diff --git a/.github/workflows/docker-build.yml b/.github/workflows/docker-build.yml
index 13973bc69..6ee66a648 100644
--- a/.github/workflows/docker-build.yml
+++ b/.github/workflows/docker-build.yml
@@ -53,26 +53,6 @@ jobs:
         with:
           username: ${{ secrets.DOCKERHUB_USERNAME }}
           password: ${{ secrets.DOCKERHUB_TOKEN }}
-      # - name: Build Docker Image
-      #   uses: docker/build-push-action@v5
-      #   id: docker_build
-      #   with:
-      #     context: .
-      #     push: false  # Do not push yet
-      #     platforms: "linux/amd64,linux/arm64/v8"
-      #     load: true
-      #     file: ${{ matrix.file }}
-      #     tags: ${{ env.TEST_TAG }}
-      # - name: Run Container
-      #   run: |
-      #     docker run -d --name test_container -p 8000:8000 ${{ needs.setup.outputs.tags }}
-      # - name: Wait for Container to Start and Check Health
-      #   run: |
-      #     timeout 40 bash -c 'until curl -f http://127.0.0.1:8000/health; do sleep 1; done' || (echo "Server did not start in time" && docker logs test_container && docker stop test_container && docker rm test_container && exit 1)
-      # - name: Stop and Remove Container
-      #   run: |
-      #     docker stop test_container
-      #     docker rm test_container
       - name: Build and Push Docker Image
         uses: docker/build-push-action@v5
         with:
diff --git a/.github/workflows/lint-js.yml b/.github/workflows/lint-js.yml
index 300903288..d381a467f 100644
--- a/.github/workflows/lint-js.yml
+++ b/.github/workflows/lint-js.yml
@@ -10,8 +10,10 @@ env:
 
 jobs:
   run-linters:
-    name: Run linters
+    name: Run Prettier
     runs-on: ubuntu-latest
+    permissions:
+      contents: write
 
     steps:
       - name: Checkout code
@@ -38,16 +40,13 @@ jobs:
           npm install
         if: ${{ steps.setup-node.outputs.cache-hit != 'true' }}
 
-      - name: Run linters
-        uses: wearerequired/lint-action@v2
+      - name: Run Prettier
+        run: |
+          cd src/frontend
+          npm run format
+      - name: Commit changes
+        uses: stefanzweifel/git-auto-commit-action@v5
         with:
-          github_token: ${{ secrets.github_token }}
-          auto_fix: true
-          git_email: "gabriel@langflow.org"
-          # Enable linters
-          # eslint: true
-          # eslint_auto_fix: true
-          prettier: true
-          prettier_auto_fix: true
-          prettier_args: '--write \"{tests,src}/**/*.{js,jsx,ts,tsx,json,md}\" --ignore-path .prettierignore'
+          commit_message: Apply Prettier formatting
+          branch: ${{ github.head_ref }}
 
diff --git a/.github/workflows/style-check-py.yml b/.github/workflows/style-check-py.yml
index 217dc5aa8..3255aa31d 100644
--- a/.github/workflows/style-check-py.yml
+++ b/.github/workflows/style-check-py.yml
@@ -36,5 +36,11 @@ jobs:
       - name: Run Ruff
         run: poetry run ruff check --output-format=github .
       - name: Run Ruff format
-        run: poetry run ruff format --check .
+        run: poetry run ruff format .
+      - name: Commit changes
+        uses: stefanzweifel/git-auto-commit-action@v5
+        with:
+          commit_message: Apply Ruff formatting
+          branch: ${{ github.head_ref }}
+
 
diff --git a/.gitignore b/.gitignore
index 216030526..243628fbb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -268,5 +268,7 @@ stuff/*
 src/frontend/playwright-report/index.html
 *.bak
 prof/*
-*-shm
-*-wal
\ No newline at end of file
+
+src/frontend/temp
+*.db-shm
+*.db-wal
diff --git a/.vscode/launch.json b/.vscode/launch.json
index 82e39fcc9..a2a38dcba 100644
--- a/.vscode/launch.json
+++ b/.vscode/launch.json
@@ -15,7 +15,9 @@
         "--log-level",
         "debug",
         "--loop",
-        "asyncio"
+        "asyncio",
+        "--reload-include",
+        "src/backend/*"
       ],
       "jinja": true,
       "justMyCode": false,
diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
index 3b89f7faa..a4f49057f 100644
--- a/CODE_OF_CONDUCT.md
+++ b/CODE_OF_CONDUCT.md
@@ -5,7 +5,7 @@
 We as members, contributors, and leaders pledge to make participation in our
 community a harassment-free experience for everyone, regardless of age, body
 size, visible or invisible disability, ethnicity, sex characteristics, gender
-identity and expression, level of experience, education, socio-economic status,
+identity and expression, level of experience, education, socioeconomic status,
 nationality, personal appearance, race, religion, or sexual identity
 and orientation.
 
diff --git a/Makefile b/Makefile
index a305ed807..5e5e24412 100644
--- a/Makefile
+++ b/Makefile
@@ -208,6 +208,7 @@ ifdef base
 endif
 
 ifdef main
+	make build_frontend
 	make build_langflow
 endif
 
diff --git a/docs/docs/administration/cli.mdx b/docs/docs/administration/cli.mdx
index 9be0a3453..4f11cc721 100644
--- a/docs/docs/administration/cli.mdx
+++ b/docs/docs/administration/cli.mdx
@@ -106,26 +106,26 @@ python -m langflow run
 
 ### Options
 
-| Option                                                     | Description                                                                                                                                                                                                                                           |
-| ---------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `--help`                                                   | Displays all available options.                                                                                                                                                                                                                       |
-| `--host`                                                   | Defines the host to bind the server to. Can be set using the `LANGFLOW_HOST` environment variable. The default is `127.0.0.1`.                                                                                                                        |
-| `--workers`                                                | Sets the number of worker processes. Can be set using the `LANGFLOW_WORKERS` environment variable. The default is `1`.                                                                                                                                |
-| `--timeout`                                                | Sets the worker timeout in seconds. The default is `60`.                                                                                                                                                                                              |
-| `--port`                                                   | Sets the port to listen on. Can be set using the `LANGFLOW_PORT` environment variable. The default is `7860`.                                                                                                                                         |
-| `--env-file`                                               | Specifies the path to the .env file containing environment variables. The default is `.env`.                                                                                                                                                          |
-| `--log-level`                                              | Defines the logging level. Can be set using the `LANGFLOW_LOG_LEVEL` environment variable. The default is `critical`.                                                                                                                                 |
-| `--components-path`                                        | Specifies the path to the directory containing custom components. Can be set using the `LANGFLOW_COMPONENTS_PATH` environment variable. The default is `langflow/components`.                                                                         |
-| `--log-file`                                               | Specifies the path to the log file. Can be set using the `LANGFLOW_LOG_FILE` environment variable. The default is `logs/langflow.log`.                                                                                                                |
-| `--cache`                                                  | Select the type of cache to use. Options are `InMemoryCache` and `SQLiteCache`. Can be set using the `LANGFLOW_LANGCHAIN_CACHE` environment variable. The default is `SQLiteCache`.                                                                   |
-| `--dev`/`--no-dev`                                         | Toggles the development mode. The default is `no-dev`.                                                                                                                                                                                                |
-| `--path`                                                   | Specifies the path to the frontend directory containing build files. This option is for development purposes only. Can be set using the `LANGFLOW_FRONTEND_PATH` environment variable.                                                                |
-| `--open-browser`/`--no-open-browser`                       | Toggles the option to open the browser after starting the server. Can be set using the `LANGFLOW_OPEN_BROWSER` environment variable. The default is `open-browser`.                                                                                   |
-| `--remove-api-keys`/`--no-remove-api-keys`                 | Toggles the option to remove API keys from the projects saved in the database. Can be set using the `LANGFLOW_REMOVE_API_KEYS` environment variable. The default is `no-remove-api-keys`.                                                             |
-| `--install-completion [bash\|zsh\|fish\|powershell\|pwsh]` | Installs completion for the specified shell.                                                                                                                                                                                                          |
-| `--show-completion [bash\|zsh\|fish\|powershell\|pwsh]`    | Shows completion for the specified shell, allowing you to copy it or customize the installation.                                                                                                                                                      |
-| `--backend-only`                                           | This parameter, with a default value of `False`, allows running only the backend server without the frontend. It can also be set using the `LANGFLOW_BACKEND_ONLY` environment variable. For more, see [Backend-only](../deployment/backend-only.md). |
-| `--store`                                                  | This parameter, with a default value of `True`, enables the store features, use `--no-store` to deactivate it. It can be configured using the `LANGFLOW_STORE` environment variable.                                                                  |
+| Option                                                     | Description                                                                                                                                                                                                                                        |
+| ---------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `--help`                                                   | Displays all available options.                                                                                                                                                                                                                    |
+| `--host`                                                   | Defines the host to bind the server to. Can be set using the `LANGFLOW_HOST` environment variable. The default is `127.0.0.1`.                                                                                                                     |
+| `--workers`                                                | Sets the number of worker processes. Can be set using the `LANGFLOW_WORKERS` environment variable. The default is `1`.                                                                                                                             |
+| `--timeout`                                                | Sets the worker timeout in seconds. The default is `60`.                                                                                                                                                                                           |
+| `--port`                                                   | Sets the port to listen on. Can be set using the `LANGFLOW_PORT` environment variable. The default is `7860`.                                                                                                                                      |
+| `--env-file`                                               | Specifies the path to the .env file containing environment variables. The default is `.env`.                                                                                                                                                       |
+| `--log-level`                                              | Defines the logging level. Can be set using the `LANGFLOW_LOG_LEVEL` environment variable. The default is `critical`.                                                                                                                              |
+| `--components-path`                                        | Specifies the path to the directory containing custom components. Can be set using the `LANGFLOW_COMPONENTS_PATH` environment variable. The default is `langflow/components`.                                                                      |
+| `--log-file`                                               | Specifies the path to the log file. Can be set using the `LANGFLOW_LOG_FILE` environment variable. The default is `logs/langflow.log`.                                                                                                             |
+| `--cache`                                                  | Select the type of cache to use. Options are `InMemoryCache` and `SQLiteCache`. Can be set using the `LANGFLOW_LANGCHAIN_CACHE` environment variable. The default is `SQLiteCache`.                                                                |
+| `--dev`/`--no-dev`                                         | Toggles the development mode. The default is `no-dev`.                                                                                                                                                                                             |
+| `--path`                                                   | Specifies the path to the frontend directory containing build files. This option is for development purposes only. Can be set using the `LANGFLOW_FRONTEND_PATH` environment variable.                                                             |
+| `--open-browser`/`--no-open-browser`                       | Toggles the option to open the browser after starting the server. Can be set using the `LANGFLOW_OPEN_BROWSER` environment variable. The default is `open-browser`.                                                                                |
+| `--remove-api-keys`/`--no-remove-api-keys`                 | Toggles the option to remove API keys from the projects saved in the database. Can be set using the `LANGFLOW_REMOVE_API_KEYS` environment variable. The default is `no-remove-api-keys`.                                                          |
+| `--install-completion [bash\|zsh\|fish\|powershell\|pwsh]` | Installs completion for the specified shell.                                                                                                                                                                                                       |
+| `--show-completion [bash\|zsh\|fish\|powershell\|pwsh]`    | Shows completion for the specified shell, allowing you to copy it or customize the installation.                                                                                                                                                   |
+| `--backend-only`                                           | This parameter, with a default value of `False`, allows running only the backend server without the frontend. It can also be set using the `LANGFLOW_BACKEND_ONLY` environment variable. For more, see [Backend-only](../deployment/backend-only). |
+| `--store`                                                  | This parameter, with a default value of `True`, enables the store features, use `--no-store` to deactivate it. It can be configured using the `LANGFLOW_STORE` environment variable.                                                               |
 
 #### CLI environment variables
 
diff --git a/docs/docs/administration/playground.mdx b/docs/docs/administration/playground.mdx
index b0e9d8bad..fd2a2b75c 100644
--- a/docs/docs/administration/playground.mdx
+++ b/docs/docs/administration/playground.mdx
@@ -44,9 +44,9 @@ Adding or removing any of the below components modifies your Playground so you c
 - Text Input
 - Chat Output
 - Text Output
-- Records Output
+- Data Output
 - Inspect Memory
 
 You can also select **Options** > **Logs** to see your flow's logs.
 
-For more information, see [Inputs and Outputs](../components/inputs-and-outputs.mdx).
+For more information, see [Inputs and Outputs](../components/inputs-and-outputs)
diff --git a/docs/docs/components/experimental.mdx b/docs/docs/components/experimental.mdx
index 036fa334c..a6f35d024 100644
--- a/docs/docs/components/experimental.mdx
+++ b/docs/docs/components/experimental.mdx
@@ -24,15 +24,15 @@ Provide the session ID to clear its message history.
 
 ---
 
-## Extract Key From Record
+## Extract Key From Data
 
 This component extracts specified keys from a record.
 
 **Parameters**
 
-- **Record:**
+- **Data:**
 
-  - **Display Name:** Record
+  - **Display Name:** Data
   - **Info:** The record from which to extract keys.
 
 - **Keys:**
@@ -112,18 +112,18 @@ Call this component without parameters to list all flows.
 
 ---
 
-## Merge Records
+## Merge Data
 
-This component merges a list of records.
+This component merges a list of Data.
 
 **Parameters**
 
-- **Records:**
-  - **Display Name:** Records
+- **Data:**
+  - **Display Name:** Data
 
 **Usage**
 
-Provide the records you want to merge.
+Provide the Data you want to merge.
 
 ---
 
@@ -138,9 +138,9 @@ This component generates a notification.
   - **Display Name:** Name
   - **Info:** The notification's name.
 
-- **Record:**
+- **Data:**
 
-  - **Display Name:** Record
+  - **Display Name:** Data
   - **Info:** Optionally, a record to store in the notification.
 
 - **Append:**
diff --git a/docs/docs/components/helpers.mdx b/docs/docs/components/helpers.mdx
index f95c43b9d..59fb63564 100644
--- a/docs/docs/components/helpers.mdx
+++ b/docs/docs/components/helpers.mdx
@@ -13,7 +13,7 @@ This component retrieves stored chat messages based on a specific session ID.
 - **Number of messages:** Number of messages to retrieve.
 - **Session ID:** The session ID of the chat history.
 - **Order:** Choose the message order, either "Ascending" or "Descending".
-- **Record template:** (Optional) Template to convert a record to text. If left empty, the system dynamically sets it to the record's text key.
+- **Data template:** (Optional) Template to convert a record to text. If left empty, the system dynamically sets it to the record's text key.
 
 ---
 
@@ -59,13 +59,13 @@ Learn more about creating custom components at [Custom Component](http://docs.la
 
 ---
 
-### Documents to records
+### Documents to Data
 
-Convert LangChain documents into records.
+Convert LangChain documents into Data.
 
 #### Parameters
 
-- **Documents:** Documents to be converted into records.
+- **Documents:** Documents to be converted into Data.
 
 ---
 
@@ -93,14 +93,14 @@ Retrieves stored chat messages based on a specific session ID.
 
 ---
 
-### Records to text
+### Data to text
 
-Convert records into plain text following a specified template.
+Convert Data into plain text following a specified template.
 
 #### Parameters
 
-- **Records:** The records to convert to text.
-- **Template:** The template used for formatting the records. It can contain keys like `{text}`, `{data}`, or any other key in the record.
+- **Data:** The Data to convert to text.
+- **Template:** The template used for formatting the Data. It can contain keys like `{text}`, `{data}`, or any other key in the record.
 
 ---
 
@@ -124,5 +124,5 @@ Update a record with text-based key/value pairs, similar to updating a Python di
 
 #### Parameters
 
-- **Record:** The record to update.
+- **Data:** The record to update.
 - **New data:** The new data to update the record with.
diff --git a/docs/docs/components/inputs-and-outputs.mdx b/docs/docs/components/inputs-and-outputs.mdx
index 484afc6b9..a35976d31 100644
--- a/docs/docs/components/inputs-and-outputs.mdx
+++ b/docs/docs/components/inputs-and-outputs.mdx
@@ -8,11 +8,11 @@ They also dynamically change the Playground and can be renamed to facilitate bui
 
 ## Inputs
 
-Inputs are components used to define where data enters your flow. They can receive data from the user, a database, or any other source that can be converted to Text or Record.
+Inputs are components used to define where data enters your flow. They can receive data from the user, a database, or any other source that can be converted to Text or Data.
 
 The difference between Chat Input and other Input components is the output format, the number of configurable fields, and the way they are displayed in the Playground.
 
-Chat Input components can output `Text` or `Record`. When you want to pass the sender name or sender to the next component, use the `Record` output. To pass only the message, use the `Text` output, useful when saving the message to a database or memory system like Zep.
+Chat Input components can output `Text` or `Data`. When you want to pass the sender name or sender to the next component, use the `Data` output. To pass only the message, use the `Text` output, useful when saving the message to a database or memory system like Zep.
 
 You can find out more about Chat Input and other Inputs [here](#chat-input).
 
@@ -38,8 +38,8 @@ This component collects user input from the chat.
 
 <Admonition type="note" title="Note">
   <p>
-    If `As Record` is `true` and the `Message` is a `Record`, the data of the
-    `Record` will be updated with the `Sender`, `Sender Name`, and `Session ID`.
+    If `As Data` is `true` and the `Message` is a `Data`, the data of the `Data`
+    will be updated with the `Sender`, `Sender Name`, and `Session ID`.
   </p>
 </Admonition>
 
@@ -70,11 +70,11 @@ The **Text Input** component adds an **Input** field on the Playground. This ena
 **Parameters**
 
 - **Value:** Specifies the text input value. This is where the user inputs text data that will be passed to the next component in the sequence. If no value is provided, it defaults to an empty string.
-- **Record Template:** Specifies how a `Record` should be converted into `Text`.
+- **Data Template:** Specifies how a `Data` should be converted into `Text`.
 
-The **Record Template** field is used to specify how a `Record` should be converted into `Text`. This is particularly useful when you want to extract specific information from a `Record` and pass it as text to the next component in the sequence.
+The **Data Template** field is used to specify how a `Data` should be converted into `Text`. This is particularly useful when you want to extract specific information from a `Data` and pass it as text to the next component in the sequence.
 
-For example, if you have a `Record` with the following structure:
+For example, if you have a `Data` with the following structure:
 
 ```json
 {
@@ -84,9 +84,9 @@ For example, if you have a `Record` with the following structure:
 }
 ```
 
-A template with `Name: {name}, Age: {age}` will convert the `Record` into a text string of `Name: John Doe, Age: 30`.
+A template with `Name: {name}, Age: {age}` will convert the `Data` into a text string of `Name: John Doe, Age: 30`.
 
-If you pass more than one `Record`, the text will be concatenated with a new line separator.
+If you pass more than one `Data`, the text will be concatenated with a new line separator.
 
 ## Outputs
 
@@ -112,8 +112,8 @@ This component sends a message to the chat.
 
 <Admonition type="note" title="Note">
   <p>
-    If `As Record` is `true` and the `Message` is a `Record`, the data in the
-    `Record` is updated with the `Sender`, `Sender Name`, and `Session ID`.
+    If `As Data` is `true` and the `Message` is a `Data`, the data in the `Data`
+    is updated with the `Sender`, `Sender Name`, and `Session ID`.
   </p>
 </Admonition>
 
@@ -154,4 +154,5 @@ The `PromptTemplate` component enables users to create prompts and define variab
 
 After defining a variable in the prompt template, it acts as its own component input. See [Prompt Customization](../administration/prompt-customization) for more details.
 
-- **template:** The template used to format an individual request.
+- **template:** The template used to format an individual request.import Admonition from "@theme/Admonition";
+  import ZoomableImage from "/src/theme/ZoomableImage.js";
diff --git a/docs/docs/components/text-and-record.mdx b/docs/docs/components/text-and-record.mdx
index 24c16e4aa..fe2e61644 100644
--- a/docs/docs/components/text-and-record.mdx
+++ b/docs/docs/components/text-and-record.mdx
@@ -1,16 +1,16 @@
-# Text and Record
+# Text and Data
 
-In Langflow 1.0, we added two main input and output types: `Text` and `Record`.
+In Langflow 1.0, we added two main input and output types: `Text` and `Data`.
 
-`Text` is a simple string input and output type, while `Record` is a structure very similar to a dictionary in Python. It is a key-value pair data structure.
+`Text` is a simple string input and output type, while `Data` is a structure very similar to a dictionary in Python. It is a key-value pair data structure.
 
 We've created a few components to help you work with these types. Let's see how a few of them work.
 
-## Records To Text
+## Data To Text
 
-This is a component that takes in Records and outputs a `Text`. It does this using a template string and concatenating the values of the `Record`, one per line.
+This is a component that takes in Data and outputs a `Text`. It does this using a template string and concatenating the values of the `Data`, one per line.
 
-If we have the following Records:
+If we have the following Data:
 
 ```json
 {
@@ -32,13 +32,13 @@ Alice: Hello!
 John: Hi!
 ```
 
-## Create Record
+## Create Data
 
-This component allows you to create a `Record` from a number of inputs. You can add as many key-value pairs as you want (as long as it is less than 15). Once you've picked that number you'll need to write the name of the Key and can pass `Text` values from other components to it.
+This component allows you to create a `Data` from a number of inputs. You can add as many key-value pairs as you want (as long as it is less than 15). Once you've picked that number you'll need to write the name of the Key and can pass `Text` values from other components to it.
 
-## Documents To Records
+## Documents To Data
 
-This component takes in a LangChain `Document` and outputs a `Record`. It does this by extracting the `page_content` and the `metadata` from the `Document` and adding them to the `Record` as text and data respectively.
+This component takes in a LangChain `Document` and outputs a `Data`. It does this by extracting the `page_content` and the `metadata` from the `Document` and adding them to the `Data` as text and data respectively.
 
 ## Why is this useful?
 
diff --git a/docs/docs/components/vector-stores.mdx b/docs/docs/components/vector-stores.mdx
index 6072abe29..51f0375d4 100644
--- a/docs/docs/components/vector-stores.mdx
+++ b/docs/docs/components/vector-stores.mdx
@@ -4,11 +4,11 @@ import Admonition from "@theme/Admonition";
 
 ### Astra DB
 
-The `Astra DB` initializes a vector store using Astra DB from records. It creates Astra DB-based vector indexes to efficiently store and retrieve documents.
+The `Astra DB` initializes a vector store using Astra DB from Data. It creates Astra DB-based vector indexes to efficiently store and retrieve documents.
 
 **Parameters:**
 
-- **Input:** Documents or records for input.
+- **Input:** Documents or Data for input.
 - **Embedding:** Embedding model Astra DB uses.
 - **Collection Name:** Name of the Astra DB collection.
 - **Token:** Authentication token for Astra DB.
@@ -99,12 +99,12 @@ For detailed documentation and integration guides, please refer to the [Chroma C
 
 ### Couchbase
 
-`Couchbase` builds a Couchbase vector store from records, streamlining the storage and retrieval of documents.
+`Couchbase` builds a Couchbase vector store from Data, streamlining the storage and retrieval of documents.
 
 **Parameters:**
 
 - **Embedding:** Model used by Couchbase.
-- **Input:** Documents or records.
+- **Input:** Documents or Data.
 - **Couchbase Cluster Connection String:** Cluster Connection string.
 - **Couchbase Cluster Username:** Cluster Username.
 - **Couchbase Cluster Password:** Cluster Password.
@@ -165,12 +165,12 @@ For more details, see the [FAISS Component Documentation](https://faiss.ai/index
 
 ### MongoDB Atlas
 
-`MongoDBAtlas` builds a MongoDB Atlas-based vector store from records, streamlining the storage and retrieval of documents.
+`MongoDBAtlas` builds a MongoDB Atlas-based vector store from Data, streamlining the storage and retrieval of documents.
 
 **Parameters:**
 
 - **Embedding:** Model used by MongoDB Atlas.
-- **Input:** Documents or records.
+- **Input:** Documents or Data.
 - **Collection Name:** Collection identifier in MongoDB Atlas.
 - **Database Name:** Database identifier.
 - **Index Name:** Index identifier.
@@ -235,11 +235,11 @@ For more details, see the [PGVector Component Documentation](https://python.lang
 
 ### Pinecone
 
-`Pinecone` constructs a Pinecone wrapper from records, setting up Pinecone-based vector indexes for document storage and retrieval.
+`Pinecone` constructs a Pinecone wrapper from Data, setting up Pinecone-based vector indexes for document storage and retrieval.
 
 **Parameters:**
 
-- **Input:** Documents or records.
+- **Input:** Documents or Data.
 - **Embedding:** Model used.
 - **Index Name:** Index identifier.
 - **Namespace:** Namespace used.
@@ -278,7 +278,7 @@ For more details, see the [PGVector Component Documentation](https://python.lang
 
 **Parameters:**
 
-- **Input:** Documents or records.
+- **Input:** Documents or Data.
 - **Embedding:** Model used.
 - **API Key:** Qdrant API key.
 - **Collection Name:** Collection identifier.
@@ -345,7 +345,7 @@ For detailed documentation, refer to the [Redis Documentation](https://python.la
 
 **Parameters:**
 
-- **Input:** Documents or records.
+- **Input:** Documents or data.
 - **Embedding:** Model used.
 - **Query Name:** Optional query name.
 - **Search Kwargs:** Advanced search parameters.
diff --git a/docs/docs/contributing/contribute-component.md b/docs/docs/contributing/contribute-component.md
index f638434e2..d252a0929 100644
--- a/docs/docs/contributing/contribute-component.md
+++ b/docs/docs/contributing/contribute-component.md
@@ -15,7 +15,7 @@ You have a new document loader called **MyCustomDocumentLoader** and it would lo
 5. Add the code to the [/components/documentloaders](https://github.com/langflow-ai/langflow/tree/dev/src/backend/base/langflow/components) folder.
 6. Add the dependency to [/documentloaders/\_\_init\_\_.py](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/components/documentloaders/__init__.py) as `from .MyCustomDocumentLoader import MyCustomDocumentLoader`.
 7. Add any new dependencies to the outer [pyproject.toml](https://github.com/langflow-ai/langflow/blob/dev/pyproject.toml#L27) file.
-8. Submit documentation for your component. For this example, you'd submit documentation to the [loaders page](https://github.com/langflow-ai/langflow/blob/dev/docs/docs/components/loaders.mdx).
+8. Submit documentation for your component. For this example, you'd submit documentation to the [loaders page](https://github.com/langflow-ai/langflow/blob/dev/docs/docs/components/loaders).
 9. Submit your changes as a pull request. The Langflow team will have a look, suggest changes, and add your component to Langflow.
 
 ## User Sharing
diff --git a/docs/docs/deployment/backend-only.md b/docs/docs/deployment/backend-only.md
index fb5efdfdb..4122373f3 100644
--- a/docs/docs/deployment/backend-only.md
+++ b/docs/docs/deployment/backend-only.md
@@ -9,11 +9,11 @@ Langflow will now serve requests to its API without the frontend running.
 
 ## Prerequisites
 
-- [Langflow installed](../getting-started/install-langflow.mdx)
+- [Langflow installed](../getting-started/install-langflow)
 
 - [OpenAI API key](https://platform.openai.com)
 
-- [A Langflow flow created](../starter-projects/basic-prompting.mdx)
+- [A Langflow flow created](../starter-projects/basic-prompting)
 
 ## Download your flow's curl call
 
@@ -120,4 +120,4 @@ The result is similar to the curl call:
 
 Your Python app POSTs to your Langflow server, and the server runs the flow and returns the result.
 
-See [API](../administration/api.mdx) for more ways to interact with your headless Langflow server.
+See [API](../administration/api) for more ways to interact with your headless Langflow server.
diff --git a/docs/docs/examples/create-record.mdx b/docs/docs/examples/create-record.mdx
index aa7a886f4..7858ca783 100644
--- a/docs/docs/examples/create-record.mdx
+++ b/docs/docs/examples/create-record.mdx
@@ -4,11 +4,11 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
 import ReactPlayer from "react-player";
 import Admonition from "@theme/Admonition";
 
-# Create Record
+# Create Data
 
-In Langflow, a `Record` has a structure very similar to a Python dictionary. It is a key-value pair data structure.
+In Langflow, a `Data` has a structure very similar to a Python dictionary. It is a key-value pair data structure.
 
-The **Create Record** component allows you to dynamically create a `Record` from a specified number of inputs. You can add as many key-value pairs as you want (as long as it is less than 15 😅). Once you've chosen the number of `Records`, add keys and fill up values, or pass on values from other components to the component using the input handles.
+The **Create Data** component allows you to dynamically create a `Data` from a specified number of inputs. You can add as many key-value pairs as you want (as long as it is less than 15 😅). Once you've chosen the number of `Data`, add keys and fill up values, or pass on values from other components to the component using the input handles.
 
 <div
   style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
diff --git a/docs/docs/getting-started/canvas.mdx b/docs/docs/getting-started/canvas.mdx
index 79205ba06..01d73699a 100644
--- a/docs/docs/getting-started/canvas.mdx
+++ b/docs/docs/getting-started/canvas.mdx
@@ -27,7 +27,7 @@ For example, the **OpenAI LLM** is a **component** of the **Basic prompting** fl
 
 A **flow** is a pipeline of components connected together in the Langflow canvas.
 
-For example, the [Basic prompting](../starter-projects/basic-prompting.mdx) flow is a pipeline of four components:
+For example, the [Basic prompting](../starter-projects/basic-prompting) flow is a pipeline of four components:
 
 <ZoomableImage
   alt="Docusaurus themed image"
@@ -117,7 +117,7 @@ You can modify the code and save it.
 
 #### Save
 
-Save your component to the **Saved** components folder for re-use.
+Save your component to the **Saved** components folder for reuse.
 
 #### Duplicate
 
@@ -143,19 +143,19 @@ Duplicate your component in the canvas.
 
 ### Group multiple components
 
-Components without input or output nodes can be grouped into a single component for re-use.
+Components without input or output nodes can be grouped into a single component for reuse.
 This is useful for combining large flows into single components (like RAG with a vector database, for example) and saves space in the canvas.
 
 1. Hold **Shift** and drag to select the **Prompt** and **OpenAI** components.
 2. Select **Group**.
 3. The components merge into a single component.
-4. To save the new component, select **Save**. It can now be re-used from the **Saved** components folder.
+4. To save the new component, select **Save**. It can now be reused from the **Saved** components folder.
 
 ## Playground
 
 Run your flow by clicking the **![Playground icon](/logos/botmessage.svg)Playground** button.
 
-For more, see [Playground](../administration/playground.mdx).
+For more, see [Playground](../administration/playground).
 
 ## API
 
@@ -210,7 +210,7 @@ The **Python Code** tab displays code to interact with your flow's `.json` file
 ### Chat Widget HTML
 
 The **Chat Widget HTML** tab displays code that can be inserted in the `<body>` of your HTML to interact with your flow.
-For more, see the [Chat widget documentation](../administration/chat-widget.mdx).
+For more, see the [Chat widget documentation](../administration/chat-widget).
 
 ### Tweaks
 
@@ -250,7 +250,7 @@ Select **Download Collection** to save your project to your local machine. This
 
 Select **Upload Collection** to upload a flow or component `.json` file from your local machine.
 
-Select **New Project** to create a new project. In addition to a blank canvas, [starter projects](../starter-projects/basic-prompting.mdx) are also available.
+Select **New Project** to create a new project. In addition to a blank canvas, [starter projects](../starter-projects/basic-prompting) are also available.
 
 ## Project options menu
 
@@ -275,4 +275,8 @@ To see options for your project, in the upper left corner of the canvas, select
 
 **Export** - Download your current project to your local machine as a `.json` file.
 
-**Undo** or **Redo** - Undo or redo your last action.
+**Undo** or **Redo** - Undo or redo your last action.import ThemedImage from "@theme/ThemedImage";
+import useBaseUrl from "@docusaurus/useBaseUrl";
+import ZoomableImage from "/src/theme/ZoomableImage.js";
+import ReactPlayer from "react-player";
+import Admonition from "@theme/Admonition";
diff --git a/docs/docs/getting-started/install-langflow.mdx b/docs/docs/getting-started/install-langflow.mdx
index 643938beb..0759397cf 100644
--- a/docs/docs/getting-started/install-langflow.mdx
+++ b/docs/docs/getting-started/install-langflow.mdx
@@ -70,7 +70,7 @@ python -m langflow run
 │ Collaborate, and contribute at our GitHub Repo 🚀 │
 ```
 
-3. Continue on to the [Quickstart](./quickstart.mdx).
+3. Continue on to the [Quickstart](./quickstart).
 
 ## HuggingFace Spaces
 
@@ -89,4 +89,7 @@ You'll be presented with the following screen:
   style={{ width: "80%", maxWidth: "800px", margin: "0 auto" }}
 />
 
-Name your Space, define the visibility (Public or Private), and click on **Duplicate Space** to start the installation process. When installation is finished, you'll be redirected to the Space's main page to start using Langflow right away!
+Name your Space, define the visibility (Public or Private), and click on **Duplicate Space** to start the installation process. When installation is finished, you'll be redirected to the Space's main page to start using Langflow right away!import ThemedImage from "@theme/ThemedImage";
+import useBaseUrl from "@docusaurus/useBaseUrl";
+import ZoomableImage from "/src/theme/ZoomableImage.js";
+import Admonition from "@theme/Admonition";
diff --git a/docs/docs/getting-started/new-to-llms.mdx b/docs/docs/getting-started/new-to-llms.mdx
index 3a38a37f4..bce3c1ec6 100644
--- a/docs/docs/getting-started/new-to-llms.mdx
+++ b/docs/docs/getting-started/new-to-llms.mdx
@@ -2,9 +2,9 @@
 
 Large Language Models, or LLMs, are part of an exciting new world in computing.
 
-We made Langflow for anyone to create with LLMs, and hope you'll feel comfortable installing Langflow and [getting started](./quickstart.mdx).
+We made Langflow for anyone to create with LLMs, and hope you'll feel comfortable installing Langflow and [getting started](./quickstart).
 
 If you want to learn more about LLMs, prompt engineering, and AI models, Langflow recommends [promptingguide.ai](https://promptingguide.ai), an open-source repository of prompt engineering content maintained by AI experts.
 PromptingGuide offers content for [beginners](https://www.promptingguide.ai/introduction/basics) and [experts](https://www.promptingguide.ai/techniques/cot), as well as the latest [research papers](https://www.promptingguide.ai/papers) and [test results](https://www.promptingguide.ai/research) fueling AI's progress.
 
-Wherever you are on your AI journey, it's helpful to keep Prompting Guide open in a tab.
\ No newline at end of file
+Wherever you are on your AI journey, it's helpful to keep Prompting Guide open in a tab.
diff --git a/docs/docs/getting-started/quickstart.mdx b/docs/docs/getting-started/quickstart.mdx
index 544b29b18..6ab1b3c6c 100644
--- a/docs/docs/getting-started/quickstart.mdx
+++ b/docs/docs/getting-started/quickstart.mdx
@@ -12,7 +12,7 @@ This guide demonstrates how to build a basic prompt flow and modify that prompt
 
 - [Python >=3.10](https://www.python.org/downloads/release/python-3100/) and [pip](https://pypi.org/project/pip/) or [pipx](https://pipx.pypa.io/stable/installation/)
 
-- [Langflow installed and running](./install-langflow.mdx)
+- [Langflow installed and running](./install-langflow)
 
 - [OpenAI API key](https://platform.openai.com)
 
@@ -77,6 +77,10 @@ By adding Langflow components to your flow, you can create all sorts of interest
 
 Here are a couple of examples:
 
-- [Memory chatbot](/starter-projects/memory-chatbot.mdx)
-- [Blog writer](/starter-projects/blog-writer.mdx)
-- [Document QA](/starter-projects/document-qa.mdx)
+- [Memory chatbot](/starter-projects/memory-chatbot)
+- [Blog writer](/starter-projects/blog-writer)
+- [Document QA](/starter-projects/document-qa)import ThemedImage from "@theme/ThemedImage";
+  import useBaseUrl from "@docusaurus/useBaseUrl";
+  import ZoomableImage from "/src/theme/ZoomableImage.js";
+  import ReactPlayer from "react-player";
+  import Admonition from "@theme/Admonition";
diff --git a/docs/docs/getting-started/rag-with-astradb.mdx b/docs/docs/getting-started/rag-with-astradb.mdx
index 8cb2593c9..3800ea96c 100644
--- a/docs/docs/getting-started/rag-with-astradb.mdx
+++ b/docs/docs/getting-started/rag-with-astradb.mdx
@@ -136,10 +136,10 @@ The RAG flow is a bit more complex. It consists of:
 
 - **Chat Input** component that defines where to put the user input coming from the Playground
 - **OpenAI Embeddings** component that generates embeddings from the user input
-- **Astra DB Search** component that retrieves the most relevant Records from the Astra DB database
-- **Text Output** component that turns the Records into Text by concatenating them and also displays it in the Playground
+- **Astra DB Search** component that retrieves the most relevant Data from the Astra DB database
+- **Text Output** component that turns the Data into Text by concatenating them and also displays it in the Playground
   - One interesting point you'll see here is that this component is named `Extracted Chunks`, and that is how it will appear in the Playground
-- **Prompt** component that takes in the user input and the retrieved Records as text and builds a prompt for the OpenAI model
+- **Prompt** component that takes in the user input and the retrieved Data as text and builds a prompt for the OpenAI model
 - **OpenAI** component that generates a response to the prompt
 - **Chat Output** component that displays the response in the Playground
 
@@ -176,7 +176,7 @@ Because this flow has a **Chat Input** and a **Text Output** component, the Pane
   style={{ width: "80%", margin: "20px auto" }}
 />
 
-Once we interact with it we get a response and the Extracted Chunks section is updated with the retrieved records.
+Once we interact with it we get a response and the Extracted Chunks section is updated with the retrieved data.
 
 <ZoomableImage
   alt="Docusaurus themed image"
@@ -192,4 +192,16 @@ And that's it! You have successfully ran a RAG application using Astra DB and La
 # Conclusion
 
 In this guide, we have learned how to run a RAG application using Astra DB and Langflow.
-We have seen how to create an Astra DB database, import the Astra DB RAG Flows project into Langflow, and run the ingestion and RAG flows.
+We have seen how to create an Astra DB database, import the Astra DB RAG Flows project into Langflow, and run the ingestion and RAG flows.import ThemedImage from "@theme/ThemedImage";
+import useBaseUrl from "@docusaurus/useBaseUrl";
+import ZoomableImage from "/src/theme/ZoomableImage.js";
+import Admonition from "@theme/Admonition";import ThemedImage from "@theme/ThemedImage";
+import useBaseUrl from "@docusaurus/useBaseUrl";
+import ZoomableImage from "/src/theme/ZoomableImage.js";
+import Admonition from "@theme/Admonition";import ThemedImage from "@theme/ThemedImage";
+import useBaseUrl from "@docusaurus/useBaseUrl";
+import ZoomableImage from "/src/theme/ZoomableImage.js";
+import Admonition from "@theme/Admonition";import ThemedImage from "@theme/ThemedImage";
+import useBaseUrl from "@docusaurus/useBaseUrl";
+import ZoomableImage from "/src/theme/ZoomableImage.js";
+import Admonition from "@theme/Admonition";
diff --git a/docs/docs/integrations/notion/list-database-properties.md b/docs/docs/integrations/notion/list-database-properties.md
index c41159893..2056fa81d 100644
--- a/docs/docs/integrations/notion/list-database-properties.md
+++ b/docs/docs/integrations/notion/list-database-properties.md
@@ -33,7 +33,7 @@ import requests
 from typing import Dict
 
 from langflow import CustomComponent
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class NotionDatabaseProperties(CustomComponent):
@@ -61,7 +61,7 @@ class NotionDatabaseProperties(CustomComponent):
         self,
         database_id: str,
         notion_secret: str,
-    ) -> Record:
+    ) -> Data:
         url = f"https://api.notion.com/v1/databases/{database_id}"
         headers = {
             "Authorization": f"Bearer {notion_secret}",
@@ -74,7 +74,7 @@ class NotionDatabaseProperties(CustomComponent):
         data = response.json()
         properties = data.get("properties", {})
 
-        record = Record(text=str(response.json()), data=properties)
+        record = Data(text=str(response.json()), data=properties)
         self.status = f"Retrieved {len(properties)} properties from the Notion database.\n {record.text}"
         return record
 ```
diff --git a/docs/docs/integrations/notion/list-pages.md b/docs/docs/integrations/notion/list-pages.md
index ea1b04950..e1f6603b3 100644
--- a/docs/docs/integrations/notion/list-pages.md
+++ b/docs/docs/integrations/notion/list-pages.md
@@ -39,7 +39,7 @@ import requests
 import json
 from typing import Dict, Any, List
 from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langflow.schema import Data
 
 class NotionListPages(CustomComponent):
     display_name = "List Pages [Notion]"
@@ -83,7 +83,7 @@ class NotionListPages(CustomComponent):
         notion_secret: str,
         database_id: str,
         query_payload: str = "{}",
-    ) -> List[Record]:
+    ) -> List[Data]:
         try:
             query_data = json.loads(query_payload)
             filter_obj = query_data.get("filter")
@@ -107,7 +107,7 @@ class NotionListPages(CustomComponent):
             response.raise_for_status()
 
             results = response.json()
-            records = []
+            data = []
             combined_text = f"Pages found: {len(results['results'])}\n\n"
             for page in results['results']:
                 page_data = {
@@ -127,14 +127,14 @@ class NotionListPages(CustomComponent):
                 )
 
                 combined_text += text
-                records.append(Record(text=text, data=page_data))
+                data.append(Data(text=text, data=page_data))
 
             self.status = combined_text.strip()
-            return records
+            return data
 
         except Exception as e:
             self.status = f"An error occurred: {str(e)}"
-            return [Record(text=self.status, data=[])]
+            return [Data(text=self.status, data=[])]
 ```
 
 <Admonition type="info" title="Example Usage">
diff --git a/docs/docs/integrations/notion/list-users.md b/docs/docs/integrations/notion/list-users.md
index 0eb8236f5..17352c0c2 100644
--- a/docs/docs/integrations/notion/list-users.md
+++ b/docs/docs/integrations/notion/list-users.md
@@ -30,7 +30,7 @@ import requests
 from typing import List
 
 from langflow import CustomComponent
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class NotionUserList(CustomComponent):
@@ -52,7 +52,7 @@ class NotionUserList(CustomComponent):
     def build(
         self,
         notion_secret: str,
-    ) -> List[Record]:
+    ) -> List[Data]:
         url = "https://api.notion.com/v1/users"
         headers = {
             "Authorization": f"Bearer {notion_secret}",
@@ -65,14 +65,14 @@ class NotionUserList(CustomComponent):
         data = response.json()
         results = data['results']
 
-        records = []
+        data = []
         for user in results:
             id = user['id']
             type = user['type']
             name = user.get('name', '')
             avatar_url = user.get('avatar_url', '')
 
-            record_data = {
+            data_dict = {
                 "id": id,
                 "type": type,
                 "name": name,
@@ -80,15 +80,15 @@ class NotionUserList(CustomComponent):
             }
 
             output = "User:\n"
-            for key, value in record_data.items():
+            for key, value in data_dict.items():
                 output += f"{key.replace('_', ' ').title()}: {value}\n"
             output += "________________________\n"
 
-            record = Record(text=output, data=record_data)
-            records.append(record)
+            record = Data(text=output, data=data_dict)
+            data.append(record)
 
-        self.status = "\n".join(record.text for record in records)
-        return records
+        self.status = "\n".join(record.text for record in data)
+        return data
 ```
 
 ## Example Usage
diff --git a/docs/docs/integrations/notion/page-content-viewer.md b/docs/docs/integrations/notion/page-content-viewer.md
index f4eeba052..070d71800 100644
--- a/docs/docs/integrations/notion/page-content-viewer.md
+++ b/docs/docs/integrations/notion/page-content-viewer.md
@@ -36,7 +36,7 @@ import requests
 from typing import Dict, Any
 
 from langflow import CustomComponent
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class NotionPageContent(CustomComponent):
@@ -64,7 +64,7 @@ class NotionPageContent(CustomComponent):
         self,
         page_id: str,
         notion_secret: str,
-    ) -> Record:
+    ) -> Data:
         blocks_url = f"https://api.notion.com/v1/blocks/{page_id}/children?page_size=100"
         headers = {
             "Authorization": f"Bearer {notion_secret}",
@@ -80,7 +80,7 @@ class NotionPageContent(CustomComponent):
         content = self.parse_blocks(blocks_data["results"])
 
         self.status = content
-        return Record(data={"content": content}, text=content)
+        return Data(data={"content": content}, text=content)
 
     def parse_blocks(self, blocks: list) -> str:
         content = ""
diff --git a/docs/docs/integrations/notion/page-update.md b/docs/docs/integrations/notion/page-update.md
index 0370a2b3a..3ed8f7740 100644
--- a/docs/docs/integrations/notion/page-update.md
+++ b/docs/docs/integrations/notion/page-update.md
@@ -26,7 +26,7 @@ import requests
 from typing import Dict, Any
 
 from langflow import CustomComponent
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class NotionPageUpdate(CustomComponent):
@@ -61,7 +61,7 @@ class NotionPageUpdate(CustomComponent):
         page_id: str,
         properties: str,
         notion_secret: str,
-    ) -> Record:
+    ) -> Data:
         url = f"https://api.notion.com/v1/pages/{page_id}"
         headers = {
             "Authorization": f"Bearer {notion_secret}",
@@ -88,7 +88,7 @@ class NotionPageUpdate(CustomComponent):
             output += f"{prop_name}: {prop_value}\n"
 
         self.status = output
-        return Record(data=updated_page)
+        return Data(data=updated_page)
 ```
 
 Let's break down the key parts of this component:
@@ -99,7 +99,7 @@ Let's break down the key parts of this component:
 
 - The component interacts with the Notion API to update the page properties. It constructs the API URL, headers, and request data based on the provided parameters.
 
-- The processed data is returned as a `Record` object, which can be connected to other components in the Langflow flow. The `Record` object contains the updated page data.
+- The processed data is returned as a `Data` object, which can be connected to other components in the Langflow flow. The `Data` object contains the updated page data.
 
 - The component also stores the updated page properties in the `status` attribute for logging and debugging purposes.
 
@@ -124,7 +124,7 @@ When using the `NotionPageUpdate` component, consider the following best practic
 
 - Ensure that you have a valid Notion integration token with the necessary permissions to update page properties.
 - Handle edge cases and error scenarios gracefully, such as invalid JSON format for properties or API request failures.
-- We recommend using an LLM to generate the inputs for this component, to allow flexibilty
+- We recommend using an LLM to generate the inputs for this component, to allow flexibility
 
 By leveraging the `NotionPageUpdate` component in Langflow, you can easily integrate updating Notion page properties into your language model workflows and build powerful applications that extend Langflow's capabilities.
 
diff --git a/docs/docs/integrations/notion/search.md b/docs/docs/integrations/notion/search.md
index a972bffc0..1e9b8c529 100644
--- a/docs/docs/integrations/notion/search.md
+++ b/docs/docs/integrations/notion/search.md
@@ -36,7 +36,7 @@ To use the `NotionSearch` component in a Langflow flow, follow these steps:
 import requests
 from typing import Dict, Any, List
 from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langflow.schema import Data
 
 class NotionSearch(CustomComponent):
     display_name = "Search Notion"
@@ -88,7 +88,7 @@ class NotionSearch(CustomComponent):
         query: str = "",
         filter_value: str = "page",
         sort_direction: str = "descending",
-    ) -> List[Record]:
+    ) -> List[Data]:
         try:
             url = "https://api.notion.com/v1/search"
             headers = {
@@ -113,7 +113,7 @@ class NotionSearch(CustomComponent):
             response.raise_for_status()
 
             results = response.json()
-            records = []
+            data = []
             combined_text = f"Results found: {len(results['results'])}\n\n"
             for result in results['results']:
                 result_data = {
@@ -135,14 +135,14 @@ class NotionSearch(CustomComponent):
 
                 text += f"type: {result['object']}\nlast_edited_time: {result['last_edited_time']}\n\n"
                 combined_text += text
-                records.append(Record(text=text, data=result_data))
+                data.append(Data(text=text, data=result_data))
 
             self.status = combined_text
-            return records
+            return data
 
         except Exception as e:
             self.status = f"An error occurred: {str(e)}"
-            return [Record(text=self.status, data=[])]
+            return [Data(text=self.status, data=[])]
 ```
 
 ## Example Usage
diff --git a/docs/docs/migration/migrating-to-one-point-zero.mdx b/docs/docs/migration/migrating-to-one-point-zero.mdx
index 611c43645..8ff848def 100644
--- a/docs/docs/migration/migrating-to-one-point-zero.mdx
+++ b/docs/docs/migration/migrating-to-one-point-zero.mdx
@@ -16,7 +16,7 @@ We have a special channel in our Discord server dedicated to Langflow 1.0 migrat
 - Continued support for LangChain and new support for multiple frameworks
 - Redesigned sidebar and customizable interaction panel
 - New Native Categories and Components
-- Improved user experience with Text and Record modes
+- Improved user experience with Text and Data modes
 - CustomComponent for all components
 - Compatibility with previous versions using Runnable Executor
 - Multiple flows in the canvas
@@ -32,49 +32,49 @@ We have a special channel in our Discord server dedicated to Langflow 1.0 migrat
 
 Langflow 1.0 introduces adds the concept of Inputs and Outputs to flows, allowing a clear definition of the data flow between components. Discover how to use Inputs and Outputs to pass data between components and create more dynamic flows.
 
-[Learn more about Inputs and Outputs](../components/inputs-and-outputs.mdx)
+[Learn more about Inputs and Outputs](../components/inputs-and-outputs)
 
 ## To Compose or Not to Compose: The Choice is Yours
 
 Even though composition is still possible in Langflow 1.0, the new standard is getting data moving through the flow. This allows for more flexibility and control over the data flow in your projects.
 
-[See our example components](../examples/create-record.mdx) for examples of interweaving LangChain components with our Core components.
+[See our example components](../examples/create-record) for examples of interweaving LangChain components with our Core components.
 
 ## Continued Support for LangChain and Multiple Frameworks
 
 Langflow 1.0 continues to support LangChain while also introducing support for multiple frameworks. This is another important boon that adding the paradigm of data flow brings to the table. Find out how to leverage the power of different frameworks in your projects.
 
-[Learn more about compatibility and updating existing flows](./compatibility.mdx)
+[Learn more about compatibility and updating existing flows](./compatibility)
 
 ## Sidebar Redesign and Customizable Playground
 
 We've expanded on the chat experience by creating a customizable interaction panel that allows you to design a panel that fits your needs and interact with it. The sidebar has also been redesigned to provide a more intuitive and user-friendly experience. Explore the new sidebar and interaction panel features to enhance your workflow.
 
-[Learn more about the Playground](../administration/playground.mdx)
+[Learn more about the Playground](../administration/playground)
 
 ## New Native Categories and Components
 
 Langflow 1.0 introduces many new native categories, including Inputs, Outputs, Helpers, Experimental, Models, and more. Discover the new components available, such as Chat Input, Prompt, Files, API Request, and others.
 
-[Learn more about new components](../components/inputs-and-outputs.mdx)
+[Learn more about new components](../components/inputs-and-outputs)
 
-## New Way of Using Langflow: Text and Record (and more to come)
+## New Way of Using Langflow: Text and Data (and more to come)
 
-With the introduction of Text and Record types connections between Components are more intuitive and easier to understand. This is the first step in a series of improvements to the way you interact with Langflow. Learn how to use Text, and Record and how they help you build better flows.
+With the introduction of Text and Data types connections between Components are more intuitive and easier to understand. This is the first step in a series of improvements to the way you interact with Langflow. Learn how to use Text, and Data and how they help you build better flows.
 
-[Learn more about Text and Record](../components/text-and-record.mdx)
+[Learn more about Text and Record](../components/text-and-record)
 
 ## CustomComponent for All Components
 
 Almost all components in Langflow 1.0 are now CustomComponents, allowing you to check and modify the code of each component. Discover how to leverage this feature to customize your components to your specific needs.
 
-[Learn more about CustomComponents](../components/custom.mdx)
+[Learn more about CustomComponents](../components/custom)
 
 ## Compatibility with Previous Versions
 
 To use flows built in previous versions of Langflow, you can utilize the experimental component Runnable Executor along with an Input and Output. **We'd love your feedback on this**. Learn how to adapt your existing flows to work seamlessly in the new version of Langflow.
 
-[Learn more about Compatibility with Previous Versions](./compatibility.mdx)
+[Learn more about Compatibility with Previous Versions](./compatibility)
 
 ## Multiple Flows in the Canvas
 
@@ -86,37 +86,37 @@ Langflow 1.0 allows you to have more than one flow in the canvas and run them se
 
 Each component now displays its status more clearly, allowing you to quickly identify any issues or errors. Explore how to use the new component status feature to troubleshoot and optimize your flows.
 
-[Learn more about Component Status](../getting-started/canvas.mdx#component)
+[Learn more about Component Status](../getting-started/canvas#component)
 
 ## Connecting Output Components
 
 You can now connect Output components to any other component (that has a Text output), providing a better understanding of the data flow. Explore the possibilities of connecting Output components and how it enhances your flow's functionality.
 
-[Learn more about Inputs and Outputs](../components/inputs-and-outputs.mdx)
+[Learn more about Inputs and Outputs](../components/inputs-and-outputs)
 
 ## Renaming and Editing Component Descriptions
 
 Langflow 1.0 allows you to rename and edit the description of each component, making it easier to understand and interact with the flow. Learn how to customize your component names and descriptions for improved clarity.
 
-[Learn more about Component Descriptions](../getting-started/canvas.mdx#component-parameters)
+[Learn more about Component Descriptions](../getting-started/canvas#component-parameters)
 
 ## Passing Tweaks and Inputs in the API
 
 Things got a whole lot easier. You can now pass tweaks and inputs in the API by referencing the Display Name of the component. Discover how to leverage this feature to dynamically control your flow's behavior.
 
-[Learn more about Tweaks and API inputs](../getting-started/canvas.mdx#tweaks)
+[Learn more about Tweaks and API inputs](../getting-started/canvas#tweaks)
 
 ## Global Variables for Text Fields
 
 Global Variables can be used in any Text Field across your projects. Learn how to define and utilize Global Variables to streamline your workflow.
 
-[Learn more about Global Variables](../administration/global-env.mdx)
+[Learn more about Global Variables](../administration/global-env)
 
 ## Experimental Components
 
 Explore the experimental components available in Langflow 1.0, such as SubFlow, which allows you to load a flow as a component dynamically, and Flow as Tool, which enables you to use a flow as a tool for an Agent.
 
-[Learn more about Experimental Components](../components/experimental.mdx)
+[Learn more about Experimental Components](../components/experimental)
 
 ## Experimental State Management System
 
diff --git a/docs/docs/starter-projects/basic-prompting.mdx b/docs/docs/starter-projects/basic-prompting.mdx
index 26b054bcc..2e83f760c 100644
--- a/docs/docs/starter-projects/basic-prompting.mdx
+++ b/docs/docs/starter-projects/basic-prompting.mdx
@@ -14,7 +14,7 @@ This article demonstrates how to use Langflow's prompt tools to issue basic prom
 
 ## Prerequisites
 
-- [Langflow installed and running](../getting-started/install-langflow.mdx)
+- [Langflow installed and running](../getting-started/install-langflow)
 
 - [OpenAI API key created](https://platform.openai.com)
 
@@ -62,4 +62,8 @@ This should be interesting...
    The **Edit Prompt** window opens.
 2. Change `Answer the user as if you were a pirate` to a different character, perhaps `Answer the user as if you were Harold Abelson.`
 3. Run the basic prompting flow again.
-   The response will be markedly different.
+   The response will be markedly different.import ThemedImage from "@theme/ThemedImage";
+   import useBaseUrl from "@docusaurus/useBaseUrl";
+   import ZoomableImage from "/src/theme/ZoomableImage.js";
+   import ReactPlayer from "react-player";
+   import Admonition from "@theme/Admonition";
diff --git a/docs/docs/starter-projects/blog-writer.mdx b/docs/docs/starter-projects/blog-writer.mdx
index 9380bf114..ec167bc55 100644
--- a/docs/docs/starter-projects/blog-writer.mdx
+++ b/docs/docs/starter-projects/blog-writer.mdx
@@ -10,7 +10,7 @@ Build a blog writer with OpenAI that uses URLs for reference content.
 
 ## Prerequisites
 
-- [Langflow installed and running](../getting-started/install-langflow.mdx)
+- [Langflow installed and running](../getting-started/install-langflow)
 
 - [OpenAI API key created](https://platform.openai.com)
 
@@ -75,4 +75,8 @@ The `reference_1` and `reference_2` values are received from the **URL** fields
 3. The **OpenAI** component constructs a blog post with the **URL** items as context.
    The default **URL** values are for web pages at `promptingguide.ai`, so your blog post will be about prompting LLMs.
 
-To write about something different, change the values in the **URL** components, and see what the LLM constructs.
+To write about something different, change the values in the **URL** components, and see what the LLM constructs.import ThemedImage from "@theme/ThemedImage";
+import useBaseUrl from "@docusaurus/useBaseUrl";
+import ZoomableImage from "/src/theme/ZoomableImage.js";
+import ReactPlayer from "react-player";
+import Admonition from "@theme/Admonition";
diff --git a/docs/docs/starter-projects/document-qa.mdx b/docs/docs/starter-projects/document-qa.mdx
index f3c4f0a0a..e3bf58778 100644
--- a/docs/docs/starter-projects/document-qa.mdx
+++ b/docs/docs/starter-projects/document-qa.mdx
@@ -10,7 +10,7 @@ Build a question-and-answer chatbot with a document loaded from local memory.
 
 ## Prerequisites
 
-- [Langflow installed and running](../getting-started/install-langflow.mdx)
+- [Langflow installed and running](../getting-started/install-langflow)
 
 - [OpenAI API key created](https://platform.openai.com)
 
@@ -67,4 +67,8 @@ Including a file with the prompt gives the **OpenAI** component context it may n
 The issue occurred during the execution of migrations in the application. Specifically, an error was raised by the Alembic library, indicating that new upgrade operations were detected that had not been accounted for in the existing migration scripts. The operation in question involved modifying the nullable property of a column (apikey, created_at) in the database, with details about the existing type (DATETIME()), existing server default, and other properties.
 ```
 
-This result indicates that the bot received the loaded document and understood the context surrounding the vague question. It also correctly identified the issue in the error log, and followed up with appropriate troubleshooting suggestions. Nice!
+This result indicates that the bot received the loaded document and understood the context surrounding the vague question. It also correctly identified the issue in the error log, and followed up with appropriate troubleshooting suggestions. Nice!import ThemedImage from "@theme/ThemedImage";
+import useBaseUrl from "@docusaurus/useBaseUrl";
+import ZoomableImage from "/src/theme/ZoomableImage.js";
+import ReactPlayer from "react-player";
+import Admonition from "@theme/Admonition";
diff --git a/docs/docs/starter-projects/memory-chatbot.mdx b/docs/docs/starter-projects/memory-chatbot.mdx
index 8e38ca3e0..28aec2baf 100644
--- a/docs/docs/starter-projects/memory-chatbot.mdx
+++ b/docs/docs/starter-projects/memory-chatbot.mdx
@@ -6,11 +6,11 @@ import Admonition from "@theme/Admonition";
 
 # Memory Chatbot
 
-This flow extends the [basic prompting flow](./basic-prompting.mdx) to include chat memory for unique SessionIDs.
+This flow extends the [basic prompting flow](./basic-prompting) to include chat memory for unique SessionIDs.
 
 ## Prerequisites
 
-- [Langflow installed and running](../getting-started/install-langflow.mdx)
+- [Langflow installed and running](../getting-started/install-langflow)
 
 - [OpenAI API key created](https://platform.openai.com)
 
@@ -81,4 +81,8 @@ To store **Session ID** as a Langflow variable, in the **Session ID** field, cli
 1. In the **Variable Name** field, enter a name like `customer_chat_emea`.
 2. In the **Value** field, enter a value like `1B5EBD79-6E9C-4533-B2C8-7E4FF29E983B`.
 3. Click **Save Variable**.
-4. Apply this variable to **Chat Input**.
+4. Apply this variable to **Chat Input**.import ThemedImage from "@theme/ThemedImage";
+   import useBaseUrl from "@docusaurus/useBaseUrl";
+   import ZoomableImage from "/src/theme/ZoomableImage.js";
+   import ReactPlayer from "react-player";
+   import Admonition from "@theme/Admonition";
diff --git a/docs/docs/starter-projects/vector-store-rag.mdx b/docs/docs/starter-projects/vector-store-rag.mdx
index d0054e6c4..39b9c636a 100644
--- a/docs/docs/starter-projects/vector-store-rag.mdx
+++ b/docs/docs/starter-projects/vector-store-rag.mdx
@@ -23,7 +23,7 @@ We've chosen [Astra DB](https://astra.datastax.com/signup?utm_source=langflow-pr
   to create your own Langflow workspace in minutes.
 </Admonition>
 
-- [Langflow installed and running](../getting-started/install-langflow.mdx)
+- [Langflow installed and running](../getting-started/install-langflow)
 
 - [OpenAI API key](https://platform.openai.com)
 
@@ -56,9 +56,9 @@ The **query** flow (top of the screen) allows users to chat with the embedded ve
 
 - **Chat Input** component defines where to put the user input coming from the Playground.
 - **OpenAI Embeddings** component generates embeddings from the user input.
-- **Astra DB Search** component retrieves the most relevant Records from the Astra DB database.
-- **Text Output** component turns the Records into Text by concatenating them and also displays it in the Playground.
-- **Prompt** component takes in the user input and the retrieved Records as text and builds a prompt for the OpenAI model.
+- **Astra DB Search** component retrieves the most relevant Data from the Astra DB database.
+- **Text Output** component turns the Data into Text by concatenating them and also displays it in the Playground.
+- **Prompt** component takes in the user input and the retrieved Data as text and builds a prompt for the OpenAI model.
 - **OpenAI** component generates a response to the prompt.
 - **Chat Output** component displays the response in the Playground.
 
@@ -106,4 +106,8 @@ AI
 You should use a 3/8 inch wrench to remove the oil drain cap.
 ```
 
-This is the size the engine manual lists as well. This confirms our flow works, because the query returns the unique knowledge we embedded from the Astra vector store.
+This is the size the engine manual lists as well. This confirms our flow works, because the query returns the unique knowledge we embedded from the Astra vector store.import ThemedImage from "@theme/ThemedImage";
+import useBaseUrl from "@docusaurus/useBaseUrl";
+import ZoomableImage from "/src/theme/ZoomableImage.js";
+import ReactPlayer from "react-player";
+import Admonition from "@theme/Admonition";
diff --git a/docs/docs/tutorials/rag-with-astradb.mdx b/docs/docs/tutorials/rag-with-astradb.mdx
index a80268376..8d0fbd41e 100644
--- a/docs/docs/tutorials/rag-with-astradb.mdx
+++ b/docs/docs/tutorials/rag-with-astradb.mdx
@@ -130,10 +130,10 @@ The RAG flow is a bit more complex. It consists of:
 
 - **Chat Input** component that defines where to put the user input coming from the Playground
 - **OpenAI Embeddings** component that generates embeddings from the user input
-- **Astra DB Search** component that retrieves the most relevant Records from the Astra DB database
-- **Text Output** component that turns the Records into Text by concatenating them and also displays it in the Playground
+- **Astra DB Search** component that retrieves the most relevant Data from the Astra DB database
+- **Text Output** component that turns the Data into Text by concatenating them and also displays it in the Playground
   - One interesting point you'll see here is that this component is named `Extracted Chunks`, and that is how it will appear in the Playground
-- **Prompt** component that takes in the user input and the retrieved Records as text and builds a prompt for the OpenAI model
+- **Prompt** component that takes in the user input and the retrieved Data as text and builds a prompt for the OpenAI model
 - **OpenAI** component that generates a response to the prompt
 - **Chat Output** component that displays the response in the Playground
 
@@ -170,7 +170,7 @@ Because this flow has a **Chat Input** and a **Text Output** component, the Pane
   style={{ width: "80%", margin: "20px auto" }}
 />
 
-Once we interact with it we get a response and the Extracted Chunks section is updated with the retrieved records.
+Once we interact with it we get a response and the Extracted Chunks section is updated with the retrieved Data.
 
 <ZoomableImage
   alt="Docusaurus themed image"
@@ -186,7 +186,4 @@ And that's it! You have successfully ran a RAG application using Astra DB and La
 # Conclusion
 
 In this guide, we have learned how to run a RAG application using Astra DB and Langflow.
-We have seen how to create an Astra DB database, import the Astra DB RAG Flows project into Langflow, and run the ingestion and RAG flows.import ThemedImage from "@theme/ThemedImage";
-import useBaseUrl from "@docusaurus/useBaseUrl";
-import ZoomableImage from "/src/theme/ZoomableImage.js";
-import Admonition from "@theme/Admonition";
+We have seen how to create an Astra DB database, import the Astra DB RAG Flows project into Langflow, and run the ingestion and RAG flows.
diff --git a/docs/package-lock.json b/docs/package-lock.json
index c91a4ec06..8709daa59 100644
--- a/docs/package-lock.json
+++ b/docs/package-lock.json
@@ -95,74 +95,74 @@
       }
     },
     "node_modules/@algolia/cache-browser-local-storage": {
-      "version": "4.23.2",
-      "resolved": "https://registry.npmjs.org/@algolia/cache-browser-local-storage/-/cache-browser-local-storage-4.23.2.tgz",
-      "integrity": "sha512-PvRQdCmtiU22dw9ZcTJkrVKgNBVAxKgD0/cfiqyxhA5+PHzA2WDt6jOmZ9QASkeM2BpyzClJb/Wr1yt2/t78Kw==",
+      "version": "4.23.3",
+      "resolved": "https://registry.npmjs.org/@algolia/cache-browser-local-storage/-/cache-browser-local-storage-4.23.3.tgz",
+      "integrity": "sha512-vRHXYCpPlTDE7i6UOy2xE03zHF2C8MEFjPN2v7fRbqVpcOvAUQK81x3Kc21xyb5aSIpYCjWCZbYZuz8Glyzyyg==",
       "dependencies": {
-        "@algolia/cache-common": "4.23.2"
+        "@algolia/cache-common": "4.23.3"
       }
     },
     "node_modules/@algolia/cache-common": {
-      "version": "4.23.2",
-      "resolved": "https://registry.npmjs.org/@algolia/cache-common/-/cache-common-4.23.2.tgz",
-      "integrity": "sha512-OUK/6mqr6CQWxzl/QY0/mwhlGvS6fMtvEPyn/7AHUx96NjqDA4X4+Ju7aXFQKh+m3jW9VPB0B9xvEQgyAnRPNw=="
+      "version": "4.23.3",
+      "resolved": "https://registry.npmjs.org/@algolia/cache-common/-/cache-common-4.23.3.tgz",
+      "integrity": "sha512-h9XcNI6lxYStaw32pHpB1TMm0RuxphF+Ik4o7tcQiodEdpKK+wKufY6QXtba7t3k8eseirEMVB83uFFF3Nu54A=="
     },
     "node_modules/@algolia/cache-in-memory": {
-      "version": "4.23.2",
-      "resolved": "https://registry.npmjs.org/@algolia/cache-in-memory/-/cache-in-memory-4.23.2.tgz",
-      "integrity": "sha512-rfbi/SnhEa3MmlqQvgYz/9NNJ156NkU6xFxjbxBtLWnHbpj+qnlMoKd+amoiacHRITpajg6zYbLM9dnaD3Bczw==",
+      "version": "4.23.3",
+      "resolved": "https://registry.npmjs.org/@algolia/cache-in-memory/-/cache-in-memory-4.23.3.tgz",
+      "integrity": "sha512-yvpbuUXg/+0rbcagxNT7un0eo3czx2Uf0y4eiR4z4SD7SiptwYTpbuS0IHxcLHG3lq22ukx1T6Kjtk/rT+mqNg==",
       "dependencies": {
-        "@algolia/cache-common": "4.23.2"
+        "@algolia/cache-common": "4.23.3"
       }
     },
     "node_modules/@algolia/client-account": {
-      "version": "4.23.2",
-      "resolved": "https://registry.npmjs.org/@algolia/client-account/-/client-account-4.23.2.tgz",
-      "integrity": "sha512-VbrOCLIN/5I7iIdskSoSw3uOUPF516k4SjDD4Qz3BFwa3of7D9A0lzBMAvQEJJEPHWdVraBJlGgdJq/ttmquJQ==",
+      "version": "4.23.3",
+      "resolved": "https://registry.npmjs.org/@algolia/client-account/-/client-account-4.23.3.tgz",
+      "integrity": "sha512-hpa6S5d7iQmretHHF40QGq6hz0anWEHGlULcTIT9tbUssWUriN9AUXIFQ8Ei4w9azD0hc1rUok9/DeQQobhQMA==",
       "dependencies": {
-        "@algolia/client-common": "4.23.2",
-        "@algolia/client-search": "4.23.2",
-        "@algolia/transporter": "4.23.2"
+        "@algolia/client-common": "4.23.3",
+        "@algolia/client-search": "4.23.3",
+        "@algolia/transporter": "4.23.3"
       }
     },
     "node_modules/@algolia/client-analytics": {
-      "version": "4.23.2",
-      "resolved": "https://registry.npmjs.org/@algolia/client-analytics/-/client-analytics-4.23.2.tgz",
-      "integrity": "sha512-lLj7irsAztGhMoEx/SwKd1cwLY6Daf1Q5f2AOsZacpppSvuFvuBrmkzT7pap1OD/OePjLKxicJS8wNA0+zKtuw==",
+      "version": "4.23.3",
+      "resolved": "https://registry.npmjs.org/@algolia/client-analytics/-/client-analytics-4.23.3.tgz",
+      "integrity": "sha512-LBsEARGS9cj8VkTAVEZphjxTjMVCci+zIIiRhpFun9jGDUlS1XmhCW7CTrnaWeIuCQS/2iPyRqSy1nXPjcBLRA==",
       "dependencies": {
-        "@algolia/client-common": "4.23.2",
-        "@algolia/client-search": "4.23.2",
-        "@algolia/requester-common": "4.23.2",
-        "@algolia/transporter": "4.23.2"
+        "@algolia/client-common": "4.23.3",
+        "@algolia/client-search": "4.23.3",
+        "@algolia/requester-common": "4.23.3",
+        "@algolia/transporter": "4.23.3"
       }
     },
     "node_modules/@algolia/client-common": {
-      "version": "4.23.2",
-      "resolved": "https://registry.npmjs.org/@algolia/client-common/-/client-common-4.23.2.tgz",
-      "integrity": "sha512-Q2K1FRJBern8kIfZ0EqPvUr3V29ICxCm/q42zInV+VJRjldAD9oTsMGwqUQ26GFMdFYmqkEfCbY4VGAiQhh22g==",
+      "version": "4.23.3",
+      "resolved": "https://registry.npmjs.org/@algolia/client-common/-/client-common-4.23.3.tgz",
+      "integrity": "sha512-l6EiPxdAlg8CYhroqS5ybfIczsGUIAC47slLPOMDeKSVXYG1n0qGiz4RjAHLw2aD0xzh2EXZ7aRguPfz7UKDKw==",
       "dependencies": {
-        "@algolia/requester-common": "4.23.2",
-        "@algolia/transporter": "4.23.2"
+        "@algolia/requester-common": "4.23.3",
+        "@algolia/transporter": "4.23.3"
       }
     },
     "node_modules/@algolia/client-personalization": {
-      "version": "4.23.2",
-      "resolved": "https://registry.npmjs.org/@algolia/client-personalization/-/client-personalization-4.23.2.tgz",
-      "integrity": "sha512-vwPsgnCGhUcHhhQG5IM27z8q7dWrN9itjdvgA6uKf2e9r7vB+WXt4OocK0CeoYQt3OGEAExryzsB8DWqdMK5wg==",
+      "version": "4.23.3",
+      "resolved": "https://registry.npmjs.org/@algolia/client-personalization/-/client-personalization-4.23.3.tgz",
+      "integrity": "sha512-3E3yF3Ocr1tB/xOZiuC3doHQBQ2zu2MPTYZ0d4lpfWads2WTKG7ZzmGnsHmm63RflvDeLK/UVx7j2b3QuwKQ2g==",
       "dependencies": {
-        "@algolia/client-common": "4.23.2",
-        "@algolia/requester-common": "4.23.2",
-        "@algolia/transporter": "4.23.2"
+        "@algolia/client-common": "4.23.3",
+        "@algolia/requester-common": "4.23.3",
+        "@algolia/transporter": "4.23.3"
       }
     },
     "node_modules/@algolia/client-search": {
-      "version": "4.23.2",
-      "resolved": "https://registry.npmjs.org/@algolia/client-search/-/client-search-4.23.2.tgz",
-      "integrity": "sha512-CxSB29OVGSE7l/iyoHvamMonzq7Ev8lnk/OkzleODZ1iBcCs3JC/XgTIKzN/4RSTrJ9QybsnlrN/bYCGufo7qw==",
+      "version": "4.23.3",
+      "resolved": "https://registry.npmjs.org/@algolia/client-search/-/client-search-4.23.3.tgz",
+      "integrity": "sha512-P4VAKFHqU0wx9O+q29Q8YVuaowaZ5EM77rxfmGnkHUJggh28useXQdopokgwMeYw2XUht49WX5RcTQ40rZIabw==",
       "dependencies": {
-        "@algolia/client-common": "4.23.2",
-        "@algolia/requester-common": "4.23.2",
-        "@algolia/transporter": "4.23.2"
+        "@algolia/client-common": "4.23.3",
+        "@algolia/requester-common": "4.23.3",
+        "@algolia/transporter": "4.23.3"
       }
     },
     "node_modules/@algolia/events": {
@@ -171,65 +171,65 @@
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     },
     "node_modules/@algolia/logger-common": {
-      "version": "4.23.2",
-      "resolved": "https://registry.npmjs.org/@algolia/logger-common/-/logger-common-4.23.2.tgz",
-      "integrity": "sha512-jGM49Q7626cXZ7qRAWXn0jDlzvoA1FvN4rKTi1g0hxKsTTSReyYk0i1ADWjChDPl3Q+nSDhJuosM2bBUAay7xw=="
+      "version": "4.23.3",
+      "resolved": "https://registry.npmjs.org/@algolia/logger-common/-/logger-common-4.23.3.tgz",
+      "integrity": "sha512-y9kBtmJwiZ9ZZ+1Ek66P0M68mHQzKRxkW5kAAXYN/rdzgDN0d2COsViEFufxJ0pb45K4FRcfC7+33YB4BLrZ+g=="
     },
     "node_modules/@algolia/logger-console": {
-      "version": "4.23.2",
-      "resolved": "https://registry.npmjs.org/@algolia/logger-console/-/logger-console-4.23.2.tgz",
-      "integrity": "sha512-oo+lnxxEmlhTBTFZ3fGz1O8PJ+G+8FiAoMY2Qo3Q4w23xocQev6KqDTA1JQAGPDxAewNA2VBwWOsVXeXFjrI/Q==",
+      "version": "4.23.3",
+      "resolved": "https://registry.npmjs.org/@algolia/logger-console/-/logger-console-4.23.3.tgz",
+      "integrity": "sha512-8xoiseoWDKuCVnWP8jHthgaeobDLolh00KJAdMe9XPrWPuf1by732jSpgy2BlsLTaT9m32pHI8CRfrOqQzHv3A==",
       "dependencies": {
-        "@algolia/logger-common": "4.23.2"
+        "@algolia/logger-common": "4.23.3"
       }
     },
     "node_modules/@algolia/recommend": {
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-      "integrity": "sha512-r3vXyErRCYJ7wg28yvBY5VSoAF8ZvlcW9/BwUzEtUsjvX/DKs24dIkuwjtuprwJJHsbyUbLApepYTR1BN4uHrg==",
+      "version": "2.4.5",
+      "resolved": "https://registry.npmjs.org/yaml/-/yaml-2.4.5.tgz",
+      "integrity": "sha512-aBx2bnqDzVOyNKfsysjA2ms5ZlnjSAW2eG3/L5G/CSujfjLJTJsEw1bGw8kCf04KodQWk1pxlGnZ56CRxiawmg==",
+      "bin": {
+        "yaml": "bin.mjs"
+      },
       "engines": {
-        "node": ">= 6"
+        "node": ">= 14"
       }
     },
     "node_modules/yargs": {
diff --git a/docs/static/data/AstraDB-RAG-Flows.json b/docs/static/data/AstraDB-RAG-Flows.json
index d8bd23eb2..b23d97a57 100644
--- a/docs/static/data/AstraDB-RAG-Flows.json
+++ b/docs/static/data/AstraDB-RAG-Flows.json
@@ -1,192 +1,190 @@
 {
-  "id": "51e2b78a-199b-4054-9f32-e288eef6924c",
+  "id": "c9e0cb46-c474-451a-8496-413f58481d92",
   "data": {
     "nodes": [
       {
-        "id": "ChatInput-yxMKE",
-        "type": "genericNode",
-        "position": {
-          "x": 1195.5276981160775,
-          "y": 209.421875
-        },
         "data": {
-          "type": "ChatInput",
+          "id": "ChatInput-IY8UK",
           "node": {
-            "template": {
-              "code": {
-                "type": "code",
-                "required": true,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": true,
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    def build_config(self):\n        build_config = super().build_config()\n        build_config[\"input_value\"] = {\n            \"input_types\": [],\n            \"display_name\": \"Message\",\n            \"multiline\": True,\n        }\n\n        return build_config\n\n    def build(\n        self,\n        sender: Optional[str] = \"User\",\n        sender_name: Optional[str] = \"User\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n    ) -> Union[Text, Record]:\n        return super().build(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n        )\n",
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "code",
-                "advanced": true,
-                "dynamic": true,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "input_value": {
-                "type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": true,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "input_value",
-                "display_name": "Message",
-                "advanced": false,
-                "input_types": [],
-                "dynamic": false,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false,
-                "value": "what is a line"
-              },
-              "return_record": {
-                "type": "bool",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "value": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "return_record",
-                "display_name": "Return Record",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Return the message as a record containing the sender, sender_name, and session_id.",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "sender": {
-                "type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": true,
-                "show": true,
-                "multiline": false,
-                "value": "User",
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "options": ["Machine", "User"],
-                "name": "sender",
-                "display_name": "Sender Type",
-                "advanced": true,
-                "dynamic": false,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"]
-              },
-              "sender_name": {
-                "type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "value": "User",
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "sender_name",
-                "display_name": "Sender Name",
-                "advanced": false,
-                "dynamic": false,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"]
-              },
-              "session_id": {
-                "type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "session_id",
-                "display_name": "Session ID",
-                "advanced": true,
-                "dynamic": false,
-                "info": "If provided, the message will be stored in the memory.",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"]
-              },
-              "_type": "CustomComponent"
-            },
-            "description": "Get chat inputs from the Playground.",
-            "icon": "ChatInput",
-            "base_classes": ["Text", "str", "object", "Record"],
-            "display_name": "Chat Input",
-            "documentation": "",
+            "base_classes": [
+              "Text",
+              "str",
+              "object",
+              "Record"
+            ],
+            "beta": false,
             "custom_fields": {
+              "input_value": null,
+              "return_record": null,
               "sender": null,
               "sender_name": null,
-              "input_value": null,
-              "session_id": null,
-              "return_record": null
+              "session_id": null
             },
-            "output_types": ["Text", "Record"],
+            "description": "Get chat inputs from the Playground.",
+            "display_name": "Chat Input",
+            "documentation": "",
             "field_formatters": {},
-            "frozen": false,
             "field_order": [],
-            "beta": false
-          },
-          "id": "ChatInput-yxMKE"
-        },
-        "selected": false,
-        "width": 384,
-        "height": 383
-      },
-      {
-        "id": "TextOutput-BDknO",
-        "type": "genericNode",
-        "position": {
-          "x": 2322.600672827879,
-          "y": 604.9467307442569
-        },
-        "data": {
-          "type": "TextOutput",
-          "node": {
+            "frozen": false,
+            "icon": "ChatInput",
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Message",
+                "method": "message_response",
+                "name": "message",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__",
+                "hidden": false
+              }
+            ],
             "template": {
-              "input_value": {
-                "type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "value": "",
+              "_type": "Component",
+              "code": {
+                "advanced": true,
+                "dynamic": true,
                 "fileTypes": [],
                 "file_path": "",
-                "password": false,
-                "name": "input_value",
-                "display_name": "Value",
-                "advanced": false,
-                "input_types": ["Record", "Text"],
-                "dynamic": false,
-                "info": "Text or Record to be passed as output.",
+                "info": "",
+                "list": false,
                 "load_from_db": false,
-                "title_case": false
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, FileInput, MultilineInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
+              "input_value": {
+                "advanced": false,
+                "display_name": "Text",
+                "dynamic": false,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "Message to be passed as input.",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "input_value",
+                "password": false,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "sender": {
+                "advanced": true,
+                "display_name": "Sender Type",
+                "dynamic": false,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "Type of sender.",
+                "input_types": [
+                  "Text"
+                ],
+                "list": true,
+                "load_from_db": false,
+                "multiline": false,
+                "name": "sender",
+                "options": [
+                  "Machine",
+                  "User"
+                ],
+                "password": false,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "User"
+              },
+              "sender_name": {
+                "advanced": true,
+                "display_name": "Sender Name",
+                "dynamic": false,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "Name of the sender.",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "sender_name",
+                "password": false,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "User"
+              },
+              "session_id": {
+                "advanced": true,
+                "display_name": "Session ID",
+                "dynamic": false,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "Session ID for the message.",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "session_id",
+                "password": false,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              }
+            }
+          },
+          "type": "ChatInput"
+        },
+        "dragging": false,
+        "height": 309,
+        "id": "ChatInput-IY8UK",
+        "position": {
+          "x": 702.4571951501161,
+          "y": 119.7726926425525
+        },
+        "positionAbsolute": {
+          "x": 702.4571951501161,
+          "y": 119.7726926425525
+        },
+        "selected": false,
+        "type": "genericNode",
+        "width": 384
+      },
+      {
+        "data": {
+          "description": "Display a text output in the Playground.",
+          "display_name": "Extracted Chunks",
+          "edited": false,
+          "id": "TextOutput-IxTee",
+          "node": {
+            "template": {
+              "_type": "Component",
               "code": {
                 "type": "code",
                 "required": true,
@@ -194,7 +192,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n    display_name = \"Text Output\"\n    description = \"Display a text output in the Playground.\"\n    icon = \"type\"\n\n    def build_config(self):\n        return {\n            \"input_value\": {\n                \"display_name\": \"Value\",\n                \"input_types\": [\"Record\", \"Text\"],\n                \"info\": \"Text or Record to be passed as output.\",\n            },\n            \"record_template\": {\n                \"display_name\": \"Record Template\",\n                \"multiline\": True,\n                \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n        return super().build(input_value=input_value, record_template=record_template)\n",
+                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass TextOutputComponent(TextComponent):\n    display_name = \"Text Output\"\n    description = \"Display a text output in the Playground.\"\n    icon = \"type\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as output.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n        )\n        self.status = self.input_value\n        return message\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,
@@ -205,534 +203,95 @@
                 "load_from_db": false,
                 "title_case": false
               },
-              "record_template": {
-                "type": "str",
+              "input_value": {
+                "load_from_db": false,
+                "list": false,
                 "required": false,
                 "placeholder": "",
-                "list": false,
                 "show": true,
-                "multiline": true,
-                "value": "{text}",
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "record_template",
-                "display_name": "Record Template",
-                "advanced": true,
+                "value": "",
+                "name": "input_value",
+                "display_name": "Text",
+                "advanced": false,
+                "input_types": [
+                  "Message"
+                ],
                 "dynamic": false,
-                "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
-                "load_from_db": false,
+                "info": "Text to be passed as output.",
                 "title_case": false,
-                "input_types": ["Text"]
-              },
-              "_type": "CustomComponent"
+                "type": "str"
+              }
             },
             "description": "Display a text output in the Playground.",
             "icon": "type",
-            "base_classes": ["object", "Text", "str"],
+            "base_classes": [
+              "Message"
+            ],
             "display_name": "Extracted Chunks",
             "documentation": "",
-            "custom_fields": {
-              "input_value": null,
-              "record_template": null
-            },
-            "output_types": ["Text"],
-            "field_formatters": {},
+            "custom_fields": {},
+            "output_types": [],
+            "pinned": false,
+            "conditional_paths": [],
             "frozen": false,
-            "field_order": [],
-            "beta": false
+            "outputs": [
+              {
+                "types": [
+                  "Message"
+                ],
+                "selected": "Message",
+                "name": "text",
+                "display_name": "Text",
+                "method": "text_response",
+                "value": "__UNDEFINED__",
+                "cache": true
+              }
+            ],
+            "field_order": [
+              "input_value"
+            ],
+            "beta": false,
+            "edited": true
           },
-          "id": "TextOutput-BDknO"
+          "type": "TextOutput"
+        },
+        "dragging": false,
+        "height": 309,
+        "id": "TextOutput-IxTee",
+        "position": {
+          "x": 2439.792450398153,
+          "y": 661.149562774499
+        },
+        "positionAbsolute": {
+          "x": 2439.792450398153,
+          "y": 661.149562774499
         },
         "selected": false,
-        "width": 384,
-        "height": 289,
-        "positionAbsolute": {
-          "x": 2322.600672827879,
-          "y": 604.9467307442569
-        },
-        "dragging": false
+        "type": "genericNode",
+        "width": 384
       },
       {
-        "id": "OpenAIEmbeddings-ZlOk1",
-        "type": "genericNode",
-        "position": {
-          "x": 1183.667250865064,
-          "y": 687.3171828430261
-        },
         "data": {
-          "type": "OpenAIEmbeddings",
+          "id": "OpenAIEmbeddings-HoSp5",
           "node": {
-            "template": {
-              "allowed_special": {
-                "type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "value": [],
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "allowed_special",
-                "display_name": "Allowed Special",
-                "advanced": true,
-                "dynamic": false,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"]
-              },
-              "chunk_size": {
-                "type": "int",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "value": 1000,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "chunk_size",
-                "display_name": "Chunk Size",
-                "advanced": true,
-                "dynamic": false,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "client": {
-                "type": "Any",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "client",
-                "display_name": "Client",
-                "advanced": true,
-                "dynamic": false,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "code": {
-                "type": "code",
-                "required": true,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": true,
-                "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n    display_name = \"OpenAI Embeddings\"\n    description = \"Generate embeddings using OpenAI models.\"\n\n    def build_config(self):\n        return {\n            \"allowed_special\": {\n                \"display_name\": \"Allowed Special\",\n                \"advanced\": True,\n                \"field_type\": \"str\",\n                \"is_list\": True,\n            },\n            \"default_headers\": {\n                \"display_name\": \"Default Headers\",\n                \"advanced\": True,\n                \"field_type\": \"dict\",\n            },\n            \"default_query\": {\n                \"display_name\": \"Default Query\",\n                \"advanced\": True,\n                \"field_type\": \"NestedDict\",\n            },\n            \"disallowed_special\": {\n                \"display_name\": \"Disallowed Special\",\n                \"advanced\": True,\n                \"field_type\": \"str\",\n                \"is_list\": True,\n            },\n            \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n            \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n            \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n            \"embedding_ctx_length\": {\n                \"display_name\": \"Embedding Context Length\",\n                \"advanced\": True,\n            },\n            \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n            \"model\": {\n                \"display_name\": \"Model\",\n                \"advanced\": False,\n                \"options\": [\n                    \"text-embedding-3-small\",\n                    \"text-embedding-3-large\",\n                    \"text-embedding-ada-002\",\n                ],\n            },\n            \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"password\": True,\n                \"advanced\": True,\n            },\n            \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n            \"openai_api_type\": {\n                \"display_name\": \"OpenAI API Type\",\n                \"advanced\": True,\n                \"password\": True,\n            },\n            \"openai_api_version\": {\n                \"display_name\": \"OpenAI API Version\",\n                \"advanced\": True,\n            },\n            \"openai_organization\": {\n                \"display_name\": \"OpenAI Organization\",\n                \"advanced\": True,\n            },\n            \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n            \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n            \"show_progress_bar\": {\n                \"display_name\": \"Show Progress Bar\",\n                \"advanced\": True,\n            },\n            \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n            \"tiktoken_model_name\": {\n                \"display_name\": \"TikToken Model Name\",\n                \"advanced\": True,\n            },\n            \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n        }\n\n    def build(\n        self,\n        openai_api_key: str,\n        default_headers: Optional[Dict[str, str]] = None,\n        default_query: Optional[NestedDict] = {},\n        allowed_special: List[str] = [],\n        disallowed_special: List[str] = [\"all\"],\n        chunk_size: int = 1000,\n        client: Optional[Any] = None,\n        deployment: str = \"text-embedding-ada-002\",\n        embedding_ctx_length: int = 8191,\n        max_retries: int = 6,\n        model: str = \"text-embedding-ada-002\",\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        openai_api_type: Optional[str] = None,\n        openai_api_version: Optional[str] = None,\n        openai_organization: Optional[str] = None,\n        openai_proxy: Optional[str] = None,\n        request_timeout: Optional[float] = None,\n        show_progress_bar: bool = False,\n        skip_empty: bool = False,\n        tiktoken_enable: bool = True,\n        tiktoken_model_name: Optional[str] = None,\n    ) -> Embeddings:\n        # This is to avoid errors with Vector Stores (e.g Chroma)\n        if disallowed_special == [\"all\"]:\n            disallowed_special = \"all\"  # type: ignore\n\n        return OpenAIEmbeddings(\n            tiktoken_enabled=tiktoken_enable,\n            default_headers=default_headers,\n            default_query=default_query,\n            allowed_special=set(allowed_special),\n            disallowed_special=\"all\",\n            chunk_size=chunk_size,\n            client=client,\n            deployment=deployment,\n            embedding_ctx_length=embedding_ctx_length,\n            max_retries=max_retries,\n            model=model,\n            model_kwargs=model_kwargs,\n            base_url=openai_api_base,\n            api_key=openai_api_key,\n            openai_api_type=openai_api_type,\n            api_version=openai_api_version,\n            organization=openai_organization,\n            openai_proxy=openai_proxy,\n            timeout=request_timeout,\n            show_progress_bar=show_progress_bar,\n            skip_empty=skip_empty,\n            tiktoken_model_name=tiktoken_model_name,\n        )\n",
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-                  "text-embedding-3-large",
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-                "name": "model",
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-                "display_name": "OpenAI API Key",
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-                "dynamic": false,
-                "info": "",
-                "load_from_db": true,
-                "title_case": false,
-                "input_types": ["Text"],
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-              },
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-                "type": "str",
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-                "load_from_db": false,
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-                "input_types": ["Text"]
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-                "display_name": "OpenAI API Version",
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-                "display_name": "Skip Empty",
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-                "info": "",
-                "load_from_db": false,
-                "title_case": false
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-              "tiktoken_enable": {
-                "type": "bool",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "value": true,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "tiktoken_enable",
-                "display_name": "TikToken Enable",
-                "advanced": true,
-                "dynamic": false,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "tiktoken_model_name": {
-                "type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "tiktoken_model_name",
-                "display_name": "TikToken Model Name",
-                "advanced": true,
-                "dynamic": false,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"]
-              },
-              "_type": "CustomComponent"
-            },
-            "description": "Generate embeddings using OpenAI models.",
-            "base_classes": ["Embeddings"],
-            "display_name": "OpenAI Embeddings",
-            "documentation": "",
+            "base_classes": [
+              "Embeddings"
+            ],
+            "beta": false,
             "custom_fields": {
-              "openai_api_key": null,
-              "default_headers": null,
-              "default_query": null,
               "allowed_special": null,
-              "disallowed_special": null,
               "chunk_size": null,
               "client": null,
+              "default_headers": null,
+              "default_query": null,
               "deployment": null,
+              "disallowed_special": null,
               "embedding_ctx_length": null,
               "max_retries": null,
               "model": null,
               "model_kwargs": null,
               "openai_api_base": null,
+              "openai_api_key": null,
               "openai_api_type": null,
               "openai_api_version": null,
               "openai_organization": null,
@@ -743,49 +302,393 @@
               "tiktoken_enable": null,
               "tiktoken_model_name": null
             },
-            "output_types": ["Embeddings"],
+            "description": "Generate embeddings using OpenAI models.",
+            "display_name": "OpenAI Embeddings",
+            "documentation": "",
             "field_formatters": {},
-            "frozen": false,
             "field_order": [],
-            "beta": false
-          },
-          "id": "OpenAIEmbeddings-ZlOk1"
-        },
-        "selected": false,
-        "width": 384,
-        "height": 383,
-        "dragging": false
-      },
-      {
-        "id": "OpenAIModel-EjXlN",
-        "type": "genericNode",
-        "position": {
-          "x": 3410.117202077183,
-          "y": 431.2038048137648
-        },
-        "data": {
-          "type": "OpenAIModel",
-          "node": {
+            "frozen": false,
+            "icon": "OpenAI",
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Embeddings",
+                "method": "build_embeddings",
+                "name": "embeddings",
+                "selected": "Embeddings",
+                "types": [
+                  "Embeddings"
+                ],
+                "value": "__UNDEFINED__",
+                "hidden": false
+              }
+            ],
             "template": {
-              "input_value": {
-                "type": "str",
-                "required": true,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "input_value",
-                "display_name": "Input",
-                "advanced": false,
+              "_type": "Component",
+              "chunk_size": {
+                "advanced": true,
+                "display_name": "Chunk Size",
                 "dynamic": false,
                 "info": "",
-                "load_from_db": false,
+                "list": false,
+                "name": "chunk_size",
+                "placeholder": "",
+                "required": false,
+                "show": true,
                 "title_case": false,
-                "input_types": ["Text"]
+                "type": "int",
+                "value": 1000
               },
+              "client": {
+                "advanced": true,
+                "display_name": "Client",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "client",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import Embeddings\nfrom langflow.io import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, Output, SecretStrInput, TextInput\n\n\nclass OpenAIEmbeddingsComponent(LCModelComponent):\n    display_name = \"OpenAI Embeddings\"\n    description = \"Generate embeddings using OpenAI models.\"\n    icon = \"OpenAI\"\n    inputs = [\n        DictInput(\n            name=\"default_headers\",\n            display_name=\"Default Headers\",\n            advanced=True,\n            info=\"Default headers to use for the API request.\",\n        ),\n        DictInput(\n            name=\"default_query\",\n            display_name=\"Default Query\",\n            advanced=True,\n            info=\"Default query parameters to use for the API request.\",\n        ),\n        IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n        TextInput(name=\"client\", display_name=\"Client\", advanced=True),\n        TextInput(name=\"deployment\", display_name=\"Deployment\", advanced=True),\n        IntInput(name=\"embedding_ctx_length\", display_name=\"Embedding Context Length\", advanced=True, value=1536),\n        IntInput(name=\"max_retries\", display_name=\"Max Retries\", value=3, advanced=True),\n        DropdownInput(\n            name=\"model\",\n            display_name=\"Model\",\n            advanced=False,\n            options=[\n                \"text-embedding-3-small\",\n                \"text-embedding-3-large\",\n                \"text-embedding-ada-002\",\n            ],\n            value=\"text-embedding-3-small\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        SecretStrInput(name=\"openai_api_base\", display_name=\"OpenAI API Base\", advanced=True),\n        SecretStrInput(name=\"openai_api_key\", display_name=\"OpenAI API Key\"),\n        SecretStrInput(name=\"openai_api_type\", display_name=\"OpenAI API Type\", advanced=True),\n        TextInput(name=\"openai_api_version\", display_name=\"OpenAI API Version\", advanced=True),\n        TextInput(\n            name=\"openai_organization\",\n            display_name=\"OpenAI Organization\",\n            advanced=True,\n        ),\n        TextInput(name=\"openai_proxy\", display_name=\"OpenAI Proxy\", advanced=True),\n        FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n        BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n        BoolInput(name=\"skip_empty\", display_name=\"Skip Empty\", advanced=True),\n        TextInput(\n            name=\"tiktoken_model_name\",\n            display_name=\"TikToken Model Name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"tiktoken_enable\",\n            display_name=\"TikToken Enable\",\n            advanced=True,\n            value=True,\n            info=\"If False, you must have transformers installed.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Embeddings\", name=\"embeddings\", method=\"build_embeddings\"),\n    ]\n\n    def build_embeddings(self) -> Embeddings:\n        return OpenAIEmbeddings(\n            tiktoken_enabled=self.tiktoken_enable,\n            default_headers=self.default_headers,\n            default_query=self.default_query,\n            allowed_special=\"all\",\n            disallowed_special=\"all\",\n            chunk_size=self.chunk_size,\n            deployment=self.deployment,\n            embedding_ctx_length=self.embedding_ctx_length,\n            max_retries=self.max_retries,\n            model=self.model,\n            model_kwargs=self.model_kwargs,\n            base_url=self.openai_api_base,\n            api_key=self.openai_api_key,\n            openai_api_type=self.openai_api_type,\n            api_version=self.openai_api_version,\n            organization=self.openai_organization,\n            openai_proxy=self.openai_proxy,\n            timeout=self.request_timeout or None,\n            show_progress_bar=self.show_progress_bar,\n            skip_empty=self.skip_empty,\n            tiktoken_model_name=self.tiktoken_model_name,\n        )\n"
+              },
+              "default_headers": {
+                "advanced": true,
+                "display_name": "Default Headers",
+                "dynamic": false,
+                "info": "Default headers to use for the API request.",
+                "list": false,
+                "name": "default_headers",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "dict",
+                "value": {}
+              },
+              "default_query": {
+                "advanced": true,
+                "display_name": "Default Query",
+                "dynamic": false,
+                "info": "Default query parameters to use for the API request.",
+                "list": false,
+                "name": "default_query",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "dict",
+                "value": {}
+              },
+              "deployment": {
+                "advanced": true,
+                "display_name": "Deployment",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "deployment",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "embedding_ctx_length": {
+                "advanced": true,
+                "display_name": "Embedding Context Length",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "embedding_ctx_length",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": 1536
+              },
+              "max_retries": {
+                "advanced": true,
+                "display_name": "Max Retries",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "max_retries",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": 3
+              },
+              "model": {
+                "advanced": false,
+                "display_name": "Model",
+                "dynamic": false,
+                "info": "",
+                "name": "model",
+                "options": [
+                  "text-embedding-3-small",
+                  "text-embedding-3-large",
+                  "text-embedding-ada-002"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "text-embedding-3-small"
+              },
+              "model_kwargs": {
+                "advanced": true,
+                "display_name": "Model Kwargs",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "model_kwargs",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "dict",
+                "value": {}
+              },
+              "openai_api_base": {
+                "advanced": true,
+                "display_name": "OpenAI API Base",
+                "dynamic": false,
+                "info": "",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "openai_api_base",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "openai_api_key": {
+                "advanced": false,
+                "display_name": "OpenAI API Key",
+                "dynamic": false,
+                "info": "",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "openai_api_key",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "openai_api_type": {
+                "advanced": true,
+                "display_name": "OpenAI API Type",
+                "dynamic": false,
+                "info": "",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "openai_api_type",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "openai_api_version": {
+                "advanced": true,
+                "display_name": "OpenAI API Version",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "openai_api_version",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "openai_organization": {
+                "advanced": true,
+                "display_name": "OpenAI Organization",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "openai_organization",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "openai_proxy": {
+                "advanced": true,
+                "display_name": "OpenAI Proxy",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "openai_proxy",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "request_timeout": {
+                "advanced": true,
+                "display_name": "Request Timeout",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "request_timeout",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "float",
+                "value": ""
+              },
+              "show_progress_bar": {
+                "advanced": true,
+                "display_name": "Show Progress Bar",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "show_progress_bar",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "bool",
+                "value": false
+              },
+              "skip_empty": {
+                "advanced": true,
+                "display_name": "Skip Empty",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "skip_empty",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "bool",
+                "value": false
+              },
+              "tiktoken_enable": {
+                "advanced": true,
+                "display_name": "TikToken Enable",
+                "dynamic": false,
+                "info": "If False, you must have transformers installed.",
+                "list": false,
+                "name": "tiktoken_enable",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "bool",
+                "value": true
+              },
+              "tiktoken_model_name": {
+                "advanced": true,
+                "display_name": "TikToken Model Name",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tiktoken_model_name",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              }
+            }
+          },
+          "type": "OpenAIEmbeddings"
+        },
+        "dragging": false,
+        "height": 395,
+        "id": "OpenAIEmbeddings-HoSp5",
+        "position": {
+          "x": 690.5967478991026,
+          "y": 597.6680004855787
+        },
+        "positionAbsolute": {
+          "x": 690.5967478991026,
+          "y": 597.6680004855787
+        },
+        "selected": false,
+        "type": "genericNode",
+        "width": 384
+      },
+      {
+        "data": {
+          "id": "OpenAIModel-ickkA",
+          "node": {
+            "template": {
+              "_type": "Component",
               "code": {
                 "type": "code",
                 "required": true,
@@ -793,7 +696,7 @@
                 "list": false,
                 "show": true,
                 "multiline": true,
-                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\"},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": [\n                    \"gpt-4-turbo-preview\",\n                    \"gpt-3.5-turbo\",\n                    \"gpt-4-0125-preview\",\n                    \"gpt-4-1106-preview\",\n                    \"gpt-4-vision-preview\",\n                    \"gpt-3.5-turbo-0125\",\n                    \"gpt-3.5-turbo-1106\",\n                ],\n                \"value\": \"gpt-4-turbo-preview\",\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float,\n        model_name: str,\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        output = ChatOpenAI(\n            max_tokens=max_tokens,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=openai_api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n",
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    inputs = [\n        MessageInput(name=\"input_value\", display_name=\"Input\"),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:\n        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict)\n        seed = self.seed\n        model_kwargs[\"seed\"] = seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n",
                 "fileTypes": [],
                 "file_path": "",
                 "password": false,
@@ -804,2059 +707,723 @@
                 "load_from_db": false,
                 "title_case": false
               },
-              "max_tokens": {
-                "type": "int",
+              "input_value": {
+                "load_from_db": false,
+                "list": false,
                 "required": false,
                 "placeholder": "",
-                "list": false,
                 "show": true,
-                "multiline": false,
-                "value": 256,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
+                "value": "",
+                "name": "input_value",
+                "display_name": "Input",
+                "advanced": false,
+                "input_types": [
+                  "Message"
+                ],
+                "dynamic": false,
+                "info": "",
+                "title_case": false,
+                "type": "str"
+              },
+              "max_tokens": {
+                "list": false,
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "value": "",
                 "name": "max_tokens",
                 "display_name": "Max Tokens",
                 "advanced": true,
                 "dynamic": false,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false
+                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+                "title_case": false,
+                "type": "int"
               },
               "model_kwargs": {
-                "type": "NestedDict",
+                "list": false,
                 "required": false,
                 "placeholder": "",
-                "list": false,
                 "show": true,
-                "multiline": false,
                 "value": {},
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
                 "name": "model_kwargs",
                 "display_name": "Model Kwargs",
                 "advanced": true,
                 "dynamic": false,
                 "info": "",
-                "load_from_db": false,
-                "title_case": false
+                "title_case": false,
+                "type": "dict"
               },
               "model_name": {
-                "type": "str",
-                "required": true,
-                "placeholder": "",
-                "list": true,
-                "show": true,
-                "multiline": false,
-                "value": "gpt-3.5-turbo",
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
                 "options": [
+                  "gpt-4o",
+                  "gpt-4-turbo",
                   "gpt-4-turbo-preview",
                   "gpt-3.5-turbo",
-                  "gpt-4-0125-preview",
-                  "gpt-4-1106-preview",
-                  "gpt-4-vision-preview",
-                  "gpt-3.5-turbo-0125",
-                  "gpt-3.5-turbo-1106"
+                  "gpt-3.5-turbo-0125"
                 ],
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "value": "gpt-3.5-turbo",
                 "name": "model_name",
                 "display_name": "Model Name",
                 "advanced": false,
                 "dynamic": false,
                 "info": "",
-                "load_from_db": false,
                 "title_case": false,
-                "input_types": ["Text"]
+                "type": "str"
               },
               "openai_api_base": {
-                "type": "str",
+                "load_from_db": false,
+                "list": false,
                 "required": false,
                 "placeholder": "",
-                "list": false,
                 "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
+                "value": "",
                 "name": "openai_api_base",
                 "display_name": "OpenAI API Base",
                 "advanced": true,
                 "dynamic": false,
-                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
-                "load_from_db": false,
+                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.",
                 "title_case": false,
-                "input_types": ["Text"]
+                "type": "str"
               },
               "openai_api_key": {
-                "type": "str",
-                "required": true,
+                "load_from_db": true,
+                "required": false,
                 "placeholder": "",
-                "list": false,
                 "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": true,
+                "value": "",
                 "name": "openai_api_key",
                 "display_name": "OpenAI API Key",
                 "advanced": false,
+                "input_types": [],
                 "dynamic": false,
                 "info": "The OpenAI API Key to use for the OpenAI model.",
-                "load_from_db": true,
                 "title_case": false,
-                "input_types": ["Text"],
-                "value": "OPENAI_API_KEY"
+                "password": true,
+                "type": "str"
               },
-              "stream": {
-                "type": "bool",
+              "output_schema": {
+                "list": true,
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "value": {},
+                "name": "output_schema",
+                "display_name": "Schema",
+                "advanced": true,
+                "dynamic": false,
+                "info": "The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.",
+                "title_case": false,
+                "type": "dict"
+              },
+              "seed": {
+                "list": false,
+                "required": false,
+                "placeholder": "",
+                "show": true,
+                "value": 1,
+                "name": "seed",
+                "display_name": "Seed",
+                "advanced": true,
+                "dynamic": false,
+                "info": "The seed controls the reproducibility of the job.",
+                "title_case": false,
+                "type": "int"
+              },
+              "stream": {
+                "list": false,
                 "required": false,
                 "placeholder": "",
-                "list": false,
                 "show": true,
-                "multiline": false,
                 "value": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
                 "name": "stream",
                 "display_name": "Stream",
                 "advanced": true,
                 "dynamic": false,
                 "info": "Stream the response from the model. Streaming works only in Chat.",
-                "load_from_db": false,
-                "title_case": false
+                "title_case": false,
+                "type": "bool"
               },
               "system_message": {
-                "type": "str",
+                "load_from_db": false,
+                "list": false,
                 "required": false,
                 "placeholder": "",
-                "list": false,
                 "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
+                "value": "",
                 "name": "system_message",
                 "display_name": "System Message",
                 "advanced": true,
                 "dynamic": false,
                 "info": "System message to pass to the model.",
-                "load_from_db": false,
                 "title_case": false,
-                "input_types": ["Text"]
+                "type": "str"
               },
               "temperature": {
-                "type": "float",
-                "required": true,
-                "placeholder": "",
                 "list": false,
+                "required": false,
+                "placeholder": "",
                 "show": true,
-                "multiline": false,
                 "value": 0.1,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
                 "name": "temperature",
                 "display_name": "Temperature",
                 "advanced": false,
                 "dynamic": false,
                 "info": "",
-                "rangeSpec": {
-                  "step_type": "float",
-                  "min": -1,
-                  "max": 1,
-                  "step": 0.1
-                },
-                "load_from_db": false,
-                "title_case": false
-              },
-              "_type": "CustomComponent"
+                "title_case": false,
+                "type": "float"
+              }
             },
             "description": "Generates text using OpenAI LLMs.",
             "icon": "OpenAI",
-            "base_classes": ["object", "Text", "str"],
+            "base_classes": [
+              "LanguageModel",
+              "Message"
+            ],
             "display_name": "OpenAI",
             "documentation": "",
-            "custom_fields": {
-              "input_value": null,
-              "openai_api_key": null,
-              "temperature": null,
-              "model_name": null,
-              "max_tokens": null,
-              "model_kwargs": null,
-              "openai_api_base": null,
-              "stream": null,
-              "system_message": null
-            },
-            "output_types": ["Text"],
-            "field_formatters": {},
+            "custom_fields": {},
+            "output_types": [],
+            "pinned": false,
+            "conditional_paths": [],
             "frozen": false,
+            "outputs": [
+              {
+                "types": [
+                  "Message"
+                ],
+                "selected": "Message",
+                "name": "text_output",
+                "display_name": "Text",
+                "method": "text_response",
+                "value": "__UNDEFINED__",
+                "cache": true,
+                "hidden": false
+              },
+              {
+                "types": [
+                  "LanguageModel"
+                ],
+                "selected": "LanguageModel",
+                "name": "model_output",
+                "display_name": "Language Model",
+                "method": "build_model",
+                "value": "__UNDEFINED__",
+                "cache": true
+              }
+            ],
             "field_order": [
+              "input_value",
               "max_tokens",
               "model_kwargs",
+              "output_schema",
               "model_name",
               "openai_api_base",
               "openai_api_key",
               "temperature",
-              "input_value",
+              "stream",
               "system_message",
-              "stream"
+              "seed"
             ],
-            "beta": false
+            "beta": false,
+            "edited": true
           },
-          "id": "OpenAIModel-EjXlN"
+          "type": "OpenAIModel",
+          "description": "Generates text using OpenAI LLMs.",
+          "display_name": "OpenAI",
+          "edited": false
+        },
+        "dragging": false,
+        "height": 623,
+        "id": "OpenAIModel-ickkA",
+        "position": {
+          "x": 3410.117202077183,
+          "y": 431.2038048137648
         },
-        "selected": true,
-        "width": 384,
-        "height": 563,
         "positionAbsolute": {
           "x": 3410.117202077183,
           "y": 431.2038048137648
         },
-        "dragging": false
+        "selected": false,
+        "type": "genericNode",
+        "width": 384
       },
       {
-        "id": "Prompt-xeI6K",
-        "type": "genericNode",
-        "position": {
-          "x": 2969.0261961391298,
-          "y": 442.1613649809069
-        },
         "data": {
-          "type": "Prompt",
+          "description": "Create a prompt template with dynamic variables.",
+          "display_name": "Prompt",
+          "id": "Prompt-jzPqb",
           "node": {
-            "template": {
-              "code": {
-                "type": "code",
-                "required": true,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": true,
-                "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Text:\n        from langflow.base.prompts.utils import dict_values_to_string\n\n        prompt_template = PromptTemplate.from_template(Text(template))\n        kwargs = dict_values_to_string(kwargs)\n        kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n        try:\n            formated_prompt = prompt_template.format(**kwargs)\n        except Exception as exc:\n            raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n        self.status = f'Prompt:\\n\"{formated_prompt}\"'\n        return formated_prompt\n",
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "code",
-                "advanced": true,
-                "dynamic": true,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "template": {
-                "type": "prompt",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "value": "{context}\n\n---\n\nGiven the context above, answer the question as best as possible.\n\nQuestion: {question}\n\nAnswer: ",
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "template",
-                "display_name": "Template",
-                "advanced": false,
-                "input_types": ["Text"],
-                "dynamic": false,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "_type": "CustomComponent",
-              "context": {
-                "field_type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": true,
-                "value": "",
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "context",
-                "display_name": "context",
-                "advanced": false,
-                "input_types": [
-                  "Document",
-                  "BaseOutputParser",
-                  "Record",
-                  "Text"
-                ],
-                "dynamic": false,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false,
-                "type": "str"
-              },
-              "question": {
-                "field_type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": true,
-                "value": "",
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "question",
-                "display_name": "question",
-                "advanced": false,
-                "input_types": [
-                  "Document",
-                  "BaseOutputParser",
-                  "Record",
-                  "Text"
-                ],
-                "dynamic": false,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false,
-                "type": "str"
-              }
+            "base_classes": [
+              "object",
+              "str",
+              "Text"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {
+              "template": [
+                "context",
+                "question"
+              ]
             },
             "description": "Create a prompt template with dynamic variables.",
-            "icon": "prompts",
-            "is_input": null,
-            "is_output": null,
-            "is_composition": null,
-            "base_classes": ["object", "Text", "str"],
-            "name": "",
             "display_name": "Prompt",
             "documentation": "",
-            "custom_fields": {
-              "template": ["context", "question"]
-            },
-            "output_types": ["Text"],
-            "full_path": null,
-            "field_formatters": {},
-            "frozen": false,
+            "error": null,
             "field_order": [],
-            "beta": false,
-            "error": null
-          },
-          "id": "Prompt-xeI6K",
-          "description": "Create a prompt template with dynamic variables.",
-          "display_name": "Prompt"
-        },
-        "selected": false,
-        "width": 384,
-        "height": 477,
-        "positionAbsolute": {
-          "x": 2969.0261961391298,
-          "y": 442.1613649809069
-        },
-        "dragging": false
-      },
-      {
-        "id": "ChatOutput-Q39I8",
-        "type": "genericNode",
-        "position": {
-          "x": 3887.2073667611485,
-          "y": 588.4801225794856
-        },
-        "data": {
-          "type": "ChatOutput",
-          "node": {
+            "frozen": false,
+            "full_path": null,
+            "icon": "prompts",
+            "is_composition": null,
+            "is_input": null,
+            "is_output": null,
+            "name": "",
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Prompt Message",
+                "method": "build_prompt",
+                "name": "prompt",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__",
+                "hidden": false
+              }
+            ],
+            "pinned": false,
             "template": {
+              "_type": "Component",
               "code": {
-                "type": "code",
-                "required": true,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": true,
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        return_record: Optional[bool] = False,\n        record_template: Optional[str] = \"{text}\",\n    ) -> Union[Text, Record]:\n        return super().build(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            return_record=return_record,\n            record_template=record_template,\n        )\n",
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "code",
                 "advanced": true,
                 "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
                 "info": "",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "input_value": {
-                "type": "str",
-                "required": false,
-                "placeholder": "",
                 "list": false,
-                "show": true,
+                "load_from_db": false,
                 "multiline": true,
-                "fileTypes": [],
-                "file_path": "",
+                "name": "code",
                 "password": false,
-                "name": "input_value",
-                "display_name": "Message",
-                "advanced": false,
-                "input_types": ["Text"],
-                "dynamic": false,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "record_template": {
-                "type": "str",
-                "required": false,
                 "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": true,
-                "value": "{text}",
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "record_template",
-                "display_name": "Record Template",
-                "advanced": true,
-                "dynamic": false,
-                "info": "In case of Message being a Record, this template will be used to convert it to text.",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"]
-              },
-              "return_record": {
-                "type": "bool",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "value": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "return_record",
-                "display_name": "Return Record",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Return the message as a record containing the sender, sender_name, and session_id.",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "sender": {
-                "type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": true,
-                "show": true,
-                "multiline": false,
-                "value": "Machine",
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "options": ["Machine", "User"],
-                "name": "sender",
-                "display_name": "Sender Type",
-                "advanced": true,
-                "dynamic": false,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"]
-              },
-              "sender_name": {
-                "type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "value": "AI",
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "sender_name",
-                "display_name": "Sender Name",
-                "advanced": false,
-                "dynamic": false,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"]
-              },
-              "session_id": {
-                "type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "session_id",
-                "display_name": "Session ID",
-                "advanced": true,
-                "dynamic": false,
-                "info": "If provided, the message will be stored in the memory.",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"]
-              },
-              "_type": "CustomComponent"
-            },
-            "description": "Display a chat message in the Playground.",
-            "icon": "ChatOutput",
-            "base_classes": ["object", "Text", "Record", "str"],
-            "display_name": "Chat Output",
-            "documentation": "",
-            "custom_fields": {
-              "sender": null,
-              "sender_name": null,
-              "input_value": null,
-              "session_id": null,
-              "return_record": null,
-              "record_template": null
-            },
-            "output_types": ["Text", "Record"],
-            "field_formatters": {},
-            "frozen": false,
-            "field_order": [],
-            "beta": false
-          },
-          "id": "ChatOutput-Q39I8"
-        },
-        "selected": false,
-        "width": 384,
-        "height": 383,
-        "positionAbsolute": {
-          "x": 3887.2073667611485,
-          "y": 588.4801225794856
-        },
-        "dragging": false
-      },
-      {
-        "id": "File-t0a6a",
-        "type": "genericNode",
-        "position": {
-          "x": 2257.233450682836,
-          "y": 1747.5389618367233
-        },
-        "data": {
-          "type": "File",
-          "node": {
-            "template": {
-              "path": {
-                "type": "file",
                 "required": true,
-                "placeholder": "",
-                "list": false,
                 "show": true,
-                "multiline": false,
-                "fileTypes": [
-                  ".txt",
-                  ".md",
-                  ".mdx",
-                  ".csv",
-                  ".json",
-                  ".yaml",
-                  ".yml",
-                  ".xml",
-                  ".html",
-                  ".htm",
-                  ".pdf",
-                  ".docx"
-                ],
-                "file_path": "51e2b78a-199b-4054-9f32-e288eef6924c/Langflow conversation.pdf",
-                "password": false,
-                "name": "path",
-                "display_name": "Path",
-                "advanced": false,
-                "dynamic": false,
-                "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx",
-                "load_from_db": false,
                 "title_case": false,
+                "type": "code",
+                "value": "from langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n"
+              },
+              "context": {
+                "advanced": false,
+                "display_name": "context",
+                "dynamic": false,
+                "field_type": "str",
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "Text"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "context",
+                "password": false,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
                 "value": ""
               },
-              "code": {
-                "type": "code",
-                "required": true,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": true,
-                "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n    display_name = \"File\"\n    description = \"A generic file loader.\"\n    icon = \"file-text\"\n\n    def build_config(self) -> Dict[str, Any]:\n        return {\n            \"path\": {\n                \"display_name\": \"Path\",\n                \"field_type\": \"file\",\n                \"file_types\": TEXT_FILE_TYPES,\n                \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n            },\n            \"silent_errors\": {\n                \"display_name\": \"Silent Errors\",\n                \"advanced\": True,\n                \"info\": \"If true, errors will not raise an exception.\",\n            },\n        }\n\n    def load_file(self, path: str, silent_errors: bool = False) -> Record:\n        resolved_path = self.resolve_path(path)\n        path_obj = Path(resolved_path)\n        extension = path_obj.suffix[1:].lower()\n        if extension == \"doc\":\n            raise ValueError(\"doc files are not supported. Please save as .docx\")\n        if extension not in TEXT_FILE_TYPES:\n            raise ValueError(f\"Unsupported file type: {extension}\")\n        record = parse_text_file_to_record(resolved_path, silent_errors)\n        self.status = record if record else \"No data\"\n        return record or Record()\n\n    def build(\n        self,\n        path: str,\n        silent_errors: bool = False,\n    ) -> Record:\n        record = self.load_file(path, silent_errors)\n        self.status = record\n        return record\n",
+              "question": {
+                "advanced": false,
+                "display_name": "question",
+                "dynamic": false,
+                "field_type": "str",
                 "fileTypes": [],
                 "file_path": "",
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "Text"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "question",
                 "password": false,
-                "name": "code",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "template": {
+                "advanced": false,
+                "display_name": "Template",
+                "dynamic": false,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "input_types": [
+                  "Text"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": false,
+                "name": "template",
+                "password": false,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "prompt",
+                "value": "{context}\n\n---\n\nGiven the context above, answer the question as best as possible.\n\nQuestion: {question}\n\nAnswer: "
+              }
+            }
+          },
+          "type": "Prompt"
+        },
+        "dragging": false,
+        "height": 525,
+        "id": "Prompt-jzPqb",
+        "position": {
+          "x": 2941.2776396951576,
+          "y": 446.43037366459487
+        },
+        "positionAbsolute": {
+          "x": 2941.2776396951576,
+          "y": 446.43037366459487
+        },
+        "selected": false,
+        "type": "genericNode",
+        "width": 384
+      },
+      {
+        "data": {
+          "id": "ChatOutput-Zy354",
+          "node": {
+            "base_classes": [
+              "object",
+              "Text",
+              "Record",
+              "str"
+            ],
+            "beta": false,
+            "custom_fields": {
+              "input_value": null,
+              "record_template": null,
+              "return_record": null,
+              "sender": null,
+              "sender_name": null,
+              "session_id": null
+            },
+            "description": "Display a chat message in the Playground.",
+            "display_name": "Chat Output",
+            "documentation": "",
+            "field_formatters": {},
+            "field_order": [],
+            "frozen": false,
+            "icon": "ChatOutput",
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Message",
+                "method": "message_response",
+                "name": "message",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "template": {
+              "_type": "Component",
+              "code": {
                 "advanced": true,
                 "dynamic": true,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "silent_errors": {
-                "type": "bool",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "value": false,
                 "fileTypes": [],
                 "file_path": "",
-                "password": false,
-                "name": "silent_errors",
-                "display_name": "Silent Errors",
-                "advanced": true,
-                "dynamic": false,
-                "info": "If true, errors will not raise an exception.",
+                "info": "",
+                "list": false,
                 "load_from_db": false,
-                "title_case": false
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
-              "_type": "CustomComponent"
-            },
-            "description": "A generic file loader.",
-            "icon": "file-text",
-            "base_classes": ["Record"],
-            "display_name": "File",
-            "documentation": "",
+              "input_value": {
+                "advanced": false,
+                "display_name": "Text",
+                "dynamic": false,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "Message to be passed as output.",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "input_value",
+                "password": false,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "sender": {
+                "advanced": true,
+                "display_name": "Sender Type",
+                "dynamic": false,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "Type of sender.",
+                "input_types": [
+                  "Text"
+                ],
+                "list": true,
+                "load_from_db": false,
+                "multiline": false,
+                "name": "sender",
+                "options": [
+                  "Machine",
+                  "User"
+                ],
+                "password": false,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "Machine"
+              },
+              "sender_name": {
+                "advanced": true,
+                "display_name": "Sender Name",
+                "dynamic": false,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "Name of the sender.",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "sender_name",
+                "password": false,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "AI"
+              },
+              "session_id": {
+                "advanced": true,
+                "display_name": "Session ID",
+                "dynamic": false,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "Session ID for the message.",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "session_id",
+                "password": false,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              }
+            }
+          },
+          "type": "ChatOutput"
+        },
+        "dragging": false,
+        "height": 309,
+        "id": "ChatOutput-Zy354",
+        "position": {
+          "x": 3998.201592537035,
+          "y": 603.4216529723935
+        },
+        "positionAbsolute": {
+          "x": 3998.201592537035,
+          "y": 603.4216529723935
+        },
+        "selected": false,
+        "type": "genericNode",
+        "width": 384
+      },
+      {
+        "data": {
+          "id": "File-28ckd",
+          "node": {
+            "base_classes": [
+              "Record"
+            ],
+            "beta": false,
             "custom_fields": {
               "path": null,
               "silent_errors": null
             },
-            "output_types": ["Record"],
+            "description": "A generic file loader.",
+            "display_name": "File",
+            "documentation": "",
             "field_formatters": {},
-            "frozen": false,
             "field_order": [],
-            "beta": false
+            "frozen": false,
+            "icon": "file-text",
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Data",
+                "method": "load_file",
+                "name": "data",
+                "selected": "Data",
+                "types": [
+                  "Data"
+                ],
+                "value": "__UNDEFINED__",
+                "hidden": false
+              }
+            ],
+            "template": {
+              "_type": "Component",
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from pathlib import Path\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_data\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, FileInput, Output\nfrom langflow.schema import Data\n\n\nclass FileComponent(Component):\n    display_name = \"File\"\n    description = \"A generic file loader.\"\n    icon = \"file-text\"\n\n    inputs = [\n        FileInput(\n            name=\"path\",\n            display_name=\"Path\",\n            file_types=TEXT_FILE_TYPES,\n            info=f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n        ),\n        BoolInput(\n            name=\"silent_errors\",\n            display_name=\"Silent Errors\",\n            advanced=True,\n            info=\"If true, errors will not raise an exception.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"load_file\"),\n    ]\n\n    def load_file(self) -> Data:\n        if not self.path:\n            raise ValueError(\"Please, upload a file to use this component.\")\n        resolved_path = self.resolve_path(self.path)\n        silent_errors = self.silent_errors\n\n        extension = Path(resolved_path).suffix[1:].lower()\n\n        if extension == \"doc\":\n            raise ValueError(\"doc files are not supported. Please save as .docx\")\n        if extension not in TEXT_FILE_TYPES:\n            raise ValueError(f\"Unsupported file type: {extension}\")\n\n        data = parse_text_file_to_data(resolved_path, silent_errors)\n        self.status = data if data else \"No data\"\n        return data or Data()\n"
+              },
+              "path": {
+                "advanced": false,
+                "display_name": "Path",
+                "dynamic": false,
+                "fileTypes": [
+                  "txt",
+                  "md",
+                  "mdx",
+                  "csv",
+                  "json",
+                  "yaml",
+                  "yml",
+                  "xml",
+                  "html",
+                  "htm",
+                  "pdf",
+                  "docx",
+                  "py",
+                  "sh",
+                  "sql",
+                  "js",
+                  "ts",
+                  "tsx"
+                ],
+                "file_path": "c9e0cb46-c474-451a-8496-413f58481d92/Context Once.json",
+                "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx, py, sh, sql, js, ts, tsx",
+                "list": false,
+                "name": "path",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "file",
+                "value": ""
+              },
+              "silent_errors": {
+                "advanced": true,
+                "display_name": "Silent Errors",
+                "dynamic": false,
+                "info": "If true, errors will not raise an exception.",
+                "list": false,
+                "name": "silent_errors",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "bool",
+                "value": false
+              }
+            }
           },
-          "id": "File-t0a6a"
+          "type": "File"
+        },
+        "dragging": false,
+        "height": 301,
+        "id": "File-28ckd",
+        "position": {
+          "x": 2257.233450682836,
+          "y": 1747.5389618367233
         },
-        "selected": false,
-        "width": 384,
-        "height": 281,
         "positionAbsolute": {
           "x": 2257.233450682836,
           "y": 1747.5389618367233
         },
-        "dragging": false
-      },
-      {
-        "id": "RecursiveCharacterTextSplitter-tR9QM",
-        "type": "genericNode",
-        "position": {
-          "x": 2791.013514133929,
-          "y": 1462.9588953494142
-        },
-        "data": {
-          "type": "RecursiveCharacterTextSplitter",
-          "node": {
-            "template": {
-              "inputs": {
-                "type": "Document",
-                "required": true,
-                "placeholder": "",
-                "list": true,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "inputs",
-                "display_name": "Input",
-                "advanced": false,
-                "input_types": ["Document", "Record"],
-                "dynamic": false,
-                "info": "The texts to split.",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "chunk_overlap": {
-                "type": "int",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "value": 200,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "chunk_overlap",
-                "display_name": "Chunk Overlap",
-                "advanced": false,
-                "dynamic": false,
-                "info": "The amount of overlap between chunks.",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "chunk_size": {
-                "type": "int",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "value": 1000,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "chunk_size",
-                "display_name": "Chunk Size",
-                "advanced": false,
-                "dynamic": false,
-                "info": "The maximum length of each chunk.",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "code": {
-                "type": "code",
-                "required": true,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": true,
-                "value": "from typing import Optional\n\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\nfrom langchain_core.documents import Document\n\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\nfrom langflow.utils.util import build_loader_repr_from_records, unescape_string\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n    display_name: str = \"Recursive Character Text Splitter\"\n    description: str = \"Split text into chunks of a specified length.\"\n    documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n    def build_config(self):\n        return {\n            \"inputs\": {\n                \"display_name\": \"Input\",\n                \"info\": \"The texts to split.\",\n                \"input_types\": [\"Document\", \"Record\"],\n            },\n            \"separators\": {\n                \"display_name\": \"Separators\",\n                \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n                \"is_list\": True,\n            },\n            \"chunk_size\": {\n                \"display_name\": \"Chunk Size\",\n                \"info\": \"The maximum length of each chunk.\",\n                \"field_type\": \"int\",\n                \"value\": 1000,\n            },\n            \"chunk_overlap\": {\n                \"display_name\": \"Chunk Overlap\",\n                \"info\": \"The amount of overlap between chunks.\",\n                \"field_type\": \"int\",\n                \"value\": 200,\n            },\n            \"code\": {\"show\": False},\n        }\n\n    def build(\n        self,\n        inputs: list[Document],\n        separators: Optional[list[str]] = None,\n        chunk_size: Optional[int] = 1000,\n        chunk_overlap: Optional[int] = 200,\n    ) -> list[Record]:\n        \"\"\"\n        Split text into chunks of a specified length.\n\n        Args:\n            separators (list[str]): The characters to split on.\n            chunk_size (int): The maximum length of each chunk.\n            chunk_overlap (int): The amount of overlap between chunks.\n            length_function (function): The function to use to calculate the length of the text.\n\n        Returns:\n            list[str]: The chunks of text.\n        \"\"\"\n\n        if separators == \"\":\n            separators = None\n        elif separators:\n            # check if the separators list has escaped characters\n            # if there are escaped characters, unescape them\n            separators = [unescape_string(x) for x in separators]\n\n        # Make sure chunk_size and chunk_overlap are ints\n        if isinstance(chunk_size, str):\n            chunk_size = int(chunk_size)\n        if isinstance(chunk_overlap, str):\n            chunk_overlap = int(chunk_overlap)\n        splitter = RecursiveCharacterTextSplitter(\n            separators=separators,\n            chunk_size=chunk_size,\n            chunk_overlap=chunk_overlap,\n        )\n        documents = []\n        for _input in inputs:\n            if isinstance(_input, Record):\n                documents.append(_input.to_lc_document())\n            else:\n                documents.append(_input)\n        docs = splitter.split_documents(documents)\n        records = self.to_records(docs)\n        self.repr_value = build_loader_repr_from_records(records)\n        return records\n",
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "code",
-                "advanced": true,
-                "dynamic": true,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "separators": {
-                "type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": true,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "separators",
-                "display_name": "Separators",
-                "advanced": false,
-                "dynamic": false,
-                "info": "The characters to split on.\nIf left empty defaults to [\"\\n\\n\", \"\\n\", \" \", \"\"].",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"],
-                "value": [""]
-              },
-              "_type": "CustomComponent"
-            },
-            "description": "Split text into chunks of a specified length.",
-            "base_classes": ["Record"],
-            "display_name": "Recursive Character Text Splitter",
-            "documentation": "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter",
-            "custom_fields": {
-              "inputs": null,
-              "separators": null,
-              "chunk_size": null,
-              "chunk_overlap": null
-            },
-            "output_types": ["Record"],
-            "field_formatters": {},
-            "frozen": false,
-            "field_order": [],
-            "beta": false
-          },
-          "id": "RecursiveCharacterTextSplitter-tR9QM"
-        },
         "selected": false,
-        "width": 384,
-        "height": 501,
-        "positionAbsolute": {
-          "x": 2791.013514133929,
-          "y": 1462.9588953494142
-        },
-        "dragging": false
+        "type": "genericNode",
+        "width": 384
       },
       {
-        "id": "AstraDBSearch-41nRz",
-        "type": "genericNode",
-        "position": {
-          "x": 1723.976434815103,
-          "y": 277.03317407245913
-        },
         "data": {
-          "type": "AstraDBSearch",
+          "id": "OpenAIEmbeddings-YeYtt",
           "node": {
-            "template": {
-              "embedding": {
-                "type": "Embeddings",
-                "required": true,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "embedding",
-                "display_name": "Embedding",
-                "advanced": false,
-                "dynamic": false,
-                "info": "Embedding to use",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "input_value": {
-                "type": "str",
-                "required": true,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "input_value",
-                "display_name": "Input Value",
-                "advanced": false,
-                "dynamic": false,
-                "info": "Input value to search",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"]
-              },
-              "api_endpoint": {
-                "type": "str",
-                "required": true,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "api_endpoint",
-                "display_name": "API Endpoint",
-                "advanced": false,
-                "dynamic": false,
-                "info": "API endpoint URL for the Astra DB service.",
-                "load_from_db": true,
-                "title_case": false,
-                "input_types": ["Text"],
-                "value": "ASTRA_DB_API_ENDPOINT"
-              },
-              "batch_size": {
-                "type": "int",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "batch_size",
-                "display_name": "Batch Size",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Optional number of records to process in a single batch.",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "bulk_delete_concurrency": {
-                "type": "int",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "bulk_delete_concurrency",
-                "display_name": "Bulk Delete Concurrency",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Optional concurrency level for bulk delete operations.",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "bulk_insert_batch_concurrency": {
-                "type": "int",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "bulk_insert_batch_concurrency",
-                "display_name": "Bulk Insert Batch Concurrency",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Optional concurrency level for bulk insert operations.",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "bulk_insert_overwrite_concurrency": {
-                "type": "int",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "bulk_insert_overwrite_concurrency",
-                "display_name": "Bulk Insert Overwrite Concurrency",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Optional concurrency level for bulk insert operations that overwrite existing records.",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "code": {
-                "type": "code",
-                "required": true,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": true,
-                "value": "from typing import List, Optional\n\nfrom langflow.components.vectorstores.AstraDB import AstraDBVectorStoreComponent\nfrom langflow.components.vectorstores.base.model import LCVectorStoreComponent\nfrom langflow.field_typing import Embeddings, Text\nfrom langflow.schema import Record\n\n\nclass AstraDBSearchComponent(LCVectorStoreComponent):\n    display_name = \"Astra DB Search\"\n    description = \"Searches an existing Astra DB Vector Store.\"\n    icon = \"AstraDB\"\n    field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"input_value\", \"embedding\"]\n\n    def build_config(self):\n        return {\n            \"search_type\": {\n                \"display_name\": \"Search Type\",\n                \"options\": [\"Similarity\", \"MMR\"],\n            },\n            \"input_value\": {\n                \"display_name\": \"Input Value\",\n                \"info\": \"Input value to search\",\n            },\n            \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n            \"collection_name\": {\n                \"display_name\": \"Collection Name\",\n                \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n            },\n            \"token\": {\n                \"display_name\": \"Token\",\n                \"info\": \"Authentication token for accessing Astra DB.\",\n                \"password\": True,\n            },\n            \"api_endpoint\": {\n                \"display_name\": \"API Endpoint\",\n                \"info\": \"API endpoint URL for the Astra DB service.\",\n            },\n            \"namespace\": {\n                \"display_name\": \"Namespace\",\n                \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n                \"advanced\": True,\n            },\n            \"metric\": {\n                \"display_name\": \"Metric\",\n                \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n                \"advanced\": True,\n            },\n            \"batch_size\": {\n                \"display_name\": \"Batch Size\",\n                \"info\": \"Optional number of records to process in a single batch.\",\n                \"advanced\": True,\n            },\n            \"bulk_insert_batch_concurrency\": {\n                \"display_name\": \"Bulk Insert Batch Concurrency\",\n                \"info\": \"Optional concurrency level for bulk insert operations.\",\n                \"advanced\": True,\n            },\n            \"bulk_insert_overwrite_concurrency\": {\n                \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n                \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n                \"advanced\": True,\n            },\n            \"bulk_delete_concurrency\": {\n                \"display_name\": \"Bulk Delete Concurrency\",\n                \"info\": \"Optional concurrency level for bulk delete operations.\",\n                \"advanced\": True,\n            },\n            \"setup_mode\": {\n                \"display_name\": \"Setup Mode\",\n                \"info\": \"Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.\",\n                \"options\": [\"Sync\", \"Async\", \"Off\"],\n                \"advanced\": True,\n            },\n            \"pre_delete_collection\": {\n                \"display_name\": \"Pre Delete Collection\",\n                \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n                \"advanced\": True,\n            },\n            \"metadata_indexing_include\": {\n                \"display_name\": \"Metadata Indexing Include\",\n                \"info\": \"Optional list of metadata fields to include in the indexing.\",\n                \"advanced\": True,\n            },\n            \"metadata_indexing_exclude\": {\n                \"display_name\": \"Metadata Indexing Exclude\",\n                \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n                \"advanced\": True,\n            },\n            \"collection_indexing_policy\": {\n                \"display_name\": \"Collection Indexing Policy\",\n                \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n                \"advanced\": True,\n            },\n            \"number_of_results\": {\n                \"display_name\": \"Number of Results\",\n                \"info\": \"Number of results to return.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        embedding: Embeddings,\n        collection_name: str,\n        input_value: Text,\n        token: str,\n        api_endpoint: str,\n        search_type: str = \"Similarity\",\n        number_of_results: int = 4,\n        namespace: Optional[str] = None,\n        metric: Optional[str] = None,\n        batch_size: Optional[int] = None,\n        bulk_insert_batch_concurrency: Optional[int] = None,\n        bulk_insert_overwrite_concurrency: Optional[int] = None,\n        bulk_delete_concurrency: Optional[int] = None,\n        setup_mode: str = \"Sync\",\n        pre_delete_collection: bool = False,\n        metadata_indexing_include: Optional[List[str]] = None,\n        metadata_indexing_exclude: Optional[List[str]] = None,\n        collection_indexing_policy: Optional[dict] = None,\n    ) -> List[Record]:\n        vector_store = AstraDBVectorStoreComponent().build(\n            embedding=embedding,\n            collection_name=collection_name,\n            token=token,\n            api_endpoint=api_endpoint,\n            namespace=namespace,\n            metric=metric,\n            batch_size=batch_size,\n            bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n            bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n            bulk_delete_concurrency=bulk_delete_concurrency,\n            setup_mode=setup_mode,\n            pre_delete_collection=pre_delete_collection,\n            metadata_indexing_include=metadata_indexing_include,\n            metadata_indexing_exclude=metadata_indexing_exclude,\n            collection_indexing_policy=collection_indexing_policy,\n        )\n        try:\n            return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)\n        except KeyError as e:\n            if \"content\" in str(e):\n                raise ValueError(\n                    \"You should ingest data through Langflow (or LangChain) to query it in Langflow. Your collection does not contain a field name 'content'.\"\n                )\n            else:\n                raise e\n",
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "code",
-                "advanced": true,
-                "dynamic": true,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "collection_indexing_policy": {
-                "type": "dict",
-                "required": false,
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-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "collection_indexing_policy",
-                "display_name": "Collection Indexing Policy",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Optional dictionary defining the indexing policy for the collection.",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "collection_name": {
-                "type": "str",
-                "required": true,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "collection_name",
-                "display_name": "Collection Name",
-                "advanced": false,
-                "dynamic": false,
-                "info": "The name of the collection within Astra DB where the vectors will be stored.",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"],
-                "value": "langflow"
-              },
-              "metadata_indexing_exclude": {
-                "type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": true,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "metadata_indexing_exclude",
-                "display_name": "Metadata Indexing Exclude",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Optional list of metadata fields to exclude from the indexing.",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"]
-              },
-              "metadata_indexing_include": {
-                "type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": true,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "metadata_indexing_include",
-                "display_name": "Metadata Indexing Include",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Optional list of metadata fields to include in the indexing.",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"]
-              },
-              "metric": {
-                "type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "metric",
-                "display_name": "Metric",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Optional distance metric for vector comparisons in the vector store.",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"]
-              },
-              "namespace": {
-                "type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "namespace",
-                "display_name": "Namespace",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Optional namespace within Astra DB to use for the collection.",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"]
-              },
-              "number_of_results": {
-                "type": "int",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "value": 4,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "number_of_results",
-                "display_name": "Number of Results",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Number of results to return.",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "pre_delete_collection": {
-                "type": "bool",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "value": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "pre_delete_collection",
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-                "advanced": true,
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-                "info": "Boolean flag to determine whether to delete the collection before creating a new one.",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "search_type": {
-                "type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": true,
-                "show": true,
-                "multiline": false,
-                "value": "Similarity",
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "options": ["Similarity", "MMR"],
-                "name": "search_type",
-                "display_name": "Search Type",
-                "advanced": false,
-                "dynamic": false,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"]
-              },
-              "setup_mode": {
-                "type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": true,
-                "show": true,
-                "multiline": false,
-                "value": "Sync",
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "options": ["Sync", "Async", "Off"],
-                "name": "setup_mode",
-                "display_name": "Setup Mode",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"]
-              },
-              "token": {
-                "type": "str",
-                "required": true,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": true,
-                "name": "token",
-                "display_name": "Token",
-                "advanced": false,
-                "dynamic": false,
-                "info": "Authentication token for accessing Astra DB.",
-                "load_from_db": true,
-                "title_case": false,
-                "input_types": ["Text"],
-                "value": "ASTRA_DB_APPLICATION_TOKEN"
-              },
-              "_type": "CustomComponent"
-            },
-            "description": "Searches an existing Astra DB Vector Store.",
-            "icon": "AstraDB",
-            "base_classes": ["Record"],
-            "display_name": "Astra DB Search",
-            "documentation": "",
-            "custom_fields": {
-              "embedding": null,
-              "collection_name": null,
-              "input_value": null,
-              "token": null,
-              "api_endpoint": null,
-              "search_type": null,
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-              "namespace": null,
-              "metric": null,
-              "batch_size": null,
-              "bulk_insert_batch_concurrency": null,
-              "bulk_insert_overwrite_concurrency": null,
-              "bulk_delete_concurrency": null,
-              "setup_mode": null,
-              "pre_delete_collection": null,
-              "metadata_indexing_include": null,
-              "metadata_indexing_exclude": null,
-              "collection_indexing_policy": null
-            },
-            "output_types": ["Record"],
-            "field_formatters": {},
-            "frozen": false,
-            "field_order": [
-              "token",
-              "api_endpoint",
-              "collection_name",
-              "input_value",
-              "embedding"
+            "base_classes": [
+              "Embeddings"
             ],
-            "beta": false
-          },
-          "id": "AstraDBSearch-41nRz"
-        },
-        "selected": false,
-        "width": 384,
-        "height": 713,
-        "dragging": false,
-        "positionAbsolute": {
-          "x": 1723.976434815103,
-          "y": 277.03317407245913
-        }
-      },
-      {
-        "id": "AstraDB-eUCSS",
-        "type": "genericNode",
-        "position": {
-          "x": 3372.04958055989,
-          "y": 1611.0742035495277
-        },
-        "data": {
-          "type": "AstraDB",
-          "node": {
-            "template": {
-              "embedding": {
-                "type": "Embeddings",
-                "required": true,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "embedding",
-                "display_name": "Embedding",
-                "advanced": false,
-                "dynamic": false,
-                "info": "Embedding to use",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "inputs": {
-                "type": "Record",
-                "required": false,
-                "placeholder": "",
-                "list": true,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "inputs",
-                "display_name": "Inputs",
-                "advanced": false,
-                "dynamic": false,
-                "info": "Optional list of records to be processed and stored in the vector store.",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "api_endpoint": {
-                "type": "str",
-                "required": true,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "api_endpoint",
-                "display_name": "API Endpoint",
-                "advanced": false,
-                "dynamic": false,
-                "info": "API endpoint URL for the Astra DB service.",
-                "load_from_db": true,
-                "title_case": false,
-                "input_types": ["Text"],
-                "value": "ASTRA_DB_API_ENDPOINT"
-              },
-              "batch_size": {
-                "type": "int",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "batch_size",
-                "display_name": "Batch Size",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Optional number of records to process in a single batch.",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "bulk_delete_concurrency": {
-                "type": "int",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "bulk_delete_concurrency",
-                "display_name": "Bulk Delete Concurrency",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Optional concurrency level for bulk delete operations.",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "bulk_insert_batch_concurrency": {
-                "type": "int",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "bulk_insert_batch_concurrency",
-                "display_name": "Bulk Insert Batch Concurrency",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Optional concurrency level for bulk insert operations.",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "bulk_insert_overwrite_concurrency": {
-                "type": "int",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "bulk_insert_overwrite_concurrency",
-                "display_name": "Bulk Insert Overwrite Concurrency",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Optional concurrency level for bulk insert operations that overwrite existing records.",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "code": {
-                "type": "code",
-                "required": true,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": true,
-                "value": "from typing import List, Optional\n\nfrom langchain_astradb import AstraDBVectorStore\nfrom langchain_astradb.utils.astradb import SetupMode\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, VectorStore\nfrom langflow.schema import Record\n\n\nclass AstraDBVectorStoreComponent(CustomComponent):\n    display_name = \"Astra DB\"\n    description = \"Builds or loads an Astra DB Vector Store.\"\n    icon = \"AstraDB\"\n    field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"inputs\", \"embedding\"]\n\n    def build_config(self):\n        return {\n            \"inputs\": {\n                \"display_name\": \"Inputs\",\n                \"info\": \"Optional list of records to be processed and stored in the vector store.\",\n            },\n            \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n            \"collection_name\": {\n                \"display_name\": \"Collection Name\",\n                \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n            },\n            \"token\": {\n                \"display_name\": \"Token\",\n                \"info\": \"Authentication token for accessing Astra DB.\",\n                \"password\": True,\n            },\n            \"api_endpoint\": {\n                \"display_name\": \"API Endpoint\",\n                \"info\": \"API endpoint URL for the Astra DB service.\",\n            },\n            \"namespace\": {\n                \"display_name\": \"Namespace\",\n                \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n                \"advanced\": True,\n            },\n            \"metric\": {\n                \"display_name\": \"Metric\",\n                \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n                \"advanced\": True,\n            },\n            \"batch_size\": {\n                \"display_name\": \"Batch Size\",\n                \"info\": \"Optional number of records to process in a single batch.\",\n                \"advanced\": True,\n            },\n            \"bulk_insert_batch_concurrency\": {\n                \"display_name\": \"Bulk Insert Batch Concurrency\",\n                \"info\": \"Optional concurrency level for bulk insert operations.\",\n                \"advanced\": True,\n            },\n            \"bulk_insert_overwrite_concurrency\": {\n                \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n                \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n                \"advanced\": True,\n            },\n            \"bulk_delete_concurrency\": {\n                \"display_name\": \"Bulk Delete Concurrency\",\n                \"info\": \"Optional concurrency level for bulk delete operations.\",\n                \"advanced\": True,\n            },\n            \"setup_mode\": {\n                \"display_name\": \"Setup Mode\",\n                \"info\": \"Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.\",\n                \"options\": [\"Sync\", \"Async\", \"Off\"],\n                \"advanced\": True,\n            },\n            \"pre_delete_collection\": {\n                \"display_name\": \"Pre Delete Collection\",\n                \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n                \"advanced\": True,\n            },\n            \"metadata_indexing_include\": {\n                \"display_name\": \"Metadata Indexing Include\",\n                \"info\": \"Optional list of metadata fields to include in the indexing.\",\n                \"advanced\": True,\n            },\n            \"metadata_indexing_exclude\": {\n                \"display_name\": \"Metadata Indexing Exclude\",\n                \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n                \"advanced\": True,\n            },\n            \"collection_indexing_policy\": {\n                \"display_name\": \"Collection Indexing Policy\",\n                \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        embedding: Embeddings,\n        token: str,\n        api_endpoint: str,\n        collection_name: str,\n        inputs: Optional[List[Record]] = None,\n        namespace: Optional[str] = None,\n        metric: Optional[str] = None,\n        batch_size: Optional[int] = None,\n        bulk_insert_batch_concurrency: Optional[int] = None,\n        bulk_insert_overwrite_concurrency: Optional[int] = None,\n        bulk_delete_concurrency: Optional[int] = None,\n        setup_mode: str = \"Async\",\n        pre_delete_collection: bool = False,\n        metadata_indexing_include: Optional[List[str]] = None,\n        metadata_indexing_exclude: Optional[List[str]] = None,\n        collection_indexing_policy: Optional[dict] = None,\n    ) -> VectorStore:\n        try:\n            setup_mode_value = SetupMode[setup_mode.upper()]\n        except KeyError:\n            raise ValueError(f\"Invalid setup mode: {setup_mode}\")\n        if inputs:\n            documents = [_input.to_lc_document() for _input in inputs]\n\n            vector_store = AstraDBVectorStore.from_documents(\n                documents=documents,\n                embedding=embedding,\n                collection_name=collection_name,\n                token=token,\n                api_endpoint=api_endpoint,\n                namespace=namespace,\n                metric=metric,\n                batch_size=batch_size,\n                bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n                bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n                bulk_delete_concurrency=bulk_delete_concurrency,\n                setup_mode=setup_mode_value,\n                pre_delete_collection=pre_delete_collection,\n                metadata_indexing_include=metadata_indexing_include,\n                metadata_indexing_exclude=metadata_indexing_exclude,\n                collection_indexing_policy=collection_indexing_policy,\n            )\n        else:\n            vector_store = AstraDBVectorStore(\n                embedding=embedding,\n                collection_name=collection_name,\n                token=token,\n                api_endpoint=api_endpoint,\n                namespace=namespace,\n                metric=metric,\n                batch_size=batch_size,\n                bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n                bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n                bulk_delete_concurrency=bulk_delete_concurrency,\n                setup_mode=setup_mode_value,\n                pre_delete_collection=pre_delete_collection,\n                metadata_indexing_include=metadata_indexing_include,\n                metadata_indexing_exclude=metadata_indexing_exclude,\n                collection_indexing_policy=collection_indexing_policy,\n            )\n\n        return vector_store\n",
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "code",
-                "advanced": true,
-                "dynamic": true,
-                "info": "",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "collection_indexing_policy": {
-                "type": "dict",
-                "required": false,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "collection_indexing_policy",
-                "display_name": "Collection Indexing Policy",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Optional dictionary defining the indexing policy for the collection.",
-                "load_from_db": false,
-                "title_case": false
-              },
-              "collection_name": {
-                "type": "str",
-                "required": true,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "collection_name",
-                "display_name": "Collection Name",
-                "advanced": false,
-                "dynamic": false,
-                "info": "The name of the collection within Astra DB where the vectors will be stored.",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"],
-                "value": "langflow"
-              },
-              "metadata_indexing_exclude": {
-                "type": "str",
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-                "list": true,
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-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "name": "metadata_indexing_exclude",
-                "display_name": "Metadata Indexing Exclude",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Optional list of metadata fields to exclude from the indexing.",
-                "load_from_db": false,
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-                "fileTypes": [],
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-                "password": false,
-                "name": "metadata_indexing_include",
-                "display_name": "Metadata Indexing Include",
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-              "metric": {
-                "type": "str",
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-                "password": false,
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-                "display_name": "Metric",
-                "advanced": true,
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-                "load_from_db": false,
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-                "type": "str",
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-                "name": "namespace",
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-                "input_types": ["Text"]
-              },
-              "pre_delete_collection": {
-                "type": "bool",
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-                "value": false,
-                "fileTypes": [],
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-                "password": false,
-                "name": "pre_delete_collection",
-                "display_name": "Pre Delete Collection",
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-                "info": "Boolean flag to determine whether to delete the collection before creating a new one.",
-                "load_from_db": false,
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-              "setup_mode": {
-                "type": "str",
-                "required": false,
-                "placeholder": "",
-                "list": true,
-                "show": true,
-                "multiline": false,
-                "value": "Async",
-                "fileTypes": [],
-                "file_path": "",
-                "password": false,
-                "options": ["Sync", "Async", "Off"],
-                "name": "setup_mode",
-                "display_name": "Setup Mode",
-                "advanced": true,
-                "dynamic": false,
-                "info": "Configuration mode for setting up the vector store, with options like \u201cSync\u201d, \u201cAsync\u201d, or \u201cOff\u201d.",
-                "load_from_db": false,
-                "title_case": false,
-                "input_types": ["Text"]
-              },
-              "token": {
-                "type": "str",
-                "required": true,
-                "placeholder": "",
-                "list": false,
-                "show": true,
-                "multiline": false,
-                "fileTypes": [],
-                "file_path": "",
-                "password": true,
-                "name": "token",
-                "display_name": "Token",
-                "advanced": false,
-                "dynamic": false,
-                "info": "Authentication token for accessing Astra DB.",
-                "load_from_db": true,
-                "title_case": false,
-                "input_types": ["Text"],
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This is to avoid errors with Vector Stores (e.g Chroma)\n        if disallowed_special == [\"all\"]:\n            disallowed_special = \"all\"  # type: ignore\n\n        return OpenAIEmbeddings(\n            tiktoken_enabled=tiktoken_enable,\n            default_headers=default_headers,\n            default_query=default_query,\n            allowed_special=set(allowed_special),\n            disallowed_special=\"all\",\n            chunk_size=chunk_size,\n            client=client,\n            deployment=deployment,\n            embedding_ctx_length=embedding_ctx_length,\n            max_retries=max_retries,\n            model=model,\n            model_kwargs=model_kwargs,\n            base_url=openai_api_base,\n            api_key=openai_api_key,\n            openai_api_type=openai_api_type,\n            api_version=openai_api_version,\n            organization=openai_organization,\n            openai_proxy=openai_proxy,\n            timeout=request_timeout,\n            show_progress_bar=show_progress_bar,\n            skip_empty=skip_empty,\n            tiktoken_model_name=tiktoken_model_name,\n        )\n",
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-            "documentation": "",
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-              "default_headers": null,
-              "default_query": null,
               "allowed_special": null,
-              "disallowed_special": null,
               "chunk_size": null,
               "client": null,
+              "default_headers": null,
+              "default_query": null,
               "deployment": null,
+              "disallowed_special": null,
               "embedding_ctx_length": null,
               "max_retries": null,
               "model": null,
               "model_kwargs": null,
               "openai_api_base": null,
+              "openai_api_key": null,
               "openai_api_type": null,
               "openai_api_version": null,
               "openai_organization": null,
@@ -2867,281 +1434,1813 @@
               "tiktoken_enable": null,
               "tiktoken_model_name": null
             },
-            "output_types": ["Embeddings"],
+            "description": "Generate embeddings using OpenAI models.",
+            "display_name": "OpenAI Embeddings",
+            "documentation": "",
             "field_formatters": {},
-            "frozen": false,
             "field_order": [],
-            "beta": false
+            "frozen": false,
+            "icon": "OpenAI",
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Embeddings",
+                "method": "build_embeddings",
+                "name": "embeddings",
+                "selected": "Embeddings",
+                "types": [
+                  "Embeddings"
+                ],
+                "value": "__UNDEFINED__",
+                "hidden": false
+              }
+            ],
+            "template": {
+              "_type": "Component",
+              "chunk_size": {
+                "advanced": true,
+                "display_name": "Chunk Size",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "chunk_size",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": 1000
+              },
+              "client": {
+                "advanced": true,
+                "display_name": "Client",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "client",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import Embeddings\nfrom langflow.io import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, Output, SecretStrInput, TextInput\n\n\nclass OpenAIEmbeddingsComponent(LCModelComponent):\n    display_name = \"OpenAI Embeddings\"\n    description = \"Generate embeddings using OpenAI models.\"\n    icon = \"OpenAI\"\n    inputs = [\n        DictInput(\n            name=\"default_headers\",\n            display_name=\"Default Headers\",\n            advanced=True,\n            info=\"Default headers to use for the API request.\",\n        ),\n        DictInput(\n            name=\"default_query\",\n            display_name=\"Default Query\",\n            advanced=True,\n            info=\"Default query parameters to use for the API request.\",\n        ),\n        IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n        TextInput(name=\"client\", display_name=\"Client\", advanced=True),\n        TextInput(name=\"deployment\", display_name=\"Deployment\", advanced=True),\n        IntInput(name=\"embedding_ctx_length\", display_name=\"Embedding Context Length\", advanced=True, value=1536),\n        IntInput(name=\"max_retries\", display_name=\"Max Retries\", value=3, advanced=True),\n        DropdownInput(\n            name=\"model\",\n            display_name=\"Model\",\n            advanced=False,\n            options=[\n                \"text-embedding-3-small\",\n                \"text-embedding-3-large\",\n                \"text-embedding-ada-002\",\n            ],\n            value=\"text-embedding-3-small\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        SecretStrInput(name=\"openai_api_base\", display_name=\"OpenAI API Base\", advanced=True),\n        SecretStrInput(name=\"openai_api_key\", display_name=\"OpenAI API Key\"),\n        SecretStrInput(name=\"openai_api_type\", display_name=\"OpenAI API Type\", advanced=True),\n        TextInput(name=\"openai_api_version\", display_name=\"OpenAI API Version\", advanced=True),\n        TextInput(\n            name=\"openai_organization\",\n            display_name=\"OpenAI Organization\",\n            advanced=True,\n        ),\n        TextInput(name=\"openai_proxy\", display_name=\"OpenAI Proxy\", advanced=True),\n        FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n        BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n        BoolInput(name=\"skip_empty\", display_name=\"Skip Empty\", advanced=True),\n        TextInput(\n            name=\"tiktoken_model_name\",\n            display_name=\"TikToken Model Name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"tiktoken_enable\",\n            display_name=\"TikToken Enable\",\n            advanced=True,\n            value=True,\n            info=\"If False, you must have transformers installed.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Embeddings\", name=\"embeddings\", method=\"build_embeddings\"),\n    ]\n\n    def build_embeddings(self) -> Embeddings:\n        return OpenAIEmbeddings(\n            tiktoken_enabled=self.tiktoken_enable,\n            default_headers=self.default_headers,\n            default_query=self.default_query,\n            allowed_special=\"all\",\n            disallowed_special=\"all\",\n            chunk_size=self.chunk_size,\n            deployment=self.deployment,\n            embedding_ctx_length=self.embedding_ctx_length,\n            max_retries=self.max_retries,\n            model=self.model,\n            model_kwargs=self.model_kwargs,\n            base_url=self.openai_api_base,\n            api_key=self.openai_api_key,\n            openai_api_type=self.openai_api_type,\n            api_version=self.openai_api_version,\n            organization=self.openai_organization,\n            openai_proxy=self.openai_proxy,\n            timeout=self.request_timeout or None,\n            show_progress_bar=self.show_progress_bar,\n            skip_empty=self.skip_empty,\n            tiktoken_model_name=self.tiktoken_model_name,\n        )\n"
+              },
+              "default_headers": {
+                "advanced": true,
+                "display_name": "Default Headers",
+                "dynamic": false,
+                "info": "Default headers to use for the API request.",
+                "list": false,
+                "name": "default_headers",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "dict",
+                "value": {}
+              },
+              "default_query": {
+                "advanced": true,
+                "display_name": "Default Query",
+                "dynamic": false,
+                "info": "Default query parameters to use for the API request.",
+                "list": false,
+                "name": "default_query",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "dict",
+                "value": {}
+              },
+              "deployment": {
+                "advanced": true,
+                "display_name": "Deployment",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "deployment",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "embedding_ctx_length": {
+                "advanced": true,
+                "display_name": "Embedding Context Length",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "embedding_ctx_length",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": 1536
+              },
+              "max_retries": {
+                "advanced": true,
+                "display_name": "Max Retries",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "max_retries",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": 3
+              },
+              "model": {
+                "advanced": false,
+                "display_name": "Model",
+                "dynamic": false,
+                "info": "",
+                "name": "model",
+                "options": [
+                  "text-embedding-3-small",
+                  "text-embedding-3-large",
+                  "text-embedding-ada-002"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "text-embedding-3-small"
+              },
+              "model_kwargs": {
+                "advanced": true,
+                "display_name": "Model Kwargs",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "model_kwargs",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "dict",
+                "value": {}
+              },
+              "openai_api_base": {
+                "advanced": true,
+                "display_name": "OpenAI API Base",
+                "dynamic": false,
+                "info": "",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "openai_api_base",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "openai_api_key": {
+                "advanced": false,
+                "display_name": "OpenAI API Key",
+                "dynamic": false,
+                "info": "",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "openai_api_key",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "openai_api_type": {
+                "advanced": true,
+                "display_name": "OpenAI API Type",
+                "dynamic": false,
+                "info": "",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "openai_api_type",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "openai_api_version": {
+                "advanced": true,
+                "display_name": "OpenAI API Version",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "openai_api_version",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "openai_organization": {
+                "advanced": true,
+                "display_name": "OpenAI Organization",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "openai_organization",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "openai_proxy": {
+                "advanced": true,
+                "display_name": "OpenAI Proxy",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "openai_proxy",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "request_timeout": {
+                "advanced": true,
+                "display_name": "Request Timeout",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "request_timeout",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "float",
+                "value": ""
+              },
+              "show_progress_bar": {
+                "advanced": true,
+                "display_name": "Show Progress Bar",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "show_progress_bar",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "bool",
+                "value": false
+              },
+              "skip_empty": {
+                "advanced": true,
+                "display_name": "Skip Empty",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "skip_empty",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "bool",
+                "value": false
+              },
+              "tiktoken_enable": {
+                "advanced": true,
+                "display_name": "TikToken Enable",
+                "dynamic": false,
+                "info": "If False, you must have transformers installed.",
+                "list": false,
+                "name": "tiktoken_enable",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "bool",
+                "value": true
+              },
+              "tiktoken_model_name": {
+                "advanced": true,
+                "display_name": "TikToken Model Name",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tiktoken_model_name",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              }
+            }
           },
-          "id": "OpenAIEmbeddings-9TPjc"
+          "type": "OpenAIEmbeddings"
+        },
+        "dragging": false,
+        "height": 395,
+        "id": "OpenAIEmbeddings-YeYtt",
+        "position": {
+          "x": 2781.1922529351923,
+          "y": 2206.267872396239
+        },
+        "positionAbsolute": {
+          "x": 2781.1922529351923,
+          "y": 2206.267872396239
         },
         "selected": false,
-        "width": 384,
-        "height": 383,
-        "positionAbsolute": {
-          "x": 2814.0402191223047,
-          "y": 1955.9268168273086
+        "type": "genericNode",
+        "width": 384
+      },
+      {
+        "data": {
+          "description": "Split text into chunks of a specified length.",
+          "display_name": "Recursive Character Text Splitter",
+          "id": "RecursiveCharacterTextSplitter-HVESL",
+          "node": {
+            "base_classes": [
+              "Data"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Split text into chunks of a specified length.",
+            "display_name": "Recursive Character Text Splitter",
+            "documentation": "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter",
+            "edited": false,
+            "field_order": [
+              "chunk_size",
+              "chunk_overlap",
+              "data_input",
+              "separators"
+            ],
+            "frozen": false,
+            "output_types": [
+              "Data"
+            ],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Data",
+                "method": "build",
+                "name": "data",
+                "selected": "Data",
+                "types": [
+                  "Data"
+                ],
+                "value": "__UNDEFINED__",
+                "hidden": false
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "chunk_overlap": {
+                "advanced": false,
+                "display_name": "Chunk Overlap",
+                "dynamic": false,
+                "info": "The amount of overlap between chunks.",
+                "list": false,
+                "name": "chunk_overlap",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": 200
+              },
+              "chunk_size": {
+                "advanced": false,
+                "display_name": "Chunk Size",
+                "dynamic": false,
+                "info": "The maximum length of each chunk.",
+                "list": false,
+                "name": "chunk_size",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": 1000
+              },
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from langchain_text_splitters import RecursiveCharacterTextSplitter\n\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DataInput, IntInput, TextInput\nfrom langflow.schema import Data\nfrom langflow.template.field.base import Output\nfrom langflow.utils.util import build_loader_repr_from_data, unescape_string\n\n\nclass RecursiveCharacterTextSplitterComponent(Component):\n    display_name: str = \"Recursive Character Text Splitter\"\n    description: str = \"Split text into chunks of a specified length.\"\n    documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n    inputs = [\n        IntInput(\n            name=\"chunk_size\",\n            display_name=\"Chunk Size\",\n            info=\"The maximum length of each chunk.\",\n            value=1000,\n        ),\n        IntInput(\n            name=\"chunk_overlap\",\n            display_name=\"Chunk Overlap\",\n            info=\"The amount of overlap between chunks.\",\n            value=200,\n        ),\n        DataInput(\n            name=\"data_input\",\n            display_name=\"Input\",\n            info=\"The texts to split.\",\n            input_types=[\"Document\", \"Data\"],\n        ),\n        TextInput(\n            name=\"separators\",\n            display_name=\"Separators\",\n            info='The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"build\"),\n    ]\n\n    def build(self) -> list[Data]:\n        \"\"\"\n        Split text into chunks of a specified length.\n\n        Args:\n            separators (list[str]): The characters to split on.\n            chunk_size (int): The maximum length of each chunk.\n            chunk_overlap (int): The amount of overlap between chunks.\n\n        Returns:\n            list[str]: The chunks of text.\n        \"\"\"\n\n        if self.separators == \"\":\n            self.separators = None\n        elif self.separators:\n            # check if the separators list has escaped characters\n            # if there are escaped characters, unescape them\n            self.separators = [unescape_string(x) for x in self.separators]\n\n        # Make sure chunk_size and chunk_overlap are ints\n        if isinstance(self.chunk_size, str):\n            self.chunk_size = int(self.chunk_size)\n        if isinstance(self.chunk_overlap, str):\n            self.chunk_overlap = int(self.chunk_overlap)\n        splitter = RecursiveCharacterTextSplitter(\n            separators=self.separators,\n            chunk_size=self.chunk_size,\n            chunk_overlap=self.chunk_overlap,\n        )\n        documents = []\n        if not isinstance(self.data_input, list):\n            self.data_input = [self.data_input]\n        for _input in self.data_input:\n            if isinstance(_input, Data):\n                documents.append(_input.to_lc_document())\n            else:\n                documents.append(_input)\n        docs = splitter.split_documents(documents)\n        data = self.to_data(docs)\n        self.repr_value = build_loader_repr_from_data(data)\n        return data\n"
+              },
+              "data_input": {
+                "advanced": false,
+                "display_name": "Input",
+                "dynamic": false,
+                "info": "The texts to split.",
+                "input_types": [
+                  "Document",
+                  "Data"
+                ],
+                "list": false,
+                "name": "data_input",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "other",
+                "value": ""
+              },
+              "separators": {
+                "advanced": false,
+                "display_name": "Separators",
+                "dynamic": false,
+                "info": "The characters to split on.\nIf left empty defaults to [\"\\n\\n\", \"\\n\", \" \", \"\"].",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": true,
+                "load_from_db": false,
+                "name": "separators",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": [
+                  "\\n"
+                ]
+              }
+            }
+          },
+          "type": "RecursiveCharacterTextSplitter"
         },
-        "dragging": false
+        "dragging": false,
+        "height": 529,
+        "id": "RecursiveCharacterTextSplitter-HVESL",
+        "position": {
+          "x": 2726.46405760335,
+          "y": 1530.1666819162674
+        },
+        "positionAbsolute": {
+          "x": 2726.46405760335,
+          "y": 1530.1666819162674
+        },
+        "selected": false,
+        "type": "genericNode",
+        "width": 384
+      },
+      {
+        "data": {
+          "description": "Implementation of Vector Store using Astra DB with search capabilities",
+          "display_name": "Astra DB Vector Store",
+          "id": "AstraDB-irvai",
+          "node": {
+            "base_classes": [
+              "Data"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Implementation of Vector Store using Astra DB with search capabilities",
+            "display_name": "Astra DB Vector Store",
+            "documentation": "https://python.langchain.com/docs/integrations/vectorstores/astradb",
+            "edited": false,
+            "field_order": [
+              "collection_name",
+              "token",
+              "api_endpoint",
+              "vector_store_inputs",
+              "embedding",
+              "namespace",
+              "metric",
+              "batch_size",
+              "bulk_insert_batch_concurrency",
+              "bulk_insert_overwrite_concurrency",
+              "bulk_delete_concurrency",
+              "setup_mode",
+              "pre_delete_collection",
+              "metadata_indexing_include",
+              "metadata_indexing_exclude",
+              "collection_indexing_policy",
+              "add_to_vector_store",
+              "search_input",
+              "search_type",
+              "number_of_results"
+            ],
+            "frozen": false,
+            "icon": "AstraDB",
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Retriever",
+                "method": "build_base_retriever",
+                "name": "base_retriever",
+                "selected": "Data",
+                "types": [
+                  "Data"
+                ],
+                "value": "__UNDEFINED__"
+              },
+              {
+                "cache": true,
+                "display_name": "Search Results",
+                "method": "search_documents",
+                "name": "search_results",
+                "selected": "Data",
+                "types": [
+                  "Data"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "add_to_vector_store": {
+                "advanced": false,
+                "display_name": "Add to Vector Store",
+                "dynamic": false,
+                "info": "If true, the Vector Store Inputs will be added to the Vector Store.",
+                "list": false,
+                "name": "add_to_vector_store",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "bool",
+                "value": true
+              },
+              "api_endpoint": {
+                "advanced": false,
+                "display_name": "API Endpoint",
+                "dynamic": false,
+                "info": "API endpoint URL for the Astra DB service.",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "api_endpoint",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "batch_size": {
+                "advanced": true,
+                "display_name": "Batch Size",
+                "dynamic": false,
+                "info": "Optional number of data to process in a single batch.",
+                "list": false,
+                "name": "batch_size",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": ""
+              },
+              "bulk_delete_concurrency": {
+                "advanced": true,
+                "display_name": "Bulk Delete Concurrency",
+                "dynamic": false,
+                "info": "Optional concurrency level for bulk delete operations.",
+                "list": false,
+                "name": "bulk_delete_concurrency",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": ""
+              },
+              "bulk_insert_batch_concurrency": {
+                "advanced": true,
+                "display_name": "Bulk Insert Batch Concurrency",
+                "dynamic": false,
+                "info": "Optional concurrency level for bulk insert operations.",
+                "list": false,
+                "name": "bulk_insert_batch_concurrency",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": ""
+              },
+              "bulk_insert_overwrite_concurrency": {
+                "advanced": true,
+                "display_name": "Bulk Insert Overwrite Concurrency",
+                "dynamic": false,
+                "info": "Optional concurrency level for bulk insert operations that overwrite existing data.",
+                "list": false,
+                "name": "bulk_insert_overwrite_concurrency",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": ""
+              },
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from loguru import logger\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent\nfrom langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput\nfrom langflow.schema import Data\n\n\nclass AstraVectorStoreComponent(LCVectorStoreComponent):\n    display_name: str = \"Astra DB Vector Store\"\n    description: str = \"Implementation of Vector Store using Astra DB with search capabilities\"\n    documentation: str = \"https://python.langchain.com/docs/integrations/vectorstores/astradb\"\n    icon: str = \"AstraDB\"\n\n    inputs = [\n        StrInput(\n            name=\"collection_name\",\n            display_name=\"Collection Name\",\n            info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n        ),\n        SecretStrInput(\n            name=\"token\",\n            display_name=\"Astra DB Application Token\",\n            info=\"Authentication token for accessing Astra DB.\",\n            value=\"ASTRA_DB_APPLICATION_TOKEN\",\n        ),\n        SecretStrInput(\n            name=\"api_endpoint\",\n            display_name=\"API Endpoint\",\n            info=\"API endpoint URL for the Astra DB service.\",\n            value=\"ASTRA_DB_API_ENDPOINT\",\n        ),\n        HandleInput(\n            name=\"vector_store_inputs\",\n            display_name=\"Vector Store Inputs\",\n            input_types=[\"Document\", \"Data\"],\n            is_list=True,\n        ),\n        HandleInput(\n            name=\"embedding\",\n            display_name=\"Embedding\",\n            input_types=[\"Embeddings\"],\n        ),\n        StrInput(\n            name=\"namespace\",\n            display_name=\"Namespace\",\n            info=\"Optional namespace within Astra DB to use for the collection.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"metric\",\n            display_name=\"Metric\",\n            info=\"Optional distance metric for vector comparisons in the vector store.\",\n            options=[\"cosine\", \"dot_product\", \"euclidean\"],\n            advanced=True,\n        ),\n        IntInput(\n            name=\"batch_size\",\n            display_name=\"Batch Size\",\n            info=\"Optional number of data to process in a single batch.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_batch_concurrency\",\n            display_name=\"Bulk Insert Batch Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_overwrite_concurrency\",\n            display_name=\"Bulk Insert Overwrite Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_delete_concurrency\",\n            display_name=\"Bulk Delete Concurrency\",\n            info=\"Optional concurrency level for bulk delete operations.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"setup_mode\",\n            display_name=\"Setup Mode\",\n            info=\"Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.\",\n            options=[\"Sync\", \"Async\", \"Off\"],\n            advanced=True,\n            value=\"Sync\",\n        ),\n        BoolInput(\n            name=\"pre_delete_collection\",\n            display_name=\"Pre Delete Collection\",\n            info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_include\",\n            display_name=\"Metadata Indexing Include\",\n            info=\"Optional list of metadata fields to include in the indexing.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_exclude\",\n            display_name=\"Metadata Indexing Exclude\",\n            info=\"Optional list of metadata fields to exclude from the indexing.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"collection_indexing_policy\",\n            display_name=\"Collection Indexing Policy\",\n            info=\"Optional dictionary defining the indexing policy for the collection.\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_to_vector_store\",\n            display_name=\"Add to Vector Store\",\n            info=\"If true, the Vector Store Inputs will be added to the Vector Store.\",\n        ),\n        MultilineInput(\n            name=\"search_input\",\n            display_name=\"Search Input\",\n        ),\n        DropdownInput(\n            name=\"search_type\",\n            display_name=\"Search Type\",\n            options=[\"Similarity\", \"MMR\"],\n            value=\"Similarity\",\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Results\",\n            info=\"Number of results to return.\",\n            advanced=True,\n            value=4,\n        ),\n    ]\n\n    def build_vector_store(self):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n            from langchain_astradb.utils.astradb import SetupMode\n        except ImportError:\n            raise ImportError(\n                \"Could not import langchain Astra DB integration package. \"\n                \"Please install it with `pip install langchain-astradb`.\"\n            )\n\n        try:\n            if not self.setup_mode:\n                self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n            setup_mode_value = SetupMode[self.setup_mode.upper()]\n        except KeyError:\n            raise ValueError(f\"Invalid setup mode: {self.setup_mode}\")\n\n        vector_store_kwargs = {\n            \"embedding\": self.embedding,\n            \"collection_name\": self.collection_name,\n            \"token\": self.token,\n            \"api_endpoint\": self.api_endpoint,\n            \"namespace\": self.namespace or None,\n            \"metric\": self.metric or None,\n            \"batch_size\": self.batch_size or None,\n            \"bulk_insert_batch_concurrency\": self.bulk_insert_batch_concurrency or None,\n            \"bulk_insert_overwrite_concurrency\": self.bulk_insert_overwrite_concurrency or None,\n            \"bulk_delete_concurrency\": self.bulk_delete_concurrency or None,\n            \"setup_mode\": setup_mode_value,\n            \"pre_delete_collection\": self.pre_delete_collection or False,\n        }\n\n        if self.metadata_indexing_include:\n            vector_store_kwargs[\"metadata_indexing_include\"] = self.metadata_indexing_include\n        elif self.metadata_indexing_exclude:\n            vector_store_kwargs[\"metadata_indexing_exclude\"] = self.metadata_indexing_exclude\n        elif self.collection_indexing_policy:\n            vector_store_kwargs[\"collection_indexing_policy\"] = self.collection_indexing_policy\n\n        try:\n            vector_store = AstraDBVectorStore(**vector_store_kwargs)\n        except Exception as e:\n            raise ValueError(f\"Error initializing AstraDBVectorStore: {str(e)}\") from e\n\n        if self.add_to_vector_store:\n            self._add_documents_to_vector_store(vector_store)\n\n        return vector_store\n\n    def build_base_retriever(self):\n        vector_store = self.build_vector_store()\n        self.status = self._astradb_collection_to_data(vector_store.collection)\n        return vector_store\n\n    def _add_documents_to_vector_store(self, vector_store):\n        documents = []\n        for _input in self.vector_store_inputs or []:\n            if isinstance(_input, Data):\n                documents.append(_input.to_lc_document())\n            else:\n                raise ValueError(\"Vector Store Inputs must be Data objects.\")\n\n        if documents and self.embedding is not None:\n            logger.debug(f\"Adding {len(documents)} documents to the Vector Store.\")\n            try:\n                vector_store.add_documents(documents)\n            except Exception as e:\n                raise ValueError(f\"Error adding documents to AstraDBVectorStore: {str(e)}\") from e\n        else:\n            logger.debug(\"No documents to add to the Vector Store.\")\n\n    def search_documents(self):\n        vector_store = self.build_vector_store()\n\n        logger.debug(f\"Search input: {self.search_input}\")\n        logger.debug(f\"Search type: {self.search_type}\")\n        logger.debug(f\"Number of results: {self.number_of_results}\")\n\n        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():\n            try:\n                if self.search_type == \"Similarity\":\n                    docs = vector_store.similarity_search(\n                        query=self.search_input,\n                        k=self.number_of_results,\n                    )\n                elif self.search_type == \"MMR\":\n                    docs = vector_store.max_marginal_relevance_search(\n                        query=self.search_input,\n                        k=self.number_of_results,\n                    )\n                else:\n                    raise ValueError(f\"Invalid search type: {self.search_type}\")\n            except Exception as e:\n                raise ValueError(f\"Error performing search in AstraDBVectorStore: {str(e)}\") from e\n\n            logger.debug(f\"Retrieved documents: {len(docs)}\")\n\n            data = [Data.from_document(doc) for doc in docs]\n            logger.debug(f\"Converted documents to data: {len(data)}\")\n            self.status = data\n            return data\n        else:\n            logger.debug(\"No search input provided. Skipping search.\")\n            return []\n\n    def _astradb_collection_to_data(self, collection):\n        data = []\n        data_dict = collection.find()\n        if data_dict and \"data\" in data_dict:\n            data_dict = data_dict[\"data\"].get(\"documents\", [])\n\n        for item in data_dict:\n            data.append(Data(content=item[\"content\"]))\n        return data\n"
+              },
+              "collection_indexing_policy": {
+                "advanced": true,
+                "display_name": "Collection Indexing Policy",
+                "dynamic": false,
+                "info": "Optional dictionary defining the indexing policy for the collection.",
+                "list": false,
+                "load_from_db": false,
+                "name": "collection_indexing_policy",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "collection_name": {
+                "advanced": false,
+                "display_name": "Collection Name",
+                "dynamic": false,
+                "info": "The name of the collection within Astra DB where the vectors will be stored.",
+                "list": false,
+                "load_from_db": false,
+                "name": "collection_name",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "langflow"
+              },
+              "embedding": {
+                "advanced": false,
+                "display_name": "Embedding",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Embeddings"
+                ],
+                "list": false,
+                "name": "embedding",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "other",
+                "value": ""
+              },
+              "metadata_indexing_exclude": {
+                "advanced": true,
+                "display_name": "Metadata Indexing Exclude",
+                "dynamic": false,
+                "info": "Optional list of metadata fields to exclude from the indexing.",
+                "list": false,
+                "load_from_db": false,
+                "name": "metadata_indexing_exclude",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "metadata_indexing_include": {
+                "advanced": true,
+                "display_name": "Metadata Indexing Include",
+                "dynamic": false,
+                "info": "Optional list of metadata fields to include in the indexing.",
+                "list": false,
+                "load_from_db": false,
+                "name": "metadata_indexing_include",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "metric": {
+                "advanced": true,
+                "display_name": "Metric",
+                "dynamic": false,
+                "info": "Optional distance metric for vector comparisons in the vector store.",
+                "name": "metric",
+                "options": [
+                  "cosine",
+                  "dot_product",
+                  "euclidean"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "namespace": {
+                "advanced": true,
+                "display_name": "Namespace",
+                "dynamic": false,
+                "info": "Optional namespace within Astra DB to use for the collection.",
+                "list": false,
+                "load_from_db": false,
+                "name": "namespace",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "number_of_results": {
+                "advanced": true,
+                "display_name": "Number of Results",
+                "dynamic": false,
+                "info": "Number of results to return.",
+                "list": false,
+                "name": "number_of_results",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": 4
+              },
+              "pre_delete_collection": {
+                "advanced": true,
+                "display_name": "Pre Delete Collection",
+                "dynamic": false,
+                "info": "Boolean flag to determine whether to delete the collection before creating a new one.",
+                "list": false,
+                "name": "pre_delete_collection",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "bool",
+                "value": false
+              },
+              "search_input": {
+                "advanced": false,
+                "display_name": "Search Input",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "search_input",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "search_type": {
+                "advanced": false,
+                "display_name": "Search Type",
+                "dynamic": false,
+                "info": "",
+                "name": "search_type",
+                "options": [
+                  "Similarity",
+                  "MMR"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "Similarity"
+              },
+              "setup_mode": {
+                "advanced": true,
+                "display_name": "Setup Mode",
+                "dynamic": false,
+                "info": "Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.",
+                "name": "setup_mode",
+                "options": [
+                  "Sync",
+                  "Async",
+                  "Off"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "Sync"
+              },
+              "token": {
+                "advanced": false,
+                "display_name": "Astra DB Application Token",
+                "dynamic": false,
+                "info": "Authentication token for accessing Astra DB.",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "token",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "vector_store_inputs": {
+                "advanced": false,
+                "display_name": "Vector Store Inputs",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Document",
+                  "Data"
+                ],
+                "list": true,
+                "name": "vector_store_inputs",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "other",
+                "value": ""
+              }
+            }
+          },
+          "type": "AstraDB"
+        },
+        "dragging": false,
+        "height": 917,
+        "id": "AstraDB-irvai",
+        "position": {
+          "x": 3329.7211874614477,
+          "y": 1559.774393811144
+        },
+        "positionAbsolute": {
+          "x": 3329.7211874614477,
+          "y": 1559.774393811144
+        },
+        "selected": false,
+        "type": "genericNode",
+        "width": 384
+      },
+      {
+        "data": {
+          "description": "Implementation of Vector Store using Astra DB with search capabilities",
+          "display_name": "Astra DB Vector Store",
+          "id": "AstraDB-wANQu",
+          "node": {
+            "base_classes": [
+              "Data"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Implementation of Vector Store using Astra DB with search capabilities",
+            "display_name": "Astra DB Vector Store",
+            "documentation": "https://python.langchain.com/docs/integrations/vectorstores/astradb",
+            "edited": false,
+            "field_order": [
+              "collection_name",
+              "token",
+              "api_endpoint",
+              "vector_store_inputs",
+              "embedding",
+              "namespace",
+              "metric",
+              "batch_size",
+              "bulk_insert_batch_concurrency",
+              "bulk_insert_overwrite_concurrency",
+              "bulk_delete_concurrency",
+              "setup_mode",
+              "pre_delete_collection",
+              "metadata_indexing_include",
+              "metadata_indexing_exclude",
+              "collection_indexing_policy",
+              "add_to_vector_store",
+              "search_input",
+              "search_type",
+              "number_of_results"
+            ],
+            "frozen": false,
+            "icon": "AstraDB",
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Retriever",
+                "method": "build_base_retriever",
+                "name": "base_retriever",
+                "selected": "Data",
+                "types": [
+                  "Data"
+                ],
+                "value": "__UNDEFINED__"
+              },
+              {
+                "cache": true,
+                "display_name": "Search Results",
+                "method": "search_documents",
+                "name": "search_results",
+                "selected": "Data",
+                "types": [
+                  "Data"
+                ],
+                "value": "__UNDEFINED__",
+                "hidden": false
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "add_to_vector_store": {
+                "advanced": false,
+                "display_name": "Add to Vector Store",
+                "dynamic": false,
+                "info": "If true, the Vector Store Inputs will be added to the Vector Store.",
+                "list": false,
+                "name": "add_to_vector_store",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "bool",
+                "value": true
+              },
+              "api_endpoint": {
+                "advanced": false,
+                "display_name": "API Endpoint",
+                "dynamic": false,
+                "info": "API endpoint URL for the Astra DB service.",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "api_endpoint",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "batch_size": {
+                "advanced": true,
+                "display_name": "Batch Size",
+                "dynamic": false,
+                "info": "Optional number of data to process in a single batch.",
+                "list": false,
+                "name": "batch_size",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": ""
+              },
+              "bulk_delete_concurrency": {
+                "advanced": true,
+                "display_name": "Bulk Delete Concurrency",
+                "dynamic": false,
+                "info": "Optional concurrency level for bulk delete operations.",
+                "list": false,
+                "name": "bulk_delete_concurrency",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": ""
+              },
+              "bulk_insert_batch_concurrency": {
+                "advanced": true,
+                "display_name": "Bulk Insert Batch Concurrency",
+                "dynamic": false,
+                "info": "Optional concurrency level for bulk insert operations.",
+                "list": false,
+                "name": "bulk_insert_batch_concurrency",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": ""
+              },
+              "bulk_insert_overwrite_concurrency": {
+                "advanced": true,
+                "display_name": "Bulk Insert Overwrite Concurrency",
+                "dynamic": false,
+                "info": "Optional concurrency level for bulk insert operations that overwrite existing data.",
+                "list": false,
+                "name": "bulk_insert_overwrite_concurrency",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": ""
+              },
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from loguru import logger\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent\nfrom langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput\nfrom langflow.schema import Data\n\n\nclass AstraVectorStoreComponent(LCVectorStoreComponent):\n    display_name: str = \"Astra DB Vector Store\"\n    description: str = \"Implementation of Vector Store using Astra DB with search capabilities\"\n    documentation: str = \"https://python.langchain.com/docs/integrations/vectorstores/astradb\"\n    icon: str = \"AstraDB\"\n\n    inputs = [\n        StrInput(\n            name=\"collection_name\",\n            display_name=\"Collection Name\",\n            info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n        ),\n        SecretStrInput(\n            name=\"token\",\n            display_name=\"Astra DB Application Token\",\n            info=\"Authentication token for accessing Astra DB.\",\n            value=\"ASTRA_DB_APPLICATION_TOKEN\",\n        ),\n        SecretStrInput(\n            name=\"api_endpoint\",\n            display_name=\"API Endpoint\",\n            info=\"API endpoint URL for the Astra DB service.\",\n            value=\"ASTRA_DB_API_ENDPOINT\",\n        ),\n        HandleInput(\n            name=\"vector_store_inputs\",\n            display_name=\"Vector Store Inputs\",\n            input_types=[\"Document\", \"Data\"],\n            is_list=True,\n        ),\n        HandleInput(\n            name=\"embedding\",\n            display_name=\"Embedding\",\n            input_types=[\"Embeddings\"],\n        ),\n        StrInput(\n            name=\"namespace\",\n            display_name=\"Namespace\",\n            info=\"Optional namespace within Astra DB to use for the collection.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"metric\",\n            display_name=\"Metric\",\n            info=\"Optional distance metric for vector comparisons in the vector store.\",\n            options=[\"cosine\", \"dot_product\", \"euclidean\"],\n            advanced=True,\n        ),\n        IntInput(\n            name=\"batch_size\",\n            display_name=\"Batch Size\",\n            info=\"Optional number of data to process in a single batch.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_batch_concurrency\",\n            display_name=\"Bulk Insert Batch Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_overwrite_concurrency\",\n            display_name=\"Bulk Insert Overwrite Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_delete_concurrency\",\n            display_name=\"Bulk Delete Concurrency\",\n            info=\"Optional concurrency level for bulk delete operations.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"setup_mode\",\n            display_name=\"Setup Mode\",\n            info=\"Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.\",\n            options=[\"Sync\", \"Async\", \"Off\"],\n            advanced=True,\n            value=\"Sync\",\n        ),\n        BoolInput(\n            name=\"pre_delete_collection\",\n            display_name=\"Pre Delete Collection\",\n            info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_include\",\n            display_name=\"Metadata Indexing Include\",\n            info=\"Optional list of metadata fields to include in the indexing.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_exclude\",\n            display_name=\"Metadata Indexing Exclude\",\n            info=\"Optional list of metadata fields to exclude from the indexing.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"collection_indexing_policy\",\n            display_name=\"Collection Indexing Policy\",\n            info=\"Optional dictionary defining the indexing policy for the collection.\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_to_vector_store\",\n            display_name=\"Add to Vector Store\",\n            info=\"If true, the Vector Store Inputs will be added to the Vector Store.\",\n        ),\n        MultilineInput(\n            name=\"search_input\",\n            display_name=\"Search Input\",\n        ),\n        DropdownInput(\n            name=\"search_type\",\n            display_name=\"Search Type\",\n            options=[\"Similarity\", \"MMR\"],\n            value=\"Similarity\",\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Results\",\n            info=\"Number of results to return.\",\n            advanced=True,\n            value=4,\n        ),\n    ]\n\n    def build_vector_store(self):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n            from langchain_astradb.utils.astradb import SetupMode\n        except ImportError:\n            raise ImportError(\n                \"Could not import langchain Astra DB integration package. \"\n                \"Please install it with `pip install langchain-astradb`.\"\n            )\n\n        try:\n            if not self.setup_mode:\n                self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n            setup_mode_value = SetupMode[self.setup_mode.upper()]\n        except KeyError:\n            raise ValueError(f\"Invalid setup mode: {self.setup_mode}\")\n\n        vector_store_kwargs = {\n            \"embedding\": self.embedding,\n            \"collection_name\": self.collection_name,\n            \"token\": self.token,\n            \"api_endpoint\": self.api_endpoint,\n            \"namespace\": self.namespace or None,\n            \"metric\": self.metric or None,\n            \"batch_size\": self.batch_size or None,\n            \"bulk_insert_batch_concurrency\": self.bulk_insert_batch_concurrency or None,\n            \"bulk_insert_overwrite_concurrency\": self.bulk_insert_overwrite_concurrency or None,\n            \"bulk_delete_concurrency\": self.bulk_delete_concurrency or None,\n            \"setup_mode\": setup_mode_value,\n            \"pre_delete_collection\": self.pre_delete_collection or False,\n        }\n\n        if self.metadata_indexing_include:\n            vector_store_kwargs[\"metadata_indexing_include\"] = self.metadata_indexing_include\n        elif self.metadata_indexing_exclude:\n            vector_store_kwargs[\"metadata_indexing_exclude\"] = self.metadata_indexing_exclude\n        elif self.collection_indexing_policy:\n            vector_store_kwargs[\"collection_indexing_policy\"] = self.collection_indexing_policy\n\n        try:\n            vector_store = AstraDBVectorStore(**vector_store_kwargs)\n        except Exception as e:\n            raise ValueError(f\"Error initializing AstraDBVectorStore: {str(e)}\") from e\n\n        if self.add_to_vector_store:\n            self._add_documents_to_vector_store(vector_store)\n\n        return vector_store\n\n    def build_base_retriever(self):\n        vector_store = self.build_vector_store()\n        self.status = self._astradb_collection_to_data(vector_store.collection)\n        return vector_store\n\n    def _add_documents_to_vector_store(self, vector_store):\n        documents = []\n        for _input in self.vector_store_inputs or []:\n            if isinstance(_input, Data):\n                documents.append(_input.to_lc_document())\n            else:\n                raise ValueError(\"Vector Store Inputs must be Data objects.\")\n\n        if documents and self.embedding is not None:\n            logger.debug(f\"Adding {len(documents)} documents to the Vector Store.\")\n            try:\n                vector_store.add_documents(documents)\n            except Exception as e:\n                raise ValueError(f\"Error adding documents to AstraDBVectorStore: {str(e)}\") from e\n        else:\n            logger.debug(\"No documents to add to the Vector Store.\")\n\n    def search_documents(self):\n        vector_store = self.build_vector_store()\n\n        logger.debug(f\"Search input: {self.search_input}\")\n        logger.debug(f\"Search type: {self.search_type}\")\n        logger.debug(f\"Number of results: {self.number_of_results}\")\n\n        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():\n            try:\n                if self.search_type == \"Similarity\":\n                    docs = vector_store.similarity_search(\n                        query=self.search_input,\n                        k=self.number_of_results,\n                    )\n                elif self.search_type == \"MMR\":\n                    docs = vector_store.max_marginal_relevance_search(\n                        query=self.search_input,\n                        k=self.number_of_results,\n                    )\n                else:\n                    raise ValueError(f\"Invalid search type: {self.search_type}\")\n            except Exception as e:\n                raise ValueError(f\"Error performing search in AstraDBVectorStore: {str(e)}\") from e\n\n            logger.debug(f\"Retrieved documents: {len(docs)}\")\n\n            data = [Data.from_document(doc) for doc in docs]\n            logger.debug(f\"Converted documents to data: {len(data)}\")\n            self.status = data\n            return data\n        else:\n            logger.debug(\"No search input provided. Skipping search.\")\n            return []\n\n    def _astradb_collection_to_data(self, collection):\n        data = []\n        data_dict = collection.find()\n        if data_dict and \"data\" in data_dict:\n            data_dict = data_dict[\"data\"].get(\"documents\", [])\n\n        for item in data_dict:\n            data.append(Data(content=item[\"content\"]))\n        return data\n"
+              },
+              "collection_indexing_policy": {
+                "advanced": true,
+                "display_name": "Collection Indexing Policy",
+                "dynamic": false,
+                "info": "Optional dictionary defining the indexing policy for the collection.",
+                "list": false,
+                "load_from_db": false,
+                "name": "collection_indexing_policy",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "collection_name": {
+                "advanced": false,
+                "display_name": "Collection Name",
+                "dynamic": false,
+                "info": "The name of the collection within Astra DB where the vectors will be stored.",
+                "list": false,
+                "load_from_db": false,
+                "name": "collection_name",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "langflow"
+              },
+              "embedding": {
+                "advanced": false,
+                "display_name": "Embedding",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Embeddings"
+                ],
+                "list": false,
+                "name": "embedding",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "other",
+                "value": ""
+              },
+              "metadata_indexing_exclude": {
+                "advanced": true,
+                "display_name": "Metadata Indexing Exclude",
+                "dynamic": false,
+                "info": "Optional list of metadata fields to exclude from the indexing.",
+                "list": false,
+                "load_from_db": false,
+                "name": "metadata_indexing_exclude",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "metadata_indexing_include": {
+                "advanced": true,
+                "display_name": "Metadata Indexing Include",
+                "dynamic": false,
+                "info": "Optional list of metadata fields to include in the indexing.",
+                "list": false,
+                "load_from_db": false,
+                "name": "metadata_indexing_include",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "metric": {
+                "advanced": true,
+                "display_name": "Metric",
+                "dynamic": false,
+                "info": "Optional distance metric for vector comparisons in the vector store.",
+                "name": "metric",
+                "options": [
+                  "cosine",
+                  "dot_product",
+                  "euclidean"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "namespace": {
+                "advanced": true,
+                "display_name": "Namespace",
+                "dynamic": false,
+                "info": "Optional namespace within Astra DB to use for the collection.",
+                "list": false,
+                "load_from_db": false,
+                "name": "namespace",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "number_of_results": {
+                "advanced": true,
+                "display_name": "Number of Results",
+                "dynamic": false,
+                "info": "Number of results to return.",
+                "list": false,
+                "name": "number_of_results",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": 4
+              },
+              "pre_delete_collection": {
+                "advanced": true,
+                "display_name": "Pre Delete Collection",
+                "dynamic": false,
+                "info": "Boolean flag to determine whether to delete the collection before creating a new one.",
+                "list": false,
+                "name": "pre_delete_collection",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "bool",
+                "value": false
+              },
+              "search_input": {
+                "advanced": false,
+                "display_name": "Search Input",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "search_input",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "search_type": {
+                "advanced": false,
+                "display_name": "Search Type",
+                "dynamic": false,
+                "info": "",
+                "name": "search_type",
+                "options": [
+                  "Similarity",
+                  "MMR"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "Similarity"
+              },
+              "setup_mode": {
+                "advanced": true,
+                "display_name": "Setup Mode",
+                "dynamic": false,
+                "info": "Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.",
+                "name": "setup_mode",
+                "options": [
+                  "Sync",
+                  "Async",
+                  "Off"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "Sync"
+              },
+              "token": {
+                "advanced": false,
+                "display_name": "Astra DB Application Token",
+                "dynamic": false,
+                "info": "Authentication token for accessing Astra DB.",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "token",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "vector_store_inputs": {
+                "advanced": false,
+                "display_name": "Vector Store Inputs",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Document",
+                  "Data"
+                ],
+                "list": true,
+                "name": "vector_store_inputs",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "other",
+                "value": ""
+              }
+            }
+          },
+          "type": "AstraDB"
+        },
+        "dragging": false,
+        "height": 917,
+        "id": "AstraDB-wANQu",
+        "position": {
+          "x": 1298.4611042465333,
+          "y": 160.7181472642742
+        },
+        "positionAbsolute": {
+          "x": 1298.4611042465333,
+          "y": 160.7181472642742
+        },
+        "selected": false,
+        "type": "genericNode",
+        "width": 384
+      },
+      {
+        "data": {
+          "id": "ParseData-C9tUn",
+          "node": {
+            "base_classes": [
+              "Message"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Convert Data into plain text following a specified template.",
+            "display_name": "Parse Data",
+            "documentation": "",
+            "field_order": [
+              "data",
+              "template",
+              "sep"
+            ],
+            "frozen": false,
+            "icon": "braces",
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Text",
+                "method": "parse_data",
+                "name": "text",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__",
+                "hidden": false
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n"
+              },
+              "data": {
+                "advanced": false,
+                "display_name": "Data",
+                "dynamic": false,
+                "info": "The data to convert to text.",
+                "input_types": [
+                  "Data"
+                ],
+                "list": false,
+                "name": "data",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "other",
+                "value": ""
+              },
+              "sep": {
+                "advanced": true,
+                "display_name": "Separator",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "name": "sep",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "---"
+              },
+              "template": {
+                "advanced": false,
+                "display_name": "Template",
+                "dynamic": false,
+                "info": "The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "template",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "{text}"
+              }
+            }
+          },
+          "type": "ParseData"
+        },
+        "dragging": false,
+        "height": 385,
+        "id": "ParseData-C9tUn",
+        "position": {
+          "x": 1911.4866480237615,
+          "y": 566.903831987901
+        },
+        "positionAbsolute": {
+          "x": 1911.4866480237615,
+          "y": 566.903831987901
+        },
+        "selected": false,
+        "type": "genericNode",
+        "width": 384
       }
     ],
     "edges": [
       {
-        "source": "TextOutput-BDknO",
-        "target": "Prompt-xeI6K",
-        "sourceHandle": "{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153TextOutput\u0153,\u0153id\u0153:\u0153TextOutput-BDknO\u0153}",
-        "targetHandle": "{\u0153fieldName\u0153:\u0153context\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}",
-        "id": "reactflow__edge-TextOutput-BDknO{\u0153baseClasses\u0153:[\u0153object\u0153,\u0153Text\u0153,\u0153str\u0153],\u0153dataType\u0153:\u0153TextOutput\u0153,\u0153id\u0153:\u0153TextOutput-BDknO\u0153}-Prompt-xeI6K{\u0153fieldName\u0153:\u0153context\u0153,\u0153id\u0153:\u0153Prompt-xeI6K\u0153,\u0153inputTypes\u0153:[\u0153Document\u0153,\u0153BaseOutputParser\u0153,\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}",
+        "className": "",
         "data": {
+          "sourceHandle": {
+            "dataType": "Prompt",
+            "id": "Prompt-jzPqb",
+            "name": "prompt",
+            "output_types": [
+              "Message"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "input_value",
+            "id": "OpenAIModel-ickkA",
+            "inputTypes": [
+              "Message"
+            ],
+            "type": "str"
+          }
+        },
+        "id": "reactflow__edge-Prompt-jzPqb{œdataTypeœ:œPromptœ,œidœ:œPrompt-jzPqbœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-ickkA{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-ickkAœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+        "selected": false,
+        "source": "Prompt-jzPqb",
+        "sourceHandle": "{œdataTypeœ:œPromptœ,œidœ:œPrompt-jzPqbœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}",
+        "style": {
+          "stroke": "#555"
+        },
+        "target": "OpenAIModel-ickkA",
+        "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-ickkAœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}"
+      },
+      {
+        "className": "",
+        "data": {
+          "sourceHandle": {
+            "dataType": "OpenAIModel",
+            "id": "OpenAIModel-ickkA",
+            "name": "text_output",
+            "output_types": [
+              "Message"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "input_value",
+            "id": "ChatOutput-Zy354",
+            "inputTypes": [
+              "Message",
+              "str"
+            ],
+            "type": "str"
+          }
+        },
+        "id": "reactflow__edge-OpenAIModel-ickkA{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-ickkAœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-Zy354{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-Zy354œ,œinputTypesœ:[œMessageœ,œstrœ],œtypeœ:œstrœ}",
+        "selected": false,
+        "source": "OpenAIModel-ickkA",
+        "sourceHandle": "{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-ickkAœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}",
+        "style": {
+          "stroke": "#555"
+        },
+        "target": "ChatOutput-Zy354",
+        "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-Zy354œ,œinputTypesœ:[œMessageœ,œstrœ],œtypeœ:œstrœ}"
+      },
+      {
+        "className": "",
+        "data": {
+          "sourceHandle": {
+            "dataType": "File",
+            "id": "File-28ckd",
+            "name": "data",
+            "output_types": [
+              "Data"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "data_input",
+            "id": "RecursiveCharacterTextSplitter-HVESL",
+            "inputTypes": [
+              "Document",
+              "Data"
+            ],
+            "type": "other"
+          }
+        },
+        "id": "reactflow__edge-File-28ckd{œdataTypeœ:œFileœ,œidœ:œFile-28ckdœ,œnameœ:œdataœ,œoutput_typesœ:[œDataœ]}-RecursiveCharacterTextSplitter-HVESL{œfieldNameœ:œdata_inputœ,œidœ:œRecursiveCharacterTextSplitter-HVESLœ,œinputTypesœ:[œDocumentœ,œDataœ],œtypeœ:œotherœ}",
+        "source": "File-28ckd",
+        "sourceHandle": "{œdataTypeœ:œFileœ,œidœ:œFile-28ckdœ,œnameœ:œdataœ,œoutput_typesœ:[œDataœ]}",
+        "target": "RecursiveCharacterTextSplitter-HVESL",
+        "targetHandle": "{œfieldNameœ:œdata_inputœ,œidœ:œRecursiveCharacterTextSplitter-HVESLœ,œinputTypesœ:[œDocumentœ,œDataœ],œtypeœ:œotherœ}"
+      },
+      {
+        "className": "",
+        "data": {
+          "sourceHandle": {
+            "dataType": "RecursiveCharacterTextSplitter",
+            "id": "RecursiveCharacterTextSplitter-HVESL",
+            "name": "data",
+            "output_types": [
+              "Data"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "vector_store_inputs",
+            "id": "AstraDB-irvai",
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-        "selected": false
-      },
-      {
-        "source": "AstraDBSearch-41nRz",
-        "sourceHandle": "{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153AstraDBSearch\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153}",
-        "target": "TextOutput-BDknO",
-        "targetHandle": "{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-BDknO\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}",
-        "data": {
-          "targetHandle": {
-            "fieldName": "input_value",
-            "id": "TextOutput-BDknO",
-            "inputTypes": ["Record", "Text"],
-            "type": "str"
-          },
-          "sourceHandle": {
-            "baseClasses": ["Record"],
-            "dataType": "AstraDBSearch",
-            "id": "AstraDBSearch-41nRz"
-          }
-        },
-        "style": {
-          "stroke": "#555"
-        },
-        "className": "stroke-gray-900 stroke-connection",
-        "id": "reactflow__edge-AstraDBSearch-41nRz{\u0153baseClasses\u0153:[\u0153Record\u0153],\u0153dataType\u0153:\u0153AstraDBSearch\u0153,\u0153id\u0153:\u0153AstraDBSearch-41nRz\u0153}-TextOutput-BDknO{\u0153fieldName\u0153:\u0153input_value\u0153,\u0153id\u0153:\u0153TextOutput-BDknO\u0153,\u0153inputTypes\u0153:[\u0153Record\u0153,\u0153Text\u0153],\u0153type\u0153:\u0153str\u0153}"
+        "id": "reactflow__edge-ChatInput-IY8UK{œdataTypeœ:œChatInputœ,œidœ:œChatInput-IY8UKœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Prompt-jzPqb{œfieldNameœ:œquestionœ,œidœ:œPrompt-jzPqbœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+        "source": "ChatInput-IY8UK",
+        "sourceHandle": "{œdataTypeœ:œChatInputœ,œidœ:œChatInput-IY8UKœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}",
+        "target": "Prompt-jzPqb",
+        "targetHandle": "{œfieldNameœ:œquestionœ,œidœ:œPrompt-jzPqbœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}"
       }
     ],
     "viewport": {
-      "x": -259.6782520315529,
-      "y": 90.3428735006047,
-      "zoom": 0.2687057134854984
+      "x": -124.92057698887993,
+      "y": 126.80996053902413,
+      "zoom": 0.370642160653555
     }
   },
   "description": "Visit https://pre-release.langflow.org/tutorials/rag-with-astradb for a detailed guide of this project.\nThis project give you both Ingestion and RAG in a single file. You'll need to visit https://astra.datastax.com/ to create an Astra DB instance, your Token and grab an API Endpoint.\nRunning this project requires you to add a file in the Files component, then define a Collection Name and click on the Play icon on the Astra DB component. \n\nAfter the ingestion ends you are ready to click on the Run button at the lower left corner and start asking questions about your data.",
   "name": "Vector Store RAG",
-  "last_tested_version": "1.0.0a0",
+  "last_tested_version": "1.0.0a61",
+  "endpoint_name": null,
   "is_component": false
-}
+}
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
index 865498299..8a2ce60c2 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -472,17 +472,17 @@ files = [
 
 [[package]]
 name = "boto3"
-version = "1.34.128"
+version = "1.34.129"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "boto3-1.34.128-py3-none-any.whl", hash = "sha256:a048ff980a81cd652724a73bc496c519b336fabe19cc8bfc6c53b2ff6eb22c7b"},
-    {file = "boto3-1.34.128.tar.gz", hash = "sha256:43a6e99f53a8d34b3b4dbe424dbcc6b894350dc41a85b0af7c7bc24a7ec2cead"},
+    {file = "boto3-1.34.129-py3-none-any.whl", hash = "sha256:cc73de1c9d953b1f9da6ee2404af717e93d888f790f3e0291b22d1b8489eb401"},
+    {file = "boto3-1.34.129.tar.gz", hash = "sha256:a7a696fd3e7f5f43a81450b441f3eb6c5a89d28efe867cd97d8fc73ea5d8c139"},
 ]
 
 [package.dependencies]
-botocore = ">=1.34.128,<1.35.0"
+botocore = ">=1.34.129,<1.35.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"
 
@@ -491,13 +491,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.34.128"
+version = "1.34.129"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "botocore-1.34.128-py3-none-any.whl", hash = "sha256:db67fda136c372ab3fa432580c819c89ba18d28a6152a4d2a7ea40d44082892e"},
-    {file = "botocore-1.34.128.tar.gz", hash = "sha256:8d8e03f7c8c080ecafda72036eb3b482d649f8417c90b5dca33b7c2c47adb0c9"},
+    {file = "botocore-1.34.129-py3-none-any.whl", hash = "sha256:86d3dd30996aa459e9c3321edac12aebe47c73cb4acc7556941f9b4c39726088"},
+    {file = "botocore-1.34.129.tar.gz", hash = "sha256:7c56e25af6112d69c5d14a15b42f76ba7687687abc463a96ac5edca19c0a9c2d"},
 ]
 
 [package.dependencies]
@@ -1005,13 +1005,13 @@ numpy = "*"
 
 [[package]]
 name = "chromadb"
-version = "0.5.0"
+version = "0.5.3"
 description = "Chroma."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "chromadb-0.5.0-py3-none-any.whl", hash = "sha256:8193dc65c143b61d8faf87f02c44ecfa778d471febd70de517f51c5d88a06009"},
-    {file = "chromadb-0.5.0.tar.gz", hash = "sha256:7954af614a9ff7b2902ddbd0a162f33f7ec0669e2429903905c4f7876d1f766f"},
+    {file = "chromadb-0.5.3-py3-none-any.whl", hash = "sha256:b3874f08356e291c68c6d2e177db472cd51f22f3af7b9746215b748fd1e29982"},
+    {file = "chromadb-0.5.3.tar.gz", hash = "sha256:05d887f56a46b2e0fc6ac5ab979503a27b9ee50d5ca9e455f83b2fb9840cd026"},
 ]
 
 [package.dependencies]
@@ -1020,10 +1020,11 @@ build = ">=1.0.3"
 chroma-hnswlib = "0.7.3"
 fastapi = ">=0.95.2"
 grpcio = ">=1.58.0"
+httpx = ">=0.27.0"
 importlib-resources = "*"
 kubernetes = ">=28.1.0"
 mmh3 = ">=4.0.1"
-numpy = ">=1.22.5"
+numpy = ">=1.22.5,<2.0.0"
 onnxruntime = ">=1.14.1"
 opentelemetry-api = ">=1.2.0"
 opentelemetry-exporter-otlp-proto-grpc = ">=1.2.0"
@@ -2439,8 +2440,8 @@ files = [
 [package.dependencies]
 cffi = {version = ">=1.12.2", markers = "platform_python_implementation == \"CPython\" and sys_platform == \"win32\""}
 greenlet = [
-    {version = ">=2.0.0", markers = "platform_python_implementation == \"CPython\" and python_version < \"3.11\""},
     {version = ">=3.0rc3", markers = "platform_python_implementation == \"CPython\" and python_version >= \"3.11\""},
+    {version = ">=2.0.0", markers = "platform_python_implementation == \"CPython\" and python_version < \"3.11\""},
 ]
 "zope.event" = "*"
 "zope.interface" = "*"
@@ -2535,6 +2536,38 @@ benchmarks = ["httplib2", "httpx", "requests", "urllib3"]
 dev = ["dpkt", "pytest", "requests"]
 examples = ["oauth2"]
 
+[[package]]
+name = "gitdb"
+version = "4.0.11"
+description = "Git Object Database"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "gitdb-4.0.11-py3-none-any.whl", hash = "sha256:81a3407ddd2ee8df444cbacea00e2d038e40150acfa3001696fe0dcf1d3adfa4"},
+    {file = "gitdb-4.0.11.tar.gz", hash = "sha256:bf5421126136d6d0af55bc1e7c1af1c397a34f5b7bd79e776cd3e89785c2b04b"},
+]
+
+[package.dependencies]
+smmap = ">=3.0.1,<6"
+
+[[package]]
+name = "gitpython"
+version = "3.1.43"
+description = "GitPython is a Python library used to interact with Git repositories"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "GitPython-3.1.43-py3-none-any.whl", hash = "sha256:eec7ec56b92aad751f9912a73404bc02ba212a23adb2c7098ee668417051a1ff"},
+    {file = "GitPython-3.1.43.tar.gz", hash = "sha256:35f314a9f878467f5453cc1fee295c3e18e52f1b99f10f6cf5b1682e968a9e7c"},
+]
+
+[package.dependencies]
+gitdb = ">=4.0.1,<5"
+
+[package.extras]
+doc = ["sphinx (==4.3.2)", "sphinx-autodoc-typehints", "sphinx-rtd-theme", "sphinxcontrib-applehelp (>=1.0.2,<=1.0.4)", "sphinxcontrib-devhelp (==1.0.2)", "sphinxcontrib-htmlhelp (>=2.0.0,<=2.0.1)", "sphinxcontrib-qthelp (==1.0.3)", "sphinxcontrib-serializinghtml (==1.1.5)"]
+test = ["coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest (>=7.3.1)", "pytest-cov", "pytest-instafail", "pytest-mock", "pytest-sugar", "typing-extensions"]
+
 [[package]]
 name = "google-ai-generativelanguage"
 version = "0.6.4"
@@ -2567,12 +2600,12 @@ files = [
 google-auth = ">=2.14.1,<3.0.dev0"
 googleapis-common-protos = ">=1.56.2,<2.0.dev0"
 grpcio = [
-    {version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
     {version = ">=1.49.1,<2.0dev", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
+    {version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
 ]
 grpcio-status = [
-    {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
     {version = ">=1.49.1,<2.0.dev0", optional = true, markers = "python_version >= \"3.11\" and extra == \"grpc\""},
+    {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""},
 ]
 proto-plus = ">=1.22.3,<2.0.0dev"
 protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0.dev0"
@@ -2641,13 +2674,13 @@ httplib2 = ">=0.19.0"
 
 [[package]]
 name = "google-cloud-aiplatform"
-version = "1.55.0"
+version = "1.56.0"
 description = "Vertex AI API client library"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "google-cloud-aiplatform-1.55.0.tar.gz", hash = "sha256:aa87cb6c49ae5fde87fb831ce8ad4a853c4656fe04babe505e9144c7a9e09c1a"},
-    {file = "google_cloud_aiplatform-1.55.0-py2.py3-none-any.whl", hash = "sha256:c6cc76ca5537f4636a0c3f8c0288d2e0d2d86ef708e562d2654313e11d6ee46a"},
+    {file = "google-cloud-aiplatform-1.56.0.tar.gz", hash = "sha256:d4cfb085427dac01142915f523949ac2955d6c7f148d95017d3286a77caf5d5e"},
+    {file = "google_cloud_aiplatform-1.56.0-py2.py3-none-any.whl", hash = "sha256:ee1ab3bd115c3caebf8ddfd3e47eeb8396a3ec2fc5f5baf1a5c295c8d64333ab"},
 ]
 
 [package.dependencies]
@@ -2669,8 +2702,8 @@ cloud-profiler = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow
 datasets = ["pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)"]
 endpoint = ["requests (>=2.28.1)"]
 full = ["cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nest-asyncio (>=1.0.0,<1.6.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)"]
-langchain = ["langchain (>=0.1.16,<0.3)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)"]
-langchain-testing = ["absl-py", "cloudpickle (>=2.2.1,<4.0)", "langchain (>=0.1.16,<0.3)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<3)", "pytest-xdist"]
+langchain = ["langchain (>=0.1.16,<0.3)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "tenacity (<=8.3)"]
+langchain-testing = ["absl-py", "cloudpickle (>=3.0,<4.0)", "langchain (>=0.1.16,<0.3)", "langchain-core (<0.2)", "langchain-google-vertexai (<2)", "openinference-instrumentation-langchain (>=0.1.19,<0.2)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<3)", "pytest-xdist", "tenacity (<=8.3)"]
 lit = ["explainable-ai-sdk (>=1.0.0)", "lit-nlp (==0.4.0)", "pandas (>=1.0.0)", "tensorflow (>=2.3.0,<3.0.0dev)"]
 metadata = ["numpy (>=1.15.0)", "pandas (>=1.0.0)"]
 pipelines = ["pyyaml (>=5.3.1,<7)"]
@@ -2680,7 +2713,7 @@ private-endpoints = ["requests (>=2.28.1)", "urllib3 (>=1.21.1,<1.27)"]
 rapid-evaluation = ["nest-asyncio (>=1.0.0,<1.6.0)", "pandas (>=1.0.0,<2.2.0)"]
 ray = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "setuptools (<70.0.0)"]
 ray-testing = ["google-cloud-bigquery", "google-cloud-bigquery-storage", "immutabledict", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pytest-xdist", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "ray[train] (==2.9.3)", "scikit-learn", "setuptools (<70.0.0)", "tensorflow", "torch (>=2.0.0,<2.1.0)", "xgboost", "xgboost-ray"]
-reasoningengine = ["cloudpickle (>=2.2.1,<4.0)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<3)"]
+reasoningengine = ["cloudpickle (>=3.0,<4.0)", "opentelemetry-exporter-gcp-trace (<2)", "opentelemetry-sdk (<2)", "pydantic (>=2.6.3,<3)"]
 tensorboard = ["tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "werkzeug (>=2.0.0,<2.1.0dev)"]
 testing = ["bigframes", "cloudpickle (<3.0)", "docker (>=5.0.3)", "explainable-ai-sdk (>=1.0.0)", "fastapi (>=0.71.0,<=0.109.1)", "google-api-core (>=2.11,<3.0.0)", "google-cloud-bigquery", "google-cloud-bigquery-storage", "google-cloud-logging (<4.0)", "google-vizier (>=0.1.6)", "grpcio-testing", "httpx (>=0.23.0,<0.25.0)", "immutabledict", "ipython", "kfp (>=2.6.0,<3.0.0)", "lit-nlp (==0.4.0)", "mlflow (>=1.27.0,<=2.1.1)", "nest-asyncio (>=1.0.0,<1.6.0)", "numpy (>=1.15.0)", "pandas (>=1.0.0)", "pandas (>=1.0.0,<2.2.0)", "pyarrow (>=10.0.1)", "pyarrow (>=14.0.0)", "pyarrow (>=3.0.0,<8.0dev)", "pyarrow (>=6.0.1)", "pydantic (<2)", "pyfakefs", "pytest-asyncio", "pytest-xdist", "pyyaml (>=5.3.1,<7)", "ray[default] (>=2.4,<2.5.dev0 || >2.9.0,!=2.9.1,!=2.9.2,<=2.9.3)", "ray[default] (>=2.5,<=2.9.3)", "requests (>=2.28.1)", "requests-toolbelt (<1.0.0)", "scikit-learn", "setuptools (<70.0.0)", "starlette (>=0.17.1)", "tensorboard-plugin-profile (>=2.4.0,<3.0.0dev)", "tensorflow (==2.13.0)", "tensorflow (==2.16.1)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.3.0,<3.0.0dev)", "tensorflow (>=2.4.0,<3.0.0dev)", "torch (>=2.0.0,<2.1.0)", "torch (>=2.2.0)", "urllib3 (>=1.21.1,<1.27)", "uvicorn[standard] (>=0.16.0)", "werkzeug (>=2.0.0,<2.1.0dev)", "xgboost"]
 vizier = ["google-vizier (>=0.1.6)"]
@@ -4140,6 +4173,22 @@ astrapy = ">=1.2,<2.0"
 langchain-core = ">=0.1.31,<0.3"
 numpy = ">=1,<2"
 
+[[package]]
+name = "langchain-aws"
+version = "0.1.6"
+description = "An integration package connecting AWS and LangChain"
+optional = false
+python-versions = "<4.0,>=3.8.1"
+files = [
+    {file = "langchain_aws-0.1.6-py3-none-any.whl", hash = "sha256:a747c282c2df29281918818238fea5f7e183cfa1b28add4a08d7f3f9a4438ca9"},
+    {file = "langchain_aws-0.1.6.tar.gz", hash = "sha256:d5ceb3db72aef98b2fb2336add7932f1ed045df8824fef24cd3613d93d8b4efb"},
+]
+
+[package.dependencies]
+boto3 = ">=1.34.51,<1.35.0"
+langchain-core = ">=0.1.45,<0.3"
+numpy = ">=1,<2"
+
 [[package]]
 name = "langchain-chroma"
 version = "0.1.1"
@@ -4200,22 +4249,25 @@ tenacity = ">=8.1.0,<9.0.0"
 
 [[package]]
 name = "langchain-core"
-version = "0.2.8"
+version = "0.2.9"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchain_core-0.2.8-py3-none-any.whl", hash = "sha256:172c81c858dc1f3123cc72b7e44e10f44c92f8a761cae18c364081f6c208e9f6"},
-    {file = "langchain_core-0.2.8.tar.gz", hash = "sha256:2db866a4514672c4875b69d5590aa2ed50aa0d144874268bef68d74b5e7f33f9"},
+    {file = "langchain_core-0.2.9-py3-none-any.whl", hash = "sha256:426a5a4fea95a5db995ba5ab560b76edd4998fb6fe52ccc28ac987092a4cbfcd"},
+    {file = "langchain_core-0.2.9.tar.gz", hash = "sha256:f1c59082642921727844e1cd0eb36d451edd1872c20e193aa3142aac03495986"},
 ]
 
 [package.dependencies]
 jsonpatch = ">=1.33,<2.0"
 langsmith = ">=0.1.75,<0.2.0"
 packaging = ">=23.2,<25"
-pydantic = ">=1,<3"
+pydantic = [
+    {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
+    {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
+]
 PyYAML = ">=5.3"
-tenacity = ">=8.1.0,<9.0.0"
+tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
 
 [[package]]
 name = "langchain-experimental"
@@ -4301,6 +4353,22 @@ httpx-sse = ">=0.3.1,<1"
 langchain-core = ">=0.2.0,<0.3"
 tokenizers = ">=0.15.1,<1"
 
+[[package]]
+name = "langchain-mongodb"
+version = "0.1.6"
+description = "An integration package connecting MongoDB and LangChain"
+optional = false
+python-versions = "<4.0,>=3.8.1"
+files = [
+    {file = "langchain_mongodb-0.1.6-py3-none-any.whl", hash = "sha256:ba5e1e388131ae00e8fcd39c4b38ce42c29f00c4bc39c58023b354b597ffa8df"},
+    {file = "langchain_mongodb-0.1.6.tar.gz", hash = "sha256:a7a6b66d5270d6f8732c4e848f9a3742bbd4485c8829a2d629332ee683b936d1"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.1.46,<0.3"
+numpy = ">=1,<2"
+pymongo = ">=4.6.1,<5.0"
+
 [[package]]
 name = "langchain-openai"
 version = "0.1.8"
@@ -4465,13 +4533,13 @@ openai = ["openai (>=0.27.8)"]
 
 [[package]]
 name = "langsmith"
-version = "0.1.78"
+version = "0.1.80"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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-    {file = "langsmith-0.1.78.tar.gz", hash = "sha256:d9112d2e9298ec6b02d3b1afec6ed557df9db3746c79d34ef3b448fc18e116cd"},
+    {file = "langsmith-0.1.80-py3-none-any.whl", hash = "sha256:951fc29576b52afd8378d41f6db343090fea863e3620f0ca97e83b221f93c94d"},
+    {file = "langsmith-0.1.80.tar.gz", hash = "sha256:a29b1dde27612308beee424f1388ad844c8e7e375bf2ac8bdf4da174013f279d"},
 ]
 
 [package.dependencies]
@@ -4481,18 +4549,19 @@ requests = ">=2,<3"
 
 [[package]]
 name = "litellm"
-version = "1.40.15"
+version = "1.40.16"
 description = "Library to easily interface with LLM API providers"
 optional = false
 python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
 files = [
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+    {file = "litellm-1.40.16-py3-none-any.whl", hash = "sha256:b78571a39a3b7819ca9cb96278f4b15b97557e2cd50e9aeee9210a7e744b8667"},
+    {file = "litellm-1.40.16.tar.gz", hash = "sha256:b32cd15c7cf9084aa0f92eb7103323741d63cc8a14e28b739054b0cb6682c431"},
 ]
 
 [package.dependencies]
 aiohttp = "*"
 click = "*"
+ijson = "*"
 importlib-metadata = ">=6.8.0"
 jinja2 = ">=3.1.2,<4.0.0"
 openai = ">=1.27.0"
@@ -4551,8 +4620,8 @@ psutil = ">=5.9.1"
 pywin32 = {version = "*", markers = "platform_system == \"Windows\""}
 pyzmq = ">=25.0.0"
 requests = [
-    {version = ">=2.26.0", markers = "python_version <= \"3.11\""},
     {version = ">=2.32.2", markers = "python_version > \"3.11\""},
+    {version = ">=2.26.0", markers = "python_version <= \"3.11\""},
 ]
 tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
 Werkzeug = ">=2.0.0"
@@ -6020,9 +6089,9 @@ files = [
 
 [package.dependencies]
 numpy = [
+    {version = ">=1.26.0,<2", markers = "python_version >= \"3.12\""},
     {version = ">=1.22.4,<2", markers = "python_version < \"3.11\""},
     {version = ">=1.23.2,<2", markers = "python_version == \"3.11\""},
-    {version = ">=1.26.0,<2", markers = "python_version >= \"3.12\""},
 ]
 python-dateutil = ">=2.8.2"
 pytz = ">=2020.1"
@@ -6427,27 +6496,28 @@ files = [
 
 [[package]]
 name = "psutil"
-version = "5.9.8"
+version = "6.0.0"
 description = "Cross-platform lib for process and system monitoring in Python."
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7"
 files = [
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 ]
 
 [package.extras]
@@ -7105,12 +7175,12 @@ image = ["Pillow (>=8.0.0)"]
 
 [[package]]
 name = "pyperclip"
-version = "1.8.2"
+version = "1.9.0"
 description = "A cross-platform clipboard module for Python. (Only handles plain text for now.)"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
@@ -8482,6 +8552,17 @@ files = [
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 ]
 
+[[package]]
+name = "smmap"
+version = "5.0.1"
+description = "A pure Python implementation of a sliding window memory map manager"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
+
 [[package]]
 name = "sniffio"
 version = "1.3.1"
@@ -8506,64 +8587,64 @@ files = [
 
 [[package]]
 name = "sqlalchemy"
-version = "2.0.30"
+version = "2.0.31"
 description = "Database Abstraction Library"
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "SQLAlchemy-2.0.31.tar.gz", hash = "sha256:b607489dd4a54de56984a0c7656247504bd5523d9d0ba799aef59d4add009484"},
 ]
 
 [package.dependencies]
-greenlet = {version = "!=0.4.17", markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\""}
+greenlet = {version = "!=0.4.17", markers = "python_version < \"3.13\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"}
 typing-extensions = ">=4.6.0"
 
 [package.extras]
@@ -10545,4 +10626,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "6cfa9f164710bf283b50a8e12e9d0c91c58f5d0176bbb277b5f5d3630ec8b2cb"
+content-hash = "332baeb07342a5ad1367ce6b13a87bd96504f628d2e91e33dfda1a8317b2de13"
diff --git a/pyproject.toml b/pyproject.toml
index ad7349594..704ec2038 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -74,7 +74,7 @@ assemblyai = "^0.26.0"
 litellm = "^1.38.0"
 chromadb = "^0.5.0"
 langchain-anthropic = "^0.1.6"
-langchain-astradb = "^0.3.0"
+langchain-astradb = "^0.3.3"
 langchain-openai = "^0.1.1"
 zep-python = { version = "^2.0.0rc5", allow-prereleases = true }
 langchain-google-vertexai = "^1.0.3"
@@ -86,8 +86,11 @@ youtube-transcript-api = "^0.6.2"
 markdown = "^3.6"
 langchain-chroma = "^0.1.1"
 upstash-vector = "^0.4.0"
+gitpython = "^3.1.43"
 cassio = { extras = ["cassio"], version = "^0.1.7", optional = true }
 unstructured = {extras = ["docx", "md", "pptx"], version = "^0.14.4"}
+langchain-aws = "^0.1.6"
+langchain-mongodb = "^0.1.6"
 
 
 [tool.poetry.group.dev.dependencies]
@@ -130,17 +133,16 @@ local = ["llama-cpp-python", "sentence-transformers", "ctransformers"]
 optional = true
 
 [tool.poetry.group.spelling.dependencies]
-codespell = "^2.2.6"
+codespell = "^2.3.0"
 
 [tool.codespell]
-skip = '.git,*.pdf,*.svg,*.pdf,*.yaml,*.ipynb,poetry.lock,*.min.js,*.css,package-lock.json,*.trig'
+skip = '.git,*.pdf,*.svg,*.pdf,*.yaml,*.ipynb,poetry.lock,*.min.js,*.css,package-lock.json,*.trig.,**/node_modules/**,./stuff/*,*.csv'
 # Ignore latin etc
 ignore-regex = '.*(Stati Uniti|Tense=Pres).*'
 
 
 [tool.pytest.ini_options]
 minversion = "6.0"
-addopts = "-ra -n auto"
 testpaths = ["tests", "integration"]
 console_output_style = "progress"
 filterwarnings = ["ignore::DeprecationWarning"]
diff --git a/src/backend/base/langflow/api/utils.py b/src/backend/base/langflow/api/utils.py
index 82bd32165..099df66c5 100644
--- a/src/backend/base/langflow/api/utils.py
+++ b/src/backend/base/langflow/api/utils.py
@@ -129,9 +129,6 @@ def update_template_field(frontend_template, key, value_dict):
             template_field["value"] = ""
         template_field["file_path"] = file_path_value
 
-    if "load_from_db" in value_dict and value_dict["load_from_db"]:
-        template_field["load_from_db"] = value_dict["load_from_db"]
-
 
 def get_file_path_value(file_path):
     """Get the file path value if the file exists, else return empty string."""
@@ -208,7 +205,7 @@ def format_elapsed_time(elapsed_time: float) -> str:
         return f"{minutes} {minutes_unit}, {seconds} {seconds_unit}"
 
 
-async def build_and_cache_graph_from_db(flow_id: str, session: Session, chat_service: "ChatService"):
+async def build_graph_from_db(flow_id: str, session: Session, chat_service: "ChatService"):
     """Build and cache the graph."""
     flow: Optional[Flow] = session.get(Flow, flow_id)
     if not flow or not flow.data:
diff --git a/src/backend/base/langflow/api/v1/chat.py b/src/backend/base/langflow/api/v1/chat.py
index 031772852..9dd911ca4 100644
--- a/src/backend/base/langflow/api/v1/chat.py
+++ b/src/backend/base/langflow/api/v1/chat.py
@@ -1,4 +1,5 @@
 import time
+import traceback
 import uuid
 from functools import partial
 from typing import TYPE_CHECKING, Annotated, Optional
@@ -9,7 +10,7 @@ from loguru import logger
 
 from langflow.api.utils import (
     build_and_cache_graph_from_data,
-    build_and_cache_graph_from_db,
+    build_graph_from_db,
     format_elapsed_time,
     format_exception_message,
     get_top_level_vertices,
@@ -23,6 +24,7 @@ from langflow.api.v1.schemas import (
     VertexBuildResponse,
     VerticesOrderResponse,
 )
+from langflow.exceptions.component import ComponentBuildException
 from langflow.schema.schema import Log
 from langflow.services.auth.utils import get_current_active_user
 from langflow.services.chat.service import ChatService
@@ -82,7 +84,7 @@ async def retrieve_vertices_order(
         flow_id_str = str(flow_id)
         # First, we need to check if the flow_id is in the cache
         if not data:
-            graph = await build_and_cache_graph_from_db(flow_id=flow_id_str, session=session, chat_service=chat_service)
+            graph = await build_graph_from_db(flow_id=flow_id_str, session=session, chat_service=chat_service)
         else:
             graph = await build_and_cache_graph_from_data(
                 flow_id=flow_id_str, graph_data=data.model_dump(), chat_service=chat_service
@@ -109,6 +111,7 @@ async def retrieve_vertices_order(
         run_id = uuid.uuid4()
         graph.set_run_id(run_id)
         vertices_to_run = list(graph.vertices_to_run) + get_top_level_vertices(graph, graph.vertices_to_run)
+        await chat_service.set_cache(str(flow_id), graph)
         return VerticesOrderResponse(ids=first_layer, run_id=run_id, vertices_to_run=vertices_to_run)
 
     except Exception as exc:
@@ -156,7 +159,7 @@ async def build_vertex(
         if not cache:
             # If there's no cache
             logger.warning(f"No cache found for {flow_id_str}. Building graph starting at {vertex_id}")
-            graph = await build_and_cache_graph_from_db(
+            graph = await build_graph_from_db(
                 flow_id=flow_id_str, session=next(get_session()), chat_service=chat_service
             )
         else:
@@ -183,22 +186,29 @@ async def build_vertex(
                 lock, set_cache_coro, graph=graph, vertex=vertex, cache=False
             )
             top_level_vertices = graph.run_manager.get_top_level_vertices(graph, next_runnable_vertices)
-            log_obj = Log(message=vertex.artifacts_raw, type=vertex.artifacts_type)
+
             result_data_response = ResultDataResponse(**result_dict.model_dump())
 
+            result_data_response = ResultDataResponse.model_validate(result_dict, from_attributes=True)
         except Exception as exc:
-            logger.exception(f"Error building Component: {exc}")
-            params = format_exception_message(exc)
+            if isinstance(exc, ComponentBuildException):
+                params = exc.message
+                tb = exc.formatted_traceback
+            else:
+                tb = traceback.format_exc()
+                logger.exception(f"Error building Component: {exc}")
+                params = format_exception_message(exc)
+            message = {"errorMessage": params, "stackTrace": tb}
             valid = False
-            log_obj = Log(message=params, type="error")
-            result_data_response = ResultDataResponse(results={})
+            output_label = vertex.outputs[0]["name"] if vertex.outputs else "output"
+            logs = {output_label: Log(message=message, type="error")}
+            result_data_response = ResultDataResponse(results={}, logs=logs)
             artifacts = {}
             # If there's an error building the vertex
             # we need to clear the cache
             await chat_service.clear_cache(flow_id_str)
 
         result_data_response.message = artifacts
-        result_data_response.logs.append(log_obj)
 
         # Log the vertex build
         if not vertex.will_stream:
@@ -220,6 +230,7 @@ async def build_vertex(
         inactivated_vertices = list(graph.inactivated_vertices)
         graph.reset_inactivated_vertices()
         graph.reset_activated_vertices()
+
         await chat_service.set_cache(flow_id_str, graph)
 
         # graph.stop_vertex tells us if the user asked
@@ -240,7 +251,7 @@ async def build_vertex(
         )
         return build_response
     except Exception as exc:
-        logger.error(f"Error building Component: {exc}")
+        logger.error(f"Error building Component:\n\n{exc}")
         logger.exception(exc)
         message = parse_exception(exc)
         raise HTTPException(status_code=500, detail=message) from exc
@@ -284,18 +295,12 @@ async def build_vertex_stream(
 
         async def stream_vertex():
             try:
-                if not session_id:
-                    cache = await chat_service.get_cache(flow_id_str)
-                    if not cache:
-                        # If there's no cache
-                        raise ValueError(f"No cache found for {flow_id_str}.")
-                    else:
-                        graph = cache.get("result")
+                cache = await chat_service.get_cache(flow_id_str)
+                if not cache:
+                    # If there's no cache
+                    raise ValueError(f"No cache found for {flow_id_str}.")
                 else:
-                    session_data = await session_service.load_session(session_id, flow_id=flow_id_str)
-                    graph, artifacts = session_data if session_data else (None, None)
-                    if not graph:
-                        raise ValueError(f"No graph found for {flow_id_str}.")
+                    graph = cache.get("result")
 
                 vertex: "InterfaceVertex" = graph.get_vertex(vertex_id)
                 if not hasattr(vertex, "stream"):
@@ -342,6 +347,7 @@ async def build_vertex_stream(
                 yield str(StreamData(event="error", data={"error": exc_message}))
             finally:
                 logger.debug("Closing stream")
+                await chat_service.set_cache(flow_id_str, graph)
                 yield str(StreamData(event="close", data={"message": "Stream closed"}))
 
         return StreamingResponse(stream_vertex(), media_type="text/event-stream")
diff --git a/src/backend/base/langflow/api/v1/endpoints.py b/src/backend/base/langflow/api/v1/endpoints.py
index 9124810c7..d69cc3612 100644
--- a/src/backend/base/langflow/api/v1/endpoints.py
+++ b/src/backend/base/langflow/api/v1/endpoints.py
@@ -1,9 +1,13 @@
+from asyncio import Lock
 from http import HTTPStatus
 from typing import TYPE_CHECKING, Annotated, List, Optional, Union
 from uuid import UUID
 
 import sqlalchemy as sa
 from fastapi import APIRouter, BackgroundTasks, Body, Depends, HTTPException, Request, UploadFile, status
+from loguru import logger
+from sqlmodel import Session, select
+
 from langflow.api.utils import update_frontend_node_with_template_values
 from langflow.api.v1.schemas import (
     ConfigResponse,
@@ -16,7 +20,7 @@ from langflow.api.v1.schemas import (
     UpdateCustomComponentRequest,
     UploadFileResponse,
 )
-from langflow.custom import CustomComponent
+from langflow.custom.custom_component.component import Component
 from langflow.custom.utils import build_custom_component_template
 from langflow.graph.graph.base import Graph
 from langflow.graph.schema import RunOutputs
@@ -26,30 +30,40 @@ from langflow.schema.graph import Tweaks
 from langflow.services.auth.utils import api_key_security, get_current_active_user
 from langflow.services.cache.utils import save_uploaded_file
 from langflow.services.database.models.flow import Flow
-from langflow.services.database.models.flow.utils import get_all_webhook_components_in_flow, get_flow_by_id
+from langflow.services.database.models.flow.utils import get_all_webhook_components_in_flow
 from langflow.services.database.models.user.model import User
-from langflow.services.deps import get_session, get_session_service, get_settings_service, get_task_service
+from langflow.services.deps import (
+    get_cache_service,
+    get_session,
+    get_session_service,
+    get_settings_service,
+    get_task_service,
+)
 from langflow.services.session.service import SessionService
 from langflow.services.task.service import TaskService
-from loguru import logger
-from sqlmodel import Session, select
 
 if TYPE_CHECKING:
-    from langflow.services.settings.manager import SettingsService
+    from langflow.services.cache.base import CacheService
+    from langflow.services.settings.service import SettingsService
 
 router = APIRouter(tags=["Base"])
 
 
 @router.get("/all", dependencies=[Depends(get_current_active_user)])
-def get_all(
+async def get_all(
     settings_service=Depends(get_settings_service),
+    cache_service: "CacheService" = Depends(dependency=get_cache_service),
+    force_refresh: bool = False,
 ):
-    from langflow.interface.types import get_all_types_dict
+    from langflow.interface.types import get_and_cache_all_types_dict
 
-    logger.debug("Building langchain types dict")
     try:
-        all_types_dict = get_all_types_dict(settings_service.settings.components_path)
-        return all_types_dict
+        async with Lock() as lock:
+            all_types_dict = await get_and_cache_all_types_dict(
+                settings_service=settings_service, cache_service=cache_service, force_refresh=force_refresh, lock=lock
+            )
+
+            return all_types_dict
     except Exception as exc:
         logger.exception(exc)
         raise HTTPException(status_code=500, detail=str(exc)) from exc
@@ -106,12 +120,10 @@ async def simple_run_flow(
 
 @router.post("/run/{flow_id_or_name}", response_model=RunResponse, response_model_exclude_none=True)
 async def simplified_run_flow(
-    db: Annotated[Session, Depends(get_session)],
     flow: Annotated[Flow, Depends(get_flow_by_id_or_endpoint_name)],
     input_request: SimplifiedAPIRequest = SimplifiedAPIRequest(),
     stream: bool = False,
     api_key_user: User = Depends(api_key_security),
-    session_service: SessionService = Depends(get_session_service),
 ):
     """
     Executes a specified flow by ID with input customization, performance enhancements through caching, and optional data streaming.
@@ -183,10 +195,10 @@ async def simplified_run_flow(
         raise HTTPException(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(exc)) from exc
 
 
-@router.post("/webhook/{flow_id}", response_model=dict, status_code=HTTPStatus.ACCEPTED)
+@router.post("/webhook/{flow_id_or_name}", response_model=dict, status_code=HTTPStatus.ACCEPTED)
 async def webhook_run_flow(
     db: Annotated[Session, Depends(get_session)],
-    flow: Annotated[Flow, Depends(get_flow_by_id)],
+    flow: Annotated[Flow, Depends(get_flow_by_id_or_endpoint_name)],
     request: Request,
     background_tasks: BackgroundTasks,
     session_service: SessionService = Depends(get_session_service),
@@ -232,7 +244,6 @@ async def webhook_run_flow(
         logger.debug("Starting background task")
         background_tasks.add_task(  # type: ignore
             simple_run_flow,
-            db=db,
             flow=flow,
             input_request=input_request,
         )
@@ -453,7 +464,7 @@ async def custom_component(
     raw_code: CustomComponentRequest,
     user: User = Depends(get_current_active_user),
 ):
-    component = CustomComponent(code=raw_code.code)
+    component = Component(code=raw_code.code)
 
     built_frontend_node, _ = build_custom_component_template(component, user_id=user.id)
 
@@ -481,7 +492,7 @@ async def custom_component_update(
 
     """
     try:
-        component = CustomComponent(code=code_request.code)
+        component = Component(code=code_request.code)
 
         component_node, cc_instance = build_custom_component_template(
             component,
diff --git a/src/backend/base/langflow/api/v1/flows.py b/src/backend/base/langflow/api/v1/flows.py
index 299a433d6..2cd16a003 100644
--- a/src/backend/base/langflow/api/v1/flows.py
+++ b/src/backend/base/langflow/api/v1/flows.py
@@ -50,6 +50,26 @@ def create_flow(
                 flow.name = f"{flow.name} ({max(numbers) + 1})"
             else:
                 flow.name = f"{flow.name} (1)"
+        # Now check if the endpoint is unique
+        if (
+            flow.endpoint_name
+            and session.exec(
+                select(Flow).where(Flow.endpoint_name == flow.endpoint_name).where(Flow.user_id == current_user.id)
+            ).first()
+        ):
+            flows = session.exec(
+                select(Flow)
+                .where(Flow.endpoint_name.like(f"{flow.endpoint_name}-%"))  # type: ignore
+                .where(Flow.user_id == current_user.id)
+            ).all()
+            if flows:
+                # The endpoitn name is like "my-endpoint","my-endpoint-1", "my-endpoint-2"
+                # so we need to get the highest number and add 1
+                # we need to get the last part of the endpoint name
+                numbers = [int(flow.endpoint_name.split("-")[-1]) for flow in flows]  # type: ignore
+                flow.endpoint_name = f"{flow.endpoint_name}-{max(numbers) + 1}"
+            else:
+                flow.endpoint_name = f"{flow.endpoint_name}-1"
 
         db_flow = Flow.model_validate(flow, from_attributes=True)
         db_flow.updated_at = datetime.now(timezone.utc)
diff --git a/src/backend/base/langflow/api/v1/login.py b/src/backend/base/langflow/api/v1/login.py
index 2637cc865..9ba570f3c 100644
--- a/src/backend/base/langflow/api/v1/login.py
+++ b/src/backend/base/langflow/api/v1/login.py
@@ -11,7 +11,7 @@ from langflow.services.auth.utils import (
 )
 from langflow.services.database.models.folder.utils import create_default_folder_if_it_doesnt_exist
 from langflow.services.deps import get_session, get_settings_service, get_variable_service
-from langflow.services.settings.manager import SettingsService
+from langflow.services.settings.service import SettingsService
 from langflow.services.variable.service import VariableService
 
 router = APIRouter(tags=["Login"])
diff --git a/src/backend/base/langflow/api/v1/monitor.py b/src/backend/base/langflow/api/v1/monitor.py
index e0608f38a..a99c86bf8 100644
--- a/src/backend/base/langflow/api/v1/monitor.py
+++ b/src/backend/base/langflow/api/v1/monitor.py
@@ -1,5 +1,6 @@
 from typing import List, Optional
 
+
 from fastapi import APIRouter, Depends, HTTPException, Query
 
 from langflow.services.deps import get_monitor_service
diff --git a/src/backend/base/langflow/api/v1/schemas.py b/src/backend/base/langflow/api/v1/schemas.py
index 1e0308bd5..c44e0f676 100644
--- a/src/backend/base/langflow/api/v1/schemas.py
+++ b/src/backend/base/langflow/api/v1/schemas.py
@@ -245,7 +245,7 @@ class VerticesOrderResponse(BaseModel):
 
 class ResultDataResponse(BaseModel):
     results: Optional[Any] = Field(default_factory=dict)
-    logs: List[Log | None] = Field(default_factory=list)
+    logs: dict[str, Log] = Field(default_factory=dict)
     message: Optional[Any] = Field(default_factory=dict)
     artifacts: Optional[Any] = Field(default_factory=dict)
     timedelta: Optional[float] = None
diff --git a/src/backend/base/langflow/base/agents/agent.py b/src/backend/base/langflow/base/agents/agent.py
index d4328032d..b24cf1984 100644
--- a/src/backend/base/langflow/base/agents/agent.py
+++ b/src/backend/base/langflow/base/agents/agent.py
@@ -4,10 +4,10 @@ from langchain.agents import AgentExecutor, BaseMultiActionAgent, BaseSingleActi
 from langchain_core.messages import BaseMessage
 from langchain_core.runnables import Runnable
 
-from langflow.base.agents.utils import get_agents_list, records_to_messages
+from langflow.base.agents.utils import data_to_messages, get_agents_list
 from langflow.custom import CustomComponent
 from langflow.field_typing import Text, Tool
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class LCAgentComponent(CustomComponent):
@@ -49,7 +49,7 @@ class LCAgentComponent(CustomComponent):
         agent: Union[Runnable, BaseSingleActionAgent, BaseMultiActionAgent, AgentExecutor],
         inputs: str,
         tools: List[Tool],
-        message_history: Optional[List[Record]] = None,
+        message_history: Optional[List[Data]] = None,
         handle_parsing_errors: bool = True,
         output_key: str = "output",
     ) -> Text:
@@ -64,7 +64,7 @@ class LCAgentComponent(CustomComponent):
             )
         input_dict: dict[str, str | list[BaseMessage]] = {"input": inputs}
         if message_history:
-            input_dict["chat_history"] = records_to_messages(message_history)
+            input_dict["chat_history"] = data_to_messages(message_history)
         result = await runnable.ainvoke(input_dict)
         self.status = result
         if output_key in result:
diff --git a/src/backend/base/langflow/base/agents/utils.py b/src/backend/base/langflow/base/agents/utils.py
index 781fa2362..2651ecb4a 100644
--- a/src/backend/base/langflow/base/agents/utils.py
+++ b/src/backend/base/langflow/base/agents/utils.py
@@ -13,7 +13,7 @@ from langchain_core.prompts import BasePromptTemplate, ChatPromptTemplate
 from langchain_core.tools import BaseTool
 from pydantic import BaseModel
 
-from langflow.schema import Record
+from langflow.schema import Data
 
 from .default_prompts import XML_AGENT_PROMPT
 
@@ -34,17 +34,17 @@ class AgentSpec(BaseModel):
     hub_repo: Optional[str] = None
 
 
-def records_to_messages(records: List[Record]) -> List[BaseMessage]:
+def data_to_messages(data: List[Data]) -> List[BaseMessage]:
     """
-    Convert a list of records to a list of messages.
+    Convert a list of data to a list of messages.
 
     Args:
-        records (List[Record]): The records to convert.
+        data (List[Data]): The data to convert.
 
     Returns:
-        List[Message]: The records as messages.
+        List[Message]: The data as messages.
     """
-    return [record.to_lc_message() for record in records]
+    return [value.to_lc_message() for value in data]
 
 
 def validate_and_create_xml_agent(
diff --git a/src/backend/base/langflow/base/constants.py b/src/backend/base/langflow/base/constants.py
index cb520a835..8c3f52845 100644
--- a/src/backend/base/langflow/base/constants.py
+++ b/src/backend/base/langflow/base/constants.py
@@ -11,7 +11,7 @@ import orjson
 
 STREAM_INFO_TEXT = "Stream the response from the model. Streaming works only in Chat."
 
-NODE_FORMAT_ATTRIBUTES = ["beta", "icon", "display_name", "description", "output_types"]
+NODE_FORMAT_ATTRIBUTES = ["beta", "icon", "display_name", "description", "output_types", "edited"]
 
 
 FIELD_FORMAT_ATTRIBUTES = [
@@ -28,6 +28,8 @@ FIELD_FORMAT_ATTRIBUTES = [
     "refresh_button",
     "refresh_button_text",
     "options",
+    "advanced",
+    "load_from_db",
 ]
 
 ORJSON_OPTIONS = orjson.OPT_INDENT_2 | orjson.OPT_SORT_KEYS | orjson.OPT_OMIT_MICROSECONDS
diff --git a/src/backend/base/langflow/base/curl/parse.py b/src/backend/base/langflow/base/curl/parse.py
index c3c2d31ce..bb58e8fa2 100644
--- a/src/backend/base/langflow/base/curl/parse.py
+++ b/src/backend/base/langflow/base/curl/parse.py
@@ -45,7 +45,7 @@ def normalize_newlines(multiline_text):
 def parse_curl_command(curl_command):
     tokens = shlex.split(normalize_newlines(curl_command))
     tokens = [token for token in tokens if token and token != " "]
-    if "curl" not in tokens[0]:
+    if tokens and "curl" not in tokens[0]:
         raise ValueError("Invalid curl command")
     args_template = {
         "command": None,
@@ -112,6 +112,10 @@ def parse_curl_command(curl_command):
 
 def parse_context(curl_command):
     method = "get"
+    if not curl_command:
+        return ParsedContext(
+            method=method, url="", data=None, headers={}, cookies={}, verify=True, auth=None, proxy=None
+        )
 
     parsed_args: ParsedArgs = parse_curl_command(curl_command)
 
diff --git a/src/backend/base/langflow/base/data/utils.py b/src/backend/base/langflow/base/data/utils.py
index 9bad1dabf..abadb2541 100644
--- a/src/backend/base/langflow/base/data/utils.py
+++ b/src/backend/base/langflow/base/data/utils.py
@@ -8,7 +8,7 @@ import chardet
 import orjson
 import yaml
 
-from langflow.schema import Record
+from langflow.schema import Data
 
 # Types of files that can be read simply by file.read()
 # and have 100% to be completely readable
@@ -33,12 +33,7 @@ TEXT_FILE_TYPES = [
     "tsx",
 ]
 
-IMG_FILE_TYPES = [
-    "jpg",
-    "jpeg",
-    "png",
-    "bmp",
-]
+IMG_FILE_TYPES = ["jpg", "jpeg", "png", "bmp", "image"]
 
 
 def normalize_text(text):
@@ -82,7 +77,7 @@ def retrieve_file_paths(
 
 # ! Removing unstructured dependency until
 # ! 3.12 is supported
-# def partition_file_to_record(file_path: str, silent_errors: bool) -> Optional[Record]:
+# def partition_file_to_data(file_path: str, silent_errors: bool) -> Optional[Data]:
 #     # Use the partition function to load the file
 #     from unstructured.partition.auto import partition  # type: ignore
 
@@ -93,11 +88,11 @@ def retrieve_file_paths(
 #             raise ValueError(f"Error loading file {file_path}: {e}") from e
 #         return None
 
-#     # Create a Record
+#     # Create a Data
 #     text = "\n\n".join([Text(el) for el in elements])
 #     metadata = elements.metadata if hasattr(elements, "metadata") else {}
 #     metadata["file_path"] = file_path
-#     record = Record(text=text, data=metadata)
+#     record = Data(text=text, data=metadata)
 #     return record
 
 
@@ -129,7 +124,7 @@ def parse_pdf_to_text(file_path: str) -> str:
         return "\n\n".join([page.extract_text() for page in reader.pages])
 
 
-def parse_text_file_to_record(file_path: str, silent_errors: bool) -> Optional[Record]:
+def parse_text_file_to_data(file_path: str, silent_errors: bool) -> Optional[Data]:
     try:
         if file_path.endswith(".pdf"):
             text = parse_pdf_to_text(file_path)
@@ -145,6 +140,7 @@ def parse_text_file_to_record(file_path: str, silent_errors: bool) -> Optional[R
                 text = {k: normalize_text(v) if isinstance(v, str) else v for k, v in text.items()}
             elif isinstance(text, list):
                 text = [normalize_text(item) if isinstance(item, str) else item for item in text]
+            text = orjson.dumps(text).decode("utf-8")
 
         elif file_path.endswith(".yaml") or file_path.endswith(".yml"):
             text = yaml.safe_load(text)
@@ -156,7 +152,7 @@ def parse_text_file_to_record(file_path: str, silent_errors: bool) -> Optional[R
             raise ValueError(f"Error loading file {file_path}: {e}") from e
         return None
 
-    record = Record(data={"file_path": file_path, "text": text})
+    record = Data(data={"file_path": file_path, "text": text})
     return record
 
 
@@ -167,21 +163,21 @@ def parse_text_file_to_record(file_path: str, silent_errors: bool) -> Optional[R
 #     silent_errors: bool,
 #     max_concurrency: int,
 #     use_multithreading: bool,
-# ) -> List[Optional[Record]]:
+# ) -> List[Optional[Data]]:
 #     if use_multithreading:
-#         records = parallel_load_records(file_paths, silent_errors, max_concurrency)
+#         data = parallel_load_data(file_paths, silent_errors, max_concurrency)
 #     else:
-#         records = [partition_file_to_record(file_path, silent_errors) for file_path in file_paths]
-#     records = list(filter(None, records))
-#     return records
+#         data = [partition_file_to_data(file_path, silent_errors) for file_path in file_paths]
+#     data = list(filter(None, data))
+#     return data
 
 
-def parallel_load_records(
+def parallel_load_data(
     file_paths: List[str],
     silent_errors: bool,
     max_concurrency: int,
-    load_function: Callable = parse_text_file_to_record,
-) -> List[Optional[Record]]:
+    load_function: Callable = parse_text_file_to_data,
+) -> List[Optional[Data]]:
     with futures.ThreadPoolExecutor(max_workers=max_concurrency) as executor:
         loaded_files = executor.map(
             lambda file_path: load_function(file_path, silent_errors),
diff --git a/src/backend/base/langflow/base/flow_processing/utils.py b/src/backend/base/langflow/base/flow_processing/utils.py
index 34b3ed1be..467a7a953 100644
--- a/src/backend/base/langflow/base/flow_processing/utils.py
+++ b/src/backend/base/langflow/base/flow_processing/utils.py
@@ -3,62 +3,65 @@ from typing import List
 from loguru import logger
 
 from langflow.graph.schema import ResultData, RunOutputs
-from langflow.schema import Record
+from langflow.schema import Data
 
 
-def build_records_from_run_outputs(run_outputs: RunOutputs) -> List[Record]:
+def build_data_from_run_outputs(run_outputs: RunOutputs) -> List[Data]:
     """
-    Build a list of records from the given RunOutputs.
+    Build a list of data from the given RunOutputs.
 
     Args:
         run_outputs (RunOutputs): The RunOutputs object containing the output data.
 
     Returns:
-        List[Record]: A list of records built from the RunOutputs.
+        List[Data]: A list of data built from the RunOutputs.
 
     """
     if not run_outputs:
         return []
-    records = []
+    data = []
     for result_data in run_outputs.outputs:
         if result_data:
-            records.extend(build_records_from_result_data(result_data))
-    return records
+            data.extend(build_data_from_result_data(result_data))
+    return data
 
 
-def build_records_from_result_data(result_data: ResultData, get_final_results_only: bool = True) -> List[Record]:
+def build_data_from_result_data(result_data: ResultData, get_final_results_only: bool = True) -> List[Data]:
     """
-    Build a list of records from the given ResultData.
+    Build a list of data from the given ResultData.
 
     Args:
         result_data (ResultData): The ResultData object containing the result data.
         get_final_results_only (bool, optional): Whether to include only final results. Defaults to True.
 
     Returns:
-        List[Record]: A list of records built from the ResultData.
+        List[Data]: A list of data built from the ResultData.
 
     """
     messages = result_data.messages
-    records = []
+
+    if not messages:
+        return []
+    data = []
 
     # Handle results without chat messages (calling flow)
     if not messages:
         # Result with a single record
         if isinstance(result_data.artifacts, dict):
-            records.append(Record(data=result_data.artifacts))
+            data.append(Data(data=result_data.artifacts))
         # List of artifacts
         elif isinstance(result_data.artifacts, list):
             for artifact in result_data.artifacts:
                 # If multiple records are found as artifacts, return as-is
-                if isinstance(artifact, Record):
-                    records.append(artifact)
+                if isinstance(artifact, Data):
+                    data.append(artifact)
                 else:
                     # Warn about unknown output type
                     logger.warning(f"Unable to build record output from unknown ResultData.artifact: {str(artifact)}")
         # Chat or text output
         elif result_data.results:
-            records.append(Record(data={"result": result_data.results}, text_key="result"))
-            return records
+            data.append(Data(data={"result": result_data.results}, text_key="result"))
+            return data
         else:
             return []
 
@@ -68,22 +71,22 @@ def build_records_from_result_data(result_data: ResultData, get_final_results_on
             result_data_dict = result_data.model_dump()
             results = result_data_dict.get("results", {})
             inner_result = results.get("result", {})
-        record = Record(data={"result": inner_result, "message": message_dict}, text_key="result")
-        records.append(record)
-    return records
+        record = Data(data={"result": inner_result, "message": message_dict}, text_key="result")
+        data.append(record)
+    return data
 
 
-def format_flow_output_records(records: List[Record]) -> str:
+def format_flow_output_data(data: List[Data]) -> str:
     """
-    Format the flow output records into a string.
+    Format the flow output data into a string.
 
     Args:
-        records (List[Record]): The list of records to format.
+        data (List[Data]): The list of data to format.
 
     Returns:
-        str: The formatted flow output records.
+        str: The formatted flow output data.
 
     """
     result = "Flow run output:\n"
-    results = "\n".join([record.result for record in records if record.data["message"]])
+    results = "\n".join([value.result for value in data if value.data["message"]])
     return result + results
diff --git a/src/backend/base/langflow/base/io/chat.py b/src/backend/base/langflow/base/io/chat.py
index d855b523a..0ee15bde3 100644
--- a/src/backend/base/langflow/base/io/chat.py
+++ b/src/backend/base/langflow/base/io/chat.py
@@ -1,13 +1,13 @@
 from typing import Optional, Union
 
 from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES
-from langflow.custom import CustomComponent
+from langflow.custom import Component
 from langflow.memory import store_message
-from langflow.schema import Record
+from langflow.schema import Data
 from langflow.schema.message import Message
 
 
-class ChatComponent(CustomComponent):
+class ChatComponent(Component):
     display_name = "Chat Component"
     description = "Use as base for chat components."
 
@@ -34,10 +34,10 @@ class ChatComponent(CustomComponent):
                 "info": "Return the message as a Message containing the sender, sender_name, and session_id.",
                 "advanced": True,
             },
-            "record_template": {
-                "display_name": "Record Template",
+            "data_template": {
+                "display_name": "Data Template",
                 "multiline": True,
-                "info": "In case of Message being a Record, this template will be used to convert it to text.",
+                "info": "In case of Message being a Data, this template will be used to convert it to text.",
                 "advanced": True,
             },
             "files": {
@@ -61,20 +61,20 @@ class ChatComponent(CustomComponent):
         self.status = messages
         return messages
 
-    def build_with_record(
+    def build_with_data(
         self,
         sender: Optional[str] = "User",
         sender_name: Optional[str] = "User",
-        input_value: Optional[Union[str, Record, Message]] = None,
+        input_value: Optional[Union[str, Data, Message]] = None,
         files: Optional[list[str]] = None,
         session_id: Optional[str] = None,
         return_message: Optional[bool] = False,
     ) -> Message:
         message: Message | None = None
 
-        if isinstance(input_value, Record):
+        if isinstance(input_value, Data):
             # Update the data of the record
-            message = Message.from_record(input_value)
+            message = Message.from_data(input_value)
         else:
             message = Message(
                 text=input_value, sender=sender, sender_name=sender_name, files=files, session_id=session_id
diff --git a/src/backend/base/langflow/base/io/text.py b/src/backend/base/langflow/base/io/text.py
index 5b6ece996..6ba7e39a3 100644
--- a/src/backend/base/langflow/base/io/text.py
+++ b/src/backend/base/langflow/base/io/text.py
@@ -1,12 +1,12 @@
 from typing import Optional
 
-from langflow.custom import CustomComponent
+from langflow.custom import Component
 from langflow.field_typing import Text
-from langflow.helpers.record import records_to_text
-from langflow.schema import Record
+from langflow.helpers.data import data_to_text
+from langflow.schema import Data
 
 
-class TextComponent(CustomComponent):
+class TextComponent(Component):
     display_name = "Text Component"
     description = "Used to pass text to the next component."
 
@@ -14,13 +14,13 @@ class TextComponent(CustomComponent):
         return {
             "input_value": {
                 "display_name": "Value",
-                "input_types": ["Text", "Record"],
-                "info": "Text or Record to be passed.",
+                "input_types": ["Text", "Data"],
+                "info": "Text or Data to be passed.",
             },
-            "record_template": {
-                "display_name": "Record Template",
+            "data_template": {
+                "display_name": "Data Template",
                 "multiline": True,
-                "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
+                "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
                 "advanced": True,
             },
         }
@@ -28,14 +28,16 @@ class TextComponent(CustomComponent):
     def build(
         self,
         input_value: Optional[Text] = "",
-        record_template: Optional[str] = "{text}",
+        data_template: Optional[str] = "{text}",
     ) -> Text:
-        if isinstance(input_value, Record):
-            if record_template == "":
-                # it should be dynamically set to the Record's .text_key value
-                # meaning, if text_key = "bacon", then record_template = "{bacon}"
-                record_template = "{" + input_value.text_key + "}"
-            input_value = records_to_text(template=record_template, records=input_value)
+        if isinstance(input_value, Data):
+            if data_template == "":
+                # it should be dynamically set to the Data's .text_key value
+                # meaning, if text_key = "bacon", then data_template = "{bacon}"
+                data_template = "{" + input_value.text_key + "}"
+            input_value = data_to_text(template=data_template, data=input_value)
+        elif not input_value:
+            input_value = ""
         self.status = input_value
         if not input_value:
             input_value = ""
diff --git a/src/backend/base/langflow/base/memory/memory.py b/src/backend/base/langflow/base/memory/memory.py
index fe372a96b..ba3735dc5 100644
--- a/src/backend/base/langflow/base/memory/memory.py
+++ b/src/backend/base/langflow/base/memory/memory.py
@@ -1,7 +1,7 @@
 from typing import Optional
 
 from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class BaseMemoryComponent(CustomComponent):
@@ -32,15 +32,15 @@ class BaseMemoryComponent(CustomComponent):
                 "info": "Order of the messages.",
                 "advanced": True,
             },
-            "record_template": {
-                "display_name": "Record Template",
+            "data_template": {
+                "display_name": "Data Template",
                 "multiline": True,
-                "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
+                "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
                 "advanced": True,
             },
         }
 
-    def get_messages(self, **kwargs) -> list[Record]:
+    def get_messages(self, **kwargs) -> list[Data]:
         raise NotImplementedError
 
     def add_message(
diff --git a/src/backend/base/langflow/base/models/model.py b/src/backend/base/langflow/base/models/model.py
index 5cc4f1066..d767d637c 100644
--- a/src/backend/base/langflow/base/models/model.py
+++ b/src/backend/base/langflow/base/models/model.py
@@ -1,18 +1,47 @@
+import json
 import warnings
 from typing import Optional, Union
 
-from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_core.language_models.llms import LLM
-from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
+from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
 
-from langflow.custom import CustomComponent
-from langflow.field_typing.prompt import Prompt
+from langflow.custom import Component
+from langflow.field_typing import LanguageModel
+from langflow.schema.message import Message
+from langflow.template.field.base import Output
 
 
-class LCModelComponent(CustomComponent):
+class LCModelComponent(Component):
     display_name: str = "Model Name"
     description: str = "Model Description"
 
+    outputs = [
+        Output(display_name="Text", name="text_output", method="text_response"),
+        Output(display_name="Language Model", name="model_output", method="build_model"),
+    ]
+
+    def _get_exception_message(self, e: Exception):
+        return str(e)
+
+    def _validate_outputs(self):
+        # At least these two outputs must be defined
+        required_output_methods = ["text_response", "build_model"]
+        output_names = [output.name for output in self.outputs]
+        for method_name in required_output_methods:
+            if method_name not in output_names:
+                raise ValueError(f"Output with name '{method_name}' must be defined.")
+            elif not hasattr(self, method_name):
+                raise ValueError(f"Method '{method_name}' must be defined.")
+
+    def text_response(self) -> Message:
+        input_value = self.input_value
+        stream = self.stream
+        system_message = self.system_message
+        output = self.build_model()
+        result = self.get_chat_result(output, stream, input_value, system_message)
+        self.status = result
+        return result
+
     def get_result(self, runnable: LLM, stream: bool, input_value: str):
         """
         Retrieves the result from the output of a Runnable object.
@@ -25,13 +54,18 @@ class LCModelComponent(CustomComponent):
         Returns:
             The result obtained from the output object.
         """
-        if stream:
-            result = runnable.stream(input_value)
-        else:
-            message = runnable.invoke(input_value)
-            result = message.content if hasattr(message, "content") else message
-            self.status = result
-        return result
+        try:
+            if stream:
+                result = runnable.stream(input_value)
+            else:
+                message = runnable.invoke(input_value)
+                result = message.content if hasattr(message, "content") else message
+                self.status = result
+            return result
+        except Exception as e:
+            if message := self._get_exception_message(e):
+                raise ValueError(message) from e
+            raise e
 
     def build_status_message(self, message: AIMessage):
         """
@@ -84,15 +118,15 @@ class LCModelComponent(CustomComponent):
         return status_message
 
     def get_chat_result(
-        self, runnable: BaseChatModel, stream: bool, input_value: str | Prompt, system_message: Optional[str] = None
+        self, runnable: LanguageModel, stream: bool, input_value: str | Message, system_message: Optional[str] = None
     ):
-        messages: list[Union[HumanMessage, SystemMessage]] = []
+        messages: list[Union[BaseMessage]] = []
         if not input_value and not system_message:
             raise ValueError("The message you want to send to the model is empty.")
         if system_message:
             messages.append(SystemMessage(content=system_message))
         if input_value:
-            if isinstance(input_value, Prompt):
+            if isinstance(input_value, Message):
                 with warnings.catch_warnings():
                     warnings.simplefilter("ignore")
                     if "prompt" in input_value:
@@ -102,15 +136,23 @@ class LCModelComponent(CustomComponent):
                         messages.append(input_value.to_lc_message())
             else:
                 messages.append(HumanMessage(content=input_value))
-        inputs = messages or {}  # type: ignore
-        if stream:
-            return runnable.stream(inputs)  # type: ignore
-        else:
-            message = runnable.invoke(inputs)  # type: ignore
-            result = message.content
-            if isinstance(message, AIMessage):
-                status_message = self.build_status_message(message)
-                self.status = status_message
+        inputs: Union[list, dict] = messages or {}
+        try:
+            if stream:
+                return runnable.stream(inputs)
             else:
-                self.status = result
-            return result
+                message = runnable.invoke(inputs)
+                result = message.content if hasattr(message, "content") else message
+                if isinstance(message, AIMessage):
+                    status_message = self.build_status_message(message)
+                    self.status = status_message
+                elif isinstance(result, dict):
+                    result = json.dumps(message, indent=4)
+                    self.status = result
+                else:
+                    self.status = result
+                return result
+        except Exception as e:
+            if message := self._get_exception_message(e):
+                raise ValueError(message) from e
+            raise e
diff --git a/src/backend/base/langflow/base/prompts/utils.py b/src/backend/base/langflow/base/prompts/utils.py
index 0fa62ea3b..948d09954 100644
--- a/src/backend/base/langflow/base/prompts/utils.py
+++ b/src/backend/base/langflow/base/prompts/utils.py
@@ -2,16 +2,15 @@ from copy import deepcopy
 
 from langchain_core.documents import Document
 
-from langflow.schema import Record
-from langflow.schema.message import Message
+from langflow.schema import Data
 
 
-def record_to_string(record: Record) -> str:
+def data_to_string(record: Data) -> str:
     """
     Convert a record to a string.
 
     Args:
-        record (Record): The record to convert.
+        record (Data): The record to convert.
 
     Returns:
         str: The record as a string.
@@ -29,22 +28,24 @@ def dict_values_to_string(d: dict) -> dict:
     Returns:
         dict: The dictionary with values converted to strings.
     """
+    from langflow.schema.message import Message
+
     # Do something similar to the above
     d_copy = deepcopy(d)
     for key, value in d_copy.items():
-        # it could be a list of records or documents or strings
+        # it could be a list of data or documents or strings
         if isinstance(value, list):
             for i, item in enumerate(value):
                 if isinstance(item, Message):
                     d_copy[key][i] = item.text
-                elif isinstance(item, Record):
-                    d_copy[key][i] = record_to_string(item)
+                elif isinstance(item, Data):
+                    d_copy[key][i] = data_to_string(item)
                 elif isinstance(item, Document):
                     d_copy[key][i] = document_to_string(item)
         elif isinstance(value, Message):
             d_copy[key] = value.text
-        elif isinstance(value, Record):
-            d_copy[key] = record_to_string(value)
+        elif isinstance(value, Data):
+            d_copy[key] = data_to_string(value)
         elif isinstance(value, Document):
             d_copy[key] = document_to_string(value)
     return d_copy
diff --git a/src/backend/base/langflow/base/tools/flow_tool.py b/src/backend/base/langflow/base/tools/flow_tool.py
index d0993bd99..4f767e4da 100644
--- a/src/backend/base/langflow/base/tools/flow_tool.py
+++ b/src/backend/base/langflow/base/tools/flow_tool.py
@@ -6,7 +6,7 @@ from langchain_core.runnables import RunnableConfig
 from langchain_core.tools import ToolException
 from pydantic.v1 import BaseModel
 
-from langflow.base.flow_processing.utils import build_records_from_result_data, format_flow_output_records
+from langflow.base.flow_processing.utils import build_data_from_result_data, format_flow_output_data
 from langflow.graph.graph.base import Graph
 from langflow.graph.vertex.base import Vertex
 from langflow.helpers.flow import build_schema_from_inputs, get_arg_names, get_flow_inputs, run_flow
@@ -59,14 +59,12 @@ class FlowTool(BaseTool):
             return "No output"
         run_output = run_outputs[0]
 
-        records = []
+        data = []
         if run_output is not None:
             for output in run_output.outputs:
                 if output:
-                    records.extend(
-                        build_records_from_result_data(output, get_final_results_only=self.get_final_results_only)
-                    )
-        return format_flow_output_records(records)
+                    data.extend(build_data_from_result_data(output, get_final_results_only=self.get_final_results_only))
+        return format_flow_output_data(data)
 
     def validate_inputs(self, args_names: List[dict[str, str]], args: Any, kwargs: Any):
         """Validate the inputs."""
@@ -107,11 +105,9 @@ class FlowTool(BaseTool):
             return "No output"
         run_output = run_outputs[0]
 
-        records = []
+        data = []
         if run_output is not None:
             for output in run_output.outputs:
                 if output:
-                    records.extend(
-                        build_records_from_result_data(output, get_final_results_only=self.get_final_results_only)
-                    )
-        return format_flow_output_records(records)
+                    data.extend(build_data_from_result_data(output, get_final_results_only=self.get_final_results_only))
+        return format_flow_output_data(data)
diff --git a/src/backend/base/langflow/base/vectorstores/model.py b/src/backend/base/langflow/base/vectorstores/model.py
new file mode 100644
index 000000000..e02a7ea23
--- /dev/null
+++ b/src/backend/base/langflow/base/vectorstores/model.py
@@ -0,0 +1,104 @@
+from typing import List
+
+from langchain_core.documents import Document
+from loguru import logger
+
+from langflow.custom import Component
+from langflow.field_typing import Retriever, Text, VectorStore
+from langflow.helpers.data import docs_to_data
+from langflow.io import Output
+from langflow.schema import Data
+
+
+class LCVectorStoreComponent(Component):
+    outputs = [
+        Output(
+            display_name="Retriever",
+            name="base_retriever",
+            method="build_base_retriever",
+        ),
+        Output(
+            display_name="Search Results",
+            name="search_results",
+            method="search_documents",
+        ),
+    ]
+
+    def _validate_outputs(self):
+        # At least these three outputs must be defined
+        required_output_methods = ["build_base_retriever", "search_documents"]
+        output_names = [output.name for output in self.outputs]
+        for method_name in required_output_methods:
+            if method_name not in output_names:
+                raise ValueError(f"Output with name '{method_name}' must be defined.")
+            elif not hasattr(self, method_name):
+                raise ValueError(f"Method '{method_name}' must be defined.")
+
+    def search_with_vector_store(
+        self,
+        input_value: Text,
+        search_type: str,
+        vector_store: VectorStore,
+        k=10,
+        **kwargs,
+    ) -> List[Data]:
+        """
+        Search for data in the vector store based on the input value and search type.
+
+        Args:
+            input_value (Text): The input value to search for.
+            search_type (str): The type of search to perform.
+            vector_store (VectorStore): The vector store to search in.
+
+        Returns:
+            List[Data]: A list of data matching the search criteria.
+
+        Raises:
+            ValueError: If invalid inputs are provided.
+        """
+
+        docs: List[Document] = []
+        if input_value and isinstance(input_value, str) and hasattr(vector_store, "search"):
+            docs = vector_store.search(query=input_value, search_type=search_type.lower(), k=k, **kwargs)
+        else:
+            raise ValueError("Invalid inputs provided.")
+        data = docs_to_data(docs)
+        self.status = data
+        return data
+
+    def build_vector_store(self) -> VectorStore:
+        """
+        Builds the Vector Store object.c
+        """
+        raise NotImplementedError("build_vector_store method must be implemented.")
+
+    def build_base_retriever(self) -> Retriever:
+        """
+        Builds the BaseRetriever object.
+        """
+        vector_store = self.build_vector_store()
+        if hasattr(vector_store, "as_retriever"):
+            return vector_store.as_retriever()
+        else:
+            raise ValueError(f"Vector Store {vector_store.__class__.__name__} does not have an as_retriever method.")
+
+    def search_documents(self) -> List[Data]:
+        """
+        Search for documents in the Chroma vector store.
+        """
+        search_query: str = self.search_query
+        if not search_query:
+            self.status = ""
+            return []
+
+        vector_store = self.build_vector_store()
+
+        logger.debug(f"Search input: {search_query}")
+        logger.debug(f"Search type: {self.search_type}")
+        logger.debug(f"Number of results: {self.number_of_results}")
+
+        search_results = self.search_with_vector_store(
+            search_query, self.search_type, vector_store, k=self.number_of_results
+        )
+        self.status = search_results
+        return search_results
diff --git a/src/backend/base/langflow/base/vectorstores/utils.py b/src/backend/base/langflow/base/vectorstores/utils.py
index 739181600..4c3cd0221 100644
--- a/src/backend/base/langflow/base/vectorstores/utils.py
+++ b/src/backend/base/langflow/base/vectorstores/utils.py
@@ -1,24 +1,24 @@
-from langflow.schema import Record
+from langflow.schema import Data
 
 
-def chroma_collection_to_records(collection_dict: dict):
+def chroma_collection_to_data(collection_dict: dict):
     """
-    Converts a collection of chroma vectors into a list of records.
+    Converts a collection of chroma vectors into a list of data.
 
     Args:
         collection_dict (dict): A dictionary containing the collection of chroma vectors.
 
     Returns:
-        list: A list of records, where each record represents a document in the collection.
+        list: A list of data, where each record represents a document in the collection.
     """
-    records = []
+    data = []
     for i, doc in enumerate(collection_dict["documents"]):
-        record_dict = {
+        data_dict = {
             "id": collection_dict["ids"][i],
             "text": doc,
         }
         if "metadatas" in collection_dict:
             for key, value in collection_dict["metadatas"][i].items():
-                record_dict[key] = value
-        records.append(Record(**record_dict))
-    return records
+                data_dict[key] = value
+        data.append(Data(**data_dict))
+    return data
diff --git a/src/backend/base/langflow/components/agents/CSVAgent.py b/src/backend/base/langflow/components/agents/CSVAgent.py
index 57774568f..21b285de2 100644
--- a/src/backend/base/langflow/components/agents/CSVAgent.py
+++ b/src/backend/base/langflow/components/agents/CSVAgent.py
@@ -1,7 +1,7 @@
 from langchain_experimental.agents.agent_toolkits.csv.base import create_csv_agent
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import AgentExecutor, BaseLanguageModel
+from langflow.field_typing import AgentExecutor, LanguageModel
 
 
 class CSVAgentComponent(CustomComponent):
@@ -11,7 +11,7 @@ class CSVAgentComponent(CustomComponent):
 
     def build_config(self):
         return {
-            "llm": {"display_name": "LLM", "type": BaseLanguageModel},
+            "llm": {"display_name": "LLM", "type": LanguageModel},
             "path": {"display_name": "Path", "field_type": "file", "suffixes": [".csv"], "file_types": [".csv"]},
             "handle_parsing_errors": {"display_name": "Handle Parse Errors", "advanced": True},
             "agent_type": {
@@ -22,7 +22,7 @@ class CSVAgentComponent(CustomComponent):
         }
 
     def build(
-        self, llm: BaseLanguageModel, path: str, handle_parsing_errors: bool = True, agent_type: str = "openai-tools"
+        self, llm: LanguageModel, path: str, handle_parsing_errors: bool = True, agent_type: str = "openai-tools"
     ) -> AgentExecutor:
         # Instantiate and return the CSV agent class with the provided llm and path
         return create_csv_agent(
diff --git a/src/backend/base/langflow/components/agents/JsonAgent.py b/src/backend/base/langflow/components/agents/JsonAgent.py
index 17826ef00..cadc684f8 100644
--- a/src/backend/base/langflow/components/agents/JsonAgent.py
+++ b/src/backend/base/langflow/components/agents/JsonAgent.py
@@ -3,7 +3,7 @@ from langchain_community.agent_toolkits import create_json_agent
 from langchain_community.agent_toolkits.json.toolkit import JsonToolkit
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 
 class JsonAgentComponent(CustomComponent):
@@ -18,7 +18,7 @@ class JsonAgentComponent(CustomComponent):
 
     def build(
         self,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         toolkit: JsonToolkit,
     ) -> AgentExecutor:
         return create_json_agent(llm=llm, toolkit=toolkit)
diff --git a/src/backend/base/langflow/components/agents/SQLAgent.py b/src/backend/base/langflow/components/agents/SQLAgent.py
index cd6b03f94..4f2a8e89e 100644
--- a/src/backend/base/langflow/components/agents/SQLAgent.py
+++ b/src/backend/base/langflow/components/agents/SQLAgent.py
@@ -6,7 +6,7 @@ from langchain_community.agent_toolkits.sql.base import create_sql_agent
 from langchain_community.utilities import SQLDatabase
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 
 class SQLAgentComponent(CustomComponent):
@@ -22,7 +22,7 @@ class SQLAgentComponent(CustomComponent):
 
     def build(
         self,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         database_uri: str,
         verbose: bool = False,
     ) -> Union[AgentExecutor, Callable]:
diff --git a/src/backend/base/langflow/components/agents/ToolCallingAgent.py b/src/backend/base/langflow/components/agents/ToolCallingAgent.py
index 91fcb1132..ddc21c3a5 100644
--- a/src/backend/base/langflow/components/agents/ToolCallingAgent.py
+++ b/src/backend/base/langflow/components/agents/ToolCallingAgent.py
@@ -1,64 +1,107 @@
-from typing import List, Optional
+from typing import List, cast
 
+from langchain.agents import AgentExecutor, BaseSingleActionAgent
 from langchain.agents.tool_calling_agent.base import create_tool_calling_agent
+from langchain_core.messages import BaseMessage, HumanMessage
 from langchain_core.prompts import ChatPromptTemplate
 
-from langflow.base.agents.agent import LCAgentComponent
-from langflow.field_typing import BaseLanguageModel, Text, Tool
-from langflow.schema import Record
+from langflow.custom import Component
+from langflow.io import BoolInput, HandleInput, Output, TextInput
+from langflow.schema import Data
+from langflow.schema.message import Message
 
 
-class ToolCallingAgentComponent(LCAgentComponent):
+class ToolCallingAgentComponent(Component):
     display_name: str = "Tool Calling Agent"
     description: str = "Agent that uses tools. Only models that are compatible with function calling are supported."
+    icon = "Agent"
 
-    def build_config(self):
-        return {
-            "llm": {"display_name": "LLM"},
-            "tools": {"display_name": "Tools"},
-            "user_prompt": {
-                "display_name": "Prompt",
-                "multiline": True,
-                "info": "This prompt must contain 'input' key.",
-            },
-            "handle_parsing_errors": {
-                "display_name": "Handle Parsing Errors",
-                "info": "If True, the agent will handle parsing errors. If False, the agent will raise an error.",
-                "advanced": True,
-            },
-            "memory": {
-                "display_name": "Memory",
-                "info": "Memory to use for the agent.",
-            },
-            "input_value": {
-                "display_name": "Inputs",
-                "info": "Input text to pass to the agent.",
-            },
-        }
+    inputs = [
+        HandleInput(
+            name="llm",
+            display_name="LLM",
+            input_types=["LanguageModel"],
+        ),
+        HandleInput(
+            name="tools",
+            display_name="Tools",
+            input_types=["Tool"],
+            is_list=True,
+        ),
+        TextInput(
+            name="user_prompt",
+            display_name="Prompt",
+            info="This prompt must contain 'input' key.",
+            value="{input}",
+        ),
+        BoolInput(
+            name="handle_parsing_errors",
+            display_name="Handle Parsing Errors",
+            info="If True, the agent will handle parsing errors. If False, the agent will raise an error.",
+            advanced=True,
+            value=True,
+        ),
+        HandleInput(
+            name="memory",
+            display_name="Memory",
+            input_types=["Data"],
+            info="Memory to use for the agent.",
+        ),
+        TextInput(
+            name="input_value",
+            display_name="Inputs",
+            info="Input text to pass to the agent.",
+        ),
+    ]
 
-    async def build(
-        self,
-        input_value: str,
-        llm: BaseLanguageModel,
-        tools: List[Tool],
-        user_prompt: str = "{input}",
-        message_history: Optional[List[Record]] = None,
-        system_message: str = "You are a helpful assistant",
-        handle_parsing_errors: bool = True,
-    ) -> Text:
-        if "input" not in user_prompt:
+    outputs = [
+        Output(display_name="Text", name="text_output", method="run_agent"),
+    ]
+
+    async def run_agent(self) -> Message:
+        if "input" not in self.user_prompt:
             raise ValueError("Prompt must contain 'input' key.")
         messages = [
-            ("system", system_message),
+            ("system", "You are a helpful assistant"),
             (
                 "placeholder",
                 "{chat_history}",
             ),
-            ("human", user_prompt),
+            ("human", self.user_prompt),
             ("placeholder", "{agent_scratchpad}"),
         ]
         prompt = ChatPromptTemplate.from_messages(messages)
-        agent = create_tool_calling_agent(llm, tools, prompt)
-        result = await self.run_agent(agent, input_value, tools, message_history, handle_parsing_errors)
+        agent = create_tool_calling_agent(self.llm, self.tools, prompt)
+
+        runnable = AgentExecutor.from_agent_and_tools(
+            agent=cast(BaseSingleActionAgent, agent),
+            tools=self.tools,
+            verbose=True,
+            handle_parsing_errors=self.handle_parsing_errors,
+        )
+        input_dict: dict[str, str | list[BaseMessage]] = {"input": self.input_value}
+        if hasattr(self, "memory") and self.memory:
+            input_dict["chat_history"] = self.convert_chat_history(self.memory)
+        result = await runnable.ainvoke(input_dict)
         self.status = result
-        return result
+
+        if "output" not in result:
+            raise ValueError("Output key not found in result. Tried 'output'.")
+
+        result_string = result["output"]
+
+        return Message(text=result_string)
+
+    def convert_chat_history(self, chat_history: List[Message | Data]) -> List[BaseMessage]:
+        messages = []
+        for item in chat_history:
+            if isinstance(item, (Message, Data)):
+                messages.append(item.to_lc_message())
+            elif isinstance(item, str):
+                messages.append(HumanMessage(content=item))
+            elif isinstance(item, dict) and "sender" in item and "text" in item:
+                messages.append(HumanMessage(content=item["text"], sender=item["sender"]))
+            else:
+                raise ValueError(f"Invalid item ({type(item)}) in chat history: {item}")
+
+        return messages
diff --git a/src/backend/base/langflow/components/agents/VectorStoreAgent.py b/src/backend/base/langflow/components/agents/VectorStoreAgent.py
index 3cba51a09..2bb324e50 100644
--- a/src/backend/base/langflow/components/agents/VectorStoreAgent.py
+++ b/src/backend/base/langflow/components/agents/VectorStoreAgent.py
@@ -4,7 +4,7 @@ from langchain.agents import AgentExecutor, create_vectorstore_agent
 from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreToolkit
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 
 class VectorStoreAgentComponent(CustomComponent):
@@ -19,7 +19,7 @@ class VectorStoreAgentComponent(CustomComponent):
 
     def build(
         self,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         vector_store_toolkit: VectorStoreToolkit,
     ) -> Union[AgentExecutor, Callable]:
         return create_vectorstore_agent(llm=llm, toolkit=vector_store_toolkit)
diff --git a/src/backend/base/langflow/components/agents/VectorStoreRouterAgent.py b/src/backend/base/langflow/components/agents/VectorStoreRouterAgent.py
index e483f0d2c..a696a19c2 100644
--- a/src/backend/base/langflow/components/agents/VectorStoreRouterAgent.py
+++ b/src/backend/base/langflow/components/agents/VectorStoreRouterAgent.py
@@ -2,7 +2,7 @@ from typing import Callable
 
 from langchain.agents import create_vectorstore_router_agent
 from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreRouterToolkit
-from langchain_core.language_models.base import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 from langflow.custom import CustomComponent
 
@@ -17,5 +17,5 @@ class VectorStoreRouterAgentComponent(CustomComponent):
             "vectorstoreroutertoolkit": {"display_name": "Vector Store Router Toolkit"},
         }
 
-    def build(self, llm: BaseLanguageModel, vectorstoreroutertoolkit: VectorStoreRouterToolkit) -> Callable:
+    def build(self, llm: LanguageModel, vectorstoreroutertoolkit: VectorStoreRouterToolkit) -> Callable:
         return create_vectorstore_router_agent(llm=llm, toolkit=vectorstoreroutertoolkit)
diff --git a/src/backend/base/langflow/components/agents/XMLAgent.py b/src/backend/base/langflow/components/agents/XMLAgent.py
index 47f823ba4..b0a9b1873 100644
--- a/src/backend/base/langflow/components/agents/XMLAgent.py
+++ b/src/backend/base/langflow/components/agents/XMLAgent.py
@@ -4,8 +4,8 @@ from langchain.agents import create_xml_agent
 from langchain_core.prompts import ChatPromptTemplate
 
 from langflow.base.agents.agent import LCAgentComponent
-from langflow.field_typing import BaseLanguageModel, Text, Tool
-from langflow.schema import Record
+from langflow.field_typing import LanguageModel, Text, Tool
+from langflow.schema import Data
 
 
 class XMLAgentComponent(LCAgentComponent):
@@ -72,11 +72,11 @@ class XMLAgentComponent(LCAgentComponent):
     async def build(
         self,
         input_value: str,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         tools: List[Tool],
         user_prompt: str = "{input}",
         system_message: str = "You are a helpful assistant",
-        message_history: Optional[List[Record]] = None,
+        message_history: Optional[List[Data]] = None,
         tool_template: str = "{name}: {description}",
         handle_parsing_errors: bool = True,
     ) -> Text:
diff --git a/src/backend/base/langflow/components/chains/ConversationChain.py b/src/backend/base/langflow/components/chains/ConversationChain.py
index 0801f4623..a400d4993 100644
--- a/src/backend/base/langflow/components/chains/ConversationChain.py
+++ b/src/backend/base/langflow/components/chains/ConversationChain.py
@@ -3,7 +3,7 @@ from typing import Optional
 from langchain.chains import ConversationChain
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, BaseMemory, Text
+from langflow.field_typing import BaseMemory, LanguageModel, Text
 
 
 class ConversationChainComponent(CustomComponent):
@@ -27,7 +27,7 @@ class ConversationChainComponent(CustomComponent):
     def build(
         self,
         input_value: Text,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         memory: Optional[BaseMemory] = None,
     ) -> Text:
         if memory is None:
diff --git a/src/backend/base/langflow/components/chains/LLMChain.py b/src/backend/base/langflow/components/chains/LLMChain.py
index 0387b50f3..b0f6913bf 100644
--- a/src/backend/base/langflow/components/chains/LLMChain.py
+++ b/src/backend/base/langflow/components/chains/LLMChain.py
@@ -4,7 +4,7 @@ from langchain.chains.llm import LLMChain
 from langchain_core.prompts import PromptTemplate
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, BaseMemory, Text
+from langflow.field_typing import BaseMemory, LanguageModel, Text
 
 
 class LLMChainComponent(CustomComponent):
@@ -21,7 +21,7 @@ class LLMChainComponent(CustomComponent):
     def build(
         self,
         template: Text,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         memory: Optional[BaseMemory] = None,
     ) -> Text:
         prompt = PromptTemplate.from_template(template)
diff --git a/src/backend/base/langflow/components/chains/LLMCheckerChain.py b/src/backend/base/langflow/components/chains/LLMCheckerChain.py
index f413081b1..9c86c31dd 100644
--- a/src/backend/base/langflow/components/chains/LLMCheckerChain.py
+++ b/src/backend/base/langflow/components/chains/LLMCheckerChain.py
@@ -1,7 +1,7 @@
 from langchain.chains import LLMCheckerChain
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, Text
+from langflow.field_typing import LanguageModel, Text
 
 
 class LLMCheckerChainComponent(CustomComponent):
@@ -21,7 +21,7 @@ class LLMCheckerChainComponent(CustomComponent):
     def build(
         self,
         input_value: Text,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
     ) -> Text:
         chain = LLMCheckerChain.from_llm(llm=llm)
         response = chain.invoke({chain.input_key: input_value})
diff --git a/src/backend/base/langflow/components/chains/LLMMathChain.py b/src/backend/base/langflow/components/chains/LLMMathChain.py
index 2bb573ef5..9cb73ef71 100644
--- a/src/backend/base/langflow/components/chains/LLMMathChain.py
+++ b/src/backend/base/langflow/components/chains/LLMMathChain.py
@@ -3,7 +3,7 @@ from typing import Optional
 from langchain.chains import LLMChain, LLMMathChain
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, BaseMemory, Text
+from langflow.field_typing import BaseMemory, LanguageModel, Text
 
 
 class LLMMathChainComponent(CustomComponent):
@@ -27,7 +27,7 @@ class LLMMathChainComponent(CustomComponent):
     def build(
         self,
         input_value: Text,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         llm_chain: LLMChain,
         input_key: str = "question",
         output_key: str = "answer",
diff --git a/src/backend/base/langflow/components/chains/RetrievalQA.py b/src/backend/base/langflow/components/chains/RetrievalQA.py
index ca9910279..0d9efdc65 100644
--- a/src/backend/base/langflow/components/chains/RetrievalQA.py
+++ b/src/backend/base/langflow/components/chains/RetrievalQA.py
@@ -4,8 +4,8 @@ from langchain.chains.retrieval_qa.base import RetrievalQA
 from langchain_core.documents import Document
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, BaseMemory, BaseRetriever, Text
-from langflow.schema import Record
+from langflow.field_typing import BaseMemory, BaseRetriever, LanguageModel, Text
+from langflow.schema import Data
 
 
 class RetrievalQAComponent(CustomComponent):
@@ -23,13 +23,13 @@ class RetrievalQAComponent(CustomComponent):
             "return_source_documents": {"display_name": "Return Source Documents"},
             "input_value": {
                 "display_name": "Input",
-                "input_types": ["Record", "Document"],
+                "input_types": ["Data", "Document"],
             },
         }
 
     def build(
         self,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         chain_type: str,
         retriever: BaseRetriever,
         input_value: str = "",
@@ -50,17 +50,17 @@ class RetrievalQAComponent(CustomComponent):
         )
         if isinstance(input_value, Document):
             input_value = input_value.page_content
-        if isinstance(input_value, Record):
+        if isinstance(input_value, Data):
             input_value = input_value.get_text()
         self.status = runnable
         result = runnable.invoke({input_key: input_value})
         result = result.content if hasattr(result, "content") else result
         # Result is a dict with keys "query",  "result" and "source_documents"
         # for now we just return the result
-        records = self.to_records(result.get("source_documents"))
+        data = self.to_data(result.get("source_documents"))
         references_str = ""
         if return_source_documents:
-            references_str = self.create_references_from_records(records)
+            references_str = self.create_references_from_data(data)
         result_str = result.get("result", "")
 
         final_result = "\n".join([Text(result_str), references_str])
diff --git a/src/backend/base/langflow/components/chains/RetrievalQAWithSourcesChain.py b/src/backend/base/langflow/components/chains/RetrievalQAWithSourcesChain.py
index 2e0fa4ced..ae79b9e16 100644
--- a/src/backend/base/langflow/components/chains/RetrievalQAWithSourcesChain.py
+++ b/src/backend/base/langflow/components/chains/RetrievalQAWithSourcesChain.py
@@ -4,7 +4,7 @@ from langchain.chains import RetrievalQAWithSourcesChain
 from langchain_core.documents import Document
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, BaseMemory, BaseRetriever, Text
+from langflow.field_typing import BaseMemory, BaseRetriever, LanguageModel, Text
 
 
 class RetrievalQAWithSourcesChainComponent(CustomComponent):
@@ -32,7 +32,7 @@ class RetrievalQAWithSourcesChainComponent(CustomComponent):
         self,
         input_value: Text,
         retriever: BaseRetriever,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         chain_type: str,
         memory: Optional[BaseMemory] = None,
         return_source_documents: Optional[bool] = True,
@@ -53,10 +53,10 @@ class RetrievalQAWithSourcesChainComponent(CustomComponent):
         result = result.content if hasattr(result, "content") else result
         # Result is a dict with keys "query",  "result" and "source_documents"
         # for now we just return the result
-        records = self.to_records(result.get("source_documents"))
+        data = self.to_data(result.get("source_documents"))
         references_str = ""
         if return_source_documents:
-            references_str = self.create_references_from_records(records)
+            references_str = self.create_references_from_data(data)
         result_str = Text(result.get("answer", ""))
         final_result = "\n".join([result_str, references_str])
         self.status = final_result
diff --git a/src/backend/base/langflow/components/chains/SQLGenerator.py b/src/backend/base/langflow/components/chains/SQLGenerator.py
index a6ff0ee2f..dc085c46e 100644
--- a/src/backend/base/langflow/components/chains/SQLGenerator.py
+++ b/src/backend/base/langflow/components/chains/SQLGenerator.py
@@ -6,7 +6,7 @@ from langchain_core.prompts import PromptTemplate
 from langchain_core.runnables import Runnable
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, Text
+from langflow.field_typing import LanguageModel, Text
 
 
 class SQLGeneratorComponent(CustomComponent):
@@ -35,7 +35,7 @@ class SQLGeneratorComponent(CustomComponent):
         self,
         input_value: Text,
         db: SQLDatabase,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         top_k: int = 5,
         prompt: Optional[Text] = None,
     ) -> Text:
diff --git a/src/backend/base/langflow/components/data/APIRequest.py b/src/backend/base/langflow/components/data/APIRequest.py
index 2065f90c7..c41b26d9f 100644
--- a/src/backend/base/langflow/components/data/APIRequest.py
+++ b/src/backend/base/langflow/components/data/APIRequest.py
@@ -1,54 +1,77 @@
 import asyncio
 import json
 from typing import Any, List, Optional
+from urllib.parse import parse_qsl, urlencode, urlparse, urlunparse
 
 import httpx
 from loguru import logger
 
 from langflow.base.curl.parse import parse_context
-from langflow.custom import CustomComponent
-from langflow.field_typing import NestedDict
-from langflow.schema import Record
+from langflow.custom import Component
+from langflow.io import DataInput, DropdownInput, IntInput, NestedDictInput, Output, TextInput
+from langflow.schema import Data
 from langflow.schema.dotdict import dotdict
 
 
-class APIRequest(CustomComponent):
-    display_name: str = "API Request"
-    description: str = "Make HTTP requests given one or more URLs."
-    output_types: list[str] = ["Record"]
-    documentation: str = "https://docs.langflow.org/components/utilities#api-request"
+class APIRequestComponent(Component):
+    display_name = "API Request"
+    description = (
+        "This component allows you to make HTTP requests to one or more URLs. "
+        "You can provide headers and body as either dictionaries or Data objects. "
+        "Additionally, you can append query parameters to the URLs.\n\n"
+        "**Note:** Check advanced options for more settings."
+    )
     icon = "Globe"
 
-    field_config = {
-        "urls": {"display_name": "URLs", "info": "URLs to make requests to."},
-        "curl": {
-            "display_name": "Curl",
-            "info": "Paste a curl command to populate the fields.",
-            "refresh_button": True,
-            "refresh_button_text": "",
-        },
-        "method": {
-            "display_name": "Method",
-            "info": "The HTTP method to use.",
-            "options": ["GET", "POST", "PATCH", "PUT"],
-            "value": "GET",
-        },
-        "headers": {
-            "display_name": "Headers",
-            "info": "The headers to send with the request.",
-            "input_types": ["Record"],
-        },
-        "body": {
-            "display_name": "Body",
-            "info": "The body to send with the request (for POST, PATCH, PUT).",
-            "input_types": ["Record"],
-        },
-        "timeout": {
-            "display_name": "Timeout",
-            "info": "The timeout to use for the request.",
-            "value": 5,
-        },
-    }
+    inputs = [
+        TextInput(
+            name="urls",
+            display_name="URLs",
+            is_list=True,
+            info="Enter one or more URLs, separated by commas.",
+        ),
+        TextInput(
+            name="curl",
+            display_name="Curl",
+            info="Paste a curl command to populate the fields. This will fill in the dictionary fields for headers and body.",
+            advanced=False,
+            refresh_button=True,
+        ),
+        DropdownInput(
+            name="method",
+            display_name="Method",
+            options=["GET", "POST", "PATCH", "PUT"],
+            value="GET",
+            info="The HTTP method to use (GET, POST, PATCH, PUT).",
+        ),
+        NestedDictInput(
+            name="headers",
+            display_name="Headers",
+            info="The headers to send with the request as a dictionary. This is populated when using the CURL field.",
+            input_types=["Data"],
+        ),
+        NestedDictInput(
+            name="body",
+            display_name="Body",
+            info="The body to send with the request as a dictionary (for POST, PATCH, PUT). This is populated when using the CURL field.",
+            input_types=["Data"],
+        ),
+        DataInput(
+            name="query_params",
+            display_name="Query Parameters",
+            info="The query parameters to append to the URL.",
+        ),
+        IntInput(
+            name="timeout",
+            display_name="Timeout",
+            value=5,
+            info="The timeout to use for the request.",
+        ),
+    ]
+
+    outputs = [
+        Output(display_name="Data", name="data", method="make_requests"),
+    ]
 
     def parse_curl(self, curl: str, build_config: dotdict) -> dotdict:
         try:
@@ -57,18 +80,21 @@ class APIRequest(CustomComponent):
             build_config["method"]["value"] = parsed.method.upper()
             build_config["headers"]["value"] = dict(parsed.headers)
 
-            try:
-                json_data = json.loads(parsed.data)
-                build_config["body"]["value"] = json_data
-            except json.JSONDecodeError as e:
-                print(e)
+            if parsed.data:
+                try:
+                    json_data = json.loads(parsed.data)
+                    build_config["body"]["value"] = json_data
+                except json.JSONDecodeError as e:
+                    logger.error(f"Error decoding JSON data: {e}")
+            else:
+                build_config["body"]["value"] = {}
         except Exception as exc:
             logger.error(f"Error parsing curl: {exc}")
             raise ValueError(f"Error parsing curl: {exc}")
         return build_config
 
     def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):
-        if field_name == "curl" and field_value is not None:
+        if field_name == "curl" and field_value:
             build_config = self.parse_curl(field_value, build_config)
         return build_config
 
@@ -80,20 +106,20 @@ class APIRequest(CustomComponent):
         headers: Optional[dict] = None,
         body: Optional[dict] = None,
         timeout: int = 5,
-    ) -> Record:
+    ) -> Data:
         method = method.upper()
         if method not in ["GET", "POST", "PATCH", "PUT", "DELETE"]:
             raise ValueError(f"Unsupported method: {method}")
 
         data = body if body else None
-        payload = json.dumps(data)
+        payload = json.dumps(data) if data else None
         try:
             response = await client.request(method, url, headers=headers, content=payload, timeout=timeout)
             try:
                 result = response.json()
             except Exception:
                 result = response.text
-            return Record(
+            return Data(
                 data={
                     "source": url,
                     "headers": headers,
@@ -102,7 +128,7 @@ class APIRequest(CustomComponent):
                 },
             )
         except httpx.TimeoutException:
-            return Record(
+            return Data(
                 data={
                     "source": url,
                     "headers": headers,
@@ -111,7 +137,7 @@ class APIRequest(CustomComponent):
                 },
             )
         except Exception as exc:
-            return Record(
+            return Data(
                 data={
                     "source": url,
                     "headers": headers,
@@ -120,36 +146,38 @@ class APIRequest(CustomComponent):
                 },
             )
 
-    async def build(
-        self,
-        method: str,
-        urls: List[str],
-        curl: Optional[str] = None,
-        headers: Optional[NestedDict] = {},
-        body: Optional[NestedDict] = {},
-        timeout: int = 5,
-    ) -> List[Record]:
-        if headers is None:
-            headers_dict = {}
-        elif isinstance(headers, Record):
-            headers_dict = headers.data
-        else:
-            headers_dict = headers
+    def add_query_params(self, url: str, params: dict) -> str:
+        url_parts = list(urlparse(url))
+        query = dict(parse_qsl(url_parts[4]))
+        query.update(params)
+        url_parts[4] = urlencode(query)
+        return urlunparse(url_parts)
 
-        bodies = []
-        if body:
-            if not isinstance(body, list):
-                bodies = [body]
-            else:
-                bodies = body
-            bodies = [b.data if isinstance(b, Record) else b for b in bodies]  # type: ignore
+    async def make_requests(self) -> List[Data]:
+        method = self.method
+        urls = [url.strip() for url in self.urls if url.strip()]
+        curl = self.curl
+        headers = self.headers or {}
+        body = self.body or {}
+        timeout = self.timeout
+        query_params = self.query_params.data if self.query_params else {}
+
+        if curl:
+            self._build_config = self.parse_curl(curl, dotdict())
+
+        if isinstance(headers, Data):
+            headers = headers.data
+
+        if isinstance(body, Data):
+            body = body.data
+
+        bodies = [body] * len(urls)
+
+        urls = [self.add_query_params(url, query_params) for url in urls]
 
-        if len(urls) != len(bodies):
-            # add bodies with None
-            bodies += [None] * (len(urls) - len(bodies))  # type: ignore
         async with httpx.AsyncClient() as client:
             results = await asyncio.gather(
-                *[self.make_request(client, method, u, headers_dict, rec, timeout) for u, rec in zip(urls, bodies)]
+                *[self.make_request(client, method, u, headers, rec, timeout) for u, rec in zip(urls, bodies)]
             )
         self.status = results
         return results
diff --git a/src/backend/base/langflow/components/data/Directory.py b/src/backend/base/langflow/components/data/Directory.py
index 4dfa51de3..0b381274d 100644
--- a/src/backend/base/langflow/components/data/Directory.py
+++ b/src/backend/base/langflow/components/data/Directory.py
@@ -1,63 +1,88 @@
-from typing import Any, Dict, List, Optional
+from typing import List, Optional
 
-from langflow.base.data.utils import parallel_load_records, parse_text_file_to_record, retrieve_file_paths
-from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langflow.base.data.utils import parallel_load_data, parse_text_file_to_data, retrieve_file_paths
+from langflow.custom import Component
+from langflow.io import BoolInput, IntInput, TextInput
+from langflow.schema import Data
+from langflow.template import Output
 
 
-class DirectoryComponent(CustomComponent):
+class DirectoryComponent(Component):
     display_name = "Directory"
     description = "Recursively load files from a directory."
     icon = "folder"
 
-    def build_config(self) -> Dict[str, Any]:
-        return {
-            "path": {"display_name": "Path"},
-            "types": {
-                "display_name": "Types",
-                "info": "File types to load. Leave empty to load all types.",
-            },
-            "depth": {"display_name": "Depth", "info": "Depth to search for files."},
-            "max_concurrency": {"display_name": "Max Concurrency", "advanced": True},
-            "load_hidden": {
-                "display_name": "Load Hidden",
-                "advanced": True,
-                "info": "If true, hidden files will be loaded.",
-            },
-            "recursive": {
-                "display_name": "Recursive",
-                "advanced": True,
-                "info": "If true, the search will be recursive.",
-            },
-            "silent_errors": {
-                "display_name": "Silent Errors",
-                "advanced": True,
-                "info": "If true, errors will not raise an exception.",
-            },
-            "use_multithreading": {
-                "display_name": "Use Multithreading",
-                "advanced": True,
-            },
-        }
+    inputs = [
+        TextInput(
+            name="path",
+            display_name="Path",
+            info="Path to the directory to load files from.",
+        ),
+        TextInput(
+            name="types",
+            display_name="Types",
+            info="File types to load. Leave empty to load all types.",
+        ),
+        IntInput(
+            name="depth",
+            display_name="Depth",
+            info="Depth to search for files.",
+            value=0,
+        ),
+        IntInput(
+            name="max_concurrency",
+            display_name="Max Concurrency",
+            advanced=True,
+            info="Maximum concurrency for loading files.",
+            value=2,
+        ),
+        BoolInput(
+            name="load_hidden",
+            display_name="Load Hidden",
+            advanced=True,
+            info="If true, hidden files will be loaded.",
+        ),
+        BoolInput(
+            name="recursive",
+            display_name="Recursive",
+            advanced=True,
+            info="If true, the search will be recursive.",
+        ),
+        BoolInput(
+            name="silent_errors",
+            display_name="Silent Errors",
+            advanced=True,
+            info="If true, errors will not raise an exception.",
+        ),
+        BoolInput(
+            name="use_multithreading",
+            display_name="Use Multithreading",
+            advanced=True,
+            info="If true, multithreading will be used.",
+        ),
+    ]
+
+    outputs = [
+        Output(display_name="Data", name="data", method="load_directory"),
+    ]
+
+    def load_directory(self) -> List[Optional[Data]]:
+        path = self.path
+        depth = self.depth
+        max_concurrency = self.max_concurrency
+        load_hidden = self.load_hidden
+        recursive = self.recursive
+        silent_errors = self.silent_errors
+        use_multithreading = self.use_multithreading
 
-    def build(
-        self,
-        path: str,
-        depth: int = 0,
-        max_concurrency: int = 2,
-        load_hidden: bool = False,
-        recursive: bool = True,
-        silent_errors: bool = False,
-        use_multithreading: bool = True,
-    ) -> List[Optional[Record]]:
         resolved_path = self.resolve_path(path)
         file_paths = retrieve_file_paths(resolved_path, load_hidden, recursive, depth)
-        loaded_records = []
+        loaded_data = []
 
         if use_multithreading:
-            loaded_records = parallel_load_records(file_paths, silent_errors, max_concurrency)
+            loaded_data = parallel_load_data(file_paths, silent_errors, max_concurrency)
         else:
-            loaded_records = [parse_text_file_to_record(file_path, silent_errors) for file_path in file_paths]
-        loaded_records = list(filter(None, loaded_records))
-        self.status = loaded_records
-        return loaded_records
+            loaded_data = [parse_text_file_to_data(file_path, silent_errors) for file_path in file_paths]
+        loaded_data = list(filter(None, loaded_data))
+        self.status = loaded_data
+        return loaded_data
diff --git a/src/backend/base/langflow/components/data/File.py b/src/backend/base/langflow/components/data/File.py
index 5ebb94cff..a0a4385e6 100644
--- a/src/backend/base/langflow/components/data/File.py
+++ b/src/backend/base/langflow/components/data/File.py
@@ -1,48 +1,48 @@
 from pathlib import Path
-from typing import Any, Dict
 
-from langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record
-from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_data
+from langflow.custom import Component
+from langflow.io import BoolInput, FileInput, Output
+from langflow.schema import Data
 
 
-class FileComponent(CustomComponent):
+class FileComponent(Component):
     display_name = "File"
     description = "A generic file loader."
     icon = "file-text"
 
-    def build_config(self) -> Dict[str, Any]:
-        return {
-            "path": {
-                "display_name": "Path",
-                "field_type": "file",
-                "file_types": TEXT_FILE_TYPES,
-                "info": f"Supported file types: {', '.join(TEXT_FILE_TYPES)}",
-            },
-            "silent_errors": {
-                "display_name": "Silent Errors",
-                "advanced": True,
-                "info": "If true, errors will not raise an exception.",
-            },
-        }
+    inputs = [
+        FileInput(
+            name="path",
+            display_name="Path",
+            file_types=TEXT_FILE_TYPES,
+            info=f"Supported file types: {', '.join(TEXT_FILE_TYPES)}",
+        ),
+        BoolInput(
+            name="silent_errors",
+            display_name="Silent Errors",
+            advanced=True,
+            info="If true, errors will not raise an exception.",
+        ),
+    ]
+
+    outputs = [
+        Output(display_name="Data", name="data", method="load_file"),
+    ]
+
+    def load_file(self) -> Data:
+        if not self.path:
+            raise ValueError("Please, upload a file to use this component.")
+        resolved_path = self.resolve_path(self.path)
+        silent_errors = self.silent_errors
+
+        extension = Path(resolved_path).suffix[1:].lower()
 
-    def load_file(self, path: str, silent_errors: bool = False) -> Record:
-        resolved_path = self.resolve_path(path)
-        path_obj = Path(resolved_path)
-        extension = path_obj.suffix[1:].lower()
         if extension == "doc":
             raise ValueError("doc files are not supported. Please save as .docx")
         if extension not in TEXT_FILE_TYPES:
             raise ValueError(f"Unsupported file type: {extension}")
-        record = parse_text_file_to_record(resolved_path, silent_errors)
-        self.status = record if record else "No data"
-        return record or Record()
 
-    def build(
-        self,
-        path: str,
-        silent_errors: bool = False,
-    ) -> Record:
-        record = self.load_file(path, silent_errors)
-        self.status = record
-        return record
+        data = parse_text_file_to_data(resolved_path, silent_errors)
+        self.status = data if data else "No data"
+        return data or Data()
diff --git a/src/backend/base/langflow/components/data/URL.py b/src/backend/base/langflow/components/data/URL.py
index 32ebc91ee..37b4e8648 100644
--- a/src/backend/base/langflow/components/data/URL.py
+++ b/src/backend/base/langflow/components/data/URL.py
@@ -1,27 +1,65 @@
-from typing import Any, Dict
+import re
 
 from langchain_community.document_loaders.web_base import WebBaseLoader
 
-from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langflow.custom import Component
+from langflow.io import Output, TextInput
+from langflow.schema import Data
 
 
-class URLComponent(CustomComponent):
+class URLComponent(Component):
     display_name = "URL"
     description = "Fetch content from one or more URLs."
     icon = "layout-template"
 
-    def build_config(self) -> Dict[str, Any]:
-        return {
-            "urls": {"display_name": "URL"},
-        }
+    inputs = [
+        TextInput(
+            name="urls",
+            display_name="URLs",
+            info="Enter one or more URLs, separated by commas.",
+            is_list=True,
+        ),
+    ]
 
-    def build(
-        self,
-        urls: list[str],
-    ) -> list[Record]:
-        loader = WebBaseLoader(web_paths=[url for url in urls if url])
+    outputs = [
+        Output(display_name="Data", name="data", method="fetch_content"),
+    ]
+
+    def ensure_url(self, string: str) -> str:
+        """
+        Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.
+        Raises an error if the string is not a valid URL.
+
+        Parameters:
+            string (str): The string to be checked and possibly modified.
+
+        Returns:
+            str: The modified string that is ensured to be a URL.
+
+        Raises:
+            ValueError: If the string is not a valid URL.
+        """
+        if not string.startswith(("http://", "https://")):
+            string = "http://" + string
+
+        # Basic URL validation regex
+        url_regex = re.compile(
+            r"^(http://|https://)?"  # http:// or https://
+            r"(([a-zA-Z0-9\.-]+)"  # domain
+            r"(\.[a-zA-Z]{2,}))"  # top-level domain
+            r"(:[0-9]{1,5})?"  # optional port
+            r"(\/.*)?$"  # optional path
+        )
+
+        if not re.match(url_regex, string):
+            raise ValueError(f"Invalid URL: {string}")
+
+        return string
+
+    def fetch_content(self) -> list[Data]:
+        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]
+        loader = WebBaseLoader(web_paths=urls, encoding="utf-8")
         docs = loader.load()
-        records = self.to_records(docs)
-        self.status = records
-        return records
+        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]
+        self.status = data
+        return data
diff --git a/src/backend/base/langflow/components/data/Webhook.py b/src/backend/base/langflow/components/data/Webhook.py
index a1989cd49..67e188f6e 100644
--- a/src/backend/base/langflow/components/data/Webhook.py
+++ b/src/backend/base/langflow/components/data/Webhook.py
@@ -1,39 +1,37 @@
 import json
-import uuid
-from typing import Any, Optional
 
-from langflow.custom import CustomComponent
-from langflow.schema import Record
-from langflow.schema.dotdict import dotdict
+from langflow.custom import Component
+from langflow.io import MultilineInput, Output
+from langflow.schema import Data
 
 
-class WebhookComponent(CustomComponent):
+class WebhookComponent(Component):
     display_name = "Webhook Input"
     description = "Defines a webhook input for the flow."
 
-    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):
-        if field_name == "webhook_id":
-            build_config["webhook_id"]["value"] = uuid.uuid4().hex
-        return build_config
+    inputs = [
+        MultilineInput(
+            name="data",
+            display_name="Data",
+            info="Use this field to quickly test the webhook component by providing a JSON payload.",
+        )
+    ]
+    outputs = [
+        Output(display_name="Data", name="output_data", method="build_data"),
+    ]
 
-    def build_config(self):
-        return {
-            "data": {
-                "display_name": "Data",
-                "info": "Use this field to quickly test the webhook component by providing a JSON payload.",
-                "multiline": True,
-            }
-        }
-
-    def build(self, data: Optional[str] = "") -> Record:
-        message = ""
+    def build_data(self) -> Data:
+        message: str | Data = ""
+        if not self.data:
+            self.status = "No data provided."
+            return Data(data={})
         try:
-            body = json.loads(data or "{}")
+            body = json.loads(self.data or "{}")
         except json.JSONDecodeError:
-            body = {"payload": data}
-            message = f"Invalid JSON payload. Please check the format.\n\n{data}"
-        record = Record(data=body)
+            body = {"payload": self.data}
+            message = f"Invalid JSON payload. Please check the format.\n\n{self.data}"
+        data = Data(data=body)
         if not message:
-            message = json.dumps(body, indent=2)
+            message = data
         self.status = message
-        return record
+        return data
diff --git a/src/backend/base/langflow/components/data/__init__.py b/src/backend/base/langflow/components/data/__init__.py
index c57cf8656..ba037a740 100644
--- a/src/backend/base/langflow/components/data/__init__.py
+++ b/src/backend/base/langflow/components/data/__init__.py
@@ -1,8 +1,7 @@
-from .APIRequest import APIRequest
+from .APIRequest import APIRequestComponent
 from .Directory import DirectoryComponent
 from .File import FileComponent
+from .URL import URLComponent
 from .Webhook import WebhookComponent
 
-from .URL import URLComponent
-
-__all__ = ["APIRequest", "DirectoryComponent", "FileComponent", "URLComponent", "WebhookComponent"]
+__all__ = ["APIRequestComponent", "DirectoryComponent", "FileComponent", "URLComponent", "WebhookComponent"]
diff --git a/src/backend/base/langflow/components/embeddings/AmazonBedrockEmbeddings.py b/src/backend/base/langflow/components/embeddings/AmazonBedrockEmbeddings.py
index e43c144b1..2c1c8bd48 100644
--- a/src/backend/base/langflow/components/embeddings/AmazonBedrockEmbeddings.py
+++ b/src/backend/base/langflow/components/embeddings/AmazonBedrockEmbeddings.py
@@ -1,42 +1,48 @@
-from typing import Optional
-
 from langchain_community.embeddings import BedrockEmbeddings
-from langchain_core.embeddings import Embeddings
-
-from langflow.custom import CustomComponent
+from langflow.base.models.model import LCModelComponent
+from langflow.field_typing import Embeddings
+from langflow.io import DropdownInput, Output, TextInput
 
 
-class AmazonBedrockEmeddingsComponent(CustomComponent):
+class AmazonBedrockEmbeddingsComponent(LCModelComponent):
     display_name: str = "Amazon Bedrock Embeddings"
     description: str = "Generate embeddings using Amazon Bedrock models."
     documentation = "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock"
+    icon = "Amazon"
 
-    def build_config(self):
-        return {
-            "model_id": {
-                "display_name": "Model Id",
-                "options": ["amazon.titan-embed-text-v1"],
-            },
-            "credentials_profile_name": {"display_name": "Credentials Profile Name"},
-            "endpoint_url": {"display_name": "Bedrock Endpoint URL"},
-            "region_name": {"display_name": "AWS Region"},
-            "code": {"show": False},
-        }
+    inputs = [
+        DropdownInput(
+            name="model_id",
+            display_name="Model Id",
+            options=["amazon.titan-embed-text-v1"],
+            value="amazon.titan-embed-text-v1",
+        ),
+        TextInput(
+            name="credentials_profile_name",
+            display_name="Credentials Profile Name",
+        ),
+        TextInput(
+            name="endpoint_url",
+            display_name="Bedrock Endpoint URL",
+        ),
+        TextInput(
+            name="region_name",
+            display_name="AWS Region",
+        ),
+    ]
 
-    def build(
-        self,
-        model_id: str = "amazon.titan-embed-text-v1",
-        credentials_profile_name: Optional[str] = None,
-        endpoint_url: Optional[str] = None,
-        region_name: Optional[str] = None,
-    ) -> Embeddings:
+    outputs = [
+        Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
+    ]
+
+    def build_embeddings(self) -> Embeddings:
         try:
             output = BedrockEmbeddings(
-                credentials_profile_name=credentials_profile_name,
-                model_id=model_id,
-                endpoint_url=endpoint_url,
-                region_name=region_name,
+                credentials_profile_name=self.credentials_profile_name,
+                model_id=self.model_id,
+                endpoint_url=self.endpoint_url,
+                region_name=self.region_name,
             )  # type: ignore
         except Exception as e:
-            raise ValueError("Could not connect to AmazonBedrock API.") from e
+            raise ValueError("Could not connect to Amazon Bedrock API.") from e
         return output
diff --git a/src/backend/base/langflow/components/embeddings/AzureOpenAIEmbeddings.py b/src/backend/base/langflow/components/embeddings/AzureOpenAIEmbeddings.py
index 4fca09762..927520c75 100644
--- a/src/backend/base/langflow/components/embeddings/AzureOpenAIEmbeddings.py
+++ b/src/backend/base/langflow/components/embeddings/AzureOpenAIEmbeddings.py
@@ -1,16 +1,15 @@
-from typing import Optional
-from langchain_core.embeddings import Embeddings
 from langchain_openai import AzureOpenAIEmbeddings
 from pydantic.v1 import SecretStr
 
-from langflow.custom import CustomComponent
+from langflow.base.models.model import LCModelComponent
+from langflow.field_typing import Embeddings
+from langflow.io import DropdownInput, IntInput, Output, SecretStrInput, TextInput
 
 
-class AzureOpenAIEmbeddingsComponent(CustomComponent):
+class AzureOpenAIEmbeddingsComponent(LCModelComponent):
     display_name: str = "Azure OpenAI Embeddings"
     description: str = "Generate embeddings using Azure OpenAI models."
     documentation: str = "https://python.langchain.com/docs/integrations/text_embedding/azureopenai"
-    beta = False
     icon = "Azure"
 
     API_VERSION_OPTIONS = [
@@ -22,57 +21,56 @@ class AzureOpenAIEmbeddingsComponent(CustomComponent):
         "2023-08-01-preview",
     ]
 
-    def build_config(self):
-        return {
-            "azure_endpoint": {
-                "display_name": "Azure Endpoint",
-                "required": True,
-                "info": "Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`",
-            },
-            "azure_deployment": {
-                "display_name": "Deployment Name",
-                "required": True,
-            },
-            "api_version": {
-                "display_name": "API Version",
-                "options": self.API_VERSION_OPTIONS,
-                "value": self.API_VERSION_OPTIONS[-1],
-                "advanced": True,
-            },
-            "api_key": {
-                "display_name": "API Key",
-                "required": True,
-                "password": True,
-            },
-            "code": {"show": False},
-            "dimensions": {
-                "display_name": "Dimensions",
-                "info": "The number of dimensions the resulting output embeddings should have. Only supported by certain models.",
-                "advanced": True,
-            },
-        }
+    inputs = [
+        TextInput(
+            name="azure_endpoint",
+            display_name="Azure Endpoint",
+            required=True,
+            info="Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/`",
+        ),
+        TextInput(
+            name="azure_deployment",
+            display_name="Deployment Name",
+            required=True,
+        ),
+        DropdownInput(
+            name="api_version",
+            display_name="API Version",
+            options=API_VERSION_OPTIONS,
+            value=API_VERSION_OPTIONS[-1],
+            advanced=True,
+        ),
+        SecretStrInput(
+            name="api_key",
+            display_name="API Key",
+            required=True,
+        ),
+        IntInput(
+            name="dimensions",
+            display_name="Dimensions",
+            info="The number of dimensions the resulting output embeddings should have. Only supported by certain models.",
+            advanced=True,
+        ),
+    ]
+
+    outputs = [
+        Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
+    ]
+
+    def build_embeddings(self) -> Embeddings:
+        if not self.api_key:
+            raise ValueError("API Key is required")
+
+        azure_api_key = SecretStr(self.api_key)
 
-    def build(
-        self,
-        azure_endpoint: str,
-        azure_deployment: str,
-        api_version: str,
-        api_key: str,
-        dimensions: Optional[int] = None,
-    ) -> Embeddings:
-        if api_key:
-            azure_api_key = SecretStr(api_key)
-        else:
-            azure_api_key = None
         try:
             embeddings = AzureOpenAIEmbeddings(
-                azure_endpoint=azure_endpoint,
-                azure_deployment=azure_deployment,
-                api_version=api_version,
+                azure_endpoint=self.azure_endpoint,
+                azure_deployment=self.azure_deployment,
+                api_version=self.api_version,
                 api_key=azure_api_key,
-                dimensions=dimensions,
+                dimensions=self.dimensions,
             )
-
         except Exception as e:
             raise ValueError("Could not connect to AzureOpenAIEmbeddings API.") from e
 
diff --git a/src/backend/base/langflow/components/embeddings/CohereEmbeddings.py b/src/backend/base/langflow/components/embeddings/CohereEmbeddings.py
index 23d855f40..7c4062c99 100644
--- a/src/backend/base/langflow/components/embeddings/CohereEmbeddings.py
+++ b/src/backend/base/langflow/components/embeddings/CohereEmbeddings.py
@@ -1,38 +1,44 @@
-from typing import Optional
-
 from langchain_community.embeddings.cohere import CohereEmbeddings
 
-from langflow.custom import CustomComponent
+from langflow.base.models.model import LCModelComponent
+from langflow.field_typing import Embeddings
+from langflow.io import DropdownInput, FloatInput, IntInput, Output, SecretStrInput, TextInput
 
 
-class CohereEmbeddingsComponent(CustomComponent):
+class CohereEmbeddingsComponent(LCModelComponent):
     display_name = "Cohere Embeddings"
     description = "Generate embeddings using Cohere models."
+    icon = "Cohere"
+    inputs = [
+        SecretStrInput(name="cohere_api_key", display_name="Cohere API Key"),
+        DropdownInput(
+            name="model",
+            display_name="Model",
+            advanced=True,
+            options=[
+                "embed-english-v2.0",
+                "embed-multilingual-v2.0",
+                "embed-english-light-v2.0",
+                "embed-multilingual-light-v2.0",
+            ],
+            value="embed-english-v2.0",
+        ),
+        TextInput(name="truncate", display_name="Truncate", advanced=True),
+        IntInput(name="max_retries", display_name="Max Retries", value=3, advanced=True),
+        TextInput(name="user_agent", display_name="User Agent", advanced=True, value="langchain"),
+        FloatInput(name="request_timeout", display_name="Request Timeout", advanced=True),
+    ]
 
-    def build_config(self):
-        return {
-            "cohere_api_key": {"display_name": "Cohere API Key", "password": True},
-            "model": {"display_name": "Model", "default": "embed-english-v2.0", "advanced": True},
-            "truncate": {"display_name": "Truncate", "advanced": True},
-            "max_retries": {"display_name": "Max Retries", "advanced": True},
-            "user_agent": {"display_name": "User Agent", "advanced": True},
-            "request_timeout": {"display_name": "Request Timeout", "advanced": True},
-        }
+    outputs = [
+        Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
+    ]
 
-    def build(
-        self,
-        request_timeout: Optional[float] = None,
-        cohere_api_key: str = "",
-        max_retries: int = 3,
-        model: str = "embed-english-v2.0",
-        truncate: Optional[str] = None,
-        user_agent: str = "langchain",
-    ) -> CohereEmbeddings:
+    def build_embeddings(self) -> Embeddings:
         return CohereEmbeddings(  # type: ignore
-            max_retries=max_retries,
-            user_agent=user_agent,
-            request_timeout=request_timeout,
-            cohere_api_key=cohere_api_key,
-            model=model,
-            truncate=truncate,
+            cohere_api_key=self.cohere_api_key,
+            model=self.model,
+            truncate=self.truncate,
+            max_retries=self.max_retries,
+            user_agent=self.user_agent,
+            request_timeout=self.request_timeout or None,
         )
diff --git a/src/backend/base/langflow/components/embeddings/HuggingFaceEmbeddings.py b/src/backend/base/langflow/components/embeddings/HuggingFaceEmbeddings.py
index 720dfa97f..86a6d909c 100644
--- a/src/backend/base/langflow/components/embeddings/HuggingFaceEmbeddings.py
+++ b/src/backend/base/langflow/components/embeddings/HuggingFaceEmbeddings.py
@@ -1,11 +1,11 @@
-from typing import Dict, Optional
-
 from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
 
-from langflow.custom import CustomComponent
+from langflow.base.models.model import LCModelComponent
+from langflow.field_typing import Embeddings
+from langflow.io import BoolInput, DictInput, TextInput, Output
 
 
-class HuggingFaceEmbeddingsComponent(CustomComponent):
+class HuggingFaceEmbeddingsComponent(LCModelComponent):
     display_name = "Hugging Face Embeddings"
     description = "Generate embeddings using HuggingFace models."
     documentation = (
@@ -13,27 +13,23 @@ class HuggingFaceEmbeddingsComponent(CustomComponent):
     )
     icon = "HuggingFace"
 
-    def build_config(self):
-        return {
-            "cache_folder": {"display_name": "Cache Folder", "advanced": True},
-            "encode_kwargs": {"display_name": "Encode Kwargs", "advanced": True, "field_type": "dict"},
-            "model_kwargs": {"display_name": "Model Kwargs", "field_type": "dict", "advanced": True},
-            "model_name": {"display_name": "Model Name"},
-            "multi_process": {"display_name": "Multi Process", "advanced": True},
-        }
+    inputs = [
+        TextInput(name="cache_folder", display_name="Cache Folder", advanced=True),
+        DictInput(name="encode_kwargs", display_name="Encode Kwargs", advanced=True),
+        DictInput(name="model_kwargs", display_name="Model Kwargs", advanced=True),
+        TextInput(name="model_name", display_name="Model Name", value="sentence-transformers/all-mpnet-base-v2"),
+        BoolInput(name="multi_process", display_name="Multi Process", advanced=True),
+    ]
 
-    def build(
-        self,
-        cache_folder: Optional[str] = None,
-        encode_kwargs: Optional[Dict] = {},
-        model_kwargs: Optional[Dict] = {},
-        model_name: str = "sentence-transformers/all-mpnet-base-v2",
-        multi_process: bool = False,
-    ) -> HuggingFaceEmbeddings:
+    outputs = [
+        Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
+    ]
+
+    def build_embeddings(self) -> Embeddings:
         return HuggingFaceEmbeddings(
-            cache_folder=cache_folder,
-            encode_kwargs=encode_kwargs,
-            model_kwargs=model_kwargs,
-            model_name=model_name,
-            multi_process=multi_process,
+            cache_folder=self.cache_folder,
+            encode_kwargs=self.encode_kwargs,
+            model_kwargs=self.model_kwargs,
+            model_name=self.model_name,
+            multi_process=self.multi_process,
         )
diff --git a/src/backend/base/langflow/components/embeddings/HuggingFaceInferenceAPIEmbeddings.py b/src/backend/base/langflow/components/embeddings/HuggingFaceInferenceAPIEmbeddings.py
index 503a6a25a..cd80f7b47 100644
--- a/src/backend/base/langflow/components/embeddings/HuggingFaceInferenceAPIEmbeddings.py
+++ b/src/backend/base/langflow/components/embeddings/HuggingFaceInferenceAPIEmbeddings.py
@@ -1,44 +1,31 @@
-from typing import Dict, Optional
-
 from langchain_community.embeddings.huggingface import HuggingFaceInferenceAPIEmbeddings
 from pydantic.v1.types import SecretStr
 
-from langflow.custom import CustomComponent
+from langflow.base.models.model import LCModelComponent
+from langflow.field_typing import Embeddings
+from langflow.io import Output, SecretStrInput, TextInput
 
 
-class HuggingFaceInferenceAPIEmbeddingsComponent(CustomComponent):
+class HuggingFaceInferenceAPIEmbeddingsComponent(LCModelComponent):
     display_name = "Hugging Face API Embeddings"
     description = "Generate embeddings using Hugging Face Inference API models."
     documentation = "https://github.com/huggingface/text-embeddings-inference"
     icon = "HuggingFace"
 
-    def build_config(self):
-        return {
-            "api_key": {"display_name": "API Key", "password": True, "advanced": True},
-            "api_url": {"display_name": "API URL", "advanced": True},
-            "model_name": {"display_name": "Model Name"},
-            "cache_folder": {"display_name": "Cache Folder", "advanced": True},
-            "encode_kwargs": {"display_name": "Encode Kwargs", "advanced": True, "field_type": "dict"},
-            "model_kwargs": {"display_name": "Model Kwargs", "field_type": "dict", "advanced": True},
-            "multi_process": {"display_name": "Multi Process", "advanced": True},
-        }
+    inputs = [
+        SecretStrInput(name="api_key", display_name="API Key", advanced=True),
+        TextInput(name="api_url", display_name="API URL", advanced=True, value="http://localhost:8080"),
+        TextInput(name="model_name", display_name="Model Name", value="BAAI/bge-large-en-v1.5"),
+    ]
 
-    def build(
-        self,
-        api_key: Optional[str] = "",
-        api_url: str = "http://localhost:8080",
-        model_name: str = "BAAI/bge-large-en-v1.5",
-        cache_folder: Optional[str] = None,
-        encode_kwargs: Optional[Dict] = {},
-        model_kwargs: Optional[Dict] = {},
-        multi_process: bool = False,
-    ) -> HuggingFaceInferenceAPIEmbeddings:
-        if api_key:
-            secret_api_key = SecretStr(api_key)
-        else:
+    outputs = [
+        Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
+    ]
+
+    def build_embeddings(self) -> Embeddings:
+        if not self.api_key:
             raise ValueError("API Key is required")
-        return HuggingFaceInferenceAPIEmbeddings(
-            api_key=secret_api_key,
-            api_url=api_url,
-            model_name=model_name,
-        )
+
+        api_key = SecretStr(self.api_key)
+
+        return HuggingFaceInferenceAPIEmbeddings(api_key=api_key, api_url=self.api_url, model_name=self.model_name)
diff --git a/src/backend/base/langflow/components/embeddings/MistalAIEmbeddings.py b/src/backend/base/langflow/components/embeddings/MistalAIEmbeddings.py
index d24c9fb30..145aed76d 100644
--- a/src/backend/base/langflow/components/embeddings/MistalAIEmbeddings.py
+++ b/src/backend/base/langflow/components/embeddings/MistalAIEmbeddings.py
@@ -1,64 +1,56 @@
 from langchain_mistralai.embeddings import MistralAIEmbeddings
 from pydantic.v1 import SecretStr
 
-from langflow.custom import CustomComponent
+from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import Embeddings
+from langflow.io import DropdownInput, IntInput, Output, SecretStrInput, TextInput
 
 
-class MistralAIEmbeddingsComponent(CustomComponent):
+class MistralAIEmbeddingsComponent(LCModelComponent):
     display_name = "MistralAI Embeddings"
     description = "Generate embeddings using MistralAI models."
+    icon = "MistralAI"
 
-    def build_config(self):
-        return {
-            "model": {
-                "display_name": "Model",
-                "advanced": False,
-                "options": ["mistral-embed"],
-                "value": "mistral-embed",
-            },
-            "mistral_api_key": {
-                "display_name": "Mistral API Key",
-                "password": True,
-                "advanced": False,
-            },
-            "max_concurrent_requests": {
-                "display_name": "Max Concurrent Requests",
-                "advanced": True,
-                "value": 64,
-            },
-            "max_retries": {
-                "display_name": "Max Retries",
-                "advanced": True,
-                "value": 5,
-            },
-            "timeout": {
-                "display_name": "Request Timeout",
-                "advanced": True,
-                "value": 120,
-            },
-            "endpoint": {"display_name": "API Endpoint", "advanced": True, "value": "https://api.mistral.ai/v1/"},
-        }
+    inputs = [
+        DropdownInput(
+            name="model",
+            display_name="Model",
+            advanced=False,
+            options=["mistral-embed"],
+            value="mistral-embed",
+        ),
+        SecretStrInput(name="mistral_api_key", display_name="Mistral API Key"),
+        IntInput(
+            name="max_concurrent_requests",
+            display_name="Max Concurrent Requests",
+            advanced=True,
+            value=64,
+        ),
+        IntInput(name="max_retries", display_name="Max Retries", advanced=True, value=5),
+        IntInput(name="timeout", display_name="Request Timeout", advanced=True, value=120),
+        TextInput(
+            name="endpoint",
+            display_name="API Endpoint",
+            advanced=True,
+            value="https://api.mistral.ai/v1/",
+        ),
+    ]
 
-    def build(
-        self,
-        mistral_api_key: str,
-        model: str = "mistral-embed",
-        max_concurrent_requests: int = 64,
-        max_retries: int = 5,
-        timeout: int = 120,
-        endpoint: str = "https://api.mistral.ai/v1/",
-    ) -> Embeddings:
-        if mistral_api_key:
-            api_key = SecretStr(mistral_api_key)
-        else:
-            api_key = None
+    outputs = [
+        Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
+    ]
+
+    def build_embeddings(self) -> Embeddings:
+        if not self.mistral_api_key:
+            raise ValueError("Mistral API Key is required")
+
+        api_key = SecretStr(self.mistral_api_key)
 
         return MistralAIEmbeddings(
             api_key=api_key,
-            model=model,
-            endpoint=endpoint,
-            max_concurrent_requests=max_concurrent_requests,
-            max_retries=max_retries,
-            timeout=timeout,
+            model=self.model,
+            endpoint=self.endpoint,
+            max_concurrent_requests=self.max_concurrent_requests,
+            max_retries=self.max_retries,
+            timeout=self.timeout,
         )
diff --git a/src/backend/base/langflow/components/embeddings/OllamaEmbeddings.py b/src/backend/base/langflow/components/embeddings/OllamaEmbeddings.py
index 8aad24735..a9f832dee 100644
--- a/src/backend/base/langflow/components/embeddings/OllamaEmbeddings.py
+++ b/src/backend/base/langflow/components/embeddings/OllamaEmbeddings.py
@@ -1,34 +1,44 @@
-from typing import Optional
-
 from langchain_community.embeddings import OllamaEmbeddings
-from langchain_core.embeddings import Embeddings
-
-from langflow.custom import CustomComponent
+from langflow.base.models.model import LCModelComponent
+from langflow.field_typing import Embeddings
+from langflow.io import FloatInput, Output, TextInput
 
 
-class OllamaEmbeddingsComponent(CustomComponent):
+class OllamaEmbeddingsComponent(LCModelComponent):
     display_name: str = "Ollama Embeddings"
     description: str = "Generate embeddings using Ollama models."
     documentation = "https://python.langchain.com/docs/integrations/text_embedding/ollama"
+    icon = "Ollama"
 
-    def build_config(self):
-        return {
-            "model": {
-                "display_name": "Ollama Model",
-            },
-            "base_url": {"display_name": "Ollama Base URL"},
-            "temperature": {"display_name": "Model Temperature"},
-            "code": {"show": False},
-        }
+    inputs = [
+        TextInput(
+            name="model",
+            display_name="Ollama Model",
+            value="llama2",
+        ),
+        TextInput(
+            name="base_url",
+            display_name="Ollama Base URL",
+            value="http://localhost:11434",
+        ),
+        FloatInput(
+            name="temperature",
+            display_name="Model Temperature",
+            advanced=True,
+        ),
+    ]
 
-    def build(
-        self,
-        model: str = "llama2",
-        base_url: str = "http://localhost:11434",
-        temperature: Optional[float] = None,
-    ) -> Embeddings:
+    outputs = [
+        Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
+    ]
+
+    def build_embeddings(self) -> Embeddings:
         try:
-            output = OllamaEmbeddings(model=model, base_url=base_url, temperature=temperature)  # type: ignore
+            output = OllamaEmbeddings(
+                model=self.model,
+                base_url=self.base_url,
+                temperature=self.temperature,
+            )  # type: ignore
         except Exception as e:
             raise ValueError("Could not connect to Ollama API.") from e
         return output
diff --git a/src/backend/base/langflow/components/embeddings/OpenAIEmbeddings.py b/src/backend/base/langflow/components/embeddings/OpenAIEmbeddings.py
index 4813fc793..0b95b9734 100644
--- a/src/backend/base/langflow/components/embeddings/OpenAIEmbeddings.py
+++ b/src/backend/base/langflow/components/embeddings/OpenAIEmbeddings.py
@@ -1,150 +1,96 @@
-from typing import Dict, List, Optional
-
 from langchain_openai.embeddings.base import OpenAIEmbeddings
-from pydantic.v1 import SecretStr
 
-from langflow.custom import CustomComponent
-from langflow.field_typing import Embeddings, NestedDict
+from langflow.base.models.model import LCModelComponent
+from langflow.field_typing import Embeddings
+from langflow.io import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, Output, SecretStrInput, TextInput
 
 
-class OpenAIEmbeddingsComponent(CustomComponent):
+class OpenAIEmbeddingsComponent(LCModelComponent):
     display_name = "OpenAI Embeddings"
     description = "Generate embeddings using OpenAI models."
+    icon = "OpenAI"
+    inputs = [
+        DictInput(
+            name="default_headers",
+            display_name="Default Headers",
+            advanced=True,
+            info="Default headers to use for the API request.",
+        ),
+        DictInput(
+            name="default_query",
+            display_name="Default Query",
+            advanced=True,
+            info="Default query parameters to use for the API request.",
+        ),
+        IntInput(name="chunk_size", display_name="Chunk Size", advanced=True, value=1000),
+        TextInput(name="client", display_name="Client", advanced=True),
+        TextInput(name="deployment", display_name="Deployment", advanced=True),
+        IntInput(name="embedding_ctx_length", display_name="Embedding Context Length", advanced=True, value=1536),
+        IntInput(name="max_retries", display_name="Max Retries", value=3, advanced=True),
+        DropdownInput(
+            name="model",
+            display_name="Model",
+            advanced=False,
+            options=[
+                "text-embedding-3-small",
+                "text-embedding-3-large",
+                "text-embedding-ada-002",
+            ],
+            value="text-embedding-3-small",
+        ),
+        DictInput(name="model_kwargs", display_name="Model Kwargs", advanced=True),
+        SecretStrInput(name="openai_api_base", display_name="OpenAI API Base", advanced=True),
+        SecretStrInput(name="openai_api_key", display_name="OpenAI API Key"),
+        SecretStrInput(name="openai_api_type", display_name="OpenAI API Type", advanced=True),
+        TextInput(name="openai_api_version", display_name="OpenAI API Version", advanced=True),
+        TextInput(
+            name="openai_organization",
+            display_name="OpenAI Organization",
+            advanced=True,
+        ),
+        TextInput(name="openai_proxy", display_name="OpenAI Proxy", advanced=True),
+        FloatInput(name="request_timeout", display_name="Request Timeout", advanced=True),
+        BoolInput(name="show_progress_bar", display_name="Show Progress Bar", advanced=True),
+        BoolInput(name="skip_empty", display_name="Skip Empty", advanced=True),
+        TextInput(
+            name="tiktoken_model_name",
+            display_name="TikToken Model Name",
+            advanced=True,
+        ),
+        BoolInput(
+            name="tiktoken_enable",
+            display_name="TikToken Enable",
+            advanced=True,
+            value=True,
+            info="If False, you must have transformers installed.",
+        ),
+    ]
 
-    def build_config(self):
-        return {
-            "allowed_special": {
-                "display_name": "Allowed Special",
-                "advanced": True,
-                "field_type": "str",
-                "is_list": True,
-            },
-            "default_headers": {
-                "display_name": "Default Headers",
-                "advanced": True,
-                "field_type": "dict",
-            },
-            "default_query": {
-                "display_name": "Default Query",
-                "advanced": True,
-                "field_type": "NestedDict",
-            },
-            "disallowed_special": {
-                "display_name": "Disallowed Special",
-                "advanced": True,
-                "field_type": "str",
-                "is_list": True,
-            },
-            "chunk_size": {"display_name": "Chunk Size", "advanced": True},
-            "client": {"display_name": "Client", "advanced": True},
-            "deployment": {"display_name": "Deployment", "advanced": True},
-            "embedding_ctx_length": {
-                "display_name": "Embedding Context Length",
-                "advanced": True,
-            },
-            "max_retries": {"display_name": "Max Retries", "advanced": True},
-            "model": {
-                "display_name": "Model",
-                "advanced": False,
-                "options": [
-                    "text-embedding-3-small",
-                    "text-embedding-3-large",
-                    "text-embedding-ada-002",
-                ],
-            },
-            "model_kwargs": {"display_name": "Model Kwargs", "advanced": True},
-            "openai_api_base": {
-                "display_name": "OpenAI API Base",
-                "password": True,
-                "advanced": True,
-            },
-            "openai_api_key": {"display_name": "OpenAI API Key", "password": True},
-            "openai_api_type": {
-                "display_name": "OpenAI API Type",
-                "advanced": True,
-                "password": True,
-            },
-            "openai_api_version": {
-                "display_name": "OpenAI API Version",
-                "advanced": True,
-            },
-            "openai_organization": {
-                "display_name": "OpenAI Organization",
-                "advanced": True,
-            },
-            "openai_proxy": {"display_name": "OpenAI Proxy", "advanced": True},
-            "request_timeout": {"display_name": "Request Timeout", "advanced": True},
-            "show_progress_bar": {
-                "display_name": "Show Progress Bar",
-                "advanced": True,
-            },
-            "skip_empty": {"display_name": "Skip Empty", "advanced": True},
-            "tiktoken_model_name": {
-                "display_name": "TikToken Model Name",
-                "advanced": True,
-            },
-            "tiktoken_enable": {"display_name": "TikToken Enable", "advanced": True},
-            "dimensions": {
-                "display_name": "Dimensions",
-                "info": "The number of dimensions the resulting output embeddings should have. Only supported by certain models.",
-                "advanced": True,
-            },
-        }
-
-    def build(
-        self,
-        openai_api_key: str,
-        default_headers: Optional[Dict[str, str]] = None,
-        default_query: Optional[NestedDict] = {},
-        allowed_special: List[str] = [],
-        disallowed_special: List[str] = ["all"],
-        chunk_size: int = 1000,
-        deployment: str = "text-embedding-ada-002",
-        embedding_ctx_length: int = 8191,
-        max_retries: int = 6,
-        model: str = "text-embedding-ada-002",
-        model_kwargs: NestedDict = {},
-        openai_api_base: Optional[str] = None,
-        openai_api_type: Optional[str] = None,
-        openai_api_version: Optional[str] = None,
-        openai_organization: Optional[str] = None,
-        openai_proxy: Optional[str] = None,
-        request_timeout: Optional[float] = None,
-        show_progress_bar: bool = False,
-        skip_empty: bool = False,
-        tiktoken_enable: bool = True,
-        tiktoken_model_name: Optional[str] = None,
-        dimensions: Optional[int] = None,
-    ) -> Embeddings:
-        # This is to avoid errors with Vector Stores (e.g Chroma)
-        if disallowed_special == ["all"]:
-            disallowed_special = "all"  # type: ignore
-        if openai_api_key:
-            api_key = SecretStr(openai_api_key)
-        else:
-            api_key = None
+    outputs = [
+        Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
+    ]
 
+    def build_embeddings(self) -> Embeddings:
         return OpenAIEmbeddings(
-            tiktoken_enabled=tiktoken_enable,
-            default_headers=default_headers,
-            default_query=default_query,
-            allowed_special=set(allowed_special),
+            tiktoken_enabled=self.tiktoken_enable,
+            default_headers=self.default_headers,
+            default_query=self.default_query,
+            allowed_special="all",
             disallowed_special="all",
-            chunk_size=chunk_size,
-            deployment=deployment,
-            embedding_ctx_length=embedding_ctx_length,
-            max_retries=max_retries,
-            model=model,
-            model_kwargs=model_kwargs,
-            base_url=openai_api_base,
-            api_key=api_key,
-            openai_api_type=openai_api_type,
-            api_version=openai_api_version,
-            organization=openai_organization,
-            openai_proxy=openai_proxy,
-            timeout=request_timeout,
-            show_progress_bar=show_progress_bar,
-            skip_empty=skip_empty,
-            tiktoken_model_name=tiktoken_model_name,
-            dimensions=dimensions,
+            chunk_size=self.chunk_size,
+            deployment=self.deployment,
+            embedding_ctx_length=self.embedding_ctx_length,
+            max_retries=self.max_retries,
+            model=self.model,
+            model_kwargs=self.model_kwargs,
+            base_url=self.openai_api_base,
+            api_key=self.openai_api_key,
+            openai_api_type=self.openai_api_type,
+            api_version=self.openai_api_version,
+            organization=self.openai_organization,
+            openai_proxy=self.openai_proxy,
+            timeout=self.request_timeout or None,
+            show_progress_bar=self.show_progress_bar,
+            skip_empty=self.skip_empty,
+            tiktoken_model_name=self.tiktoken_model_name,
         )
diff --git a/src/backend/base/langflow/components/embeddings/VertexAIEmbeddings.py b/src/backend/base/langflow/components/embeddings/VertexAIEmbeddings.py
index c0d249326..28b1f1c7c 100644
--- a/src/backend/base/langflow/components/embeddings/VertexAIEmbeddings.py
+++ b/src/backend/base/langflow/components/embeddings/VertexAIEmbeddings.py
@@ -1,90 +1,119 @@
-from typing import List, Optional
-
-from langchain_google_vertexai import VertexAIEmbeddings
-
-from langflow.custom import CustomComponent
+from langflow.base.models.model import LCModelComponent
+from langflow.field_typing import Embeddings
+from langflow.io import BoolInput, DictInput, FileInput, FloatInput, IntInput, Output, TextInput
 
 
-class VertexAIEmbeddingsComponent(CustomComponent):
+class VertexAIEmbeddingsComponent(LCModelComponent):
     display_name = "VertexAI Embeddings"
     description = "Generate embeddings using Google Cloud VertexAI models."
+    icon = "VertexAI"
 
-    def build_config(self):
-        return {
-            "credentials": {
-                "display_name": "Credentials",
-                "value": "",
-                "file_types": [".json"],
-                "field_type": "file",
-            },
-            "instance": {
-                "display_name": "instance",
-                "advanced": True,
-                "field_type": "dict",
-            },
-            "location": {
-                "display_name": "Location",
-                "value": "us-central1",
-                "advanced": True,
-            },
-            "max_output_tokens": {"display_name": "Max Output Tokens", "value": 128},
-            "max_retries": {
-                "display_name": "Max Retries",
-                "value": 6,
-                "advanced": True,
-            },
-            "model_name": {
-                "display_name": "Model Name",
-                "value": "textembedding-gecko",
-            },
-            "n": {"display_name": "N", "value": 1, "advanced": True},
-            "project": {"display_name": "Project", "advanced": True},
-            "request_parallelism": {
-                "display_name": "Request Parallelism",
-                "value": 5,
-                "advanced": True,
-            },
-            "stop": {"display_name": "Stop", "advanced": True},
-            "streaming": {
-                "display_name": "Streaming",
-                "value": False,
-                "advanced": True,
-            },
-            "temperature": {"display_name": "Temperature", "value": 0.0},
-            "top_k": {"display_name": "Top K", "value": 40, "advanced": True},
-            "top_p": {"display_name": "Top P", "value": 0.95, "advanced": True},
-        }
+    inputs = [
+        FileInput(
+            name="credentials",
+            display_name="Credentials",
+            value="",
+            file_types=["json"],  # Removed the dot
+        ),
+        DictInput(
+            name="instance",
+            display_name="Instance",
+            advanced=True,
+        ),
+        TextInput(
+            name="location",
+            display_name="Location",
+            value="us-central1",
+            advanced=True,
+        ),
+        IntInput(
+            name="max_output_tokens",
+            display_name="Max Output Tokens",
+            value=128,
+        ),
+        IntInput(
+            name="max_retries",
+            display_name="Max Retries",
+            value=6,
+            advanced=True,
+        ),
+        TextInput(
+            name="model_name",
+            display_name="Model Name",
+            value="textembedding-gecko",
+        ),
+        IntInput(
+            name="n",
+            display_name="N",
+            value=1,
+            advanced=True,
+        ),
+        TextInput(
+            name="project",
+            display_name="Project",
+            advanced=True,
+        ),
+        IntInput(
+            name="request_parallelism",
+            display_name="Request Parallelism",
+            value=5,
+            advanced=True,
+        ),
+        TextInput(
+            name="stop",
+            display_name="Stop",
+            advanced=True,
+        ),
+        BoolInput(
+            name="streaming",
+            display_name="Streaming",
+            value=False,
+            advanced=True,
+        ),
+        FloatInput(
+            name="temperature",
+            display_name="Temperature",
+            value=0.0,
+        ),
+        IntInput(
+            name="top_k",
+            display_name="Top K",
+            value=40,
+            advanced=True,
+        ),
+        FloatInput(
+            name="top_p",
+            display_name="Top P",
+            value=0.95,
+            advanced=True,
+        ),
+    ]
+
+    outputs = [
+        Output(display_name="Embeddings", name="embeddings", method="build_embeddings"),
+    ]
+
+    def build_embeddings(self) -> Embeddings:
+        try:
+            from langchain_google_vertexai import VertexAIEmbeddings
+        except ImportError:
+            raise ImportError(
+                "Please install the langchain-google-vertexai package to use the VertexAIEmbeddings component."
+            )
 
-    def build(
-        self,
-        instance: Optional[str] = None,
-        credentials: Optional[str] = None,
-        location: str = "us-central1",
-        max_output_tokens: int = 128,
-        max_retries: int = 6,
-        model_name: str = "textembedding-gecko",
-        n: int = 1,
-        project: Optional[str] = None,
-        request_parallelism: int = 5,
-        stop: Optional[List[str]] = None,
-        streaming: bool = False,
-        temperature: float = 0.0,
-        top_k: int = 40,
-        top_p: float = 0.95,
-    ) -> VertexAIEmbeddings:
         return VertexAIEmbeddings(
-            instance=instance,
-            credentials=credentials,
-            location=location,
-            max_output_tokens=max_output_tokens,
-            max_retries=max_retries,
-            model_name=model_name,
-            n=n,
-            project=project,
-            request_parallelism=request_parallelism,
-            stop=stop,
-            streaming=streaming,
-            temperature=temperature,
-            top_k=top_k,
-            top_p=top_p,
+            instance=self.instance,
+            credentials=self.credentials,
+            location=self.location,
+            max_output_tokens=self.max_output_tokens,
+            max_retries=self.max_retries,
+            model_name=self.model_name,
+            n=self.n,
+            project=self.project,
+            request_parallelism=self.request_parallelism,
+            stop=self.stop,
+            streaming=self.streaming,
+            temperature=self.temperature,
+            top_k=self.top_k,
+            top_p=self.top_p,
         )
diff --git a/src/backend/base/langflow/components/embeddings/__init__.py b/src/backend/base/langflow/components/embeddings/__init__.py
index f2d1bc48e..a55a13ffe 100644
--- a/src/backend/base/langflow/components/embeddings/__init__.py
+++ b/src/backend/base/langflow/components/embeddings/__init__.py
@@ -1,4 +1,4 @@
-from .AmazonBedrockEmbeddings import AmazonBedrockEmeddingsComponent
+from .AmazonBedrockEmbeddings import AmazonBedrockEmbeddingsComponent
 from .AzureOpenAIEmbeddings import AzureOpenAIEmbeddingsComponent
 from .CohereEmbeddings import CohereEmbeddingsComponent
 from .HuggingFaceEmbeddings import HuggingFaceEmbeddingsComponent
@@ -8,7 +8,7 @@ from .OpenAIEmbeddings import OpenAIEmbeddingsComponent
 from .VertexAIEmbeddings import VertexAIEmbeddingsComponent
 
 __all__ = [
-    "AmazonBedrockEmeddingsComponent",
+    "AmazonBedrockEmbeddingsComponent",
     "AzureOpenAIEmbeddingsComponent",
     "CohereEmbeddingsComponent",
     "HuggingFaceEmbeddingsComponent",
diff --git a/src/backend/base/langflow/components/experimental/AgentComponent.py b/src/backend/base/langflow/components/experimental/AgentComponent.py
index abd8826d4..657dadc0a 100644
--- a/src/backend/base/langflow/components/experimental/AgentComponent.py
+++ b/src/backend/base/langflow/components/experimental/AgentComponent.py
@@ -5,8 +5,8 @@ from langchain_core.prompts.chat import HumanMessagePromptTemplate, SystemMessag
 
 from langflow.base.agents.agent import LCAgentComponent
 from langflow.base.agents.utils import AGENTS, AgentSpec, get_agents_list
-from langflow.field_typing import BaseLanguageModel, Text, Tool
-from langflow.schema import Record
+from langflow.field_typing import LanguageModel, Text, Tool
+from langflow.schema import Data
 from langflow.schema.dotdict import dotdict
 
 
@@ -145,11 +145,11 @@ class AgentComponent(LCAgentComponent):
         self,
         agent_name: str,
         input_value: str,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         tools: List[Tool],
         system_message: str = "You are a helpful assistant. Help the user answer any questions.",
         user_prompt: str = "{input}",
-        message_history: Optional[List[Record]] = None,
+        message_history: Optional[List[Data]] = None,
         tool_template: str = "{name}: {description}",
         handle_parsing_errors: bool = True,
     ) -> Text:
diff --git a/src/backend/base/langflow/components/experimental/ClearMessageHistory.py b/src/backend/base/langflow/components/experimental/ClearMessageHistory.py
index dacfaccb4..4cdcf3212 100644
--- a/src/backend/base/langflow/components/experimental/ClearMessageHistory.py
+++ b/src/backend/base/langflow/components/experimental/ClearMessageHistory.py
@@ -21,6 +21,6 @@ class ClearMessageHistoryComponent(CustomComponent):
         session_id: str,
     ) -> None:
         delete_messages(session_id=session_id)
-        records = get_messages(session_id=session_id)
-        self.records = records
-        return records
+        data = get_messages(session_id=session_id)
+        self.data = data
+        return data
diff --git a/src/backend/base/langflow/components/experimental/ConditionalRouter.py b/src/backend/base/langflow/components/experimental/ConditionalRouter.py
new file mode 100644
index 000000000..697b67fd6
--- /dev/null
+++ b/src/backend/base/langflow/components/experimental/ConditionalRouter.py
@@ -0,0 +1,86 @@
+from langflow.custom import Component
+from langflow.io import BoolInput, DropdownInput, MessageInput, Output, TextInput
+from langflow.schema.message import Message
+
+
+class ConditionalRouterComponent(Component):
+    display_name = "Conditional Router"
+    description = "Routes an input message to a corresponding output based on text comparison."
+    icon = "equal"
+
+    inputs = [
+        TextInput(
+            name="input_text",
+            display_name="Input Text",
+            info="The primary text input for the operation.",
+        ),
+        TextInput(
+            name="match_text",
+            display_name="Match Text",
+            info="The text input to compare against.",
+        ),
+        DropdownInput(
+            name="operator",
+            display_name="Operator",
+            options=["equals", "not equals", "contains", "starts with", "ends with"],
+            info="The operator to apply for comparing the texts.",
+            value="equals",
+            advanced=True,
+        ),
+        BoolInput(
+            name="case_sensitive",
+            display_name="Case Sensitive",
+            info="If true, the comparison will be case sensitive.",
+            value=False,
+            advanced=True,
+        ),
+        MessageInput(
+            name="message",
+            display_name="Message",
+            info="The message to pass through either route.",
+        ),
+    ]
+
+    outputs = [
+        Output(display_name="True Route", name="true_result", method="true_response"),
+        Output(display_name="False Route", name="false_result", method="false_response"),
+    ]
+
+    def evaluate_condition(self, input_text: str, match_text: str, operator: str, case_sensitive: bool) -> bool:
+        if not case_sensitive:
+            input_text = input_text.lower()
+            match_text = match_text.lower()
+
+        if operator == "equals":
+            return input_text == match_text
+        elif operator == "not equals":
+            return input_text != match_text
+        elif operator == "contains":
+            return match_text in input_text
+        elif operator == "starts with":
+            return input_text.startswith(match_text)
+        elif operator == "ends with":
+            return input_text.endswith(match_text)
+        return False
+
+    def true_response(self) -> Message:
+        result = self.evaluate_condition(self.input_text, self.match_text, self.operator, self.case_sensitive)
+        if result:
+            self.stop("false_result")
+            response = self.message if self.message else self.input_text
+            self.status = response
+            return response
+        else:
+            self.stop("true_result")
+            return Message()
+
+    def false_response(self) -> Message:
+        result = self.evaluate_condition(self.input_text, self.match_text, self.operator, self.case_sensitive)
+        if not result:
+            self.stop("true_result")
+            response = self.message if self.message else self.input_text
+            self.status = response
+            return response
+        else:
+            self.stop("false_result")
+            return Message()
diff --git a/src/backend/base/langflow/components/experimental/Embed.py b/src/backend/base/langflow/components/experimental/Embed.py
index 177eb135c..a67c6c4cf 100644
--- a/src/backend/base/langflow/components/experimental/Embed.py
+++ b/src/backend/base/langflow/components/experimental/Embed.py
@@ -1,6 +1,6 @@
 from langflow.custom import CustomComponent
-from langflow.schema import Record
 from langflow.field_typing import Embeddings
+from langflow.schema import Data
 
 
 class EmbedComponent(CustomComponent):
@@ -9,7 +9,7 @@ class EmbedComponent(CustomComponent):
     def build_config(self):
         return {"texts": {"display_name": "Texts"}, "embbedings": {"display_name": "Embeddings"}}
 
-    def build(self, texts: list[str], embbedings: Embeddings) -> Record:
-        vectors = Record(vector=embbedings.embed_documents(texts))
+    def build(self, texts: list[str], embbedings: Embeddings) -> Data:
+        vectors = Data(vector=embbedings.embed_documents(texts))
         self.status = vectors
         return vectors
diff --git a/src/backend/base/langflow/components/experimental/ExtractDataFromRecord.py b/src/backend/base/langflow/components/experimental/ExtractDataFromRecord.py
deleted file mode 100644
index b1d6ecd40..000000000
--- a/src/backend/base/langflow/components/experimental/ExtractDataFromRecord.py
+++ /dev/null
@@ -1,45 +0,0 @@
-from langflow.custom import CustomComponent
-from langflow.schema import Record
-
-
-class ExtractKeyFromRecordComponent(CustomComponent):
-    display_name = "Extract Key From Record"
-    description = "Extracts a key from a record."
-    beta: bool = True
-
-    field_config = {
-        "record": {"display_name": "Record"},
-        "keys": {
-            "display_name": "Keys",
-            "info": "The keys to extract from the record.",
-            "input_types": [],
-        },
-        "silent_error": {
-            "display_name": "Silent Errors",
-            "info": "If True, errors will not be raised.",
-            "advanced": True,
-        },
-    }
-
-    def build(self, record: Record, keys: list[str], silent_error: bool = True) -> Record:
-        """
-        Extracts the keys from a record.
-
-        Args:
-            record (Record): The record from which to extract the keys.
-            keys (list[str]): The keys to extract from the record.
-            silent_error (bool): If True, errors will not be raised.
-
-        Returns:
-            dict: The extracted keys.
-        """
-        extracted_keys = {}
-        for key in keys:
-            try:
-                extracted_keys[key] = getattr(record, key)
-            except AttributeError:
-                if not silent_error:
-                    raise KeyError(f"The key '{key}' does not exist in the record.")
-        return_record = Record(data=extracted_keys)
-        self.status = return_record
-        return return_record
diff --git a/src/backend/base/langflow/components/experimental/ExtractKeyFromData.py b/src/backend/base/langflow/components/experimental/ExtractKeyFromData.py
new file mode 100644
index 000000000..a8ca78262
--- /dev/null
+++ b/src/backend/base/langflow/components/experimental/ExtractKeyFromData.py
@@ -0,0 +1,45 @@
+from langflow.custom import CustomComponent
+from langflow.schema import Data
+
+
+class ExtractKeyFromDataComponent(CustomComponent):
+    display_name = "Extract Key From Data"
+    description = "Extracts a key from a data."
+    beta: bool = True
+
+    field_config = {
+        "data": {"display_name": "Data"},
+        "keys": {
+            "display_name": "Keys",
+            "info": "The keys to extract from the data.",
+            "input_types": [],
+        },
+        "silent_error": {
+            "display_name": "Silent Errors",
+            "info": "If True, errors will not be raised.",
+            "advanced": True,
+        },
+    }
+
+    def build(self, data: Data, keys: list[str], silent_error: bool = True) -> Data:
+        """
+        Extracts the keys from a data.
+
+        Args:
+            data (Data): The data from which to extract the keys.
+            keys (list[str]): The keys to extract from the data.
+            silent_error (bool): If True, errors will not be raised.
+
+        Returns:
+            dict: The extracted keys.
+        """
+        extracted_keys = {}
+        for key in keys:
+            try:
+                extracted_keys[key] = getattr(data, key)
+            except AttributeError:
+                if not silent_error:
+                    raise KeyError(f"The key '{key}' does not exist in the data.")
+        return_data = Data(data=extracted_keys)
+        self.status = return_data
+        return return_data
diff --git a/src/backend/base/langflow/components/experimental/FlowTool.py b/src/backend/base/langflow/components/experimental/FlowTool.py
index eaebb0c6e..7ba8c6ab6 100644
--- a/src/backend/base/langflow/components/experimental/FlowTool.py
+++ b/src/backend/base/langflow/components/experimental/FlowTool.py
@@ -7,7 +7,7 @@ from langflow.custom import CustomComponent
 from langflow.field_typing import Tool
 from langflow.graph.graph.base import Graph
 from langflow.helpers.flow import get_flow_inputs
-from langflow.schema import Record
+from langflow.schema import Data
 from langflow.schema.dotdict import dotdict
 
 
@@ -17,10 +17,10 @@ class FlowToolComponent(CustomComponent):
     field_order = ["flow_name", "name", "description", "return_direct"]
 
     def get_flow_names(self) -> List[str]:
-        flow_records = self.list_flows()
-        return [flow_record.data["name"] for flow_record in flow_records]
+        flow_datas = self.list_flows()
+        return [flow_data.data["name"] for flow_data in flow_datas]
 
-    def get_flow(self, flow_name: str) -> Optional[Record]:
+    def get_flow(self, flow_name: str) -> Optional[Data]:
         """
         Retrieves a flow by its name.
 
@@ -30,10 +30,10 @@ class FlowToolComponent(CustomComponent):
         Returns:
             Optional[Text]: The flow record if found, None otherwise.
         """
-        flow_records = self.list_flows()
-        for flow_record in flow_records:
-            if flow_record.data["name"] == flow_name:
-                return flow_record
+        flow_datas = self.list_flows()
+        for flow_data in flow_datas:
+            if flow_data.data["name"] == flow_name:
+                return flow_data
         return None
 
     def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):
@@ -69,10 +69,10 @@ class FlowToolComponent(CustomComponent):
 
     async def build(self, flow_name: str, name: str, description: str, return_direct: bool = False) -> Tool:
         FlowTool.update_forward_refs()
-        flow_record = self.get_flow(flow_name)
-        if not flow_record:
+        flow_data = self.get_flow(flow_name)
+        if not flow_data:
             raise ValueError("Flow not found.")
-        graph = Graph.from_payload(flow_record.data["data"])
+        graph = Graph.from_payload(flow_data.data["data"])
         inputs = get_flow_inputs(graph)
         tool = FlowTool(
             name=name,
@@ -80,7 +80,7 @@ class FlowToolComponent(CustomComponent):
             graph=graph,
             return_direct=return_direct,
             inputs=inputs,
-            flow_id=str(flow_record.id),
+            flow_id=str(flow_data.id),
             user_id=str(self._user_id),
         )
         description_repr = repr(tool.description).strip("'")
diff --git a/src/backend/base/langflow/components/experimental/ListFlows.py b/src/backend/base/langflow/components/experimental/ListFlows.py
index 07b4a4bbc..38fb2b967 100644
--- a/src/backend/base/langflow/components/experimental/ListFlows.py
+++ b/src/backend/base/langflow/components/experimental/ListFlows.py
@@ -1,7 +1,7 @@
 from typing import List
 
 from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class ListFlowsComponent(CustomComponent):
@@ -15,7 +15,7 @@ class ListFlowsComponent(CustomComponent):
 
     def build(
         self,
-    ) -> List[Record]:
+    ) -> List[Data]:
         flows = self.list_flows()
         self.status = flows
         return flows
diff --git a/src/backend/base/langflow/components/experimental/Listen.py b/src/backend/base/langflow/components/experimental/Listen.py
index be7ddb8e3..03a81d130 100644
--- a/src/backend/base/langflow/components/experimental/Listen.py
+++ b/src/backend/base/langflow/components/experimental/Listen.py
@@ -1,5 +1,5 @@
 from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class ListenComponent(CustomComponent):
@@ -15,7 +15,7 @@ class ListenComponent(CustomComponent):
             },
         }
 
-    def build(self, name: str) -> Record:
+    def build(self, name: str) -> Data:
         state = self.get_state(name)
         self.status = state
         return state
diff --git a/src/backend/base/langflow/components/experimental/MergeData.py b/src/backend/base/langflow/components/experimental/MergeData.py
new file mode 100644
index 000000000..1b4c47e6a
--- /dev/null
+++ b/src/backend/base/langflow/components/experimental/MergeData.py
@@ -0,0 +1,36 @@
+from langflow.custom import CustomComponent
+from langflow.schema import Data
+
+
+class MergeDataComponent(CustomComponent):
+    display_name = "Merge Data"
+    description = "Merges data."
+    beta: bool = True
+
+    field_config = {
+        "data": {"display_name": "Data"},
+    }
+
+    def build(self, data: list[Data]) -> Data:
+        if not data:
+            return Data()
+        if len(data) == 1:
+            return data[0]
+        merged_data = Data()
+        for value in data:
+            if merged_data is None:
+                merged_data = value
+            else:
+                merged_data += value
+        self.status = merged_data
+        return merged_data
+
+
+if __name__ == "__main__":
+    data = [
+        Data(data={"key1": "value1"}),
+        Data(data={"key2": "value2"}),
+    ]
+    component = MergeDataComponent()
+    result = component.build(data)
+    print(result)
diff --git a/src/backend/base/langflow/components/experimental/MergeRecords.py b/src/backend/base/langflow/components/experimental/MergeRecords.py
deleted file mode 100644
index c938b4473..000000000
--- a/src/backend/base/langflow/components/experimental/MergeRecords.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from langflow.custom import CustomComponent
-from langflow.schema import Record
-
-
-class MergeRecordsComponent(CustomComponent):
-    display_name = "Merge Records"
-    description = "Merges records."
-    beta: bool = True
-
-    field_config = {
-        "records": {"display_name": "Records"},
-    }
-
-    def build(self, records: list[Record]) -> Record:
-        if not records:
-            return Record()
-        if len(records) == 1:
-            return records[0]
-        merged_record = Record()
-        for record in records:
-            if merged_record is None:
-                merged_record = record
-            else:
-                merged_record += record
-        self.status = merged_record
-        return merged_record
-
-
-if __name__ == "__main__":
-    records = [
-        Record(data={"key1": "value1"}),
-        Record(data={"key2": "value2"}),
-    ]
-    component = MergeRecordsComponent()
-    result = component.build(records)
-    print(result)
diff --git a/src/backend/base/langflow/components/experimental/Notify.py b/src/backend/base/langflow/components/experimental/Notify.py
index bf4391682..e4bd0b090 100644
--- a/src/backend/base/langflow/components/experimental/Notify.py
+++ b/src/backend/base/langflow/components/experimental/Notify.py
@@ -1,7 +1,7 @@
 from typing import Optional
 
 from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class NotifyComponent(CustomComponent):
@@ -13,29 +13,29 @@ class NotifyComponent(CustomComponent):
     def build_config(self):
         return {
             "name": {"display_name": "Name", "info": "The name of the notification."},
-            "record": {"display_name": "Record", "info": "The record to store."},
+            "data": {"display_name": "Data", "info": "The data to store."},
             "append": {
                 "display_name": "Append",
                 "info": "If True, the record will be appended to the notification.",
             },
         }
 
-    def build(self, name: str, record: Optional[Record] = None, append: bool = False) -> Record:
-        if record and not isinstance(record, Record):
-            if isinstance(record, str):
-                record = Record(text=record)
-            elif isinstance(record, dict):
-                record = Record(data=record)
+    def build(self, name: str, data: Optional[Data] = None, append: bool = False) -> Data:
+        if data and not isinstance(data, Data):
+            if isinstance(data, str):
+                data = Data(text=data)
+            elif isinstance(data, dict):
+                data = Data(data=data)
             else:
-                record = Record(text=str(record))
-        elif not record:
-            record = Record(text="")
-        if record:
+                data = Data(text=str(data))
+        elif not data:
+            data = Data(text="")
+        if data:
             if append:
-                self.append_state(name, record)
+                self.append_state(name, data)
             else:
-                self.update_state(name, record)
+                self.update_state(name, data)
         else:
             self.status = "No record provided."
-        self.status = record
-        return record
+        self.status = data
+        return data
diff --git a/src/backend/base/langflow/components/experimental/Pass.py b/src/backend/base/langflow/components/experimental/Pass.py
index 3fdb438a0..d21fe0887 100644
--- a/src/backend/base/langflow/components/experimental/Pass.py
+++ b/src/backend/base/langflow/components/experimental/Pass.py
@@ -2,7 +2,7 @@ from typing import Union
 
 from langflow.custom import CustomComponent
 from langflow.field_typing import Text
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class PassComponent(CustomComponent):
@@ -15,16 +15,16 @@ class PassComponent(CustomComponent):
             "ignored_input": {
                 "display_name": "Ignored Input",
                 "info": "This input is ignored. It's used to control the flow in the graph.",
-                "input_types": ["Text", "Record"],
+                "input_types": ["Text", "Data"],
             },
             "forwarded_input": {
                 "display_name": "Input",
                 "info": "This input is forwarded by the component.",
-                "input_types": ["Text", "Record"],
+                "input_types": ["Text", "Data"],
             },
         }
 
-    def build(self, ignored_input: Text, forwarded_input: Text) -> Union[Text, Record]:
+    def build(self, ignored_input: Text, forwarded_input: Text) -> Union[Text, Data]:
         # The ignored_input is not used in the logic, it's just there for graph flow control
         self.status = forwarded_input
         return forwarded_input
diff --git a/src/backend/base/langflow/components/experimental/RunFlow.py b/src/backend/base/langflow/components/experimental/RunFlow.py
index d2e7dd285..d5b2362fb 100644
--- a/src/backend/base/langflow/components/experimental/RunFlow.py
+++ b/src/backend/base/langflow/components/experimental/RunFlow.py
@@ -1,10 +1,10 @@
 from typing import Any, List, Optional
 
-from langflow.base.flow_processing.utils import build_records_from_run_outputs
+from langflow.base.flow_processing.utils import build_data_from_run_outputs
 from langflow.custom import CustomComponent
 from langflow.field_typing import NestedDict, Text
 from langflow.graph.schema import RunOutputs
-from langflow.schema import Record, dotdict
+from langflow.schema import Data, dotdict
 
 
 class RunFlowComponent(CustomComponent):
@@ -13,8 +13,8 @@ class RunFlowComponent(CustomComponent):
     beta: bool = True
 
     def get_flow_names(self) -> List[str]:
-        flow_records = self.list_flows()
-        return [flow_record.data["name"] for flow_record in flow_records]
+        flow_data = self.list_flows()
+        return [flow_data.data["name"] for flow_data in flow_data]
 
     def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):
         if field_name == "flow_name":
@@ -40,17 +40,17 @@ class RunFlowComponent(CustomComponent):
             },
         }
 
-    async def build(self, input_value: Text, flow_name: str, tweaks: NestedDict) -> List[Record]:
+    async def build(self, input_value: Text, flow_name: str, tweaks: NestedDict) -> List[Data]:
         results: List[Optional[RunOutputs]] = await self.run_flow(
             inputs={"input_value": input_value}, flow_name=flow_name, tweaks=tweaks
         )
         if isinstance(results, list):
-            records = []
+            data = []
             for result in results:
                 if result:
-                    records.extend(build_records_from_run_outputs(result))
+                    data.extend(build_data_from_run_outputs(result))
         else:
-            records = build_records_from_run_outputs()(results)
+            data = build_data_from_run_outputs()(results)
 
-        self.status = records
-        return records
+        self.status = data
+        return data
diff --git a/src/backend/base/langflow/components/experimental/SelectivePassThrough.py b/src/backend/base/langflow/components/experimental/SelectivePassThrough.py
new file mode 100644
index 000000000..a0aba72ff
--- /dev/null
+++ b/src/backend/base/langflow/components/experimental/SelectivePassThrough.py
@@ -0,0 +1,75 @@
+from langflow.custom import Component
+from langflow.field_typing import Text
+from langflow.io import BoolInput, DropdownInput, Output, TextInput
+
+
+class SelectivePassThroughComponent(Component):
+    display_name = "Selective Pass Through"
+    description = "Passes the specified value if a specified condition is met."
+    icon = "filter"
+
+    inputs = [
+        TextInput(
+            name="input_value",
+            display_name="Input Value",
+            info="The primary input value to evaluate.",
+        ),
+        TextInput(
+            name="comparison_value",
+            display_name="Comparison Value",
+            info="The value to compare against the input value.",
+        ),
+        DropdownInput(
+            name="operator",
+            display_name="Operator",
+            options=["equals", "not equals", "contains", "starts with", "ends with"],
+            info="Condition to evaluate the input value.",
+        ),
+        TextInput(
+            name="value_to_pass",
+            display_name="Value to Pass",
+            info="The value to pass if the condition is met.",
+        ),
+        BoolInput(
+            name="case_sensitive",
+            display_name="Case Sensitive",
+            info="If true, the comparison will be case sensitive.",
+            value=False,
+            advanced=True,
+        ),
+    ]
+
+    outputs = [
+        Output(display_name="Passed Output", name="passed_output", method="pass_through"),
+    ]
+
+    def evaluate_condition(self, input_value: str, comparison_value: str, operator: str, case_sensitive: bool) -> bool:
+        if not case_sensitive:
+            input_value = input_value.lower()
+            comparison_value = comparison_value.lower()
+
+        if operator == "equals":
+            return input_value == comparison_value
+        elif operator == "not equals":
+            return input_value != comparison_value
+        elif operator == "contains":
+            return comparison_value in input_value
+        elif operator == "starts with":
+            return input_value.startswith(comparison_value)
+        elif operator == "ends with":
+            return input_value.endswith(comparison_value)
+        return False
+
+    def pass_through(self) -> Text:
+        input_value = self.input_value
+        comparison_value = self.comparison_value
+        operator = self.operator
+        value_to_pass = self.value_to_pass
+        case_sensitive = self.case_sensitive
+
+        if self.evaluate_condition(input_value, comparison_value, operator, case_sensitive):
+            self.status = value_to_pass
+            return value_to_pass
+        else:
+            self.status = ""
+            return ""
diff --git a/src/backend/base/langflow/components/experimental/SplitText.py b/src/backend/base/langflow/components/experimental/SplitText.py
index 7156371c3..73e87504c 100644
--- a/src/backend/base/langflow/components/experimental/SplitText.py
+++ b/src/backend/base/langflow/components/experimental/SplitText.py
@@ -1,49 +1,69 @@
-from typing import Optional
+from typing import List
 
-from langflow.custom import CustomComponent
-from langflow.field_typing import Text
-from langflow.schema import Record
+from langchain_text_splitters import CharacterTextSplitter
+from langflow.custom import Component
+from langflow.io import HandleInput, IntInput, Output, TextInput
+from langflow.schema import Data
 from langflow.utils.util import unescape_string
 
 
-class SplitTextComponent(CustomComponent):
+class SplitTextComponent(Component):
     display_name: str = "Split Text"
-    description: str = "Split text into chunks of a specified length."
+    description: str = "Split text into chunks based on specified criteria."
+    icon = "scissors-line-dashed"
 
-    def build_config(self):
-        return {
-            "inputs": {
-                "display_name": "Inputs",
-                "info": "Texts to split.",
-                "input_types": ["Record", "Text"],
-            },
-            "separator": {
-                "display_name": "Separator",
-                "info": 'The character to split on. Defaults to " ".',
-            },
-            "truncate_size": {
-                "display_name": "Truncate Size",
-                "info": "The maximum length (in number of characters) of each chunk to keep. Defaults to 0 (no truncation).",
-            },
-        }
+    inputs = [
+        HandleInput(
+            name="data_inputs",
+            display_name="Data Inputs",
+            info="The data to split.",
+            input_types=["Data"],
+            is_list=True,
+        ),
+        IntInput(
+            name="chunk_overlap",
+            display_name="Chunk Overlap",
+            info="Number of characters to overlap between chunks.",
+            value=200,
+        ),
+        IntInput(
+            name="chunk_size",
+            display_name="Chunk Size",
+            info="The maximum number of characters in each chunk.",
+            value=1000,
+        ),
+        TextInput(
+            name="separator",
+            display_name="Separator",
+            info="The character to split on. Defaults to newline.",
+            value="\n",
+        ),
+    ]
 
-    def build(
-        self,
-        inputs: list[Text],
-        separator: str = " ",
-        truncate_size: Optional[int] = 0,
-    ) -> list[Record]:
-        separator = unescape_string(separator)
+    outputs = [
+        Output(display_name="Chunks", name="chunks", method="split_text"),
+    ]
 
-        outputs = []
-        for text in inputs:
-            chunks = text.split(separator)
+    def _docs_to_data(self, docs):
+        data = []
+        for doc in docs:
+            data.append(Data(text=doc.page_content, data=doc.metadata))
+        return data
 
-            if truncate_size:
-                chunks = [chunk[:truncate_size] for chunk in chunks]
+    def split_text(self) -> List[Data]:
+        separator = unescape_string(self.separator)
 
-            for chunk in chunks:
-                outputs.append(Record(data={"parent": text, "text": chunk}))
+        documents = []
+        for _input in self.data_inputs:
+            if isinstance(_input, Data):
+                documents.append(_input.to_lc_document())
 
-        self.status = outputs
-        return outputs
+        splitter = CharacterTextSplitter(
+            chunk_overlap=self.chunk_overlap,
+            chunk_size=self.chunk_size,
+            separator=separator,
+        )
+        docs = splitter.split_documents(documents)
+        data = self._docs_to_data(docs)
+        self.status = data
+        return data
diff --git a/src/backend/base/langflow/components/experimental/SubFlow.py b/src/backend/base/langflow/components/experimental/SubFlow.py
index 76a9538a4..b0631ee99 100644
--- a/src/backend/base/langflow/components/experimental/SubFlow.py
+++ b/src/backend/base/langflow/components/experimental/SubFlow.py
@@ -1,33 +1,34 @@
 from typing import Any, List, Optional
 
-from loguru import logger
-
-from langflow.base.flow_processing.utils import build_records_from_result_data
+from langflow.base.flow_processing.utils import build_data_from_result_data
 from langflow.custom import CustomComponent
 from langflow.graph.graph.base import Graph
 from langflow.graph.schema import RunOutputs
 from langflow.graph.vertex.base import Vertex
 from langflow.helpers.flow import get_flow_inputs
-from langflow.schema import Record
+from langflow.schema import Data
 from langflow.schema.dotdict import dotdict
-from langflow.template.field.base import TemplateField
+from langflow.template.field.base import Input
+from loguru import logger
 
 
 class SubFlowComponent(CustomComponent):
     display_name = "Sub Flow"
-    description = "Dynamically Generates a Component from a Flow. The output is a list of records with keys 'result' and 'message'."
+    description = (
+        "Dynamically Generates a Component from a Flow. The output is a list of data with keys 'result' and 'message'."
+    )
     beta: bool = True
     field_order = ["flow_name"]
 
     def get_flow_names(self) -> List[str]:
-        flow_records = self.list_flows()
-        return [flow_record.data["name"] for flow_record in flow_records]
+        flow_datas = self.list_flows()
+        return [flow_data.data["name"] for flow_data in flow_datas]
 
-    def get_flow(self, flow_name: str) -> Optional[Record]:
-        flow_records = self.list_flows()
-        for flow_record in flow_records:
-            if flow_record.data["name"] == flow_name:
-                return flow_record
+    def get_flow(self, flow_name: str) -> Optional[Data]:
+        flow_datas = self.list_flows()
+        for flow_data in flow_datas:
+            if flow_data.data["name"] == flow_name:
+                return flow_data
         return None
 
     def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):
@@ -40,10 +41,10 @@ class SubFlowComponent(CustomComponent):
                 del build_config[key]
         if field_value is not None and field_name == "flow_name":
             try:
-                flow_record = self.get_flow(field_value)
-                if not flow_record:
+                flow_data = self.get_flow(field_value)
+                if not flow_data:
                     raise ValueError(f"Flow {field_value} not found.")
-                graph = Graph.from_payload(flow_record.data["data"])
+                graph = Graph.from_payload(flow_data.data["data"])
                 # Get all inputs from the graph
                 inputs = get_flow_inputs(graph)
                 # Add inputs to the build config
@@ -54,14 +55,14 @@ class SubFlowComponent(CustomComponent):
         return build_config
 
     def add_inputs_to_build_config(self, inputs: List[Vertex], build_config: dotdict):
-        new_fields: list[TemplateField] = []
+        new_fields: list[Input] = []
         for vertex in inputs:
-            field = TemplateField(
+            field = Input(
                 display_name=vertex.display_name,
                 name=vertex.id,
                 info=vertex.description,
                 field_type="str",
-                default=None,
+                value=None,
             )
             new_fields.append(field)
         logger.debug(new_fields)
@@ -93,7 +94,7 @@ class SubFlowComponent(CustomComponent):
             },
         }
 
-    async def build(self, flow_name: str, get_final_results_only: bool = True, **kwargs) -> List[Record]:
+    async def build(self, flow_name: str, get_final_results_only: bool = True, **kwargs) -> List[Data]:
         tweaks = {key: {"input_value": value} for key, value in kwargs.items()}
         run_outputs: List[Optional[RunOutputs]] = await self.run_flow(
             tweaks=tweaks,
@@ -103,12 +104,12 @@ class SubFlowComponent(CustomComponent):
             return []
         run_output = run_outputs[0]
 
-        records = []
+        data = []
         if run_output is not None:
             for output in run_output.outputs:
                 if output:
-                    records.extend(build_records_from_result_data(output, get_final_results_only))
+                    data.extend(build_data_from_result_data(output, get_final_results_only))
 
-        self.status = records
-        logger.debug(records)
-        return records
+        self.status = data
+        logger.debug(data)
+        return data
diff --git a/src/backend/base/langflow/components/experimental/TextOperator.py b/src/backend/base/langflow/components/experimental/TextOperator.py
deleted file mode 100644
index ea79e92e7..000000000
--- a/src/backend/base/langflow/components/experimental/TextOperator.py
+++ /dev/null
@@ -1,76 +0,0 @@
-from typing import Optional, Union
-
-from langflow.custom import CustomComponent
-from langflow.field_typing import Text
-from langflow.schema import Record
-
-
-class TextOperatorComponent(CustomComponent):
-    display_name = "Text Operator"
-    description = "Compares two text inputs based on a specified condition such as equality or inequality, with optional case sensitivity."
-
-    def build_config(self) -> dict:
-        return {
-            "input_text": {
-                "display_name": "Input Text",
-                "info": "The primary text input for the operation.",
-            },
-            "match_text": {
-                "display_name": "Match Text",
-                "info": "The text input to compare against.",
-            },
-            "operator": {
-                "display_name": "Operator",
-                "info": "The operator to apply for comparing the texts.",
-                "options": ["equals", "not equals", "contains", "starts with", "ends with", "exists"],
-            },
-            "case_sensitive": {
-                "display_name": "Case Sensitive",
-                "info": "If true, the comparison will be case sensitive.",
-                "field_type": "bool",
-                "default": False,
-            },
-            "true_output": {
-                "display_name": "Output",
-                "info": "The output to return or display when the comparison is true.",
-                "input_types": ["Text", "Record"],  # Allow both text and record types
-            },
-        }
-
-    def build(
-        self,
-        input_text: Text,
-        match_text: Text,
-        operator: Text,
-        case_sensitive: bool = False,
-        true_output: Optional[Text] = "",
-    ) -> Union[Text, Record]:
-        if not input_text or not match_text:
-            raise ValueError("Both 'input_text' and 'match_text' must be provided and non-empty.")
-
-        if not case_sensitive:
-            input_text = input_text.lower()
-            match_text = match_text.lower()
-
-        result = False
-        if operator == "equals":
-            result = input_text == match_text
-        elif operator == "not equals":
-            result = input_text != match_text
-        elif operator == "contains":
-            result = match_text in input_text
-        elif operator == "starts with":
-            result = input_text.startswith(match_text)
-        elif operator == "ends with":
-            result = input_text.endswith(match_text)
-
-        output_record = true_output if true_output else input_text
-
-        if result:
-            self.status = output_record
-            return output_record
-        else:
-            self.status = "Comparison failed, stopping execution."
-            self.stop()
-
-        return output_record
diff --git a/src/backend/base/langflow/components/experimental/__init__.py b/src/backend/base/langflow/components/experimental/__init__.py
index a8e83125c..15c0806db 100644
--- a/src/backend/base/langflow/components/experimental/__init__.py
+++ b/src/backend/base/langflow/components/experimental/__init__.py
@@ -1,30 +1,36 @@
+from .AgentComponent import AgentComponent
 from .ClearMessageHistory import ClearMessageHistoryComponent
-from .ExtractDataFromRecord import ExtractKeyFromRecordComponent
+from .ExtractKeyFromData import ExtractKeyFromDataComponent
 from .FlowTool import FlowToolComponent
-from .ListFlows import ListFlowsComponent
 from .Listen import ListenComponent
-from .MergeRecords import MergeRecordsComponent
+from .ListFlows import ListFlowsComponent
+from .MergeData import MergeDataComponent
 from .Notify import NotifyComponent
 from .PythonFunction import PythonFunctionComponent
 from .RunFlow import RunFlowComponent
 from .RunnableExecutor import RunnableExecComponent
+from .SplitText import SplitTextComponent
 from .SQLExecutor import SQLExecutorComponent
 from .SubFlow import SubFlowComponent
-from .AgentComponent import AgentComponent
+from .ConditionalRouter import ConditionalRouterComponent
+from .SelectivePassThrough import SelectivePassThroughComponent
+
 
 __all__ = [
     "AgentComponent",
     "ClearMessageHistoryComponent",
-    "ExtractKeyFromRecordComponent",
+    "ConditionalRouterComponent",
+    "ExtractKeyFromDataComponent",
     "FlowToolComponent",
-    "ListFlowsComponent",
     "ListenComponent",
-    "MergeRecordsComponent",
+    "ListFlowsComponent",
+    "MergeDataComponent",
     "NotifyComponent",
     "PythonFunctionComponent",
     "RunFlowComponent",
     "RunnableExecComponent",
+    "SplitTextComponent",
     "SQLExecutorComponent",
     "SubFlowComponent",
-    "PythonFunctionComponent",
+    "SelectivePassThroughComponent",
 ]
diff --git a/src/backend/base/langflow/components/helpers/CodeBlockExtractor.py b/src/backend/base/langflow/components/helpers/CodeBlockExtractor.py
new file mode 100644
index 000000000..316ed8a95
--- /dev/null
+++ b/src/backend/base/langflow/components/helpers/CodeBlockExtractor.py
@@ -0,0 +1,25 @@
+import re
+
+from langflow.custom import Component
+from langflow.field_typing import Input, Output, Text
+
+
+class CodeBlockExtractor(Component):
+    display_name = "Code Block Extractor"
+    description = "Extracts code block from text."
+
+    inputs = [Input(name="text", field_type=Text, description="Text to extract code blocks from.")]
+
+    outputs = [Output(name="code_block", display_name="Code Block", method="get_code_block")]
+
+    def get_code_block(self) -> Text:
+        text = self.text.strip()
+        # Extract code block
+        # It may start with ``` or ```language
+        # It may end with ```
+        pattern = r"^```(?:\w+)?\s*\n(.*?)(?=^```)```"
+        match = re.search(pattern, text, re.MULTILINE)
+        code_block = ""
+        if match:
+            code_block = match.group(1)
+        return code_block
diff --git a/src/backend/base/langflow/components/helpers/CombineText.py b/src/backend/base/langflow/components/helpers/CombineText.py
index bedc4293d..cb7ff8d04 100644
--- a/src/backend/base/langflow/components/helpers/CombineText.py
+++ b/src/backend/base/langflow/components/helpers/CombineText.py
@@ -1,29 +1,37 @@
-from langflow.custom import CustomComponent
-from langflow.field_typing import Text
+from langflow.custom import Component
+from langflow.io import Output, TextInput
+from langflow.schema.message import Message
 
 
-class CombineTextComponent(CustomComponent):
+class CombineTextComponent(Component):
     display_name = "Combine Text"
     description = "Concatenate two text sources into a single text chunk using a specified delimiter."
     icon = "merge"
 
-    def build_config(self):
-        return {
-            "text1": {
-                "display_name": "First Text",
-                "info": "The first text input to concatenate.",
-            },
-            "text2": {
-                "display_name": "Second Text",
-                "info": "The second text input to concatenate.",
-            },
-            "delimiter": {
-                "display_name": "Delimiter",
-                "info": "A string used to separate the two text inputs. Defaults to a whitespace.",
-            },
-        }
+    inputs = [
+        TextInput(
+            name="text1",
+            display_name="First Text",
+            info="The first text input to concatenate.",
+        ),
+        TextInput(
+            name="text2",
+            display_name="Second Text",
+            info="The second text input to concatenate.",
+        ),
+        TextInput(
+            name="delimiter",
+            display_name="Delimiter",
+            info="A string used to separate the two text inputs. Defaults to a whitespace.",
+            value=" ",
+        ),
+    ]
 
-    def build(self, text1: str, text2: str, delimiter: str = " ") -> Text:
-        combined = delimiter.join([text1, text2])
+    outputs = [
+        Output(display_name="Combined Text", name="combined_text", method="combine_texts"),
+    ]
+
+    def combine_texts(self) -> Message:
+        combined = self.delimiter.join([self.text1, self.text2])
         self.status = combined
-        return combined
+        return Message(text=combined)
diff --git a/src/backend/base/langflow/components/helpers/CombineTextsUnsorted.py b/src/backend/base/langflow/components/helpers/CombineTextsUnsorted.py
deleted file mode 100644
index 67d315739..000000000
--- a/src/backend/base/langflow/components/helpers/CombineTextsUnsorted.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from langflow.custom import CustomComponent
-from langflow.field_typing import Text
-
-
-class CombineTextsUnsortedComponent(CustomComponent):
-    display_name = "Combine Texts (Unsorted)"
-    description = "Concatenate text sources into a single text chunk using a specified delimiter."
-    icon = "merge"
-
-    def build_config(self):
-        return {
-            "texts": {
-                "display_name": "Texts",
-                "info": "The first text input to concatenate.",
-            },
-            "delimiter": {
-                "display_name": "Delimiter",
-                "info": "A string used to separate the two text inputs. Defaults to a whitespace.",
-            },
-        }
-
-    def build(self, texts: list[str], delimiter: str = " ") -> Text:
-        combined = delimiter.join(texts)
-        self.status = combined
-        return combined
diff --git a/src/backend/base/langflow/components/helpers/CreateRecord.py b/src/backend/base/langflow/components/helpers/CreateData.py
similarity index 77%
rename from src/backend/base/langflow/components/helpers/CreateRecord.py
rename to src/backend/base/langflow/components/helpers/CreateData.py
index a4a02e76b..c39199f4c 100644
--- a/src/backend/base/langflow/components/helpers/CreateRecord.py
+++ b/src/backend/base/langflow/components/helpers/CreateData.py
@@ -2,14 +2,14 @@ from typing import Any
 
 from langflow.custom import CustomComponent
 from langflow.field_typing.range_spec import RangeSpec
-from langflow.schema import Record
+from langflow.schema import Data
 from langflow.schema.dotdict import dotdict
-from langflow.template.field.base import TemplateField
+from langflow.template.field.base import Input
 
 
-class CreateRecordComponent(CustomComponent):
-    display_name = "Create Record"
-    description = "Dynamically create a Record with a specified number of fields."
+class CreateDataComponent(CustomComponent):
+    display_name = "Create Data"
+    description = "Dynamically create a Data with a specified number of fields."
     field_order = ["number_of_fields", "text_key"]
 
     def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None):
@@ -22,7 +22,7 @@ class CreateRecordComponent(CustomComponent):
             existing_fields = {}
             if field_value_int > 15:
                 build_config["number_of_fields"]["value"] = 15
-                raise ValueError("Number of fields cannot exceed 15. Try using a Component to combine two Records.")
+                raise ValueError("Number of fields cannot exceed 15. Try using a Component to combine two Data.")
             if len(build_config) > len(default_keys) + field_value_int:
                 # back up the existing template fields
                 for key in build_config.copy():
@@ -35,12 +35,12 @@ class CreateRecordComponent(CustomComponent):
                     field = existing_fields[key]
                     build_config[key] = field
                 else:
-                    field = TemplateField(
+                    field = Input(
                         display_name=f"Field {i}",
                         name=key,
                         info=f"Key for field {i}.",
                         field_type="dict",
-                        input_types=["Text", "Record"],
+                        input_types=["Text", "Data"],
                     )
                     build_config[field.name] = field.to_dict()
 
@@ -67,15 +67,15 @@ class CreateRecordComponent(CustomComponent):
         number_of_fields: int = 0,
         text_key: str = "text",
         **kwargs,
-    ) -> Record:
+    ) -> Data:
         data = {}
         for value_dict in kwargs.values():
             if isinstance(value_dict, dict):
-                # Check if the value of the value_dict is a Record
+                # Check if the value of the value_dict is a Data
                 value_dict = {
-                    key: value.get_text() if isinstance(value, Record) else value for key, value in value_dict.items()
+                    key: value.get_text() if isinstance(value, Data) else value for key, value in value_dict.items()
                 }
                 data.update(value_dict)
-        return_record = Record(data=data, text_key=text_key)
-        self.status = return_record
-        return return_record
+        return_data = Data(data=data, text_key=text_key)
+        self.status = return_data
+        return return_data
diff --git a/src/backend/base/langflow/components/helpers/CustomComponent.py b/src/backend/base/langflow/components/helpers/CustomComponent.py
index 7313323a9..791932c2d 100644
--- a/src/backend/base/langflow/components/helpers/CustomComponent.py
+++ b/src/backend/base/langflow/components/helpers/CustomComponent.py
@@ -1,16 +1,24 @@
 # from langflow.field_typing import Data
-from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langflow.custom import Component
+from langflow.io import Output, TextInput
+from langflow.schema import Data
 
 
-class Component(CustomComponent):
+class CustomComponent(Component):
     display_name = "Custom Component"
     description = "Use as a template to create your own component."
     documentation: str = "http://docs.langflow.org/components/custom"
     icon = "custom_components"
 
-    def build_config(self):
-        return {"param": {"display_name": "Parameter"}}
+    inputs = [
+        TextInput(name="input_value", display_name="Input Value", value="Hello, World!"),
+    ]
 
-    def build(self, param: str) -> Record:
-        return Record(data=param)
+    outputs = [
+        Output(display_name="Output", name="output", method="build_output"),
+    ]
+
+    def build_output(self) -> Data:
+        data = Data(value=self.input_value)
+        self.status = data
+        return data
diff --git a/src/backend/base/langflow/components/helpers/DocumentToRecord.py b/src/backend/base/langflow/components/helpers/DocumentToRecord.py
deleted file mode 100644
index 5adaf7ab4..000000000
--- a/src/backend/base/langflow/components/helpers/DocumentToRecord.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from typing import List
-
-from langchain_core.documents import Document
-
-from langflow.custom import CustomComponent
-from langflow.schema import Record
-
-
-class DocumentToRecordComponent(CustomComponent):
-    display_name = "Documents To Records"
-    description = "Convert LangChain Documents into Records."
-
-    field_config = {
-        "documents": {"display_name": "Documents"},
-    }
-
-    def build(self, documents: List[Document]) -> List[Record]:
-        if isinstance(documents, Document):
-            documents = [documents]
-        records = [Record.from_document(document) for document in documents]
-        self.status = records
-        return records
diff --git a/src/backend/base/langflow/components/helpers/DocumentsToData.py b/src/backend/base/langflow/components/helpers/DocumentsToData.py
new file mode 100644
index 000000000..13111db59
--- /dev/null
+++ b/src/backend/base/langflow/components/helpers/DocumentsToData.py
@@ -0,0 +1,23 @@
+from typing import List
+
+from langchain_core.documents import Document
+
+from langflow.custom import CustomComponent
+from langflow.schema import Data
+
+
+class DocumentsToDataComponent(CustomComponent):
+    display_name = "Documents ⇢ Data"
+    description = "Convert LangChain Documents into Data."
+    icon = "LangChain"
+
+    field_config = {
+        "documents": {"display_name": "Documents"},
+    }
+
+    def build(self, documents: List[Document]) -> List[Data]:
+        if isinstance(documents, Document):
+            documents = [documents]
+        data = [Data.from_document(document) for document in documents]
+        self.status = data
+        return data
diff --git a/src/backend/base/langflow/components/helpers/FilterData.py b/src/backend/base/langflow/components/helpers/FilterData.py
new file mode 100644
index 000000000..71e91e5f4
--- /dev/null
+++ b/src/backend/base/langflow/components/helpers/FilterData.py
@@ -0,0 +1,41 @@
+from typing import List
+
+from langflow.custom import Component
+from langflow.io import DataInput, Output, TextInput
+from langflow.schema import Data
+
+
+class FilterDataComponent(Component):
+    display_name = "Filter Data"
+    description = "Filters a Data object based on a list of keys."
+    icon = "filter"
+
+    inputs = [
+        DataInput(
+            name="data",
+            display_name="Data",
+            info="Data object to filter.",
+        ),
+        TextInput(
+            name="filter_criteria",
+            display_name="Filter Criteria",
+            info="List of keys to filter by.",
+            is_list=True,
+        ),
+    ]
+
+    outputs = [
+        Output(display_name="Filtered Data", name="filtered_data", method="filter_data"),
+    ]
+
+    def filter_data(self) -> Data:
+        filter_criteria: List[str] = self.filter_criteria
+        data = self.data.data if isinstance(self.data, Data) else {}
+
+        # Filter the data
+        filtered = {key: value for key, value in data.items() if key in filter_criteria}
+
+        # Create a new Data object with the filtered data
+        filtered_data = Data(data=filtered)
+        self.status = filtered_data
+        return filtered_data
diff --git a/src/backend/base/langflow/components/helpers/Memory.py b/src/backend/base/langflow/components/helpers/Memory.py
new file mode 100644
index 000000000..0d73d0499
--- /dev/null
+++ b/src/backend/base/langflow/components/helpers/Memory.py
@@ -0,0 +1,87 @@
+from langflow.custom import Component
+from langflow.helpers.data import data_to_text
+from langflow.io import DropdownInput, IntInput, MultilineInput, Output, TextInput
+from langflow.memory import get_messages
+from langflow.schema import Data
+from langflow.schema.message import Message
+
+
+class MemoryComponent(Component):
+    display_name = "Chat Memory"
+    description = "Retrieves stored chat messages."
+    icon = "message-square-more"
+
+    inputs = [
+        DropdownInput(
+            name="sender",
+            display_name="Sender Type",
+            options=["Machine", "User", "Machine and User"],
+            value="Machine and User",
+            info="Type of sender.",
+            advanced=True,
+        ),
+        TextInput(
+            name="sender_name",
+            display_name="Sender Name",
+            info="Name of the sender.",
+            advanced=True,
+        ),
+        IntInput(
+            name="n_messages",
+            display_name="Number of Messages",
+            value=100,
+            info="Number of messages to retrieve.",
+            advanced=True,
+        ),
+        TextInput(
+            name="session_id",
+            display_name="Session ID",
+            info="Session ID of the chat history.",
+            advanced=True,
+        ),
+        DropdownInput(
+            name="order",
+            display_name="Order",
+            options=["Ascending", "Descending"],
+            value="Ascending",
+            info="Order of the messages.",
+            advanced=True,
+        ),
+        MultilineInput(
+            name="template",
+            display_name="Template",
+            info="The template to use for formatting the data. It can contain the keys {text}, {sender} or any other key in the message data.",
+            value="{sender_name}: {text}",
+            advanced=True,
+        ),
+    ]
+
+    outputs = [
+        Output(display_name="Chat History", name="messages", method="retrieve_messages"),
+        Output(display_name="Messages (Text)", name="messages_text", method="retrieve_messages_as_text"),
+    ]
+
+    def retrieve_messages(self) -> Data:
+        sender = self.sender
+        sender_name = self.sender_name
+        session_id = self.session_id
+        n_messages = self.n_messages
+        order = "DESC" if self.order == "Descending" else "ASC"
+
+        if sender == "Machine and User":
+            sender = None
+
+        messages = get_messages(
+            sender=sender,
+            sender_name=sender_name,
+            session_id=session_id,
+            limit=n_messages,
+            order=order,
+        )
+        self.status = messages
+        return messages
+
+    def retrieve_messages_as_text(self) -> Message:
+        messages_text = data_to_text(self.template, self.retrieve_messages())
+        self.status = messages_text
+        return Message(text=messages_text)
diff --git a/src/backend/base/langflow/components/helpers/MemoryComponent.py b/src/backend/base/langflow/components/helpers/MemoryComponent.py
deleted file mode 100644
index 235370bee..000000000
--- a/src/backend/base/langflow/components/helpers/MemoryComponent.py
+++ /dev/null
@@ -1,82 +0,0 @@
-from typing import Optional
-
-from langflow.base.memory.memory import BaseMemoryComponent
-from langflow.field_typing import Text
-from langflow.helpers.record import messages_to_text
-from langflow.memory import get_messages
-from langflow.schema.message import Message
-
-
-class MemoryComponent(BaseMemoryComponent):
-    display_name = "Chat Memory"
-    description = "Retrieves stored chat messages given a specific Session ID."
-    beta: bool = True
-    icon = "history"
-
-    def build_config(self):
-        return {
-            "sender": {
-                "options": ["Machine", "User", "Machine and User"],
-                "display_name": "Sender Type",
-            },
-            "sender_name": {"display_name": "Sender Name", "advanced": True},
-            "n_messages": {
-                "display_name": "Number of Messages",
-                "info": "Number of messages to retrieve.",
-            },
-            "session_id": {
-                "display_name": "Session ID",
-                "info": "Session ID of the chat history.",
-                "input_types": ["Text"],
-            },
-            "order": {
-                "options": ["Ascending", "Descending"],
-                "display_name": "Order",
-                "info": "Order of the messages.",
-                "advanced": True,
-            },
-            "record_template": {
-                "display_name": "Record Template",
-                "multiline": True,
-                "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
-                "advanced": True,
-            },
-        }
-
-    def get_messages(self, **kwargs) -> list[Message]:  # type: ignore
-        # Validate kwargs by checking if it contains the correct keys
-        if "sender" not in kwargs:
-            kwargs["sender"] = None
-        if "sender_name" not in kwargs:
-            kwargs["sender_name"] = None
-        if "session_id" not in kwargs:
-            kwargs["session_id"] = None
-        if "limit" not in kwargs:
-            kwargs["limit"] = 5
-        if "order" not in kwargs:
-            kwargs["order"] = "Descending"
-
-        kwargs["order"] = "DESC" if kwargs["order"] == "Descending" else "ASC"
-        if kwargs["sender"] == "Machine and User":
-            kwargs["sender"] = None
-        return get_messages(**kwargs)
-
-    def build(
-        self,
-        sender: Optional[str] = "Machine and User",
-        sender_name: Optional[str] = None,
-        session_id: Optional[str] = None,
-        n_messages: int = 5,
-        order: Optional[str] = "Descending",
-        record_template: Optional[str] = "{sender_name}: {text}",
-    ) -> Text:
-        messages = self.get_messages(
-            sender=sender,
-            sender_name=sender_name,
-            session_id=session_id,
-            limit=n_messages,
-            order=order,
-        )
-        messages_str = messages_to_text(template=record_template or "", messages=messages)
-        self.status = messages_str
-        return messages_str
diff --git a/src/backend/base/langflow/components/helpers/MessageHistory.py b/src/backend/base/langflow/components/helpers/MessageHistory.py
deleted file mode 100644
index 90191e7d0..000000000
--- a/src/backend/base/langflow/components/helpers/MessageHistory.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from typing import List, Optional
-
-from langflow.custom import CustomComponent
-from langflow.memory import get_messages
-from langflow.schema import Record
-
-
-class MessageHistoryComponent(CustomComponent):
-    display_name = "Memory"
-    description = "Retrieves stored chat messages."
-
-    def build_config(self):
-        return {
-            "sender": {
-                "options": ["Machine", "User", "Machine and User"],
-                "display_name": "Sender Type",
-                "advanced": True,
-            },
-            "sender_name": {"display_name": "Sender Name", "advanced": True},
-            "n_messages": {
-                "display_name": "Number of Messages",
-                "info": "Number of messages to retrieve.",
-                "advanced": True,
-            },
-            "session_id": {
-                "display_name": "Session ID",
-                "info": "Session ID of the chat history.",
-                "input_types": ["Text"],
-                "advanced": True,
-            },
-            "order": {
-                "options": ["Ascending", "Descending"],
-                "display_name": "Order",
-                "info": "Order of the messages.",
-                "advanced": True,
-            },
-        }
-
-    def build(
-        self,
-        sender: Optional[str] = "Machine and User",
-        sender_name: Optional[str] = None,
-        session_id: Optional[str] = None,
-        n_messages: int = 100,
-        order: Optional[str] = "Descending",
-    ) -> List[Record]:
-        order = "DESC" if order == "Descending" else "ASC"
-        if sender == "Machine and User":
-            sender = None
-        messages = get_messages(
-            sender=sender,
-            sender_name=sender_name,
-            session_id=session_id,
-            limit=n_messages,
-            order=order,
-        )
-        self.status = messages
-        return messages
diff --git a/src/backend/base/langflow/components/helpers/ParseData.py b/src/backend/base/langflow/components/helpers/ParseData.py
new file mode 100644
index 000000000..32482bcdb
--- /dev/null
+++ b/src/backend/base/langflow/components/helpers/ParseData.py
@@ -0,0 +1,33 @@
+from langflow.custom import Component
+from langflow.helpers.data import data_to_text
+from langflow.io import DataInput, MultilineInput, Output, StrInput
+from langflow.schema.message import Message
+
+
+class ParseDataComponent(Component):
+    display_name = "Parse Data"
+    description = "Convert Data into plain text following a specified template."
+    icon = "braces"
+
+    inputs = [
+        DataInput(name="data", display_name="Data", info="The data to convert to text."),
+        MultilineInput(
+            name="template",
+            display_name="Template",
+            info="The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.",
+            value="{text}",
+        ),
+        StrInput(name="sep", display_name="Separator", advanced=True, value="\n"),
+    ]
+
+    outputs = [
+        Output(display_name="Text", name="text", method="parse_data"),
+    ]
+
+    def parse_data(self) -> Message:
+        data = self.data if isinstance(self.data, list) else [self.data]
+        template = self.template
+
+        result_string = data_to_text(template, data, sep=self.sep)
+        self.status = result_string
+        return Message(text=result_string)
diff --git a/src/backend/base/langflow/components/helpers/RecordsToText.py b/src/backend/base/langflow/components/helpers/RecordsToText.py
deleted file mode 100644
index 049c99243..000000000
--- a/src/backend/base/langflow/components/helpers/RecordsToText.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from langflow.custom import CustomComponent
-from langflow.field_typing import Text
-from langflow.helpers.record import records_to_text
-from langflow.schema import Record
-
-
-class RecordsToTextComponent(CustomComponent):
-    display_name = "Records To Text"
-    description = "Convert Records into plain text following a specified template."
-
-    def build_config(self):
-        return {
-            "records": {
-                "display_name": "Records",
-                "info": "The records to convert to text.",
-            },
-            "template": {
-                "display_name": "Template",
-                "info": "The template to use for formatting the records. It can contain the keys {text}, {data} or any other key in the Record.",
-                "multiline": True,
-            },
-        }
-
-    def build(
-        self,
-        records: list[Record],
-        template: str = "Text: {text}\nData: {data}",
-    ) -> Text:
-        if not records:
-            return ""
-        if isinstance(records, Record):
-            records = [records]
-
-        result_string = records_to_text(template, records)
-        self.status = result_string
-        return result_string
diff --git a/src/backend/base/langflow/components/helpers/ShouldRunNext.py b/src/backend/base/langflow/components/helpers/ShouldRunNext.py
index 0d20706ea..7ca5651a7 100644
--- a/src/backend/base/langflow/components/helpers/ShouldRunNext.py
+++ b/src/backend/base/langflow/components/helpers/ShouldRunNext.py
@@ -2,14 +2,14 @@ from langchain_core.messages import BaseMessage
 from langchain_core.prompts import PromptTemplate
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, Text
+from langflow.field_typing import LanguageModel, Text
 
 
 class ShouldRunNextComponent(CustomComponent):
     display_name = "Should Run Next"
     description = "Determines if a vertex is runnable."
 
-    def build(self, llm: BaseLanguageModel, question: str, context: str, retries: int = 3) -> Text:
+    def build(self, llm: LanguageModel, question: str, context: str, retries: int = 3) -> Text:
         template = "Given the following question and the context below, answer with a yes or no.\n\n{error_message}\n\nQuestion: {question}\n\nContext: {context}\n\nAnswer:"
 
         prompt = PromptTemplate.from_template(template)
diff --git a/src/backend/base/langflow/components/helpers/UpdateRecord.py b/src/backend/base/langflow/components/helpers/UpdateData.py
similarity index 53%
rename from src/backend/base/langflow/components/helpers/UpdateRecord.py
rename to src/backend/base/langflow/components/helpers/UpdateData.py
index e3153d6d7..eebd35ec8 100644
--- a/src/backend/base/langflow/components/helpers/UpdateRecord.py
+++ b/src/backend/base/langflow/components/helpers/UpdateData.py
@@ -1,15 +1,15 @@
 from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langflow.schema import Data
 
 
-class UpdateRecordComponent(CustomComponent):
-    display_name = "Update Record"
-    description = "Update Record with text-based key/value pairs, similar to updating a Python dictionary."
+class UpdateDataComponent(CustomComponent):
+    display_name = "Update Data"
+    description = "Update Data with text-based key/value pairs, similar to updating a Python dictionary."
 
     def build_config(self):
         return {
-            "record": {
-                "display_name": "Record",
+            "data": {
+                "display_name": "Data",
                 "info": "The record to update.",
             },
             "new_data": {
@@ -21,19 +21,19 @@ class UpdateRecordComponent(CustomComponent):
 
     def build(
         self,
-        record: Record,
+        data: Data,
         new_data: dict,
-    ) -> Record:
+    ) -> Data:
         """
         Updates a record with new data.
 
         Args:
-            record (Record): The record to update.
+            record (Data): The record to update.
             new_data (dict): The new data to update the record with.
 
         Returns:
-            Record: The updated record.
+            Data: The updated record.
         """
-        record.data.update(new_data)
-        self.status = record
-        return record
+        data.data.update(new_data)
+        self.status = data
+        return data
diff --git a/src/backend/base/langflow/components/helpers/__init__.py b/src/backend/base/langflow/components/helpers/__init__.py
index af3524d8e..1337aebd4 100644
--- a/src/backend/base/langflow/components/helpers/__init__.py
+++ b/src/backend/base/langflow/components/helpers/__init__.py
@@ -1,17 +1,15 @@
-from .CreateRecord import CreateRecordComponent
+from .CreateData import CreateDataComponent
 from .CustomComponent import Component
-from .DocumentToRecord import DocumentToRecordComponent
+from .ParseData import ParseDataComponent
+from .DocumentsToData import DocumentsToDataComponent
 from .IDGenerator import UUIDGeneratorComponent
-from .MessageHistory import MessageHistoryComponent
-from .UpdateRecord import UpdateRecordComponent
-from .RecordsToText import RecordsToTextComponent
+from .UpdateData import UpdateDataComponent
 
 __all__ = [
     "Component",
-    "UpdateRecordComponent",
-    "DocumentToRecordComponent",
+    "UpdateDataComponent",
+    "DocumentsToDataComponent",
     "UUIDGeneratorComponent",
-    "RecordsToTextComponent",
-    "CreateRecordComponent",
-    "MessageHistoryComponent",
+    "ParseDataComponent",
+    "CreateDataComponent",
 ]
diff --git a/src/backend/base/langflow/components/inputs/ChatInput.py b/src/backend/base/langflow/components/inputs/ChatInput.py
index 88dc73b2c..847b6809a 100644
--- a/src/backend/base/langflow/components/inputs/ChatInput.py
+++ b/src/backend/base/langflow/components/inputs/ChatInput.py
@@ -1,9 +1,7 @@
-from typing import Optional
-
+from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES
 from langflow.base.io.chat import ChatComponent
+from langflow.io import DropdownInput, FileInput, MultilineInput, Output, TextInput
 from langflow.schema.message import Message
-from langflow.field_typing import Text
-from typing import Union
 
 
 class ChatInput(ChatComponent):
@@ -11,34 +9,53 @@ class ChatInput(ChatComponent):
     description = "Get chat inputs from the Playground."
     icon = "ChatInput"
 
-    def build_config(self):
-        build_config = super().build_config()
-        build_config["input_value"] = {
-            "input_types": [],
-            "display_name": "Text",
-            "multiline": True,
-        }
-        build_config["return_message"] = {
-            "display_name": "Return Record",
-            "advanced": True,
-        }
+    inputs = [
+        MultilineInput(
+            name="input_value",
+            display_name="Text",
+            value="",
+            info="Message to be passed as input.",
+        ),
+        DropdownInput(
+            name="sender",
+            display_name="Sender Type",
+            options=["Machine", "User"],
+            value="User",
+            info="Type of sender.",
+            advanced=True,
+        ),
+        TextInput(
+            name="sender_name",
+            display_name="Sender Name",
+            info="Name of the sender.",
+            value="User",
+            advanced=True,
+        ),
+        TextInput(name="session_id", display_name="Session ID", info="Session ID for the message.", advanced=True),
+        FileInput(
+            name="files",
+            display_name="Files",
+            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,
+            info="Files to be sent with the message.",
+            advanced=True,
+            is_list=True,
+        ),
+    ]
+    outputs = [
+        Output(display_name="Message", name="message", method="message_response"),
+    ]
 
-        return build_config
-
-    def build(
-        self,
-        sender: Optional[str] = "User",
-        sender_name: Optional[str] = "User",
-        input_value: Optional[str] = None,
-        files: Optional[list[str]] = None,
-        session_id: Optional[str] = None,
-        return_message: Optional[bool] = True,
-    ) -> Union[Message, Text]:
-        return super().build_with_record(
-            sender=sender,
-            sender_name=sender_name,
-            input_value=input_value,
-            files=files,
-            session_id=session_id,
-            return_message=return_message,
+    def message_response(self) -> Message:
+        message = Message(
+            text=self.input_value,
+            sender=self.sender,
+            sender_name=self.sender_name,
+            session_id=self.session_id,
+            files=self.files,
         )
+        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):
+            self.store_message(message)
+            self.message.value = message
+
+        self.status = message
+        return message
diff --git a/src/backend/base/langflow/components/inputs/Prompt.py b/src/backend/base/langflow/components/inputs/Prompt.py
deleted file mode 100644
index a6140deee..000000000
--- a/src/backend/base/langflow/components/inputs/Prompt.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from langflow.custom import CustomComponent
-from langflow.field_typing import TemplateField
-from langflow.field_typing.prompt import Prompt
-
-
-class PromptComponent(CustomComponent):
-    display_name: str = "Prompt"
-    description: str = "Create a prompt template with dynamic variables."
-    icon = "prompts"
-
-    def build_config(self):
-        return {
-            "template": TemplateField(display_name="Template"),
-            "code": TemplateField(advanced=True),
-        }
-
-    async def build(
-        self,
-        template: str,
-        **kwargs,
-    ) -> Prompt:
-        prompt = await Prompt.from_template_and_variables(template, kwargs)
-        self.status = prompt.format_text()
-        return prompt
diff --git a/src/backend/base/langflow/components/inputs/TextInput.py b/src/backend/base/langflow/components/inputs/TextInput.py
index 4edf21723..201031ebc 100644
--- a/src/backend/base/langflow/components/inputs/TextInput.py
+++ b/src/backend/base/langflow/components/inputs/TextInput.py
@@ -1,32 +1,26 @@
-from typing import Optional
-
 from langflow.base.io.text import TextComponent
-from langflow.field_typing import Text
+from langflow.io import Output, TextInput
+from langflow.schema.message import Message
 
 
-class TextInput(TextComponent):
+class TextInputComponent(TextComponent):
     display_name = "Text Input"
     description = "Get text inputs from the Playground."
     icon = "type"
 
-    def build_config(self):
-        return {
-            "input_value": {
-                "display_name": "Text",
-                "input_types": ["Record", "Text"],
-                "info": "Text or Record to be passed as input.",
-            },
-            "record_template": {
-                "display_name": "Record Template",
-                "multiline": True,
-                "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
-                "advanced": True,
-            },
-        }
+    inputs = [
+        TextInput(
+            name="input_value",
+            display_name="Text",
+            info="Text to be passed as input.",
+        ),
+    ]
+    outputs = [
+        Output(display_name="Text", name="text", method="text_response"),
+    ]
 
-    def build(
-        self,
-        input_value: Optional[Text] = "",
-        record_template: Optional[str] = "",
-    ) -> Text:
-        return super().build(input_value=input_value, record_template=record_template)
+    def text_response(self) -> Message:
+        message = Message(
+            text=self.input_value,
+        )
+        return message
diff --git a/src/backend/base/langflow/components/inputs/__init__.py b/src/backend/base/langflow/components/inputs/__init__.py
index 4dff479de..48fc9a189 100644
--- a/src/backend/base/langflow/components/inputs/__init__.py
+++ b/src/backend/base/langflow/components/inputs/__init__.py
@@ -1,5 +1,4 @@
 from .ChatInput import ChatInput
-from .Prompt import PromptComponent
-from .TextInput import TextInput
+from .TextInput import TextInputComponent
 
-__all__ = ["ChatInput", "PromptComponent", "TextInput"]
+__all__ = ["ChatInput", "TextInputComponent"]
diff --git a/src/backend/base/langflow/components/langchain_utilities/SearchApi.py b/src/backend/base/langflow/components/langchain_utilities/SearchApi.py
index 3e6721fd6..5dfd55250 100644
--- a/src/backend/base/langflow/components/langchain_utilities/SearchApi.py
+++ b/src/backend/base/langflow/components/langchain_utilities/SearchApi.py
@@ -3,7 +3,7 @@ from typing import Optional
 from langchain_community.utilities.searchapi import SearchApiAPIWrapper
 
 from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langflow.schema import Data
 from langflow.services.database.models.base import orjson_dumps
 
 
@@ -37,7 +37,7 @@ class SearchApi(CustomComponent):
         engine: str,
         api_key: str,
         params: Optional[dict] = None,
-    ) -> Record:
+    ) -> Data:
         if params is None:
             params = {}
 
@@ -48,6 +48,6 @@ class SearchApi(CustomComponent):
 
         result = orjson_dumps(results, indent_2=False)
 
-        record = Record(data=result)
+        record = Data(data=result)
         self.status = record
         return record
diff --git a/src/backend/base/langflow/components/memories/AstraDBMessageReader.py b/src/backend/base/langflow/components/memories/AstraDBMessageReader.py
index 8d845be73..dfb8ef86a 100644
--- a/src/backend/base/langflow/components/memories/AstraDBMessageReader.py
+++ b/src/backend/base/langflow/components/memories/AstraDBMessageReader.py
@@ -1,7 +1,7 @@
 from typing import Optional, cast
 
 from langflow.base.memory.memory import BaseMemoryComponent
-from langflow.schema.record import Record
+from langflow.schema import Data
 
 
 class AstraDBMessageReaderComponent(BaseMemoryComponent):
@@ -38,7 +38,7 @@ class AstraDBMessageReaderComponent(BaseMemoryComponent):
             },
         }
 
-    def get_messages(self, **kwargs) -> list[Record]:
+    def get_messages(self, **kwargs) -> list[Data]:
         """
         Retrieves messages from the AstraDBChatMessageHistory memory.
 
@@ -46,7 +46,7 @@ class AstraDBMessageReaderComponent(BaseMemoryComponent):
             memory (AstraDBChatMessageHistory): The AstraDBChatMessageHistory instance to retrieve messages from.
 
         Returns:
-            list[Record]: A list of Record objects representing the search results.
+            list[Data]: A list of Data objects representing the search results.
         """
         try:
             from langchain_astradb.chat_message_histories import AstraDBChatMessageHistory
@@ -62,7 +62,7 @@ class AstraDBMessageReaderComponent(BaseMemoryComponent):
 
         # Get messages from the memory
         messages = memory.messages
-        results = [Record.from_lc_message(message) for message in messages]
+        results = [Data.from_lc_message(message) for message in messages]
 
         return list(results)
 
@@ -73,7 +73,7 @@ class AstraDBMessageReaderComponent(BaseMemoryComponent):
         token: str,
         api_endpoint: str,
         namespace: Optional[str] = None,
-    ) -> list[Record]:
+    ) -> list[Data]:
         try:
             from langchain_astradb.chat_message_histories import AstraDBChatMessageHistory
         except ImportError:
@@ -90,7 +90,7 @@ class AstraDBMessageReaderComponent(BaseMemoryComponent):
             namespace=namespace,
         )
 
-        records = self.get_messages(memory=memory)
-        self.status = records
+        data = self.get_messages(memory=memory)
+        self.status = data
 
-        return records
+        return data
diff --git a/src/backend/base/langflow/components/memories/AstraDBMessageWriter.py b/src/backend/base/langflow/components/memories/AstraDBMessageWriter.py
index 353223945..c929aca9e 100644
--- a/src/backend/base/langflow/components/memories/AstraDBMessageWriter.py
+++ b/src/backend/base/langflow/components/memories/AstraDBMessageWriter.py
@@ -3,7 +3,7 @@ from typing import Optional
 from langchain_core.messages import BaseMessage
 
 from langflow.base.memory.memory import BaseMemoryComponent
-from langflow.schema.record import Record
+from langflow.schema import Data
 
 
 class AstraDBMessageWriterComponent(BaseMemoryComponent):
@@ -13,8 +13,8 @@ class AstraDBMessageWriterComponent(BaseMemoryComponent):
     def build_config(self):
         return {
             "input_value": {
-                "display_name": "Input Record",
-                "info": "Record to write to Astra DB.",
+                "display_name": "Input Data",
+                "info": "Data to write to Astra DB.",
             },
             "session_id": {
                 "display_name": "Session ID",
@@ -97,13 +97,13 @@ class AstraDBMessageWriterComponent(BaseMemoryComponent):
 
     def build(
         self,
-        input_value: Record,
+        input_value: Data,
         session_id: str,
         collection_name: str,
         token: str,
         api_endpoint: str,
         namespace: Optional[str] = None,
-    ) -> Record:
+    ) -> Data:
         try:
             from langchain_astradb.chat_message_histories import AstraDBChatMessageHistory
         except ImportError:
diff --git a/src/backend/base/langflow/components/memories/CassandraMessageReader.py b/src/backend/base/langflow/components/memories/CassandraMessageReader.py
index 3fd11d772..a8bd1c365 100644
--- a/src/backend/base/langflow/components/memories/CassandraMessageReader.py
+++ b/src/backend/base/langflow/components/memories/CassandraMessageReader.py
@@ -3,7 +3,7 @@ from typing import Optional, cast
 from langchain_community.chat_message_histories import CassandraChatMessageHistory
 
 from langflow.base.memory.memory import BaseMemoryComponent
-from langflow.schema.record import Record
+from langflow.schema.data import Data
 
 
 class CassandraMessageReaderComponent(BaseMemoryComponent):
@@ -38,7 +38,7 @@ class CassandraMessageReaderComponent(BaseMemoryComponent):
             },
         }
 
-    def get_messages(self, **kwargs) -> list[Record]:
+    def get_messages(self, **kwargs) -> list[Data]:
         """
         Retrieves messages from the CassandraChatMessageHistory memory.
 
@@ -46,7 +46,7 @@ class CassandraMessageReaderComponent(BaseMemoryComponent):
             memory (CassandraChatMessageHistory): The CassandraChatMessageHistory instance to retrieve messages from.
 
         Returns:
-            list[Record]: A list of Record objects representing the search results.
+            list[Data]: A list of Data objects representing the search results.
         """
         memory: CassandraChatMessageHistory = cast(CassandraChatMessageHistory, kwargs.get("memory"))
         if not memory:
@@ -54,7 +54,7 @@ class CassandraMessageReaderComponent(BaseMemoryComponent):
 
         # Get messages from the memory
         messages = memory.messages
-        results = [Record.from_lc_message(message) for message in messages]
+        results = [Data.from_lc_message(message) for message in messages]
 
         return list(results)
 
@@ -65,7 +65,7 @@ class CassandraMessageReaderComponent(BaseMemoryComponent):
         token: str,
         database_id: str,
         keyspace: Optional[str] = None,
-    ) -> list[Record]:
+    ) -> list[Data]:
         try:
             import cassio
         except ImportError:
@@ -80,7 +80,7 @@ class CassandraMessageReaderComponent(BaseMemoryComponent):
             keyspace=keyspace,
         )
 
-        records = self.get_messages(memory=memory)
-        self.status = records
+        data = self.get_messages(memory=memory)
+        self.status = data
 
-        return records
+        return data
diff --git a/src/backend/base/langflow/components/memories/CassandraMessageWriter.py b/src/backend/base/langflow/components/memories/CassandraMessageWriter.py
index c8e3831a5..15da27274 100644
--- a/src/backend/base/langflow/components/memories/CassandraMessageWriter.py
+++ b/src/backend/base/langflow/components/memories/CassandraMessageWriter.py
@@ -4,7 +4,7 @@ from langchain_community.chat_message_histories import CassandraChatMessageHisto
 from langchain_core.messages import BaseMessage
 
 from langflow.base.memory.memory import BaseMemoryComponent
-from langflow.schema.record import Record
+from langflow.schema.data import Data
 
 
 class CassandraMessageWriterComponent(BaseMemoryComponent):
@@ -14,8 +14,8 @@ class CassandraMessageWriterComponent(BaseMemoryComponent):
     def build_config(self):
         return {
             "input_value": {
-                "display_name": "Input Record",
-                "info": "Record to write to Cassandra.",
+                "display_name": "Input Data",
+                "info": "Data to write to Cassandra.",
             },
             "session_id": {
                 "display_name": "Session ID",
@@ -93,14 +93,14 @@ class CassandraMessageWriterComponent(BaseMemoryComponent):
 
     def build(
         self,
-        input_value: Record,
+        input_value: Data,
         session_id: str,
         table_name: str,
         token: str,
         database_id: str,
         keyspace: Optional[str] = None,
         ttl_seconds: Optional[int] = None,
-    ) -> Record:
+    ) -> Data:
         try:
             import cassio
         except ImportError:
diff --git a/src/backend/base/langflow/components/memories/ZepMessageReader.py b/src/backend/base/langflow/components/memories/ZepMessageReader.py
index feef017a6..89a16587b 100644
--- a/src/backend/base/langflow/components/memories/ZepMessageReader.py
+++ b/src/backend/base/langflow/components/memories/ZepMessageReader.py
@@ -4,7 +4,7 @@ from langchain_community.chat_message_histories.zep import SearchScope, SearchTy
 
 from langflow.base.memory.memory import BaseMemoryComponent
 from langflow.field_typing import Text
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class ZepMessageReaderComponent(BaseMemoryComponent):
@@ -60,7 +60,7 @@ class ZepMessageReaderComponent(BaseMemoryComponent):
             },
         }
 
-    def get_messages(self, **kwargs) -> list[Record]:
+    def get_messages(self, **kwargs) -> list[Data]:
         """
         Retrieves messages from the ZepChatMessageHistory memory.
 
@@ -75,7 +75,7 @@ class ZepMessageReaderComponent(BaseMemoryComponent):
             limit (int, optional): The maximum number of search results to return. Defaults to None.
 
         Returns:
-            list[Record]: A list of Record objects representing the search results.
+            list[Data]: A list of Data objects representing the search results.
         """
         memory: ZepChatMessageHistory = cast(ZepChatMessageHistory, kwargs.get("memory"))
         if not memory:
@@ -103,10 +103,10 @@ class ZepMessageReaderComponent(BaseMemoryComponent):
                 result_dict["metadata"] = result.metadata
                 result_dict["score"] = result.score
                 result_dicts.append(result_dict)
-            results = [Record(data=result_dict) for result_dict in result_dicts]
+            results = [Data(data=result_dict) for result_dict in result_dicts]
         else:
             messages = memory.messages
-            results = [Record.from_lc_message(message) for message in messages]
+            results = [Data.from_lc_message(message) for message in messages]
         return results
 
     def build(
@@ -119,7 +119,7 @@ class ZepMessageReaderComponent(BaseMemoryComponent):
         search_scope: str = SearchScope.messages,
         search_type: str = SearchType.similarity,
         limit: Optional[int] = None,
-    ) -> list[Record]:
+    ) -> list[Data]:
         try:
             # Monkeypatch API_BASE_PATH to
             # avoid 404
@@ -139,12 +139,12 @@ class ZepMessageReaderComponent(BaseMemoryComponent):
 
         zep_client = ZepClient(api_url=url, api_key=api_key)
         memory = ZepChatMessageHistory(session_id=session_id, zep_client=zep_client)
-        records = self.get_messages(
+        data = self.get_messages(
             memory=memory,
             query=query,
             search_scope=search_scope,
             search_type=search_type,
             limit=limit,
         )
-        self.status = records
-        return records
+        self.status = data
+        return data
diff --git a/src/backend/base/langflow/components/memories/ZepMessageWriter.py b/src/backend/base/langflow/components/memories/ZepMessageWriter.py
index c3d55a721..cc343488e 100644
--- a/src/backend/base/langflow/components/memories/ZepMessageWriter.py
+++ b/src/backend/base/langflow/components/memories/ZepMessageWriter.py
@@ -2,7 +2,7 @@ from typing import TYPE_CHECKING, Optional
 
 from langflow.base.memory.memory import BaseMemoryComponent
 from langflow.field_typing import Text
-from langflow.schema import Record
+from langflow.schema import Data
 
 if TYPE_CHECKING:
     from zep_python.langchain import ZepChatMessageHistory
@@ -35,8 +35,8 @@ class ZepMessageWriterComponent(BaseMemoryComponent):
                 "advanced": True,
             },
             "input_value": {
-                "display_name": "Input Record",
-                "info": "Record to write to Zep.",
+                "display_name": "Input Data",
+                "info": "Data to write to Zep.",
             },
             "api_base_path": {
                 "display_name": "API Base Path",
@@ -78,12 +78,12 @@ class ZepMessageWriterComponent(BaseMemoryComponent):
 
     def build(
         self,
-        input_value: Record,
+        input_value: Data,
         session_id: Text,
         api_base_path: str = "api/v1",
         url: Optional[Text] = None,
         api_key: Optional[Text] = None,
-    ) -> Record:
+    ) -> Data:
         try:
             # Monkeypatch API_BASE_PATH to
             # avoid 404
diff --git a/src/backend/base/langflow/components/model_specs/AmazonBedrockSpecs.py b/src/backend/base/langflow/components/model_specs/AmazonBedrockSpecs.py
index 0e27e620f..6799e589d 100644
--- a/src/backend/base/langflow/components/model_specs/AmazonBedrockSpecs.py
+++ b/src/backend/base/langflow/components/model_specs/AmazonBedrockSpecs.py
@@ -3,7 +3,7 @@ from typing import Optional
 from langchain_community.llms.bedrock import Bedrock
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 
 class AmazonBedrockComponent(CustomComponent):
@@ -46,7 +46,7 @@ class AmazonBedrockComponent(CustomComponent):
         endpoint_url: Optional[str] = None,
         streaming: bool = False,
         cache: Optional[bool] = None,
-    ) -> BaseLanguageModel:
+    ) -> LanguageModel:
         try:
             output = Bedrock(
                 credentials_profile_name=credentials_profile_name,
diff --git a/src/backend/base/langflow/components/model_specs/AnthropicLLMSpecs.py b/src/backend/base/langflow/components/model_specs/AnthropicLLMSpecs.py
index 786d558bb..eaabb6212 100644
--- a/src/backend/base/langflow/components/model_specs/AnthropicLLMSpecs.py
+++ b/src/backend/base/langflow/components/model_specs/AnthropicLLMSpecs.py
@@ -1,10 +1,10 @@
 from typing import Optional
 
 from langchain_anthropic import ChatAnthropic
-from langchain_core.language_models import BaseLanguageModel
 from pydantic.v1 import SecretStr
 
 from langflow.custom import CustomComponent
+from langflow.field_typing import LanguageModel
 
 
 class ChatAntropicSpecsComponent(CustomComponent):
@@ -54,10 +54,10 @@ class ChatAntropicSpecsComponent(CustomComponent):
         self,
         model: str,
         anthropic_api_key: Optional[str] = None,
-        max_tokens: Optional[int] = None,
+        max_tokens: Optional[int] = 1000,
         temperature: Optional[float] = None,
         api_endpoint: Optional[str] = None,
-    ) -> BaseLanguageModel:
+    ) -> LanguageModel:
         # Set default API endpoint if not provided
         if not api_endpoint:
             api_endpoint = "https://api.anthropic.com"
diff --git a/src/backend/base/langflow/components/model_specs/AzureChatOpenAISpecs.py b/src/backend/base/langflow/components/model_specs/AzureChatOpenAISpecs.py
index 947a1e2a3..18cceed6f 100644
--- a/src/backend/base/langflow/components/model_specs/AzureChatOpenAISpecs.py
+++ b/src/backend/base/langflow/components/model_specs/AzureChatOpenAISpecs.py
@@ -1,10 +1,10 @@
 from typing import Optional
 
-from langchain_core.language_models import BaseLanguageModel
 from langchain_openai import AzureChatOpenAI
 from pydantic.v1 import SecretStr
 
 from langflow.custom import CustomComponent
+from langflow.field_typing import LanguageModel
 
 
 class AzureChatOpenAISpecsComponent(CustomComponent):
@@ -81,7 +81,7 @@ class AzureChatOpenAISpecsComponent(CustomComponent):
         api_version: str,
         temperature: float = 0.7,
         max_tokens: Optional[int] = 1000,
-    ) -> BaseLanguageModel:
+    ) -> LanguageModel:
         if api_key:
             azure_api_key = SecretStr(api_key)
         else:
diff --git a/src/backend/base/langflow/components/model_specs/BaiduQianfanChatEndpointsSpecs.py b/src/backend/base/langflow/components/model_specs/BaiduQianfanChatEndpointsSpecs.py
index a353410ad..3764203f9 100644
--- a/src/backend/base/langflow/components/model_specs/BaiduQianfanChatEndpointsSpecs.py
+++ b/src/backend/base/langflow/components/model_specs/BaiduQianfanChatEndpointsSpecs.py
@@ -1,11 +1,10 @@
 from typing import Optional
 
 from langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
-
 from pydantic.v1 import SecretStr
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 
 class QianfanChatEndpointComponent(CustomComponent):
@@ -80,7 +79,7 @@ class QianfanChatEndpointComponent(CustomComponent):
         temperature: Optional[float] = None,
         penalty_score: Optional[float] = None,
         endpoint: Optional[str] = None,
-    ) -> BaseLanguageModel:
+    ) -> LanguageModel:
         try:
             output = QianfanChatEndpoint(  # type: ignore
                 model=model,
diff --git a/src/backend/base/langflow/components/model_specs/BaiduQianfanLLMEndpointsSpecs.py b/src/backend/base/langflow/components/model_specs/BaiduQianfanLLMEndpointsSpecs.py
index 273bb5d98..e12ff565f 100644
--- a/src/backend/base/langflow/components/model_specs/BaiduQianfanLLMEndpointsSpecs.py
+++ b/src/backend/base/langflow/components/model_specs/BaiduQianfanLLMEndpointsSpecs.py
@@ -3,7 +3,7 @@ from typing import Optional
 from langchain_community.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 
 class QianfanLLMEndpointComponent(CustomComponent):
@@ -78,7 +78,7 @@ class QianfanLLMEndpointComponent(CustomComponent):
         temperature: Optional[float] = None,
         penalty_score: Optional[float] = None,
         endpoint: Optional[str] = None,
-    ) -> BaseLanguageModel:
+    ) -> LanguageModel:
         try:
             output = QianfanLLMEndpoint(  # type: ignore
                 model=model,
diff --git a/src/backend/base/langflow/components/model_specs/ChatAnthropicSpecs.py b/src/backend/base/langflow/components/model_specs/ChatAnthropicSpecs.py
deleted file mode 100644
index 7e4000d9b..000000000
--- a/src/backend/base/langflow/components/model_specs/ChatAnthropicSpecs.py
+++ /dev/null
@@ -1,88 +0,0 @@
-from typing import Optional
-
-from langchain_anthropic import ChatAnthropic
-from pydantic.v1.types import SecretStr
-
-
-from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
-
-
-class AnthropicLLM(CustomComponent):
-    display_name: str = "Anthropic"
-    description: str = "Generate text using Anthropic Chat&Completion LLMs."
-    icon = "Anthropic"
-
-    field_order = [
-        "model",
-        "anthropic_api_key",
-        "max_tokens",
-        "temperature",
-        "anthropic_api_url",
-    ]
-
-    def build_config(self):
-        return {
-            "model": {
-                "display_name": "Model Name",
-                "options": [
-                    "claude-3-opus-20240229",
-                    "claude-3-sonnet-20240229",
-                    "claude-3-haiku-20240307",
-                    "claude-2.1",
-                    "claude-2.0",
-                    "claude-instant-1.2",
-                    "claude-instant-1",
-                ],
-                "info": "Name of the model to use.",
-                "required": True,
-                "value": "claude-3-opus-20240229",
-            },
-            "anthropic_api_key": {
-                "display_name": "Anthropic API Key",
-                "required": True,
-                "password": True,
-                "info": "Your Anthropic API key.",
-            },
-            "max_tokens": {
-                "display_name": "Max Tokens",
-                "advanced": True,
-                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
-            },
-            "temperature": {
-                "display_name": "Temperature",
-                "field_type": "float",
-                "value": 0.1,
-            },
-            "anthropic_api_url": {
-                "display_name": "Anthropic API URL",
-                "advanced": True,
-                "info": "Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.",
-            },
-            "code": {"show": False},
-        }
-
-    def build(
-        self,
-        model: str,
-        anthropic_api_key: Optional[str] = None,
-        max_tokens: Optional[int] = None,
-        temperature: Optional[float] = None,
-        anthropic_api_url: Optional[str] = None,
-    ) -> BaseLanguageModel:
-        # Set default API endpoint if not provided
-        if not anthropic_api_url:
-            anthropic_api_url = "https://api.anthropic.com"
-
-        try:
-            output = ChatAnthropic(
-                model_name=model,
-                anthropic_api_key=(SecretStr(anthropic_api_key) if anthropic_api_key else None),
-                max_tokens_to_sample=max_tokens,  # type: ignore
-                temperature=temperature,
-                anthropic_api_url=anthropic_api_url,
-            )
-        except Exception as e:
-            raise ValueError("Could not connect to Anthropic API.") from e
-
-        return output
diff --git a/src/backend/base/langflow/components/model_specs/ChatLiteLLMSpecs.py b/src/backend/base/langflow/components/model_specs/ChatLiteLLMSpecs.py
index b3bce849e..1e7c32242 100644
--- a/src/backend/base/langflow/components/model_specs/ChatLiteLLMSpecs.py
+++ b/src/backend/base/langflow/components/model_specs/ChatLiteLLMSpecs.py
@@ -3,7 +3,7 @@ from typing import Any, Dict, Optional
 from langchain_community.chat_models.litellm import ChatLiteLLM, ChatLiteLLMException
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 
 class ChatLiteLLMComponent(CustomComponent):
@@ -116,7 +116,7 @@ class ChatLiteLLMComponent(CustomComponent):
         max_tokens: int = 256,
         max_retries: int = 6,
         verbose: bool = False,
-    ) -> BaseLanguageModel:
+    ) -> LanguageModel:
         try:
             import litellm  # type: ignore
 
diff --git a/src/backend/base/langflow/components/model_specs/ChatMistralSpecs.py b/src/backend/base/langflow/components/model_specs/ChatMistralSpecs.py
index 73bbc3220..e977e5713 100644
--- a/src/backend/base/langflow/components/model_specs/ChatMistralSpecs.py
+++ b/src/backend/base/langflow/components/model_specs/ChatMistralSpecs.py
@@ -4,7 +4,7 @@ from langchain_mistralai import ChatMistralAI
 from pydantic.v1 import SecretStr
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 
 class MistralAIModelComponent(CustomComponent):
@@ -68,7 +68,7 @@ class MistralAIModelComponent(CustomComponent):
         mistral_api_key: Optional[str] = None,
         max_tokens: Optional[int] = None,
         mistral_api_base: Optional[str] = None,
-    ) -> BaseLanguageModel:
+    ) -> LanguageModel:
         # Set default API endpoint if not provided
         if not mistral_api_base:
             mistral_api_base = "https://api.mistral.ai"
diff --git a/src/backend/base/langflow/components/model_specs/ChatOpenAISpecs.py b/src/backend/base/langflow/components/model_specs/ChatOpenAISpecs.py
index 76974a00f..f4fe8af96 100644
--- a/src/backend/base/langflow/components/model_specs/ChatOpenAISpecs.py
+++ b/src/backend/base/langflow/components/model_specs/ChatOpenAISpecs.py
@@ -5,7 +5,7 @@ from pydantic.v1 import SecretStr
 
 from langflow.base.models.openai_constants import MODEL_NAMES
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, NestedDict
+from langflow.field_typing import LanguageModel, NestedDict
 
 
 class ChatOpenAIComponent(CustomComponent):
@@ -57,7 +57,7 @@ class ChatOpenAIComponent(CustomComponent):
         openai_api_base: Optional[str] = None,
         openai_api_key: Optional[str] = None,
         temperature: float = 0.7,
-    ) -> BaseLanguageModel:
+    ) -> LanguageModel:
         if not openai_api_base:
             openai_api_base = "https://api.openai.com/v1"
         if openai_api_key:
diff --git a/src/backend/base/langflow/components/model_specs/ChatVertexAISpecs.py b/src/backend/base/langflow/components/model_specs/ChatVertexAISpecs.py
index 0df7a0465..02cc675e9 100644
--- a/src/backend/base/langflow/components/model_specs/ChatVertexAISpecs.py
+++ b/src/backend/base/langflow/components/model_specs/ChatVertexAISpecs.py
@@ -2,8 +2,9 @@ from typing import Optional
 
 from langchain_community.chat_models.vertexai import ChatVertexAI
 
+
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 
 class ChatVertexAIComponent(CustomComponent):
@@ -16,7 +17,7 @@ class ChatVertexAIComponent(CustomComponent):
             "credentials": {
                 "display_name": "Credentials",
                 "field_type": "file",
-                "file_types": [".json"],
+                "file_types": ["json"],
                 "file_path": None,
             },
             "examples": {
@@ -71,7 +72,7 @@ class ChatVertexAIComponent(CustomComponent):
         top_k: int = 40,
         top_p: float = 0.95,
         verbose: bool = False,
-    ) -> BaseLanguageModel:
+    ) -> LanguageModel:
         return ChatVertexAI(
             credentials=credentials,
             location=location,
diff --git a/src/backend/base/langflow/components/model_specs/CohereSpecs.py b/src/backend/base/langflow/components/model_specs/CohereSpecs.py
index 2e2a1fa7e..f65a441f1 100644
--- a/src/backend/base/langflow/components/model_specs/CohereSpecs.py
+++ b/src/backend/base/langflow/components/model_specs/CohereSpecs.py
@@ -1,7 +1,7 @@
 from typing import Optional
 
 from langchain_cohere import ChatCohere
-from langchain_core.language_models.base import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 from pydantic.v1 import SecretStr
 
 from langflow.custom import CustomComponent
@@ -29,7 +29,7 @@ class CohereComponent(CustomComponent):
         cohere_api_key: str,
         max_tokens: Optional[int] = 256,
         temperature: float = 0.75,
-    ) -> BaseLanguageModel:
+    ) -> LanguageModel:
         if cohere_api_key:
             api_key = SecretStr(cohere_api_key)
         else:
diff --git a/src/backend/base/langflow/components/model_specs/GoogleGenerativeAISpecs.py b/src/backend/base/langflow/components/model_specs/GoogleGenerativeAISpecs.py
index 534085938..cf71ddd39 100644
--- a/src/backend/base/langflow/components/model_specs/GoogleGenerativeAISpecs.py
+++ b/src/backend/base/langflow/components/model_specs/GoogleGenerativeAISpecs.py
@@ -4,7 +4,7 @@ from langchain_google_genai import ChatGoogleGenerativeAI  # type: ignore
 from pydantic.v1.types import SecretStr
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, RangeSpec
+from langflow.field_typing import LanguageModel, RangeSpec
 
 
 class GoogleGenerativeAIComponent(CustomComponent):
@@ -62,7 +62,7 @@ class GoogleGenerativeAIComponent(CustomComponent):
         top_k: Optional[int] = None,
         top_p: Optional[float] = None,
         n: Optional[int] = 1,
-    ) -> BaseLanguageModel:
+    ) -> LanguageModel:
         return ChatGoogleGenerativeAI(
             model=model,
             max_output_tokens=max_output_tokens or None,  # type: ignore
diff --git a/src/backend/base/langflow/components/model_specs/GroqModelSpecs.py b/src/backend/base/langflow/components/model_specs/GroqModelSpecs.py
index ec203471c..4851e700b 100644
--- a/src/backend/base/langflow/components/model_specs/GroqModelSpecs.py
+++ b/src/backend/base/langflow/components/model_specs/GroqModelSpecs.py
@@ -6,7 +6,7 @@ from pydantic.v1 import SecretStr
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.groq_constants import MODEL_NAMES
 from langflow.base.models.model import LCModelComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 
 class GroqModelSpecs(LCModelComponent):
@@ -74,7 +74,7 @@ class GroqModelSpecs(LCModelComponent):
         temperature: float = 0.1,
         n: Optional[int] = 1,
         stream: bool = False,
-    ) -> BaseLanguageModel:
+    ) -> LanguageModel:
         return ChatGroq(
             model_name=model_name,
             max_tokens=max_tokens or None,  # type: ignore
diff --git a/src/backend/base/langflow/components/model_specs/HuggingFaceEndpointsSpecs.py b/src/backend/base/langflow/components/model_specs/HuggingFaceEndpointsSpecs.py
index 4de68365f..23a2e50c1 100644
--- a/src/backend/base/langflow/components/model_specs/HuggingFaceEndpointsSpecs.py
+++ b/src/backend/base/langflow/components/model_specs/HuggingFaceEndpointsSpecs.py
@@ -3,7 +3,7 @@ from typing import Optional
 from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 
 class HuggingFaceEndpointsComponent(CustomComponent):
@@ -32,7 +32,7 @@ class HuggingFaceEndpointsComponent(CustomComponent):
         task: str = "text2text-generation",
         huggingfacehub_api_token: Optional[str] = None,
         model_kwargs: Optional[dict] = None,
-    ) -> BaseLanguageModel:
+    ) -> LanguageModel:
         try:
             output = HuggingFaceEndpoint(  # type: ignore
                 endpoint_url=endpoint_url,
diff --git a/src/backend/base/langflow/components/model_specs/OllamaLLMSpecs.py b/src/backend/base/langflow/components/model_specs/OllamaLLMSpecs.py
index 1c416f1b1..76372f8cd 100644
--- a/src/backend/base/langflow/components/model_specs/OllamaLLMSpecs.py
+++ b/src/backend/base/langflow/components/model_specs/OllamaLLMSpecs.py
@@ -3,7 +3,7 @@ from typing import List, Optional
 from langchain_community.llms.ollama import Ollama
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 
 class OllamaLLM(CustomComponent):
@@ -118,7 +118,7 @@ class OllamaLLM(CustomComponent):
         tfs_z: Optional[float] = None,
         top_k: Optional[int] = None,
         top_p: Optional[int] = None,
-    ) -> BaseLanguageModel:
+    ) -> LanguageModel:
         if not base_url:
             base_url = "http://localhost:11434"
 
diff --git a/src/backend/base/langflow/components/model_specs/VertexAISpecs.py b/src/backend/base/langflow/components/model_specs/VertexAISpecs.py
index 49b120d35..d7e646c5f 100644
--- a/src/backend/base/langflow/components/model_specs/VertexAISpecs.py
+++ b/src/backend/base/langflow/components/model_specs/VertexAISpecs.py
@@ -2,8 +2,9 @@ from typing import Dict, Optional
 
 from langchain_community.llms.vertexai import VertexAI
 
+
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 
 class VertexAIComponent(CustomComponent):
@@ -16,7 +17,7 @@ class VertexAIComponent(CustomComponent):
             "credentials": {
                 "display_name": "Credentials",
                 "field_type": "file",
-                "file_types": [".json"],
+                "file_types": ["json"],
                 "required": False,
                 "value": None,
             },
@@ -129,7 +130,7 @@ class VertexAIComponent(CustomComponent):
         top_p: float = 0.95,
         tuned_model_name: Optional[str] = None,
         verbose: bool = False,
-    ) -> BaseLanguageModel:
+    ) -> LanguageModel:
         return VertexAI(
             credentials=credentials,
             location=location,
diff --git a/src/backend/base/langflow/components/model_specs/__init__.py b/src/backend/base/langflow/components/model_specs/__init__.py
index eb5c3d822..18b52a41f 100644
--- a/src/backend/base/langflow/components/model_specs/__init__.py
+++ b/src/backend/base/langflow/components/model_specs/__init__.py
@@ -1,7 +1,7 @@
 from .AmazonBedrockSpecs import AmazonBedrockComponent
 from .BaiduQianfanChatEndpointsSpecs import QianfanChatEndpointComponent
 from .BaiduQianfanLLMEndpointsSpecs import QianfanLLMEndpointComponent
-from .ChatAnthropicSpecs import AnthropicLLM
+
 from .ChatLiteLLMSpecs import ChatLiteLLMComponent
 from .ChatOllamaEndpointSpecs import ChatOllamaComponent
 from .ChatOpenAISpecs import ChatOpenAIComponent
@@ -14,7 +14,6 @@ from .VertexAISpecs import VertexAIComponent
 
 __all__ = [
     "AmazonBedrockComponent",
-    "ChatAntropicSpecsComponent",
     "AzureChatOpenAISpecsComponent",
     "QianfanChatEndpointComponent",
     "QianfanLLMEndpointComponent",
diff --git a/src/backend/base/langflow/components/models/AmazonBedrockModel.py b/src/backend/base/langflow/components/models/AmazonBedrockModel.py
index 99229deb2..ba06c6723 100644
--- a/src/backend/base/langflow/components/models/AmazonBedrockModel.py
+++ b/src/backend/base/langflow/components/models/AmazonBedrockModel.py
@@ -1,90 +1,84 @@
-from typing import Optional
-
-from langchain_community.chat_models.bedrock import BedrockChat
+from langchain_aws import ChatBedrock
 
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
-from langflow.field_typing import Text
+from langflow.field_typing import LanguageModel
+from langflow.io import BoolInput, DictInput, DropdownInput, MessageInput, Output, StrInput
 
 
 class AmazonBedrockComponent(LCModelComponent):
     display_name: str = "Amazon Bedrock"
     description: str = "Generate text using Amazon Bedrock LLMs."
     icon = "Amazon"
-    field_order = [
-        "model_id",
-        "credentials_profile_name",
-        "region_name",
-        "model_kwargs",
-        "endpoint_url",
-        "cache",
-        "stream",
-        "input_value",
-        "system_message",
+
+    inputs = [
+        MessageInput(name="input_value", display_name="Input"),
+        DropdownInput(
+            name="model_id",
+            display_name="Model ID",
+            options=[
+                "amazon.titan-text-express-v1",
+                "amazon.titan-text-lite-v1",
+                "amazon.titan-text-premier-v1:0",
+                "amazon.titan-embed-text-v1",
+                "amazon.titan-embed-text-v2:0",
+                "amazon.titan-embed-image-v1",
+                "amazon.titan-image-generator-v1",
+                "anthropic.claude-v2",
+                "anthropic.claude-v2:1",
+                "anthropic.claude-3-sonnet-20240229-v1:0",
+                "anthropic.claude-3-haiku-20240307-v1:0",
+                "anthropic.claude-3-opus-20240229-v1:0",
+                "anthropic.claude-instant-v1",
+                "ai21.j2-mid-v1",
+                "ai21.j2-ultra-v1",
+                "cohere.command-text-v14",
+                "cohere.command-light-text-v14",
+                "cohere.command-r-v1:0",
+                "cohere.command-r-plus-v1:0",
+                "cohere.embed-english-v3",
+                "cohere.embed-multilingual-v3",
+                "meta.llama2-13b-chat-v1",
+                "meta.llama2-70b-chat-v1",
+                "meta.llama3-8b-instruct-v1:0",
+                "meta.llama3-70b-instruct-v1:0",
+                "mistral.mistral-7b-instruct-v0:2",
+                "mistral.mixtral-8x7b-instruct-v0:1",
+                "mistral.mistral-large-2402-v1:0",
+                "mistral.mistral-small-2402-v1:0",
+                "stability.stable-diffusion-xl-v0",
+                "stability.stable-diffusion-xl-v1",
+            ],
+            value="anthropic.claude-3-haiku-20240307-v1:0",
+        ),
+        StrInput(name="credentials_profile_name", display_name="Credentials Profile Name"),
+        StrInput(name="region_name", display_name="Region Name"),
+        DictInput(name="model_kwargs", display_name="Model Kwargs", advanced=True),
+        StrInput(name="endpoint_url", display_name="Endpoint URL"),
+        BoolInput(name="cache", display_name="Cache"),
+        StrInput(
+            name="system_message",
+            display_name="System Message",
+            info="System message to pass to the model.",
+            advanced=True,
+        ),
+        BoolInput(name="stream", display_name="Stream", info=STREAM_INFO_TEXT, advanced=True),
+    ]
+    outputs = [
+        Output(display_name="Text", name="text_output", method="text_response"),
+        Output(display_name="Language Model", name="model_output", method="build_model"),
     ]
 
-    def build_config(self):
-        return {
-            "model_id": {
-                "display_name": "Model Id",
-                "options": [
-                    "amazon.titan-text-express-v1",
-                    "amazon.titan-text-lite-v1",
-                    "amazon.titan-embed-text-v1",
-                    "amazon.titan-embed-image-v1",
-                    "amazon.titan-image-generator-v1",
-                    "anthropic.claude-v2",
-                    "anthropic.claude-v2:1",
-                    "anthropic.claude-3-sonnet-20240229-v1:0",
-                    "anthropic.claude-3-haiku-20240307-v1:0",
-                    "anthropic.claude-instant-v1",
-                    "ai21.j2-mid-v1",
-                    "ai21.j2-ultra-v1",
-                    "cohere.command-text-v14",
-                    "cohere.command-light-text-v14",
-                    "cohere.embed-english-v3",
-                    "cohere.embed-multilingual-v3",
-                    "meta.llama2-13b-chat-v1",
-                    "meta.llama2-70b-chat-v1",
-                    "mistral.mistral-7b-instruct-v0:2",
-                    "mistral.mixtral-8x7b-instruct-v0:1",
-                ],
-            },
-            "credentials_profile_name": {"display_name": "Credentials Profile Name"},
-            "endpoint_url": {"display_name": "Endpoint URL"},
-            "region_name": {"display_name": "Region Name"},
-            "model_kwargs": {
-                "display_name": "Model Kwargs",
-                "advanced": True,
-            },
-            "cache": {"display_name": "Cache"},
-            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
-            "system_message": {
-                "display_name": "System Message",
-                "info": "System message to pass to the model.",
-                "advanced": True,
-            },
-            "stream": {
-                "display_name": "Stream",
-                "info": STREAM_INFO_TEXT,
-                "advanced": True,
-            },
-        }
-
-    def build(
-        self,
-        input_value: Text,
-        system_message: Optional[str] = None,
-        model_id: str = "anthropic.claude-instant-v1",
-        credentials_profile_name: Optional[str] = None,
-        region_name: Optional[str] = None,
-        model_kwargs: Optional[dict] = None,
-        endpoint_url: Optional[str] = None,
-        cache: Optional[bool] = None,
-        stream: bool = False,
-    ) -> Text:
+    def build_model(self) -> LanguageModel:
+        model_id = self.model_id
+        credentials_profile_name = self.credentials_profile_name
+        region_name = self.region_name
+        model_kwargs = self.model_kwargs
+        endpoint_url = self.endpoint_url
+        cache = self.cache
+        stream = self.stream
         try:
-            output = BedrockChat(
+            output = ChatBedrock(  # type: ignore
                 credentials_profile_name=credentials_profile_name,
                 model_id=model_id,
                 region_name=region_name,
@@ -92,8 +86,7 @@ class AmazonBedrockComponent(LCModelComponent):
                 endpoint_url=endpoint_url,
                 streaming=stream,
                 cache=cache,
-            )  # type: ignore
+            )
         except Exception as e:
             raise ValueError("Could not connect to AmazonBedrock API.") from e
-
-        return self.get_chat_result(output, stream, input_value, system_message)
+        return output
diff --git a/src/backend/base/langflow/components/models/AnthropicModel.py b/src/backend/base/langflow/components/models/AnthropicModel.py
index bac7708d4..dd7ccb4f5 100644
--- a/src/backend/base/langflow/components/models/AnthropicModel.py
+++ b/src/backend/base/langflow/components/models/AnthropicModel.py
@@ -1,105 +1,106 @@
-from typing import Optional
-
 from langchain_anthropic.chat_models import ChatAnthropic
 from pydantic.v1 import SecretStr
 
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
-from langflow.field_typing import Text
+from langflow.field_typing import LanguageModel
+from langflow.io import BoolInput, DropdownInput, FloatInput, IntInput, Output, SecretStrInput, TextInput
 
 
-class AnthropicLLM(LCModelComponent):
-    display_name: str = "Anthropic"
-    description: str = "Generate text using Anthropic Chat&Completion LLMs."
+class AnthropicModelComponent(LCModelComponent):
+    display_name = "Anthropic"
+    description = "Generate text using Anthropic Chat&Completion LLMs with prefill support."
     icon = "Anthropic"
 
-    field_order = [
-        "model",
-        "anthropic_api_key",
-        "max_tokens",
-        "temperature",
-        "anthropic_api_url",
-        "input_value",
-        "system_message",
-        "stream",
+    inputs = [
+        TextInput(name="input_value", display_name="Input"),
+        IntInput(
+            name="max_tokens",
+            display_name="Max Tokens",
+            advanced=True,
+            value=4096,
+            info="The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+        ),
+        DropdownInput(
+            name="model",
+            display_name="Model Name",
+            options=[
+                "claude-3-5-sonnet-20240620",
+                "claude-3-opus-20240229",
+                "claude-3-sonnet-20240229",
+                "claude-3-haiku-20240307",
+            ],
+            info="https://python.langchain.com/docs/integrations/chat/anthropic",
+            value="claude-3-5-sonnet-20240620",
+        ),
+        SecretStrInput(
+            name="anthropic_api_key",
+            display_name="Anthropic API Key",
+            info="Your Anthropic API key.",
+        ),
+        FloatInput(name="temperature", display_name="Temperature", value=0.1),
+        TextInput(
+            name="anthropic_api_url",
+            display_name="Anthropic API URL",
+            advanced=True,
+            info="Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.",
+        ),
+        BoolInput(name="stream", display_name="Stream", info=STREAM_INFO_TEXT, advanced=True, value=False),
+        TextInput(
+            name="system_message",
+            display_name="System Message",
+            info="System message to pass to the model.",
+            advanced=True,
+        ),
+        TextInput(
+            name="prefill",
+            display_name="Prefill",
+            info="Prefill text to guide the model's response.",
+            advanced=True,
+        ),
+    ]
+    outputs = [
+        Output(display_name="Text", name="text_output", method="text_response"),
+        Output(display_name="Language Model", name="model_output", method="build_model"),
     ]
 
-    def build_config(self):
-        return {
-            "model": {
-                "display_name": "Model Name",
-                "options": [
-                    "claude-3-opus-20240229",
-                    "claude-3-sonnet-20240229",
-                    "claude-3-haiku-20240307",
-                    "claude-2.1",
-                    "claude-2.0",
-                    "claude-instant-1.2",
-                    "claude-instant-1",
-                ],
-                "info": "https://python.langchain.com/docs/integrations/chat/anthropic",
-                "required": True,
-                "value": "claude-3-opus-20240229",
-            },
-            "anthropic_api_key": {
-                "display_name": "Anthropic API Key",
-                "required": True,
-                "password": True,
-                "info": "Your Anthropic API key.",
-            },
-            "max_tokens": {
-                "display_name": "Max Tokens",
-                "advanced": True,
-                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
-            },
-            "temperature": {
-                "display_name": "Temperature",
-                "field_type": "float",
-                "value": 0.1,
-            },
-            "anthropic_api_url": {
-                "display_name": "Anthropic API URL",
-                "advanced": True,
-                "info": "Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.",
-            },
-            "code": {"show": False},
-            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
-            "stream": {
-                "display_name": "Stream",
-                "advanced": True,
-                "info": STREAM_INFO_TEXT,
-            },
-            "system_message": {
-                "display_name": "System Message",
-                "advanced": True,
-                "info": "System message to pass to the model.",
-            },
-        }
-
-    def build(
-        self,
-        model: str,
-        input_value: Text,
-        system_message: Optional[str] = None,
-        anthropic_api_key: Optional[str] = None,
-        max_tokens: Optional[int] = None,
-        temperature: Optional[float] = None,
-        anthropic_api_url: Optional[str] = None,
-        stream: bool = False,
-    ) -> Text:
-        # Set default API endpoint if not provided
-        if not anthropic_api_url:
-            anthropic_api_url = "https://api.anthropic.com"
+    def build_model(self) -> LanguageModel:
+        model = self.model
+        anthropic_api_key = self.anthropic_api_key
+        max_tokens = self.max_tokens
+        temperature = self.temperature
+        anthropic_api_url = self.anthropic_api_url or "https://api.anthropic.com"
 
         try:
             output = ChatAnthropic(
-                model_name=model,
+                model=model,
                 anthropic_api_key=(SecretStr(anthropic_api_key) if anthropic_api_key else None),
                 max_tokens_to_sample=max_tokens,  # type: ignore
                 temperature=temperature,
                 anthropic_api_url=anthropic_api_url,
+                streaming=self.stream,
             )
         except Exception as e:
             raise ValueError("Could not connect to Anthropic API.") from e
 
-        return self.get_chat_result(output, stream, input_value, system_message)
+        return output
+
+    def _get_exception_message(self, exception: Exception) -> str | None:
+        """
+        Get a message from an Anthropic exception.
+
+        Args:
+            exception (Exception): The exception to get the message from.
+
+        Returns:
+            str: The message from the exception.
+        """
+        try:
+            from anthropic import BadRequestError
+        except ImportError:
+            return None
+        if isinstance(exception, BadRequestError):
+            message = exception.body.get("error", {}).get("message")  # type: ignore
+            if message:
+                return message
+        return None
diff --git a/src/backend/base/langflow/components/models/AzureOpenAIModel.py b/src/backend/base/langflow/components/models/AzureOpenAIModel.py
index 97ee88920..9a2ebbbe0 100644
--- a/src/backend/base/langflow/components/models/AzureOpenAIModel.py
+++ b/src/backend/base/langflow/components/models/AzureOpenAIModel.py
@@ -1,11 +1,10 @@
-from typing import Optional
-
 from langchain_openai import AzureChatOpenAI
 from pydantic.v1 import SecretStr
 
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
-from langflow.field_typing import Text
+from langflow.field_typing import LanguageModel
+from langflow.io import BoolInput, DropdownInput, FloatInput, IntInput, MessageInput, Output, SecretStrInput, StrInput
 
 
 class AzureChatOpenAIComponent(LCModelComponent):
@@ -15,26 +14,14 @@ class AzureChatOpenAIComponent(LCModelComponent):
     beta = False
     icon = "Azure"
 
-    field_order = [
-        "model",
-        "azure_endpoint",
-        "azure_deployment",
-        "api_version",
-        "api_key",
-        "temperature",
-        "max_tokens",
-        "input_value",
-        "system_message",
-        "stream",
-    ]
-
     AZURE_OPENAI_MODELS = [
         "gpt-35-turbo",
         "gpt-35-turbo-16k",
         "gpt-35-turbo-instruct",
         "gpt-4",
         "gpt-4-32k",
-        "gpt-4-vision",
+        "gpt-4o",
+        "gpt-4-turbo",
     ]
 
     AZURE_OPENAI_API_VERSIONS = [
@@ -45,69 +32,67 @@ class AzureChatOpenAIComponent(LCModelComponent):
         "2023-08-01-preview",
         "2023-09-01-preview",
         "2023-12-01-preview",
+        "2024-04-09",
+        "2024-05-13",
     ]
 
-    def build_config(self):
-        return {
-            "model": {
-                "display_name": "Model Name",
-                "value": self.AZURE_OPENAI_MODELS[0],
-                "options": self.AZURE_OPENAI_MODELS,
-            },
-            "azure_endpoint": {
-                "display_name": "Azure Endpoint",
-                "info": "Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`",
-            },
-            "azure_deployment": {
-                "display_name": "Deployment Name",
-            },
-            "api_version": {
-                "display_name": "API Version",
-                "options": self.AZURE_OPENAI_API_VERSIONS,
-                "value": self.AZURE_OPENAI_API_VERSIONS[-1],
-                "advanced": True,
-            },
-            "api_key": {"display_name": "API Key", "password": True},
-            "temperature": {
-                "display_name": "Temperature",
-                "value": 0.7,
-            },
-            "max_tokens": {
-                "display_name": "Max Tokens",
-                "advanced": True,
-                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
-            },
-            "code": {"show": False},
-            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
-            "stream": {
-                "display_name": "Stream",
-                "info": STREAM_INFO_TEXT,
-                "advanced": True,
-            },
-            "system_message": {
-                "display_name": "System Message",
-                "info": "System message to pass to the model.",
-                "advanced": True,
-            },
-        }
+    inputs = [
+        DropdownInput(
+            name="model",
+            display_name="Model Name",
+            options=AZURE_OPENAI_MODELS,
+            value=AZURE_OPENAI_MODELS[0],
+        ),
+        StrInput(
+            name="azure_endpoint",
+            display_name="Azure Endpoint",
+            info="Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/`",
+        ),
+        StrInput(name="azure_deployment", display_name="Deployment Name"),
+        DropdownInput(
+            name="api_version",
+            display_name="API Version",
+            options=AZURE_OPENAI_API_VERSIONS,
+            value=AZURE_OPENAI_API_VERSIONS[-1],
+            advanced=True,
+        ),
+        SecretStrInput(name="api_key", display_name="API Key", password=True),
+        FloatInput(name="temperature", display_name="Temperature", value=0.7),
+        IntInput(
+            name="max_tokens",
+            display_name="Max Tokens",
+            advanced=True,
+            info="The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+        ),
+        MessageInput(name="input_value", display_name="Input"),
+        BoolInput(name="stream", display_name="Stream", info=STREAM_INFO_TEXT, advanced=True),
+        StrInput(
+            name="system_message",
+            display_name="System Message",
+            advanced=True,
+            info="System message to pass to the model.",
+        ),
+    ]
+    outputs = [
+        Output(display_name="Text", name="text_output", method="text_response"),
+        Output(display_name="Language Model", name="model_output", method="model_response"),
+    ]
+
+    def model_response(self) -> LanguageModel:
+        model = self.model
+        azure_endpoint = self.azure_endpoint
+        azure_deployment = self.azure_deployment
+        api_version = self.api_version
+        api_key = self.api_key
+        temperature = self.temperature
+        max_tokens = self.max_tokens
+        stream = self.stream
 
-    def build(
-        self,
-        model: str,
-        azure_endpoint: str,
-        input_value: Text,
-        azure_deployment: str,
-        api_version: str,
-        api_key: str,
-        temperature: float,
-        system_message: Optional[str] = None,
-        max_tokens: Optional[int] = 1000,
-        stream: bool = False,
-    ) -> Text:
         if api_key:
             secret_api_key = SecretStr(api_key)
         else:
             secret_api_key = None
+
         try:
             output = AzureChatOpenAI(
                 model=model,
@@ -117,8 +102,9 @@ class AzureChatOpenAIComponent(LCModelComponent):
                 api_key=secret_api_key,
                 temperature=temperature,
                 max_tokens=max_tokens or None,
+                streaming=stream,
             )
         except Exception as e:
             raise ValueError("Could not connect to AzureOpenAI API.") from e
 
-        return self.get_chat_result(output, stream, input_value, system_message)
+        return output
diff --git a/src/backend/base/langflow/components/models/BaiduQianfanChatModel.py b/src/backend/base/langflow/components/models/BaiduQianfanChatModel.py
index aaae3112f..e131fb1a7 100644
--- a/src/backend/base/langflow/components/models/BaiduQianfanChatModel.py
+++ b/src/backend/base/langflow/components/models/BaiduQianfanChatModel.py
@@ -1,112 +1,103 @@
-from typing import Optional
-
 from langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
 from pydantic.v1 import SecretStr
 
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
-from langflow.field_typing import Text
+from langflow.field_typing.constants import LanguageModel
+from langflow.io import BoolInput, DropdownInput, FloatInput, Output, SecretStrInput, TextInput
 
 
 class QianfanChatEndpointComponent(LCModelComponent):
     display_name: str = "Qianfan"
     description: str = "Generate text using Baidu Qianfan LLMs."
-    documentation: str = "https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint."
+    documentation: str = "https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint"
     icon = "BaiduQianfan"
 
-    field_order = [
-        "model",
-        "qianfan_ak",
-        "qianfan_sk",
-        "top_p",
-        "temperature",
-        "penalty_score",
-        "endpoint",
-        "input_value",
-        "system_message",
-        "stream",
+    inputs = [
+        TextInput(
+            name="input_value",
+            display_name="Input",
+        ),
+        DropdownInput(
+            name="model",
+            display_name="Model Name",
+            options=[
+                "ERNIE-Bot",
+                "ERNIE-Bot-turbo",
+                "BLOOMZ-7B",
+                "Llama-2-7b-chat",
+                "Llama-2-13b-chat",
+                "Llama-2-70b-chat",
+                "Qianfan-BLOOMZ-7B-compressed",
+                "Qianfan-Chinese-Llama-2-7B",
+                "ChatGLM2-6B-32K",
+                "AquilaChat-7B",
+            ],
+            info="https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint",
+            value="ERNIE-Bot-turbo",
+        ),
+        SecretStrInput(
+            name="qianfan_ak",
+            display_name="Qianfan Ak",
+            info="which you could get from  https://cloud.baidu.com/product/wenxinworkshop",
+        ),
+        SecretStrInput(
+            name="qianfan_sk",
+            display_name="Qianfan Sk",
+            info="which you could get from  https://cloud.baidu.com/product/wenxinworkshop",
+        ),
+        FloatInput(
+            name="top_p",
+            display_name="Top p",
+            info="Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
+            value=0.8,
+            advanced=True,
+        ),
+        FloatInput(
+            name="temperature",
+            display_name="Temperature",
+            info="Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
+            value=0.95,
+        ),
+        FloatInput(
+            name="penalty_score",
+            display_name="Penalty Score",
+            info="Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
+            value=1.0,
+            advanced=True,
+        ),
+        TextInput(
+            name="endpoint",
+            display_name="Endpoint",
+            info="Endpoint of the Qianfan LLM, required if custom model used.",
+        ),
+        BoolInput(
+            name="stream",
+            display_name="Stream",
+            info=STREAM_INFO_TEXT,
+            advanced=True,
+        ),
+        TextInput(
+            name="system_message",
+            display_name="System Message",
+            info="System message to pass to the model.",
+            advanced=True,
+        ),
+    ]
+    outputs = [
+        Output(display_name="Text", name="text_output", method="text_response"),
+        Output(display_name="Language Model", name="model_output", method="build_model"),
     ]
 
-    def build_config(self):
-        return {
-            "model": {
-                "display_name": "Model Name",
-                "options": [
-                    "ERNIE-Bot",
-                    "ERNIE-Bot-turbo",
-                    "BLOOMZ-7B",
-                    "Llama-2-7b-chat",
-                    "Llama-2-13b-chat",
-                    "Llama-2-70b-chat",
-                    "Qianfan-BLOOMZ-7B-compressed",
-                    "Qianfan-Chinese-Llama-2-7B",
-                    "ChatGLM2-6B-32K",
-                    "AquilaChat-7B",
-                ],
-                "info": "https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint",
-                "value": "ERNIE-Bot-turbo",
-            },
-            "qianfan_ak": {
-                "display_name": "Qianfan Ak",
-                "password": True,
-                "info": "which you could get from  https://cloud.baidu.com/product/wenxinworkshop",
-            },
-            "qianfan_sk": {
-                "display_name": "Qianfan Sk",
-                "password": True,
-                "info": "which you could get from  https://cloud.baidu.com/product/wenxinworkshop",
-            },
-            "top_p": {
-                "display_name": "Top p",
-                "field_type": "float",
-                "info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
-                "value": 0.8,
-                "advanced": True,
-            },
-            "temperature": {
-                "display_name": "Temperature",
-                "field_type": "float",
-                "info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
-                "value": 0.95,
-            },
-            "penalty_score": {
-                "display_name": "Penalty Score",
-                "field_type": "float",
-                "info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
-                "value": 1.0,
-                "advanced": True,
-            },
-            "endpoint": {
-                "display_name": "Endpoint",
-                "info": "Endpoint of the Qianfan LLM, required if custom model used.",
-            },
-            "code": {"show": False},
-            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
-            "stream": {
-                "display_name": "Stream",
-                "info": STREAM_INFO_TEXT,
-                "advanced": True,
-            },
-            "system_message": {
-                "display_name": "System Message",
-                "info": "System message to pass to the model.",
-                "advanced": True,
-            },
-        }
+    def build_model(self) -> LanguageModel:  # type: ignore[type-var]
+        model = self.model
+        qianfan_ak = self.qianfan_ak
+        qianfan_sk = self.qianfan_sk
+        top_p = self.top_p
+        temperature = self.temperature
+        penalty_score = self.penalty_score
+        endpoint = self.endpoint
 
-    def build(
-        self,
-        input_value: Text,
-        qianfan_ak: str,
-        qianfan_sk: str,
-        model: str,
-        top_p: Optional[float] = None,
-        temperature: Optional[float] = None,
-        penalty_score: Optional[float] = None,
-        endpoint: Optional[str] = None,
-        stream: bool = False,
-        system_message: Optional[str] = None,
-    ) -> Text:
         try:
             output = QianfanChatEndpoint(  # type: ignore
                 model=model,
@@ -120,4 +111,4 @@ class QianfanChatEndpointComponent(LCModelComponent):
         except Exception as e:
             raise ValueError("Could not connect to Baidu Qianfan API.") from e
 
-        return self.get_chat_result(output, stream, input_value, system_message)
+        return output  # type: ignore
diff --git a/src/backend/base/langflow/components/models/ChatLiteLLMModel.py b/src/backend/base/langflow/components/models/ChatLiteLLMModel.py
index aa3cf6976..e43854bc7 100644
--- a/src/backend/base/langflow/components/models/ChatLiteLLMModel.py
+++ b/src/backend/base/langflow/components/models/ChatLiteLLMModel.py
@@ -1,155 +1,143 @@
-from typing import Any, Dict, Optional
+from typing import Optional
 
 from langchain_community.chat_models.litellm import ChatLiteLLM, ChatLiteLLMException
 
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
-from langflow.field_typing import Text
+from langflow.field_typing import LanguageModel
+from langflow.io import (
+    BoolInput,
+    DictInput,
+    DropdownInput,
+    FloatInput,
+    IntInput,
+    MessageInput,
+    Output,
+    SecretStrInput,
+    StrInput,
+)
 
 
 class ChatLiteLLMModelComponent(LCModelComponent):
     display_name = "LiteLLM"
     description = "`LiteLLM` collection of large language models."
     documentation = "https://python.langchain.com/docs/integrations/chat/litellm"
-    field_order = [
-        "model",
-        "api_key",
-        "provider",
-        "temperature",
-        "model_kwargs",
-        "top_p",
-        "top_k",
-        "n",
-        "max_tokens",
-        "max_retries",
-        "verbose",
-        "stream",
-        "input_value",
-        "system_message",
+    icon = "LiteLLM"
+
+    inputs = [
+        MessageInput(name="input_value", display_name="Input"),
+        StrInput(
+            name="model",
+            display_name="Model name",
+            advanced=False,
+            required=True,
+            info="The name of the model to use. For example, `gpt-3.5-turbo`.",
+        ),
+        SecretStrInput(
+            name="api_key",
+            display_name="API key",
+            advanced=False,
+            required=False,
+        ),
+        DropdownInput(
+            name="provider",
+            display_name="Provider",
+            info="The provider of the API key.",
+            options=[
+                "OpenAI",
+                "Azure",
+                "Anthropic",
+                "Replicate",
+                "Cohere",
+                "OpenRouter",
+            ],
+        ),
+        FloatInput(
+            name="temperature",
+            display_name="Temperature",
+            advanced=False,
+            required=False,
+            value=0.7,
+        ),
+        DictInput(
+            name="model_kwargs",
+            display_name="Model kwargs",
+            advanced=True,
+            required=False,
+            value={},
+        ),
+        FloatInput(
+            name="top_p",
+            display_name="Top p",
+            advanced=True,
+            required=False,
+        ),
+        IntInput(
+            name="top_k",
+            display_name="Top k",
+            advanced=True,
+            required=False,
+        ),
+        IntInput(
+            name="n",
+            display_name="N",
+            advanced=True,
+            required=False,
+            info="Number of chat completions to generate for each prompt. "
+            "Note that the API may not return the full n completions if duplicates are generated.",
+            value=1,
+        ),
+        IntInput(
+            name="max_tokens",
+            display_name="Max tokens",
+            advanced=False,
+            value=256,
+            info="The maximum number of tokens to generate for each chat completion.",
+        ),
+        IntInput(
+            name="max_retries",
+            display_name="Max retries",
+            advanced=True,
+            required=False,
+            value=6,
+        ),
+        BoolInput(
+            name="verbose",
+            display_name="Verbose",
+            advanced=True,
+            required=False,
+            value=False,
+        ),
+        BoolInput(
+            name="stream",
+            display_name="Stream",
+            info=STREAM_INFO_TEXT,
+            advanced=True,
+        ),
+        StrInput(
+            name="system_message",
+            display_name="System Message",
+            info="System message to pass to the model.",
+            advanced=True,
+        ),
     ]
 
-    def build_config(self):
-        return {
-            "model": {
-                "display_name": "Model name",
-                "field_type": "str",
-                "advanced": False,
-                "required": True,
-                "info": "The name of the model to use. For example, `gpt-3.5-turbo`.",
-            },
-            "api_key": {
-                "display_name": "API key",
-                "field_type": "str",
-                "advanced": False,
-                "required": False,
-                "password": True,
-            },
-            "provider": {
-                "display_name": "Provider",
-                "info": "The provider of the API key.",
-                "options": [
-                    "OpenAI",
-                    "Azure",
-                    "Anthropic",
-                    "Replicate",
-                    "Cohere",
-                    "OpenRouter",
-                ],
-            },
-            "temperature": {
-                "display_name": "Temperature",
-                "field_type": "float",
-                "advanced": False,
-                "required": False,
-                "default": 0.7,
-            },
-            "model_kwargs": {
-                "display_name": "Model kwargs",
-                "field_type": "dict",
-                "advanced": True,
-                "required": False,
-                "default": {},
-            },
-            "top_p": {
-                "display_name": "Top p",
-                "field_type": "float",
-                "advanced": True,
-                "required": False,
-            },
-            "top_k": {
-                "display_name": "Top k",
-                "field_type": "int",
-                "advanced": True,
-                "required": False,
-            },
-            "n": {
-                "display_name": "N",
-                "field_type": "int",
-                "advanced": True,
-                "required": False,
-                "info": "Number of chat completions to generate for each prompt. "
-                "Note that the API may not return the full n completions if duplicates are generated.",
-                "default": 1,
-            },
-            "max_tokens": {
-                "display_name": "Max tokens",
-                "advanced": False,
-                "default": 256,
-                "info": "The maximum number of tokens to generate for each chat completion.",
-            },
-            "max_retries": {
-                "display_name": "Max retries",
-                "field_type": "int",
-                "advanced": True,
-                "required": False,
-                "default": 6,
-            },
-            "verbose": {
-                "display_name": "Verbose",
-                "field_type": "bool",
-                "advanced": True,
-                "required": False,
-                "default": False,
-            },
-            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
-            "stream": {
-                "display_name": "Stream",
-                "info": STREAM_INFO_TEXT,
-                "advanced": True,
-            },
-            "system_message": {
-                "display_name": "System Message",
-                "info": "System message to pass to the model.",
-                "advanced": True,
-            },
-        }
+    outputs = [
+        Output(display_name="Text", name="text_output", method="text_response"),
+        Output(display_name="Language Model", name="model_output", method="build_model"),
+    ]
 
-    def build(
-        self,
-        input_value: Text,
-        model: str,
-        provider: str,
-        api_key: Optional[str] = None,
-        stream: bool = False,
-        temperature: Optional[float] = 0.7,
-        model_kwargs: Optional[Dict[str, Any]] = {},
-        top_p: Optional[float] = None,
-        top_k: Optional[int] = None,
-        n: int = 1,
-        max_tokens: int = 256,
-        max_retries: int = 6,
-        verbose: bool = False,
-        system_message: Optional[str] = None,
-    ) -> Text:
+    def build_model(self) -> LanguageModel:
         try:
             import litellm  # type: ignore
 
             litellm.drop_params = True
-            litellm.set_verbose = verbose
+            litellm.set_verbose = self.verbose
         except ImportError:
             raise ChatLiteLLMException(
                 "Could not import litellm python package. " "Please install it with `pip install litellm`"
             )
+
         provider_map = {
             "OpenAI": "openai_api_key",
             "Azure": "azure_api_key",
@@ -158,27 +146,28 @@ class ChatLiteLLMModelComponent(LCModelComponent):
             "Cohere": "cohere_api_key",
             "OpenRouter": "openrouter_api_key",
         }
+
         # Set the API key based on the provider
         api_keys: dict[str, Optional[str]] = {v: None for v in provider_map.values()}
 
-        if variable_name := provider_map.get(provider):
-            api_keys[variable_name] = api_key
+        if variable_name := provider_map.get(self.provider):
+            api_keys[variable_name] = self.api_key
         else:
             raise ChatLiteLLMException(
-                f"Provider {provider} is not supported. Supported providers are: {', '.join(provider_map.keys())}"
+                f"Provider {self.provider} is not supported. Supported providers are: {', '.join(provider_map.keys())}"
             )
 
         output = ChatLiteLLM(
-            model=model,
+            model=self.model,
             client=None,
-            streaming=stream,
-            temperature=temperature,
-            model_kwargs=model_kwargs if model_kwargs is not None else {},
-            top_p=top_p,
-            top_k=top_k,
-            n=n,
-            max_tokens=max_tokens,
-            max_retries=max_retries,
+            streaming=self.stream,
+            temperature=self.temperature,
+            model_kwargs=self.model_kwargs if self.model_kwargs is not None else {},
+            top_p=self.top_p,
+            top_k=self.top_k,
+            n=self.n,
+            max_tokens=self.max_tokens,
+            max_retries=self.max_retries,
             openai_api_key=api_keys["openai_api_key"],
             azure_api_key=api_keys["azure_api_key"],
             anthropic_api_key=api_keys["anthropic_api_key"],
@@ -186,4 +175,6 @@ class ChatLiteLLMModelComponent(LCModelComponent):
             cohere_api_key=api_keys["cohere_api_key"],
             openrouter_api_key=api_keys["openrouter_api_key"],
         )
-        return self.get_chat_result(output, stream, input_value, system_message)
+
+        return output
+        return output
diff --git a/src/backend/base/langflow/components/models/CohereModel.py b/src/backend/base/langflow/components/models/CohereModel.py
index b5ecbab9f..305f8b4c2 100644
--- a/src/backend/base/langflow/components/models/CohereModel.py
+++ b/src/backend/base/langflow/components/models/CohereModel.py
@@ -1,73 +1,54 @@
-from typing import Optional
-
 from langchain_cohere import ChatCohere
+from langchain_core.language_models.chat_models import BaseChatModel
 from pydantic.v1 import SecretStr
 
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
-from langflow.field_typing import Text
+from langflow.field_typing import LanguageModel
+from langflow.io import BoolInput, FloatInput, MessageInput, Output, SecretStrInput, StrInput
 
 
 class CohereComponent(LCModelComponent):
     display_name = "Cohere"
     description = "Generate text using Cohere LLMs."
     documentation = "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere"
-
     icon = "Cohere"
 
-    field_order = [
-        "cohere_api_key",
-        "max_tokens",
-        "temperature",
-        "input_value",
-        "system_message",
-        "stream",
+    inputs = [
+        SecretStrInput(
+            name="cohere_api_key",
+            display_name="Cohere API Key",
+            info="The Cohere API Key to use for the Cohere model.",
+            advanced=False,
+            value="COHERE_API_KEY",
+        ),
+        FloatInput(name="temperature", display_name="Temperature", value=0.75),
+        MessageInput(name="input_value", display_name="Input"),
+        BoolInput(name="stream", display_name="Stream", info=STREAM_INFO_TEXT, advanced=True),
+        StrInput(
+            name="system_message",
+            display_name="System Message",
+            info="System message to pass to the model.",
+            advanced=True,
+        ),
+    ]
+    outputs = [
+        Output(display_name="Text", name="text_output", method="text_response"),
+        Output(display_name="Language Model", name="model_output", method="build_model"),
     ]
 
-    def build_config(self):
-        return {
-            "cohere_api_key": {
-                "display_name": "Cohere API Key",
-                "type": "password",
-                "password": True,
-                "required": True,
-            },
-            "max_tokens": {
-                "display_name": "Max Tokens",
-                "advanced": True,
-                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
-            },
-            "temperature": {
-                "display_name": "Temperature",
-                "default": 0.75,
-                "type": "float",
-                "show": True,
-            },
-            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
-            "stream": {
-                "display_name": "Stream",
-                "info": STREAM_INFO_TEXT,
-                "advanced": True,
-            },
-            "system_message": {
-                "display_name": "System Message",
-                "info": "System message to pass to the model.",
-                "advanced": True,
-            },
-        }
+    def build_model(self) -> LanguageModel | BaseChatModel:
+        cohere_api_key = self.cohere_api_key
+        temperature = self.temperature
 
-    def build(
-        self,
-        cohere_api_key: str,
-        input_value: Text,
-        temperature: float = 0.75,
-        stream: bool = False,
-        system_message: Optional[str] = None,
-    ) -> Text:
-        api_key = SecretStr(cohere_api_key)
-        output = ChatCohere(  # type: ignore
+        if cohere_api_key:
+            api_key = SecretStr(cohere_api_key)
+        else:
+            api_key = None
+
+        output = ChatCohere(
+            temperature=temperature or 0.75,
             cohere_api_key=api_key,
-            temperature=temperature,
         )
-        return self.get_chat_result(output, stream, input_value, system_message)
-        return self.get_chat_result(output, stream, input_value, system_message)
+
+        return output
diff --git a/src/backend/base/langflow/components/models/GoogleGenerativeAIModel.py b/src/backend/base/langflow/components/models/GoogleGenerativeAIModel.py
index 0c2b8eef5..377e307a9 100644
--- a/src/backend/base/langflow/components/models/GoogleGenerativeAIModel.py
+++ b/src/backend/base/langflow/components/models/GoogleGenerativeAIModel.py
@@ -1,10 +1,9 @@
-from typing import Optional
-
-from langchain_google_genai import ChatGoogleGenerativeAI
 from pydantic.v1 import SecretStr
-from langflow.field_typing import Text, RangeSpec
+
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
+from langflow.field_typing import LanguageModel
+from langflow.io import BoolInput, DropdownInput, FloatInput, IntInput, MessageInput, Output, SecretStrInput, StrInput
 
 
 class GoogleGenerativeAIComponent(LCModelComponent):
@@ -12,91 +11,90 @@ class GoogleGenerativeAIComponent(LCModelComponent):
     description: str = "Generate text using Google Generative AI."
     icon = "GoogleGenerativeAI"
 
-    field_order = [
-        "google_api_key",
-        "model",
-        "max_output_tokens",
-        "temperature",
-        "top_k",
-        "top_p",
-        "n",
-        "input_value",
-        "system_message",
-        "stream",
+    inputs = [
+        SecretStrInput(
+            name="google_api_key",
+            display_name="Google API Key",
+            info="The Google API Key to use for the Google Generative AI.",
+        ),
+        DropdownInput(
+            name="model",
+            display_name="Model",
+            info="The name of the model to use.",
+            options=["gemini-1.5-pro", "gemini-1.5-flash"],
+            value="gemini-1.5-pro",
+        ),
+        IntInput(
+            name="max_output_tokens",
+            display_name="Max Output Tokens",
+            info="The maximum number of tokens to generate.",
+            advanced=True,
+        ),
+        FloatInput(
+            name="temperature",
+            display_name="Temperature",
+            info="Run inference with this temperature. Must by in the closed interval [0.0, 1.0].",
+            value=0.1,
+        ),
+        IntInput(
+            name="top_k",
+            display_name="Top K",
+            info="Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.",
+            advanced=True,
+        ),
+        FloatInput(
+            name="top_p",
+            display_name="Top P",
+            info="The maximum cumulative probability of tokens to consider when sampling.",
+            advanced=True,
+        ),
+        IntInput(
+            name="n",
+            display_name="N",
+            info="Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.",
+            advanced=True,
+        ),
+        MessageInput(
+            name="input_value",
+            display_name="Input",
+            info="The input to the model.",
+            input_types=["Text", "Data", "Prompt"],
+        ),
+        BoolInput(name="stream", display_name="Stream", info=STREAM_INFO_TEXT, advanced=True),
+        StrInput(
+            name="system_message",
+            display_name="System Message",
+            info="System message to pass to the model.",
+            advanced=True,
+        ),
+    ]
+    outputs = [
+        Output(display_name="Text", name="text_output", method="text_response"),
+        Output(display_name="Language Model", name="model_output", method="build_model"),
     ]
 
-    def build_config(self):
-        return {
-            "google_api_key": {
-                "display_name": "Google API Key",
-                "info": "The Google API Key to use for the Google Generative AI.",
-            },
-            "max_output_tokens": {
-                "display_name": "Max Output Tokens",
-                "info": "The maximum number of tokens to generate.",
-                "advanced": True,
-            },
-            "temperature": {
-                "display_name": "Temperature",
-                "info": "Run inference with this temperature. Must by in the closed interval [0.0, 1.0].",
-            },
-            "top_k": {
-                "display_name": "Top K",
-                "info": "Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.",
-                "rangeSpec": RangeSpec(min=0, max=2, step=0.1),
-                "advanced": True,
-            },
-            "top_p": {
-                "display_name": "Top P",
-                "info": "The maximum cumulative probability of tokens to consider when sampling.",
-                "advanced": True,
-            },
-            "n": {
-                "display_name": "N",
-                "info": "Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.",
-                "advanced": True,
-            },
-            "model": {
-                "display_name": "Model",
-                "info": "The name of the model to use. Supported examples: gemini-pro",
-                "options": ["gemini-pro", "gemini-pro-vision"],
-            },
-            "code": {
-                "advanced": True,
-            },
-            "input_value": {"display_name": "Input", "info": "The input to the model."},
-            "stream": {
-                "display_name": "Stream",
-                "info": STREAM_INFO_TEXT,
-                "advanced": True,
-            },
-            "system_message": {
-                "display_name": "System Message",
-                "info": "System message to pass to the model.",
-                "advanced": True,
-            },
-        }
+    def build_model(self) -> LanguageModel:
+        try:
+            from langchain_google_genai import ChatGoogleGenerativeAI
+        except ImportError:
+            raise ImportError("The 'langchain_google_genai' package is required to use the Google Generative AI model.")
 
-    def build(
-        self,
-        google_api_key: str,
-        model: str,
-        input_value: Text,
-        max_output_tokens: Optional[int] = None,
-        temperature: float = 0.1,
-        top_k: Optional[int] = None,
-        top_p: Optional[float] = None,
-        n: Optional[int] = 1,
-        stream: bool = False,
-        system_message: Optional[str] = None,
-    ) -> Text:
-        output = ChatGoogleGenerativeAI(
+        google_api_key = self.google_api_key
+        model = self.model
+        max_output_tokens = self.max_output_tokens
+        temperature = self.temperature
+        top_k = self.top_k
+        top_p = self.top_p
+        n = self.n
+
+        output = ChatGoogleGenerativeAI(  # type: ignore
             model=model,
-            max_output_tokens=max_output_tokens or None,  # type: ignore
+            max_output_tokens=max_output_tokens or None,
             temperature=temperature,
             top_k=top_k or None,
-            top_p=top_p or None,  # type: ignore
+            top_p=top_p or None,
             n=n or 1,
             google_api_key=SecretStr(google_api_key),
         )
-        return self.get_chat_result(output, stream, input_value, system_message)
+
+        return output
diff --git a/src/backend/base/langflow/components/models/GroqModel.py b/src/backend/base/langflow/components/models/GroqModel.py
index 91959e751..e7f5330c8 100644
--- a/src/backend/base/langflow/components/models/GroqModel.py
+++ b/src/backend/base/langflow/components/models/GroqModel.py
@@ -1,12 +1,11 @@
-from typing import Optional
-
 from langchain_groq import ChatGroq
-from langflow.base.models.groq_constants import MODEL_NAMES
 from pydantic.v1 import SecretStr
 
 from langflow.base.constants import STREAM_INFO_TEXT
+from langflow.base.models.groq_constants import MODEL_NAMES
 from langflow.base.models.model import LCModelComponent
-from langflow.field_typing import Text
+from langflow.field_typing import LanguageModel
+from langflow.io import BoolInput, DropdownInput, FloatInput, IntInput, SecretStrInput, TextInput
 
 
 class GroqModel(LCModelComponent):
@@ -14,82 +13,78 @@ class GroqModel(LCModelComponent):
     description: str = "Generate text using Groq."
     icon = "Groq"
 
-    field_order = [
-        "groq_api_key",
-        "model",
-        "max_output_tokens",
-        "temperature",
-        "top_k",
-        "top_p",
-        "n",
-        "input_value",
-        "system_message",
-        "stream",
+    inputs = [
+        SecretStrInput(
+            name="groq_api_key",
+            display_name="Groq API Key",
+            info="API key for the Groq API.",
+        ),
+        TextInput(
+            name="groq_api_base",
+            display_name="Groq API Base",
+            info="Base URL path for API requests, leave blank if not using a proxy or service emulator.",
+            advanced=True,
+        ),
+        IntInput(
+            name="max_tokens",
+            display_name="Max Output Tokens",
+            info="The maximum number of tokens to generate.",
+            advanced=True,
+        ),
+        FloatInput(
+            name="temperature",
+            display_name="Temperature",
+            info="Run inference with this temperature. Must by in the closed interval [0.0, 1.0].",
+            value=0.1,
+        ),
+        IntInput(
+            name="n",
+            display_name="N",
+            info="Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.",
+            advanced=True,
+        ),
+        DropdownInput(
+            name="model_name",
+            display_name="Model",
+            info="The name of the model to use.",
+            options=MODEL_NAMES,
+        ),
+        TextInput(
+            name="input_value",
+            display_name="Input",
+            info="The input to the model.",
+        ),
+        BoolInput(
+            name="stream",
+            display_name="Stream",
+            info=STREAM_INFO_TEXT,
+            advanced=True,
+        ),
+        TextInput(
+            name="system_message",
+            display_name="System Message",
+            info="System message to pass to the model.",
+            advanced=True,
+        ),
     ]
 
-    def build_config(self):
-        return {
-            "groq_api_key": {
-                "display_name": "Groq API Key",
-                "info": "API key for the Groq API.",
-                "password": True,
-            },
-            "groq_api_base": {
-                "display_name": "Groq API Base",
-                "info": "Base URL path for API requests, leave blank if not using a proxy or service emulator.",
-                "advanced": True,
-            },
-            "max_tokens": {
-                "display_name": "Max Output Tokens",
-                "info": "The maximum number of tokens to generate.",
-                "advanced": True,
-            },
-            "temperature": {
-                "display_name": "Temperature",
-                "info": "Run inference with this temperature. Must by in the closed interval [0.0, 1.0].",
-            },
-            "n": {
-                "display_name": "N",
-                "info": "Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.",
-                "advanced": True,
-            },
-            "model_name": {
-                "display_name": "Model",
-                "info": "The name of the model to use. Supported examples: gemini-pro",
-                "options": MODEL_NAMES,
-            },
-            "input_value": {"display_name": "Input", "info": "The input to the model."},
-            "stream": {
-                "display_name": "Stream",
-                "info": STREAM_INFO_TEXT,
-                "advanced": True,
-            },
-            "system_message": {
-                "display_name": "System Message",
-                "info": "System message to pass to the model.",
-                "advanced": True,
-            },
-        }
+    def build_model(self) -> LanguageModel:
+        groq_api_key = self.groq_api_key
+        model_name = self.model_name
+        max_tokens = self.max_tokens
+        temperature = self.temperature
+        groq_api_base = self.groq_api_base
+        n = self.n
+        stream = self.stream
 
-    def build(
-        self,
-        groq_api_key: str,
-        model_name: str,
-        input_value: Text,
-        groq_api_base: Optional[str] = None,
-        max_tokens: Optional[int] = None,
-        temperature: float = 0.1,
-        n: Optional[int] = 1,
-        stream: bool = False,
-        system_message: Optional[str] = None,
-    ) -> Text:
         output = ChatGroq(
-            model_name=model_name,
-            max_tokens=max_tokens or None,  # type: ignore
+            model=model_name,
+            max_tokens=max_tokens or None,
             temperature=temperature,
-            groq_api_base=groq_api_base,
+            base_url=groq_api_base,
             n=n or 1,
-            groq_api_key=SecretStr(groq_api_key),
+            api_key=SecretStr(groq_api_key),
             streaming=stream,
         )
-        return self.get_chat_result(output, stream, input_value, system_message)
+
+        return output
diff --git a/src/backend/base/langflow/components/models/HuggingFaceModel.py b/src/backend/base/langflow/components/models/HuggingFaceModel.py
index 949598b2d..021f15793 100644
--- a/src/backend/base/langflow/components/models/HuggingFaceModel.py
+++ b/src/backend/base/langflow/components/models/HuggingFaceModel.py
@@ -1,11 +1,10 @@
-from typing import Optional
-
 from langchain_community.chat_models.huggingface import ChatHuggingFace
 from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
 
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
-from langflow.field_typing import Text
+from langflow.field_typing import LanguageModel
+from langflow.io import BoolInput, DictInput, DropdownInput, MessageInput, Output, SecretStrInput, StrInput
 
 
 class HuggingFaceEndpointsComponent(LCModelComponent):
@@ -13,64 +12,45 @@ class HuggingFaceEndpointsComponent(LCModelComponent):
     description: str = "Generate text using Hugging Face Inference APIs."
     icon = "HuggingFace"
 
-    field_order = [
-        "endpoint_url",
-        "task",
-        "huggingfacehub_api_token",
-        "model_kwargs",
-        "input_value",
-        "system_message",
-        "stream",
+    inputs = [
+        MessageInput(name="input_value", display_name="Input"),
+        SecretStrInput(name="endpoint_url", display_name="Endpoint URL", password=True),
+        DropdownInput(
+            name="task",
+            display_name="Task",
+            options=["text2text-generation", "text-generation", "summarization"],
+        ),
+        SecretStrInput(name="huggingfacehub_api_token", display_name="API token", password=True),
+        DictInput(name="model_kwargs", display_name="Model Keyword Arguments", advanced=True),
+        BoolInput(name="stream", display_name="Stream", info=STREAM_INFO_TEXT, advanced=True),
+        StrInput(
+            name="system_message",
+            display_name="System Message",
+            info="System message to pass to the model.",
+            advanced=True,
+        ),
     ]
 
-    def build_config(self):
-        return {
-            "endpoint_url": {"display_name": "Endpoint URL", "password": True},
-            "task": {
-                "display_name": "Task",
-                "options": ["text2text-generation", "text-generation", "summarization"],
-            },
-            "huggingfacehub_api_token": {"display_name": "API token", "password": True},
-            "model_kwargs": {
-                "display_name": "Model Keyword Arguments",
-                "field_type": "code",
-                "advanced": True,
-            },
-            "code": {"show": False},
-            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
-            "stream": {
-                "display_name": "Stream",
-                "info": STREAM_INFO_TEXT,
-                "advanced": True,
-            },
-            "system_message": {
-                "display_name": "System Message",
-                "info": "System message to pass to the model.",
-                "advanced": True,
-            },
-        }
+    outputs = [
+        Output(display_name="Text", name="text_output", method="text_response"),
+        Output(display_name="Language Model", name="model_output", method="build_model"),
+    ]
+
+    def build_model(self) -> LanguageModel:
+        endpoint_url = self.endpoint_url
+        task = self.task
+        huggingfacehub_api_token = self.huggingfacehub_api_token
+        model_kwargs = self.model_kwargs or {}
 
-    def build(
-        self,
-        input_value: Text,
-        endpoint_url: str,
-        model: Optional[str] = None,
-        task: str = "text2text-generation",
-        huggingfacehub_api_token: Optional[str] = None,
-        model_kwargs: Optional[dict] = None,
-        stream: bool = False,
-        system_message: Optional[str] = None,
-    ) -> Text:
         try:
             llm = HuggingFaceEndpoint(  # type: ignore
                 endpoint_url=endpoint_url,
                 task=task,
                 huggingfacehub_api_token=huggingfacehub_api_token,
-                model_kwargs=model_kwargs or {},
-                model=model or "",
+                model_kwargs=model_kwargs,
             )
         except Exception as e:
             raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
+
         output = ChatHuggingFace(llm=llm)
-        return self.get_chat_result(output, stream, input_value, system_message)
-        return self.get_chat_result(output, stream, input_value, system_message)
+        return output
diff --git a/src/backend/base/langflow/components/models/MistralModel.py b/src/backend/base/langflow/components/models/MistralModel.py
index 75937e70d..d9058a4f3 100644
--- a/src/backend/base/langflow/components/models/MistralModel.py
+++ b/src/backend/base/langflow/components/models/MistralModel.py
@@ -1,11 +1,10 @@
-from typing import Optional
-
 from langchain_mistralai import ChatMistralAI
 from pydantic.v1 import SecretStr
 
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
-from langflow.field_typing import Text
+from langflow.field_typing import LanguageModel
+from langflow.io import BoolInput, DropdownInput, FloatInput, IntInput, MessageInput, Output, SecretStrInput, StrInput
 
 
 class MistralAIModelComponent(LCModelComponent):
@@ -13,119 +12,83 @@ class MistralAIModelComponent(LCModelComponent):
     description = "Generates text using MistralAI LLMs."
     icon = "MistralAI"
 
-    field_order = [
-        "max_tokens",
-        "model_kwargs",
-        "model_name",
-        "mistral_api_base",
-        "mistral_api_key",
-        "temperature",
-        "input_value",
-        "system_message",
-        "stream",
+    inputs = [
+        MessageInput(name="input_value", display_name="Input"),
+        IntInput(
+            name="max_tokens",
+            display_name="Max Tokens",
+            advanced=True,
+            info="The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+        ),
+        DropdownInput(
+            name="model_name",
+            display_name="Model Name",
+            advanced=False,
+            options=[
+                "open-mixtral-8x7b",
+                "open-mixtral-8x22b",
+                "mistral-small-latest",
+                "mistral-medium-latest",
+                "mistral-large-latest",
+                "codestral-latest",
+            ],
+            value="codestral-latest",
+        ),
+        StrInput(
+            name="mistral_api_base",
+            display_name="Mistral API Base",
+            advanced=True,
+            info=(
+                "The base URL of the Mistral API. Defaults to https://api.mistral.ai/v1. "
+                "You can change this to use other APIs like JinaChat, LocalAI and Prem."
+            ),
+        ),
+        SecretStrInput(
+            name="mistral_api_key",
+            display_name="Mistral API Key",
+            info="The Mistral API Key to use for the Mistral model.",
+            advanced=False,
+        ),
+        FloatInput(name="temperature", display_name="Temperature", advanced=False, value=0.1),
+        BoolInput(name="stream", display_name="Stream", info=STREAM_INFO_TEXT, advanced=True),
+        StrInput(
+            name="system_message",
+            display_name="System Message",
+            info="System message to pass to the model.",
+            advanced=True,
+        ),
+        IntInput(name="max_retries", display_name="Max Retries", advanced=True),
+        IntInput(name="timeout", display_name="Timeout", advanced=True),
+        IntInput(name="max_concurrent_requests", display_name="Max Concurrent Requests", advanced=True),
+        FloatInput(name="top_p", display_name="Top P", advanced=True),
+        IntInput(name="random_seed", display_name="Random Seed", value=1, advanced=True),
+        BoolInput(name="safe_mode", display_name="Safe Mode", advanced=True),
     ]
 
-    def build_config(self):
-        return {
-            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
-            "max_tokens": {
-                "display_name": "Max Tokens",
-                "advanced": True,
-                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
-            },
-            "model_name": {
-                "display_name": "Model Name",
-                "advanced": False,
-                "options": [
-                    "open-mistral-7b",
-                    "open-mixtral-8x7b",
-                    "open-mixtral-8x22b",
-                    "mistral-small-latest",
-                    "mistral-medium-latest",
-                    "mistral-large-latest",
-                ],
-                "value": "open-mistral-7b",
-            },
-            "mistral_api_base": {
-                "display_name": "Mistral API Base",
-                "advanced": True,
-                "info": (
-                    "The base URL of the Mistral API. Defaults to https://api.mistral.ai.\n\n"
-                    "You can change this to use other APIs like JinaChat, LocalAI and Prem."
-                ),
-            },
-            "mistral_api_key": {
-                "display_name": "Mistral API Key",
-                "info": "The Mistral API Key to use for the Mistral model.",
-                "advanced": False,
-                "password": True,
-            },
-            "temperature": {
-                "display_name": "Temperature",
-                "advanced": False,
-                "value": 0.1,
-            },
-            "stream": {
-                "display_name": "Stream",
-                "info": STREAM_INFO_TEXT,
-                "advanced": True,
-            },
-            "system_message": {
-                "display_name": "System Message",
-                "info": "System message to pass to the model.",
-                "advanced": True,
-            },
-            "max_retries": {
-                "display_name": "Max Retries",
-                "advanced": True,
-            },
-            "timeout": {
-                "display_name": "Timeout",
-                "advanced": True,
-            },
-            "max_concurrent_requests": {
-                "display_name": "Max Concurrent Requests",
-                "advanced": True,
-            },
-            "top_p": {
-                "display_name": "Top P",
-                "advanced": True,
-            },
-            "random_seed": {
-                "display_name": "Random Seed",
-                "advanced": True,
-            },
-            "safe_mode": {
-                "display_name": "Safe Mode",
-                "advanced": True,
-            },
-        }
+    outputs = [
+        Output(display_name="Text", name="text_output", method="text_response"),
+        Output(display_name="Language Model", name="model_output", method="build_model"),
+    ]
+
+    def build_model(self) -> LanguageModel:
+        mistral_api_key = self.mistral_api_key
+        temperature = self.temperature
+        model_name = self.model_name
+        max_tokens = self.max_tokens
+        mistral_api_base = self.mistral_api_base or "https://api.mistral.ai/v1"
+        max_retries = self.max_retries
+        timeout = self.timeout
+        max_concurrent_requests = self.max_concurrent_requests
+        top_p = self.top_p
+        random_seed = self.random_seed
+        safe_mode = self.safe_mode
 
-    def build(
-        self,
-        input_value: Text,
-        mistral_api_key: str,
-        model_name: str,
-        temperature: float = 0.1,
-        max_tokens: Optional[int] = 256,
-        mistral_api_base: Optional[str] = None,
-        stream: bool = False,
-        system_message: Optional[str] = None,
-        max_retries: int = 5,
-        timeout: int = 120,
-        max_concurrent_requests: int = 64,
-        top_p: float = 1,
-        random_seed: Optional[int] = None,
-        safe_mode: bool = False,
-    ) -> Text:
-        if not mistral_api_base:
-            mistral_api_base = "https://api.mistral.ai"
         if mistral_api_key:
             api_key = SecretStr(mistral_api_key)
         else:
             api_key = None
 
-        chat_model = ChatMistralAI(
+        output = ChatMistralAI(
             max_tokens=max_tokens or None,
             model_name=model_name,
             endpoint=mistral_api_base,
@@ -139,4 +102,4 @@ class MistralAIModelComponent(LCModelComponent):
             safe_mode=safe_mode,
         )
 
-        return self.get_chat_result(chat_model, stream, input_value, system_message)
+        return output
diff --git a/src/backend/base/langflow/components/models/OllamaModel.py b/src/backend/base/langflow/components/models/OllamaModel.py
index cca2a0f48..71a62cd68 100644
--- a/src/backend/base/langflow/components/models/OllamaModel.py
+++ b/src/backend/base/langflow/components/models/OllamaModel.py
@@ -1,11 +1,12 @@
-from typing import Any, Dict, List, Optional
+from typing import Any
 
 import httpx
-from langchain_community.chat_models.ollama import ChatOllama
+from langchain_community.chat_models import ChatOllama
 
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
-from langflow.field_typing import Text
+from langflow.field_typing import LanguageModel
+from langflow.io import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, MessageInput, Output, StrInput
 
 
 class ChatOllamaComponent(LCModelComponent):
@@ -13,199 +14,6 @@ class ChatOllamaComponent(LCModelComponent):
     description = "Generate text using Ollama Local LLMs."
     icon = "Ollama"
 
-    field_order = [
-        "base_url",
-        "headers",
-        "keep_alive_flag",
-        "keep_alive",
-        "metadata",
-        "model",
-        "temperature",
-        "cache",
-        "format",
-        "metadata",
-        "mirostat",
-        "mirostat_eta",
-        "mirostat_tau",
-        "num_ctx",
-        "num_gpu",
-        "num_thread",
-        "repeat_last_n",
-        "repeat_penalty",
-        "tfs_z",
-        "timeout",
-        "top_k",
-        "top_p",
-        "verbose",
-        "tags",
-        "stop",
-        "system",
-        "template",
-        "input_value",
-        "system_message",
-        "stream",
-    ]
-
-    def build_config(self) -> dict:
-        return {
-            "base_url": {
-                "display_name": "Base URL",
-                "info": "Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.",
-            },
-            "format": {
-                "display_name": "Format",
-                "info": "Specify the format of the output (e.g., json)",
-                "advanced": True,
-            },
-            "headers": {
-                "display_name": "Headers",
-                "advanced": True,
-            },
-            "keep_alive_flag": {
-                "display_name": "Unload interval",
-                "options": ["Keep", "Immediately", "Minute", "Hour", "sec"],
-                "real_time_refresh": True,
-                "refresh_button": True,
-            },
-            "keep_alive": {
-                "display_name": "interval",
-                "info": "How long the model will stay loaded into memory.",
-            },
-            "model": {
-                "display_name": "Model Name",
-                "options": [],
-                "info": "Refer to https://ollama.ai/library for more models.",
-                "real_time_refresh": True,
-                "refresh_button": True,
-            },
-            "temperature": {
-                "display_name": "Temperature",
-                "field_type": "float",
-                "value": 0.8,
-                "info": "Controls the creativity of model responses.",
-            },
-            "metadata": {
-                "display_name": "Metadata",
-                "info": "Metadata to add to the run trace.",
-                "advanced": True,
-            },
-            "mirostat": {
-                "display_name": "Mirostat",
-                "options": ["Disabled", "Mirostat", "Mirostat 2.0"],
-                "info": "Enable/disable Mirostat sampling for controlling perplexity.",
-                "advanced": False,
-                "real_time_refresh": True,
-                "refresh_button": True,
-            },
-            "mirostat_eta": {
-                "display_name": "Mirostat Eta",
-                "field_type": "float",
-                "info": "Learning rate for Mirostat algorithm. (Default: 0.1)",
-                "advanced": True,
-                "real_time_refresh": True,
-            },
-            "mirostat_tau": {
-                "display_name": "Mirostat Tau",
-                "field_type": "float",
-                "info": "Controls the balance between coherence and diversity of the output. (Default: 5.0)",
-                "advanced": True,
-                "real_time_refresh": True,
-            },
-            "num_ctx": {
-                "display_name": "Context Window Size",
-                "field_type": "int",
-                "info": "Size of the context window for generating tokens. (Default: 2048)",
-                "advanced": True,
-            },
-            "num_gpu": {
-                "display_name": "Number of GPUs",
-                "field_type": "int",
-                "info": "Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)",
-                "advanced": True,
-            },
-            "num_thread": {
-                "display_name": "Number of Threads",
-                "field_type": "int",
-                "info": "Number of threads to use during computation. (Default: detected for optimal performance)",
-                "advanced": True,
-            },
-            "repeat_last_n": {
-                "display_name": "Repeat Last N",
-                "field_type": "int",
-                "info": "How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)",
-                "advanced": True,
-            },
-            "repeat_penalty": {
-                "display_name": "Repeat Penalty",
-                "field_type": "float",
-                "info": "Penalty for repetitions in generated text. (Default: 1.1)",
-                "advanced": True,
-            },
-            "tfs_z": {
-                "display_name": "TFS Z",
-                "field_type": "float",
-                "info": "Tail free sampling value. (Default: 1)",
-                "advanced": True,
-            },
-            "timeout": {
-                "display_name": "Timeout",
-                "field_type": "int",
-                "info": "Timeout for the request stream.",
-                "advanced": True,
-            },
-            "top_k": {
-                "display_name": "Top K",
-                "field_type": "int",
-                "info": "Limits token selection to top K. (Default: 40)",
-                "advanced": True,
-            },
-            "top_p": {
-                "display_name": "Top P",
-                "field_type": "float",
-                "info": "Works together with top-k. (Default: 0.9)",
-                "advanced": True,
-            },
-            "verbose": {
-                "display_name": "Verbose",
-                "field_type": "bool",
-                "info": "Whether to print out response text.",
-            },
-            "tags": {
-                "display_name": "Tags",
-                "field_type": "list",
-                "info": "Tags to add to the run trace.",
-                "advanced": True,
-            },
-            "stop": {
-                "display_name": "Stop Tokens",
-                "field_type": "list",
-                "info": "List of tokens to signal the model to stop generating text.",
-                "advanced": True,
-            },
-            "system": {
-                "display_name": "System",
-                "field_type": "str",
-                "info": "System to use for generating text.",
-                "advanced": True,
-            },
-            "template": {
-                "display_name": "Template",
-                "field_type": "str",
-                "info": "Template to use for generating text.",
-                "advanced": True,
-            },
-            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
-            "stream": {
-                "display_name": "Stream",
-                "info": STREAM_INFO_TEXT,
-            },
-            "system_message": {
-                "display_name": "System Message",
-                "info": "System message to pass to the model.",
-                "advanced": True,
-            },
-        }
-
     def update_build_config(self, build_config: dict, field_value: Any, field_name: str | None = None):
         if field_name == "mirostat":
             if field_value == "Disabled":
@@ -247,7 +55,7 @@ class ChatOllamaComponent(LCModelComponent):
 
         return build_config
 
-    def get_model(self, url: str) -> List[str]:
+    def get_model(self, url: str) -> list[str]:
         try:
             with httpx.Client() as client:
                 response = client.get(url)
@@ -257,88 +65,206 @@ class ChatOllamaComponent(LCModelComponent):
                 model_names = [model["name"] for model in data.get("models", [])]
                 return model_names
         except Exception as e:
-            raise ValueError("Could not retrieve models") from e
-            return [""]
+            raise ValueError("Could not retrieve models. Please, make sure Ollama is running.") from e
 
-    def build(
-        self,
-        base_url: Optional[str],
-        model: str,
-        input_value: Text,
-        mirostat: Optional[str] = "Disabled",
-        mirostat_eta: Optional[float] = None,
-        mirostat_tau: Optional[float] = None,
-        repeat_last_n: Optional[int] = None,
-        verbose: Optional[bool] = None,
-        keep_alive: Optional[int] = None,
-        keep_alive_flag: Optional[str] = "Keep",
-        num_ctx: Optional[int] = None,
-        num_gpu: Optional[int] = None,
-        format: Optional[str] = None,
-        metadata: Optional[Dict] = None,
-        num_thread: Optional[int] = None,
-        repeat_penalty: Optional[float] = None,
-        stop: Optional[List[str]] = None,
-        system: Optional[str] = None,
-        tags: Optional[List[str]] = None,
-        temperature: Optional[float] = None,
-        template: Optional[str] = None,
-        tfs_z: Optional[float] = None,
-        timeout: Optional[int] = None,
-        top_k: Optional[int] = None,
-        top_p: Optional[int] = None,
-        stream: bool = False,
-        system_message: Optional[str] = None,
-    ) -> Text:
-        if not base_url:
-            base_url = "http://localhost:11434"
+    inputs = [
+        StrInput(
+            name="base_url",
+            display_name="Base URL",
+            info="Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.",
+            value="http://localhost:11434",
+        ),
+        DropdownInput(
+            name="model",
+            display_name="Model Name",
+            value="llama2",
+            info="Refer to https://ollama.ai/library for more models.",
+            refresh_button=True,
+        ),
+        FloatInput(
+            name="temperature",
+            display_name="Temperature",
+            value=0.2,
+            info="Controls the creativity of model responses.",
+        ),
+        StrInput(
+            name="format",
+            display_name="Format",
+            info="Specify the format of the output (e.g., json).",
+            advanced=True,
+        ),
+        DictInput(
+            name="metadata",
+            display_name="Metadata",
+            info="Metadata to add to the run trace.",
+            advanced=True,
+        ),
+        DropdownInput(
+            name="mirostat",
+            display_name="Mirostat",
+            options=["Disabled", "Mirostat", "Mirostat 2.0"],
+            info="Enable/disable Mirostat sampling for controlling perplexity.",
+            value="Disabled",
+            advanced=True,
+        ),
+        FloatInput(
+            name="mirostat_eta",
+            display_name="Mirostat Eta",
+            info="Learning rate for Mirostat algorithm. (Default: 0.1)",
+            advanced=True,
+        ),
+        FloatInput(
+            name="mirostat_tau",
+            display_name="Mirostat Tau",
+            info="Controls the balance between coherence and diversity of the output. (Default: 5.0)",
+            advanced=True,
+        ),
+        IntInput(
+            name="num_ctx",
+            display_name="Context Window Size",
+            info="Size of the context window for generating tokens. (Default: 2048)",
+            advanced=True,
+        ),
+        IntInput(
+            name="num_gpu",
+            display_name="Number of GPUs",
+            info="Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)",
+            advanced=True,
+        ),
+        IntInput(
+            name="num_thread",
+            display_name="Number of Threads",
+            info="Number of threads to use during computation. (Default: detected for optimal performance)",
+            advanced=True,
+        ),
+        IntInput(
+            name="repeat_last_n",
+            display_name="Repeat Last N",
+            info="How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)",
+            advanced=True,
+        ),
+        FloatInput(
+            name="repeat_penalty",
+            display_name="Repeat Penalty",
+            info="Penalty for repetitions in generated text. (Default: 1.1)",
+            advanced=True,
+        ),
+        FloatInput(
+            name="tfs_z",
+            display_name="TFS Z",
+            info="Tail free sampling value. (Default: 1)",
+            advanced=True,
+        ),
+        IntInput(
+            name="timeout",
+            display_name="Timeout",
+            info="Timeout for the request stream.",
+            advanced=True,
+        ),
+        IntInput(
+            name="top_k",
+            display_name="Top K",
+            info="Limits token selection to top K. (Default: 40)",
+            advanced=True,
+        ),
+        FloatInput(
+            name="top_p",
+            display_name="Top P",
+            info="Works together with top-k. (Default: 0.9)",
+            advanced=True,
+        ),
+        BoolInput(
+            name="verbose",
+            display_name="Verbose",
+            info="Whether to print out response text.",
+        ),
+        StrInput(
+            name="tags",
+            display_name="Tags",
+            info="Comma-separated list of tags to add to the run trace.",
+            advanced=True,
+        ),
+        StrInput(
+            name="stop_tokens",
+            display_name="Stop Tokens",
+            info="Comma-separated list of tokens to signal the model to stop generating text.",
+            advanced=True,
+        ),
+        StrInput(
+            name="system",
+            display_name="System",
+            info="System to use for generating text.",
+            advanced=True,
+        ),
+        StrInput(
+            name="template",
+            display_name="Template",
+            info="Template to use for generating text.",
+            advanced=True,
+        ),
+        MessageInput(
+            name="input_value",
+            display_name="Input",
+        ),
+        BoolInput(
+            name="stream",
+            display_name="Stream",
+            info=STREAM_INFO_TEXT,
+        ),
+        StrInput(
+            name="system_message",
+            display_name="System Message",
+            info="System message to pass to the model.",
+            advanced=True,
+        ),
+    ]
+    outputs = [
+        Output(display_name="Text", name="text_output", method="text_response"),
+        Output(display_name="Language Model", name="model_output", method="build_model"),
+    ]
 
-        if keep_alive_flag == "Minute":
-            keep_alive_instance = f"{keep_alive}m"
-        elif keep_alive_flag == "Hour":
-            keep_alive_instance = f"{keep_alive}h"
-        elif keep_alive_flag == "sec":
-            keep_alive_instance = f"{keep_alive}s"
-        elif keep_alive_flag == "Keep":
-            keep_alive_instance = "-1"
-        elif keep_alive_flag == "Immediately":
-            keep_alive_instance = "0"
+    def build_model(self) -> LanguageModel:
+        # Mapping mirostat settings to their corresponding values
+        mirostat_options = {"Mirostat": 1, "Mirostat 2.0": 2}
+
+        # Default to 0 for 'Disabled'
+        mirostat_value = mirostat_options.get(self.mirostat, 0)  # type: ignore
+
+        # Set mirostat_eta and mirostat_tau to None if mirostat is disabled
+        if mirostat_value == 0:
+            mirostat_eta = None
+            mirostat_tau = None
         else:
-            keep_alive_instance = "Invalid option"
-
-        mirostat_instance = 0
-
-        if mirostat == "disable":
-            mirostat_instance = 0
+            mirostat_eta = self.mirostat_eta
+            mirostat_tau = self.mirostat_tau
 
         # Mapping system settings to their corresponding values
         llm_params = {
-            "base_url": base_url,
-            "model": model,
-            "mirostat": mirostat_instance,
-            "keep_alive": keep_alive_instance,
-            "format": format,
-            "metadata": metadata,
-            "tags": tags,
+            "base_url": self.base_url,
+            "model": self.model,
+            "mirostat": mirostat_value,
+            "format": self.format,
+            "metadata": self.metadata,
+            "tags": self.tags.split(",") if self.tags else None,
             "mirostat_eta": mirostat_eta,
             "mirostat_tau": mirostat_tau,
-            "num_ctx": num_ctx,
-            "num_gpu": num_gpu,
-            "num_thread": num_thread,
-            "repeat_last_n": repeat_last_n,
-            "repeat_penalty": repeat_penalty,
-            "temperature": temperature,
-            "stop": stop,
-            "system": system,
-            "template": template,
-            "tfs_z": tfs_z,
-            "timeout": timeout,
-            "top_k": top_k,
-            "top_p": top_p,
-            "verbose": verbose,
+            "num_ctx": self.num_ctx or None,
+            "num_gpu": self.num_gpu or None,
+            "num_thread": self.num_thread or None,
+            "repeat_last_n": self.repeat_last_n or None,
+            "repeat_penalty": self.repeat_penalty or None,
+            "temperature": self.temperature or None,
+            "stop": self.stop_tokens.split(",") if self.stop_tokens else None,
+            "system": self.system,
+            "template": self.template,
+            "tfs_z": self.tfs_z or None,
+            "timeout": self.timeout or None,
+            "top_k": self.top_k or None,
+            "top_p": self.top_p or None,
+            "verbose": self.verbose,
         }
 
-        # None Value remove
+        # Remove parameters with None values
         llm_params = {k: v for k, v in llm_params.items() if v is not None}
 
         try:
@@ -346,4 +272,4 @@ class ChatOllamaComponent(LCModelComponent):
         except Exception as e:
             raise ValueError("Could not initialize Ollama LLM.") from e
 
-        return self.get_chat_result(output, stream, input_value, system_message)
+        return output
diff --git a/src/backend/base/langflow/components/models/OpenAIModel.py b/src/backend/base/langflow/components/models/OpenAIModel.py
index 329b0357f..6545cf3dc 100644
--- a/src/backend/base/langflow/components/models/OpenAIModel.py
+++ b/src/backend/base/langflow/components/models/OpenAIModel.py
@@ -1,4 +1,5 @@
-from typing import Optional
+import operator
+from functools import reduce
 
 from langchain_openai import ChatOpenAI
 from pydantic.v1 import SecretStr
@@ -6,7 +7,17 @@ from pydantic.v1 import SecretStr
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
 from langflow.base.models.openai_constants import MODEL_NAMES
-from langflow.field_typing import NestedDict, Text
+from langflow.field_typing import LanguageModel
+from langflow.inputs import (
+    BoolInput,
+    DictInput,
+    DropdownInput,
+    FloatInput,
+    IntInput,
+    MessageInput,
+    SecretStrInput,
+    StrInput,
+)
 
 
 class OpenAIModelComponent(LCModelComponent):
@@ -14,92 +25,103 @@ class OpenAIModelComponent(LCModelComponent):
     description = "Generates text using OpenAI LLMs."
     icon = "OpenAI"
 
-    field_order = [
-        "max_tokens",
-        "model_kwargs",
-        "model_name",
-        "openai_api_base",
-        "openai_api_key",
-        "temperature",
-        "input_value",
-        "system_message",
-        "stream",
+    inputs = [
+        MessageInput(name="input_value", display_name="Input"),
+        IntInput(
+            name="max_tokens",
+            display_name="Max Tokens",
+            advanced=True,
+            info="The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+        ),
+        DictInput(name="model_kwargs", display_name="Model Kwargs", advanced=True),
+        DictInput(
+            name="output_schema",
+            is_list=True,
+            display_name="Schema",
+            advanced=True,
+            info="The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.",
+        ),
+        DropdownInput(
+            name="model_name", display_name="Model Name", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]
+        ),
+        StrInput(
+            name="openai_api_base",
+            display_name="OpenAI API Base",
+            advanced=True,
+            info="The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.",
+        ),
+        SecretStrInput(
+            name="openai_api_key",
+            display_name="OpenAI API Key",
+            info="The OpenAI API Key to use for the OpenAI model.",
+            advanced=False,
+            value="OPENAI_API_KEY",
+        ),
+        FloatInput(name="temperature", display_name="Temperature", value=0.1),
+        BoolInput(name="stream", display_name="Stream", info=STREAM_INFO_TEXT, advanced=True),
+        StrInput(
+            name="system_message",
+            display_name="System Message",
+            info="System message to pass to the model.",
+            advanced=True,
+        ),
+        IntInput(
+            name="seed",
+            display_name="Seed",
+            info="The seed controls the reproducibility of the job.",
+            advanced=True,
+            value=1,
+        ),
     ]
 
-    def build_config(self):
-        return {
-            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
-            "max_tokens": {
-                "display_name": "Max Tokens",
-                "advanced": True,
-                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
-            },
-            "model_kwargs": {
-                "display_name": "Model Kwargs",
-                "advanced": True,
-            },
-            "model_name": {
-                "display_name": "Model Name",
-                "advanced": False,
-                "options": MODEL_NAMES,
-            },
-            "openai_api_base": {
-                "display_name": "OpenAI API Base",
-                "advanced": True,
-                "info": (
-                    "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\n"
-                    "You can change this to use other APIs like JinaChat, LocalAI and Prem."
-                ),
-            },
-            "openai_api_key": {
-                "display_name": "OpenAI API Key",
-                "info": "The OpenAI API Key to use for the OpenAI model.",
-                "advanced": False,
-                "password": True,
-            },
-            "temperature": {
-                "display_name": "Temperature",
-                "advanced": False,
-                "value": 0.1,
-            },
-            "stream": {
-                "display_name": "Stream",
-                "info": STREAM_INFO_TEXT,
-                "advanced": True,
-            },
-            "system_message": {
-                "display_name": "System Message",
-                "info": "System message to pass to the model.",
-                "advanced": True,
-            },
-        }
+    def build_model(self) -> LanguageModel:
+        # self.output_schea is a list of dictionaries
+        # let's convert it to a dictionary
+        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})
+        openai_api_key = self.openai_api_key
+        temperature = self.temperature
+        model_name: str = self.model_name
+        max_tokens = self.max_tokens
+        model_kwargs = self.model_kwargs or {}
+        openai_api_base = self.openai_api_base or "https://api.openai.com/v1"
+        json_mode = bool(output_schema_dict)
+        seed = self.seed
+        model_kwargs["seed"] = seed
 
-    def build(
-        self,
-        input_value: Text,
-        openai_api_key: str,
-        temperature: float = 0.1,
-        model_name: str = "gpt-3.5-turbo",
-        max_tokens: Optional[int] = 256,
-        model_kwargs: NestedDict = {},
-        openai_api_base: Optional[str] = None,
-        stream: bool = False,
-        system_message: Optional[str] = None,
-    ) -> Text:
-        if not openai_api_base:
-            openai_api_base = "https://api.openai.com/v1"
         if openai_api_key:
             api_key = SecretStr(openai_api_key)
         else:
             api_key = None
-
         output = ChatOpenAI(
             max_tokens=max_tokens or None,
             model_kwargs=model_kwargs,
             model=model_name,
             base_url=openai_api_base,
             api_key=api_key,
-            temperature=temperature,
+            temperature=temperature or 0.1,
         )
+        if json_mode:
+            output = output.with_structured_output(schema=output_schema_dict, method="json_mode")  # type: ignore
 
-        return self.get_chat_result(output, stream, input_value, system_message)
+        return output
+
+    def _get_exception_message(self, e: Exception):
+        """
+        Get a message from an OpenAI exception.
+
+        Args:
+            exception (Exception): The exception to get the message from.
+
+        Returns:
+            str: The message from the exception.
+        """
+
+        try:
+            from openai import BadRequestError
+        except ImportError:
+            return
+        if isinstance(e, BadRequestError):
+            message = e.body.get("message")  # type: ignore
+            if message:
+                return message
+        return
diff --git a/src/backend/base/langflow/components/models/VertexAiModel.py b/src/backend/base/langflow/components/models/VertexAiModel.py
index 33bbbbc46..fb06ac5f2 100644
--- a/src/backend/base/langflow/components/models/VertexAiModel.py
+++ b/src/backend/base/langflow/components/models/VertexAiModel.py
@@ -1,8 +1,9 @@
-from typing import Optional
+from langchain_google_vertexai import ChatVertexAI
 
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
-from langflow.field_typing import Text
+from langflow.field_typing import LanguageModel
+from langflow.io import BoolInput, FileInput, FloatInput, IntInput, MessageInput, MultilineInput, Output, StrInput
 
 
 class ChatVertexAIComponent(LCModelComponent):
@@ -10,103 +11,58 @@ class ChatVertexAIComponent(LCModelComponent):
     description = "Generate text using Vertex AI LLMs."
     icon = "VertexAI"
 
-    field_order = [
-        "credentials",
-        "project",
-        "examples",
-        "location",
-        "max_output_tokens",
-        "model_name",
-        "temperature",
-        "top_k",
-        "top_p",
-        "verbose",
-        "input_value",
-        "system_message",
-        "stream",
+    inputs = [
+        MessageInput(name="input_value", display_name="Input"),
+        FileInput(
+            name="credentials",
+            display_name="Credentials",
+            info="Path to the JSON file containing the credentials.",
+            file_types=["json"],
+            advanced=True,
+        ),
+        StrInput(name="project", display_name="Project", info="The project ID."),
+        MultilineInput(
+            name="examples",
+            display_name="Examples",
+            info="Examples to pass to the model.",
+            advanced=True,
+        ),
+        StrInput(name="location", display_name="Location", value="us-central1", advanced=True),
+        IntInput(
+            name="max_output_tokens",
+            display_name="Max Output Tokens",
+            value=128,
+            advanced=True,
+        ),
+        StrInput(name="model_name", display_name="Model Name", value="gemini-1.5-pro"),
+        FloatInput(name="temperature", display_name="Temperature", value=0.0),
+        IntInput(name="top_k", display_name="Top K", value=40, advanced=True),
+        FloatInput(name="top_p", display_name="Top P", value=0.95, advanced=True),
+        BoolInput(name="verbose", display_name="Verbose", value=False, advanced=True),
+        BoolInput(name="stream", display_name="Stream", info=STREAM_INFO_TEXT, advanced=True),
+        StrInput(
+            name="system_message",
+            display_name="System Message",
+            info="System message to pass to the model.",
+            advanced=True,
+        ),
+    ]
+    outputs = [
+        Output(display_name="Text", name="text_output", method="text_response"),
+        Output(display_name="Language Model", name="model_output", method="build_model"),
     ]
 
-    def build_config(self):
-        return {
-            "credentials": {
-                "display_name": "Credentials",
-                "field_type": "file",
-                "file_types": [".json"],
-                "file_path": None,
-            },
-            "examples": {
-                "display_name": "Examples",
-                "multiline": True,
-            },
-            "location": {
-                "display_name": "Location",
-                "value": "us-central1",
-            },
-            "max_output_tokens": {
-                "display_name": "Max Output Tokens",
-                "value": 128,
-                "advanced": True,
-            },
-            "model_name": {
-                "display_name": "Model Name",
-                "value": "chat-bison",
-            },
-            "project": {
-                "display_name": "Project",
-            },
-            "temperature": {
-                "display_name": "Temperature",
-                "value": 0.0,
-            },
-            "top_k": {
-                "display_name": "Top K",
-                "value": 40,
-                "advanced": True,
-            },
-            "top_p": {
-                "display_name": "Top P",
-                "value": 0.95,
-                "advanced": True,
-            },
-            "verbose": {
-                "display_name": "Verbose",
-                "value": False,
-                "advanced": True,
-            },
-            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
-            "stream": {
-                "display_name": "Stream",
-                "info": STREAM_INFO_TEXT,
-                "advanced": True,
-            },
-            "system_message": {
-                "display_name": "System Message",
-                "info": "System message to pass to the model.",
-                "advanced": True,
-            },
-        }
+    def build_model(self) -> LanguageModel:
+        credentials = self.credentials
+        location = self.location
+        max_output_tokens = self.max_output_tokens
+        model_name = self.model_name
+        project = self.project
+        temperature = self.temperature
+        top_k = self.top_k
+        top_p = self.top_p
+        verbose = self.verbose
 
-    def build(
-        self,
-        input_value: Text,
-        credentials: Optional[str],
-        project: str,
-        location: str = "us-central1",
-        max_output_tokens: int = 128,
-        model_name: str = "chat-bison",
-        temperature: float = 0.0,
-        top_k: int = 40,
-        top_p: float = 0.95,
-        verbose: bool = False,
-        stream: bool = False,
-        system_message: Optional[str] = None,
-    ) -> Text:
-        try:
-            from langchain_google_vertexai import ChatVertexAI  # type: ignore
-        except ImportError:
-            raise ImportError(
-                "To use the ChatVertexAI model, you need to install the langchain-google-vertexai package."
-            )
         output = ChatVertexAI(
             credentials=credentials,
             location=location,
@@ -119,4 +75,4 @@ class ChatVertexAIComponent(LCModelComponent):
             verbose=verbose,
         )
 
-        return self.get_chat_result(output, stream, input_value, system_message)
+        return output
diff --git a/src/backend/base/langflow/components/models/__init__.py b/src/backend/base/langflow/components/models/__init__.py
index 9db6caa26..a348f230a 100644
--- a/src/backend/base/langflow/components/models/__init__.py
+++ b/src/backend/base/langflow/components/models/__init__.py
@@ -1,5 +1,5 @@
 from .AmazonBedrockModel import AmazonBedrockComponent
-from .AnthropicModel import AnthropicLLM
+from .AnthropicModel import AnthropicModelComponent
 from .AzureOpenAIModel import AzureChatOpenAIComponent
 from .BaiduQianfanChatModel import QianfanChatEndpointComponent
 from .ChatLiteLLMModel import ChatLiteLLMModelComponent
@@ -13,7 +13,7 @@ from .VertexAiModel import ChatVertexAIComponent
 __all__ = [
     "ChatLiteLLMModelComponent",
     "AmazonBedrockComponent",
-    "AnthropicLLM",
+    "AnthropicModelComponent",
     "AzureChatOpenAIComponent",
     "QianfanChatEndpointComponent",
     "CohereComponent",
diff --git a/src/backend/base/langflow/components/outputs/ChatOutput.py b/src/backend/base/langflow/components/outputs/ChatOutput.py
index 6278fd74f..f2e5b426d 100644
--- a/src/backend/base/langflow/components/outputs/ChatOutput.py
+++ b/src/backend/base/langflow/components/outputs/ChatOutput.py
@@ -1,7 +1,5 @@
-from typing import Optional, Union
-
 from langflow.base.io.chat import ChatComponent
-from langflow.field_typing import Text
+from langflow.io import DropdownInput, Output, TextInput
 from langflow.schema.message import Message
 
 
@@ -10,20 +8,46 @@ class ChatOutput(ChatComponent):
     description = "Display a chat message in the Playground."
     icon = "ChatOutput"
 
-    def build(
-        self,
-        sender: Optional[str] = "Machine",
-        sender_name: Optional[str] = "AI",
-        input_value: Optional[str] = None,
-        session_id: Optional[str] = None,
-        files: Optional[list[str]] = None,
-        return_message: Optional[bool] = False,
-    ) -> Union[Message, Text]:
-        return super().build_with_record(
-            sender=sender,
-            sender_name=sender_name,
-            input_value=input_value,
-            session_id=session_id,
-            files=files,
-            return_message=return_message,
+    inputs = [
+        TextInput(
+            name="input_value",
+            display_name="Text",
+            info="Message to be passed as output.",
+        ),
+        DropdownInput(
+            name="sender",
+            display_name="Sender Type",
+            options=["Machine", "User"],
+            value="Machine",
+            advanced=True,
+            info="Type of sender.",
+        ),
+        TextInput(
+            name="sender_name", display_name="Sender Name", info="Name of the sender.", value="AI", advanced=True
+        ),
+        TextInput(name="session_id", display_name="Session ID", info="Session ID for the message.", advanced=True),
+        TextInput(
+            name="data_template",
+            display_name="Data Template",
+            value="{text}",
+            advanced=True,
+            info="Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
+        ),
+    ]
+    outputs = [
+        Output(display_name="Message", name="message", method="message_response"),
+    ]
+
+    def message_response(self) -> Message:
+        message = Message(
+            text=self.input_value,
+            sender=self.sender,
+            sender_name=self.sender_name,
+            session_id=self.session_id,
         )
+        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):
+            self.store_message(message)
+            self.message.value = message
+
+        self.status = message
+        return message
diff --git a/src/backend/base/langflow/components/outputs/RecordsOutput.py b/src/backend/base/langflow/components/outputs/RecordsOutput.py
deleted file mode 100644
index cbd268ea6..000000000
--- a/src/backend/base/langflow/components/outputs/RecordsOutput.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from langflow.custom import CustomComponent
-from langflow.schema import Record
-
-
-class RecordOutput(CustomComponent):
-    display_name = "Records Output"
-    description = "Display Records as a Table"
-
-    def build_config(self):
-        return {
-            "input_value": {
-                "display_name": "Records",
-                "input_types": ["Record"],
-                "info": "Record or Record list to be passed as input.",
-            },
-        }
-
-    def build(self, input_value: Record) -> Record:
-        self.status = input_value
-        return input_value
diff --git a/src/backend/base/langflow/components/outputs/TextOutput.py b/src/backend/base/langflow/components/outputs/TextOutput.py
index 9096b7a4d..5e3809a2b 100644
--- a/src/backend/base/langflow/components/outputs/TextOutput.py
+++ b/src/backend/base/langflow/components/outputs/TextOutput.py
@@ -1,28 +1,27 @@
-from typing import Optional
-
 from langflow.base.io.text import TextComponent
-from langflow.field_typing import Text
+from langflow.io import Output, TextInput
+from langflow.schema.message import Message
 
 
-class TextOutput(TextComponent):
+class TextOutputComponent(TextComponent):
     display_name = "Text Output"
     description = "Display a text output in the Playground."
     icon = "type"
 
-    def build_config(self):
-        return {
-            "input_value": {
-                "display_name": "Text",
-                "input_types": ["Record", "Text"],
-                "info": "Text or Record to be passed as output.",
-            },
-            "record_template": {
-                "display_name": "Record Template",
-                "multiline": True,
-                "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
-                "advanced": True,
-            },
-        }
+    inputs = [
+        TextInput(
+            name="input_value",
+            display_name="Text",
+            info="Text to be passed as output.",
+        ),
+    ]
+    outputs = [
+        Output(display_name="Text", name="text", method="text_response"),
+    ]
 
-    def build(self, input_value: Optional[Text] = "", record_template: Optional[str] = "") -> Text:
-        return super().build(input_value=input_value, record_template=record_template)
+    def text_response(self) -> Message:
+        message = Message(
+            text=self.input_value,
+        )
+        self.status = self.input_value
+        return message
diff --git a/src/backend/base/langflow/components/outputs/__init__.py b/src/backend/base/langflow/components/outputs/__init__.py
index cf395bb86..35200e0fe 100644
--- a/src/backend/base/langflow/components/outputs/__init__.py
+++ b/src/backend/base/langflow/components/outputs/__init__.py
@@ -1,4 +1,4 @@
 from .ChatOutput import ChatOutput
-from .TextOutput import TextOutput
+from .TextOutput import TextOutputComponent
 
-__all__ = ["ChatOutput", "TextOutput"]
+__all__ = ["ChatOutput", "TextOutputComponent"]
diff --git a/src/backend/base/langflow/components/prompts/Prompt.py b/src/backend/base/langflow/components/prompts/Prompt.py
index d2d9f78a0..adb2b49d8 100644
--- a/src/backend/base/langflow/components/prompts/Prompt.py
+++ b/src/backend/base/langflow/components/prompts/Prompt.py
@@ -1,24 +1,24 @@
-from langflow.custom import CustomComponent
-from langflow.field_typing import TemplateField
-from langflow.field_typing.prompt import Prompt
+from langflow.custom import Component
+from langflow.io import Output, PromptInput
+from langflow.schema.message import Message
 
 
-class PromptComponent(CustomComponent):
-    display_name: str = "Empty Prompt"
+class PromptComponent(Component):
+    display_name: str = "Prompt"
     description: str = "Create a prompt template with dynamic variables."
     icon = "prompts"
 
-    def build_config(self):
-        return {
-            "template": TemplateField(display_name="Template"),
-            "code": TemplateField(advanced=True),
-        }
+    inputs = [
+        PromptInput(name="template", display_name="Template"),
+    ]
 
-    async def build(
+    outputs = [
+        Output(display_name="Prompt Message", name="prompt", method="build_prompt"),
+    ]
+
+    async def build_prompt(
         self,
-        template: Prompt,
-        **kwargs,
-    ) -> Prompt:
-        prompt = await Prompt.from_template_and_variables(template, kwargs)  # type: ignore
-        self.status = prompt.format_text()
+    ) -> Message:
+        prompt = await Message.from_template_and_variables(**self._attributes)
+        self.status = prompt.text
         return prompt
diff --git a/src/backend/base/langflow/components/prompts/__init__.py b/src/backend/base/langflow/components/prompts/__init__.py
new file mode 100644
index 000000000..7a0a9cdf0
--- /dev/null
+++ b/src/backend/base/langflow/components/prompts/__init__.py
@@ -0,0 +1,3 @@
+from .Prompt import PromptComponent
+
+__all__ = ["PromptComponent"]
diff --git a/src/backend/base/langflow/components/retrievers/AmazonKendra.py b/src/backend/base/langflow/components/retrievers/AmazonKendra.py
index 23ab9191a..ff830f2ed 100644
--- a/src/backend/base/langflow/components/retrievers/AmazonKendra.py
+++ b/src/backend/base/langflow/components/retrievers/AmazonKendra.py
@@ -1,9 +1,9 @@
 from typing import Optional
 
 from langchain_community.retrievers import AmazonKendraRetriever
-from langchain_core.retrievers import BaseRetriever
 
 from langflow.custom import CustomComponent
+from langflow.field_typing import Retriever
 
 
 class AmazonKendraRetrieverComponent(CustomComponent):
@@ -36,7 +36,7 @@ class AmazonKendraRetrieverComponent(CustomComponent):
         credentials_profile_name: Optional[str] = None,
         attribute_filter: Optional[dict] = None,
         user_context: Optional[dict] = None,
-    ) -> BaseRetriever:
+    ) -> Retriever:
         try:
             output = AmazonKendraRetriever(
                 index_id=index_id,
diff --git a/src/backend/base/langflow/components/retrievers/MetalRetriever.py b/src/backend/base/langflow/components/retrievers/MetalRetriever.py
index 55fbcff0d..104adcbde 100644
--- a/src/backend/base/langflow/components/retrievers/MetalRetriever.py
+++ b/src/backend/base/langflow/components/retrievers/MetalRetriever.py
@@ -1,10 +1,10 @@
 from typing import Optional
 
 from langchain_community.retrievers import MetalRetriever
-from langchain_core.retrievers import BaseRetriever
 from metal_sdk.metal import Metal  # type: ignore
 
 from langflow.custom import CustomComponent
+from langflow.field_typing import Retriever
 
 
 class MetalRetrieverComponent(CustomComponent):
@@ -20,7 +20,7 @@ class MetalRetrieverComponent(CustomComponent):
             "code": {"show": False},
         }
 
-    def build(self, api_key: str, client_id: str, index_id: str, params: Optional[dict] = None) -> BaseRetriever:
+    def build(self, api_key: str, client_id: str, index_id: str, params: Optional[dict] = None) -> Retriever:
         try:
             metal = Metal(api_key=api_key, client_id=client_id, index_id=index_id)
         except Exception as e:
diff --git a/src/backend/base/langflow/components/retrievers/MultiQueryRetriever.py b/src/backend/base/langflow/components/retrievers/MultiQueryRetriever.py
index d9197ece2..f7b2eda6b 100644
--- a/src/backend/base/langflow/components/retrievers/MultiQueryRetriever.py
+++ b/src/backend/base/langflow/components/retrievers/MultiQueryRetriever.py
@@ -3,7 +3,7 @@ from typing import Optional
 from langchain.retrievers import MultiQueryRetriever
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, BaseRetriever, PromptTemplate, Text
+from langflow.field_typing import BaseRetriever, LanguageModel, PromptTemplate, Text
 
 
 class MultiQueryRetrieverComponent(CustomComponent):
@@ -39,7 +39,7 @@ class MultiQueryRetrieverComponent(CustomComponent):
 
     def build(
         self,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         retriever: BaseRetriever,
         prompt: Optional[Text] = None,
         parser_key: str = "lines",
diff --git a/src/backend/base/langflow/components/retrievers/SelfQueryRetriever.py b/src/backend/base/langflow/components/retrievers/SelfQueryRetriever.py
index 7dc53caff..fd61a32de 100644
--- a/src/backend/base/langflow/components/retrievers/SelfQueryRetriever.py
+++ b/src/backend/base/langflow/components/retrievers/SelfQueryRetriever.py
@@ -4,8 +4,8 @@ from langchain.retrievers.self_query.base import SelfQueryRetriever
 from langchain_core.vectorstores import VectorStore
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, Text
-from langflow.schema import Record
+from langflow.field_typing import LanguageModel, Text
+from langflow.schema import Data
 from langflow.schema.message import Message
 
 
@@ -43,11 +43,11 @@ class SelfQueryRetrieverComponent(CustomComponent):
         self,
         query: Message,
         vectorstore: VectorStore,
-        attribute_infos: list[Record],
+        attribute_infos: list[Data],
         document_content_description: Text,
-        llm: BaseLanguageModel,
-    ) -> Record:
-        metadata_field_infos = [AttributeInfo(**record.data) for record in attribute_infos]
+        llm: LanguageModel,
+    ) -> Data:
+        metadata_field_infos = [AttributeInfo(**value.data) for value in attribute_infos]
         self_query_retriever = SelfQueryRetriever.from_llm(
             llm=llm,
             vectorstore=vectorstore,
@@ -60,9 +60,10 @@ class SelfQueryRetrieverComponent(CustomComponent):
             input_text = query.text
         elif isinstance(query, str):
             input_text = query
-        else:
+
+        if not isinstance(query, str):
             raise ValueError(f"Query type {type(query)} not supported.")
-        documents = self_query_retriever.invoke(input=input_text)  # type: ignore
-        records = [Record.from_document(document) for document in documents]
-        self.status = records  # type: ignore
-        return records  # type: ignore
+        documents = self_query_retriever.invoke(input=input_text)
+        data = [Data.from_document(document) for document in documents]
+        self.status = data
+        return data
diff --git a/src/backend/base/langflow/components/retrievers/VectaraSelfQueryRetriver.py b/src/backend/base/langflow/components/retrievers/VectaraSelfQueryRetriver.py
index 0c5c4fff5..42ffd92d6 100644
--- a/src/backend/base/langflow/components/retrievers/VectaraSelfQueryRetriver.py
+++ b/src/backend/base/langflow/components/retrievers/VectaraSelfQueryRetriver.py
@@ -3,11 +3,11 @@ from typing import List
 
 from langchain.chains.query_constructor.base import AttributeInfo
 from langchain.retrievers.self_query.base import SelfQueryRetriever
-from langchain_core.language_models import BaseLanguageModel
-from langchain_core.retrievers import BaseRetriever
 from langchain_core.vectorstores import VectorStore
 
 from langflow.custom import CustomComponent
+from langflow.field_typing.constants import LanguageModel
+from langflow.field_typing import Retriever
 
 
 class VectaraSelfQueryRetriverComponent(CustomComponent):
@@ -38,9 +38,9 @@ class VectaraSelfQueryRetriverComponent(CustomComponent):
         self,
         vectorstore: VectorStore,
         document_content_description: str,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
         metadata_field_info: List[str],
-    ) -> BaseRetriever:
+    ) -> Retriever:
         metadata_field_obj = []
 
         for meta in metadata_field_info:
diff --git a/src/backend/base/langflow/components/textsplitters/CharacterTextSplitter.py b/src/backend/base/langflow/components/textsplitters/CharacterTextSplitter.py
index 9f60d7c88..c0f00b078 100644
--- a/src/backend/base/langflow/components/textsplitters/CharacterTextSplitter.py
+++ b/src/backend/base/langflow/components/textsplitters/CharacterTextSplitter.py
@@ -3,7 +3,7 @@ from typing import List
 from langchain_text_splitters import CharacterTextSplitter
 
 from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langflow.schema import Data
 from langflow.utils.util import unescape_string
 
 
@@ -13,7 +13,7 @@ class CharacterTextSplitterComponent(CustomComponent):
 
     def build_config(self):
         return {
-            "inputs": {"display_name": "Input", "input_types": ["Document", "Record"]},
+            "inputs": {"display_name": "Input", "input_types": ["Document", "Data"]},
             "chunk_overlap": {"display_name": "Chunk Overlap", "default": 200},
             "chunk_size": {"display_name": "Chunk Size", "default": 1000},
             "separator": {"display_name": "Separator", "default": "\n"},
@@ -21,16 +21,16 @@ class CharacterTextSplitterComponent(CustomComponent):
 
     def build(
         self,
-        inputs: List[Record],
+        inputs: List[Data],
         chunk_overlap: int = 200,
         chunk_size: int = 1000,
         separator: str = "\n",
-    ) -> List[Record]:
+    ) -> List[Data]:
         # separator may come escaped from the frontend
         separator = unescape_string(separator)
         documents = []
         for _input in inputs:
-            if isinstance(_input, Record):
+            if isinstance(_input, Data):
                 documents.append(_input.to_lc_document())
             else:
                 documents.append(_input)
@@ -39,6 +39,6 @@ class CharacterTextSplitterComponent(CustomComponent):
             chunk_size=chunk_size,
             separator=separator,
         ).split_documents(documents)
-        records = self.to_records(docs)
-        self.status = records
-        return records
+        data = self.to_data(docs)
+        self.status = data
+        return data
diff --git a/src/backend/base/langflow/components/textsplitters/LanguageRecursiveTextSplitter.py b/src/backend/base/langflow/components/textsplitters/LanguageRecursiveTextSplitter.py
index a43fdcd72..4c074e861 100644
--- a/src/backend/base/langflow/components/textsplitters/LanguageRecursiveTextSplitter.py
+++ b/src/backend/base/langflow/components/textsplitters/LanguageRecursiveTextSplitter.py
@@ -3,7 +3,7 @@ from typing import List, Optional
 from langchain_text_splitters import Language, RecursiveCharacterTextSplitter
 
 from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class LanguageRecursiveTextSplitterComponent(CustomComponent):
@@ -14,7 +14,7 @@ class LanguageRecursiveTextSplitterComponent(CustomComponent):
     def build_config(self):
         options = [x.value for x in Language]
         return {
-            "inputs": {"display_name": "Input", "input_types": ["Document", "Record"]},
+            "inputs": {"display_name": "Input", "input_types": ["Document", "Data"]},
             "separator_type": {
                 "display_name": "Separator Type",
                 "info": "The type of separator to use.",
@@ -44,11 +44,11 @@ class LanguageRecursiveTextSplitterComponent(CustomComponent):
 
     def build(
         self,
-        inputs: List[Record],
+        inputs: List[Data],
         chunk_size: Optional[int] = 1000,
         chunk_overlap: Optional[int] = 200,
         separator_type: str = "Python",
-    ) -> list[Record]:
+    ) -> list[Data]:
         """
         Split text into chunks of a specified length.
 
@@ -75,10 +75,10 @@ class LanguageRecursiveTextSplitterComponent(CustomComponent):
         )
         documents = []
         for _input in inputs:
-            if isinstance(_input, Record):
+            if isinstance(_input, Data):
                 documents.append(_input.to_lc_document())
             else:
                 documents.append(_input)
         docs = splitter.split_documents(documents)
-        records = self.to_records(docs)
-        return records
+        data = self.to_data(docs)
+        return data
diff --git a/src/backend/base/langflow/components/textsplitters/RecursiveCharacterTextSplitter.py b/src/backend/base/langflow/components/textsplitters/RecursiveCharacterTextSplitter.py
index 77fcfa62a..ab4308afe 100644
--- a/src/backend/base/langflow/components/textsplitters/RecursiveCharacterTextSplitter.py
+++ b/src/backend/base/langflow/components/textsplitters/RecursiveCharacterTextSplitter.py
@@ -1,89 +1,86 @@
-from typing import Optional
-
-from langchain_core.documents import Document
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 
-from langflow.custom import CustomComponent
-from langflow.schema import Record
-from langflow.utils.util import build_loader_repr_from_records, unescape_string
+from langflow.custom import Component
+from langflow.inputs.inputs import DataInput, IntInput, TextInput
+from langflow.schema import Data
+from langflow.template.field.base import Output
+from langflow.utils.util import build_loader_repr_from_data, unescape_string
 
 
-class RecursiveCharacterTextSplitterComponent(CustomComponent):
+class RecursiveCharacterTextSplitterComponent(Component):
     display_name: str = "Recursive Character Text Splitter"
     description: str = "Split text into chunks of a specified length."
     documentation: str = "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter"
 
-    def build_config(self):
-        return {
-            "inputs": {
-                "display_name": "Input",
-                "info": "The texts to split.",
-                "input_types": ["Document", "Record"],
-            },
-            "separators": {
-                "display_name": "Separators",
-                "info": 'The characters to split on.\nIf left empty defaults to ["\\n\\n", "\\n", " ", ""].',
-                "is_list": True,
-            },
-            "chunk_size": {
-                "display_name": "Chunk Size",
-                "info": "The maximum length of each chunk.",
-                "field_type": "int",
-                "value": 1000,
-            },
-            "chunk_overlap": {
-                "display_name": "Chunk Overlap",
-                "info": "The amount of overlap between chunks.",
-                "field_type": "int",
-                "value": 200,
-            },
-            "code": {"show": False},
-        }
+    inputs = [
+        IntInput(
+            name="chunk_size",
+            display_name="Chunk Size",
+            info="The maximum length of each chunk.",
+            value=1000,
+        ),
+        IntInput(
+            name="chunk_overlap",
+            display_name="Chunk Overlap",
+            info="The amount of overlap between chunks.",
+            value=200,
+        ),
+        DataInput(
+            name="data_input",
+            display_name="Input",
+            info="The texts to split.",
+            input_types=["Document", "Data"],
+        ),
+        TextInput(
+            name="separators",
+            display_name="Separators",
+            info='The characters to split on.\nIf left empty defaults to ["\\n\\n", "\\n", " ", ""].',
+            is_list=True,
+        ),
+    ]
+    outputs = [
+        Output(display_name="Data", name="data", method="build"),
+    ]
 
-    def build(
-        self,
-        inputs: list[Document],
-        separators: Optional[list[str]] = None,
-        chunk_size: Optional[int] = 1000,
-        chunk_overlap: Optional[int] = 200,
-    ) -> list[Record]:
+    def build(self) -> list[Data]:
         """
         Split text into chunks of a specified length.
 
         Args:
-            separators (list[str]): The characters to split on.
+            separators (list[str] | None): The characters to split on.
             chunk_size (int): The maximum length of each chunk.
             chunk_overlap (int): The amount of overlap between chunks.
-            length_function (function): The function to use to calculate the length of the text.
 
         Returns:
             list[str]: The chunks of text.
         """
 
-        if separators == "":
-            separators = None
-        elif separators:
+        if self.separators == "":
+            self.separators: list[str] | None = None
+        elif self.separators:
             # check if the separators list has escaped characters
             # if there are escaped characters, unescape them
-            separators = [unescape_string(x) for x in separators]
+            self.separators = [unescape_string(x) for x in self.separators]
 
         # Make sure chunk_size and chunk_overlap are ints
-        if isinstance(chunk_size, str):
-            chunk_size = int(chunk_size)
-        if isinstance(chunk_overlap, str):
-            chunk_overlap = int(chunk_overlap)
+        if self.chunk_size:
+            self.chunk_size: int = int(self.chunk_size)
+        if self.chunk_overlap:
+            self.chunk_overlap: int = int(self.chunk_overlap)
         splitter = RecursiveCharacterTextSplitter(
-            separators=separators,
-            chunk_size=chunk_size,
-            chunk_overlap=chunk_overlap,
+            separators=self.separators,
+            chunk_size=self.chunk_size,
+            chunk_overlap=self.chunk_overlap,
         )
         documents = []
-        for _input in inputs:
-            if isinstance(_input, Record):
+        if not isinstance(self.data_input, list):
+            self.data_input: list[Data] = [self.data_input]
+        for _input in self.data_input:
+            if isinstance(_input, Data):
                 documents.append(_input.to_lc_document())
             else:
                 documents.append(_input)
         docs = splitter.split_documents(documents)
-        records = self.to_records(docs)
-        self.repr_value = build_loader_repr_from_records(records)
-        return records
+        data = self.to_data(docs)
+        self.repr_value = build_loader_repr_from_data(data)
+        return data
diff --git a/src/backend/base/langflow/components/toolkits/OpenAPIToolkit.py b/src/backend/base/langflow/components/toolkits/OpenAPIToolkit.py
index a24798cef..0639dae0c 100644
--- a/src/backend/base/langflow/components/toolkits/OpenAPIToolkit.py
+++ b/src/backend/base/langflow/components/toolkits/OpenAPIToolkit.py
@@ -6,7 +6,7 @@ from langchain_community.tools.json.tool import JsonSpec
 from langchain_community.utilities.requests import TextRequestsWrapper
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel
+from langflow.field_typing import LanguageModel
 
 
 class OpenAPIToolkitComponent(CustomComponent):
@@ -19,7 +19,7 @@ class OpenAPIToolkitComponent(CustomComponent):
             "requests_wrapper": {"display_name": "Text Requests Wrapper"},
         }
 
-    def build(self, llm: BaseLanguageModel, path: str, allow_dangerous_requests: bool = False) -> BaseToolkit:
+    def build(self, llm: LanguageModel, path: str, allow_dangerous_requests: bool = False) -> BaseToolkit:
         if path.endswith("yaml") or path.endswith("yml"):
             yaml_dict = yaml.load(open(path, "r"), Loader=yaml.FullLoader)
             spec = JsonSpec(dict_=yaml_dict)
diff --git a/src/backend/base/langflow/components/toolkits/VectorStoreRouterToolkit.py b/src/backend/base/langflow/components/toolkits/VectorStoreRouterToolkit.py
index 13fff14a2..6e5b5d613 100644
--- a/src/backend/base/langflow/components/toolkits/VectorStoreRouterToolkit.py
+++ b/src/backend/base/langflow/components/toolkits/VectorStoreRouterToolkit.py
@@ -3,7 +3,7 @@ from typing import List, Union
 from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo, VectorStoreRouterToolkit
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, Tool
+from langflow.field_typing import LanguageModel, Tool
 
 
 class VectorStoreRouterToolkitComponent(CustomComponent):
@@ -16,9 +16,7 @@ class VectorStoreRouterToolkitComponent(CustomComponent):
             "llm": {"display_name": "LLM"},
         }
 
-    def build(
-        self, vectorstores: List[VectorStoreInfo], llm: BaseLanguageModel
-    ) -> Union[Tool, VectorStoreRouterToolkit]:
+    def build(self, vectorstores: List[VectorStoreInfo], llm: LanguageModel) -> Union[Tool, VectorStoreRouterToolkit]:
         print("vectorstores", vectorstores)
         print("llm", llm)
         return VectorStoreRouterToolkit(vectorstores=vectorstores, llm=llm)
diff --git a/src/backend/base/langflow/components/toolkits/VectorStoreToolkit.py b/src/backend/base/langflow/components/toolkits/VectorStoreToolkit.py
index 2f788fcb9..fc63bb66f 100644
--- a/src/backend/base/langflow/components/toolkits/VectorStoreToolkit.py
+++ b/src/backend/base/langflow/components/toolkits/VectorStoreToolkit.py
@@ -3,7 +3,7 @@ from typing import Union
 from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo, VectorStoreToolkit
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import BaseLanguageModel, Tool
+from langflow.field_typing import LanguageModel, Tool
 
 
 class VectorStoreToolkitComponent(CustomComponent):
@@ -19,6 +19,6 @@ class VectorStoreToolkitComponent(CustomComponent):
     def build(
         self,
         vectorstore_info: VectorStoreInfo,
-        llm: BaseLanguageModel,
+        llm: LanguageModel,
     ) -> Union[Tool, VectorStoreToolkit]:
         return VectorStoreToolkit(vectorstore_info=vectorstore_info, llm=llm)
diff --git a/src/backend/base/langflow/components/tools/PythonREPLTool.py b/src/backend/base/langflow/components/tools/PythonREPLTool.py
index f2f3b4b52..914b6b965 100644
--- a/src/backend/base/langflow/components/tools/PythonREPLTool.py
+++ b/src/backend/base/langflow/components/tools/PythonREPLTool.py
@@ -36,7 +36,7 @@ class PythonREPLToolComponent(CustomComponent):
                 imported_module = importlib.import_module(module)
                 global_dict[imported_module.__name__] = imported_module
             except ImportError:
-                print(f"Could not import module {module}")
+                raise ImportError(f"Could not import module {module}")
         return global_dict
 
     def build(
diff --git a/src/backend/base/langflow/components/tools/SearchApi.py b/src/backend/base/langflow/components/tools/SearchApi.py
index 3e6721fd6..5dfd55250 100644
--- a/src/backend/base/langflow/components/tools/SearchApi.py
+++ b/src/backend/base/langflow/components/tools/SearchApi.py
@@ -3,7 +3,7 @@ from typing import Optional
 from langchain_community.utilities.searchapi import SearchApiAPIWrapper
 
 from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langflow.schema import Data
 from langflow.services.database.models.base import orjson_dumps
 
 
@@ -37,7 +37,7 @@ class SearchApi(CustomComponent):
         engine: str,
         api_key: str,
         params: Optional[dict] = None,
-    ) -> Record:
+    ) -> Data:
         if params is None:
             params = {}
 
@@ -48,6 +48,6 @@ class SearchApi(CustomComponent):
 
         result = orjson_dumps(results, indent_2=False)
 
-        record = Record(data=result)
+        record = Data(data=result)
         self.status = record
         return record
diff --git a/src/backend/base/langflow/components/vectorsearch/AstraDBSearch.py b/src/backend/base/langflow/components/vectorsearch/AstraDBSearch.py
index 107c6b80a..0de882841 100644
--- a/src/backend/base/langflow/components/vectorsearch/AstraDBSearch.py
+++ b/src/backend/base/langflow/components/vectorsearch/AstraDBSearch.py
@@ -1,9 +1,9 @@
 from typing import List, Optional
 
-from langflow.components.vectorstores.AstraDB import AstraDBVectorStoreComponent
-from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.base.vectorstores.model import LCVectorStoreComponent
+from langflow.components.vectorstores.AstraDB import AstraVectorStoreComponent
 from langflow.field_typing import Embeddings, Text
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class AstraDBSearchComponent(LCVectorStoreComponent):
@@ -48,7 +48,7 @@ class AstraDBSearchComponent(LCVectorStoreComponent):
             },
             "batch_size": {
                 "display_name": "Batch Size",
-                "info": "Optional number of records to process in a single batch.",
+                "info": "Optional number of data to process in a single batch.",
                 "advanced": True,
             },
             "bulk_insert_batch_concurrency": {
@@ -58,7 +58,7 @@ class AstraDBSearchComponent(LCVectorStoreComponent):
             },
             "bulk_insert_overwrite_concurrency": {
                 "display_name": "Bulk Insert Overwrite Concurrency",
-                "info": "Optional concurrency level for bulk insert operations that overwrite existing records.",
+                "info": "Optional concurrency level for bulk insert operations that overwrite existing data.",
                 "advanced": True,
             },
             "bulk_delete_concurrency": {
@@ -119,8 +119,8 @@ class AstraDBSearchComponent(LCVectorStoreComponent):
         metadata_indexing_include: Optional[List[str]] = None,
         metadata_indexing_exclude: Optional[List[str]] = None,
         collection_indexing_policy: Optional[dict] = None,
-    ) -> List[Record]:
-        vector_store = AstraDBVectorStoreComponent().build(
+    ) -> List[Data]:
+        vector_store = AstraVectorStoreComponent().build(
             embedding=embedding,
             collection_name=collection_name,
             token=token,
diff --git a/src/backend/base/langflow/components/vectorsearch/CassandraSearch.py b/src/backend/base/langflow/components/vectorsearch/CassandraSearch.py
index 8ee558276..38257a84a 100644
--- a/src/backend/base/langflow/components/vectorsearch/CassandraSearch.py
+++ b/src/backend/base/langflow/components/vectorsearch/CassandraSearch.py
@@ -1,11 +1,12 @@
 from typing import Any, List, Optional, Tuple
 
-from langflow.components.vectorstores.Cassandra import CassandraVectorStoreComponent
-from langflow.components.vectorstores.base.model import LCVectorStoreComponent
-from langflow.field_typing import Embeddings, Text
-from langflow.schema import Record
 from langchain_community.utilities.cassandra import SetupMode
 
+from langflow.base.vectorstores.model import LCVectorStoreComponent
+from langflow.components.vectorstores.Cassandra import CassandraVectorStoreComponent
+from langflow.field_typing import Embeddings, Text
+from langflow.schema import Data
+
 
 class CassandraSearchComponent(LCVectorStoreComponent):
     display_name = "Cassandra Search"
@@ -72,7 +73,7 @@ class CassandraSearchComponent(LCVectorStoreComponent):
         keyspace: Optional[str] = None,
         body_index_options: Optional[List[Tuple[str, Any]]] = None,
         setup_mode: SetupMode = SetupMode.SYNC,
-    ) -> List[Record]:
+    ) -> List[Data]:
         vector_store = CassandraVectorStoreComponent().build(
             embedding=embedding,
             table_name=table_name,
diff --git a/src/backend/base/langflow/components/vectorsearch/ChromaSearch.py b/src/backend/base/langflow/components/vectorsearch/ChromaSearch.py
index 3d4687522..f0ffbbca9 100644
--- a/src/backend/base/langflow/components/vectorsearch/ChromaSearch.py
+++ b/src/backend/base/langflow/components/vectorsearch/ChromaSearch.py
@@ -3,9 +3,11 @@ from typing import List, Optional
 import chromadb
 from chromadb.config import Settings
 from langchain_chroma import Chroma
-from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+
+
+from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.field_typing import Embeddings, Text
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class ChromaSearchComponent(LCVectorStoreComponent):
@@ -68,7 +70,7 @@ class ChromaSearchComponent(LCVectorStoreComponent):
         chroma_server_host: Optional[str] = None,
         chroma_server_http_port: Optional[int] = None,
         chroma_server_grpc_port: Optional[int] = None,
-    ) -> List[Record]:
+    ) -> List[Data]:
         """
         Builds the Vector Store or BaseRetriever object.
 
@@ -86,7 +88,7 @@ class ChromaSearchComponent(LCVectorStoreComponent):
         - chroma_server_grpc_port (int, optional): The gRPC port for the Chroma server. Defaults to None.
 
         Returns:
-        - List[Record]: The list of records.
+        - List[Data]: The list of data.
         """
 
         # Chroma settings
diff --git a/src/backend/base/langflow/components/vectorsearch/CouchbaseSearch.py b/src/backend/base/langflow/components/vectorsearch/CouchbaseSearch.py
index 2aa23c490..45d3f23c3 100644
--- a/src/backend/base/langflow/components/vectorsearch/CouchbaseSearch.py
+++ b/src/backend/base/langflow/components/vectorsearch/CouchbaseSearch.py
@@ -1,9 +1,9 @@
 from typing import List
 
-from langflow.components.vectorstores.base.model import LCVectorStoreComponent
-from langflow.components.vectorstores.Couchbase import CouchbaseComponent
+from langflow.base.vectorstores.model import LCVectorStoreComponent
+from langflow.components.vectorstores.Couchbase import CouchbaseVectorStoreComponent
 from langflow.field_typing import Embeddings, Text
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class CouchbaseSearchComponent(LCVectorStoreComponent):
@@ -51,8 +51,8 @@ class CouchbaseSearchComponent(LCVectorStoreComponent):
         couchbase_connection_string: str = "",
         couchbase_username: str = "",
         couchbase_password: str = "",
-    ) -> List[Record]:
-        vector_store = CouchbaseComponent().build(
+    ) -> List[Data]:
+        vector_store = CouchbaseVectorStoreComponent().build(
             couchbase_connection_string=couchbase_connection_string,
             couchbase_username=couchbase_username,
             couchbase_password=couchbase_password,
diff --git a/src/backend/base/langflow/components/vectorsearch/FAISSSearch.py b/src/backend/base/langflow/components/vectorsearch/FAISSSearch.py
index d68f455cc..b76377b3f 100644
--- a/src/backend/base/langflow/components/vectorsearch/FAISSSearch.py
+++ b/src/backend/base/langflow/components/vectorsearch/FAISSSearch.py
@@ -2,9 +2,9 @@ from typing import List
 
 from langchain_community.vectorstores.faiss import FAISS
 
-from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.field_typing import Embeddings, Text
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class FAISSSearchComponent(LCVectorStoreComponent):
@@ -35,7 +35,7 @@ class FAISSSearchComponent(LCVectorStoreComponent):
         folder_path: str,
         number_of_results: int = 4,
         index_name: str = "langflow_index",
-    ) -> List[Record]:
+    ) -> List[Data]:
         if not folder_path:
             raise ValueError("Folder path is required to save the FAISS index.")
         path = self.resolve_path(folder_path)
diff --git a/src/backend/base/langflow/components/vectorsearch/MongoDBAtlasVectorSearch.py b/src/backend/base/langflow/components/vectorsearch/MongoDBAtlasVectorSearch.py
index 0ecde1688..436f49419 100644
--- a/src/backend/base/langflow/components/vectorsearch/MongoDBAtlasVectorSearch.py
+++ b/src/backend/base/langflow/components/vectorsearch/MongoDBAtlasVectorSearch.py
@@ -1,9 +1,9 @@
 from typing import List, Optional
 
-from langflow.components.vectorstores.base.model import LCVectorStoreComponent
-from langflow.components.vectorstores.MongoDBAtlasVector import MongoDBAtlasComponent
+from langflow.base.vectorstores.model import LCVectorStoreComponent
+from langflow.components.vectorstores.MongoDBAtlasVector import MongoVectorStoreComponent
 from langflow.field_typing import Embeddings, NestedDict, Text
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class MongoDBAtlasSearchComponent(LCVectorStoreComponent):
@@ -41,9 +41,9 @@ class MongoDBAtlasSearchComponent(LCVectorStoreComponent):
         index_name: str = "",
         mongodb_atlas_cluster_uri: str = "",
         search_kwargs: Optional[NestedDict] = None,
-    ) -> List[Record]:
+    ) -> List[Data]:
         search_kwargs = search_kwargs or {}
-        vector_store = MongoDBAtlasComponent().build(
+        vector_store = MongoVectorStoreComponent().build(
             mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri,
             collection_name=collection_name,
             db_name=db_name,
diff --git a/src/backend/base/langflow/components/vectorsearch/PineconeSearch.py b/src/backend/base/langflow/components/vectorsearch/PineconeSearch.py
index e995f86f8..55c11d44d 100644
--- a/src/backend/base/langflow/components/vectorsearch/PineconeSearch.py
+++ b/src/backend/base/langflow/components/vectorsearch/PineconeSearch.py
@@ -2,14 +2,14 @@ from typing import List, Optional
 
 from langchain_pinecone._utilities import DistanceStrategy
 
-from langflow.components.vectorstores.base.model import LCVectorStoreComponent
-from langflow.components.vectorstores.Pinecone import PineconeComponent
+from langflow.base.vectorstores.model import LCVectorStoreComponent
+from langflow.components.vectorstores.Pinecone import PineconeVectorStoreComponent
 from langflow.field_typing import Embeddings, Text
 from langflow.field_typing.constants import NestedDict
-from langflow.schema import Record
+from langflow.schema import Data
 
 
-class PineconeSearchComponent(PineconeComponent, LCVectorStoreComponent):
+class PineconeSearchComponent(PineconeVectorStoreComponent, LCVectorStoreComponent):
     display_name = "Pinecone Search"
     description = "Search a Pinecone Vector Store for similar documents."
     icon = "Pinecone"
@@ -70,7 +70,7 @@ class PineconeSearchComponent(PineconeComponent, LCVectorStoreComponent):
         namespace: Optional[str] = "default",
         search_type: str = "similarity",
         search_kwargs: Optional[NestedDict] = None,
-    ) -> List[Record]:  # type: ignore[override]
+    ) -> List[Data]:  # type: ignore[override]
         vector_store = super().build(
             embedding=embedding,
             distance_strategy=distance_strategy,
diff --git a/src/backend/base/langflow/components/vectorsearch/QdrantSearch.py b/src/backend/base/langflow/components/vectorsearch/QdrantSearch.py
index a64343e17..3e0613f7c 100644
--- a/src/backend/base/langflow/components/vectorsearch/QdrantSearch.py
+++ b/src/backend/base/langflow/components/vectorsearch/QdrantSearch.py
@@ -1,12 +1,12 @@
 from typing import List, Optional
 
-from langflow.components.vectorstores.base.model import LCVectorStoreComponent
-from langflow.components.vectorstores.Qdrant import QdrantComponent
+from langflow.base.vectorstores.model import LCVectorStoreComponent
+from langflow.components.vectorstores.Qdrant import QdrantVectorStoreComponent
 from langflow.field_typing import Embeddings, NestedDict, Text
-from langflow.schema import Record
+from langflow.schema import Data
 
 
-class QdrantSearchComponent(QdrantComponent, LCVectorStoreComponent):
+class QdrantSearchComponent(QdrantVectorStoreComponent, LCVectorStoreComponent):
     display_name = "Qdrant Search"
     description = "Construct Qdrant wrapper from a list of texts."
     icon = "Qdrant"
@@ -70,7 +70,7 @@ class QdrantSearchComponent(QdrantComponent, LCVectorStoreComponent):
         search_kwargs: Optional[NestedDict] = None,
         timeout: Optional[int] = None,
         url: Optional[str] = None,
-    ) -> List[Record]:  # type: ignore[override]
+    ) -> List[Data]:  # type: ignore[override]
         vector_store = super().build(
             embedding=embedding,
             collection_name=collection_name,
diff --git a/src/backend/base/langflow/components/vectorsearch/RedisSearch.py b/src/backend/base/langflow/components/vectorsearch/RedisSearch.py
index 75aba7f8a..097c32f4c 100644
--- a/src/backend/base/langflow/components/vectorsearch/RedisSearch.py
+++ b/src/backend/base/langflow/components/vectorsearch/RedisSearch.py
@@ -2,13 +2,13 @@ from typing import List, Optional
 
 from langchain_core.embeddings import Embeddings
 
-from langflow.components.vectorstores.base.model import LCVectorStoreComponent
-from langflow.components.vectorstores.Redis import RedisComponent
+from langflow.base.vectorstores.model import LCVectorStoreComponent
+from langflow.components.vectorstores.Redis import RedisVectorStoreComponent
 from langflow.field_typing import Text
-from langflow.schema import Record
+from langflow.schema import Data
 
 
-class RedisSearchComponent(RedisComponent, LCVectorStoreComponent):
+class RedisSearchComponent(RedisVectorStoreComponent, LCVectorStoreComponent):
     """
     A custom component for implementing a Vector Store using Redis.
     """
@@ -55,7 +55,7 @@ class RedisSearchComponent(RedisComponent, LCVectorStoreComponent):
         redis_index_name: str,
         number_of_results: int = 4,
         schema: Optional[str] = None,
-    ) -> List[Record]:
+    ) -> List[Data]:
         """
         Builds the Vector Store or BaseRetriever object.
 
diff --git a/src/backend/base/langflow/components/vectorsearch/SupabaseVectorStoreSearch.py b/src/backend/base/langflow/components/vectorsearch/SupabaseVectorStoreSearch.py
index aef1c13b7..8fcd485f0 100644
--- a/src/backend/base/langflow/components/vectorsearch/SupabaseVectorStoreSearch.py
+++ b/src/backend/base/langflow/components/vectorsearch/SupabaseVectorStoreSearch.py
@@ -3,9 +3,9 @@ from typing import List
 from langchain_community.vectorstores.supabase import SupabaseVectorStore
 from supabase.client import Client, create_client
 
-from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.field_typing import Embeddings, Text
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class SupabaseSearchComponent(LCVectorStoreComponent):
@@ -43,7 +43,7 @@ class SupabaseSearchComponent(LCVectorStoreComponent):
         supabase_service_key: str = "",
         supabase_url: str = "",
         table_name: str = "",
-    ) -> List[Record]:
+    ) -> List[Data]:
         supabase: Client = create_client(supabase_url, supabase_key=supabase_service_key)
         vector_store = SupabaseVectorStore(
             client=supabase,
diff --git a/src/backend/base/langflow/components/vectorsearch/UpstashSearch.py b/src/backend/base/langflow/components/vectorsearch/UpstashSearch.py
index 506896e2b..cf778ed7a 100644
--- a/src/backend/base/langflow/components/vectorsearch/UpstashSearch.py
+++ b/src/backend/base/langflow/components/vectorsearch/UpstashSearch.py
@@ -2,10 +2,10 @@ from typing import List, Optional
 
 from langchain_core.embeddings import Embeddings
 
-from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.components.vectorstores.Upstash import UpstashVectorStoreComponent
 from langflow.field_typing import Text
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class UpstashSearchComponent(UpstashVectorStoreComponent, LCVectorStoreComponent):
@@ -29,7 +29,7 @@ class UpstashSearchComponent(UpstashVectorStoreComponent, LCVectorStoreComponent
                 "options": ["Similarity", "MMR"],
             },
             "input_value": {"display_name": "Input"},
-            "inputs": {"display_name": "Input", "input_types": ["Document", "Record"]},
+            "inputs": {"display_name": "Input", "input_types": ["Document", "Data"]},
             "embedding": {
                 "display_name": "Embedding",
                 "input_types": ["Embeddings"],
@@ -64,7 +64,7 @@ class UpstashSearchComponent(UpstashVectorStoreComponent, LCVectorStoreComponent
         index_token: Optional[str] = None,
         embedding: Optional[Embeddings] = None,
         number_of_results: int = 4,
-    ) -> List[Record]:
+    ) -> List[Data]:
         vector_store = super().build(
             embedding=embedding,
             text_key=text_key,
diff --git a/src/backend/base/langflow/components/vectorsearch/VectaraSearch.py b/src/backend/base/langflow/components/vectorsearch/VectaraSearch.py
index 459054f67..38888d8ac 100644
--- a/src/backend/base/langflow/components/vectorsearch/VectaraSearch.py
+++ b/src/backend/base/langflow/components/vectorsearch/VectaraSearch.py
@@ -2,13 +2,13 @@ from typing import List
 
 from langchain_community.vectorstores.vectara import Vectara
 
-from langflow.components.vectorstores.base.model import LCVectorStoreComponent
-from langflow.components.vectorstores.Vectara import VectaraComponent
+from langflow.base.vectorstores.model import LCVectorStoreComponent
+from langflow.components.vectorstores.Vectara import VectaraVectorStoreComponent
 from langflow.field_typing import Text
-from langflow.schema import Record
+from langflow.schema import Data
 
 
-class VectaraSearchComponent(VectaraComponent, LCVectorStoreComponent):
+class VectaraSearchComponent(VectaraVectorStoreComponent, LCVectorStoreComponent):
     display_name: str = "Vectara Search"
     description: str = "Search a Vectara Vector Store for similar documents."
     documentation = "https://python.langchain.com/docs/integrations/vectorstores/vectara"
@@ -49,7 +49,7 @@ class VectaraSearchComponent(VectaraComponent, LCVectorStoreComponent):
         vectara_corpus_id: str,
         vectara_api_key: str,
         number_of_results: int = 4,
-    ) -> List[Record]:
+    ) -> List[Data]:
         source = "Langflow"
         vector_store = Vectara(
             vectara_customer_id=vectara_customer_id,
diff --git a/src/backend/base/langflow/components/vectorsearch/WeaviateSearch.py b/src/backend/base/langflow/components/vectorsearch/WeaviateSearch.py
index b70dfa41d..db5c0a738 100644
--- a/src/backend/base/langflow/components/vectorsearch/WeaviateSearch.py
+++ b/src/backend/base/langflow/components/vectorsearch/WeaviateSearch.py
@@ -2,10 +2,10 @@ from typing import List, Optional
 
 from langchain_core.embeddings import Embeddings
 
-from langflow.components.vectorstores.base.model import LCVectorStoreComponent
+from langflow.base.vectorstores.model import LCVectorStoreComponent
 from langflow.components.vectorstores.Weaviate import WeaviateVectorStoreComponent
 from langflow.field_typing import Text
-from langflow.schema import Record
+from langflow.schema import Data
 
 
 class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreComponent):
@@ -68,7 +68,7 @@ class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreCompo
         text_key: str = "text",
         embedding: Optional[Embeddings] = None,
         attributes: Optional[list] = None,
-    ) -> List[Record]:
+    ) -> List[Data]:
         vector_store = super().build(
             url=url,
             api_key=api_key,
diff --git a/src/backend/base/langflow/components/vectorsearch/pgvectorSearch.py b/src/backend/base/langflow/components/vectorsearch/pgvectorSearch.py
index 304439ff4..5e7a171b5 100644
--- a/src/backend/base/langflow/components/vectorsearch/pgvectorSearch.py
+++ b/src/backend/base/langflow/components/vectorsearch/pgvectorSearch.py
@@ -2,13 +2,13 @@ from typing import List
 
 from langchain_core.embeddings import Embeddings
 
-from langflow.components.vectorstores.base.model import LCVectorStoreComponent
-from langflow.components.vectorstores.pgvector import PGVectorComponent
+from langflow.base.vectorstores.model import LCVectorStoreComponent
+from langflow.components.vectorstores.pgvector import PGVectorStoreComponent
 from langflow.field_typing import Text
-from langflow.schema import Record
+from langflow.schema import Data
 
 
-class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent):
+class PGVectorSearchComponent(PGVectorStoreComponent, LCVectorStoreComponent):
     display_name: str = "PGVector Search"
     description: str = "Search a PGVector Store for similar documents."
     documentation = "https://python.langchain.com/docs/integrations/vectorstores/pgvector"
@@ -48,7 +48,7 @@ class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent):
         pg_server_url: str,
         collection_name: str,
         number_of_results: int = 4,
-    ) -> List[Record]:
+    ) -> List[Data]:
         """
         Builds the Vector Store or BaseRetriever object.
 
diff --git a/src/backend/base/langflow/components/vectorstores/AstraDB.py b/src/backend/base/langflow/components/vectorstores/AstraDB.py
index a8f0516d7..2be70ac80 100644
--- a/src/backend/base/langflow/components/vectorstores/AstraDB.py
+++ b/src/backend/base/langflow/components/vectorstores/AstraDB.py
@@ -1,114 +1,139 @@
-from typing import List, Optional, Union
+from loguru import logger
 
-from langflow.custom import CustomComponent
-from langflow.field_typing import Embeddings, VectorStore
-from langflow.schema import Record
-from langchain_core.retrievers import BaseRetriever
+from langflow.base.vectorstores.model import LCVectorStoreComponent
+from langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput
+from langflow.schema import Data
 
 
-class AstraDBVectorStoreComponent(CustomComponent):
-    display_name = "Astra DB"
-    description = "Builds or loads an Astra DB Vector Store."
-    icon = "AstraDB"
-    field_order = ["token", "api_endpoint", "collection_name", "inputs", "embedding"]
+class AstraVectorStoreComponent(LCVectorStoreComponent):
+    display_name: str = "Astra DB Vector Store"
+    description: str = "Implementation of Vector Store using Astra DB with search capabilities"
+    documentation: str = "https://python.langchain.com/docs/integrations/vectorstores/astradb"
+    icon: str = "AstraDB"
 
-    def build_config(self):
-        return {
-            "inputs": {
-                "display_name": "Inputs",
-                "info": "Optional list of records to be processed and stored in the vector store.",
-            },
-            "embedding": {"display_name": "Embedding", "info": "Embedding to use"},
-            "collection_name": {
-                "display_name": "Collection Name",
-                "info": "The name of the collection within Astra DB where the vectors will be stored.",
-            },
-            "token": {
-                "display_name": "Astra DB Application Token",
-                "info": "Authentication token for accessing Astra DB.",
-                "password": True,
-            },
-            "api_endpoint": {
-                "display_name": "API Endpoint",
-                "info": "API endpoint URL for the Astra DB service.",
-            },
-            "namespace": {
-                "display_name": "Namespace",
-                "info": "Optional namespace within Astra DB to use for the collection.",
-                "advanced": True,
-            },
-            "metric": {
-                "display_name": "Metric",
-                "info": "Optional distance metric for vector comparisons in the vector store.",
-                "advanced": True,
-            },
-            "batch_size": {
-                "display_name": "Batch Size",
-                "info": "Optional number of records to process in a single batch.",
-                "advanced": True,
-            },
-            "bulk_insert_batch_concurrency": {
-                "display_name": "Bulk Insert Batch Concurrency",
-                "info": "Optional concurrency level for bulk insert operations.",
-                "advanced": True,
-            },
-            "bulk_insert_overwrite_concurrency": {
-                "display_name": "Bulk Insert Overwrite Concurrency",
-                "info": "Optional concurrency level for bulk insert operations that overwrite existing records.",
-                "advanced": True,
-            },
-            "bulk_delete_concurrency": {
-                "display_name": "Bulk Delete Concurrency",
-                "info": "Optional concurrency level for bulk delete operations.",
-                "advanced": True,
-            },
-            "setup_mode": {
-                "display_name": "Setup Mode",
-                "info": "Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.",
-                "options": ["Sync", "Async", "Off"],
-                "advanced": True,
-            },
-            "pre_delete_collection": {
-                "display_name": "Pre Delete Collection",
-                "info": "Boolean flag to determine whether to delete the collection before creating a new one.",
-                "advanced": True,
-            },
-            "metadata_indexing_include": {
-                "display_name": "Metadata Indexing Include",
-                "info": "Optional list of metadata fields to include in the indexing.",
-                "advanced": True,
-            },
-            "metadata_indexing_exclude": {
-                "display_name": "Metadata Indexing Exclude",
-                "info": "Optional list of metadata fields to exclude from the indexing.",
-                "advanced": True,
-            },
-            "collection_indexing_policy": {
-                "display_name": "Collection Indexing Policy",
-                "info": "Optional dictionary defining the indexing policy for the collection.",
-                "advanced": True,
-            },
-        }
+    inputs = [
+        StrInput(
+            name="collection_name",
+            display_name="Collection Name",
+            info="The name of the collection within Astra DB where the vectors will be stored.",
+        ),
+        SecretStrInput(
+            name="token",
+            display_name="Astra DB Application Token",
+            info="Authentication token for accessing Astra DB.",
+            value="ASTRA_DB_APPLICATION_TOKEN",
+        ),
+        SecretStrInput(
+            name="api_endpoint",
+            display_name="API Endpoint",
+            info="API endpoint URL for the Astra DB service.",
+            value="ASTRA_DB_API_ENDPOINT",
+        ),
+        HandleInput(
+            name="vector_store_inputs",
+            display_name="Vector Store Inputs",
+            input_types=["Document", "Data"],
+            is_list=True,
+        ),
+        HandleInput(
+            name="embedding",
+            display_name="Embedding",
+            input_types=["Embeddings"],
+        ),
+        StrInput(
+            name="namespace",
+            display_name="Namespace",
+            info="Optional namespace within Astra DB to use for the collection.",
+            advanced=True,
+        ),
+        DropdownInput(
+            name="metric",
+            display_name="Metric",
+            info="Optional distance metric for vector comparisons in the vector store.",
+            options=["cosine", "dot_product", "euclidean"],
+            advanced=True,
+        ),
+        IntInput(
+            name="batch_size",
+            display_name="Batch Size",
+            info="Optional number of data to process in a single batch.",
+            advanced=True,
+        ),
+        IntInput(
+            name="bulk_insert_batch_concurrency",
+            display_name="Bulk Insert Batch Concurrency",
+            info="Optional concurrency level for bulk insert operations.",
+            advanced=True,
+        ),
+        IntInput(
+            name="bulk_insert_overwrite_concurrency",
+            display_name="Bulk Insert Overwrite Concurrency",
+            info="Optional concurrency level for bulk insert operations that overwrite existing data.",
+            advanced=True,
+        ),
+        IntInput(
+            name="bulk_delete_concurrency",
+            display_name="Bulk Delete Concurrency",
+            info="Optional concurrency level for bulk delete operations.",
+            advanced=True,
+        ),
+        DropdownInput(
+            name="setup_mode",
+            display_name="Setup Mode",
+            info="Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.",
+            options=["Sync", "Async", "Off"],
+            advanced=True,
+            value="Sync",
+        ),
+        BoolInput(
+            name="pre_delete_collection",
+            display_name="Pre Delete Collection",
+            info="Boolean flag to determine whether to delete the collection before creating a new one.",
+            advanced=True,
+        ),
+        StrInput(
+            name="metadata_indexing_include",
+            display_name="Metadata Indexing Include",
+            info="Optional list of metadata fields to include in the indexing.",
+            advanced=True,
+        ),
+        StrInput(
+            name="metadata_indexing_exclude",
+            display_name="Metadata Indexing Exclude",
+            info="Optional list of metadata fields to exclude from the indexing.",
+            advanced=True,
+        ),
+        StrInput(
+            name="collection_indexing_policy",
+            display_name="Collection Indexing Policy",
+            info="Optional dictionary defining the indexing policy for the collection.",
+            advanced=True,
+        ),
+        BoolInput(
+            name="add_to_vector_store",
+            display_name="Add to Vector Store",
+            info="If true, the Vector Store Inputs will be added to the Vector Store.",
+        ),
+        MultilineInput(
+            name="search_input",
+            display_name="Search Input",
+        ),
+        DropdownInput(
+            name="search_type",
+            display_name="Search Type",
+            options=["Similarity", "MMR"],
+            value="Similarity",
+        ),
+        IntInput(
+            name="number_of_results",
+            display_name="Number of Results",
+            info="Number of results to return.",
+            advanced=True,
+            value=4,
+        ),
+    ]
 
-    def build(
-        self,
-        embedding: Embeddings,
-        token: str,
-        api_endpoint: str,
-        collection_name: str,
-        inputs: Optional[List[Record]] = None,
-        namespace: Optional[str] = None,
-        metric: Optional[str] = None,
-        batch_size: Optional[int] = None,
-        bulk_insert_batch_concurrency: Optional[int] = None,
-        bulk_insert_overwrite_concurrency: Optional[int] = None,
-        bulk_delete_concurrency: Optional[int] = None,
-        setup_mode: str = "Sync",
-        pre_delete_collection: bool = False,
-        metadata_indexing_include: Optional[List[str]] = None,
-        metadata_indexing_exclude: Optional[List[str]] = None,
-        collection_indexing_policy: Optional[dict] = None,
-    ) -> Union[VectorStore, BaseRetriever]:
+    def build_vector_store(self):
         try:
             from langchain_astradb import AstraDBVectorStore
             from langchain_astradb.utils.astradb import SetupMode
@@ -119,47 +144,103 @@ class AstraDBVectorStoreComponent(CustomComponent):
             )
 
         try:
-            setup_mode_value = SetupMode[setup_mode.upper()]
+            if not self.setup_mode:
+                self.setup_mode = self._inputs["setup_mode"].options[0]
+
+            setup_mode_value = SetupMode[self.setup_mode.upper()]
         except KeyError:
-            raise ValueError(f"Invalid setup mode: {setup_mode}")
-        if inputs:
-            documents = [_input.to_lc_document() for _input in inputs]
+            raise ValueError(f"Invalid setup mode: {self.setup_mode}")
 
-            vector_store = AstraDBVectorStore.from_documents(
-                documents=documents,
-                embedding=embedding,
-                collection_name=collection_name,
-                token=token,
-                api_endpoint=api_endpoint,
-                namespace=namespace,
-                metric=metric,
-                batch_size=batch_size,
-                bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,
-                bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,
-                bulk_delete_concurrency=bulk_delete_concurrency,
-                setup_mode=setup_mode_value,
-                pre_delete_collection=pre_delete_collection,
-                metadata_indexing_include=metadata_indexing_include,
-                metadata_indexing_exclude=metadata_indexing_exclude,
-                collection_indexing_policy=collection_indexing_policy,
-            )
-        else:
-            vector_store = AstraDBVectorStore(
-                embedding=embedding,
-                collection_name=collection_name,
-                token=token,
-                api_endpoint=api_endpoint,
-                namespace=namespace,
-                metric=metric,
-                batch_size=batch_size,
-                bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,
-                bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,
-                bulk_delete_concurrency=bulk_delete_concurrency,
-                setup_mode=setup_mode_value,
-                pre_delete_collection=pre_delete_collection,
-                metadata_indexing_include=metadata_indexing_include,
-                metadata_indexing_exclude=metadata_indexing_exclude,
-                collection_indexing_policy=collection_indexing_policy,
-            )
+        vector_store_kwargs = {
+            "embedding": self.embedding,
+            "collection_name": self.collection_name,
+            "token": self.token,
+            "api_endpoint": self.api_endpoint,
+            "namespace": self.namespace or None,
+            "metric": self.metric or None,
+            "batch_size": self.batch_size or None,
+            "bulk_insert_batch_concurrency": self.bulk_insert_batch_concurrency or None,
+            "bulk_insert_overwrite_concurrency": self.bulk_insert_overwrite_concurrency or None,
+            "bulk_delete_concurrency": self.bulk_delete_concurrency or None,
+            "setup_mode": setup_mode_value,
+            "pre_delete_collection": self.pre_delete_collection or False,
+        }
 
+        if self.metadata_indexing_include:
+            vector_store_kwargs["metadata_indexing_include"] = self.metadata_indexing_include
+        elif self.metadata_indexing_exclude:
+            vector_store_kwargs["metadata_indexing_exclude"] = self.metadata_indexing_exclude
+        elif self.collection_indexing_policy:
+            vector_store_kwargs["collection_indexing_policy"] = self.collection_indexing_policy
+
+        try:
+            vector_store = AstraDBVectorStore(**vector_store_kwargs)
+        except Exception as e:
+            raise ValueError(f"Error initializing AstraDBVectorStore: {str(e)}") from e
+
+        if self.add_to_vector_store:
+            self._add_documents_to_vector_store(vector_store)
+
+        self.status = self._astradb_collection_to_data(vector_store.collection)
         return vector_store
+
+    def _add_documents_to_vector_store(self, vector_store):
+        documents = []
+        for _input in self.vector_store_inputs or []:
+            if isinstance(_input, Data):
+                documents.append(_input.to_lc_document())
+            else:
+                raise ValueError("Vector Store Inputs must be Data objects.")
+
+        if documents and self.embedding is not None:
+            logger.debug(f"Adding {len(documents)} documents to the Vector Store.")
+            try:
+                vector_store.add_documents(documents)
+            except Exception as e:
+                raise ValueError(f"Error adding documents to AstraDBVectorStore: {str(e)}") from e
+        else:
+            logger.debug("No documents to add to the Vector Store.")
+
+    def search_documents(self):
+        vector_store = self.build_vector_store()
+
+        logger.debug(f"Search input: {self.search_input}")
+        logger.debug(f"Search type: {self.search_type}")
+        logger.debug(f"Number of results: {self.number_of_results}")
+
+        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
+            try:
+                if self.search_type == "Similarity":
+                    docs = vector_store.similarity_search(
+                        query=self.search_input,
+                        k=self.number_of_results,
+                    )
+                elif self.search_type == "MMR":
+                    docs = vector_store.max_marginal_relevance_search(
+                        query=self.search_input,
+                        k=self.number_of_results,
+                    )
+                else:
+                    raise ValueError(f"Invalid search type: {self.search_type}")
+            except Exception as e:
+                raise ValueError(f"Error performing search in AstraDBVectorStore: {str(e)}") from e
+
+            logger.debug(f"Retrieved documents: {len(docs)}")
+
+            data = [Data.from_document(doc) for doc in docs]
+            logger.debug(f"Converted documents to data: {len(data)}")
+            self.status = data
+            return data
+        else:
+            logger.debug("No search input provided. Skipping search.")
+            return []
+
+    def _astradb_collection_to_data(self, collection):
+        data = []
+        data_dict = collection.find()
+        if data_dict and "data" in data_dict:
+            data_dict = data_dict["data"].get("documents", [])
+
+        for item in data_dict:
+            data.append(Data(content=item["content"]))
+        return data
diff --git a/src/backend/base/langflow/components/vectorstores/Cassandra.py b/src/backend/base/langflow/components/vectorstores/Cassandra.py
index 34c21ccd0..4446feabe 100644
--- a/src/backend/base/langflow/components/vectorstores/Cassandra.py
+++ b/src/backend/base/langflow/components/vectorstores/Cassandra.py
@@ -1,79 +1,104 @@
-from typing import Any, List, Optional, Tuple
+from typing import List
+
 from langchain_community.vectorstores import Cassandra
-from langchain_community.utilities.cassandra import SetupMode
+from langchain_core.retrievers import BaseRetriever
 
-from langflow.custom import CustomComponent
-from langflow.field_typing import Embeddings, VectorStore
-from langflow.schema import Record
+from langflow.custom import Component
+from langflow.helpers.data import docs_to_data
+from langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
+from langflow.schema import Data
 
 
-class CassandraVectorStoreComponent(CustomComponent):
+class CassandraVectorStoreComponent(Component):
     display_name = "Cassandra"
-    description = "Builds or loads a Cassandra Vector Store."
+    description = "Cassandra Vector Store with search capabilities"
+    documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/cassandra"
     icon = "Cassandra"
-    field_order = ["token", "database_id", "table_name", "inputs", "embedding"]
 
-    def build_config(self):
-        return {
-            "inputs": {
-                "display_name": "Inputs",
-                "info": "Optional list of records to be processed and stored in the vector store.",
-            },
-            "embedding": {"display_name": "Embedding", "info": "Embedding to use"},
-            "token": {
-                "display_name": "Token",
-                "info": "Authentication token for accessing Cassandra on Astra DB.",
-                "password": True,
-            },
-            "database_id": {
-                "display_name": "Database ID",
-                "info": "The Astra database ID.",
-            },
-            "table_name": {
-                "display_name": "Table Name",
-                "info": "The name of the table where vectors will be stored.",
-            },
-            "keyspace": {
-                "display_name": "Keyspace",
-                "info": "Optional key space within Astra DB. The keyspace should already be created.",
-                "advanced": True,
-            },
-            "ttl_seconds": {
-                "display_name": "TTL Seconds",
-                "info": "Optional time-to-live for the added texts.",
-                "advanced": True,
-            },
-            "batch_size": {
-                "display_name": "Batch Size",
-                "info": "Optional number of records to process in a single batch.",
-                "advanced": True,
-            },
-            "body_index_options": {
-                "display_name": "Body Index Options",
-                "info": "Optional options used to create the body index.",
-                "advanced": True,
-            },
-            "setup_mode": {
-                "display_name": "Setup Mode",
-                "info": "Configuration mode for setting up the Cassandra table, with options like 'Sync', 'Async', or 'Off'.",
-                "options": ["Sync", "Async", "Off"],
-                "advanced": True,
-            },
-        }
+    inputs = [
+        SecretStrInput(
+            name="token",
+            display_name="Token",
+            info="Authentication token for accessing Cassandra on Astra DB.",
+            required=True,
+        ),
+        StrInput(name="database_id", display_name="Database ID", info="The Astra database ID.", required=True),
+        StrInput(
+            name="table_name",
+            display_name="Table Name",
+            info="The name of the table where vectors will be stored.",
+            required=True,
+        ),
+        StrInput(
+            name="keyspace",
+            display_name="Keyspace",
+            info="Optional key space within Astra DB. The keyspace should already be created.",
+            advanced=True,
+        ),
+        IntInput(
+            name="ttl_seconds",
+            display_name="TTL Seconds",
+            info="Optional time-to-live for the added texts.",
+            advanced=True,
+        ),
+        IntInput(
+            name="batch_size",
+            display_name="Batch Size",
+            info="Optional number of data to process in a single batch.",
+            value=16,
+            advanced=True,
+        ),
+        StrInput(
+            name="body_index_options",
+            display_name="Body Index Options",
+            info="Optional options used to create the body index.",
+            advanced=True,
+        ),
+        DropdownInput(
+            name="setup_mode",
+            display_name="Setup Mode",
+            info="Configuration mode for setting up the Cassandra table, with options like 'Sync', 'Async', or 'Off'.",
+            options=["Sync", "Async", "Off"],
+            value="Sync",
+            advanced=True,
+        ),
+        HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
+        HandleInput(
+            name="vector_store_inputs",
+            display_name="Vector Store Inputs",
+            input_types=["Document", "Data"],
+            is_list=True,
+        ),
+        BoolInput(
+            name="add_to_vector_store",
+            display_name="Add to Vector Store",
+            info="If true, the Vector Store Inputs will be added to the Vector Store.",
+        ),
+        StrInput(name="search_input", display_name="Search Input"),
+        IntInput(
+            name="number_of_results",
+            display_name="Number of Results",
+            info="Number of results to return.",
+            value=4,
+            advanced=True,
+        ),
+    ]
 
-    def build(
-        self,
-        embedding: Embeddings,
-        token: str,
-        database_id: str,
-        inputs: Optional[List[Record]] = None,
-        keyspace: Optional[str] = None,
-        table_name: str = "",
-        ttl_seconds: Optional[int] = None,
-        batch_size: int = 16,
-        body_index_options: Optional[List[Tuple[str, Any]]] = None,
-        setup_mode: SetupMode = SetupMode.SYNC,
-    ) -> VectorStore:
+    outputs = [
+        Output(display_name="Vector Store", name="vector_store", method="build_vector_store", output_type=Cassandra),
+        Output(
+            display_name="Base Retriever",
+            name="base_retriever",
+            method="build_base_retriever",
+            output_type=BaseRetriever,
+        ),
+        Output(display_name="Search Results", name="search_results", method="search_documents"),
+    ]
+
+    def build_vector_store(self) -> Cassandra:
+        return self._build_cassandra()
+
+    def _build_cassandra(self) -> Cassandra:
         try:
             import cassio
         except ImportError:
@@ -82,29 +107,68 @@ class CassandraVectorStoreComponent(CustomComponent):
             )
 
         cassio.init(
-            database_id=database_id,
-            token=token,
+            database_id=self.database_id,
+            token=self.token,
         )
 
-        if inputs:
-            documents = [_input.to_lc_document() for _input in inputs]
-            table = Cassandra.from_documents(
-                documents=documents,
-                embedding=embedding,
-                table_name=table_name,
-                keyspace=keyspace,
-                ttl_seconds=ttl_seconds,
-                batch_size=batch_size,
-                body_index_options=body_index_options,
-            )
+        if self.add_to_vector_store:
+            documents = []
+            for _input in self.vector_store_inputs or []:
+                if isinstance(_input, Data):
+                    documents.append(_input.to_lc_document())
+                else:
+                    documents.append(_input)
+
+            if documents:
+                table = Cassandra.from_documents(
+                    documents=documents,
+                    embedding=self.embedding,
+                    table_name=self.table_name,
+                    keyspace=self.keyspace,
+                    ttl_seconds=self.ttl_seconds,
+                    batch_size=self.batch_size,
+                    body_index_options=self.body_index_options,
+                )
+            else:
+                table = Cassandra(
+                    embedding=self.embedding,
+                    table_name=self.table_name,
+                    keyspace=self.keyspace,
+                    ttl_seconds=self.ttl_seconds,
+                    body_index_options=self.body_index_options,
+                    setup_mode=self.setup_mode,
+                )
         else:
             table = Cassandra(
-                embedding=embedding,
-                table_name=table_name,
-                keyspace=keyspace,
-                ttl_seconds=ttl_seconds,
-                body_index_options=body_index_options,
-                setup_mode=setup_mode,
+                embedding=self.embedding,
+                table_name=self.table_name,
+                keyspace=self.keyspace,
+                ttl_seconds=self.ttl_seconds,
+                body_index_options=self.body_index_options,
+                setup_mode=self.setup_mode,
             )
 
         return table
+
+    def search_documents(self) -> List[Data]:
+        vector_store = self._build_cassandra()
+
+        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
+            try:
+                docs = vector_store.similarity_search(
+                    query=self.search_input,
+                    k=self.number_of_results,
+                )
+            except KeyError as e:
+                if "content" in str(e):
+                    raise ValueError(
+                        "You should ingest data through Langflow (or LangChain) to query it in Langflow. Your collection does not contain a field name 'content'."
+                    )
+                else:
+                    raise e
+
+            data = docs_to_data(docs)
+            self.status = data
+            return data
+        else:
+            return []
diff --git a/src/backend/base/langflow/components/vectorstores/Chroma.py b/src/backend/base/langflow/components/vectorstores/Chroma.py
index f7080d2fc..a8b25a5c0 100644
--- a/src/backend/base/langflow/components/vectorstores/Chroma.py
+++ b/src/backend/base/langflow/components/vectorstores/Chroma.py
@@ -1,134 +1,169 @@
 from copy import deepcopy
-from typing import List, Optional, Union
+from typing import TYPE_CHECKING
 
-import chromadb
 from chromadb.config import Settings
-from langchain_chroma import Chroma
-from langchain_core.embeddings import Embeddings
-from langchain_core.retrievers import BaseRetriever
-from langchain_core.vectorstores import VectorStore
-from langflow.base.vectorstores.utils import chroma_collection_to_records
-from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langchain_chroma.vectorstores import Chroma
+from loguru import logger
+
+from langflow.base.vectorstores.model import LCVectorStoreComponent
+from langflow.base.vectorstores.utils import chroma_collection_to_data
+from langflow.io import BoolInput, DataInput, DropdownInput, HandleInput, IntInput, StrInput, TextInput
+from langflow.schema import Data
+
+if TYPE_CHECKING:
+    from langchain_chroma import Chroma
 
 
-class ChromaComponent(CustomComponent):
+class ChromaVectorStoreComponent(LCVectorStoreComponent):
     """
-    A custom component for implementing a Vector Store using Chroma.
+    Chroma Vector Store with search capabilities
     """
 
-    display_name: str = "Chroma"
-    description: str = "Implementation of Vector Store using Chroma"
+    display_name: str = "Chroma DB"
+    description: str = "Chroma Vector Store with search capabilities"
     documentation = "https://python.langchain.com/docs/integrations/vectorstores/chroma"
     icon = "Chroma"
 
-    def build_config(self):
+    inputs = [
+        StrInput(
+            name="collection_name",
+            display_name="Collection Name",
+            value="langflow",
+        ),
+        StrInput(
+            name="persist_directory",
+            display_name="Persist Directory",
+        ),
+        TextInput(
+            name="search_query",
+            display_name="Search Query",
+        ),
+        DataInput(
+            name="ingest_data",
+            display_name="Ingest Data",
+        ),
+        HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
+        StrInput(
+            name="chroma_server_cors_allow_origins",
+            display_name="Server CORS Allow Origins",
+            advanced=True,
+        ),
+        StrInput(
+            name="chroma_server_host",
+            display_name="Server Host",
+            advanced=True,
+        ),
+        IntInput(
+            name="chroma_server_http_port",
+            display_name="Server HTTP Port",
+            advanced=True,
+        ),
+        IntInput(
+            name="chroma_server_grpc_port",
+            display_name="Server gRPC Port",
+            advanced=True,
+        ),
+        BoolInput(
+            name="chroma_server_ssl_enabled",
+            display_name="Server SSL Enabled",
+            advanced=True,
+        ),
+        BoolInput(
+            name="allow_duplicates",
+            display_name="Allow Duplicates",
+            advanced=True,
+            info="If false, will not add documents that are already in the Vector Store.",
+        ),
+        DropdownInput(
+            name="search_type",
+            display_name="Search Type",
+            options=["Similarity", "MMR"],
+            value="Similarity",
+            advanced=True,
+        ),
+        IntInput(
+            name="number_of_results",
+            display_name="Number of Results",
+            info="Number of results to return.",
+            advanced=True,
+            value=10,
+        ),
+        IntInput(
+            name="limit",
+            display_name="Limit",
+            advanced=True,
+            info="Limit the number of records to compare when Allow Duplicates is False.",
+        ),
+    ]
+
+    def build_vector_store(self) -> "Chroma":
         """
-        Builds the configuration for the component.
-
-        Returns:
-        - dict: A dictionary containing the configuration options for the component.
+        Builds the Chroma object.
         """
-        return {
-            "collection_name": {"display_name": "Collection Name", "value": "langflow"},
-            "index_directory": {"display_name": "Persist Directory"},
-            "code": {"advanced": True, "display_name": "Code"},
-            "inputs": {"display_name": "Input", "input_types": ["Document", "Record"]},
-            "embedding": {"display_name": "Embedding"},
-            "chroma_server_cors_allow_origins": {
-                "display_name": "Server CORS Allow Origins",
-                "advanced": True,
-            },
-            "chroma_server_host": {"display_name": "Server Host", "advanced": True},
-            "chroma_server_http_port": {"display_name": "Server HTTP Port", "advanced": True},
-            "chroma_server_grpc_port": {
-                "display_name": "Server gRPC Port",
-                "advanced": True,
-            },
-            "chroma_server_ssl_enabled": {
-                "display_name": "Server SSL Enabled",
-                "advanced": True,
-            },
-            "allow_duplicates": {
-                "display_name": "Allow Duplicates",
-                "advanced": True,
-                "info": "If false, will not add documents that are already in the Vector Store.",
-            },
-        }
-
-    def build(
-        self,
-        collection_name: str,
-        embedding: Embeddings,
-        chroma_server_ssl_enabled: bool,
-        index_directory: Optional[str] = None,
-        inputs: Optional[List[Record]] = None,
-        chroma_server_cors_allow_origins: List[str] = [],
-        chroma_server_host: Optional[str] = None,
-        chroma_server_http_port: Optional[int] = None,
-        chroma_server_grpc_port: Optional[int] = None,
-        allow_duplicates: bool = False,
-    ) -> Union[VectorStore, BaseRetriever]:
-        """
-        Builds the Vector Store or BaseRetriever object.
-
-        Args:
-        - collection_name (str): The name of the collection.
-        - embedding (Embeddings): The embeddings to use for the Vector Store.
-        - chroma_server_ssl_enabled (bool): Whether to enable SSL for the Chroma server.
-        - index_directory (Optional[str]): The directory to persist the Vector Store to.
-        - inputs (Optional[List[Record]]): The input records to use for the Vector Store.
-        - chroma_server_cors_allow_origins (List[str]): The CORS allow origins for the Chroma server.
-        - chroma_server_host (Optional[str]): The host for the Chroma server.
-        - chroma_server_http_port (Optional[int]): The HTTP port for the Chroma server.
-        - chroma_server_grpc_port (Optional[int]): The gRPC port for the Chroma server.
-        - allow_duplicates (bool): Whether to allow duplicates in the Vector Store.
-
-        Returns:
-        - Union[VectorStore, BaseRetriever]: The Vector Store or BaseRetriever object.
-        """
-
+        try:
+            from chromadb import Client
+            from langchain_chroma import Chroma
+        except ImportError:
+            raise ImportError(
+                "Could not import Chroma integration package. " "Please install it with `pip install langchain-chroma`."
+            )
         # Chroma settings
         chroma_settings = None
         client = None
-        if chroma_server_host is not None:
+        if self.chroma_server_host:
             chroma_settings = Settings(
-                chroma_server_cors_allow_origins=chroma_server_cors_allow_origins or [],
-                chroma_server_host=chroma_server_host,
-                chroma_server_http_port=chroma_server_http_port or None,
-                chroma_server_grpc_port=chroma_server_grpc_port or None,
-                chroma_server_ssl_enabled=chroma_server_ssl_enabled,
+                chroma_server_cors_allow_origins=self.chroma_server_cors_allow_origins or [],
+                chroma_server_host=self.chroma_server_host,
+                chroma_server_http_port=self.chroma_server_http_port or None,
+                chroma_server_grpc_port=self.chroma_server_grpc_port or None,
+                chroma_server_ssl_enabled=self.chroma_server_ssl_enabled,
             )
-            client = chromadb.HttpClient(settings=chroma_settings)
+            client = Client(settings=chroma_settings)
 
-        # Check index_directory and expand it if it is a relative path
-        if index_directory is not None:
-            index_directory = self.resolve_path(index_directory)
+        # Check persist_directory and expand it if it is a relative path
+        if self.persist_directory is not None:
+            persist_directory = self.resolve_path(self.persist_directory)
+        else:
+            persist_directory = None
 
         chroma = Chroma(
-            persist_directory=index_directory or None,
+            persist_directory=persist_directory,
             client=client,
-            embedding_function=embedding,
-            collection_name=collection_name,
+            embedding_function=self.embedding,
+            collection_name=self.collection_name,
         )
-        if allow_duplicates:
-            stored_records = []
+
+        self._add_documents_to_vector_store(chroma)
+        self.status = chroma_collection_to_data(chroma.get(limit=self.limit))
+        return chroma
+
+    def _add_documents_to_vector_store(self, vector_store: "Chroma") -> None:
+        """
+        Adds documents to the Vector Store.
+        """
+        if not self.ingest_data:
+            self.status = ""
+            return
+
+        _stored_documents_without_id = []
+        if self.allow_duplicates:
+            stored_data = []
         else:
-            stored_records = chroma_collection_to_records(chroma.get())
-            _stored_documents_without_id = []
-            for record in deepcopy(stored_records):
-                del record.id
-                _stored_documents_without_id.append(record)
+            stored_data = chroma_collection_to_data(vector_store.get(self.limit))
+            for value in deepcopy(stored_data):
+                del value.id
+                _stored_documents_without_id.append(value)
+
         documents = []
-        for _input in inputs or []:
-            if isinstance(_input, Record):
+        for _input in self.ingest_data or []:
+            if isinstance(_input, Data):
                 if _input not in _stored_documents_without_id:
                     documents.append(_input.to_lc_document())
             else:
-                raise ValueError("Inputs must be a Record objects.")
+                raise ValueError("Vector Store Inputs must be Data objects.")
 
-        if documents and embedding is not None:
-            chroma.add_documents(documents)
-
-        self.status = stored_records
-        return chroma
+        if documents and self.embedding is not None:
+            logger.debug(f"Adding {len(documents)} documents to the Vector Store.")
+            vector_store.add_documents(documents)
+        else:
+            logger.debug("No documents to add to the Vector Store.")
diff --git a/src/backend/base/langflow/components/vectorstores/Couchbase.py b/src/backend/base/langflow/components/vectorstores/Couchbase.py
index ffc17f1b6..deac2b478 100644
--- a/src/backend/base/langflow/components/vectorstores/Couchbase.py
+++ b/src/backend/base/langflow/components/vectorstores/Couchbase.py
@@ -1,94 +1,139 @@
 from datetime import timedelta
-from typing import List, Optional, Union
+from typing import List
 
+from langchain_community.vectorstores import CouchbaseVectorStore
 from langchain_core.retrievers import BaseRetriever
 
-from langflow.custom import CustomComponent
-from langflow.field_typing import Embeddings, VectorStore
-from langflow.schema import Record
+from langflow.custom import Component
+from langflow.helpers.data import docs_to_data
+from langflow.io import BoolInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
+from langflow.schema import Data
 
 
-class CouchbaseComponent(CustomComponent):
+class CouchbaseVectorStoreComponent(Component):
     display_name = "Couchbase"
-    description = "Construct a `Couchbase Vector Search` vector store from raw documents."
-    documentation = "https://python.langchain.com/docs/integrations/vectorstores/couchbase"
+    description = "Couchbase Vector Store with search capabilities"
+    documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/couchbase"
     icon = "Couchbase"
-    field_order = [
-        "couchbase_connection_string",
-        "couchbase_username",
-        "couchbase_password",
-        "bucket_name",
-        "scope_name",
-        "collection_name",
-        "index_name",
+
+    inputs = [
+        StrInput(name="couchbase_connection_string", display_name="Couchbase Cluster connection string", required=True),
+        StrInput(name="couchbase_username", display_name="Couchbase username", required=True),
+        SecretStrInput(name="couchbase_password", display_name="Couchbase password", required=True),
+        StrInput(name="bucket_name", display_name="Bucket Name", required=True),
+        StrInput(name="scope_name", display_name="Scope Name", required=True),
+        StrInput(name="collection_name", display_name="Collection Name", required=True),
+        StrInput(name="index_name", display_name="Index Name", required=True),
+        HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
+        HandleInput(
+            name="vector_store_inputs",
+            display_name="Vector Store Inputs",
+            input_types=["Document", "Data"],
+            is_list=True,
+        ),
+        BoolInput(
+            name="add_to_vector_store",
+            display_name="Add to Vector Store",
+            info="If true, the Vector Store Inputs will be added to the Vector Store.",
+        ),
+        StrInput(name="search_input", display_name="Search Input"),
+        IntInput(
+            name="number_of_results",
+            display_name="Number of Results",
+            info="Number of results to return.",
+            value=4,
+            advanced=True,
+        ),
     ]
 
-    def build_config(self):
-        return {
-            "inputs": {"display_name": "Input", "input_types": ["Document", "Record"]},
-            "embedding": {"display_name": "Embedding"},
-            "couchbase_connection_string": {"display_name": "Couchbase Cluster connection string", "required": True},
-            "couchbase_username": {"display_name": "Couchbase username", "required": True},
-            "couchbase_password": {"display_name": "Couchbase password", "password": True, "required": True},
-            "bucket_name": {"display_name": "Bucket Name", "required": True},
-            "scope_name": {"display_name": "Scope Name", "required": True},
-            "collection_name": {"display_name": "Collection Name", "required": True},
-            "index_name": {"display_name": "Index Name", "required": True},
-        }
+    outputs = [
+        Output(
+            display_name="Vector Store",
+            name="vector_store",
+            method="build_vector_store",
+            output_type=CouchbaseVectorStore,
+        ),
+        Output(
+            display_name="Base Retriever",
+            name="base_retriever",
+            method="build_base_retriever",
+            output_type=BaseRetriever,
+        ),
+        Output(display_name="Search Results", name="search_results", method="search_documents"),
+    ]
 
-    def build(
-        self,
-        embedding: Embeddings,
-        inputs: Optional[List[Record]] = None,
-        bucket_name: str = "",
-        scope_name: str = "",
-        collection_name: str = "",
-        index_name: str = "",
-        couchbase_connection_string: str = "",
-        couchbase_username: str = "",
-        couchbase_password: str = "",
-    ) -> Union[VectorStore, BaseRetriever]:
+    def build_vector_store(self) -> CouchbaseVectorStore:
+        return self._build_couchbase()
+
+    def _build_couchbase(self) -> CouchbaseVectorStore:
         try:
             from couchbase.auth import PasswordAuthenticator  # type: ignore
             from couchbase.cluster import Cluster  # type: ignore
             from couchbase.options import ClusterOptions  # type: ignore
-            from langchain_community.vectorstores import CouchbaseVectorStore
         except ImportError as e:
             raise ImportError(
                 "Failed to import Couchbase dependencies. Install it using `pip install langflow[couchbase] --pre`"
             ) from e
 
         try:
-            auth = PasswordAuthenticator(couchbase_username, couchbase_password)
+            auth = PasswordAuthenticator(self.couchbase_username, self.couchbase_password)
             options = ClusterOptions(auth)
-            cluster = Cluster(couchbase_connection_string, options)
+            cluster = Cluster(self.couchbase_connection_string, options)
 
             cluster.wait_until_ready(timedelta(seconds=5))
         except Exception as e:
             raise ValueError(f"Failed to connect to Couchbase: {e}")
-        documents = []
-        for _input in inputs or []:
-            if isinstance(_input, Record):
-                documents.append(_input.to_lc_document())
+
+        if self.add_to_vector_store:
+            documents = []
+            for _input in self.vector_store_inputs or []:
+                if isinstance(_input, Data):
+                    documents.append(_input.to_lc_document())
+                else:
+                    documents.append(_input)
+
+            if documents:
+                couchbase_vs = CouchbaseVectorStore.from_documents(
+                    documents=documents,
+                    cluster=cluster,
+                    bucket_name=self.bucket_name,
+                    scope_name=self.scope_name,
+                    collection_name=self.collection_name,
+                    embedding=self.embedding,
+                    index_name=self.index_name,
+                )
             else:
-                documents.append(_input)
-        if documents:
-            vector_store = CouchbaseVectorStore.from_documents(
-                documents=documents,
-                cluster=cluster,
-                bucket_name=bucket_name,
-                scope_name=scope_name,
-                collection_name=collection_name,
-                embedding=embedding,
-                index_name=index_name,
-            )
+                couchbase_vs = CouchbaseVectorStore(
+                    cluster=cluster,
+                    bucket_name=self.bucket_name,
+                    scope_name=self.scope_name,
+                    collection_name=self.collection_name,
+                    embedding=self.embedding,
+                    index_name=self.index_name,
+                )
         else:
-            vector_store = CouchbaseVectorStore(
+            couchbase_vs = CouchbaseVectorStore(
                 cluster=cluster,
-                bucket_name=bucket_name,
-                scope_name=scope_name,
-                collection_name=collection_name,
-                embedding=embedding,
-                index_name=index_name,
+                bucket_name=self.bucket_name,
+                scope_name=self.scope_name,
+                collection_name=self.collection_name,
+                embedding=self.embedding,
+                index_name=self.index_name,
             )
-        return vector_store
+
+        return couchbase_vs
+
+    def search_documents(self) -> List[Data]:
+        vector_store = self._build_couchbase()
+
+        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
+            docs = vector_store.similarity_search(
+                query=self.search_input,
+                k=self.number_of_results,
+            )
+
+            data = docs_to_data(docs)
+            self.status = data
+            return data
+        else:
+            return []
diff --git a/src/backend/base/langflow/components/vectorstores/FAISS.py b/src/backend/base/langflow/components/vectorstores/FAISS.py
index 3efd5b722..dab0a069c 100644
--- a/src/backend/base/langflow/components/vectorstores/FAISS.py
+++ b/src/backend/base/langflow/components/vectorstores/FAISS.py
@@ -1,46 +1,161 @@
-from typing import List, Text, Union
+from typing import List
 
-from langchain_community.vectorstores.faiss import FAISS
-from langchain_core.retrievers import BaseRetriever
-from langchain_core.vectorstores import VectorStore
+from langchain_community.vectorstores import FAISS
+from loguru import logger
 
-from langflow.custom import CustomComponent
-from langflow.field_typing import Embeddings
-from langflow.schema import Record
+from langflow.base.vectorstores.model import LCVectorStoreComponent
+from langflow.field_typing import Text
+from langflow.helpers.data import docs_to_data
+from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
+from langflow.schema import Data
 
 
-class FAISSComponent(CustomComponent):
-    display_name = "FAISS"
-    description = "Ingest documents into FAISS Vector Store."
+class FaissVectorStoreComponent(LCVectorStoreComponent):
+    """
+    FAISS Vector Store with search capabilities
+    """
+
+    display_name: str = "FAISS"
+    description: str = "FAISS Vector Store with search capabilities"
     documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss"
+    icon = "FAISS"
 
-    def build_config(self):
-        return {
-            "inputs": {"display_name": "Input", "input_types": ["Document", "Record"]},
-            "embedding": {"display_name": "Embedding"},
-            "folder_path": {
-                "display_name": "Folder Path",
-                "info": "Path to save the FAISS index. It will be relative to where Langflow is running.",
-            },
-            "index_name": {"display_name": "Index Name"},
-        }
+    inputs = [
+        StrInput(
+            name="folder_path",
+            display_name="Folder Path",
+            info="Path to save the FAISS index. It will be relative to where Langflow is running.",
+        ),
+        StrInput(
+            name="index_name",
+            display_name="Index Name",
+            value="langflow_index",
+        ),
+        HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
+        StrInput(
+            name="vector_store_inputs",
+            display_name="Vector Store Inputs",
+            input_types=["Document", "Data"],
+            is_list=True,
+        ),
+        BoolInput(
+            name="add_to_vector_store",
+            display_name="Add to Vector Store",
+            info="If true, the Vector Store Inputs will be added to the Vector Store.",
+        ),
+        BoolInput(
+            name="allow_dangerous_deserialization",
+            display_name="Allow Dangerous Deserialization",
+            info="Set to True to allow loading pickle files from untrusted sources. Only enable this if you trust the source of the data.",
+            advanced=True,
+            value=False,
+        ),
+        StrInput(
+            name="search_input",
+            display_name="Search Input",
+        ),
+        IntInput(
+            name="number_of_results",
+            display_name="Number of Results",
+            info="Number of results to return.",
+            advanced=True,
+            value=4,
+        ),
+    ]
 
-    def build(
-        self,
-        embedding: Embeddings,
-        inputs: List[Record],
-        folder_path: str,
-        index_name: str = "langflow_index",
-    ) -> Union[VectorStore, FAISS, BaseRetriever]:
-        documents = []
-        for _input in inputs or []:
-            if isinstance(_input, Record):
-                documents.append(_input.to_lc_document())
-            else:
-                documents.append(_input)
-        vector_store = FAISS.from_documents(documents=documents, embedding=embedding)
-        if not folder_path:
+    outputs = [
+        Output(
+            display_name="Vector Store",
+            name="vector_store",
+            method="build_vector_store",
+        ),
+        Output(
+            display_name="Base Retriever",
+            name="base_retriever",
+            method="build_base_retriever",
+        ),
+        Output(
+            display_name="Search Results",
+            name="search_results",
+            method="search_documents",
+        ),
+    ]
+
+    def build_vector_store(self) -> FAISS:
+        """
+        Builds the FAISS object.
+        """
+        if not self.folder_path:
             raise ValueError("Folder path is required to save the FAISS index.")
-        path = self.resolve_path(folder_path)
-        vector_store.save_local(Text(path), index_name)
-        return vector_store
+        path = self.resolve_path(self.folder_path)
+
+        if self.add_to_vector_store:
+            documents = []
+            for _input in self.vector_store_inputs or []:
+                if isinstance(_input, Data):
+                    documents.append(_input.to_lc_document())
+                else:
+                    documents.append(_input)
+
+            faiss = FAISS.from_documents(documents=documents, embedding=self.embedding)
+            faiss.save_local(Text(path), self.index_name)
+        else:
+            try:
+                faiss = FAISS.load_local(
+                    folder_path=Text(path),
+                    embeddings=self.embedding,
+                    index_name=self.index_name,
+                    allow_dangerous_deserialization=self.allow_dangerous_deserialization,
+                )
+            except Exception as e:
+                raise ValueError(
+                    "Failed to load the FAISS index. Make sure the index was created with trusted data. "
+                    "If you trust the data source, you can set `allow_dangerous_deserialization` to `True` "
+                    "in the component's advanced settings to enable deserialization."
+                ) from e
+
+        return faiss
+
+    def search_documents(self) -> List[Data]:
+        """
+        Search for documents in the FAISS vector store.
+        """
+        if not self.folder_path:
+            raise ValueError("Folder path is required to load the FAISS index.")
+        path = self.resolve_path(self.folder_path)
+
+        try:
+            vector_store = FAISS.load_local(
+                folder_path=Text(path),
+                embeddings=self.embedding,
+                index_name=self.index_name,
+                allow_dangerous_deserialization=self.allow_dangerous_deserialization,
+            )
+        except Exception as e:
+            raise ValueError(
+                "Failed to load the FAISS index. Make sure the index was created with trusted data. "
+                "If you trust the data source, you can set `allow_dangerous_deserialization` to `True` "
+                "in the component's advanced settings to enable deserialization."
+            ) from e
+
+        if not vector_store:
+            raise ValueError("Failed to load the FAISS index.")
+
+        logger.debug(f"Search input: {self.search_input}")
+        logger.debug(f"Number of results: {self.number_of_results}")
+
+        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
+            docs = vector_store.similarity_search(
+                query=self.search_input,
+                k=self.number_of_results,
+            )
+
+            logger.debug(f"Retrieved documents: {len(docs)}")
+
+            data = docs_to_data(docs)
+            logger.debug(f"Converted documents to data: {len(data)}")
+            logger.debug(data)
+            return data  # Return the search results data
+        else:
+            logger.debug("No search input provided. Skipping search.")
+            return []
diff --git a/src/backend/base/langflow/components/vectorstores/MongoDBAtlasVector.py b/src/backend/base/langflow/components/vectorstores/MongoDBAtlasVector.py
index 61c4933e9..68ced0269 100644
--- a/src/backend/base/langflow/components/vectorstores/MongoDBAtlasVector.py
+++ b/src/backend/base/langflow/components/vectorstores/MongoDBAtlasVector.py
@@ -1,64 +1,121 @@
-from typing import List, Optional
+from typing import List
 
-from langchain_community.vectorstores.mongodb_atlas import MongoDBAtlasVectorSearch
+from langchain_community.vectorstores import MongoDBAtlasVectorSearch
+from langchain_core.retrievers import BaseRetriever
 
-from langflow.custom import CustomComponent
-from langflow.field_typing import Embeddings
-from langflow.schema import Record
+from langflow.custom import Component
+from langflow.helpers.data import docs_to_data
+from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
+from langflow.schema import Data
 
 
-class MongoDBAtlasComponent(CustomComponent):
+class MongoVectorStoreComponent(Component):
     display_name = "MongoDB Atlas"
-    description = "Construct a `MongoDB Atlas Vector Search` vector store from raw documents."
+    description = "MongoDB Atlas Vector Store with search capabilities"
+    documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/mongodb_atlas"
     icon = "MongoDB"
 
-    def build_config(self):
-        return {
-            "inputs": {"display_name": "Input", "input_types": ["Document", "Record"]},
-            "embedding": {"display_name": "Embedding"},
-            "collection_name": {"display_name": "Collection Name"},
-            "db_name": {"display_name": "Database Name"},
-            "index_name": {"display_name": "Index Name"},
-            "mongodb_atlas_cluster_uri": {"display_name": "MongoDB Atlas Cluster URI"},
-        }
+    inputs = [
+        StrInput(name="mongodb_atlas_cluster_uri", display_name="MongoDB Atlas Cluster URI", required=True),
+        StrInput(name="db_name", display_name="Database Name", required=True),
+        StrInput(name="collection_name", display_name="Collection Name", required=True),
+        StrInput(name="index_name", display_name="Index Name", required=True),
+        HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
+        HandleInput(
+            name="vector_store_inputs",
+            display_name="Vector Store Inputs",
+            input_types=["Document", "Data"],
+            is_list=True,
+        ),
+        BoolInput(
+            name="add_to_vector_store",
+            display_name="Add to Vector Store",
+            info="If true, the Vector Store Inputs will be added to the Vector Store.",
+        ),
+        StrInput(name="search_input", display_name="Search Input"),
+        IntInput(
+            name="number_of_results",
+            display_name="Number of Results",
+            info="Number of results to return.",
+            value=4,
+            advanced=True,
+        ),
+    ]
 
-    def build(
-        self,
-        embedding: Embeddings,
-        inputs: Optional[List[Record]] = None,
-        collection_name: str = "",
-        db_name: str = "",
-        index_name: str = "",
-        mongodb_atlas_cluster_uri: str = "",
-    ) -> MongoDBAtlasVectorSearch:
+    outputs = [
+        Output(
+            display_name="Vector Store",
+            name="vector_store",
+            method="build_vector_store",
+            output_type=MongoDBAtlasVectorSearch,
+        ),
+        Output(
+            display_name="Base Retriever",
+            name="base_retriever",
+            method="build_base_retriever",
+            output_type=BaseRetriever,
+        ),
+        Output(display_name="Search Results", name="search_results", method="search_documents"),
+    ]
+
+    def build_vector_store(self) -> MongoDBAtlasVectorSearch:
+        return self._build_mongodb_atlas()
+
+    def _build_mongodb_atlas(self) -> MongoDBAtlasVectorSearch:
         try:
             from pymongo import MongoClient
         except ImportError:
             raise ImportError("Please install pymongo to use MongoDB Atlas Vector Store")
+
         try:
-            mongo_client: MongoClient = MongoClient(mongodb_atlas_cluster_uri)
-            collection = mongo_client[db_name][collection_name]
+            mongo_client: MongoClient = MongoClient(self.mongodb_atlas_cluster_uri)
+            collection = mongo_client[self.db_name][self.collection_name]
         except Exception as e:
             raise ValueError(f"Failed to connect to MongoDB Atlas: {e}")
-        documents = []
-        for _input in inputs or []:
-            if isinstance(_input, Record):
-                documents.append(_input.to_lc_document())
+
+        if self.add_to_vector_store:
+            documents = []
+            for _input in self.vector_store_inputs or []:
+                if isinstance(_input, Data):
+                    documents.append(_input.to_lc_document())
+                else:
+                    documents.append(_input)
+
+            if documents:
+                vector_store = MongoDBAtlasVectorSearch.from_documents(
+                    documents=documents,
+                    embedding=self.embedding,
+                    collection=collection,
+                    db_name=self.db_name,
+                    index_name=self.index_name,
+                    mongodb_atlas_cluster_uri=self.mongodb_atlas_cluster_uri,
+                )
             else:
-                documents.append(_input)
-        if documents:
-            vector_store = MongoDBAtlasVectorSearch.from_documents(
-                documents=documents,
-                embedding=embedding,
-                collection=collection,
-                db_name=db_name,
-                index_name=index_name,
-                mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri,
-            )
+                vector_store = MongoDBAtlasVectorSearch(
+                    embedding=self.embedding,
+                    collection=collection,
+                    index_name=self.index_name,
+                )
         else:
             vector_store = MongoDBAtlasVectorSearch(
-                embedding=embedding,
+                embedding=self.embedding,
                 collection=collection,
-                index_name=index_name,
+                index_name=self.index_name,
             )
+
         return vector_store
+
+    def search_documents(self) -> List[Data]:
+        vector_store = self._build_mongodb_atlas()
+
+        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
+            docs = vector_store.similarity_search(
+                query=self.search_input,
+                k=self.number_of_results,
+            )
+
+            data = docs_to_data(docs)
+            self.status = data
+            return data
+        else:
+            return []
diff --git a/src/backend/base/langflow/components/vectorstores/Pinecone.py b/src/backend/base/langflow/components/vectorstores/Pinecone.py
index 135dd7501..57d9137b6 100644
--- a/src/backend/base/langflow/components/vectorstores/Pinecone.py
+++ b/src/backend/base/langflow/components/vectorstores/Pinecone.py
@@ -1,151 +1,114 @@
-from typing import List, Optional, Union
+from typing import List
 
-from langchain_core.documents import Document
 from langchain_core.retrievers import BaseRetriever
-from langchain_core.vectorstores import VectorStore
-from langchain_pinecone._utilities import DistanceStrategy
-from langchain_pinecone.vectorstores import PineconeVectorStore
+from langchain_pinecone import Pinecone
 
-from langflow.custom import CustomComponent
-from langflow.field_typing import Embeddings
-from langflow.schema import Record
+from langflow.custom import Component
+from langflow.helpers.data import docs_to_data
+from langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
+from langflow.schema import Data
 
 
-class PineconeComponent(CustomComponent):
+class PineconeVectorStoreComponent(Component):
     display_name = "Pinecone"
-    description = "Construct Pinecone wrapper from raw documents."
+    description = "Pinecone Vector Store with search capabilities"
+    documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/pinecone"
     icon = "Pinecone"
-    field_order = ["index_name", "namespace", "distance_strategy", "pinecone_api_key", "documents", "embedding"]
 
-    def build_config(self):
-        distance_options = [e.value.title().replace("_", " ") for e in DistanceStrategy]
-        distance_value = distance_options[0]
-        return {
-            "inputs": {"display_name": "Input", "input_types": ["Document", "Record"]},
-            "embedding": {"display_name": "Embedding"},
-            "index_name": {"display_name": "Index Name"},
-            "namespace": {"display_name": "Namespace"},
-            "text_key": {"display_name": "Text Key"},
-            "distance_strategy": {
-                "display_name": "Distance Strategy",
-                # get values from enum
-                # and make them title case for display
-                "options": distance_options,
-                "advanced": True,
-                "value": distance_value,
-            },
-            "pinecone_api_key": {
-                "display_name": "Pinecone API Key",
-                "default": "",
-                "password": True,
-                "required": True,
-            },
-            "pool_threads": {
-                "display_name": "Pool Threads",
-                "default": 1,
-                "advanced": True,
-            },
-        }
+    inputs = [
+        StrInput(name="index_name", display_name="Index Name", required=True),
+        StrInput(name="namespace", display_name="Namespace", info="Namespace for the index."),
+        DropdownInput(
+            name="distance_strategy",
+            display_name="Distance Strategy",
+            options=["Cosine", "Euclidean", "Dot Product"],
+            value="Cosine",
+            advanced=True,
+        ),
+        SecretStrInput(name="pinecone_api_key", display_name="Pinecone API Key", required=True),
+        HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
+        StrInput(
+            name="text_key",
+            display_name="Text Key",
+            info="Key in the record to use as text.",
+            value="text",
+            advanced=True,
+        ),
+        HandleInput(
+            name="vector_store_inputs",
+            display_name="Vector Store Inputs",
+            input_types=["Document", "Data"],
+            is_list=True,
+        ),
+        BoolInput(
+            name="add_to_vector_store",
+            display_name="Add to Vector Store",
+            info="If true, the Vector Store Inputs will be added to the Vector Store.",
+        ),
+        StrInput(name="search_input", display_name="Search Input"),
+        IntInput(
+            name="number_of_results",
+            display_name="Number of Results",
+            info="Number of results to return.",
+            value=4,
+            advanced=True,
+        ),
+    ]
 
-    def from_existing_index(
-        self,
-        index_name: str,
-        embedding: Embeddings,
-        pinecone_api_key: str | None,
-        text_key: str = "text",
-        namespace: Optional[str] = None,
-        distance_strategy: DistanceStrategy = DistanceStrategy.COSINE,
-        pool_threads: int = 4,
-    ) -> PineconeVectorStore:
-        """Load pinecone vectorstore from index name."""
-        pinecone_index = PineconeVectorStore.get_pinecone_index(
-            index_name, pool_threads, pinecone_api_key=pinecone_api_key
-        )
-        return PineconeVectorStore(
-            index=pinecone_index,
-            embedding=embedding,
-            text_key=text_key,
-            namespace=namespace,
-            distance_strategy=distance_strategy,
+    outputs = [
+        Output(display_name="Vector Store", name="vector_store", method="build_vector_store", output_type=Pinecone),
+        Output(
+            display_name="Base Retriever",
+            name="base_retriever",
+            method="build_base_retriever",
+            output_type=BaseRetriever,
+        ),
+        Output(display_name="Search Results", name="search_results", method="search_documents"),
+    ]
+
+    def build_vector_store(self) -> Pinecone:
+        return self._build_pinecone()
+
+    def _build_pinecone(self) -> Pinecone:
+        from langchain_pinecone._utilities import DistanceStrategy
+        from langchain_pinecone.vectorstores import Pinecone
+
+        distance_strategy = self.distance_strategy.replace(" ", "_").upper()
+        _distance_strategy = DistanceStrategy[distance_strategy]
+
+        pinecone = Pinecone(
+            index_name=self.index_name,
+            embedding=self.embedding,
+            text_key=self.text_key,
+            namespace=self.namespace,
+            distance_strategy=_distance_strategy,
+            pinecone_api_key=self.pinecone_api_key,
         )
 
-    def from_documents(
-        self,
-        documents: List[Document],
-        embedding: Embeddings,
-        index_name: str,
-        pinecone_api_key: str | None,
-        text_key: str = "text",
-        namespace: Optional[str] = None,
-        pool_threads: int = 4,
-        distance_strategy: DistanceStrategy = DistanceStrategy.COSINE,
-        batch_size: int = 32,
-        upsert_kwargs: Optional[dict] = None,
-        embeddings_chunk_size: int = 1000,
-    ) -> PineconeVectorStore:
-        """Create a new pinecone vectorstore from documents."""
-        texts = [d.page_content for d in documents]
-        metadatas = [d.metadata for d in documents]
-        pinecone = self.from_existing_index(
-            index_name=index_name,
-            embedding=embedding,
-            pinecone_api_key=pinecone_api_key,
-            text_key=text_key,
-            namespace=namespace,
-            distance_strategy=distance_strategy,
-            pool_threads=pool_threads,
-        )
-        pinecone.add_texts(
-            texts,
-            metadatas=metadatas,
-            ids=None,
-            namespace=namespace,
-            batch_size=batch_size,
-            embedding_chunk_size=embeddings_chunk_size,
-            **(upsert_kwargs or {}),
-        )
+        if self.add_to_vector_store:
+            documents = []
+            for _input in self.vector_store_inputs or []:
+                if isinstance(_input, Data):
+                    documents.append(_input.to_lc_document())
+                else:
+                    documents.append(_input)
+
+            if documents:
+                pinecone.add_documents(documents)
+
         return pinecone
 
-    def build(
-        self,
-        embedding: Embeddings,
-        distance_strategy: str,
-        inputs: Optional[List[Record]] = None,
-        text_key: str = "text",
-        pool_threads: int = 4,
-        index_name: Optional[str] = None,
-        pinecone_api_key: Optional[str] = None,
-        namespace: Optional[str] = "default",
-    ) -> Union[VectorStore, BaseRetriever]:
-        # get distance strategy from string
-        distance_strategy = distance_strategy.replace(" ", "_").upper()
-        _distance_strategy = DistanceStrategy[distance_strategy]
-        if not index_name:
-            raise ValueError("Index Name is required.")
-        documents = []
-        for _input in inputs or []:
-            if isinstance(_input, Record):
-                documents.append(_input.to_lc_document())
-            else:
-                documents.append(_input)
-        if documents:
-            return self.from_documents(
-                documents=documents,
-                embedding=embedding,
-                index_name=index_name,
-                pinecone_api_key=pinecone_api_key,
-                text_key=text_key,
-                namespace=namespace,
-                distance_strategy=_distance_strategy,
-                pool_threads=pool_threads,
+    def search_documents(self) -> List[Data]:
+        vector_store = self._build_pinecone()
+
+        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
+            docs = vector_store.similarity_search(
+                query=self.search_input,
+                k=self.number_of_results,
             )
 
-        return self.from_existing_index(
-            index_name=index_name,
-            embedding=embedding,
-            pinecone_api_key=pinecone_api_key,
-            text_key=text_key,
-            namespace=namespace,
-            distance_strategy=_distance_strategy,
-            pool_threads=pool_threads,
-        )
+            data = docs_to_data(docs)
+            self.status = data
+            return data
+        else:
+            return []
diff --git a/src/backend/base/langflow/components/vectorstores/Qdrant.py b/src/backend/base/langflow/components/vectorstores/Qdrant.py
index 6c1bdbcb6..f1c25aa57 100644
--- a/src/backend/base/langflow/components/vectorstores/Qdrant.py
+++ b/src/backend/base/langflow/components/vectorstores/Qdrant.py
@@ -1,114 +1,133 @@
-from typing import Optional, Union
+from typing import List
 
-from langchain_community.vectorstores.qdrant import Qdrant
+from langchain_community.vectorstores import Qdrant
 from langchain_core.retrievers import BaseRetriever
-from langchain_core.vectorstores import VectorStore
 
-from langflow.custom import CustomComponent
-from langflow.field_typing import Embeddings
-from langflow.schema import Record
+from langflow.custom import Component
+from langflow.helpers.data import docs_to_data
+from langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
+from langflow.schema import Data
 
 
-class QdrantComponent(CustomComponent):
+class QdrantVectorStoreComponent(Component):
     display_name = "Qdrant"
-    description = "Construct Qdrant wrapper from a list of texts."
+    description = "Qdrant Vector Store with search capabilities"
+    documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/qdrant"
     icon = "Qdrant"
 
-    def build_config(self):
-        return {
-            "inputs": {"display_name": "Input", "input_types": ["Document", "Record"]},
-            "embedding": {"display_name": "Embedding"},
-            "api_key": {"display_name": "API Key", "password": True, "advanced": True},
-            "collection_name": {"display_name": "Collection Name"},
-            "content_payload_key": {
-                "display_name": "Content Payload Key",
-                "advanced": True,
-            },
-            "distance_func": {"display_name": "Distance Function", "advanced": True},
-            "grpc_port": {"display_name": "gRPC Port", "advanced": True},
-            "host": {"display_name": "Host", "advanced": True},
-            "https": {"display_name": "HTTPS", "advanced": True},
-            "location": {"display_name": "Location", "advanced": True},
-            "metadata_payload_key": {
-                "display_name": "Metadata Payload Key",
-                "advanced": True,
-            },
-            "path": {"display_name": "Path", "advanced": True},
-            "port": {"display_name": "Port", "advanced": True},
-            "prefer_grpc": {"display_name": "Prefer gRPC", "advanced": True},
-            "prefix": {"display_name": "Prefix", "advanced": True},
-            "timeout": {"display_name": "Timeout", "advanced": True},
-            "url": {"display_name": "URL", "advanced": True},
+    inputs = [
+        StrInput(name="collection_name", display_name="Collection Name", required=True),
+        StrInput(name="host", display_name="Host", value="localhost", advanced=True),
+        IntInput(name="port", display_name="Port", value=6333, advanced=True),
+        IntInput(name="grpc_port", display_name="gRPC Port", value=6334, advanced=True),
+        SecretStrInput(name="api_key", display_name="API Key", advanced=True),
+        StrInput(name="prefix", display_name="Prefix", advanced=True),
+        IntInput(name="timeout", display_name="Timeout", advanced=True),
+        StrInput(name="path", display_name="Path", advanced=True),
+        StrInput(name="url", display_name="URL", advanced=True),
+        DropdownInput(
+            name="distance_func",
+            display_name="Distance Function",
+            options=["Cosine", "Euclidean", "Dot Product"],
+            value="Cosine",
+            advanced=True,
+        ),
+        StrInput(name="content_payload_key", display_name="Content Payload Key", value="page_content", advanced=True),
+        StrInput(name="metadata_payload_key", display_name="Metadata Payload Key", value="metadata", advanced=True),
+        HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
+        HandleInput(
+            name="vector_store_inputs",
+            display_name="Vector Store Inputs",
+            input_types=["Document", "Data"],
+            is_list=True,
+        ),
+        BoolInput(
+            name="add_to_vector_store",
+            display_name="Add to Vector Store",
+            info="If true, the Vector Store Inputs will be added to the Vector Store.",
+        ),
+        StrInput(name="search_input", display_name="Search Input"),
+        IntInput(
+            name="number_of_results",
+            display_name="Number of Results",
+            info="Number of results to return.",
+            value=4,
+            advanced=True,
+        ),
+    ]
+
+    outputs = [
+        Output(display_name="Vector Store", name="vector_store", method="build_vector_store", output_type=Qdrant),
+        Output(
+            display_name="Base Retriever",
+            name="base_retriever",
+            method="build_base_retriever",
+            output_type=BaseRetriever,
+        ),
+        Output(display_name="Search Results", name="search_results", method="search_documents"),
+    ]
+
+    def build_vector_store(self) -> Qdrant:
+        return self._build_qdrant()
+
+    def _build_qdrant(self) -> Qdrant:
+        qdrant_kwargs = {
+            "collection_name": self.collection_name,
+            "content_payload_key": self.content_payload_key,
+            "distance_func": self.distance_func,
+            "metadata_payload_key": self.metadata_payload_key,
         }
 
-    def build(
-        self,
-        embedding: Embeddings,
-        collection_name: str,
-        inputs: Optional[Record] = None,
-        api_key: Optional[str] = None,
-        content_payload_key: str = "page_content",
-        distance_func: str = "Cosine",
-        grpc_port: int = 6334,
-        https: bool = False,
-        host: Optional[str] = None,
-        location: Optional[str] = None,
-        metadata_payload_key: str = "metadata",
-        path: Optional[str] = None,
-        port: Optional[int] = 6333,
-        prefer_grpc: bool = False,
-        prefix: Optional[str] = None,
-        timeout: Optional[int] = None,
-        url: Optional[str] = None,
-    ) -> Union[VectorStore, Qdrant, BaseRetriever]:
-        documents = []
-        for _input in inputs or []:
-            if isinstance(_input, Record):
-                documents.append(_input.to_lc_document())
+        server_kwargs = {
+            "host": self.host,
+            "port": self.port,
+            "grpc_port": self.grpc_port,
+            "api_key": self.api_key,
+            "prefix": self.prefix,
+            "timeout": self.timeout,
+            "path": self.path,
+            "url": self.url,
+        }
+
+        # Remove None values from server_kwargs
+        server_kwargs = {k: v for k, v in server_kwargs.items() if v is not None}
+
+        if self.add_to_vector_store:
+            documents = []
+            for _input in self.vector_store_inputs or []:
+                if isinstance(_input, Data):
+                    documents.append(_input.to_lc_document())
+                else:
+                    documents.append(_input)
+
+            if documents:
+                qdrant = Qdrant.from_documents(
+                    documents, embedding=self.embedding, client_kwargs=server_kwargs, **qdrant_kwargs
+                )
             else:
-                documents.append(_input)
-        if not documents:
+                from qdrant_client import QdrantClient
+
+                client = QdrantClient(**server_kwargs)
+                qdrant = Qdrant(embedding_function=self.embedding.embed_query, client=client, **qdrant_kwargs)
+        else:
             from qdrant_client import QdrantClient
 
-            client = QdrantClient(
-                location=location,
-                url=url,
-                port=port,
-                grpc_port=grpc_port,
-                https=https,
-                prefix=prefix,
-                timeout=timeout,
-                prefer_grpc=prefer_grpc,
-                api_key=api_key,
-                host=host,
-                path=path,
+            client = QdrantClient(**server_kwargs)
+            qdrant = Qdrant(embedding_function=self.embedding.embed_query, client=client, **qdrant_kwargs)
+
+        return qdrant
+
+    def search_documents(self) -> List[Data]:
+        vector_store = self._build_qdrant()
+
+        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
+            docs = vector_store.similarity_search(
+                query=self.search_input,
+                k=self.number_of_results,
             )
-            vs = Qdrant(
-                client=client,
-                collection_name=collection_name,
-                embeddings=embedding,
-                content_payload_key=content_payload_key,
-                metadata_payload_key=metadata_payload_key,
-            )
-            return vs
+
+            data = docs_to_data(docs)
+            self.status = data
+            return data
         else:
-            vs = Qdrant.from_documents(
-                documents=documents,  # type: ignore
-                embedding=embedding,
-                api_key=api_key,
-                collection_name=collection_name,
-                content_payload_key=content_payload_key,
-                distance_func=distance_func,
-                grpc_port=grpc_port,
-                host=host,
-                https=https,
-                location=location,
-                metadata_payload_key=metadata_payload_key,
-                path=path,
-                port=port,
-                prefer_grpc=prefer_grpc,
-                prefix=prefix,
-                timeout=timeout,
-                url=url,
-            )
-        return vs
+            return []
diff --git a/src/backend/base/langflow/components/vectorstores/Redis.py b/src/backend/base/langflow/components/vectorstores/Redis.py
index c35ec018e..fd7b70678 100644
--- a/src/backend/base/langflow/components/vectorstores/Redis.py
+++ b/src/backend/base/langflow/components/vectorstores/Redis.py
@@ -1,15 +1,14 @@
-from typing import Optional, Union
+from typing import Optional, cast
 
 from langchain_community.vectorstores.redis import Redis
 from langchain_core.embeddings import Embeddings
-from langchain_core.retrievers import BaseRetriever
-from langchain_core.vectorstores import VectorStore
 
 from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langflow.field_typing import VectorStore
+from langflow.schema import Data
 
 
-class RedisComponent(CustomComponent):
+class RedisVectorStoreComponent(CustomComponent):
     """
     A custom component for implementing a Vector Store using Redis.
     """
@@ -28,7 +27,7 @@ class RedisComponent(CustomComponent):
         return {
             "index_name": {"display_name": "Index Name", "value": "your_index"},
             "code": {"show": False, "display_name": "Code"},
-            "inputs": {"display_name": "Input", "input_types": ["Document", "Record"]},
+            "inputs": {"display_name": "Input", "input_types": ["Document", "Data"]},
             "embedding": {"display_name": "Embedding"},
             "schema": {"display_name": "Schema", "file_types": [".yaml"]},
             "redis_server_url": {
@@ -44,8 +43,8 @@ class RedisComponent(CustomComponent):
         redis_server_url: str,
         redis_index_name: str,
         schema: Optional[str] = None,
-        inputs: Optional[Record] = None,
-    ) -> Union[VectorStore, BaseRetriever]:
+        inputs: Optional[Data] = None,
+    ) -> VectorStore:
         """
         Builds the Vector Store or BaseRetriever object.
 
@@ -60,7 +59,7 @@ class RedisComponent(CustomComponent):
         """
         documents = []
         for _input in inputs or []:
-            if isinstance(_input, Record):
+            if isinstance(_input, Data):
                 documents.append(_input.to_lc_document())
             else:
                 documents.append(_input)
@@ -81,4 +80,4 @@ class RedisComponent(CustomComponent):
                 redis_url=redis_server_url,
                 index_name=redis_index_name,
             )
-        return redis_vs
+        return cast(VectorStore, redis_vs)
diff --git a/src/backend/base/langflow/components/vectorstores/SupabaseVectorStore.py b/src/backend/base/langflow/components/vectorstores/SupabaseVectorStore.py
index e7c847f2b..f3b8ef273 100644
--- a/src/backend/base/langflow/components/vectorstores/SupabaseVectorStore.py
+++ b/src/backend/base/langflow/components/vectorstores/SupabaseVectorStore.py
@@ -1,49 +1,101 @@
-from typing import List, Optional, Union
+from typing import List
 
-from langchain_community.vectorstores.supabase import SupabaseVectorStore
+from langchain_community.vectorstores import SupabaseVectorStore
 from langchain_core.retrievers import BaseRetriever
-from langchain_core.vectorstores import VectorStore
 from supabase.client import Client, create_client
 
-from langflow.custom import CustomComponent
-from langflow.field_typing import Embeddings
-from langflow.schema import Record
+from langflow.custom import Component
+from langflow.helpers.data import docs_to_data
+from langflow.io import HandleInput, IntInput, Output, StrInput
+from langflow.schema import Data
 
 
-class SupabaseComponent(CustomComponent):
+class SupabaseVectorStoreComponent(Component):
     display_name = "Supabase"
-    description = "Return VectorStore initialized from texts and embeddings."
+    description = "Supabase Vector Store with search capabilities"
+    documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/supabase"
+    icon = "Supabase"
 
-    def build_config(self):
-        return {
-            "inputs": {"display_name": "Input", "input_types": ["Document", "Record"]},
-            "embedding": {"display_name": "Embedding"},
-            "query_name": {"display_name": "Query Name"},
-            "supabase_service_key": {"display_name": "Supabase Service Key"},
-            "supabase_url": {"display_name": "Supabase URL"},
-            "table_name": {"display_name": "Table Name", "advanced": True},
-        }
+    inputs = [
+        StrInput(name="supabase_url", display_name="Supabase URL", required=True),
+        StrInput(name="supabase_service_key", display_name="Supabase Service Key", required=True),
+        StrInput(name="table_name", display_name="Table Name", advanced=True),
+        StrInput(name="query_name", display_name="Query Name"),
+        HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
+        HandleInput(
+            name="vector_store_inputs",
+            display_name="Vector Store Inputs",
+            input_types=["Document", "Data"],
+            is_list=True,
+        ),
+        StrInput(name="search_input", display_name="Search Input"),
+        IntInput(
+            name="number_of_results",
+            display_name="Number of Results",
+            info="Number of results to return.",
+            value=4,
+            advanced=True,
+        ),
+    ]
+
+    outputs = [
+        Output(
+            display_name="Vector Store",
+            name="vector_store",
+            method="build_vector_store",
+            output_type=SupabaseVectorStore,
+        ),
+        Output(
+            display_name="Base Retriever",
+            name="base_retriever",
+            method="build_base_retriever",
+            output_type=BaseRetriever,
+        ),
+        Output(display_name="Search Results", name="search_results", method="search_documents"),
+    ]
+
+    def build_vector_store(self) -> SupabaseVectorStore:
+        return self._build_supabase()
+
+    def _build_supabase(self) -> SupabaseVectorStore:
+        supabase: Client = create_client(self.supabase_url, supabase_key=self.supabase_service_key)
 
-    def build(
-        self,
-        embedding: Embeddings,
-        inputs: Optional[List[Record]] = None,
-        query_name: str = "",
-        supabase_service_key: str = "",
-        supabase_url: str = "",
-        table_name: str = "",
-    ) -> Union[VectorStore, SupabaseVectorStore, BaseRetriever]:
-        supabase: Client = create_client(supabase_url, supabase_key=supabase_service_key)
         documents = []
-        for _input in inputs or []:
-            if isinstance(_input, Record):
+        for _input in self.vector_store_inputs or []:
+            if isinstance(_input, Data):
                 documents.append(_input.to_lc_document())
             else:
                 documents.append(_input)
-        return SupabaseVectorStore.from_documents(
-            documents=documents,
-            embedding=embedding,
-            query_name=query_name,
-            client=supabase,
-            table_name=table_name,
-        )
+
+        if documents:
+            supabase_vs = SupabaseVectorStore.from_documents(
+                documents=documents,
+                embedding=self.embedding,
+                query_name=self.query_name,
+                client=supabase,
+                table_name=self.table_name,
+            )
+        else:
+            supabase_vs = SupabaseVectorStore(
+                client=supabase,
+                embedding=self.embedding,
+                table_name=self.table_name,
+                query_name=self.query_name,
+            )
+
+        return supabase_vs
+
+    def search_documents(self) -> List[Data]:
+        vector_store = self._build_supabase()
+
+        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
+            docs = vector_store.similarity_search(
+                query=self.search_input,
+                k=self.number_of_results,
+            )
+
+            data = docs_to_data(docs)
+            self.status = data
+            return data
+        else:
+            return []
diff --git a/src/backend/base/langflow/components/vectorstores/Upstash.py b/src/backend/base/langflow/components/vectorstores/Upstash.py
index 2695abecc..3793254a6 100644
--- a/src/backend/base/langflow/components/vectorstores/Upstash.py
+++ b/src/backend/base/langflow/components/vectorstores/Upstash.py
@@ -1,89 +1,134 @@
-from typing import List, Optional, Union
+from typing import List
 
-from langchain_community.vectorstores.upstash import UpstashVectorStore
-from langchain_core.embeddings import Embeddings
+from langchain_community.vectorstores import UpstashVectorStore
 from langchain_core.retrievers import BaseRetriever
-from langchain_core.vectorstores import VectorStore
 
-from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langflow.custom import Component
+from langflow.helpers.data import docs_to_data
+from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
+from langflow.schema import Data
 
 
-class UpstashVectorStoreComponent(CustomComponent):
-    """
-    A custom component for implementing a Vector Store using Upstash.
-    """
+class UpstashVectorStoreComponent(Component):
+    display_name = "Upstash"
+    description = "Upstash Vector Store with search capabilities"
+    documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/upstash"
+    icon = "Upstash"
 
-    display_name: str = "Upstash"
-    description: str = "Create and Utilize an Upstash Vector Store"
+    inputs = [
+        StrInput(name="index_url", display_name="Index URL", info="The URL of the Upstash index.", required=True),
+        StrInput(
+            name="index_token", display_name="Index Token", info="The token for the Upstash index.", required=True
+        ),
+        StrInput(
+            name="text_key",
+            display_name="Text Key",
+            info="The key in the record to use as text.",
+            value="text",
+            advanced=True,
+        ),
+        HandleInput(
+            name="embedding",
+            display_name="Embedding",
+            input_types=["Embeddings"],
+            info="To use Upstash's embeddings, don't provide an embedding.",
+        ),
+        HandleInput(
+            name="vector_store_inputs",
+            display_name="Vector Store Inputs",
+            input_types=["Document", "Data"],
+            is_list=True,
+        ),
+        BoolInput(
+            name="add_to_vector_store",
+            display_name="Add to Vector Store",
+            info="If true, the Vector Store Inputs will be added to the Vector Store.",
+        ),
+        StrInput(name="search_input", display_name="Search Input"),
+        IntInput(
+            name="number_of_results",
+            display_name="Number of Results",
+            info="Number of results to return.",
+            value=4,
+            advanced=True,
+        ),
+    ]
 
-    def build_config(self):
-        """
-        Builds the configuration for the component.
+    outputs = [
+        Output(
+            display_name="Vector Store",
+            name="vector_store",
+            method="build_vector_store",
+            output_type=UpstashVectorStore,
+        ),
+        Output(
+            display_name="Base Retriever",
+            name="base_retriever",
+            method="build_base_retriever",
+            output_type=BaseRetriever,
+        ),
+        Output(display_name="Search Results", name="search_results", method="search_documents"),
+    ]
 
-        Returns:
-        - dict: A dictionary containing the configuration options for the component.
-        """
-        return {
-            "inputs": {"display_name": "Input", "input_types": ["Document", "Record"]},
-            "embedding": {
-                "display_name": "Embedding",
-                "input_types": ["Embeddings"],
-                "info": "To use Upstash's embeddings, don't provide an embedding.",
-            },
-            "index_url": {
-                "display_name": "Index URL",
-                "info": "The URL of the Upstash index.",
-            },
-            "index_token": {
-                "display_name": "Index Token",
-                "info": "The token for the Upstash index.",
-            },
-            "text_key": {
-                "display_name": "Text Key",
-                "info": "The key in the record to use as text.",
-                "advanced": True,
-            },
-        }
+    def build_vector_store(self) -> UpstashVectorStore:
+        return self._build_upstash()
 
-    def build(
-        self,
-        inputs: Optional[List[Record]] = None,
-        text_key: str = "text",
-        index_url: Optional[str] = None,
-        index_token: Optional[str] = None,
-        embedding: Optional[Embeddings] = None,
-    ) -> Union[VectorStore, BaseRetriever]:
-        documents = []
-        for _input in inputs or []:
-            if isinstance(_input, Record):
-                documents.append(_input.to_lc_document())
+    def _build_upstash(self) -> UpstashVectorStore:
+        use_upstash_embedding = self.embedding is None
+
+        if self.add_to_vector_store:
+            documents = []
+            for _input in self.vector_store_inputs or []:
+                if isinstance(_input, Data):
+                    documents.append(_input.to_lc_document())
+                else:
+                    documents.append(_input)
+
+            if documents:
+                if use_upstash_embedding:
+                    upstash_vs = UpstashVectorStore(
+                        embedding=use_upstash_embedding,
+                        text_key=self.text_key,
+                        index_url=self.index_url,
+                        index_token=self.index_token,
+                    )
+                    upstash_vs.add_documents(documents)
+                else:
+                    upstash_vs = UpstashVectorStore.from_documents(
+                        documents=documents,
+                        embedding=self.embedding,
+                        text_key=self.text_key,
+                        index_url=self.index_url,
+                        index_token=self.index_token,
+                    )
             else:
-                documents.append(_input)
-
-        use_upstash_embedding = embedding is None
-        if not documents:
-            upstash_vs = UpstashVectorStore(
-                embedding=embedding or use_upstash_embedding,
-                text_key=text_key,
-                index_url=index_url,
-                index_token=index_token,
-            )
-        else:
-            if use_upstash_embedding:
                 upstash_vs = UpstashVectorStore(
-                    embedding=use_upstash_embedding,
-                    text_key=text_key,
-                    index_url=index_url,
-                    index_token=index_token,
-                )
-                upstash_vs.add_documents(documents)
-            elif embedding:
-                upstash_vs = UpstashVectorStore.from_documents(
-                    documents=documents,  # type: ignore
-                    embedding=embedding,
-                    text_key=text_key,
-                    index_url=index_url,
-                    index_token=index_token,
+                    embedding=self.embedding or use_upstash_embedding,
+                    text_key=self.text_key,
+                    index_url=self.index_url,
+                    index_token=self.index_token,
                 )
+        else:
+            upstash_vs = UpstashVectorStore(
+                embedding=self.embedding or use_upstash_embedding,
+                text_key=self.text_key,
+                index_url=self.index_url,
+                index_token=self.index_token,
+            )
+
         return upstash_vs
+
+    def search_documents(self) -> List[Data]:
+        vector_store = self._build_upstash()
+
+        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
+            docs = vector_store.similarity_search(
+                query=self.search_input,
+                k=self.number_of_results,
+            )
+
+            data = docs_to_data(docs)
+            self.status = data
+            return data
+        else:
+            return []
diff --git a/src/backend/base/langflow/components/vectorstores/Vectara.py b/src/backend/base/langflow/components/vectorstores/Vectara.py
index 5a51b5a1b..2c454a7b1 100644
--- a/src/backend/base/langflow/components/vectorstores/Vectara.py
+++ b/src/backend/base/langflow/components/vectorstores/Vectara.py
@@ -1,90 +1,99 @@
-import tempfile
-import urllib
-import urllib.request
-from typing import List, Optional, Union
+from typing import List
 
 from langchain_community.embeddings import FakeEmbeddings
-from langchain_community.vectorstores.vectara import Vectara
-from langchain_core.vectorstores import VectorStore
+from langchain_community.vectorstores import Vectara
+from langchain_core.retrievers import BaseRetriever
 
-from langflow.custom import CustomComponent
-from langflow.field_typing import BaseRetriever
-from langflow.schema import Record
+from langflow.custom import Component
+from langflow.helpers.data import docs_to_data
+from langflow.io import BoolInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
+from langflow.schema import Data
 
 
-class VectaraComponent(CustomComponent):
-    display_name: str = "Vectara"
-    description: str = "Implementation of Vector Store using Vectara"
-    documentation = "https://python.langchain.com/docs/integrations/vectorstores/vectara"
+class VectaraVectorStoreComponent(Component):
+    display_name = "Vectara"
+    description = "Vectara Vector Store with search capabilities"
+    documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/vectara"
     icon = "Vectara"
-    field_config = {
-        "vectara_customer_id": {
-            "display_name": "Vectara Customer ID",
-        },
-        "vectara_corpus_id": {
-            "display_name": "Vectara Corpus ID",
-        },
-        "vectara_api_key": {
-            "display_name": "Vectara API Key",
-            "password": True,
-        },
-        "inputs": {
-            "display_name": "Input",
-            "input_types": ["Document", "Record"],
-            "info": "If provided, will be upserted to corpus (optional)",
-        },
-        "files_url": {
-            "display_name": "Files Url",
-            "info": "Make vectara object using url of files (optional)",
-        },
-    }
 
-    def build(
-        self,
-        vectara_customer_id: str,
-        vectara_corpus_id: str,
-        vectara_api_key: str,
-        files_url: Optional[List[str]] = None,
-        inputs: Optional[Record] = None,
-    ) -> Union[VectorStore, BaseRetriever]:
+    inputs = [
+        StrInput(name="vectara_customer_id", display_name="Vectara Customer ID", required=True),
+        StrInput(name="vectara_corpus_id", display_name="Vectara Corpus ID", required=True),
+        SecretStrInput(name="vectara_api_key", display_name="Vectara API Key", required=True),
+        HandleInput(
+            name="vector_store_inputs",
+            display_name="Vector Store Inputs",
+            input_types=["Document", "Data"],
+            is_list=True,
+        ),
+        BoolInput(
+            name="add_to_vector_store",
+            display_name="Add to Vector Store",
+            info="If true, the Vector Store Inputs will be added to the Vector Store.",
+        ),
+        StrInput(name="search_input", display_name="Search Input"),
+        IntInput(
+            name="number_of_results",
+            display_name="Number of Results",
+            info="Number of results to return.",
+            value=4,
+            advanced=True,
+        ),
+    ]
+
+    outputs = [
+        Output(display_name="Vector Store", name="vector_store", method="build_vector_store", output_type=Vectara),
+        Output(
+            display_name="Base Retriever",
+            name="base_retriever",
+            method="build_base_retriever",
+            output_type=BaseRetriever,
+        ),
+        Output(display_name="Search Results", name="search_results", method="search_documents"),
+    ]
+
+    def build_vector_store(self) -> Vectara:
+        return self._build_vectara()
+
+    def _build_vectara(self) -> Vectara:
         source = "Langflow"
 
-        documents = []
-        for _input in inputs or []:
-            if isinstance(_input, Record):
-                documents.append(_input.to_lc_document())
-            else:
-                documents.append(_input)
+        if self.add_to_vector_store:
+            documents = []
+            for _input in self.vector_store_inputs or []:
+                if isinstance(_input, Data):
+                    documents.append(_input.to_lc_document())
+                else:
+                    documents.append(_input)
 
-        if documents:
-            return Vectara.from_documents(
-                documents=documents,  # type: ignore
-                embedding=FakeEmbeddings(size=768),
-                vectara_customer_id=vectara_customer_id,
-                vectara_corpus_id=vectara_corpus_id,
-                vectara_api_key=vectara_api_key,
-                source=source,
-            )
-
-        if files_url is not None:
-            files_list = []
-            for url in files_url:
-                name = tempfile.NamedTemporaryFile().name
-                urllib.request.urlretrieve(url, name)
-                files_list.append(name)
-
-            return Vectara.from_files(
-                files=files_list,
-                embedding=FakeEmbeddings(size=768),
-                vectara_customer_id=vectara_customer_id,
-                vectara_corpus_id=vectara_corpus_id,
-                vectara_api_key=vectara_api_key,
-                source=source,
-            )
+            if documents:
+                return Vectara.from_documents(
+                    documents=documents,
+                    embedding=FakeEmbeddings(size=768),
+                    vectara_customer_id=self.vectara_customer_id,
+                    vectara_corpus_id=self.vectara_corpus_id,
+                    vectara_api_key=self.vectara_api_key,
+                    source=source,
+                )
 
         return Vectara(
-            vectara_customer_id=vectara_customer_id,
-            vectara_corpus_id=vectara_corpus_id,
-            vectara_api_key=vectara_api_key,
+            vectara_customer_id=self.vectara_customer_id,
+            vectara_corpus_id=self.vectara_corpus_id,
+            vectara_api_key=self.vectara_api_key,
             source=source,
         )
+
+    def search_documents(self) -> List[Data]:
+        vector_store = self._build_vectara()
+
+        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
+            docs = vector_store.similarity_search(
+                query=self.search_input,
+                k=self.number_of_results,
+            )
+
+            data = docs_to_data(docs)
+            self.status = data
+            return data
+        else:
+            return []
diff --git a/src/backend/base/langflow/components/vectorstores/Weaviate.py b/src/backend/base/langflow/components/vectorstores/Weaviate.py
index fafa2f390..26981c04b 100644
--- a/src/backend/base/langflow/components/vectorstores/Weaviate.py
+++ b/src/backend/base/langflow/components/vectorstores/Weaviate.py
@@ -1,108 +1,106 @@
-from typing import Optional, Union
+from typing import List
 
 import weaviate  # type: ignore
 from langchain_community.vectorstores import Weaviate
-from langchain_core.documents import Document
-from langchain_core.embeddings import Embeddings
 from langchain_core.retrievers import BaseRetriever
-from langchain_core.vectorstores import VectorStore
 
-from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langflow.custom import Component
+from langflow.helpers.data import docs_to_data
+from langflow.io import BoolInput, HandleInput, IntInput, Output, SecretStrInput, StrInput
+from langflow.schema import Data
 
 
-class WeaviateVectorStoreComponent(CustomComponent):
-    display_name: str = "Weaviate"
-    description: str = "Implementation of Vector Store using Weaviate"
-    documentation = "https://python.langchain.com/docs/integrations/vectorstores/weaviate"
-    field_config = {
-        "url": {"display_name": "Weaviate URL", "value": "http://localhost:8080"},
-        "api_key": {
-            "display_name": "API Key",
-            "password": True,
-            "required": False,
-        },
-        "index_name": {
-            "display_name": "Index name",
-            "required": False,
-        },
-        "text_key": {
-            "display_name": "Text Key",
-            "required": False,
-            "advanced": True,
-            "value": "text",
-        },
-        "inputs": {"display_name": "Input", "input_types": ["Document", "Record"]},
-        "embedding": {"display_name": "Embedding"},
-        "attributes": {
-            "display_name": "Attributes",
-            "required": False,
-            "is_list": True,
-            "field_type": "str",
-            "advanced": True,
-        },
-        "search_by_text": {
-            "display_name": "Search By Text",
-            "field_type": "bool",
-            "advanced": True,
-        },
-        "code": {"show": False},
-    }
+class WeaviateVectorStoreComponent(Component):
+    display_name = "Weaviate"
+    description = "Weaviate Vector Store with search capabilities"
+    documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/weaviate"
+    icon = "Weaviate"
 
-    def build(
-        self,
-        url: str,
-        index_name: str,
-        search_by_text: bool = False,
-        api_key: Optional[str] = None,
-        text_key: str = "text",
-        embedding: Optional[Embeddings] = None,
-        inputs: Optional[Record] = None,
-        attributes: Optional[list] = None,
-    ) -> Union[VectorStore, BaseRetriever]:
-        if api_key:
-            auth_config = weaviate.AuthApiKey(api_key=api_key)
-            client = weaviate.Client(url=url, auth_client_secret=auth_config)
+    inputs = [
+        StrInput(name="url", display_name="Weaviate URL", value="http://localhost:8080", required=True),
+        SecretStrInput(name="api_key", display_name="API Key", required=False),
+        StrInput(name="index_name", display_name="Index Name", required=True),
+        StrInput(name="text_key", display_name="Text Key", value="text", advanced=True),
+        HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
+        HandleInput(
+            name="vector_store_inputs",
+            display_name="Vector Store Inputs",
+            input_types=["Document", "Data"],
+            is_list=True,
+        ),
+        BoolInput(
+            name="add_to_vector_store",
+            display_name="Add to Vector Store",
+            info="If true, the Vector Store Inputs will be added to the Vector Store.",
+        ),
+        StrInput(name="search_input", display_name="Search Input"),
+        IntInput(
+            name="number_of_results",
+            display_name="Number of Results",
+            info="Number of results to return.",
+            value=4,
+            advanced=True,
+        ),
+        BoolInput(name="search_by_text", display_name="Search By Text", advanced=True),
+    ]
+
+    outputs = [
+        Output(display_name="Vector Store", name="vector_store", method="build_vector_store", output_type=Weaviate),
+        Output(
+            display_name="Base Retriever",
+            name="base_retriever",
+            method="build_base_retriever",
+            output_type=BaseRetriever,
+        ),
+        Output(display_name="Search Results", name="search_results", method="search_documents"),
+    ]
+
+    def build_vector_store(self) -> Weaviate:
+        return self._build_weaviate()
+
+    def _build_weaviate(self) -> Weaviate:
+        if self.api_key:
+            auth_config = weaviate.AuthApiKey(api_key=self.api_key)
+            client = weaviate.Client(url=self.url, auth_client_secret=auth_config)
         else:
-            client = weaviate.Client(url=url)
+            client = weaviate.Client(url=self.url)
 
-        def _to_pascal_case(word: str):
-            if word and not word[0].isupper():
-                word = word.capitalize()
+        if self.add_to_vector_store:
+            documents = []
+            for _input in self.vector_store_inputs or []:
+                if isinstance(_input, Data):
+                    documents.append(_input.to_lc_document())
+                else:
+                    documents.append(_input)
 
-            if word.isidentifier():
-                return word
-
-            word = word.replace("-", " ").replace("_", " ")
-            parts = word.split()
-            pascal_case_word = "".join([part.capitalize() for part in parts])
-
-            return pascal_case_word
-
-        index_name = _to_pascal_case(index_name) if index_name else None
-        if not index_name:
-            raise ValueError("Index name is required")
-        documents: list[Document] = []
-        for _input in inputs or []:
-            if isinstance(_input, Record):
-                documents.append(_input.to_lc_document())
-            elif isinstance(_input, Document):
-                documents.append(_input)
-
-        if documents and embedding is not None:
-            return Weaviate.from_documents(
-                client=client,
-                index_name=index_name,
-                documents=documents,
-                embedding=embedding,
-                by_text=search_by_text,
-            )
+            if documents and self.embedding:
+                return Weaviate.from_documents(
+                    client=client,
+                    index_name=self.index_name,
+                    documents=documents,
+                    embedding=self.embedding,
+                    by_text=self.search_by_text,
+                )
 
         return Weaviate(
             client=client,
-            index_name=index_name,
-            text_key=text_key,
-            embedding=embedding,
-            by_text=search_by_text,
-            attributes=attributes if attributes is not None else [],
+            index_name=self.index_name,
+            text_key=self.text_key,
+            embedding=self.embedding,
+            by_text=self.search_by_text,
         )
+
+    def search_documents(self) -> List[Data]:
+        vector_store = self._build_weaviate()
+
+        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
+            docs = vector_store.similarity_search(
+                query=self.search_input,
+                k=self.number_of_results,
+            )
+
+            data = docs_to_data(docs)
+            self.status = data
+            return data
+        else:
+            return []
diff --git a/src/backend/base/langflow/components/vectorstores/base/__init__.py b/src/backend/base/langflow/components/vectorstores/base/__init__.py
deleted file mode 100644
index 93e42c4aa..000000000
--- a/src/backend/base/langflow/components/vectorstores/base/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .model import LCVectorStoreComponent
-
-__all__ = ["LCVectorStoreComponent"]
diff --git a/src/backend/base/langflow/components/vectorstores/base/model.py b/src/backend/base/langflow/components/vectorstores/base/model.py
deleted file mode 100644
index 18a37c9cf..000000000
--- a/src/backend/base/langflow/components/vectorstores/base/model.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from typing import List, Union
-
-from langchain_core.documents import Document
-from langchain_core.retrievers import BaseRetriever
-from langchain_core.vectorstores import VectorStore
-
-from langflow.custom import CustomComponent
-from langflow.field_typing import Text
-from langflow.helpers.record import docs_to_records
-from langflow.schema import Record
-
-
-class LCVectorStoreComponent(CustomComponent):
-    display_name: str = "LC Vector Store"
-    description: str = "Search a LC Vector Store for similar documents."
-
-    def search_with_vector_store(
-        self,
-        input_value: Text,
-        search_type: str,
-        vector_store: Union[VectorStore, BaseRetriever],
-        k=10,
-        **kwargs,
-    ) -> List[Record]:
-        """
-        Search for records in the vector store based on the input value and search type.
-
-        Args:
-            input_value (Text): The input value to search for.
-            search_type (str): The type of search to perform.
-            vector_store (VectorStore): The vector store to search in.
-
-        Returns:
-            List[Record]: A list of records matching the search criteria.
-
-        Raises:
-            ValueError: If invalid inputs are provided.
-        """
-
-        docs: List[Document] = []
-        if input_value and isinstance(input_value, str) and hasattr(vector_store, "search"):
-            docs = vector_store.search(query=input_value, search_type=search_type.lower(), k=k, **kwargs)
-        else:
-            raise ValueError("Invalid inputs provided.")
-        records = docs_to_records(docs)
-        self.status = records
-        return records
diff --git a/src/backend/base/langflow/components/vectorstores/pgvector.py b/src/backend/base/langflow/components/vectorstores/pgvector.py
index 3ea7b6eb6..48f8ac13c 100644
--- a/src/backend/base/langflow/components/vectorstores/pgvector.py
+++ b/src/backend/base/langflow/components/vectorstores/pgvector.py
@@ -1,81 +1,101 @@
-from typing import Optional, Union
+from typing import List
 
-from langchain_community.vectorstores.pgvector import PGVector
-from langchain_core.embeddings import Embeddings
-from langchain_core.retrievers import BaseRetriever
-from langchain_core.vectorstores import VectorStore
+from langchain.schema import BaseRetriever
+from langchain_community.vectorstores import PGVector
 
-from langflow.custom import CustomComponent
-from langflow.schema import Record
+from langflow.custom import Component
+from langflow.helpers.data import docs_to_data
+from langflow.io import BoolInput, HandleInput, IntInput, Output, StrInput
+from langflow.schema import Data
 
 
-class PGVectorComponent(CustomComponent):
-    """
-    A custom component for implementing a Vector Store using PostgreSQL.
-    """
+class PGVectorStoreComponent(Component):
+    display_name = "PGVector"
+    description = "PGVector Vector Store with search capabilities"
+    documentation = "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/pgvector"
+    icon = "PGVector"
 
-    display_name: str = "PGVector"
-    description: str = "Implementation of Vector Store using PostgreSQL"
-    documentation = "https://python.langchain.com/docs/integrations/vectorstores/pgvector"
+    inputs = [
+        StrInput(name="pg_server_url", display_name="PostgreSQL Server Connection String", required=True),
+        StrInput(name="collection_name", display_name="Table", required=True),
+        HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
+        HandleInput(
+            name="vector_store_inputs",
+            display_name="Vector Store Inputs",
+            input_types=["Document", "Data"],
+            is_list=True,
+        ),
+        BoolInput(
+            name="add_to_vector_store",
+            display_name="Add to Vector Store",
+            info="If true, the Vector Store Inputs will be added to the Vector Store.",
+        ),
+        StrInput(name="search_input", display_name="Search Input"),
+        IntInput(
+            name="number_of_results",
+            display_name="Number of Results",
+            info="Number of results to return.",
+            value=4,
+            advanced=True,
+        ),
+    ]
 
-    def build_config(self):
-        """
-        Builds the configuration for the component.
+    outputs = [
+        Output(display_name="Vector Store", name="vector_store", method="build_vector_store", output_type=PGVector),
+        Output(
+            display_name="Base Retriever",
+            name="base_retriever",
+            method="build_base_retriever",
+            output_type=BaseRetriever,
+        ),
+        Output(display_name="Search Results", name="search_results", method="search_documents"),
+    ]
 
-        Returns:
-        - dict: A dictionary containing the configuration options for the component.
-        """
-        return {
-            "code": {"show": False},
-            "inputs": {"display_name": "Input", "input_types": ["Document", "Record"]},
-            "embedding": {"display_name": "Embedding"},
-            "pg_server_url": {
-                "display_name": "PostgreSQL Server Connection String",
-                "advanced": False,
-            },
-            "collection_name": {"display_name": "Table", "advanced": False},
-        }
+    def build_vector_store(self) -> PGVector:
+        return self._build_pgvector()
 
-    def build(
-        self,
-        embedding: Embeddings,
-        pg_server_url: str,
-        collection_name: str,
-        inputs: Optional[Record] = None,
-    ) -> Union[VectorStore, BaseRetriever]:
-        """
-        Builds the Vector Store or BaseRetriever object.
+    def _build_pgvector(self) -> PGVector:
+        if self.add_to_vector_store:
+            documents = []
+            for _input in self.vector_store_inputs or []:
+                if isinstance(_input, Data):
+                    documents.append(_input.to_lc_document())
+                else:
+                    documents.append(_input)
 
-        Args:
-        - embedding (Embeddings): The embeddings to use for the Vector Store.
-        - documents (Optional[Document]): The documents to use for the Vector Store.
-        - collection_name (str): The name of the PG table.
-        - pg_server_url (str): The URL for the PG server.
-
-        Returns:
-        - VectorStore: The Vector Store object.
-        """
-
-        documents = []
-        for _input in inputs or []:
-            if isinstance(_input, Record):
-                documents.append(_input.to_lc_document())
-            else:
-                documents.append(_input)
-        try:
-            if documents is None:
-                vector_store = PGVector.from_existing_index(
-                    embedding=embedding,
-                    collection_name=collection_name,
-                    connection_string=pg_server_url,
+            if documents:
+                pgvector = PGVector.from_documents(
+                    embedding=self.embedding,
+                    documents=documents,
+                    collection_name=self.collection_name,
+                    connection_string=self.pg_server_url,
                 )
             else:
-                vector_store = PGVector.from_documents(
-                    embedding=embedding,
-                    documents=documents,  # type: ignore
-                    collection_name=collection_name,
-                    connection_string=pg_server_url,
+                pgvector = PGVector.from_existing_index(
+                    embedding=self.embedding,
+                    collection_name=self.collection_name,
+                    connection_string=self.pg_server_url,
                 )
-        except Exception as e:
-            raise RuntimeError(f"Failed to build PGVector: {e}")
-        return vector_store
+        else:
+            pgvector = PGVector.from_existing_index(
+                embedding=self.embedding,
+                collection_name=self.collection_name,
+                connection_string=self.pg_server_url,
+            )
+
+        return pgvector
+
+    def search_documents(self) -> List[Data]:
+        vector_store = self._build_pgvector()
+
+        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():
+            docs = vector_store.similarity_search(
+                query=self.search_input,
+                k=self.number_of_results,
+            )
+
+            data = docs_to_data(docs)
+            self.status = data
+            return data
+        else:
+            return []
diff --git a/src/backend/base/langflow/custom/__init__.py b/src/backend/base/langflow/custom/__init__.py
index bd789498a..c6ce56c3e 100644
--- a/src/backend/base/langflow/custom/__init__.py
+++ b/src/backend/base/langflow/custom/__init__.py
@@ -1,3 +1,4 @@
 from langflow.custom.custom_component import CustomComponent
+from langflow.custom.custom_component.component import Component
 
-__all__ = ["CustomComponent"]
+__all__ = ["CustomComponent", "Component"]
diff --git a/src/backend/base/langflow/custom/attributes.py b/src/backend/base/langflow/custom/attributes.py
index 7bcfb5f4b..d96500c78 100644
--- a/src/backend/base/langflow/custom/attributes.py
+++ b/src/backend/base/langflow/custom/attributes.py
@@ -31,6 +31,18 @@ def getattr_return_bool(value):
         return value
 
 
+def getattr_return_list_of_str(value):
+    if isinstance(value, list):
+        return [str(val) for val in value]
+    return []
+
+
+def getattr_return_list_of_object(value):
+    if isinstance(value, list):
+        return value
+    return []
+
+
 ATTR_FUNC_MAPPING: dict[str, Callable] = {
     "display_name": getattr_return_str,
     "description": getattr_return_str,
@@ -40,4 +52,7 @@ ATTR_FUNC_MAPPING: dict[str, Callable] = {
     "frozen": getattr_return_bool,
     "is_input": getattr_return_bool,
     "is_output": getattr_return_bool,
+    "conditional_paths": getattr_return_list_of_str,
+    "outputs": getattr_return_list_of_object,
+    "inputs": getattr_return_list_of_object,
 }
diff --git a/src/backend/base/langflow/custom/code_parser/code_parser.py b/src/backend/base/langflow/custom/code_parser/code_parser.py
index 705e779f4..9dc736dc0 100644
--- a/src/backend/base/langflow/custom/code_parser/code_parser.py
+++ b/src/backend/base/langflow/custom/code_parser/code_parser.py
@@ -1,10 +1,9 @@
 import ast
 import inspect
-import operator
 import traceback
 from typing import Any, Dict, List, Type, Union
 
-from cachetools import TTLCache, cachedmethod, keys
+from cachetools import TTLCache, keys
 from fastapi import HTTPException
 from loguru import logger
 
@@ -22,6 +21,32 @@ def get_data_type():
     return Data
 
 
+def find_class_ast_node(class_obj):
+    """Finds the AST node corresponding to the given class object."""
+    # Get the source file where the class is defined
+    source_file = inspect.getsourcefile(class_obj)
+    if not source_file:
+        return None, []
+
+    # Read the source code from the file
+    with open(source_file, "r") as file:
+        source_code = file.read()
+
+    # Parse the source code into an AST
+    tree = ast.parse(source_code)
+
+    # Search for the class definition node in the AST
+    class_node = None
+    import_nodes = []
+    for node in ast.walk(tree):
+        if isinstance(node, ast.ClassDef) and node.name == class_obj.__name__:
+            class_node = node
+        elif isinstance(node, (ast.Import, ast.ImportFrom)):
+            import_nodes.append(node)
+
+    return class_node, import_nodes
+
+
 def imports_key(*args, **kwargs):
     imports = kwargs.pop("imports")
     key = keys.methodkey(*args, **kwargs)
@@ -114,7 +139,7 @@ class CodeParser:
             arg_dict["type"] = ast.unparse(arg.annotation)
         return arg_dict
 
-    @cachedmethod(operator.attrgetter("cache"))
+    # @cachedmethod(operator.attrgetter("cache"))
     def construct_eval_env(self, return_type_str: str, imports) -> dict:
         """
         Constructs an evaluation environment with the necessary imports for the return type,
@@ -136,7 +161,6 @@ class CodeParser:
                     exec(f"import {module} as {alias if alias else module}", eval_env)
         return eval_env
 
-    @cachedmethod(cache=operator.attrgetter("cache"))
     def parse_callable_details(self, node: ast.FunctionDef) -> Dict[str, Any]:
         """
         Extracts details from a single function or method node.
@@ -157,7 +181,7 @@ class CodeParser:
             doc=ast.get_docstring(node),
             args=self.parse_function_args(node),
             body=self.parse_function_body(node),
-            return_type=return_type or get_data_type(),
+            return_type=return_type,
             has_return=self.parse_return_statement(node),
         )
 
@@ -297,7 +321,6 @@ class CodeParser:
             bases = self.execute_and_inspect_classes(self.code)
         except Exception as e:
             # If the code cannot be executed, return an empty list
-            logger.debug(e)
             bases = []
             raise e
         return bases
@@ -306,16 +329,37 @@ class CodeParser:
         """
         Extracts "classes" from the code, including inheritance and init methods.
         """
-        bases = self.get_base_classes() or [ast.unparse(b) for b in node.bases]
+        if node.name in ["CustomComponent", "Component", "BaseComponent"]:
+            return
+        bases = self.get_base_classes()
+        nodes = []
+        for base in bases:
+            if base.__name__ == node.name or base.__name__ in ["CustomComponent", "Component", "BaseComponent"]:
+                continue
+            try:
+                class_node, import_nodes = find_class_ast_node(base)
+                if class_node is None:
+                    continue
+                for import_node in import_nodes:
+                    self.parse_imports(import_node)
+                nodes.append(class_node)
+            except Exception as exc:
+                logger.error(f"Error finding base class node: {exc}")
+                pass
+        nodes.insert(0, node)
         class_details = ClassCodeDetails(
             name=node.name,
             doc=ast.get_docstring(node),
-            bases=bases,
+            bases=[b.__name__ for b in bases],
             attributes=[],
             methods=[],
             init=None,
         )
+        for node in nodes:
+            self.process_class_node(node, class_details)
+        self.data["classes"].append(class_details.model_dump())
 
+    def process_class_node(self, node, class_details):
         for stmt in node.body:
             if isinstance(stmt, ast.Assign):
                 if attr := self.parse_assign(stmt):
@@ -330,8 +374,6 @@ class CodeParser:
                 else:
                     class_details.methods.append(method)
 
-        self.data["classes"].append(class_details.model_dump())
-
     def parse_global_vars(self, node: ast.Assign) -> None:
         """
         Extracts global variables from the code.
@@ -349,9 +391,9 @@ class CodeParser:
         # Get the base classes at two levels of inheritance
         bases = []
         for base in dunder_class.__bases__:
-            bases.append(base.__name__)
+            bases.append(base)
             for bases_base in base.__bases__:
-                bases.append(bases_base.__name__)
+                bases.append(bases_base)
         return bases
 
     def parse_code(self) -> Dict[str, Any]:
diff --git a/src/backend/base/langflow/custom/custom_component/base_component.py b/src/backend/base/langflow/custom/custom_component/base_component.py
new file mode 100644
index 000000000..098942dd4
--- /dev/null
+++ b/src/backend/base/langflow/custom/custom_component/base_component.py
@@ -0,0 +1,94 @@
+import operator
+import warnings
+from typing import Any, ClassVar, Optional
+
+from cachetools import TTLCache, cachedmethod
+from fastapi import HTTPException
+
+from langflow.custom.attributes import ATTR_FUNC_MAPPING
+from langflow.custom.code_parser import CodeParser
+from langflow.custom.eval import eval_custom_component_code
+from langflow.utils import validate
+
+
+class ComponentCodeNullError(HTTPException):
+    pass
+
+
+class ComponentFunctionEntrypointNameNullError(HTTPException):
+    pass
+
+
+class BaseComponent:
+    ERROR_CODE_NULL: ClassVar[str] = "Python code must be provided."
+    ERROR_FUNCTION_ENTRYPOINT_NAME_NULL: ClassVar[str] = "The name of the entrypoint function must be provided."
+
+    code: Optional[str] = None
+    _function_entrypoint_name: str = "build"
+    field_config: dict = {}
+    _user_id: Optional[str]
+
+    def __init__(self, **data):
+        self.cache = TTLCache(maxsize=1024, ttl=60)
+        for key, value in data.items():
+            if key == "user_id":
+                setattr(self, "_user_id", value)
+            else:
+                setattr(self, key, value)
+
+    def __setattr__(self, key, value):
+        if key == "_user_id" and hasattr(self, "_user_id"):
+            warnings.warn("user_id is immutable and cannot be changed.")
+        super().__setattr__(key, value)
+
+    @cachedmethod(cache=operator.attrgetter("cache"))
+    def get_code_tree(self, code: str):
+        parser = CodeParser(code)
+        return parser.parse_code()
+
+    def get_function(self):
+        if not self.code:
+            raise ComponentCodeNullError(
+                status_code=400,
+                detail={"error": self.ERROR_CODE_NULL, "traceback": ""},
+            )
+
+        if not self._function_entrypoint_name:
+            raise ComponentFunctionEntrypointNameNullError(
+                status_code=400,
+                detail={
+                    "error": self.ERROR_FUNCTION_ENTRYPOINT_NAME_NULL,
+                    "traceback": "",
+                },
+            )
+
+        return validate.create_function(self.code, self._function_entrypoint_name)
+
+    def build_template_config(self) -> dict:
+        """
+        Builds the template configuration for the custom component.
+
+        Returns:
+            A dictionary representing the template configuration.
+        """
+        if not self.code:
+            return {}
+
+        cc_class = eval_custom_component_code(self.code)
+        component_instance = cc_class()
+        template_config = {}
+
+        for attribute, func in ATTR_FUNC_MAPPING.items():
+            if hasattr(component_instance, attribute):
+                value = getattr(component_instance, attribute)
+                if value is not None:
+                    template_config[attribute] = func(value=value)
+
+        for key in template_config.copy():
+            if key not in ATTR_FUNC_MAPPING.keys():
+                template_config.pop(key, None)
+
+        return template_config
+
+    def build(self, *args: Any, **kwargs: Any) -> Any:
+        raise NotImplementedError
diff --git a/src/backend/base/langflow/custom/custom_component/component.py b/src/backend/base/langflow/custom/custom_component/component.py
index d45b5daed..5e5c14a3c 100644
--- a/src/backend/base/langflow/custom/custom_component/component.py
+++ b/src/backend/base/langflow/custom/custom_component/component.py
@@ -1,90 +1,196 @@
-import operator
-import warnings
-from typing import Any, ClassVar, Optional
+import inspect
+from typing import TYPE_CHECKING, Any, AsyncIterator, Callable, ClassVar, Generator, Iterator, List, Optional, Union
+from uuid import UUID
 
-from cachetools import TTLCache, cachedmethod
-from fastapi import HTTPException
+import yaml
+from pydantic import BaseModel
 
-from langflow.custom.attributes import ATTR_FUNC_MAPPING
-from langflow.custom.code_parser import CodeParser
-from langflow.custom.eval import eval_custom_component_code
-from langflow.utils import validate
+from langflow.inputs.inputs import InputTypes
+from langflow.schema.artifact import get_artifact_type, post_process_raw
+from langflow.schema.data import Data
+from langflow.schema.message import Message
+from langflow.template.field.base import UNDEFINED, Output
+
+from .custom_component import CustomComponent
+
+if TYPE_CHECKING:
+    from langflow.graph.vertex.base import Vertex
 
 
-class ComponentCodeNullError(HTTPException):
-    pass
+def recursive_serialize_or_str(obj):
+    try:
+        if isinstance(obj, dict):
+            return {k: recursive_serialize_or_str(v) for k, v in obj.items()}
+        elif isinstance(obj, list):
+            return [recursive_serialize_or_str(v) for v in obj]
+        elif isinstance(obj, BaseModel):
+            return {k: recursive_serialize_or_str(v) for k, v in obj.model_dump().items()}
+        elif isinstance(obj, (AsyncIterator, Generator, Iterator)):
+            # contain memory addresses
+            # without consuming the iterator
+            # return list(obj) consumes the iterator
+            # return f"{obj}" this generates '<generator object BaseChatModel.stream at 0x33e9ec770>'
+            # it is not useful
+            return "Unconsumed Stream"
+        return str(obj)
+    except Exception:
+        return str(obj)
 
 
-class ComponentFunctionEntrypointNameNullError(HTTPException):
-    pass
-
-
-class Component:
-    ERROR_CODE_NULL: ClassVar[str] = "Python code must be provided."
-    ERROR_FUNCTION_ENTRYPOINT_NAME_NULL: ClassVar[str] = "The name of the entrypoint function must be provided."
-
-    code: Optional[str] = None
-    _function_entrypoint_name: str = "build"
-    field_config: dict = {}
-    _user_id: Optional[str]
+class Component(CustomComponent):
+    inputs: List[InputTypes] = []
+    outputs: List[Output] = []
+    code_class_base_inheritance: ClassVar[str] = "Component"
 
     def __init__(self, **data):
-        self.cache = TTLCache(maxsize=1024, ttl=60)
-        for key, value in data.items():
-            if key == "user_id":
-                setattr(self, "_user_id", value)
-            else:
-                setattr(self, key, value)
+        super().__init__(**data)
+        self._inputs: dict[str, InputTypes] = {}
+        self._results: dict[str, Any] = {}
+        self._attributes: dict[str, Any] = {}
+        if self.inputs is not None:
+            self.map_inputs(self.inputs)
 
-    def __setattr__(self, key, value):
-        if key == "_user_id" and hasattr(self, "_user_id"):
-            warnings.warn("user_id is immutable and cannot be changed.")
-        super().__setattr__(key, value)
+    def __getattr__(self, name: str) -> Any:
+        if "_attributes" in self.__dict__ and name in self.__dict__["_attributes"]:
+            return self.__dict__["_attributes"][name]
+        if "_inputs" in self.__dict__ and name in self.__dict__["_inputs"]:
+            return self.__dict__["_inputs"][name].value
+        raise AttributeError(f"{name} not found in {self.__class__.__name__}")
 
-    @cachedmethod(cache=operator.attrgetter("cache"))
-    def get_code_tree(self, code: str):
-        parser = CodeParser(code)
-        return parser.parse_code()
+    # def __getattribute__(self, name: str) -> Any:
+    #     try:
+    #         return super().__getattribute__(name)
+    #     except AttributeError:
+    #         return self.__getattr__(name)
 
-    def get_function(self):
-        if not self.code:
-            raise ComponentCodeNullError(
-                status_code=400,
-                detail={"error": self.ERROR_CODE_NULL, "traceback": ""},
-            )
+    def map_inputs(self, inputs: List[InputTypes]):
+        self.inputs = inputs
+        for input_ in inputs:
+            if input_.name is None:
+                raise ValueError("Input name cannot be None.")
+            self._inputs[input_.name] = input_
 
-        if not self._function_entrypoint_name:
-            raise ComponentFunctionEntrypointNameNullError(
-                status_code=400,
-                detail={
-                    "error": self.ERROR_FUNCTION_ENTRYPOINT_NAME_NULL,
-                    "traceback": "",
-                },
-            )
+    def validate(self, params: dict):
+        self._validate_inputs(params)
+        self._validate_outputs()
 
-        return validate.create_function(self.code, self._function_entrypoint_name)
+    def _validate_outputs(self):
+        # Raise Error if some rule isn't met
+        pass
 
-    def build_template_config(self) -> dict:
+    def _validate_inputs(self, params: dict):
+        # Params keys are the `name` attribute of the Input objects
+        for key, value in params.copy().items():
+            if key not in self._inputs:
+                continue
+            input_ = self._inputs[key]
+            # BaseInputMixin has a `validate_assignment=True`
+            input_.value = value
+            params[input_.name] = input_.value
+
+    def set_attributes(self, params: dict):
+        self._validate_inputs(params)
+        _attributes = {}
+        for key, value in params.items():
+            if key in self.__dict__:
+                raise ValueError(f"Key {key} already exists in {self.__class__.__name__}")
+            _attributes[key] = value
+        for key, input_obj in self._inputs.items():
+            if key not in _attributes:
+                _attributes[key] = input_obj.value or None
+        self._attributes = _attributes
+
+    def _set_outputs(self, outputs: List[dict]):
+        self.outputs = [Output(**output) for output in outputs]
+        for output in self.outputs:
+            setattr(self, output.name, output)
+
+    async def build_results(self, vertex: "Vertex"):
+        _results = {}
+        _artifacts = {}
+        if hasattr(self, "outputs"):
+            self._set_outputs(vertex.outputs)
+            for output in self.outputs:
+                # Build the output if it's connected to some other vertex
+                # or if it's not connected to any vertex
+                if not vertex.outgoing_edges or output.name in vertex.edges_source_names:
+                    if output.method is None:
+                        raise ValueError(f"Output {output.name} does not have a method defined.")
+                    method: Callable = getattr(self, output.method)
+                    if output.cache and output.value != UNDEFINED:
+                        _results[output.name] = output.value
+                    else:
+                        result = method()
+                        # If the method is asynchronous, we need to await it
+                        if inspect.iscoroutinefunction(method):
+                            result = await result
+                        if isinstance(result, Message) and result.flow_id is None and vertex.graph.flow_id is not None:
+                            result.set_flow_id(vertex.graph.flow_id)
+                        _results[output.name] = result
+                        output.value = result
+                        custom_repr = self.custom_repr()
+                        if custom_repr is None and isinstance(result, (dict, Data, str)):
+                            custom_repr = result
+                        if not isinstance(custom_repr, str):
+                            custom_repr = str(custom_repr)
+                        raw = result
+                        if self.status is None:
+                            artifact_value = raw
+                        else:
+                            artifact_value = self.status
+                            raw = self.status
+
+                        if hasattr(raw, "data") and raw is not None:
+                            raw = raw.data
+                        if raw is None:
+                            raw = custom_repr
+
+                        elif hasattr(raw, "model_dump") and raw is not None:
+                            raw = raw.model_dump()
+                        if raw is None and isinstance(result, (dict, Data, str)):
+                            raw = result.data if isinstance(result, Data) else result
+                        artifact_type = get_artifact_type(artifact_value, result)
+                        raw = post_process_raw(raw, artifact_type)
+                        artifact = {"repr": custom_repr, "raw": raw, "type": artifact_type}
+                        _artifacts[output.name] = artifact
+        self._artifacts = _artifacts
+        self._results = _results
+        return _results, _artifacts
+
+    def custom_repr(self):
+        if self.repr_value == "":
+            self.repr_value = self.status
+        if isinstance(self.repr_value, dict):
+            return yaml.dump(self.repr_value)
+        if isinstance(self.repr_value, str):
+            return self.repr_value
+        if isinstance(self.repr_value, BaseModel) and not isinstance(self.repr_value, Data):
+            return str(self.repr_value)
+        return self.repr_value
+
+    def build_inputs(self, user_id: Optional[Union[str, UUID]] = None):
         """
-        Builds the template configuration for the custom component.
+        Builds the inputs for the custom component.
+
+        Args:
+            user_id (Optional[Union[str, UUID]], optional): The user ID. Defaults to None.
 
         Returns:
-            A dictionary representing the template configuration.
+            List[Input]: The list of inputs.
         """
-        if not self.code:
+        # This function is similar to build_config, but it will process the inputs
+        # and return them as a dict with keys being the Input.name and values being the Input.model_dump()
+        self.inputs = self.template_config.get("inputs", [])
+        if not self.inputs:
             return {}
+        build_config = {_input.name: _input.model_dump(by_alias=True, exclude_none=True) for _input in self.inputs}
+        return build_config
 
-        cc_class = eval_custom_component_code(self.code)
-        component_instance = cc_class()
-        template_config = {}
-
-        for attribute, func in ATTR_FUNC_MAPPING.items():
-            if hasattr(component_instance, attribute):
-                value = getattr(component_instance, attribute)
-                if value is not None:
-                    template_config[attribute] = func(value=value)
-
-        return template_config
-
-    def build(self, *args: Any, **kwargs: Any) -> Any:
-        raise NotImplementedError
+    def _get_field_order(self):
+        try:
+            inputs = self.template_config["inputs"]
+            return [field.name for field in inputs]
+        except KeyError:
+            return []
+            return []
+            return []
diff --git a/src/backend/base/langflow/custom/custom_component/custom_component.py b/src/backend/base/langflow/custom/custom_component/custom_component.py
index af7062346..c9bc1bfdf 100644
--- a/src/backend/base/langflow/custom/custom_component/custom_component.py
+++ b/src/backend/base/langflow/custom/custom_component/custom_component.py
@@ -1,23 +1,26 @@
-import operator
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, Callable, ClassVar, List, Optional, Sequence, Union
 from uuid import UUID
 
 import yaml
-from cachetools import TTLCache, cachedmethod
+from cachetools import TTLCache
 from langchain_core.documents import Document
-from langflow.custom.code_parser.utils import (
+from pydantic import BaseModel
+
+from langflow.custom.custom_component.base_component import BaseComponent
+from langflow.helpers.flow import list_flows, load_flow, run_flow
+from langflow.schema import Data
+from langflow.schema.artifact import get_artifact_type
+from langflow.schema.dotdict import dotdict
+from langflow.schema.message import Message
+from langflow.schema.schema import Log
+from langflow.services.deps import get_storage_service, get_variable_service, session_scope
+from langflow.services.storage.service import StorageService
+from langflow.type_extraction.type_extraction import (
     extract_inner_type_from_generic_alias,
     extract_union_types_from_generic_alias,
 )
-from langflow.custom.custom_component.component import Component
-from langflow.helpers.flow import list_flows, load_flow, run_flow
-from langflow.schema import Record
-from langflow.schema.dotdict import dotdict
-from langflow.services.deps import get_storage_service, get_variable_service, session_scope
-from langflow.services.storage.service import StorageService
 from langflow.utils import validate
-from pydantic import BaseModel
 
 if TYPE_CHECKING:
     from langflow.graph.graph.base import Graph
@@ -25,7 +28,10 @@ if TYPE_CHECKING:
     from langflow.services.storage.service import StorageService
 
 
-class CustomComponent(Component):
+LoggableType = Union[str, dict, list, int, float, bool, None, Data, Message]
+
+
+class CustomComponent(BaseComponent):
     """
     Represents a custom component in Langflow.
 
@@ -65,8 +71,6 @@ class CustomComponent(Component):
     """The default frozen state of the component. Defaults to False."""
     build_parameters: Optional[dict] = None
     """The build parameters of the component. Defaults to None."""
-    selected_output_type: Optional[str] = None
-    """The selected output type of the component. Defaults to None."""
     vertex: Optional["Vertex"] = None
     """The edge target parameter of the component. Defaults to None."""
     code_class_base_inheritance: ClassVar[str] = "CustomComponent"
@@ -76,7 +80,8 @@ class CustomComponent(Component):
     user_id: Optional[Union[UUID, str]] = None
     status: Optional[Any] = None
     """The status of the component. This is displayed on the frontend. Defaults to None."""
-    _flows_records: Optional[List[Record]] = None
+    _flows_data: Optional[List[Data]] = None
+    _logs: List[Log] = []
 
     def update_state(self, name: str, value: Any):
         if not self.vertex:
@@ -86,11 +91,15 @@ class CustomComponent(Component):
         except Exception as e:
             raise ValueError(f"Error updating state: {e}")
 
-    def stop(self):
+    def stop(self, output_name: str | None = None):
+        if not output_name and self.vertex and len(self.vertex.outputs) == 1:
+            output_name = self.vertex.outputs[0]["name"]
+        else:
+            raise ValueError("You must specify an output name to call stop")
         if not self.vertex:
             raise ValueError("Vertex is not set")
         try:
-            self.graph.mark_branch(self.vertex.id, "INACTIVE")
+            self.graph.mark_branch(vertex_id=self.vertex.id, output_name=output_name, state="INACTIVE")
         except Exception as e:
             raise ValueError(f"Error stopping {self.display_name}: {e}")
 
@@ -159,9 +168,9 @@ class CustomComponent(Component):
             self.repr_value = self.status
         if isinstance(self.repr_value, dict):
             self.repr_value = yaml.dump(self.repr_value)
-        if isinstance(self.repr_value, BaseModel) and not isinstance(self.repr_value, Record):
+        if isinstance(self.repr_value, BaseModel) and not isinstance(self.repr_value, Data):
             self.repr_value = str(self.repr_value)
-        elif hasattr(self.repr_value, "to_json") and not isinstance(self.repr_value, Record):
+        elif hasattr(self.repr_value, "to_json") and not isinstance(self.repr_value, Data):
             self.repr_value = self.repr_value.to_json()
         return self.repr_value
 
@@ -193,9 +202,9 @@ class CustomComponent(Component):
         """
         return self.get_code_tree(self.code or "")
 
-    def to_records(self, data: Any, keys: Optional[List[str]] = None, silent_errors: bool = False) -> List[Record]:
+    def to_data(self, data: Any, keys: Optional[List[str]] = None, silent_errors: bool = False) -> List[Data]:
         """
-        Converts input data into a list of Record objects.
+        Converts input data into a list of Data objects.
 
         Args:
             data (Any): The input data to be converted. It can be a single item or a sequence of items.
@@ -206,7 +215,7 @@ class CustomComponent(Component):
                 Defaults to None, in which case the default keys "text" and "data" are used.
 
         Returns:
-            List[Record]: A list of Record objects.
+            List[Data]: A list of Data objects.
 
         Raises:
             ValueError: If the input data is not of a valid type or if the specified keys are not found in the data.
@@ -214,7 +223,7 @@ class CustomComponent(Component):
         """
         if not keys:
             keys = []
-        records = []
+        data_objects = []
         if not isinstance(data, Sequence):
             data = [data]
         for item in data:
@@ -240,28 +249,28 @@ class CustomComponent(Component):
             else:
                 raise ValueError(f"Invalid data type: {type(item)}")
 
-            records.append(Record(data=data_dict))
+            data_objects.append(Data(data=data_dict))
 
-        return records
+        return data_objects
 
-    def create_references_from_records(self, records: List[Record], include_data: bool = False) -> str:
+    def create_references_from_data(self, data: List[Data], include_data: bool = False) -> str:
         """
-        Create references from a list of records.
+        Create references from a list of data.
 
         Args:
-            records (List[dict]): A list of records, where each record is a dictionary.
+            data (List[dict]): A list of data, where each record is a dictionary.
             include_data (bool, optional): Whether to include data in the references. Defaults to False.
 
         Returns:
             str: A string containing the references in markdown format.
         """
-        if not records:
+        if not data:
             return ""
         markdown_string = "---\n"
-        for record in records:
-            markdown_string += f"- Text: {record.get_text()}"
+        for value in data:
+            markdown_string += f"- Text: {value.get_text()}"
             if include_data:
-                markdown_string += f" Data: {record.data}"
+                markdown_string += f" Data: {value.data}"
             markdown_string += "\n"
         return markdown_string
 
@@ -273,7 +282,7 @@ class CustomComponent(Component):
         Returns:
             list: The arguments of the function entrypoint.
         """
-        build_method = self.get_build_method()
+        build_method = self.get_method(self.function_entrypoint_name)
         if not build_method:
             return []
 
@@ -284,8 +293,7 @@ class CustomComponent(Component):
                 arg["type"] = "Data"
         return args
 
-    @cachedmethod(operator.attrgetter("cache"))
-    def get_build_method(self):
+    def get_method(self, method_name: str):
         """
         Gets the build method for the custom component.
 
@@ -295,15 +303,15 @@ class CustomComponent(Component):
         if not self.code:
             return {}
 
-        component_classes = [cls for cls in self.tree["classes"] if self.code_class_base_inheritance in cls["bases"]]
+        component_classes = [
+            cls for cls in self.tree["classes"] if "Component" in cls["bases"] or "CustomComponent" in cls["bases"]
+        ]
         if not component_classes:
             return {}
 
         # Assume the first Component class is the one we're interested in
         component_class = component_classes[0]
-        build_methods = [
-            method for method in component_class["methods"] if method["name"] == self.function_entrypoint_name
-        ]
+        build_methods = [method for method in component_class["methods"] if method["name"] == (method_name)]
 
         return build_methods[0] if build_methods else {}
 
@@ -315,12 +323,14 @@ class CustomComponent(Component):
         Returns:
             List[Any]: The return type of the function entrypoint.
         """
-        build_method = self.get_build_method()
+        return self.get_method_return_type(self.function_entrypoint_name)
+
+    def get_method_return_type(self, method_name: str):
+        build_method = self.get_method(method_name)
         if not build_method or not build_method.get("has_return"):
             return []
         return_type = build_method["return_type"]
 
-        # If list or List is in the return type, then we remove it and return the inner type
         if hasattr(return_type, "__origin__") and return_type.__origin__ in [
             list,
             List,
@@ -448,7 +458,7 @@ class CustomComponent(Component):
     ) -> Any:
         return await run_flow(inputs=inputs, flow_id=flow_id, flow_name=flow_name, tweaks=tweaks, user_id=self._user_id)
 
-    def list_flows(self) -> List[Record]:
+    def list_flows(self) -> List[Data]:
         if not self._user_id:
             raise ValueError("Session is invalid")
         try:
@@ -468,4 +478,13 @@ class CustomComponent(Component):
             Any: The result of the build process.
         """
         raise NotImplementedError
-        raise NotImplementedError
+
+    def log(self, message: LoggableType | list[LoggableType]):
+        """
+        Logs a message.
+
+        Args:
+            message (LoggableType | list[LoggableType]): The message to log.
+        """
+        log = Log(message=message, type=get_artifact_type(message))
+        self._logs.append(log)
diff --git a/src/backend/base/langflow/custom/directory_reader/directory_reader.py b/src/backend/base/langflow/custom/directory_reader/directory_reader.py
index 52a310314..31fbd4165 100644
--- a/src/backend/base/langflow/custom/directory_reader/directory_reader.py
+++ b/src/backend/base/langflow/custom/directory_reader/directory_reader.py
@@ -1,4 +1,5 @@
 import ast
+import asyncio
 import os
 import zlib
 from pathlib import Path
@@ -220,8 +221,6 @@ class DirectoryReader:
             return False, "Empty file"
         elif not self.validate_code(file_content):
             return False, "Syntax error"
-        elif not self.validate_build(file_content):
-            return False, "Missing build function"
         elif self._is_type_hint_used_in_args("Optional", file_content) and not self._is_type_hint_imported(
             "Optional", file_content
         ):
@@ -286,6 +285,85 @@ class DirectoryReader:
         logger.debug("-------------------- Component menu list built --------------------")
         return response
 
+    async def process_file_async(self, file_path):
+        try:
+            file_content = self.read_file_content(file_path)
+        except Exception as exc:
+            logger.exception(exc)
+            logger.error(f"Error while reading file {file_path}: {str(exc)}")
+            return False, f"Could not read {file_path}"
+
+        if file_content is None:
+            return False, f"Could not read {file_path}"
+        elif self.is_empty_file(file_content):
+            return False, "Empty file"
+        elif not self.validate_code(file_content):
+            return False, "Syntax error"
+        elif self._is_type_hint_used_in_args("Optional", file_content) and not self._is_type_hint_imported(
+            "Optional", file_content
+        ):
+            return (
+                False,
+                "Type hint 'Optional' is used but not imported in the code.",
+            )
+        else:
+            if self.compress_code_field:
+                file_content = str(StringCompressor(file_content).compress_string())
+            return True, file_content
+
+    async def get_output_types_from_code_async(self, code: str):
+        return await asyncio.to_thread(self.get_output_types_from_code, code)
+
+    async def abuild_component_menu_list(self, file_paths):
+        response = {"menu": []}
+        logger.debug("-------------------- Async Building component menu list --------------------")
+
+        tasks = [self.process_file_async(file_path) for file_path in file_paths]
+        results = await asyncio.gather(*tasks)
+
+        for file_path, (validation_result, result_content) in zip(file_paths, results):
+            menu_name = os.path.basename(os.path.dirname(file_path))
+            filename = os.path.basename(file_path)
+
+            if not validation_result:
+                logger.error(f"Error while processing file {file_path}")
+
+            menu_result = self.find_menu(response, menu_name) or {
+                "name": menu_name,
+                "path": os.path.dirname(file_path),
+                "components": [],
+            }
+            component_name = filename.split(".")[0]
+
+            if "_" in component_name:
+                component_name_camelcase = " ".join(word.title() for word in component_name.split("_"))
+            else:
+                component_name_camelcase = component_name
+
+            if validation_result:
+                try:
+                    output_types = await self.get_output_types_from_code_async(result_content)
+                except Exception as exc:
+                    logger.exception(f"Error while getting output types from code: {str(exc)}")
+                    output_types = [component_name_camelcase]
+            else:
+                output_types = [component_name_camelcase]
+
+            component_info = {
+                "name": component_name_camelcase,
+                "output_types": output_types,
+                "file": filename,
+                "code": result_content if validation_result else "",
+                "error": "" if validation_result else result_content,
+            }
+            menu_result["components"].append(component_info)
+
+            if menu_result not in response["menu"]:
+                response["menu"].append(menu_result)
+
+        logger.debug("-------------------- Component menu list built --------------------")
+        return response
+
     @staticmethod
     def get_output_types_from_code(code: str) -> list:
         """
diff --git a/src/backend/base/langflow/custom/directory_reader/utils.py b/src/backend/base/langflow/custom/directory_reader/utils.py
index ddd24d8f3..331b72d2a 100644
--- a/src/backend/base/langflow/custom/directory_reader/utils.py
+++ b/src/backend/base/langflow/custom/directory_reader/utils.py
@@ -51,6 +51,16 @@ def build_and_validate_all_files(reader: DirectoryReader, file_list):
     return valid_components, invalid_components
 
 
+async def abuild_and_validate_all_files(reader: DirectoryReader, file_list):
+    """Build and validate all files"""
+    data = await reader.abuild_component_menu_list(file_list)
+
+    valid_components = reader.filter_loaded_components(data=data, with_errors=False)
+    invalid_components = reader.filter_loaded_components(data=data, with_errors=True)
+
+    return valid_components, invalid_components
+
+
 def load_files_from_path(path: str):
     """Load all files from a given path"""
     reader = DirectoryReader(path, False)
@@ -71,6 +81,19 @@ def build_custom_component_list_from_path(path: str):
     return merge_nested_dicts_with_renaming(valid_menu, invalid_menu)
 
 
+async def abuild_custom_component_list_from_path(path: str):
+    """Build a list of custom components for the langchain from a given path"""
+    file_list = load_files_from_path(path)
+    reader = DirectoryReader(path, False)
+
+    valid_components, invalid_components = await abuild_and_validate_all_files(reader, file_list)
+
+    valid_menu = build_valid_menu(valid_components)
+    invalid_menu = build_invalid_menu(invalid_components)
+
+    return merge_nested_dicts_with_renaming(valid_menu, invalid_menu)
+
+
 def create_invalid_component_template(component, component_name):
     """Create a template for an invalid component."""
     component_code = component["code"]
diff --git a/src/backend/base/langflow/custom/utils.py b/src/backend/base/langflow/custom/utils.py
index 93f08f633..f15762b99 100644
--- a/src/backend/base/langflow/custom/utils.py
+++ b/src/backend/base/langflow/custom/utils.py
@@ -11,9 +11,9 @@ from loguru import logger
 from pydantic import BaseModel
 
 from langflow.custom import CustomComponent
-from langflow.custom.attributes import ATTR_FUNC_MAPPING
-from langflow.custom.code_parser.utils import extract_inner_type
+from langflow.custom.custom_component.component import Component
 from langflow.custom.directory_reader.utils import (
+    abuild_custom_component_list_from_path,
     build_custom_component_list_from_path,
     determine_component_name,
     merge_nested_dicts_with_renaming,
@@ -21,9 +21,11 @@ from langflow.custom.directory_reader.utils import (
 from langflow.custom.eval import eval_custom_component_code
 from langflow.custom.schema import MissingDefault
 from langflow.field_typing.range_spec import RangeSpec
+from langflow.helpers.custom import format_type
 from langflow.schema import dotdict
-from langflow.template.field.base import TemplateField
-from langflow.template.frontend_node.custom_components import CustomComponentFrontendNode
+from langflow.template.field.base import Input
+from langflow.template.frontend_node.custom_components import ComponentFrontendNode, CustomComponentFrontendNode
+from langflow.type_extraction.type_extraction import extract_inner_type
 from langflow.utils import validate
 from langflow.utils.util import get_base_classes
 
@@ -101,6 +103,8 @@ def extract_type_from_optional(field_type):
     Returns:
     str: The extracted type, or an empty string if no type was found.
     """
+    if "optional" not in field_type.lower():
+        return field_type
     match = re.search(r"\[(.*?)\]$", field_type)
     return match[1] if match else field_type
 
@@ -147,7 +151,11 @@ def add_new_custom_field(
     # Check field_config if any of the keys are in it
     # if it is, update the value
     display_name = field_config.pop("display_name", None)
-    field_type = field_config.pop("field_type", field_type)
+    if not field_type:
+        if "type" in field_config and field_config["type"] is not None:
+            field_type = field_config.pop("type")
+        elif "field_type" in field_config and field_config["field_type"] is not None:
+            field_type = field_config.pop("field_type")
     field_contains_list = "list" in field_type.lower()
     field_type = process_type(field_type)
     field_value = field_config.pop("value", field_value)
@@ -174,7 +182,7 @@ def add_new_custom_field(
     required = field_config.pop("required", field_required)
     placeholder = field_config.pop("placeholder", "")
 
-    new_field = TemplateField(
+    new_field = Input(
         name=field_name,
         field_type=field_type,
         value=field_value,
@@ -236,13 +244,56 @@ def add_extra_fields(frontend_node, field_config, function_args):
             )
 
 
-def get_field_dict(field: Union[TemplateField, dict]):
-    """Get the field dictionary from a TemplateField or a dict"""
-    if isinstance(field, TemplateField):
+def get_field_dict(field: Union[Input, dict]):
+    """Get the field dictionary from a Input or a dict"""
+    if isinstance(field, Input):
         return dotdict(field.model_dump(by_alias=True, exclude_none=True))
     return field
 
 
+def run_build_inputs(
+    custom_component: Component,
+    user_id: Optional[Union[str, UUID]] = None,
+):
+    """Run the build inputs of a custom component."""
+    try:
+        field_config = custom_component.build_inputs(user_id=user_id)
+        # add_extra_fields(frontend_node, field_config, field_config.values())
+        return field_config
+    except Exception as exc:
+        logger.error(f"Error running build inputs: {exc}")
+        raise HTTPException(status_code=500, detail=str(exc)) from exc
+
+
+def get_component_instance(custom_component: CustomComponent, user_id: Optional[Union[str, UUID]] = None):
+    try:
+        if custom_component.code is None:
+            raise ValueError("Code is None")
+        elif isinstance(custom_component.code, str):
+            custom_class = eval_custom_component_code(custom_component.code)
+        else:
+            raise ValueError("Invalid code type")
+    except Exception as exc:
+        logger.error(f"Error while evaluating custom component code: {str(exc)}")
+        raise HTTPException(
+            status_code=400,
+            detail={
+                "error": ("Invalid type convertion. Please check your code and try again."),
+                "traceback": traceback.format_exc(),
+            },
+        ) from exc
+
+    try:
+        custom_instance = custom_class(user_id=user_id)
+        return custom_instance
+    except Exception as exc:
+        logger.error(f"Error while instantiating custom component: {str(exc)}")
+        if hasattr(exc, "detail") and "traceback" in exc.detail:
+            logger.error(exc.detail["traceback"])
+
+        raise exc
+
+
 def run_build_config(
     custom_component: CustomComponent,
     user_id: Optional[Union[str, UUID]] = None,
@@ -271,8 +322,8 @@ def run_build_config(
         build_config: Dict = custom_instance.build_config()
 
         for field_name, field in build_config.copy().items():
-            # Allow user to build TemplateField as well
-            # as a dict with the same keys as TemplateField
+            # Allow user to build Input as well
+            # as a dict with the same keys as Input
             field_dict = get_field_dict(field)
             # Let's check if "rangeSpec" is a RangeSpec object
             if "rangeSpec" in field_dict and isinstance(field_dict["rangeSpec"], RangeSpec):
@@ -289,28 +340,8 @@ def run_build_config(
         raise exc
 
 
-def sanitize_template_config(template_config):
-    """Sanitize the template config"""
-
-    for key in template_config.copy():
-        if key not in ATTR_FUNC_MAPPING.keys():
-            template_config.pop(key, None)
-
-    return template_config
-
-
-def build_frontend_node(template_config):
-    """Build a frontend node for a custom component"""
-    try:
-        sanitized_template_config = sanitize_template_config(template_config)
-        return CustomComponentFrontendNode(**sanitized_template_config)
-    except Exception as exc:
-        logger.error(f"Error while building base frontend node: {exc}")
-        raise exc
-
-
 def add_code_field(frontend_node: CustomComponentFrontendNode, raw_code, field_config):
-    code_field = TemplateField(
+    code_field = Input(
         dynamic=True,
         required=True,
         placeholder="",
@@ -327,13 +358,39 @@ def add_code_field(frontend_node: CustomComponentFrontendNode, raw_code, field_c
     return frontend_node
 
 
+def build_custom_component_template_from_inputs(
+    custom_component: Union[Component, CustomComponent], user_id: Optional[Union[str, UUID]] = None
+):
+    # The List of Inputs fills the role of the build_config and the entrypoint_args
+    field_config = custom_component.template_config
+    frontend_node = ComponentFrontendNode.from_inputs(**field_config)
+    frontend_node = add_code_field(frontend_node, custom_component.code, field_config.get("code", {}))
+    # But we now need to calculate the return_type of the methods in the outputs
+    for output in frontend_node.outputs:
+        if output.types:
+            continue
+        return_types = custom_component.get_method_return_type(output.method)
+        return_types = [format_type(return_type) for return_type in return_types]
+        output.add_types(return_types)
+        output.set_selected()
+    # Validate that there is not name overlap between inputs and outputs
+    frontend_node.validate_component()
+    # ! This should be removed when we have a better way to handle this
+    frontend_node.set_base_classes_from_outputs()
+    reorder_fields(frontend_node, custom_component._get_field_order())
+    cc_instance = get_component_instance(custom_component, user_id=user_id)
+    return frontend_node.to_dict(add_name=False), cc_instance
+
+
 def build_custom_component_template(
     custom_component: CustomComponent,
     user_id: Optional[Union[str, UUID]] = None,
 ) -> Tuple[Dict[str, Any], CustomComponent]:
-    """Build a custom component template for the langchain"""
+    """Build a custom component template"""
     try:
-        frontend_node = build_frontend_node(custom_component.template_config)
+        if "inputs" in custom_component.template_config:
+            return build_custom_component_template_from_inputs(custom_component, user_id=user_id)
+        frontend_node = CustomComponentFrontendNode(**custom_component.template_config)
 
         field_config, custom_instance = run_build_config(
             custom_component,
@@ -369,7 +426,7 @@ def create_component_template(component):
     component_code = component["code"]
     component_output_types = component["output_types"]
 
-    component_extractor = CustomComponent(code=component_code)
+    component_extractor = Component(code=component_code)
 
     component_template, _ = build_custom_component_template(component_extractor)
     if not component_template["output_types"] and component_output_types:
@@ -403,6 +460,31 @@ def build_custom_components(components_paths: List[str]):
     return custom_components_from_file
 
 
+async def abuild_custom_components(components_paths: List[str]):
+    """Build custom components from the specified paths."""
+    if not components_paths:
+        return {}
+
+    logger.info(f"Building custom components from {components_paths}")
+    custom_components_from_file: dict = {}
+    processed_paths = set()
+    for path in components_paths:
+        path_str = str(path)
+        if path_str in processed_paths:
+            continue
+
+        custom_component_dict = await abuild_custom_component_list_from_path(path_str)
+        if custom_component_dict:
+            category = next(iter(custom_component_dict))
+            logger.info(f"Loading {len(custom_component_dict[category])} component(s) from category {category}")
+            custom_components_from_file = merge_nested_dicts_with_renaming(
+                custom_components_from_file, custom_component_dict
+            )
+        processed_paths.add(path_str)
+
+    return custom_components_from_file
+
+
 def update_field_dict(
     custom_component_instance: "CustomComponent",
     field_dict: Dict,
@@ -434,9 +516,9 @@ def update_field_dict(
     return build_config
 
 
-def sanitize_field_config(field_config: Union[Dict, TemplateField]):
+def sanitize_field_config(field_config: Union[Dict, Input]):
     # If any of the already existing keys are in field_config, remove them
-    if isinstance(field_config, TemplateField):
+    if isinstance(field_config, Input):
         field_dict = field_config.to_dict()
     else:
         field_dict = field_config
@@ -451,6 +533,11 @@ def sanitize_field_config(field_config: Union[Dict, TemplateField]):
         "show",
     ]:
         field_dict.pop(key, None)
+
+    # Remove field_type and type because they were extracted already
+    field_dict.pop("field_type", None)
+    field_dict.pop("type", None)
+
     return field_dict
 
 
diff --git a/src/backend/base/langflow/template/frontend_node/formatter/__init__.py b/src/backend/base/langflow/exceptions/__init__.py
similarity index 100%
rename from src/backend/base/langflow/template/frontend_node/formatter/__init__.py
rename to src/backend/base/langflow/exceptions/__init__.py
diff --git a/src/backend/base/langflow/exceptions/component.py b/src/backend/base/langflow/exceptions/component.py
new file mode 100644
index 000000000..a4a557565
--- /dev/null
+++ b/src/backend/base/langflow/exceptions/component.py
@@ -0,0 +1,6 @@
+# Create an exception class that receives the message and the formatted traceback
+class ComponentBuildException(Exception):
+    def __init__(self, message: str, formatted_traceback: str):
+        self.message = message
+        self.formatted_traceback = formatted_traceback
+        super().__init__(message)
diff --git a/src/backend/base/langflow/field_typing/__init__.py b/src/backend/base/langflow/field_typing/__init__.py
index 67dfec050..f967116f5 100644
--- a/src/backend/base/langflow/field_typing/__init__.py
+++ b/src/backend/base/langflow/field_typing/__init__.py
@@ -7,6 +7,7 @@ from .constants import (
     BaseLLM,
     BaseLoader,
     BaseMemory,
+    BaseChatModel,
     BaseOutputParser,
     BasePromptTemplate,
     BaseRetriever,
@@ -24,23 +25,30 @@ from .constants import (
     TextSplitter,
     Tool,
     VectorStore,
+    Retriever,
 )
-from .prompt import Prompt
 from .range_spec import RangeSpec
 
 
-def _import_template_field():
-    from langflow.template.field.base import TemplateField
+def _import_input_class():
+    from langflow.template.field.base import Input
 
-    return TemplateField
+    return Input
+
+
+def _import_output_class():
+    from langflow.template.field.base import Output
+
+    return Output
 
 
 def __getattr__(name: str) -> Any:
     # This is to avoid circular imports
-    if name == "TemplateField":
-        return _import_template_field()
-    elif name == "RangeSpec":
+    if name == "Input":
+        return _import_input_class()
         return RangeSpec
+    elif name == "Output":
+        return _import_output_class()
     # The other names should work as if they were imported from constants
     # Import the constants module langflow.field_typing.constants
     from . import constants
@@ -49,30 +57,31 @@ def __getattr__(name: str) -> Any:
 
 
 __all__ = [
-    "NestedDict",
-    "Data",
-    "Tool",
-    "PromptTemplate",
-    "Chain",
+    "AgentExecutor",
     "BaseChatMemory",
-    "BaseLLM",
     "BaseLanguageModel",
+    "BaseLLM",
     "BaseLoader",
     "BaseMemory",
     "BaseOutputParser",
-    "BaseRetriever",
-    "VectorStore",
-    "Embeddings",
-    "TextSplitter",
-    "Document",
-    "AgentExecutor",
-    "Text",
-    "Object",
-    "Callable",
     "BasePromptTemplate",
+    "BaseRetriever",
+    "Callable",
+    "Chain",
     "ChatPromptTemplate",
-    "Prompt",
-    "RangeSpec",
-    "TemplateField",
     "Code",
+    "Data",
+    "Document",
+    "Embeddings",
+    "Input",
+    "NestedDict",
+    "Object",
+    "PromptTemplate",
+    "RangeSpec",
+    "Text",
+    "TextSplitter",
+    "Tool",
+    "VectorStore",
+    "BaseChatModel",
+    "Retriever",
 ]
diff --git a/src/backend/base/langflow/field_typing/constants.py b/src/backend/base/langflow/field_typing/constants.py
index 807f9a77e..8ba7e544d 100644
--- a/src/backend/base/langflow/field_typing/constants.py
+++ b/src/backend/base/langflow/field_typing/constants.py
@@ -1,4 +1,4 @@
-from typing import Callable, Dict, Text, Union
+from typing import Callable, Dict, Text, TypeAlias, TypeVar, Union
 
 from langchain.agents.agent import AgentExecutor
 from langchain.chains.base import Chain
@@ -7,6 +7,7 @@ from langchain_core.document_loaders import BaseLoader
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 from langchain_core.language_models import BaseLanguageModel, BaseLLM
+from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_core.memory import BaseMemory
 from langchain_core.output_parsers import BaseOutputParser
 from langchain_core.prompts import BasePromptTemplate, ChatPromptTemplate, PromptTemplate
@@ -15,10 +16,9 @@ from langchain_core.tools import Tool
 from langchain_core.vectorstores import VectorStore
 from langchain_text_splitters import TextSplitter
 
-from langflow.field_typing.prompt import Prompt
-
-# Type alias for more complex dicts
-NestedDict = Dict[str, Union[str, Dict]]
+NestedDict: TypeAlias = Dict[str, Union[str, Dict]]
+LanguageModel = TypeVar("LanguageModel", BaseLanguageModel, BaseLLM, BaseChatModel)
+Retriever: TypeAlias = BaseRetriever
 
 
 class Object:
@@ -51,6 +51,7 @@ LANGCHAIN_BASE_TYPES = {
     "BaseOutputParser": BaseOutputParser,
     "BaseMemory": BaseMemory,
     "BaseChatMemory": BaseChatMemory,
+    "BaseChatModel": BaseChatModel,
 }
 # Langchain base types plus Python base types
 CUSTOM_COMPONENT_SUPPORTED_TYPES = {
@@ -60,5 +61,6 @@ CUSTOM_COMPONENT_SUPPORTED_TYPES = {
     "Text": Text,
     "Object": Object,
     "Callable": Callable,
-    "Prompt": Prompt,
+    "LanguageModel": LanguageModel,
+    "Retriever": Retriever,
 }
diff --git a/src/backend/base/langflow/field_typing/prompt.py b/src/backend/base/langflow/field_typing/prompt.py
deleted file mode 100644
index f7cdece35..000000000
--- a/src/backend/base/langflow/field_typing/prompt.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from langchain_core.load import load
-from langchain_core.messages import HumanMessage
-from langchain_core.prompts import BaseChatPromptTemplate, ChatPromptTemplate, PromptTemplate
-
-from langflow.base.prompts.utils import dict_values_to_string
-from langflow.schema.message import Message
-from langflow.schema.record import Record
-
-
-class Prompt(Record):
-    def load_lc_prompt(self):
-        if "prompt" not in self:
-            raise ValueError("Prompt is required.")
-        return load(self.prompt)
-
-    @classmethod
-    def from_lc_prompt(
-        cls,
-        prompt: BaseChatPromptTemplate,
-    ):
-        prompt_json = prompt.to_json()
-        return cls(prompt=prompt_json)
-
-    def format_text(self):
-        prompt_template = PromptTemplate.from_template(self.template)
-        variables_with_str_values = dict_values_to_string(self.variables)
-        formatted_prompt = prompt_template.format(**variables_with_str_values)
-        self.text = formatted_prompt
-        return formatted_prompt
-
-    @classmethod
-    async def from_template_and_variables(cls, template: str, variables: dict):
-        instance = cls(template=template, variables=variables)
-        contents = [{"type": "text", "text": instance.format_text()}]
-        # Get all Message instances from the kwargs
-        for value in variables.values():
-            if isinstance(value, Message):
-                content_dicts = await value.get_file_content_dicts()
-                contents.extend(content_dicts)
-        prompt_template = ChatPromptTemplate.from_messages([HumanMessage(content=contents)])  # type: ignore
-        instance.prompt = prompt_template.to_json()
-        return instance
diff --git a/src/backend/base/langflow/graph/edge/base.py b/src/backend/base/langflow/graph/edge/base.py
index b99785ea6..ca4856b23 100644
--- a/src/backend/base/langflow/graph/edge/base.py
+++ b/src/backend/base/langflow/graph/edge/base.py
@@ -1,7 +1,7 @@
 from typing import TYPE_CHECKING, Any, List, Optional
 
 from loguru import logger
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, field_validator
 
 from langflow.schema.schema import INPUT_FIELD_NAME
 from langflow.services.monitor.utils import log_message
@@ -11,9 +11,22 @@ if TYPE_CHECKING:
 
 
 class SourceHandle(BaseModel):
-    baseClasses: List[str] = Field(..., description="List of base classes for the source handle.")
+    baseClasses: list[str] = Field(default_factory=list, description="List of base classes for the source handle.")
     dataType: str = Field(..., description="Data type for the source handle.")
     id: str = Field(..., description="Unique identifier for the source handle.")
+    name: Optional[str] = Field(None, description="Name of the source handle.")
+    output_types: List[str] = Field(default_factory=list, description="List of output types for the source handle.")
+
+    @field_validator("name", mode="before")
+    @classmethod
+    def validate_name(cls, v, _info):
+        if _info.data["dataType"] == "GroupNode":
+            # 'OpenAIModel-u4iGV_text_output'
+            splits = v.split("_", 1)
+            if len(splits) != 2:
+                raise ValueError(f"Invalid source handle name {v}")
+            v = splits[1]
+        return v
 
 
 class TargetHandle(BaseModel):
@@ -47,6 +60,27 @@ class Edge:
         self.validate_edge(source, target)
 
     def validate_handles(self, source, target) -> None:
+        if isinstance(self._source_handle, str) or self.source_handle.baseClasses:
+            self._legacy_validate_handles(source, target)
+        else:
+            self._validate_handles(source, target)
+
+    def _validate_handles(self, source, target) -> None:
+        if self.target_handle.inputTypes is None:
+            self.valid_handles = self.target_handle.type in self.source_handle.output_types
+
+        elif self.source_handle.output_types is not None:
+            self.valid_handles = (
+                any(output_type in self.target_handle.inputTypes for output_type in self.source_handle.output_types)
+                or self.target_handle.type in self.source_handle.output_types
+            )
+
+        if not self.valid_handles:
+            logger.debug(self.source_handle)
+            logger.debug(self.target_handle)
+            raise ValueError(f"Edge between {source.vertex_type} and {target.vertex_type} " f"has invalid handles")
+
+    def _legacy_validate_handles(self, source, target) -> None:
         if self.target_handle.inputTypes is None:
             self.valid_handles = self.target_handle.type in self.source_handle.baseClasses
         else:
@@ -67,6 +101,48 @@ class Edge:
         self.target_handle = state.get("target_handle")
 
     def validate_edge(self, source, target) -> None:
+        # If the self.source_handle has baseClasses, then we are using the legacy
+        # way of defining the source and target handles
+        if isinstance(self._source_handle, str) or self.source_handle.baseClasses:
+            self._legacy_validate_edge(source, target)
+        else:
+            self._validate_edge(source, target)
+
+    def _validate_edge(self, source, target) -> None:
+        # Validate that the outputs of the source node are valid inputs
+        # for the target node
+        # .outputs is a list of Output objects as dictionaries
+        # meaning: check for "types" key in each dictionary
+        self.source_types = [output for output in source.outputs if output["name"] == self.source_handle.name]
+        self.target_reqs = target.required_inputs + target.optional_inputs
+        # Both lists contain strings and sometimes a string contains the value we are
+        # looking for e.g. comgin_out=["Chain"] and target_reqs=["LLMChain"]
+        # so we need to check if any of the strings in source_types is in target_reqs
+        self.valid = any(
+            any(output_type in target_req for output_type in output["types"])
+            for output in self.source_types
+            for target_req in self.target_reqs
+        )
+        # Get what type of input the target node is expecting
+
+        # Update the matched type to be the first found match
+        self.matched_type = next(
+            (
+                output_type
+                for output in self.source_types
+                for output_type in output["types"]
+                for target_req in self.target_reqs
+                if output_type in target_req
+            ),
+            None,
+        )
+        no_matched_type = self.matched_type is None
+        if no_matched_type:
+            logger.debug(self.source_types)
+            logger.debug(self.target_reqs)
+            raise ValueError(f"Edge between {source.vertex_type} and {target.vertex_type} " f"has no matched type. ")
+
+    def _legacy_validate_edge(self, source, target) -> None:
         # Validate that the outputs of the source node are valid inputs
         # for the target node
         self.source_types = source.output
diff --git a/src/backend/base/langflow/graph/graph/base.py b/src/backend/base/langflow/graph/graph/base.py
index 5e176ef81..a0e4d9a24 100644
--- a/src/backend/base/langflow/graph/graph/base.py
+++ b/src/backend/base/langflow/graph/graph/base.py
@@ -5,6 +5,9 @@ from functools import partial
 from itertools import chain
 from typing import TYPE_CHECKING, Dict, Generator, List, Optional, Tuple, Type, Union
 
+from loguru import logger
+
+from langflow.exceptions.component import ComponentBuildException
 from langflow.graph.edge.base import ContractEdge
 from langflow.graph.graph.constants import lazy_load_vertex_dict
 from langflow.graph.graph.runnable_vertices_manager import RunnableVerticesManager
@@ -13,13 +16,12 @@ from langflow.graph.graph.utils import process_flow
 from langflow.graph.schema import InterfaceComponentTypes, RunOutputs
 from langflow.graph.vertex.base import Vertex
 from langflow.graph.vertex.types import InterfaceVertex, StateVertex
-from langflow.schema import Record
+from langflow.schema import Data
 from langflow.schema.schema import INPUT_FIELD_NAME, InputType
 from langflow.services.cache.utils import CacheMiss
 from langflow.services.chat.service import ChatService
 from langflow.services.deps import get_chat_service
 from langflow.services.monitor.utils import log_transaction
-from loguru import logger
 
 if TYPE_CHECKING:
     from langflow.graph.schema import ResultData
@@ -80,7 +82,7 @@ class Graph:
         self.define_vertices_lists()
         self.state_manager = GraphStateManager()
 
-    def get_state(self, name: str) -> Optional[Record]:
+    def get_state(self, name: str) -> Optional[Data]:
         """
         Returns the state of the graph with the given name.
 
@@ -88,17 +90,17 @@ class Graph:
             name (str): The name of the state.
 
         Returns:
-            Optional[Record]: The state record, or None if the state does not exist.
+            Optional[Data]: The state record, or None if the state does not exist.
         """
         return self.state_manager.get_state(name, run_id=self._run_id)
 
-    def update_state(self, name: str, record: Union[str, Record], caller: Optional[str] = None) -> None:
+    def update_state(self, name: str, record: Union[str, Data], caller: Optional[str] = None) -> None:
         """
         Updates the state of the graph with the given name.
 
         Args:
             name (str): The name of the state.
-            record (Union[str, Record]): The new state record.
+            record (Union[str, Data]): The new state record.
             caller (Optional[str], optional): The ID of the vertex that is updating the state. Defaults to None.
         """
         if caller:
@@ -153,13 +155,13 @@ class Graph:
         """
         self.activated_vertices = []
 
-    def append_state(self, name: str, record: Union[str, Record], caller: Optional[str] = None) -> None:
+    def append_state(self, name: str, record: Union[str, Data], caller: Optional[str] = None) -> None:
         """
         Appends the state of the graph with the given name.
 
         Args:
             name (str): The name of the state.
-            record (Union[str, Record]): The state record to append.
+            record (Union[str, Data]): The state record to append.
             caller (Optional[str], optional): The ID of the vertex that is updating the state. Defaults to None.
         """
         if caller:
@@ -288,6 +290,13 @@ class Graph:
                 raise ValueError(f"Vertex {vertex_id} not found")
             vertex.update_raw_params({"session_id": session_id})
         # Process the graph
+        try:
+            cache_service = get_chat_service()
+            if self.flow_id:
+                await cache_service.set_cache(self.flow_id, self)
+        except Exception as exc:
+            logger.exception(exc)
+
         try:
             start_component_id = next(
                 (vertex_id for vertex_id in self._is_input_vertices if "chat" in vertex_id.lower()), None
@@ -458,6 +467,8 @@ class Graph:
         """
         Resets the inactivated vertices in the graph.
         """
+        for vertex_id in self.inactivated_vertices.copy():
+            self.mark_vertex(vertex_id, "ACTIVE")
         self.inactivated_vertices = []
         self.inactivated_vertices = set()
 
@@ -471,7 +482,7 @@ class Graph:
         vertex = self.get_vertex(vertex_id)
         vertex.set_state(state)
 
-    def mark_branch(self, vertex_id: str, state: str, visited: Optional[set] = None):
+    def mark_branch(self, vertex_id: str, state: str, visited: Optional[set] = None, output_name: Optional[str] = None):
         """Marks a branch of the graph."""
         if visited is None:
             visited = set()
@@ -482,8 +493,21 @@ class Graph:
         self.mark_vertex(vertex_id, state)
 
         for child_id in self.parent_child_map[vertex_id]:
+            # Only child_id that have an edge with the vertex_id through the output_name
+            # should be marked
+            if output_name:
+                edge = self.get_edge(vertex_id, child_id)
+                if edge and edge.source_handle.name != output_name:
+                    continue
             self.mark_branch(child_id, state)
 
+    def get_edge(self, source_id: str, target_id: str) -> Optional[ContractEdge]:
+        """Returns the edge between two vertices."""
+        for edge in self.edges:
+            if edge.source_id == source_id and edge.target_id == target_id:
+                return edge
+        return None
+
     def build_parent_child_map(self, vertices: List[Vertex]):
         parent_child_map = defaultdict(list)
         for vertex in vertices:
@@ -497,10 +521,38 @@ class Graph:
         self._updates += 1
 
     def __getstate__(self):
-        return self.raw_graph_data
+        # Get all attributes that are useful in runs.
+        # We don't need to save the state_manager because it is
+        # a singleton and it is not necessary to save it
+        return {
+            "vertices": self.vertices,
+            "edges": self.edges,
+            "flow_id": self.flow_id,
+            "user_id": self.user_id,
+            "raw_graph_data": self.raw_graph_data,
+            "top_level_vertices": self.top_level_vertices,
+            "inactivated_vertices": self.inactivated_vertices,
+            "run_manager": self.run_manager.to_dict(),
+            "_run_id": self._run_id,
+            "in_degree_map": self.in_degree_map,
+            "parent_child_map": self.parent_child_map,
+            "predecessor_map": self.predecessor_map,
+            "successor_map": self.successor_map,
+            "activated_vertices": self.activated_vertices,
+            "vertices_layers": self.vertices_layers,
+            "vertices_to_run": self.vertices_to_run,
+            "stop_vertex": self.stop_vertex,
+            "vertex_map": self.vertex_map,
+        }
 
     def __setstate__(self, state):
-        self.__init__(**state)
+        run_manager = state["run_manager"]
+        if isinstance(run_manager, RunnableVerticesManager):
+            state["run_manager"] = run_manager
+        else:
+            state["run_manager"] = RunnableVerticesManager.from_dict(run_manager)
+        self.__dict__.update(state)
+        self.state_manager = GraphStateManager()
 
     @classmethod
     def from_payload(cls, payload: Dict, flow_id: Optional[str] = None, user_id: Optional[str] = None) -> "Graph":
@@ -526,6 +578,7 @@ class Graph:
                 raise ValueError(
                     f"Invalid payload. Expected keys 'nodes' and 'edges'. Found {list(payload.keys())}"
                 ) from exc
+
             raise ValueError(f"Error while creating graph from payload: {exc}") from exc
 
     def __eq__(self, other: object) -> bool:
@@ -704,6 +757,20 @@ class Graph:
         except KeyError:
             raise ValueError(f"Vertex {vertex_id} not found")
 
+    def get_root_of_group_node(self, vertex_id: str) -> Vertex:
+        """Returns the root of a group node."""
+        if vertex_id in self.top_level_vertices:
+            # Get all vertices with vertex_id as .parent_node_id
+            # then get the one at the top
+            vertices = [vertex for vertex in self.vertices if vertex.parent_node_id == vertex_id]
+            # Now go through successors of the vertices
+            # and get the one that none of its successors is in vertices
+            for vertex in vertices:
+                successors = self.get_all_successors(vertex, recursive=False)
+                if not any(successor in vertices for successor in successors):
+                    return vertex
+        raise ValueError(f"Vertex {vertex_id} is not a top level vertex or no root vertex found")
+
     async def build_vertex(
         self,
         chat_service: ChatService,
@@ -746,6 +813,7 @@ class Graph:
                     # Now set update the vertex with the cached vertex
                     vertex._built = cached_vertex._built
                     vertex.result = cached_vertex.result
+                    vertex.results = cached_vertex.results
                     vertex.artifacts = cached_vertex.artifacts
                     vertex._built_object = cached_vertex._built_object
                     vertex._custom_component = cached_vertex._custom_component
@@ -769,7 +837,8 @@ class Graph:
             log_transaction(flow_id, vertex, status="success")
             return result_dict, params, valid, artifacts, vertex
         except Exception as exc:
-            logger.exception(f"Error building Component: {exc}")
+            if not isinstance(exc, ComponentBuildException):
+                logger.exception(f"Error building Component:\n\n{exc}")
             flow_id = self.flow_id
             log_transaction(flow_id, vertex, status="failure", error=str(exc))
             raise exc
@@ -1062,7 +1131,6 @@ class Graph:
         # Initial setup
         visited = set()  # To keep track of visited vertices
         excluded = set()  # To keep track of vertices that should be excluded
-        stack = [vertex_id]  # Use a list as a stack for DFS
 
         def get_successors(vertex, recursive=True):
             # Recursively get the successors of the current vertex
@@ -1078,7 +1146,12 @@ class Graph:
                 successors_result.append(successor)
             return successors_result
 
-        stop_or_start_vertex = self.get_vertex(vertex_id)
+        try:
+            stop_or_start_vertex = self.get_vertex(vertex_id)
+            stack = [vertex_id]  # Use a list as a stack for DFS
+        except ValueError:
+            stop_or_start_vertex = self.get_root_of_group_node(vertex_id)
+            stack = [stop_or_start_vertex.id]
         stop_predecessors = [pre.id for pre in stop_or_start_vertex.predecessors]
         # DFS to collect all vertices that can reach the specified vertex
         while stack:
@@ -1134,6 +1207,7 @@ class Graph:
         )
         layers: List[List[str]] = []
         visited = set(queue)
+
         current_layer = 0
         while queue:
             layers.append([])  # Start a new layer
diff --git a/src/backend/base/langflow/graph/graph/constants.py b/src/backend/base/langflow/graph/graph/constants.py
index 8f5840524..ca04e81c6 100644
--- a/src/backend/base/langflow/graph/graph/constants.py
+++ b/src/backend/base/langflow/graph/graph/constants.py
@@ -21,6 +21,7 @@ class VertexTypesDict(LazyLoadDictBase):
     def get_type_dict(self):
         return {
             **{t: types.CustomComponentVertex for t in ["CustomComponent"]},
+            **{t: types.ComponentVertex for t in ["Component"]},
             **{t: types.InterfaceVertex for t in CHAT_COMPONENTS},
         }
 
diff --git a/src/backend/base/langflow/graph/graph/runnable_vertices_manager.py b/src/backend/base/langflow/graph/graph/runnable_vertices_manager.py
index 7081cc2ff..63b0aacaf 100644
--- a/src/backend/base/langflow/graph/graph/runnable_vertices_manager.py
+++ b/src/backend/base/langflow/graph/graph/runnable_vertices_manager.py
@@ -13,6 +13,33 @@ class RunnableVerticesManager:
         self.run_predecessors = defaultdict(set)  # Tracks predecessors for each vertex
         self.vertices_to_run = set()  # Set of vertices that are ready to run
 
+    def to_dict(self) -> dict:
+        return {
+            "run_map": self.run_map,
+            "run_predecessors": self.run_predecessors,
+            "vertices_to_run": self.vertices_to_run,
+        }
+
+    @classmethod
+    def from_dict(cls, data: dict) -> "RunnableVerticesManager":
+        instance = cls()
+        instance.run_map = data["run_map"]
+        instance.run_predecessors = data["run_predecessors"]
+        instance.vertices_to_run = data["vertices_to_run"]
+        return instance
+
+    def __getstate__(self) -> object:
+        return {
+            "run_map": self.run_map,
+            "run_predecessors": self.run_predecessors,
+            "vertices_to_run": self.vertices_to_run,
+        }
+
+    def __setstate__(self, state: dict) -> None:
+        self.run_map = state["run_map"]
+        self.run_predecessors = state["run_predecessors"]
+        self.vertices_to_run = state["vertices_to_run"]
+
     def is_vertex_runnable(self, vertex_id: str) -> bool:
         """Determines if a vertex is runnable."""
 
diff --git a/src/backend/base/langflow/graph/schema.py b/src/backend/base/langflow/graph/schema.py
index b95e0dbe0..8f4b3249e 100644
--- a/src/backend/base/langflow/graph/schema.py
+++ b/src/backend/base/langflow/graph/schema.py
@@ -11,7 +11,7 @@ from langflow.utils.schemas import ChatOutputResponse, ContainsEnumMeta
 class ResultData(BaseModel):
     results: Optional[Any] = Field(default_factory=dict)
     artifacts: Optional[Any] = Field(default_factory=dict)
-    logs: Optional[List[dict]] = Field(default_factory=list)
+    logs: Optional[dict] = Field(default_factory=dict)
     messages: Optional[list[ChatOutputResponse]] = Field(default_factory=list)
     timedelta: Optional[float] = None
     duration: Optional[str] = None
@@ -30,17 +30,19 @@ class ResultData(BaseModel):
     def validate_model(cls, values):
         if not values.get("logs") and values.get("artifacts"):
             # Build the log from the artifacts
-            message = values["artifacts"]
 
-            # ! Temporary fix
-            if not isinstance(message, dict):
-                message = {"message": message}
+            for key in values["artifacts"]:
+                message = values["artifacts"][key]
 
-            if "stream_url" in message and "type" in message:
-                stream_url = StreamURL(location=message["stream_url"])
-                values["logs"] = [Log(message=stream_url, type=message["type"])]
-            elif "type" in message:
-                values["logs"] = [Log(message=message, type=message["type"])]
+                # ! Temporary fix
+                if message is None:
+                    continue
+
+                if "stream_url" in message and "type" in message:
+                    stream_url = StreamURL(location=message["stream_url"])
+                    values["logs"].update({key: Log(message=stream_url, type=message["type"])})
+                elif "type" in message:
+                    values["logs"].update({Log(message=message, type=message["type"])})
         return values
 
 
@@ -51,7 +53,7 @@ class InterfaceComponentTypes(str, Enum, metaclass=ContainsEnumMeta):
     ChatOutput = "ChatOutput"
     TextInput = "TextInput"
     TextOutput = "TextOutput"
-    RecordsOutput = "RecordsOutput"
+    DataOutput = "DataOutput"
 
     def __contains__(cls, item):
         try:
@@ -63,7 +65,7 @@ class InterfaceComponentTypes(str, Enum, metaclass=ContainsEnumMeta):
 
 
 CHAT_COMPONENTS = [InterfaceComponentTypes.ChatInput, InterfaceComponentTypes.ChatOutput]
-RECORDS_COMPONENTS = [InterfaceComponentTypes.RecordsOutput]
+RECORDS_COMPONENTS = [InterfaceComponentTypes.DataOutput]
 INPUT_COMPONENTS = [
     InterfaceComponentTypes.ChatInput,
     InterfaceComponentTypes.TextInput,
@@ -71,7 +73,7 @@ INPUT_COMPONENTS = [
 OUTPUT_COMPONENTS = [
     InterfaceComponentTypes.ChatOutput,
     InterfaceComponentTypes.TextOutput,
-    InterfaceComponentTypes.RecordsOutput,
+    InterfaceComponentTypes.DataOutput,
 ]
 
 
diff --git a/src/backend/base/langflow/graph/utils.py b/src/backend/base/langflow/graph/utils.py
index 89db4f0aa..7183b923f 100644
--- a/src/backend/base/langflow/graph/utils.py
+++ b/src/backend/base/langflow/graph/utils.py
@@ -5,7 +5,7 @@ from langchain_core.documents import Document
 from pydantic import BaseModel
 
 from langflow.interface.utils import extract_input_variables_from_prompt
-from langflow.schema import Record
+from langflow.schema.data import Data
 from langflow.schema.message import Message
 
 
@@ -54,6 +54,7 @@ def flatten_list(list_of_lists: list[Union[list, Any]]) -> list:
 def serialize_field(value):
     """Unified serialization function for handling both BaseModel and Document types,
     including handling lists of these types."""
+
     if isinstance(value, (list, tuple)):
         return [serialize_field(v) for v in value]
     elif isinstance(value, Document):
@@ -68,7 +69,7 @@ def serialize_field(value):
 def get_artifact_type(value, build_result) -> str:
     result = ArtifactType.UNKNOWN
     match value:
-        case Record():
+        case Data():
             result = ArtifactType.RECORD
 
         case str():
diff --git a/src/backend/base/langflow/graph/vertex/base.py b/src/backend/base/langflow/graph/vertex/base.py
index 64c951ce3..a57628a79 100644
--- a/src/backend/base/langflow/graph/vertex/base.py
+++ b/src/backend/base/langflow/graph/vertex/base.py
@@ -2,17 +2,20 @@ import ast
 import asyncio
 import inspect
 import os
+import traceback
 import types
 from enum import Enum
-from typing import TYPE_CHECKING, Any, AsyncIterator, Callable, Dict, Iterator, List, Mapping, Optional
+from typing import TYPE_CHECKING, Any, AsyncIterator, Callable, Dict, Iterator, List, Mapping, Optional, Set
 
 from loguru import logger
 
+from langflow.exceptions.component import ComponentBuildException
 from langflow.graph.schema import INPUT_COMPONENTS, OUTPUT_COMPONENTS, InterfaceComponentTypes, ResultData
-from langflow.graph.utils import ArtifactType, UnbuiltObject, UnbuiltResult
+from langflow.graph.utils import UnbuiltObject, UnbuiltResult
 from langflow.interface.initialize import loading
 from langflow.interface.listing import lazy_load_dict
-from langflow.schema.schema import INPUT_FIELD_NAME
+from langflow.schema.artifact import ArtifactType
+from langflow.schema.schema import INPUT_FIELD_NAME, Log, build_logs
 from langflow.services.deps import get_storage_service
 from langflow.services.monitor.utils import log_transaction
 from langflow.utils.constants import DIRECT_TYPES
@@ -58,13 +61,14 @@ class Vertex:
         self.graph = graph
         self._data = data
         self.base_type: Optional[str] = base_type
+        self.outputs: List[Dict] = []
         self._parse_data()
         self._built_object = UnbuiltObject()
         self._built_result = None
         self._built = False
         self.artifacts: Dict[str, Any] = {}
-        self.artifacts_raw: Any = None
-        self.artifacts_type: Optional[str] = None
+        self.artifacts_raw: Dict[str, Any] = {}
+        self.artifacts_type: Dict[str, str] = {}
         self.steps: List[Callable] = [self._build]
         self.steps_ran: List[Callable] = []
         self.task_id: Optional[str] = None
@@ -75,6 +79,8 @@ class Vertex:
         self.parent_is_top_level = False
         self.layer = None
         self.result: Optional[ResultData] = None
+        self.results: Dict[str, Any] = {}
+        self.logs: Dict[str, Log] = {}
         try:
             self.is_interface_component = self.vertex_type in InterfaceComponentTypes
         except ValueError:
@@ -84,6 +90,9 @@ class Vertex:
         self.build_times: List[float] = []
         self.state = VertexStates.ACTIVE
 
+    def add_result(self, name: str, result: Any):
+        self.results[name] = result
+
     def update_graph_state(self, key, new_state, append: bool):
         if append:
             self.graph.append_state(key, new_state, caller=self.id)
@@ -134,6 +143,18 @@ class Vertex:
     def edges(self) -> List["ContractEdge"]:
         return self.graph.get_vertex_edges(self.id)
 
+    @property
+    def outgoing_edges(self) -> List["ContractEdge"]:
+        return [edge for edge in self.edges if edge.source_id == self.id]
+
+    @property
+    def incoming_edges(self) -> List["ContractEdge"]:
+        return [edge for edge in self.edges if edge.target_id == self.id]
+
+    @property
+    def edges_source_names(self) -> Set[str | None]:
+        return {edge.source_handle.name for edge in self.edges}
+
     @property
     def predecessors(self) -> List["Vertex"]:
         return self.graph.get_predecessors(self)
@@ -147,84 +168,57 @@ class Vertex:
         return self.graph.successor_map.get(self.id, [])
 
     def __getstate__(self):
-        return {
-            "_data": self._data,
-            "params": {},
-            "base_type": self.base_type,
-            "base_name": self.base_name,
-            "is_task": self.is_task,
-            "id": self.id,
-            "_built_object": UnbuiltObject(),
-            "_built": False,
-            "parent_node_id": self.parent_node_id,
-            "parent_is_top_level": self.parent_is_top_level,
-            "load_from_db_fields": self.load_from_db_fields,
-            "is_input": self.is_input,
-            "is_output": self.is_output,
-        }
+        state = self.__dict__.copy()
+        state["_lock"] = None  # Locks are not serializable
+        state["_built_object"] = None if isinstance(self._built_object, UnbuiltObject) else self._built_object
+        state["_built_result"] = None if isinstance(self._built_result, UnbuiltResult) else self._built_result
+        return state
 
     def __setstate__(self, state):
-        self._lock = asyncio.Lock()
-        self._data = state["_data"]
-        self.params = state["params"]
-        self.base_type = state["base_type"]
-        self.is_task = state["is_task"]
-        self.id = state["id"]
-        self.frozen = state.get("frozen", False)
-        self.is_input = state.get("is_input", False)
-        self.is_output = state.get("is_output", False)
-        self.base_name = state["base_name"]
-        self._parse_data()
-        if "_built_object" in state:
-            self._built_object = state["_built_object"]
-            self._built = state["_built"]
-        else:
-            self._built_object = UnbuiltObject()
-            self._built = False
-        if "_built_result" in state:
-            self._built_result = state["_built_result"]
-        else:
-            self._built_result = UnbuiltResult()
-        self.artifacts: Dict[str, Any] = {}
-        self.task_id: Optional[str] = None
-        self.parent_node_id = state["parent_node_id"]
-        self.parent_is_top_level = state["parent_is_top_level"]
-        self.load_from_db_fields = state["load_from_db_fields"]
-        self.layer = state.get("layer")
-        self.steps = state.get("steps", [self._build])
+        self.__dict__.update(state)
+        self._lock = asyncio.Lock()  # Reinitialize the lock
+        self._built_object = state.get("_built_object") or UnbuiltObject()
+        self._built_result = state.get("_built_result") or UnbuiltResult()
 
     def set_top_level(self, top_level_vertices: List[str]) -> None:
         self.parent_is_top_level = self.parent_node_id in top_level_vertices
 
     def _parse_data(self) -> None:
         self.data = self._data["data"]
-        self.output = self.data["node"]["base_classes"]
-        self.display_name = self.data["node"].get("display_name", self.id.split("-")[0])
+        if self.data["node"]["template"]["_type"] == "Component":
+            if "outputs" not in self.data["node"]:
+                raise ValueError(f"Outputs not found for {self.display_name}")
+            self.outputs = self.data["node"]["outputs"]
+        else:
+            self.outputs = self.data["node"].get("outputs", [])
+            self.output = self.data["node"]["base_classes"]
+
+        self.display_name: str = self.data["node"].get("display_name", self.id.split("-")[0])
+
+        self.description: str = self.data["node"].get("description", "")
+        self.frozen: bool = self.data["node"].get("frozen", False)
 
-        self.description = self.data["node"].get("description", "")
-        self.frozen = self.data["node"].get("frozen", False)
-        self.selected_output_type = self.data["node"].get("selected_output_type")
         self.is_input = self.data["node"].get("is_input") or self.is_input
         self.is_output = self.data["node"].get("is_output") or self.is_output
         template_dicts = {key: value for key, value in self.data["node"]["template"].items() if isinstance(value, dict)}
 
         self.has_session_id = "session_id" in template_dicts
 
-        self.required_inputs = [
-            template_dicts[key]["type"] for key, value in template_dicts.items() if value["required"]
-        ]
-        self.optional_inputs = [
-            template_dicts[key]["type"] for key, value in template_dicts.items() if not value["required"]
-        ]
-        # Add the template_dicts[key]["input_types"] to the optional_inputs
-        self.optional_inputs.extend(
-            [input_type for value in template_dicts.values() for input_type in value.get("input_types", [])]
-        )
+        self.required_inputs: list[str] = []
+        self.optional_inputs: list[str] = []
+        for value_dict in template_dicts.values():
+            list_to_append = self.required_inputs if value_dict.get("required") else self.optional_inputs
+
+            if "type" in value_dict:
+                list_to_append.append(value_dict["type"])
+            if "input_types" in value_dict:
+                list_to_append.extend(value_dict["input_types"])
 
         template_dict = self.data["node"]["template"]
         self.vertex_type = (
             self.data["type"]
-            if "Tool" not in self.output or template_dict["_type"].islower()
+            if "Tool" not in [type_ for out in self.outputs for type_ in out["types"]]
+            or template_dict["_type"].islower()
             else template_dict["_type"]
         )
 
@@ -277,7 +271,7 @@ class Vertex:
             # We check this to make sure params with the same name but different target_id
             # don't get overwritten
             if param_key in template_dict and edge.target_id == self.id:
-                if template_dict[param_key]["list"]:
+                if template_dict[param_key].get("list"):
                     if param_key not in params:
                         params[param_key] = []
                     params[param_key].append(self.graph.get_vertex(edge.source_id))
@@ -318,9 +312,15 @@ class Vertex:
                 elif field.get("required"):
                     field_display_name = field.get("display_name")
                     logger.warning(
-                        f"File path not found for {field_display_name} in component {self.display_name}. Setting to None."
+                        f"File path not found for {field_display_name} in component {self.display_name}. "
+                        "Setting to None."
                     )
                     params[field_name] = None
+                else:
+                    if field["list"]:
+                        params[field_name] = []
+                    else:
+                        params[field_name] = None
 
             elif field.get("type") in DIRECT_TYPES and params.get(field_name) is None:
                 val = field.get("value")
@@ -355,6 +355,11 @@ class Vertex:
                         params[field_name] = [unescape_string(v) for v in val]
                     elif isinstance(val, str):
                         params[field_name] = unescape_string(val)
+                elif field.get("type") == "bool" and val is not None:
+                    if isinstance(val, bool):
+                        params[field_name] = val
+                    elif isinstance(val, str):
+                        params[field_name] = val != ""
                 elif val is not None and val != "":
                     params[field_name] = val
 
@@ -452,6 +457,7 @@ class Vertex:
         result_dict = ResultData(
             results=result_dict,
             artifacts=artifacts,
+            logs=self.logs,
             messages=messages,
             component_display_name=self.display_name,
             component_id=self.id,
@@ -531,11 +537,11 @@ class Vertex:
         """
         flow_id = self.graph.flow_id
         if not self._built:
-            log_transaction(flow_id, vertex=self, target=requester, status="error")
+            log_transaction(flow_id, source=self, target=requester, status="error")
             raise ValueError(f"Component {self.display_name} has not been built yet")
 
         result = self._built_result if self.use_result else self._built_object
-        log_transaction(flow_id, vertex=self, target=requester, status="success")
+        log_transaction(flow_id, source=self, target=requester, status="success")
         return result
 
     async def _build_vertex_and_update_params(self, key, vertex: "Vertex"):
@@ -561,7 +567,7 @@ class Vertex:
         for vertex in vertices:
             result = await vertex.get_result(self)
             # Weird check to see if the params[key] is a list
-            # because sometimes it is a Record and breaks the code
+            # because sometimes it is a Data and breaks the code
             if not isinstance(self.params[key], list):
                 self.params[key] = [self.params[key]]
 
@@ -577,7 +583,7 @@ class Vertex:
                     logger.exception(e)
                     raise ValueError(
                         f"Params {key} ({self.params[key]}) is not a list and cannot be extended with {result}"
-                        f"Error building Component {self.display_name}: {str(e)}"
+                        f"Error building Component {self.display_name}:\n\n{str(e)}"
                     ) from e
 
     def _handle_func(self, key, result):
@@ -614,11 +620,12 @@ class Vertex:
                 fallback_to_env_vars=fallback_to_env_vars,
                 vertex=self,
             )
+            self.logs = build_logs(self, result)
             self._update_built_object_and_artifacts(result)
         except Exception as exc:
+            tb = traceback.format_exc()
             logger.exception(exc)
-
-            raise ValueError(f"Error building Component {self.display_name}: {str(exc)}") from exc
+            raise ComponentBuildException(f"Error building Component {self.display_name}:\n\n{exc}", tb) from exc
 
     def _update_built_object_and_artifacts(self, result):
         """
@@ -631,6 +638,7 @@ class Vertex:
                 self._custom_component, self._built_object, self.artifacts = result
                 self.artifacts_raw = self.artifacts.get("raw", None)
                 self.artifacts_type = self.artifacts.get("type", None) or ArtifactType.UNKNOWN.value
+                self.artifacts = {self.outputs[0]["name"]: self.artifacts}
         else:
             self._built_object = result
 
diff --git a/src/backend/base/langflow/graph/vertex/types.py b/src/backend/base/langflow/graph/vertex/types.py
index 5c698e144..b453aff50 100644
--- a/src/backend/base/langflow/graph/vertex/types.py
+++ b/src/backend/base/langflow/graph/vertex/types.py
@@ -1,20 +1,25 @@
 import json
-from typing import AsyncIterator, Dict, Iterator, List, Generator
+from typing import TYPE_CHECKING, Any, AsyncIterator, Dict, Generator, Iterator, List
 
 import yaml
 from langchain_core.messages import AIMessage, AIMessageChunk
 from loguru import logger
 
-from langflow.graph.schema import CHAT_COMPONENTS, RECORDS_COMPONENTS, InterfaceComponentTypes
-from langflow.graph.utils import ArtifactType, UnbuiltObject, serialize_field
+from langflow.graph.schema import CHAT_COMPONENTS, RECORDS_COMPONENTS, InterfaceComponentTypes, ResultData
+from langflow.graph.utils import UnbuiltObject, serialize_field
 from langflow.graph.vertex.base import Vertex
-from langflow.schema import Record
+from langflow.schema import Data
+from langflow.schema.artifact import ArtifactType
 from langflow.schema.message import Message
 from langflow.schema.schema import INPUT_FIELD_NAME
-from langflow.services.monitor.utils import log_vertex_build
-from langflow.utils.schemas import ChatOutputResponse, RecordOutputResponse
+from langflow.services.monitor.utils import log_transaction, log_vertex_build
+from langflow.template.field.base import UNDEFINED
+from langflow.utils.schemas import ChatOutputResponse, DataOutputResponse
 from langflow.utils.util import unescape_string
 
+if TYPE_CHECKING:
+    from langflow.graph.edge.base import ContractEdge
+
 
 class CustomComponentVertex(Vertex):
     def __init__(self, data: Dict, graph):
@@ -25,9 +30,135 @@ class CustomComponentVertex(Vertex):
             return self.artifacts["repr"] or super()._built_object_repr()
 
 
-class InterfaceVertex(Vertex):
+class ComponentVertex(Vertex):
     def __init__(self, data: Dict, graph):
-        super().__init__(data, graph=graph, base_type="custom_components", is_task=True)
+        super().__init__(data, graph=graph, base_type="component")
+
+    def _built_object_repr(self):
+        if self.artifacts and "repr" in self.artifacts:
+            return self.artifacts["repr"] or super()._built_object_repr()
+
+    def _update_built_object_and_artifacts(self, result):
+        """
+        Updates the built object and its artifacts.
+        """
+        if isinstance(result, tuple):
+            if len(result) == 2:
+                self._built_object, self.artifacts = result
+            elif len(result) == 3:
+                self._custom_component, self._built_object, self.artifacts = result
+                for key in self.artifacts:
+                    self.artifacts_raw[key] = self.artifacts[key].get("raw", None)
+                    self.artifacts_type[key] = self.artifacts[key].get("type", None) or ArtifactType.UNKNOWN.value
+        else:
+            self._built_object = result
+
+        for key, value in self._built_object.items():
+            self.add_result(key, value)
+
+    def get_edge_with_target(self, target_id: str) -> Generator["ContractEdge", None, None]:
+        """
+        Get the edge with the target id.
+
+        Args:
+            target_id: The target id of the edge.
+
+        Returns:
+            The edge with the target id.
+        """
+        for edge in self.edges:
+            if edge.target_id == target_id:
+                yield edge
+
+    async def _get_result(self, requester: "Vertex") -> Any:
+        """
+        Retrieves the result of the built component.
+
+        If the component has not been built yet, a ValueError is raised.
+
+        Returns:
+            The built result if use_result is True, else the built object.
+        """
+        if not self._built:
+            log_transaction(source=self, target=requester, flow_id=self.graph.flow_id, status="error")
+            raise ValueError(f"Component {self.display_name} has not been built yet")
+
+        if requester is None:
+            raise ValueError("Requester Vertex is None")
+
+        edges = self.get_edge_with_target(requester.id)
+        result = UNDEFINED
+        edge = None
+        for edge in edges:
+            if edge is not None and edge.source_handle.name in self.results:
+                result = self.results[edge.source_handle.name]
+                break
+        if result is UNDEFINED:
+            if edge is None:
+                raise ValueError(f"Edge not found between {self.display_name} and {requester.display_name}")
+            elif edge.source_handle.name not in self.results:
+                raise ValueError(f"Result not found for {edge.source_handle.name}. Results: {self.results}")
+            else:
+                raise ValueError(f"Result not found for {edge.source_handle.name}")
+        log_transaction(source=self, target=requester, flow_id=self.graph.flow_id, status="success")
+        return result
+
+    def extract_messages_from_artifacts(self, artifacts: Dict[str, Any]) -> List[dict]:
+        """
+        Extracts messages from the artifacts.
+
+        Args:
+            artifacts (Dict[str, Any]): The artifacts to extract messages from.
+
+        Returns:
+            List[str]: The extracted messages.
+        """
+        messages = []
+        for key in artifacts:
+            artifact = artifacts[key]
+            if any(
+                key not in artifact for key in ["text", "sender", "sender_name", "session_id", "stream_url"]
+            ) and not isinstance(artifact, Message):
+                continue
+            message_dict = artifact if isinstance(artifact, dict) else artifact.model_dump()
+            try:
+                messages.append(
+                    ChatOutputResponse(
+                        message=message_dict["text"],
+                        sender=message_dict.get("sender"),
+                        sender_name=message_dict.get("sender_name"),
+                        session_id=message_dict.get("session_id"),
+                        stream_url=message_dict.get("stream_url"),
+                        files=[
+                            {"path": file} if isinstance(file, str) else file for file in message_dict.get("files", [])
+                        ],
+                        component_id=self.id,
+                        type=self.artifacts_type[key],
+                    ).model_dump(exclude_none=True)
+                )
+            except KeyError:
+                pass
+        return messages
+
+    def _finalize_build(self):
+        result_dict = self.get_built_result()
+        # We need to set the artifacts to pass information
+        # to the frontend
+        messages = self.extract_messages_from_artifacts(result_dict)
+        result_dict = ResultData(
+            results=result_dict,
+            artifacts=self.artifacts,
+            logs=self.logs,
+            messages=messages,
+            component_display_name=self.display_name,
+            component_id=self.id,
+        )
+        self.set_result(result_dict)
+
+
+class InterfaceVertex(ComponentVertex):
+    def __init__(self, data: Dict, graph):
+        super().__init__(data, graph=graph)
         self.steps = [self._build, self._run]
 
     def build_stream_url(self):
@@ -43,8 +174,8 @@ class InterfaceVertex(Vertex):
             # dump as a yaml string
             if isinstance(self.artifacts, dict):
                 _artifacts = [self.artifacts]
-            elif hasattr(self.artifacts, "records"):
-                _artifacts = self.artifacts.records
+            elif hasattr(self.artifacts, "data"):
+                _artifacts = self.artifacts.data
             else:
                 _artifacts = self.artifacts
             artifacts = []
@@ -67,7 +198,7 @@ class InterfaceVertex(Vertex):
         object using the `from_message` method. If `_built_object` is not an instance of
         `UnbuiltObject`, it checks the type of `_built_object` and performs specific
         operations accordingly. If `_built_object` is a dictionary, it converts it into a
-        code block. If `_built_object` is an instance of `Record`, it assigns the `text`
+        code block. If `_built_object` is an instance of `Data`, it assigns the `text`
         attribute to the `message` variable. If `message` is an instance of `AsyncIterator`
         or `Iterator`, it builds a stream URL and sets `message` to an empty string. If
         `_built_object` is not a string, it converts it to a string. If `message` is a
@@ -88,31 +219,37 @@ class InterfaceVertex(Vertex):
         if isinstance(message, str):
             message = unescape_string(message)
         stream_url = None
-        if isinstance(self._built_object, (AIMessage, AIMessageChunk)):
+        if "text" in self.results:
+            text_output = self.results["text"]
+        elif "message" in self.results:
+            text_output = self.results["message"].text
+        else:
+            text_output = message
+        if isinstance(text_output, (AIMessage, AIMessageChunk)):
             artifacts = ChatOutputResponse.from_message(
-                self._built_object,
+                text_output,
                 sender=sender,
                 sender_name=sender_name,
             )
-        elif not isinstance(self._built_object, UnbuiltObject):
-            if isinstance(self._built_object, dict):
+        elif not isinstance(text_output, UnbuiltObject):
+            if isinstance(text_output, dict):
                 # Turn the dict into a pleasing to
                 # read JSON inside a code block
-                message = dict_to_codeblock(self._built_object)
-            elif isinstance(self._built_object, (Message, Generator)):
-                if isinstance(message, (AsyncIterator, Iterator, Generator)):
-                    stream_url = self.build_stream_url()
-                    message = ""
-                    if hasattr(self._built_object, "text"):
-                        self._built_object.text = message
-                else:
-                    message = self._built_object.text
-            elif not isinstance(self._built_object, str):
-                message = str(self._built_object)
+                message = dict_to_codeblock(text_output)
+            elif isinstance(text_output, Data):
+                message = text_output.text
+            elif isinstance(message, (AsyncIterator, Iterator)):
+                stream_url = self.build_stream_url()
+                message = ""
+                self.results["text"] = message
+                self.results["message"].text = message
+                self._built_object = self.results
+            elif not isinstance(text_output, str):
+                message = str(text_output)
             # if the message is a generator or iterator
             # it means that it is a stream of messages
             else:
-                message = self._built_object
+                message = text_output
             artifact_type = ArtifactType.STREAM if stream_url is not None else ArtifactType.OBJECT
             artifacts = ChatOutputResponse(
                 message=message,
@@ -129,15 +266,15 @@ class InterfaceVertex(Vertex):
 
         return message
 
-    def _process_record_component(self):
+    def _process_data_component(self):
         """
         Process the record component of the vertex.
 
-        If the built object is an instance of `Record`, it calls the `model_dump` method
+        If the built object is an instance of `Data`, it calls the `model_dump` method
         and assigns the result to the `artifacts` attribute.
 
         If the built object is a list, it iterates over each element and checks if it is
-        an instance of `Record`. If it is, it calls the `model_dump` method and appends
+        an instance of `Data`. If it is, it calls the `model_dump` method and appends
         the result to the `artifacts` list. If it is not, it raises a `ValueError` if the
         `ignore_errors` parameter is set to `False`, or logs an error message if it is set
         to `True`.
@@ -146,22 +283,22 @@ class InterfaceVertex(Vertex):
             The built object.
 
         Raises:
-            ValueError: If an element in the list is not an instance of `Record` and
+            ValueError: If an element in the list is not an instance of `Data` and
                 `ignore_errors` is set to `False`.
         """
-        if isinstance(self._built_object, Record):
+        if isinstance(self._built_object, Data):
             artifacts = [self._built_object.data]
         elif isinstance(self._built_object, list):
             artifacts = []
             ignore_errors = self.params.get("ignore_errors", False)
-            for record in self._built_object:
-                if isinstance(record, Record):
-                    artifacts.append(record.data)
+            for value in self._built_object:
+                if isinstance(value, Data):
+                    artifacts.append(value.data)
                 elif ignore_errors:
-                    logger.error(f"Record expected, but got {record} of type {type(record)}")
+                    logger.error(f"Data expected, but got {value} of type {type(value)}")
                 else:
-                    raise ValueError(f"Record expected, but got {record} of type {type(record)}")
-        self.artifacts = RecordOutputResponse(records=artifacts)
+                    raise ValueError(f"Data expected, but got {value} of type {type(value)}")
+        self.artifacts = DataOutputResponse(data=artifacts)
         return self._built_object
 
     async def _run(self, *args, **kwargs):
@@ -169,10 +306,10 @@ class InterfaceVertex(Vertex):
             if self.vertex_type in CHAT_COMPONENTS:
                 message = self._process_chat_component()
             elif self.vertex_type in RECORDS_COMPONENTS:
-                message = self._process_record_component()
+                message = self._process_data_component()
             if isinstance(self._built_object, (AsyncIterator, Iterator)):
-                if self.params.get("return_record", False):
-                    self._built_object = Record(text=message, data=self.artifacts)
+                if self.params.get("return_data", False):
+                    self._built_object = Data(text=message, data=self.artifacts)
                 else:
                     self._built_object = message
             self._built_result = self._built_object
@@ -205,13 +342,35 @@ class InterfaceVertex(Vertex):
             files=[{"path": file} if isinstance(file, str) else file for file in self.params.get("files", [])],
             type=ArtifactType.OBJECT.value,
         ).model_dump()
+
+        message = Message(
+            text=complete_message,
+            sender=self.params.get("sender", ""),
+            sender_name=self.params.get("sender_name", ""),
+            files=self.params.get("files", []),
+            flow_id=self.graph.flow_id,
+            session_id=self.params.get("session_id", ""),
+        )
         self.params[INPUT_FIELD_NAME] = complete_message
-        self._built_object = Record(text=complete_message, data=self.artifacts)
-        self._built_result = complete_message
+        if isinstance(self._built_object, dict):
+            for key, value in self._built_object.items():
+                if hasattr(value, "text") and (isinstance(value.text, (AsyncIterator, Iterator)) or value.text == ""):
+                    self._built_object[key] = message
+        else:
+            self._built_object = message
+            self.artifacts_type = ArtifactType.MESSAGE
+
         # Update artifacts with the message
         # and remove the stream_url
         self._finalize_build()
         logger.debug(f"Streamed message: {complete_message}")
+        # Set the result in the vertex of origin
+        edges = self.get_edge_with_target(self.id)
+        for edge in edges:
+            origin_vertex = self.graph.get_vertex(edge.source_id)
+            for key, value in origin_vertex.results.items():
+                if isinstance(value, (AsyncIterator, Iterator)):
+                    origin_vertex.results[key] = complete_message
 
         await log_vertex_build(
             flow_id=self.graph.flow_id,
@@ -244,6 +403,10 @@ class StateVertex(Vertex):
         successors = self.graph.successor_map.get(self.id, [])
         return successors + self.graph.activated_vertices
 
+    def _built_object_repr(self):
+        if self.artifacts and "repr" in self.artifacts:
+            return self.artifacts["repr"] or super()._built_object_repr()
+
 
 def dict_to_codeblock(d: dict) -> str:
     serialized = {key: serialize_field(val) for key, val in d.items()}
diff --git a/src/backend/base/langflow/helpers/__init__.py b/src/backend/base/langflow/helpers/__init__.py
index 38b460af2..70c5733f6 100644
--- a/src/backend/base/langflow/helpers/__init__.py
+++ b/src/backend/base/langflow/helpers/__init__.py
@@ -1,3 +1,3 @@
-from .record import docs_to_records, records_to_text, messages_to_text
+from .data import data_to_text, docs_to_data, messages_to_text
 
-__all__ = ["docs_to_records", "records_to_text", "messages_to_text"]
+__all__ = ["docs_to_data", "data_to_text", "messages_to_text"]
diff --git a/src/backend/base/langflow/helpers/custom.py b/src/backend/base/langflow/helpers/custom.py
new file mode 100644
index 000000000..bdbb128f4
--- /dev/null
+++ b/src/backend/base/langflow/helpers/custom.py
@@ -0,0 +1,13 @@
+from typing import Any
+
+
+def format_type(type_: Any) -> str:
+    if type_ == str:
+        type_ = "Text"
+    elif hasattr(type_, "__name__"):
+        type_ = type_.__name__
+    elif hasattr(type_, "__class__"):
+        type_ = type_.__class__.__name__
+    else:
+        type_ = str(type_)
+    return type_
diff --git a/src/backend/base/langflow/helpers/record.py b/src/backend/base/langflow/helpers/data.py
similarity index 60%
rename from src/backend/base/langflow/helpers/record.py
rename to src/backend/base/langflow/helpers/data.py
index 88d0bcd13..381037078 100644
--- a/src/backend/base/langflow/helpers/record.py
+++ b/src/backend/base/langflow/helpers/data.py
@@ -2,45 +2,45 @@ from typing import Union
 
 from langchain_core.documents import Document
 
-from langflow.schema import Record
+from langflow.schema import Data
 from langflow.schema.message import Message
 
 
-def docs_to_records(documents: list[Document]) -> list[Record]:
+def docs_to_data(documents: list[Document]) -> list[Data]:
     """
-    Converts a list of Documents to a list of Records.
+    Converts a list of Documents to a list of Data.
 
     Args:
         documents (list[Document]): The list of Documents to convert.
 
     Returns:
-        list[Record]: The converted list of Records.
+        list[Data]: The converted list of Data.
     """
-    return [Record.from_document(document) for document in documents]
+    return [Data.from_document(document) for document in documents]
 
 
-def records_to_text(template: str, records: Union[Record, list[Record]]) -> str:
+def data_to_text(template: str, data: Union[Data, list[Data]], sep: str = "\n") -> str:
     """
-    Converts a list of Records to a list of texts.
+    Converts a list of Data to a list of texts.
 
     Args:
-        records (list[Record]): The list of Records to convert.
+        data (list[Data]): The list of Data to convert.
 
     Returns:
         list[str]: The converted list of texts.
     """
-    if isinstance(records, (Record)):
-        records = [records]
+    if isinstance(data, (Data)):
+        data = [data]
     # Check if there are any format strings in the template
-    _records = []
-    for record in records:
+    _data = []
+    for value in data:
         # If it is not a record, create one with the key "text"
-        if not isinstance(record, Record):
-            record = Record(text=record)
-        _records.append(record)
+        if not isinstance(value, Data):
+            value = Data(text=value)
+        _data.append(value)
 
-    formated_records = [template.format(data=record.data, **record.data) for record in _records]
-    return "\n".join(formated_records)
+    formated_data = [template.format(data=value.data, **value.data) for value in _data]
+    return sep.join(formated_data)
 
 
 def messages_to_text(template: str, messages: Union[Message, list[Message]]) -> str:
diff --git a/src/backend/base/langflow/helpers/flow.py b/src/backend/base/langflow/helpers/flow.py
index 61674942a..9507ff7be 100644
--- a/src/backend/base/langflow/helpers/flow.py
+++ b/src/backend/base/langflow/helpers/flow.py
@@ -6,7 +6,7 @@ from pydantic.v1 import BaseModel, Field, create_model
 from sqlmodel import Session, select
 
 from langflow.graph.schema import RunOutputs
-from langflow.schema import Record
+from langflow.schema import Data
 from langflow.schema.schema import INPUT_FIELD_NAME
 from langflow.services.database.models.flow import Flow
 from langflow.services.deps import get_session, get_settings_service, session_scope
@@ -22,7 +22,7 @@ INPUT_TYPE_MAP = {
 }
 
 
-def list_flows(*, user_id: Optional[str] = None) -> List[Record]:
+def list_flows(*, user_id: Optional[str] = None) -> List[Data]:
     if not user_id:
         raise ValueError("Session is invalid")
     try:
@@ -31,8 +31,8 @@ def list_flows(*, user_id: Optional[str] = None) -> List[Record]:
                 select(Flow).where(Flow.user_id == user_id).where(Flow.is_component == False)  # noqa
             ).all()
 
-            flows_records = [flow.to_record() for flow in flows]
-            return flows_records
+            flows_data = [flow.to_data() for flow in flows]
+            return flows_data
     except Exception as e:
         raise ValueError(f"Error listing flows: {e}")
 
@@ -142,7 +142,7 @@ async def flow_function({func_args}):
     tweaks = {{ {arg_mappings} }}
     from langflow.helpers.flow import run_flow
     from langchain_core.tools import ToolException
-    from langflow.base.flow_processing.utils import build_records_from_result_data, format_flow_output_records
+    from langflow.base.flow_processing.utils import build_data_from_result_data, format_flow_output_data
     try:
         run_outputs = await run_flow(
             tweaks={{key: {{'input_value': value}} for key, value in tweaks.items()}},
@@ -153,12 +153,12 @@ async def flow_function({func_args}):
                 return []
         run_output = run_outputs[0]
 
-        records = []
+        data = []
         if run_output is not None:
             for output in run_output.outputs:
                 if output:
-                    records.extend(build_records_from_result_data(output, get_final_results_only=True))
-        return format_flow_output_records(records)
+                    data.extend(build_data_from_result_data(output, get_final_results_only=True))
+        return format_flow_output_data(data)
     except Exception as e:
         raise ToolException(f'Error running flow: ' + e)
 """
@@ -170,22 +170,22 @@ async def flow_function({func_args}):
 
 
 def build_function_and_schema(
-    flow_record: Record, graph: "Graph", user_id: str | UUID | None
+    flow_data: Data, graph: "Graph", user_id: str | UUID | None
 ) -> Tuple[Callable[..., Awaitable[Any]], Type[BaseModel]]:
     """
     Builds a dynamic function and schema for a given flow.
 
     Args:
-        flow_record (Record): The flow record containing information about the flow.
+        flow_data (Data): The flow record containing information about the flow.
         graph (Graph): The graph representing the flow.
 
     Returns:
         Tuple[Callable, BaseModel]: A tuple containing the dynamic function and the schema.
     """
-    flow_id = flow_record.id
+    flow_id = flow_data.id
     inputs = get_flow_inputs(graph)
     dynamic_flow_function = generate_function_for_flow(inputs, flow_id, user_id=user_id)
-    schema = build_schema_from_inputs(flow_record.name, inputs)
+    schema = build_schema_from_inputs(flow_data.name, inputs)
     return dynamic_flow_function, schema
 
 
@@ -197,7 +197,7 @@ def get_flow_inputs(graph: "Graph") -> List["Vertex"]:
         graph (Graph): The graph object representing the flow.
 
     Returns:
-        List[Record]: A list of input records, where each record contains the ID, name, and description of the input vertex.
+        List[Data]: A list of input data, where each record contains the ID, name, and description of the input vertex.
     """
     inputs = []
     for vertex in graph.vertices:
diff --git a/src/backend/base/langflow/initial_setup/setup.py b/src/backend/base/langflow/initial_setup/setup.py
index 8209a9d00..075768db4 100644
--- a/src/backend/base/langflow/initial_setup/setup.py
+++ b/src/backend/base/langflow/initial_setup/setup.py
@@ -1,3 +1,4 @@
+import copy
 import json
 import os
 import shutil
@@ -5,6 +6,7 @@ from collections import defaultdict
 from copy import deepcopy
 from datetime import datetime, timezone
 from pathlib import Path
+from typing import Awaitable
 from uuid import UUID
 
 import orjson
@@ -13,13 +15,14 @@ from loguru import logger
 from sqlmodel import select
 
 from langflow.base.constants import FIELD_FORMAT_ATTRIBUTES, NODE_FORMAT_ATTRIBUTES, ORJSON_OPTIONS
-from langflow.interface.types import get_all_components
+from langflow.graph.graph.base import Graph
 from langflow.services.auth.utils import create_super_user
 from langflow.services.database.models.flow.model import Flow, FlowCreate
 from langflow.services.database.models.folder.model import Folder, FolderCreate
 from langflow.services.database.models.folder.utils import create_default_folder_if_it_doesnt_exist
 from langflow.services.database.models.user.crud import get_user_by_username
 from langflow.services.deps import get_settings_service, get_storage_service, get_variable_service, session_scope
+from langflow.template.field.prompt import DEFAULT_PROMPT_INTUT_TYPES
 
 STARTER_FOLDER_NAME = "Starter Projects"
 STARTER_FOLDER_DESCRIPTION = "Starter projects to help you get started in Langflow."
@@ -43,50 +46,182 @@ def update_projects_components_with_latest_component_versions(project_data, all_
             latest_template = latest_node.get("template")
             node_data["template"]["code"] = latest_template["code"]
 
-            for attr in NODE_FORMAT_ATTRIBUTES:
-                if attr in latest_node:
-                    # Check if it needs to be updated
-                    if latest_node[attr] != node_data.get(attr):
-                        node_changes_log[node_data["display_name"]].append(
-                            {
-                                "attr": attr,
-                                "old_value": node_data.get(attr),
-                                "new_value": latest_node[attr],
-                            }
-                        )
-                        node_data[attr] = latest_node[attr]
-
-            for field_name, field_dict in latest_template.items():
-                if field_name not in node_data["template"]:
-                    continue
-                # The idea here is to update some attributes of the field
-                for attr in FIELD_FORMAT_ATTRIBUTES:
-                    if attr in field_dict and attr in node_data["template"].get(field_name):
-                        # Check if it needs to be updated
-                        if field_dict[attr] != node_data["template"][field_name][attr]:
+            if "outputs" in latest_node:
+                node_data["outputs"] = latest_node["outputs"]
+            if node_data["template"]["_type"] != latest_template["_type"]:
+                node_data["template"]["_type"] = latest_template["_type"]
+                if node_data.get("display_name") != "Prompt":
+                    node_data["template"] = latest_template
+                else:
+                    for key, value in latest_template.items():
+                        if key not in node_data["template"]:
                             node_changes_log[node_data["display_name"]].append(
                                 {
-                                    "attr": f"{field_name}.{attr}",
-                                    "old_value": node_data["template"][field_name][attr],
-                                    "new_value": field_dict[attr],
+                                    "attr": key,
+                                    "old_value": None,
+                                    "new_value": value,
                                 }
                             )
+                            node_data["template"][key] = value
+                        elif isinstance(value, dict) and value.get("value"):
+                            node_changes_log[node_data["display_name"]].append(
+                                {
+                                    "attr": key,
+                                    "old_value": node_data["template"][key],
+                                    "new_value": value,
+                                }
+                            )
+                            node_data["template"][key]["value"] = value["value"]
+                    for key, value in node_data["template"].items():
+                        if key not in latest_template:
+                            node_data["template"][key]["input_types"] = DEFAULT_PROMPT_INTUT_TYPES
+                node_changes_log[node_data["display_name"]].append(
+                    {
+                        "attr": "_type",
+                        "old_value": node_data["template"]["_type"],
+                        "new_value": latest_template["_type"],
+                    }
+                )
+            else:
+                for attr in NODE_FORMAT_ATTRIBUTES:
+                    if attr in latest_node:
+                        # Check if it needs to be updated
+                        if latest_node[attr] != node_data.get(attr):
+                            node_changes_log[node_data["display_name"]].append(
+                                {
+                                    "attr": attr,
+                                    "old_value": node_data.get(attr),
+                                    "new_value": latest_node[attr],
+                                }
+                            )
+                            node_data[attr] = latest_node[attr]
+
+                for field_name, field_dict in latest_template.items():
+                    if field_name not in node_data["template"]:
+                        continue
+                    # The idea here is to update some attributes of the field
+                    for attr in FIELD_FORMAT_ATTRIBUTES:
+                        if attr in field_dict and attr in node_data["template"].get(field_name):
+                            # Check if it needs to be updated
+                            if field_dict[attr] != node_data["template"][field_name][attr]:
+                                node_changes_log[node_data["display_name"]].append(
+                                    {
+                                        "attr": f"{field_name}.{attr}",
+                                        "old_value": node_data["template"][field_name][attr],
+                                        "new_value": field_dict[attr],
+                                    }
+                                )
+                                node_data["template"][field_name][attr] = field_dict[attr]
                             node_data["template"][field_name][attr] = field_dict[attr]
             # Remove fields that are not in the latest template
             if node_data.get("display_name") != "Prompt":
                 for field_name in list(node_data["template"].keys()):
                     if field_name not in latest_template:
                         node_data["template"].pop(field_name)
+    project_data_copy = update_new_output(project_data_copy)
     log_node_changes(node_changes_log)
     return project_data_copy
 
 
+def scape_json_parse(json_string: str) -> dict:
+    if isinstance(json_string, dict):
+        return json_string
+    parsed_string = json_string.replace("œ", '"')
+    return json.loads(parsed_string)
+
+
+def update_new_output(data):
+    nodes = copy.deepcopy(data["nodes"])
+    edges = copy.deepcopy(data["edges"])
+
+    for edge in edges:
+        if "sourceHandle" in edge and "targetHandle" in edge:
+            new_source_handle = scape_json_parse(edge["sourceHandle"])
+            new_target_handle = scape_json_parse(edge["targetHandle"])
+            _id = new_source_handle["id"]
+            source_node_index = next((index for (index, d) in enumerate(nodes) if d["id"] == _id), -1)
+            source_node = nodes[source_node_index] if source_node_index != -1 else None
+
+            if "baseClasses" in new_source_handle:
+                if "output_types" not in new_source_handle:
+                    if source_node and "node" in source_node["data"] and "output_types" in source_node["data"]["node"]:
+                        new_source_handle["output_types"] = source_node["data"]["node"]["output_types"]
+                    else:
+                        new_source_handle["output_types"] = new_source_handle["baseClasses"]
+                del new_source_handle["baseClasses"]
+
+            if "inputTypes" in new_target_handle and new_target_handle["inputTypes"]:
+                intersection = [
+                    type_ for type_ in new_source_handle["output_types"] if type_ in new_target_handle["inputTypes"]
+                ]
+            else:
+                intersection = [
+                    type_ for type_ in new_source_handle["output_types"] if type_ == new_target_handle["type"]
+                ]
+
+            selected = intersection[0] if intersection else None
+            if "name" not in new_source_handle:
+                new_source_handle["name"] = " | ".join(new_source_handle["output_types"])
+            new_source_handle["output_types"] = [selected] if selected else []
+
+            if source_node and not source_node["data"]["node"].get("outputs"):
+                if "outputs" not in source_node["data"]["node"]:
+                    source_node["data"]["node"]["outputs"] = []
+                types = source_node["data"]["node"].get(
+                    "output_types", source_node["data"]["node"].get("base_classes", [])
+                )
+                if not any(output.get("selected") == selected for output in source_node["data"]["node"]["outputs"]):
+                    source_node["data"]["node"]["outputs"].append(
+                        {
+                            "types": types,
+                            "selected": selected,
+                            "name": " | ".join(types),
+                            "display_name": " | ".join(types),
+                        }
+                    )
+            deduplicated_outputs = []
+            if source_node is None:
+                source_node = {"data": {"node": {"outputs": []}}}
+
+            for output in source_node["data"]["node"]["outputs"]:
+                if output["name"] not in [d["name"] for d in deduplicated_outputs]:
+                    deduplicated_outputs.append(output)
+            source_node["data"]["node"]["outputs"] = deduplicated_outputs
+
+            edge["sourceHandle"] = escape_json_dump(new_source_handle)
+            edge["data"]["sourceHandle"] = new_source_handle
+            edge["data"]["targetHandle"] = new_target_handle
+    # The above sets the edges but some of the sourceHandles do not have valid name
+    # which can be found in the nodes. We need to update the sourceHandle with the
+    # name from node['data']['node']['outputs']
+    for node in nodes:
+        if "outputs" in node["data"]["node"]:
+            for output in node["data"]["node"]["outputs"]:
+                for edge in edges:
+                    if node["id"] != edge["source"] or output.get("method") is None:
+                        continue
+                    source_handle = scape_json_parse(edge["sourceHandle"])
+                    if source_handle["output_types"] == output.get("types") and source_handle["name"] != output["name"]:
+                        source_handle["name"] = output["name"]
+                        if isinstance(source_handle, str):
+                            source_handle = scape_json_parse(source_handle)
+                        edge["sourceHandle"] = escape_json_dump(source_handle)
+                        edge["data"]["sourceHandle"] = source_handle
+
+    data_copy = copy.deepcopy(data)
+    data_copy["nodes"] = nodes
+    data_copy["edges"] = edges
+    return data_copy
+
+
 def update_edges_with_latest_component_versions(project_data):
     edge_changes_log = defaultdict(list)
     project_data_copy = deepcopy(project_data)
     for edge in project_data_copy.get("edges", []):
         source_handle = edge.get("data").get("sourceHandle")
+        source_handle = scape_json_parse(source_handle)
         target_handle = edge.get("data").get("targetHandle")
+        target_handle = scape_json_parse(target_handle)
         # Now find the source and target nodes in the nodes list
         source_node = next(
             (node for node in project_data.get("nodes", []) if node.get("id") == edge.get("source")), None
@@ -97,16 +232,40 @@ def update_edges_with_latest_component_versions(project_data):
         if source_node and target_node:
             source_node_data = source_node.get("data").get("node")
             target_node_data = target_node.get("data").get("node")
-            new_base_classes = source_node_data.get("base_classes")
-            if source_handle["baseClasses"] != new_base_classes:
+            output_data = next(
+                (output for output in source_node_data.get("outputs", []) if output["name"] == source_handle["name"]),
+                None,
+            )
+            if not output_data:
+                output_data = next(
+                    (
+                        output
+                        for output in source_node_data.get("outputs", [])
+                        if output["display_name"] == source_handle["name"]
+                    ),
+                    None,
+                )
+                if output_data:
+                    source_handle["name"] = output_data["name"]
+            if output_data:
+                if len(output_data.get("types")) == 1:
+                    new_output_types = output_data.get("types")
+                elif output_data.get("selected"):
+                    new_output_types = [output_data.get("selected")]
+                else:
+                    new_output_types = []
+            else:
+                new_output_types = []
+
+            if source_handle["output_types"] != new_output_types:
                 edge_changes_log[source_node_data["display_name"]].append(
                     {
-                        "attr": "baseClasses",
-                        "old_value": source_handle["baseClasses"],
-                        "new_value": new_base_classes,
+                        "attr": "output_types",
+                        "old_value": source_handle["output_types"],
+                        "new_value": new_output_types,
                     }
                 )
-                source_handle["baseClasses"] = new_base_classes
+                source_handle["output_types"] = new_output_types
 
             field_name = target_handle.get("fieldName")
             if field_name in target_node_data.get("template"):
@@ -121,20 +280,30 @@ def update_edges_with_latest_component_versions(project_data):
                     target_handle["inputTypes"] = target_node_data.get("template").get(field_name).get("input_types")
             escaped_source_handle = escape_json_dump(source_handle)
             escaped_target_handle = escape_json_dump(target_handle)
-            if edge["sourceHandle"] != escaped_source_handle:
+            try:
+                old_escape_source_handle = escape_json_dump(json.loads(edge["sourceHandle"]))
+
+            except json.JSONDecodeError:
+                old_escape_source_handle = edge["sourceHandle"]
+
+            try:
+                old_escape_target_handle = escape_json_dump(json.loads(edge["targetHandle"]))
+            except json.JSONDecodeError:
+                old_escape_target_handle = edge["targetHandle"]
+            if old_escape_source_handle != escaped_source_handle:
                 edge_changes_log[source_node_data["display_name"]].append(
                     {
                         "attr": "sourceHandle",
-                        "old_value": edge["sourceHandle"],
+                        "old_value": old_escape_source_handle,
                         "new_value": escaped_source_handle,
                     }
                 )
                 edge["sourceHandle"] = escaped_source_handle
-            if edge["targetHandle"] != escaped_target_handle:
+            if old_escape_target_handle != escaped_target_handle:
                 edge_changes_log[target_node_data["display_name"]].append(
                     {
                         "attr": "targetHandle",
-                        "old_value": edge["targetHandle"],
+                        "old_value": old_escape_target_handle,
                         "new_value": escaped_target_handle,
                     }
                 )
@@ -368,10 +537,9 @@ def find_existing_flow(session, flow_id, flow_endpoint_name):
     return None
 
 
-def create_or_update_starter_projects():
-    components_paths = get_settings_service().settings.components_path
+async def create_or_update_starter_projects(get_all_components_coro: Awaitable[dict]):
     try:
-        all_types_dict = get_all_components(components_paths, as_dict=True)
+        all_types_dict = await get_all_components_coro
     except Exception as e:
         logger.exception(f"Error loading components: {e}")
         raise e
@@ -394,6 +562,10 @@ def create_or_update_starter_projects():
                 project_data, all_types_dict
             )
             updated_project_data = update_edges_with_latest_component_versions(updated_project_data)
+            try:
+                Graph.from_payload(updated_project_data)
+            except Exception as e:
+                logger.error(e)
             if updated_project_data != project_data:
                 project_data = updated_project_data
                 # We also need to update the project data in the file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json
index b03836d2a..9a7b17413 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json	
@@ -5,69 +5,70 @@
         "className": "",
         "data": {
           "sourceHandle": {
-            "baseClasses": ["object", "Text", "str"],
-            "dataType": "OpenAIModel",
-            "id": "OpenAIModel-NDBjF"
+            "dataType": "ChatInput",
+            "id": "ChatInput-F6inY",
+            "name": "message",
+            "output_types": ["Message"]
           },
           "targetHandle": {
-            "fieldName": "input_value",
-            "id": "ChatOutput-JkVmc",
-            "inputTypes": ["Text"],
+            "fieldName": "user_input",
+            "id": "Prompt-graFQ",
+            "inputTypes": ["Document", "Message", "Record", "Text"],
             "type": "str"
           }
         },
-        "id": "reactflow__edge-OpenAIModel-NDBjF{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-NDBjFœ}-ChatOutput-JkVmc{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-JkVmcœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
-        "source": "OpenAIModel-NDBjF",
-        "sourceHandle": "{œbaseClassesœ: [œobjectœ, œTextœ, œstrœ], œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-NDBjFœ}",
+        "id": "reactflow__edge-ChatInput-F6inY{œdataTypeœ:œChatInputœ,œidœ:œChatInput-F6inYœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Prompt-graFQ{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-graFQœ,œinputTypesœ:[œDocumentœ,œMessageœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
+        "source": "ChatInput-F6inY",
+        "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-F6inYœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
         "style": {
           "stroke": "#555"
         },
-        "target": "ChatOutput-JkVmc",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-JkVmcœ, œinputTypesœ: [œTextœ], œtypeœ: œstrœ}"
+        "target": "Prompt-graFQ",
+        "targetHandle": "{œfieldNameœ: œuser_inputœ, œidœ: œPrompt-graFQœ, œinputTypesœ: [œDocumentœ, œMessageœ, œRecordœ, œTextœ], œtypeœ: œstrœ}"
       },
       {
         "className": "",
         "data": {
           "sourceHandle": {
-            "baseClasses": ["object", "str", "Text"],
             "dataType": "Prompt",
-            "id": "Prompt-WSII4"
+            "id": "Prompt-graFQ",
+            "name": "prompt",
+            "output_types": ["Message"]
           },
           "targetHandle": {
             "fieldName": "input_value",
-            "id": "OpenAIModel-NDBjF",
-            "inputTypes": ["Text", "Record", "Prompt"],
+            "id": "OpenAIModel-5alpQ",
+            "inputTypes": ["Message"],
             "type": "str"
           }
         },
-        "id": "reactflow__edge-Prompt-WSII4{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-WSII4œ}-OpenAIModel-NDBjF{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-NDBjFœ,œinputTypesœ:[œTextœ,œRecordœ,œPromptœ],œtypeœ:œstrœ}",
-        "source": "Prompt-WSII4",
-        "sourceHandle": "{œbaseClassesœ: [œobjectœ, œstrœ, œTextœ], œdataTypeœ: œPromptœ, œidœ: œPrompt-WSII4œ}",
-        "style": {
-          "stroke": "#555"
-        },
-        "target": "OpenAIModel-NDBjF",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-NDBjFœ, œinputTypesœ: [œTextœ, œRecordœ, œPromptœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-Prompt-graFQ{œdataTypeœ:œPromptœ,œidœ:œPrompt-graFQœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-5alpQ{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-5alpQœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+        "source": "Prompt-graFQ",
+        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-graFQœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "OpenAIModel-5alpQ",
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-5alpQœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
       },
       {
+        "className": "",
         "data": {
           "sourceHandle": {
-            "baseClasses": ["Message", "object", "str", "Text"],
-            "dataType": "ChatInput",
-            "id": "ChatInput-kltLA"
+            "dataType": "OpenAIModel",
+            "id": "OpenAIModel-5alpQ",
+            "name": "text_output",
+            "output_types": ["Message"]
           },
           "targetHandle": {
-            "fieldName": "user_input",
-            "id": "Prompt-WSII4",
-            "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"],
+            "fieldName": "input_value",
+            "id": "ChatOutput-axUHy",
+            "inputTypes": ["Message", "str"],
             "type": "str"
           }
         },
-        "id": "reactflow__edge-ChatInput-kltLA{œbaseClassesœ:[œMessageœ,œobjectœ,œstrœ,œTextœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-kltLAœ}-Prompt-WSII4{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-WSII4œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
-        "source": "ChatInput-kltLA",
-        "sourceHandle": "{œbaseClassesœ: [œMessageœ, œobjectœ, œstrœ, œTextœ], œdataTypeœ: œChatInputœ, œidœ: œChatInput-kltLAœ}",
-        "target": "Prompt-WSII4",
-        "targetHandle": "{œfieldNameœ: œuser_inputœ, œidœ: œPrompt-WSII4œ, œinputTypesœ: [œDocumentœ, œBaseOutputParserœ, œRecordœ, œTextœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-OpenAIModel-5alpQ{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-5alpQœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-axUHy{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-axUHyœ,œinputTypesœ:[œMessageœ,œstrœ],œtypeœ:œstrœ}",
+        "source": "OpenAIModel-5alpQ",
+        "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-5alpQœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "ChatOutput-axUHy",
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-axUHyœ, œinputTypesœ: [œMessageœ, œstrœ], œtypeœ: œstrœ}"
       }
     ],
     "nodes": [
@@ -75,7 +76,7 @@
         "data": {
           "description": "Create a prompt template with dynamic variables.",
           "display_name": "Prompt",
-          "id": "Prompt-WSII4",
+          "id": "Prompt-graFQ",
           "node": {
             "base_classes": ["object", "str", "Text"],
             "beta": false,
@@ -95,9 +96,21 @@
             "is_input": null,
             "is_output": null,
             "name": "",
-            "output_types": ["Prompt"],
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Prompt Message",
+                "hidden": false,
+                "method": "build_prompt",
+                "name": "prompt",
+                "selected": "Message",
+                "types": ["Message"],
+                "value": "__UNDEFINED__"
+              }
+            ],
             "template": {
-              "_type": "CustomComponent",
+              "_type": "Component",
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -114,7 +127,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import CustomComponent\nfrom langflow.field_typing import TemplateField\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    async def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Prompt:\n        prompt = await Prompt.from_template_and_variables(template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
+                "value": "from langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n"
               },
               "template": {
                 "advanced": false,
@@ -144,12 +157,7 @@
                 "fileTypes": [],
                 "file_path": "",
                 "info": "",
-                "input_types": [
-                  "Document",
-                  "BaseOutputParser",
-                  "Record",
-                  "Text"
-                ],
+                "input_types": ["Document", "Message", "Record", "Text"],
                 "list": false,
                 "load_from_db": false,
                 "multiline": true,
@@ -167,15 +175,15 @@
           "type": "Prompt"
         },
         "dragging": false,
-        "height": 419,
-        "id": "Prompt-WSII4",
+        "height": 479,
+        "id": "Prompt-graFQ",
         "position": {
-          "x": 18.562420355453696,
-          "y": -284.15095348876025
+          "x": 53.588791333410654,
+          "y": -107.07318910019967
         },
         "positionAbsolute": {
-          "x": 18.562420355453696,
-          "y": -284.15095348876025
+          "x": 53.588791333410654,
+          "y": -107.07318910019967
         },
         "selected": false,
         "type": "genericNode",
@@ -183,269 +191,7 @@
       },
       {
         "data": {
-          "description": "Generates text using OpenAI LLMs.",
-          "display_name": "OpenAI",
-          "id": "OpenAIModel-NDBjF",
-          "node": {
-            "base_classes": ["object", "Text", "str"],
-            "beta": false,
-            "custom_fields": {
-              "input_value": null,
-              "max_tokens": null,
-              "model_kwargs": null,
-              "model_name": null,
-              "openai_api_base": null,
-              "openai_api_key": null,
-              "stream": null,
-              "system_message": null,
-              "temperature": null
-            },
-            "description": "Generates text using OpenAI LLMs.",
-            "display_name": "OpenAI",
-            "documentation": "",
-            "field_formatters": {},
-            "field_order": [
-              "max_tokens",
-              "model_kwargs",
-              "model_name",
-              "openai_api_base",
-              "openai_api_key",
-              "temperature",
-              "input_value",
-              "system_message",
-              "stream"
-            ],
-            "frozen": false,
-            "icon": "OpenAI",
-            "output_types": ["Text"],
-            "template": {
-              "_type": "CustomComponent",
-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "code",
-                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\", \"Prompt\"]},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-3.5-turbo\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n"
-              },
-              "input_value": {
-                "advanced": false,
-                "display_name": "Input",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": ["Text", "Record", "Prompt"],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "input_value",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "max_tokens": {
-                "advanced": true,
-                "display_name": "Max Tokens",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "max_tokens",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "int",
-                "value": 256
-              },
-              "model_kwargs": {
-                "advanced": true,
-                "display_name": "Model Kwargs",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "model_kwargs",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "NestedDict",
-                "value": {}
-              },
-              "model_name": {
-                "advanced": false,
-                "display_name": "Model Name",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": ["Text"],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "model_name",
-                "options": [
-                  "gpt-4o",
-                  "gpt-4-turbo",
-                  "gpt-4-turbo-preview",
-                  "gpt-3.5-turbo",
-                  "gpt-3.5-turbo-0125"
-                ],
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "gpt-3.5-turbo"
-              },
-              "openai_api_base": {
-                "advanced": true,
-                "display_name": "OpenAI API Base",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
-                "input_types": ["Text"],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "openai_api_base",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "openai_api_key": {
-                "advanced": false,
-                "display_name": "OpenAI API Key",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "The OpenAI API Key to use for the OpenAI model.",
-                "input_types": ["Text"],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "openai_api_key",
-                "password": true,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": ""
-              },
-              "stream": {
-                "advanced": true,
-                "display_name": "Stream",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Stream the response from the model. Streaming works only in Chat.",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "stream",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "bool",
-                "value": false
-              },
-              "system_message": {
-                "advanced": true,
-                "display_name": "System Message",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "System message to pass to the model.",
-                "input_types": ["Text"],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "system_message",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "temperature": {
-                "advanced": false,
-                "display_name": "Temperature",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "temperature",
-                "password": false,
-                "placeholder": "",
-                "rangeSpec": {
-                  "max": 1,
-                  "min": -1,
-                  "step": 0.1,
-                  "step_type": "float"
-                },
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "float",
-                "value": 0.1
-              }
-            }
-          },
-          "type": "OpenAIModel"
-        },
-        "dragging": false,
-        "height": 571,
-        "id": "OpenAIModel-NDBjF",
-        "position": {
-          "x": 634.8148772766217,
-          "y": 27.035057029045305
-        },
-        "positionAbsolute": {
-          "x": 634.8148772766217,
-          "y": 27.035057029045305
-        },
-        "selected": false,
-        "type": "genericNode",
-        "width": 384
-      },
-      {
-        "data": {
-          "id": "ChatOutput-JkVmc",
+          "id": "ChatOutput-axUHy",
           "node": {
             "base_classes": ["Record", "Text", "str", "object"],
             "beta": false,
@@ -464,9 +210,20 @@
             "field_order": [],
             "frozen": false,
             "icon": "ChatOutput",
-            "output_types": ["Message", "Text"],
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Message",
+                "method": "message_response",
+                "name": "message",
+                "selected": "Message",
+                "types": ["Message"],
+                "value": "__UNDEFINED__"
+              }
+            ],
             "template": {
-              "_type": "CustomComponent",
+              "_type": "Component",
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -483,7 +240,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        files: Optional[list[str]] = None,\n        return_message: Optional[bool] = False,\n    ) -> Union[Message, Text]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            files=files,\n            return_message=return_message,\n        )\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "input_value": {
                 "advanced": false,
@@ -491,8 +248,8 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "",
-                "input_types": ["Text"],
+                "info": "Message to be passed as output.",
+                "input_types": ["Message", "str"],
                 "list": false,
                 "load_from_db": false,
                 "multiline": true,
@@ -502,7 +259,8 @@
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "sender": {
                 "advanced": true,
@@ -510,7 +268,7 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "",
+                "info": "Type of sender.",
                 "input_types": ["Text"],
                 "list": true,
                 "load_from_db": false,
@@ -526,16 +284,16 @@
                 "value": "Machine"
               },
               "sender_name": {
-                "advanced": false,
+                "advanced": true,
                 "display_name": "Sender Name",
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "",
-                "input_types": ["Text"],
+                "info": "Name of the sender.",
+                "input_types": ["Message", "str"],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
+                "multiline": true,
                 "name": "sender_name",
                 "password": false,
                 "placeholder": "",
@@ -551,33 +309,34 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "If provided, the message will be stored in the memory.",
-                "input_types": ["Text"],
+                "info": "Session ID for the message.",
+                "input_types": ["Message", "str"],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
+                "multiline": true,
                 "name": "session_id",
                 "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               }
             }
           },
           "type": "ChatOutput"
         },
         "dragging": false,
-        "height": 391,
-        "id": "ChatOutput-JkVmc",
+        "height": 357,
+        "id": "ChatOutput-axUHy",
         "position": {
-          "x": 1183.52086970399,
-          "y": -21.518887039580306
+          "x": 1193.250417197867,
+          "y": 71.88476890163852
         },
         "positionAbsolute": {
-          "x": 1183.52086970399,
-          "y": -21.518887039580306
+          "x": 1193.250417197867,
+          "y": 71.88476890163852
         },
         "selected": false,
         "type": "genericNode",
@@ -585,14 +344,13 @@
       },
       {
         "data": {
-          "id": "ChatInput-kltLA",
+          "id": "ChatInput-F6inY",
           "node": {
-            "base_classes": ["Message", "object", "str", "Text"],
+            "base_classes": ["object", "Record", "str", "Text"],
             "beta": false,
             "custom_fields": {
-              "files": null,
               "input_value": null,
-              "return_message": null,
+              "return_record": null,
               "sender": null,
               "sender_name": null,
               "session_id": null
@@ -604,9 +362,21 @@
             "field_order": [],
             "frozen": false,
             "icon": "ChatInput",
-            "output_types": ["Message", "Text"],
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Message",
+                "hidden": false,
+                "method": "message_response",
+                "name": "message",
+                "selected": "Message",
+                "types": ["Message"],
+                "value": "__UNDEFINED__"
+              }
+            ],
             "template": {
-              "_type": "CustomComponent",
+              "_type": "Component",
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -623,49 +393,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Optional\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.schema.message import Message\nfrom langflow.field_typing import Text\nfrom typing import Union\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    def build_config(self):\n        build_config = super().build_config()\n        build_config[\"input_value\"] = {\n            \"input_types\": [],\n            \"display_name\": \"Text\",\n            \"multiline\": True,\n        }\n        build_config[\"return_message\"] = {\n            \"display_name\": \"Return Record\",\n            \"advanced\": True,\n        }\n\n        return build_config\n\n    def build(\n        self,\n        sender: Optional[str] = \"User\",\n        sender_name: Optional[str] = \"User\",\n        input_value: Optional[str] = None,\n        files: Optional[list[str]] = None,\n        session_id: Optional[str] = None,\n        return_message: Optional[bool] = True,\n    ) -> Union[Message, Text]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            files=files,\n            session_id=session_id,\n            return_message=return_message,\n        )\n"
-              },
-              "files": {
-                "advanced": true,
-                "display_name": "Files",
-                "dynamic": false,
-                "fileTypes": [
-                  ".txt",
-                  ".md",
-                  ".mdx",
-                  ".csv",
-                  ".json",
-                  ".yaml",
-                  ".yml",
-                  ".xml",
-                  ".html",
-                  ".htm",
-                  ".pdf",
-                  ".docx",
-                  ".py",
-                  ".sh",
-                  ".sql",
-                  ".js",
-                  ".ts",
-                  ".tsx",
-                  ".jpg",
-                  ".jpeg",
-                  ".png",
-                  ".bmp"
-                ],
-                "file_path": "",
-                "info": "Files to be sent with the message.",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "files",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "file",
-                "value": ""
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, FileInput, MultilineInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "input_value": {
                 "advanced": false,
@@ -673,8 +401,8 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "",
-                "input_types": [],
+                "info": "Message to be passed as input.",
+                "input_types": ["Message", "str"],
                 "list": false,
                 "load_from_db": false,
                 "multiline": true,
@@ -685,26 +413,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "str",
-                "value": "what do you see?"
-              },
-              "return_message": {
-                "advanced": true,
-                "display_name": "Return Record",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "return_message",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "bool",
-                "value": true
+                "value": ""
               },
               "sender": {
                 "advanced": true,
@@ -712,7 +421,7 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "",
+                "info": "Type of sender.",
                 "input_types": ["Text"],
                 "list": true,
                 "load_from_db": false,
@@ -733,11 +442,11 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "",
-                "input_types": ["Text"],
+                "info": "Name of the sender.",
+                "input_types": ["Message", "str"],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
+                "multiline": true,
                 "name": "sender_name",
                 "password": false,
                 "placeholder": "",
@@ -753,33 +462,292 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "If provided, the message will be stored in the memory.",
-                "input_types": ["Text"],
+                "info": "Session ID for the message.",
+                "input_types": ["Message", "str"],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
+                "multiline": true,
                 "name": "session_id",
                 "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               }
             }
           },
           "type": "ChatInput"
         },
         "dragging": false,
-        "height": 289,
-        "id": "ChatInput-kltLA",
+        "height": 357,
+        "id": "ChatInput-F6inY",
         "position": {
-          "x": -560.3246254009209,
-          "y": -435.0506368105706
+          "x": -495.2223093083827,
+          "y": -232.56998443685862
         },
         "positionAbsolute": {
-          "x": -560.3246254009209,
-          "y": -435.0506368105706
+          "x": -495.2223093083827,
+          "y": -232.56998443685862
+        },
+        "selected": false,
+        "type": "genericNode",
+        "width": 384
+      },
+      {
+        "data": {
+          "description": "Generates text using OpenAI LLMs.",
+          "display_name": "OpenAI",
+          "edited": false,
+          "id": "OpenAIModel-5alpQ",
+          "node": {
+            "base_classes": ["LanguageModel", "Message"],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Generates text using OpenAI LLMs.",
+            "display_name": "OpenAI",
+            "documentation": "",
+            "edited": true,
+            "field_order": [
+              "input_value",
+              "max_tokens",
+              "model_kwargs",
+              "output_schema",
+              "model_name",
+              "openai_api_base",
+              "openai_api_key",
+              "temperature",
+              "stream",
+              "system_message",
+              "seed"
+            ],
+            "frozen": false,
+            "icon": "OpenAI",
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Text",
+                "hidden": false,
+                "method": "text_response",
+                "name": "text_output",
+                "selected": "Message",
+                "types": ["Message"],
+                "value": "__UNDEFINED__"
+              },
+              {
+                "cache": true,
+                "display_name": "Language Model",
+                "method": "build_model",
+                "name": "model_output",
+                "selected": "LanguageModel",
+                "types": ["LanguageModel"],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    inputs = [\n        MessageInput(name=\"input_value\", display_name=\"Input\"),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:\n        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict)\n        seed = self.seed\n        model_kwargs[\"seed\"] = seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n"
+              },
+              "input_value": {
+                "advanced": false,
+                "display_name": "Input",
+                "dynamic": false,
+                "info": "",
+                "input_types": ["Message"],
+                "list": false,
+                "load_from_db": false,
+                "name": "input_value",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "max_tokens": {
+                "advanced": true,
+                "display_name": "Max Tokens",
+                "dynamic": false,
+                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+                "list": false,
+                "name": "max_tokens",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": ""
+              },
+              "model_kwargs": {
+                "advanced": true,
+                "display_name": "Model Kwargs",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "model_kwargs",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "dict",
+                "value": {}
+              },
+              "model_name": {
+                "advanced": false,
+                "display_name": "Model Name",
+                "dynamic": false,
+                "info": "",
+                "name": "model_name",
+                "options": [
+                  "gpt-4o",
+                  "gpt-4-turbo",
+                  "gpt-4-turbo-preview",
+                  "gpt-3.5-turbo",
+                  "gpt-3.5-turbo-0125"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "gpt-4o"
+              },
+              "openai_api_base": {
+                "advanced": true,
+                "display_name": "OpenAI API Base",
+                "dynamic": false,
+                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.",
+                "list": false,
+                "load_from_db": false,
+                "name": "openai_api_base",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "openai_api_key": {
+                "advanced": false,
+                "display_name": "OpenAI API Key",
+                "dynamic": false,
+                "info": "The OpenAI API Key to use for the OpenAI model.",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "openai_api_key",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "output_schema": {
+                "advanced": true,
+                "display_name": "Schema",
+                "dynamic": false,
+                "info": "The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.",
+                "list": true,
+                "name": "output_schema",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "dict",
+                "value": {}
+              },
+              "seed": {
+                "advanced": true,
+                "display_name": "Seed",
+                "dynamic": false,
+                "info": "The seed controls the reproducibility of the job.",
+                "list": false,
+                "name": "seed",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": 1
+              },
+              "stream": {
+                "advanced": true,
+                "display_name": "Stream",
+                "dynamic": false,
+                "info": "Stream the response from the model. Streaming works only in Chat.",
+                "list": false,
+                "name": "stream",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "bool",
+                "value": false
+              },
+              "system_message": {
+                "advanced": true,
+                "display_name": "System Message",
+                "dynamic": false,
+                "info": "System message to pass to the model.",
+                "list": false,
+                "load_from_db": false,
+                "name": "system_message",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "temperature": {
+                "advanced": false,
+                "display_name": "Temperature",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "temperature",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "float",
+                "value": 0.1
+              }
+            }
+          },
+          "type": "OpenAIModel"
+        },
+        "dragging": false,
+        "height": 623,
+        "id": "OpenAIModel-5alpQ",
+        "position": {
+          "x": 576.388859357137,
+          "y": 131.1662189663108
+        },
+        "positionAbsolute": {
+          "x": 576.388859357137,
+          "y": 131.1662189663108
         },
         "selected": true,
         "type": "genericNode",
@@ -787,14 +755,15 @@
       }
     ],
     "viewport": {
-      "x": 223.38563623650703,
-      "y": 271.96191180648566,
-      "zoom": 0.5138985141032123
+      "x": 0,
+      "y": 0,
+      "zoom": 1
     }
   },
   "description": "This flow will get you experimenting with the basics of the UI, the Chat and the Prompt component. \n\nTry changing the Template in it to see how the model behaves. \nYou can change it to this and a Text Input into the `type_of_person` variable : \"Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: \" ",
-  "id": "ad43b14f-6ec7-496f-9564-aad928603084",
+  "endpoint_name": null,
+  "id": "a186d643-9741-4d3f-b84e-74d5ac368621",
   "is_component": false,
-  "last_tested_version": "1.0.0a52",
+  "last_tested_version": "1.0.0a61",
   "name": "Basic Prompting (Hello, World)"
 }
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writer.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writer.json
new file mode 100644
index 000000000..8c425e2c3
--- /dev/null
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writer.json	
@@ -0,0 +1,1337 @@
+{
+    "data": {
+        "edges": [
+            {
+                "className": "",
+                "data": {
+                    "sourceHandle": {
+                        "dataType": "TextInput",
+                        "id": "TextInput-DbgJ3",
+                        "name": "text",
+                        "output_types": [
+                            "Message"
+                        ]
+                    },
+                    "targetHandle": {
+                        "fieldName": "instructions",
+                        "id": "Prompt-jJ1i7",
+                        "inputTypes": [
+                            "Message",
+                            "Text"
+                        ],
+                        "type": "str"
+                    }
+                },
+                "id": "reactflow__edge-TextInput-DbgJ3{œdataTypeœ:œTextInputœ,œidœ:œTextInput-DbgJ3œ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-Prompt-jJ1i7{œfieldNameœ:œinstructionsœ,œidœ:œPrompt-jJ1i7œ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "source": "TextInput-DbgJ3",
+                "sourceHandle": "{œdataTypeœ: œTextInputœ, œidœ: œTextInput-DbgJ3œ, œnameœ: œtextœ, œoutput_typesœ: [œMessageœ]}",
+                "target": "Prompt-jJ1i7",
+                "targetHandle": "{œfieldNameœ: œinstructionsœ, œidœ: œPrompt-jJ1i7œ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
+            },
+            {
+                "className": "",
+                "data": {
+                    "sourceHandle": {
+                        "dataType": "URL",
+                        "id": "URL-43SB8",
+                        "name": "data",
+                        "output_types": [
+                            "Data"
+                        ]
+                    },
+                    "targetHandle": {
+                        "fieldName": "data",
+                        "id": "ParseData-34pEF",
+                        "inputTypes": [
+                            "Data"
+                        ],
+                        "type": "other"
+                    }
+                },
+                "id": "reactflow__edge-URL-43SB8{œdataTypeœ:œURLœ,œidœ:œURL-43SB8œ,œnameœ:œdataœ,œoutput_typesœ:[œDataœ]}-ParseData-34pEF{œfieldNameœ:œdataœ,œidœ:œParseData-34pEFœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
+                "selected": false,
+                "source": "URL-43SB8",
+                "sourceHandle": "{œdataTypeœ: œURLœ, œidœ: œURL-43SB8œ, œnameœ: œdataœ, œoutput_typesœ: [œDataœ]}",
+                "target": "ParseData-34pEF",
+                "targetHandle": "{œfieldNameœ: œdataœ, œidœ: œParseData-34pEFœ, œinputTypesœ: [œDataœ], œtypeœ: œotherœ}"
+            },
+            {
+                "className": "",
+                "data": {
+                    "sourceHandle": {
+                        "dataType": "URL",
+                        "id": "URL-eFIqb",
+                        "name": "data",
+                        "output_types": [
+                            "Data"
+                        ]
+                    },
+                    "targetHandle": {
+                        "fieldName": "data",
+                        "id": "ParseData-iAHWq",
+                        "inputTypes": [
+                            "Data"
+                        ],
+                        "type": "other"
+                    }
+                },
+                "id": "reactflow__edge-URL-eFIqb{œdataTypeœ:œURLœ,œidœ:œURL-eFIqbœ,œnameœ:œdataœ,œoutput_typesœ:[œDataœ]}-ParseData-iAHWq{œfieldNameœ:œdataœ,œidœ:œParseData-iAHWqœ,œinputTypesœ:[œDataœ],œtypeœ:œotherœ}",
+                "source": "URL-eFIqb",
+                "sourceHandle": "{œdataTypeœ: œURLœ, œidœ: œURL-eFIqbœ, œnameœ: œdataœ, œoutput_typesœ: [œDataœ]}",
+                "target": "ParseData-iAHWq",
+                "targetHandle": "{œfieldNameœ: œdataœ, œidœ: œParseData-iAHWqœ, œinputTypesœ: [œDataœ], œtypeœ: œotherœ}"
+            },
+            {
+                "className": "",
+                "data": {
+                    "sourceHandle": {
+                        "dataType": "ParseData",
+                        "id": "ParseData-34pEF",
+                        "name": "text",
+                        "output_types": [
+                            "Message"
+                        ]
+                    },
+                    "targetHandle": {
+                        "fieldName": "reference_2",
+                        "id": "Prompt-jJ1i7",
+                        "inputTypes": [
+                            "Message",
+                            "Text"
+                        ],
+                        "type": "str"
+                    }
+                },
+                "id": "reactflow__edge-ParseData-34pEF{œdataTypeœ:œParseDataœ,œidœ:œParseData-34pEFœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-Prompt-jJ1i7{œfieldNameœ:œreference_2œ,œidœ:œPrompt-jJ1i7œ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "source": "ParseData-34pEF",
+                "sourceHandle": "{œdataTypeœ: œParseDataœ, œidœ: œParseData-34pEFœ, œnameœ: œtextœ, œoutput_typesœ: [œMessageœ]}",
+                "target": "Prompt-jJ1i7",
+                "targetHandle": "{œfieldNameœ: œreference_2œ, œidœ: œPrompt-jJ1i7œ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
+            },
+            {
+                "className": "",
+                "data": {
+                    "sourceHandle": {
+                        "dataType": "ParseData",
+                        "id": "ParseData-iAHWq",
+                        "name": "text",
+                        "output_types": [
+                            "Message"
+                        ]
+                    },
+                    "targetHandle": {
+                        "fieldName": "reference_1",
+                        "id": "Prompt-jJ1i7",
+                        "inputTypes": [
+                            "Message",
+                            "Text"
+                        ],
+                        "type": "str"
+                    }
+                },
+                "id": "reactflow__edge-ParseData-iAHWq{œdataTypeœ:œParseDataœ,œidœ:œParseData-iAHWqœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-Prompt-jJ1i7{œfieldNameœ:œreference_1œ,œidœ:œPrompt-jJ1i7œ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+                "source": "ParseData-iAHWq",
+                "sourceHandle": "{œdataTypeœ: œParseDataœ, œidœ: œParseData-iAHWqœ, œnameœ: œtextœ, œoutput_typesœ: [œMessageœ]}",
+                "target": "Prompt-jJ1i7",
+                "targetHandle": "{œfieldNameœ: œreference_1œ, œidœ: œPrompt-jJ1i7œ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
+            },
+            {
+                "className": "",
+                "data": {
+                    "sourceHandle": {
+                        "dataType": "OpenAIModel",
+                        "id": "OpenAIModel-slJZS",
+                        "name": "text_output",
+                        "output_types": [
+                            "Message"
+                        ]
+                    },
+                    "targetHandle": {
+                        "fieldName": "input_value",
+                        "id": "ChatOutput-LkJX7",
+                        "inputTypes": [
+                            "Message",
+                            "str"
+                        ],
+                        "type": "str"
+                    }
+                },
+                "id": "reactflow__edge-OpenAIModel-slJZS{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-slJZSœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-LkJX7{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-LkJX7œ,œinputTypesœ:[œMessageœ,œstrœ],œtypeœ:œstrœ}",
+                "source": "OpenAIModel-slJZS",
+                "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-slJZSœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
+                "target": "ChatOutput-LkJX7",
+                "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-LkJX7œ, œinputTypesœ: [œMessageœ, œstrœ], œtypeœ: œstrœ}"
+            },
+            {
+                "className": "",
+                "data": {
+                    "sourceHandle": {
+                        "dataType": "Prompt",
+                        "id": "Prompt-jJ1i7",
+                        "name": "prompt",
+                        "output_types": [
+                            "Message"
+                        ]
+                    },
+                    "targetHandle": {
+                        "fieldName": "input_value",
+                        "id": "OpenAIModel-slJZS",
+                        "inputTypes": [
+                            "Message"
+                        ],
+                        "type": "str"
+                    }
+                },
+                "id": "reactflow__edge-Prompt-jJ1i7{œdataTypeœ:œPromptœ,œidœ:œPrompt-jJ1i7œ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-slJZS{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-slJZSœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "source": "Prompt-jJ1i7",
+                "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-jJ1i7œ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
+                "target": "OpenAIModel-slJZS",
+                "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-slJZSœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+            }
+        ],
+        "nodes": [
+            {
+                "data": {
+                    "description": "Create a prompt template with dynamic variables.",
+                    "display_name": "Prompt",
+                    "id": "Prompt-jJ1i7",
+                    "node": {
+                        "base_classes": [
+                            "object",
+                            "str",
+                            "Text"
+                        ],
+                        "beta": false,
+                        "conditional_paths": [],
+                        "custom_fields": {
+                            "template": [
+                                "reference_1",
+                                "reference_2",
+                                "instructions"
+                            ]
+                        },
+                        "description": "Create a prompt template with dynamic variables.",
+                        "display_name": "Prompt",
+                        "documentation": "",
+                        "error": null,
+                        "field_order": [],
+                        "frozen": false,
+                        "full_path": null,
+                        "icon": "prompts",
+                        "is_composition": null,
+                        "is_input": null,
+                        "is_output": null,
+                        "name": "",
+                        "output_types": [],
+                        "outputs": [
+                            {
+                                "cache": true,
+                                "display_name": "Prompt Message",
+                                "method": "build_prompt",
+                                "name": "prompt",
+                                "selected": "Message",
+                                "types": [
+                                    "Message"
+                                ],
+                                "value": "__UNDEFINED__"
+                            }
+                        ],
+                        "pinned": false,
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "advanced": true,
+                                "dynamic": true,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "code",
+                                "password": false,
+                                "placeholder": "",
+                                "required": true,
+                                "show": true,
+                                "title_case": false,
+                                "type": "code",
+                                "value": "from langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n"
+                            },
+                            "instructions": {
+                                "advanced": false,
+                                "display_name": "instructions",
+                                "dynamic": false,
+                                "field_type": "str",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "input_types": [
+                                    "Message",
+                                    "Text"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "instructions",
+                                "password": false,
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": ""
+                            },
+                            "reference_1": {
+                                "advanced": false,
+                                "display_name": "reference_1",
+                                "dynamic": false,
+                                "field_type": "str",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "input_types": [
+                                    "Message",
+                                    "Text"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "reference_1",
+                                "password": false,
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": ""
+                            },
+                            "reference_2": {
+                                "advanced": false,
+                                "display_name": "reference_2",
+                                "dynamic": false,
+                                "field_type": "str",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "input_types": [
+                                    "Message",
+                                    "Text"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "reference_2",
+                                "password": false,
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": ""
+                            },
+                            "template": {
+                                "advanced": false,
+                                "display_name": "Template",
+                                "dynamic": false,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "input_types": [
+                                    "Text"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": false,
+                                "name": "template",
+                                "password": false,
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "prompt",
+                                "value": "Reference 1:\n\n{reference_1}\n\n---\n\nReference 2:\n\n{reference_2}\n\n---\n\n{instructions}\n\nBlog: \n\n\n"
+                            }
+                        }
+                    },
+                    "type": "Prompt"
+                },
+                "dragging": false,
+                "height": 619,
+                "id": "Prompt-jJ1i7",
+                "position": {
+                    "x": 1378.0386633467044,
+                    "y": 547.0254869963999
+                },
+                "positionAbsolute": {
+                    "x": 1378.0386633467044,
+                    "y": 547.0254869963999
+                },
+                "selected": false,
+                "type": "genericNode",
+                "width": 384
+            },
+            {
+                "data": {
+                    "id": "URL-43SB8",
+                    "node": {
+                        "base_classes": [
+                            "Record"
+                        ],
+                        "beta": false,
+                        "custom_fields": {
+                            "urls": null
+                        },
+                        "description": "Fetch content from one or more URLs.",
+                        "display_name": "URL",
+                        "documentation": "",
+                        "field_formatters": {},
+                        "field_order": [],
+                        "frozen": false,
+                        "icon": "layout-template",
+                        "output_types": [],
+                        "outputs": [
+                            {
+                                "cache": true,
+                                "display_name": "Data",
+                                "method": "fetch_content",
+                                "name": "data",
+                                "selected": "Data",
+                                "types": [
+                                    "Data"
+                                ],
+                                "value": "__UNDEFINED__"
+                            }
+                        ],
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "advanced": true,
+                                "dynamic": true,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "code",
+                                "password": false,
+                                "placeholder": "",
+                                "required": true,
+                                "show": true,
+                                "title_case": false,
+                                "type": "code",
+                                "value": "import re\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.io import Output, TextInput\nfrom langflow.schema import Data\n\n\nclass URLComponent(Component):\n    display_name = \"URL\"\n    description = \"Fetch content from one or more URLs.\"\n    icon = \"layout-template\"\n\n    inputs = [\n        TextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs, separated by commas.\",\n            is_list=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n    ]\n\n    def ensure_url(self, string: str) -> str:\n        \"\"\"\n        Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n        Raises an error if the string is not a valid URL.\n\n        Parameters:\n            string (str): The string to be checked and possibly modified.\n\n        Returns:\n            str: The modified string that is ensured to be a URL.\n\n        Raises:\n            ValueError: If the string is not a valid URL.\n        \"\"\"\n        if not string.startswith((\"http://\", \"https://\")):\n            string = \"http://\" + string\n\n        # Basic URL validation regex\n        url_regex = re.compile(\n            r\"^(http://|https://)?\"  # http:// or https://\n            r\"(([a-zA-Z0-9\\.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,}))\"  # top-level domain\n            r\"(:[0-9]{1,5})?\"  # optional port\n            r\"(\\/.*)?$\"  # optional path\n        )\n\n        if not re.match(url_regex, string):\n            raise ValueError(f\"Invalid URL: {string}\")\n\n        return string\n\n    def fetch_content(self) -> list[Data]:\n        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n        loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n        docs = loader.load()\n        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n"
+                            },
+                            "urls": {
+                                "advanced": false,
+                                "display_name": "URLs",
+                                "dynamic": false,
+                                "info": "Enter one or more URLs, separated by commas.",
+                                "list": true,
+                                "load_from_db": false,
+                                "name": "urls",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": [
+                                    "https://www.promptingguide.ai/introduction/basics"
+                                ]
+                            }
+                        }
+                    },
+                    "type": "URL"
+                },
+                "dragging": false,
+                "height": 301,
+                "id": "URL-43SB8",
+                "position": {
+                    "x": 129.9069887328102,
+                    "y": 1026.1629590683015
+                },
+                "positionAbsolute": {
+                    "x": 129.9069887328102,
+                    "y": 1026.1629590683015
+                },
+                "selected": false,
+                "type": "genericNode",
+                "width": 384
+            },
+            {
+                "data": {
+                    "id": "URL-eFIqb",
+                    "node": {
+                        "base_classes": [
+                            "Record"
+                        ],
+                        "beta": false,
+                        "custom_fields": {
+                            "urls": null
+                        },
+                        "description": "Fetch content from one or more URLs.",
+                        "display_name": "URL",
+                        "documentation": "",
+                        "field_formatters": {},
+                        "field_order": [],
+                        "frozen": false,
+                        "icon": "layout-template",
+                        "output_types": [],
+                        "outputs": [
+                            {
+                                "cache": true,
+                                "display_name": "Data",
+                                "method": "fetch_content",
+                                "name": "data",
+                                "selected": "Data",
+                                "types": [
+                                    "Data"
+                                ],
+                                "value": "__UNDEFINED__"
+                            }
+                        ],
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "advanced": true,
+                                "dynamic": true,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "code",
+                                "password": false,
+                                "placeholder": "",
+                                "required": true,
+                                "show": true,
+                                "title_case": false,
+                                "type": "code",
+                                "value": "import re\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import Component\nfrom langflow.io import Output, TextInput\nfrom langflow.schema import Data\n\n\nclass URLComponent(Component):\n    display_name = \"URL\"\n    description = \"Fetch content from one or more URLs.\"\n    icon = \"layout-template\"\n\n    inputs = [\n        TextInput(\n            name=\"urls\",\n            display_name=\"URLs\",\n            info=\"Enter one or more URLs, separated by commas.\",\n            is_list=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"fetch_content\"),\n    ]\n\n    def ensure_url(self, string: str) -> str:\n        \"\"\"\n        Ensures the given string is a URL by adding 'http://' if it doesn't start with 'http://' or 'https://'.\n        Raises an error if the string is not a valid URL.\n\n        Parameters:\n            string (str): The string to be checked and possibly modified.\n\n        Returns:\n            str: The modified string that is ensured to be a URL.\n\n        Raises:\n            ValueError: If the string is not a valid URL.\n        \"\"\"\n        if not string.startswith((\"http://\", \"https://\")):\n            string = \"http://\" + string\n\n        # Basic URL validation regex\n        url_regex = re.compile(\n            r\"^(http://|https://)?\"  # http:// or https://\n            r\"(([a-zA-Z0-9\\.-]+)\"  # domain\n            r\"(\\.[a-zA-Z]{2,}))\"  # top-level domain\n            r\"(:[0-9]{1,5})?\"  # optional port\n            r\"(\\/.*)?$\"  # optional path\n        )\n\n        if not re.match(url_regex, string):\n            raise ValueError(f\"Invalid URL: {string}\")\n\n        return string\n\n    def fetch_content(self) -> list[Data]:\n        urls = [self.ensure_url(url.strip()) for url in self.urls if url.strip()]\n        loader = WebBaseLoader(web_paths=urls, encoding=\"utf-8\")\n        docs = loader.load()\n        data = [Data(text=doc.page_content, **doc.metadata) for doc in docs]\n        self.status = data\n        return data\n"
+                            },
+                            "urls": {
+                                "advanced": false,
+                                "display_name": "URLs",
+                                "dynamic": false,
+                                "info": "Enter one or more URLs, separated by commas.",
+                                "list": true,
+                                "load_from_db": false,
+                                "name": "urls",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": [
+                                    "https://www.promptingguide.ai/techniques/prompt_chaining"
+                                ]
+                            }
+                        }
+                    },
+                    "type": "URL"
+                },
+                "dragging": false,
+                "height": 301,
+                "id": "URL-eFIqb",
+                "position": {
+                    "x": 109.01828882212544,
+                    "y": 635.7038211214808
+                },
+                "positionAbsolute": {
+                    "x": 109.01828882212544,
+                    "y": 635.7038211214808
+                },
+                "selected": false,
+                "type": "genericNode",
+                "width": 384
+            },
+            {
+                "data": {
+                    "description": "Get text inputs from the Playground.",
+                    "display_name": "Instructions",
+                    "edited": false,
+                    "id": "TextInput-DbgJ3",
+                    "node": {
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "beta": false,
+                        "conditional_paths": [],
+                        "custom_fields": {},
+                        "description": "Get text inputs from the Playground.",
+                        "display_name": "Instructions",
+                        "documentation": "",
+                        "edited": true,
+                        "field_order": [
+                            "input_value"
+                        ],
+                        "frozen": false,
+                        "icon": "type",
+                        "output_types": [],
+                        "outputs": [
+                            {
+                                "cache": true,
+                                "display_name": "Text",
+                                "method": "text_response",
+                                "name": "text",
+                                "selected": "Message",
+                                "types": [
+                                    "Message"
+                                ],
+                                "value": "__UNDEFINED__"
+                            }
+                        ],
+                        "pinned": false,
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "advanced": true,
+                                "dynamic": true,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "code",
+                                "password": false,
+                                "placeholder": "",
+                                "required": true,
+                                "show": true,
+                                "title_case": false,
+                                "type": "code",
+                                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n        )\n        return message\n"
+                            },
+                            "input_value": {
+                                "advanced": false,
+                                "display_name": "Text",
+                                "dynamic": false,
+                                "info": "Text to be passed as input.",
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "name": "input_value",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": "Use the references above for style to write a new blog/tutorial about prompt engineering techniques. Suggest non-covered topics."
+                            }
+                        }
+                    },
+                    "type": "TextInput"
+                },
+                "dragging": false,
+                "height": 309,
+                "id": "TextInput-DbgJ3",
+                "position": {
+                    "x": 668.3436449795839,
+                    "y": 213.40493638517057
+                },
+                "positionAbsolute": {
+                    "x": 668.3436449795839,
+                    "y": 213.40493638517057
+                },
+                "selected": false,
+                "type": "genericNode",
+                "width": 384
+            },
+            {
+                "data": {
+                    "id": "ParseData-34pEF",
+                    "node": {
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "beta": false,
+                        "conditional_paths": [],
+                        "custom_fields": {},
+                        "description": "Convert Data into plain text following a specified template.",
+                        "display_name": "Parse Data",
+                        "documentation": "",
+                        "field_order": [
+                            "data",
+                            "template",
+                            "sep"
+                        ],
+                        "frozen": false,
+                        "icon": "braces",
+                        "output_types": [],
+                        "outputs": [
+                            {
+                                "cache": true,
+                                "display_name": "Text",
+                                "method": "parse_data",
+                                "name": "text",
+                                "selected": "Message",
+                                "types": [
+                                    "Message"
+                                ],
+                                "value": "__UNDEFINED__"
+                            }
+                        ],
+                        "pinned": false,
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "advanced": true,
+                                "dynamic": true,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "code",
+                                "password": false,
+                                "placeholder": "",
+                                "required": true,
+                                "show": true,
+                                "title_case": false,
+                                "type": "code",
+                                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n"
+                            },
+                            "data": {
+                                "advanced": false,
+                                "display_name": "Data",
+                                "dynamic": false,
+                                "info": "The data to convert to text.",
+                                "input_types": [
+                                    "Data"
+                                ],
+                                "list": false,
+                                "name": "data",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "other",
+                                "value": ""
+                            },
+                            "sep": {
+                                "advanced": true,
+                                "display_name": "Separator",
+                                "dynamic": false,
+                                "info": "",
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "sep",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": "---"
+                            },
+                            "template": {
+                                "advanced": false,
+                                "display_name": "Template",
+                                "dynamic": false,
+                                "info": "The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.",
+                                "multiline": true,
+                                "name": "template",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": "{text}"
+                            }
+                        }
+                    },
+                    "type": "ParseData"
+                },
+                "dragging": false,
+                "height": 377,
+                "id": "ParseData-34pEF",
+                "position": {
+                    "x": 697.109388389247,
+                    "y": 993.1273555676513
+                },
+                "positionAbsolute": {
+                    "x": 697.109388389247,
+                    "y": 993.1273555676513
+                },
+                "selected": false,
+                "type": "genericNode",
+                "width": 384
+            },
+            {
+                "data": {
+                    "id": "ParseData-iAHWq",
+                    "node": {
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "beta": false,
+                        "conditional_paths": [],
+                        "custom_fields": {},
+                        "description": "Convert Data into plain text following a specified template.",
+                        "display_name": "Parse Data",
+                        "documentation": "",
+                        "field_order": [
+                            "data",
+                            "template",
+                            "sep"
+                        ],
+                        "frozen": false,
+                        "icon": "braces",
+                        "output_types": [],
+                        "outputs": [
+                            {
+                                "cache": true,
+                                "display_name": "Text",
+                                "method": "parse_data",
+                                "name": "text",
+                                "selected": "Message",
+                                "types": [
+                                    "Message"
+                                ],
+                                "value": "__UNDEFINED__"
+                            }
+                        ],
+                        "pinned": false,
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "advanced": true,
+                                "dynamic": true,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "code",
+                                "password": false,
+                                "placeholder": "",
+                                "required": true,
+                                "show": true,
+                                "title_case": false,
+                                "type": "code",
+                                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n"
+                            },
+                            "data": {
+                                "advanced": false,
+                                "display_name": "Data",
+                                "dynamic": false,
+                                "info": "The data to convert to text.",
+                                "input_types": [
+                                    "Data"
+                                ],
+                                "list": false,
+                                "name": "data",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "other",
+                                "value": ""
+                            },
+                            "sep": {
+                                "advanced": true,
+                                "display_name": "Separator",
+                                "dynamic": false,
+                                "info": "",
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "sep",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": "---"
+                            },
+                            "template": {
+                                "advanced": false,
+                                "display_name": "Template",
+                                "dynamic": false,
+                                "info": "The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.",
+                                "multiline": true,
+                                "name": "template",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": "{text}"
+                            }
+                        }
+                    },
+                    "type": "ParseData"
+                },
+                "dragging": false,
+                "height": 377,
+                "id": "ParseData-iAHWq",
+                "position": {
+                    "x": 674.3059180422167,
+                    "y": 594.1081812719365
+                },
+                "positionAbsolute": {
+                    "x": 674.3059180422167,
+                    "y": 594.1081812719365
+                },
+                "selected": false,
+                "type": "genericNode",
+                "width": 384
+            },
+            {
+                "data": {
+                    "description": "Generates text using OpenAI LLMs.",
+                    "display_name": "OpenAI",
+                    "edited": false,
+                    "id": "OpenAIModel-slJZS",
+                    "node": {
+                        "base_classes": [
+                            "LanguageModel",
+                            "Message"
+                        ],
+                        "beta": false,
+                        "conditional_paths": [],
+                        "custom_fields": {},
+                        "description": "Generates text using OpenAI LLMs.",
+                        "display_name": "OpenAI",
+                        "documentation": "",
+                        "edited": true,
+                        "field_order": [
+                            "input_value",
+                            "max_tokens",
+                            "model_kwargs",
+                            "output_schema",
+                            "model_name",
+                            "openai_api_base",
+                            "openai_api_key",
+                            "temperature",
+                            "stream",
+                            "system_message",
+                            "seed"
+                        ],
+                        "frozen": false,
+                        "icon": "OpenAI",
+                        "output_types": [],
+                        "outputs": [
+                            {
+                                "cache": true,
+                                "display_name": "Text",
+                                "method": "text_response",
+                                "name": "text_output",
+                                "selected": "Message",
+                                "types": [
+                                    "Message"
+                                ],
+                                "value": "__UNDEFINED__"
+                            },
+                            {
+                                "cache": true,
+                                "display_name": "Language Model",
+                                "method": "build_model",
+                                "name": "model_output",
+                                "selected": "LanguageModel",
+                                "types": [
+                                    "LanguageModel"
+                                ],
+                                "value": "__UNDEFINED__"
+                            }
+                        ],
+                        "pinned": false,
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "advanced": true,
+                                "dynamic": true,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "code",
+                                "password": false,
+                                "placeholder": "",
+                                "required": true,
+                                "show": true,
+                                "title_case": false,
+                                "type": "code",
+                                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    inputs = [\n        MessageInput(name=\"input_value\", display_name=\"Input\"),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:\n        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict)\n        seed = self.seed\n        model_kwargs[\"seed\"] = seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n"
+                            },
+                            "input_value": {
+                                "advanced": false,
+                                "display_name": "Input",
+                                "dynamic": false,
+                                "info": "",
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "name": "input_value",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": ""
+                            },
+                            "max_tokens": {
+                                "advanced": true,
+                                "display_name": "Max Tokens",
+                                "dynamic": false,
+                                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+                                "list": false,
+                                "name": "max_tokens",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "int",
+                                "value": ""
+                            },
+                            "model_kwargs": {
+                                "advanced": true,
+                                "display_name": "Model Kwargs",
+                                "dynamic": false,
+                                "info": "",
+                                "list": false,
+                                "name": "model_kwargs",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "dict",
+                                "value": {}
+                            },
+                            "model_name": {
+                                "advanced": false,
+                                "display_name": "Model Name",
+                                "dynamic": false,
+                                "info": "",
+                                "name": "model_name",
+                                "options": [
+                                    "gpt-4o",
+                                    "gpt-4-turbo",
+                                    "gpt-4-turbo-preview",
+                                    "gpt-3.5-turbo",
+                                    "gpt-3.5-turbo-0125"
+                                ],
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": "gpt-3.5-turbo"
+                            },
+                            "openai_api_base": {
+                                "advanced": true,
+                                "display_name": "OpenAI API Base",
+                                "dynamic": false,
+                                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.",
+                                "list": false,
+                                "load_from_db": false,
+                                "name": "openai_api_base",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": ""
+                            },
+                            "openai_api_key": {
+                                "advanced": false,
+                                "display_name": "OpenAI API Key",
+                                "dynamic": false,
+                                "info": "The OpenAI API Key to use for the OpenAI model.",
+                                "input_types": [],
+                                "load_from_db": true,
+                                "name": "openai_api_key",
+                                "password": true,
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": ""
+                            },
+                            "output_schema": {
+                                "advanced": true,
+                                "display_name": "Schema",
+                                "dynamic": false,
+                                "info": "The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.",
+                                "list": true,
+                                "name": "output_schema",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "dict",
+                                "value": {}
+                            },
+                            "seed": {
+                                "advanced": true,
+                                "display_name": "Seed",
+                                "dynamic": false,
+                                "info": "The seed controls the reproducibility of the job.",
+                                "list": false,
+                                "name": "seed",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "int",
+                                "value": 1
+                            },
+                            "stream": {
+                                "advanced": true,
+                                "display_name": "Stream",
+                                "dynamic": false,
+                                "info": "Stream the response from the model. Streaming works only in Chat.",
+                                "list": false,
+                                "name": "stream",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "bool",
+                                "value": false
+                            },
+                            "system_message": {
+                                "advanced": true,
+                                "display_name": "System Message",
+                                "dynamic": false,
+                                "info": "System message to pass to the model.",
+                                "list": false,
+                                "load_from_db": false,
+                                "name": "system_message",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": ""
+                            },
+                            "temperature": {
+                                "advanced": false,
+                                "display_name": "Temperature",
+                                "dynamic": false,
+                                "info": "",
+                                "list": false,
+                                "name": "temperature",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "float",
+                                "value": 0.1
+                            }
+                        }
+                    },
+                    "type": "OpenAIModel"
+                },
+                "dragging": false,
+                "height": 623,
+                "id": "OpenAIModel-slJZS",
+                "position": {
+                    "x": 1968.999112433115,
+                    "y": 528.8142375467121
+                },
+                "positionAbsolute": {
+                    "x": 1968.999112433115,
+                    "y": 528.8142375467121
+                },
+                "selected": false,
+                "type": "genericNode",
+                "width": 384
+            },
+            {
+                "data": {
+                    "id": "ChatOutput-LkJX7",
+                    "node": {
+                        "base_classes": [
+                            "Message"
+                        ],
+                        "beta": false,
+                        "conditional_paths": [],
+                        "custom_fields": {},
+                        "description": "Display a chat message in the Playground.",
+                        "display_name": "Chat Output",
+                        "documentation": "",
+                        "field_order": [
+                            "input_value",
+                            "sender",
+                            "sender_name",
+                            "session_id",
+                            "data_template"
+                        ],
+                        "frozen": false,
+                        "icon": "ChatOutput",
+                        "output_types": [],
+                        "outputs": [
+                            {
+                                "cache": true,
+                                "display_name": "Message",
+                                "method": "message_response",
+                                "name": "message",
+                                "selected": "Message",
+                                "types": [
+                                    "Message"
+                                ],
+                                "value": "__UNDEFINED__"
+                            }
+                        ],
+                        "pinned": false,
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "advanced": true,
+                                "dynamic": true,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "code",
+                                "password": false,
+                                "placeholder": "",
+                                "required": true,
+                                "show": true,
+                                "title_case": false,
+                                "type": "code",
+                                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+                            },
+                            "data_template": {
+                                "advanced": true,
+                                "display_name": "Data Template",
+                                "dynamic": false,
+                                "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
+                                "input_types": [
+                                    "Message",
+                                    "str"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "data_template",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": "{text}"
+                            },
+                            "input_value": {
+                                "advanced": false,
+                                "display_name": "Text",
+                                "dynamic": false,
+                                "info": "Message to be passed as output.",
+                                "input_types": [
+                                    "Message",
+                                    "str"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "input_value",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": ""
+                            },
+                            "sender": {
+                                "advanced": true,
+                                "display_name": "Sender Type",
+                                "dynamic": false,
+                                "info": "Type of sender.",
+                                "name": "sender",
+                                "options": [
+                                    "Machine",
+                                    "User"
+                                ],
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": "Machine"
+                            },
+                            "sender_name": {
+                                "advanced": true,
+                                "display_name": "Sender Name",
+                                "dynamic": false,
+                                "info": "Name of the sender.",
+                                "input_types": [
+                                    "Message",
+                                    "str"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "sender_name",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": "AI"
+                            },
+                            "session_id": {
+                                "advanced": true,
+                                "display_name": "Session ID",
+                                "dynamic": false,
+                                "info": "Session ID for the message.",
+                                "input_types": [
+                                    "Message",
+                                    "str"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "session_id",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": ""
+                            }
+                        }
+                    },
+                    "type": "ChatOutput"
+                },
+                "dragging": false,
+                "height": 309,
+                "id": "ChatOutput-LkJX7",
+                "position": {
+                    "x": 2668.5087497211402,
+                    "y": 859.3268817022193
+                },
+                "positionAbsolute": {
+                    "x": 2668.5087497211402,
+                    "y": 859.3268817022193
+                },
+                "selected": false,
+                "type": "genericNode",
+                "width": 384
+            }
+        ],
+        "viewport": {
+            "x": 40.848461446679266,
+            "y": 89.0650521913791,
+            "zoom": 0.3782109149354305
+        }
+    },
+    "description": "This flow can be used to create a blog post following instructions from the user, using two other blogs as reference.",
+    "endpoint_name": null,
+    "id": "abcd5472-71fb-431c-9a08-6fd7781ffaa4",
+    "is_component": false,
+    "last_tested_version": "1.0.0a61",
+    "name": "Blog Writer"
+}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json
deleted file mode 100644
index 044ff92b0..000000000
--- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json	
+++ /dev/null
@@ -1,1059 +0,0 @@
-{
-  "data": {
-    "edges": [
-      {
-        "className": "stroke-gray-900 stroke-connection",
-        "data": {
-          "sourceHandle": {
-            "baseClasses": [
-              "Record"
-            ],
-            "dataType": "URL",
-            "id": "URL-HYPkR"
-          },
-          "targetHandle": {
-            "fieldName": "reference_2",
-            "id": "Prompt-Rse03",
-            "inputTypes": [
-              "Document",
-              "BaseOutputParser",
-              "Record",
-              "Text"
-            ],
-            "type": "str"
-          }
-        },
-        "id": "reactflow__edge-URL-HYPkR{œbaseClassesœ:[œRecordœ],œdataTypeœ:œURLœ,œidœ:œURL-HYPkRœ}-Prompt-Rse03{œfieldNameœ:œreference_2œ,œidœ:œPrompt-Rse03œ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
-        "selected": false,
-        "source": "URL-HYPkR",
-        "sourceHandle": "{œbaseClassesœ: [œRecordœ], œdataTypeœ: œURLœ, œidœ: œURL-HYPkRœ}",
-        "style": {
-          "stroke": "#555"
-        },
-        "target": "Prompt-Rse03",
-        "targetHandle": "{œfieldNameœ: œreference_2œ, œidœ: œPrompt-Rse03œ, œinputTypesœ: [œDocumentœ, œBaseOutputParserœ, œRecordœ, œTextœ], œtypeœ: œstrœ}"
-      },
-      {
-        "className": "stroke-gray-900 stroke-connection",
-        "data": {
-          "sourceHandle": {
-            "baseClasses": [
-              "str",
-              "Text",
-              "object"
-            ],
-            "dataType": "OpenAIModel",
-            "id": "OpenAIModel-gi29P"
-          },
-          "targetHandle": {
-            "fieldName": "input_value",
-            "id": "ChatOutput-JPlxl",
-            "inputTypes": [
-              "Text"
-            ],
-            "type": "str"
-          }
-        },
-        "id": "reactflow__edge-OpenAIModel-gi29P{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-gi29Pœ}-ChatOutput-JPlxl{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-JPlxlœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
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-        "data": {
-          "description": "Create a prompt template with dynamic variables.",
-          "display_name": "Prompt",
-          "id": "Prompt-Rse03",
-          "node": {
-            "base_classes": [
-              "object",
-              "Text",
-              "str"
-            ],
-            "beta": false,
-            "custom_fields": {
-              "template": [
-                "reference_1",
-                "reference_2",
-                "instructions"
-              ]
-            },
-            "description": "Create a prompt template with dynamic variables.",
-            "display_name": "Prompt",
-            "documentation": "",
-            "error": null,
-            "field_formatters": {},
-            "field_order": [],
-            "frozen": false,
-            "full_path": null,
-            "icon": "prompts",
-            "is_composition": null,
-            "is_input": null,
-            "is_output": null,
-            "name": "",
-            "output_types": [
-              "Prompt"
-            ],
-            "template": {
-              "_type": "CustomComponent",
-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "code",
-                "value": "from langflow.custom import CustomComponent\nfrom langflow.field_typing import TemplateField\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    async def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Prompt:\n        prompt = await Prompt.from_template_and_variables(template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
-              },
-              "instructions": {
-                "advanced": false,
-                "display_name": "instructions",
-                "dynamic": false,
-                "field_type": "str",
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Document",
-                  "BaseOutputParser",
-                  "Record",
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "instructions",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": ""
-              },
-              "reference_1": {
-                "advanced": false,
-                "display_name": "reference_1",
-                "dynamic": false,
-                "field_type": "str",
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Document",
-                  "BaseOutputParser",
-                  "Record",
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "reference_1",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": ""
-              },
-              "reference_2": {
-                "advanced": false,
-                "display_name": "reference_2",
-                "dynamic": false,
-                "field_type": "str",
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Document",
-                  "BaseOutputParser",
-                  "Record",
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "reference_2",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": ""
-              },
-              "template": {
-                "advanced": false,
-                "display_name": "Template",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "template",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "prompt",
-                "value": "Reference 1:\n\n{reference_1}\n\n---\n\nReference 2:\n\n{reference_2}\n\n---\n\n{instructions}\n\nBlog: \n\n\n"
-              }
-            }
-          },
-          "type": "Prompt"
-        },
-        "dragging": false,
-        "height": 571,
-        "id": "Prompt-Rse03",
-        "position": {
-          "x": 1331.381712783371,
-          "y": 535.0279854229713
-        },
-        "positionAbsolute": {
-          "x": 1331.381712783371,
-          "y": 535.0279854229713
-        },
-        "selected": false,
-        "type": "genericNode",
-        "width": 384
-      },
-      {
-        "data": {
-          "id": "URL-HYPkR",
-          "node": {
-            "base_classes": [
-              "Record"
-            ],
-            "beta": false,
-            "custom_fields": {
-              "urls": null
-            },
-            "description": "Fetch content from one or more URLs.",
-            "display_name": "URL",
-            "documentation": "",
-            "field_formatters": {},
-            "field_order": [],
-            "frozen": false,
-            "icon": "layout-template",
-            "output_types": [
-              "Record"
-            ],
-            "template": {
-              "_type": "CustomComponent",
-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "code",
-                "value": "from typing import Any, Dict\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\n\n\nclass URLComponent(CustomComponent):\n    display_name = \"URL\"\n    description = \"Fetch content from one or more URLs.\"\n    icon = \"layout-template\"\n\n    def build_config(self) -> Dict[str, Any]:\n        return {\n            \"urls\": {\"display_name\": \"URL\"},\n        }\n\n    def build(\n        self,\n        urls: list[str],\n    ) -> list[Record]:\n        loader = WebBaseLoader(web_paths=[url for url in urls if url])\n        docs = loader.load()\n        records = self.to_records(docs)\n        self.status = records\n        return records\n"
-              },
-              "urls": {
-                "advanced": false,
-                "display_name": "URL",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "urls",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": [
-                  "https://www.promptingguide.ai/techniques/prompt_chaining"
-                ]
-              }
-            }
-          },
-          "type": "URL"
-        },
-        "dragging": false,
-        "height": 281,
-        "id": "URL-HYPkR",
-        "position": {
-          "x": 568.2971412887712,
-          "y": 700.9983368007821
-        },
-        "positionAbsolute": {
-          "x": 568.2971412887712,
-          "y": 700.9983368007821
-        },
-        "selected": false,
-        "type": "genericNode",
-        "width": 384
-      },
-      {
-        "data": {
-          "id": "ChatOutput-JPlxl",
-          "node": {
-            "base_classes": [
-              "Text",
-              "Record",
-              "object",
-              "str"
-            ],
-            "beta": false,
-            "custom_fields": {
-              "input_value": null,
-              "record_template": null,
-              "return_record": null,
-              "sender": null,
-              "sender_name": null,
-              "session_id": null
-            },
-            "description": "Display a chat message in the Playground.",
-            "display_name": "Chat Output",
-            "documentation": "",
-            "field_formatters": {},
-            "field_order": [],
-            "frozen": false,
-            "icon": "ChatOutput",
-            "output_types": [
-              "Message",
-              "Text"
-            ],
-            "template": {
-              "_type": "CustomComponent",
-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "code",
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        files: Optional[list[str]] = None,\n        return_message: Optional[bool] = False,\n    ) -> Union[Message, Text]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            files=files,\n            return_message=return_message,\n        )\n"
-              },
-              "input_value": {
-                "advanced": false,
-                "display_name": "Text",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "input_value",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "sender": {
-                "advanced": true,
-                "display_name": "Sender Type",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "sender",
-                "options": [
-                  "Machine",
-                  "User"
-                ],
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "Machine"
-              },
-              "sender_name": {
-                "advanced": false,
-                "display_name": "Sender Name",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "sender_name",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "AI"
-              },
-              "session_id": {
-                "advanced": true,
-                "display_name": "Session ID",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "If provided, the message will be stored in the memory.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "session_id",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              }
-            }
-          },
-          "type": "ChatOutput"
-        },
-        "height": 383,
-        "id": "ChatOutput-JPlxl",
-        "position": {
-          "x": 2503.8617424688505,
-          "y": 789.3005578928434
-        },
-        "selected": false,
-        "type": "genericNode",
-        "width": 384
-      },
-      {
-        "data": {
-          "id": "OpenAIModel-gi29P",
-          "node": {
-            "base_classes": [
-              "str",
-              "Text",
-              "object"
-            ],
-            "beta": false,
-            "custom_fields": {
-              "input_value": null,
-              "max_tokens": null,
-              "model_kwargs": null,
-              "model_name": null,
-              "openai_api_base": null,
-              "openai_api_key": null,
-              "stream": null,
-              "system_message": null,
-              "temperature": null
-            },
-            "description": "Generates text using OpenAI LLMs.",
-            "display_name": "OpenAI",
-            "documentation": "",
-            "field_formatters": {},
-            "field_order": [
-              "max_tokens",
-              "model_kwargs",
-              "model_name",
-              "openai_api_base",
-              "openai_api_key",
-              "temperature",
-              "input_value",
-              "system_message",
-              "stream"
-            ],
-            "frozen": false,
-            "icon": "OpenAI",
-            "output_types": [
-              "Text"
-            ],
-            "template": {
-              "_type": "CustomComponent",
-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "code",
-                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\", \"Prompt\"]},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-3.5-turbo\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n"
-              },
-              "input_value": {
-                "advanced": false,
-                "display_name": "Input",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text",
-                  "Record",
-                  "Prompt"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "input_value",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "max_tokens": {
-                "advanced": true,
-                "display_name": "Max Tokens",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "max_tokens",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "int",
-                "value": "1024"
-              },
-              "model_kwargs": {
-                "advanced": true,
-                "display_name": "Model Kwargs",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "model_kwargs",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "NestedDict",
-                "value": {}
-              },
-              "model_name": {
-                "advanced": false,
-                "display_name": "Model Name",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "model_name",
-                "options": [
-                  "gpt-4o",
-                  "gpt-4-turbo",
-                  "gpt-4-turbo-preview",
-                  "gpt-3.5-turbo",
-                  "gpt-3.5-turbo-0125"
-                ],
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "gpt-3.5-turbo"
-              },
-              "openai_api_base": {
-                "advanced": true,
-                "display_name": "OpenAI API Base",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "openai_api_base",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "openai_api_key": {
-                "advanced": false,
-                "display_name": "OpenAI API Key",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "The OpenAI API Key to use for the OpenAI model.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": true,
-                "multiline": false,
-                "name": "openai_api_key",
-                "password": true,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "OPENAI_API_KEY"
-              },
-              "stream": {
-                "advanced": true,
-                "display_name": "Stream",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Stream the response from the model. Streaming works only in Chat.",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "stream",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "bool",
-                "value": true
-              },
-              "system_message": {
-                "advanced": true,
-                "display_name": "System Message",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "System message to pass to the model.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "system_message",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "temperature": {
-                "advanced": false,
-                "display_name": "Temperature",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "temperature",
-                "password": false,
-                "placeholder": "",
-                "rangeSpec": {
-                  "max": 1,
-                  "min": -1,
-                  "step": 0.1,
-                  "step_type": "float"
-                },
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "float",
-                "value": "0.1"
-              }
-            }
-          },
-          "type": "OpenAIModel"
-        },
-        "dragging": false,
-        "height": 563,
-        "id": "OpenAIModel-gi29P",
-        "position": {
-          "x": 1917.7089968570963,
-          "y": 575.9186499244129
-        },
-        "positionAbsolute": {
-          "x": 1917.7089968570963,
-          "y": 575.9186499244129
-        },
-        "selected": false,
-        "type": "genericNode",
-        "width": 384
-      },
-      {
-        "data": {
-          "id": "URL-2cX90",
-          "node": {
-            "base_classes": [
-              "Record"
-            ],
-            "beta": false,
-            "custom_fields": {
-              "urls": null
-            },
-            "description": "Fetch content from one or more URLs.",
-            "display_name": "URL",
-            "documentation": "",
-            "field_formatters": {},
-            "field_order": [],
-            "frozen": false,
-            "icon": "layout-template",
-            "output_types": [
-              "Record"
-            ],
-            "template": {
-              "_type": "CustomComponent",
-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "code",
-                "value": "from typing import Any, Dict\n\nfrom langchain_community.document_loaders.web_base import WebBaseLoader\n\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\n\n\nclass URLComponent(CustomComponent):\n    display_name = \"URL\"\n    description = \"Fetch content from one or more URLs.\"\n    icon = \"layout-template\"\n\n    def build_config(self) -> Dict[str, Any]:\n        return {\n            \"urls\": {\"display_name\": \"URL\"},\n        }\n\n    def build(\n        self,\n        urls: list[str],\n    ) -> list[Record]:\n        loader = WebBaseLoader(web_paths=[url for url in urls if url])\n        docs = loader.load()\n        records = self.to_records(docs)\n        self.status = records\n        return records\n"
-              },
-              "urls": {
-                "advanced": false,
-                "display_name": "URL",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "urls",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": [
-                  "https://www.promptingguide.ai/introduction/basics"
-                ]
-              }
-            }
-          },
-          "type": "URL"
-        },
-        "dragging": false,
-        "height": 281,
-        "id": "URL-2cX90",
-        "position": {
-          "x": 573.961301764604,
-          "y": 336.41463436122086
-        },
-        "positionAbsolute": {
-          "x": 573.961301764604,
-          "y": 336.41463436122086
-        },
-        "selected": false,
-        "type": "genericNode",
-        "width": 384
-      },
-      {
-        "data": {
-          "id": "TextInput-og8Or",
-          "node": {
-            "base_classes": [
-              "object",
-              "Text",
-              "str"
-            ],
-            "beta": false,
-            "custom_fields": {
-              "input_value": null,
-              "record_template": null
-            },
-            "description": "Get text inputs from the Playground.",
-            "display_name": "Instructions",
-            "documentation": "",
-            "field_formatters": {},
-            "field_order": [],
-            "frozen": false,
-            "icon": "type",
-            "output_types": [
-              "Text"
-            ],
-            "template": {
-              "_type": "CustomComponent",
-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "code",
-                "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextInput(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n\n    def build_config(self):\n        return {\n            \"input_value\": {\n                \"display_name\": \"Value\",\n                \"input_types\": [\"Record\", \"Text\"],\n                \"info\": \"Text or Record to be passed as input.\",\n            },\n            \"record_template\": {\n                \"display_name\": \"Record Template\",\n                \"multiline\": True,\n                \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Optional[str] = \"\",\n        record_template: Optional[str] = \"\",\n    ) -> Text:\n        return super().build(input_value=input_value, record_template=record_template)\n"
-              },
-              "input_value": {
-                "advanced": false,
-                "display_name": "Value",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Text or Record to be passed as input.",
-                "input_types": [
-                  "Record",
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "input_value",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "Use the references above for style to write a new blog/tutorial about prompt engineering techniques. Suggest non-covered topics."
-              },
-              "record_template": {
-                "advanced": true,
-                "display_name": "Record Template",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "record_template",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": ""
-              }
-            }
-          },
-          "type": "TextInput"
-        },
-        "dragging": false,
-        "height": 289,
-        "id": "TextInput-og8Or",
-        "position": {
-          "x": 569.9387927203336,
-          "y": 1095.3352160671316
-        },
-        "positionAbsolute": {
-          "x": 569.9387927203336,
-          "y": 1095.3352160671316
-        },
-        "selected": false,
-        "type": "genericNode",
-        "width": 384
-      }
-    ],
-    "viewport": {
-      "x": -214.14726025721177,
-      "y": -35.83855793844168,
-      "zoom": 0.47344308394045925
-    }
-  },
-  "description": "This flow can be used to create a blog post following instructions from the user, using two other blogs as reference.",
-  "id": "6ad5559d-fb66-4fdc-8f98-96f4ac12799d",
-  "is_component": false,
-  "last_tested_version": "1.0.0a0",
-  "name": "Blog Writer"
-}
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json
index 8bcaab3f3..09429630d 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json	
@@ -1,995 +1,1026 @@
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-        "style": {
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-            "dataType": "File",
-            "id": "File-6TEsD"
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-          "targetHandle": {
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-            "id": "Prompt-tHwPf",
-            "inputTypes": [
-              "Document",
-              "BaseOutputParser",
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-            "type": "str"
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-          "display_name": "Prompt",
-          "id": "Prompt-tHwPf",
-          "node": {
-            "base_classes": [
-              "object",
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-            "custom_fields": {
-              "template": [
-                "Document",
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-            },
-            "description": "Create a prompt template with dynamic variables.",
-            "display_name": "Prompt",
-            "documentation": "",
-            "error": null,
-            "field_formatters": {},
-            "field_order": [],
-            "frozen": false,
-            "full_path": null,
-            "icon": "prompts",
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-            "is_input": null,
-            "is_output": null,
-            "name": "",
-            "output_types": [
-              "Prompt"
-            ],
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-                "display_name": "Document",
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-                "field_type": "str",
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-                "display_name": "Question",
-                "dynamic": false,
-                "field_type": "str",
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Document",
-                  "BaseOutputParser",
-                  "Record",
-                  "Text"
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-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "Question",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": ""
-              },
-              "_type": "CustomComponent",
-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "code",
-                "value": "from langflow.custom import CustomComponent\nfrom langflow.field_typing import TemplateField\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    async def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Prompt:\n        prompt = await Prompt.from_template_and_variables(template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
-              },
-              "template": {
-                "advanced": false,
-                "display_name": "Template",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "template",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "prompt",
-                "value": "Answer user's questions based on the document below:\n\n---\n\n{Document}\n\n---\n\nQuestion:\n{Question}\n\nAnswer:\n"
-              }
-            }
-          },
-          "type": "Prompt"
-        },
-        "dragging": false,
-        "height": 479,
-        "id": "Prompt-tHwPf",
-        "position": {
-          "x": 585.7906101139403,
-          "y": 117.52115876762832
-        },
-        "positionAbsolute": {
-          "x": 585.7906101139403,
-          "y": 117.52115876762832
-        },
-        "selected": false,
-        "type": "genericNode",
-        "width": 384
-      },
-      {
-        "data": {
-          "id": "File-6TEsD",
-          "node": {
-            "base_classes": [
-              "Record"
-            ],
-            "beta": false,
-            "custom_fields": {
-              "path": null,
-              "silent_errors": null
-            },
-            "description": "A generic file loader.",
-            "display_name": "Files",
-            "documentation": "",
-            "field_formatters": {},
-            "field_order": [],
-            "frozen": false,
-            "output_types": [
-              "Record"
-            ],
-            "template": {
-              "_type": "CustomComponent",
-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "code",
-                "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n    display_name = \"Files\"\n    description = \"A generic file loader.\"\n\n    def build_config(self) -> Dict[str, Any]:\n        return {\n            \"path\": {\n                \"display_name\": \"Path\",\n                \"field_type\": \"file\",\n                \"file_types\": TEXT_FILE_TYPES,\n                \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n            },\n            \"silent_errors\": {\n                \"display_name\": \"Silent Errors\",\n                \"advanced\": True,\n                \"info\": \"If true, errors will not raise an exception.\",\n            },\n        }\n\n    def load_file(self, path: str, silent_errors: bool = False) -> Record:\n        resolved_path = self.resolve_path(path)\n        path_obj = Path(resolved_path)\n        extension = path_obj.suffix[1:].lower()\n        if extension == \"doc\":\n            raise ValueError(\"doc files are not supported. Please save as .docx\")\n        if extension not in TEXT_FILE_TYPES:\n            raise ValueError(f\"Unsupported file type: {extension}\")\n        record = parse_text_file_to_record(resolved_path, silent_errors)\n        self.status = record if record else \"No data\"\n        return record or Record()\n\n    def build(\n        self,\n        path: str,\n        silent_errors: bool = False,\n    ) -> Record:\n        record = self.load_file(path, silent_errors)\n        self.status = record\n        return record\n"
-              },
-              "path": {
-                "advanced": false,
-                "display_name": "Path",
-                "dynamic": false,
-                "fileTypes": [
-                  ".txt",
-                  ".md",
-                  ".mdx",
-                  ".csv",
-                  ".json",
-                  ".yaml",
-                  ".yml",
-                  ".xml",
-                  ".html",
-                  ".htm",
-                  ".pdf",
-                  ".docx"
-                ],
-                "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "path",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "file",
-                "value": ""
-              },
-              "silent_errors": {
-                "advanced": true,
-                "display_name": "Silent Errors",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "If true, errors will not raise an exception.",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "silent_errors",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "bool",
-                "value": false
-              }
-            }
-          },
-          "type": "File"
-        },
-        "dragging": false,
-        "height": 282,
-        "id": "File-6TEsD",
-        "position": {
-          "x": -18.636536329280602,
-          "y": 3.951948774836353
-        },
-        "positionAbsolute": {
-          "x": -18.636536329280602,
-          "y": 3.951948774836353
-        },
-        "selected": false,
-        "type": "genericNode",
-        "width": 384
-      },
-      {
-        "data": {
-          "id": "ChatInput-MsSJ9",
-          "node": {
-            "base_classes": [
-              "str",
-              "Record",
-              "Text",
-              "object"
-            ],
-            "beta": false,
-            "custom_fields": {
-              "input_value": null,
-              "return_record": null,
-              "sender": null,
-              "sender_name": null,
-              "session_id": null
-            },
-            "description": "Get chat inputs from the Playground.",
-            "display_name": "Chat Input",
-            "documentation": "",
-            "field_formatters": {},
-            "field_order": [],
-            "frozen": false,
-            "icon": "ChatInput",
-            "output_types": [
-              "Message",
-              "Text"
-            ],
-            "template": {
-              "_type": "CustomComponent",
-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "code",
-                "value": "from typing import Optional\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.schema.message import Message\nfrom langflow.field_typing import Text\nfrom typing import Union\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    def build_config(self):\n        build_config = super().build_config()\n        build_config[\"input_value\"] = {\n            \"input_types\": [],\n            \"display_name\": \"Text\",\n            \"multiline\": True,\n        }\n        build_config[\"return_message\"] = {\n            \"display_name\": \"Return Record\",\n            \"advanced\": True,\n        }\n\n        return build_config\n\n    def build(\n        self,\n        sender: Optional[str] = \"User\",\n        sender_name: Optional[str] = \"User\",\n        input_value: Optional[str] = None,\n        files: Optional[list[str]] = None,\n        session_id: Optional[str] = None,\n        return_message: Optional[bool] = True,\n    ) -> Union[Message, Text]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            files=files,\n            session_id=session_id,\n            return_message=return_message,\n        )\n"
-              },
-              "input_value": {
-                "advanced": false,
-                "display_name": "Text",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [],
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "input_value",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": ""
-              },
-              "sender": {
-                "advanced": true,
-                "display_name": "Sender Type",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "sender",
-                "options": [
-                  "Machine",
-                  "User"
-                ],
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "User"
-              },
-              "sender_name": {
-                "advanced": false,
-                "display_name": "Sender Name",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "sender_name",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "User"
-              },
-              "session_id": {
-                "advanced": true,
-                "display_name": "Session ID",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "If provided, the message will be stored in the memory.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "session_id",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              }
-            }
-          },
-          "type": "ChatInput"
-        },
-        "dragging": false,
-        "height": 377,
-        "id": "ChatInput-MsSJ9",
-        "position": {
-          "x": -28.80036300619821,
-          "y": 379.81180230285355
-        },
-        "positionAbsolute": {
-          "x": -28.80036300619821,
-          "y": 379.81180230285355
-        },
-        "selected": true,
-        "type": "genericNode",
-        "width": 384
-      },
-      {
-        "data": {
-          "id": "ChatOutput-F5Awj",
-          "node": {
-            "base_classes": [
-              "str",
-              "Record",
-              "Text",
-              "object"
-            ],
-            "beta": false,
-            "custom_fields": {
-              "input_value": null,
-              "return_record": null,
-              "sender": null,
-              "sender_name": null,
-              "session_id": null
-            },
-            "description": "Display a chat message in the Playground.",
-            "display_name": "Chat Output",
-            "documentation": "",
-            "field_formatters": {},
-            "field_order": [],
-            "frozen": false,
-            "icon": "ChatOutput",
-            "output_types": [
-              "Message",
-              "Text"
-            ],
-            "template": {
-              "_type": "CustomComponent",
-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "code",
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        files: Optional[list[str]] = None,\n        return_message: Optional[bool] = False,\n    ) -> Union[Message, Text]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            files=files,\n            return_message=return_message,\n        )\n"
-              },
-              "input_value": {
-                "advanced": false,
-                "display_name": "Text",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "input_value",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "sender": {
-                "advanced": true,
-                "display_name": "Sender Type",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "sender",
-                "options": [
-                  "Machine",
-                  "User"
-                ],
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "Machine"
-              },
-              "sender_name": {
-                "advanced": false,
-                "display_name": "Sender Name",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "sender_name",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "AI"
-              },
-              "session_id": {
-                "advanced": true,
-                "display_name": "Session ID",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "If provided, the message will be stored in the memory.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "session_id",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              }
-            }
-          },
-          "type": "ChatOutput"
-        },
-        "dragging": false,
-        "height": 385,
-        "id": "ChatOutput-F5Awj",
-        "position": {
-          "x": 1733.3012915204283,
-          "y": 168.76098809939327
-        },
-        "positionAbsolute": {
-          "x": 1733.3012915204283,
-          "y": 168.76098809939327
-        },
-        "selected": false,
-        "type": "genericNode",
-        "width": 384
-      },
-      {
-        "data": {
-          "id": "OpenAIModel-Bt067",
-          "node": {
-            "base_classes": [
-              "object",
-              "str",
-              "Text"
-            ],
-            "beta": false,
-            "custom_fields": {
-              "input_value": null,
-              "max_tokens": null,
-              "model_kwargs": null,
-              "model_name": null,
-              "openai_api_base": null,
-              "openai_api_key": null,
-              "stream": null,
-              "system_message": null,
-              "temperature": null
-            },
-            "description": "Generates text using OpenAI LLMs.",
-            "display_name": "OpenAI",
-            "documentation": "",
-            "field_formatters": {},
-            "field_order": [
-              "max_tokens",
-              "model_kwargs",
-              "model_name",
-              "openai_api_base",
-              "openai_api_key",
-              "temperature",
-              "input_value",
-              "system_message",
-              "stream"
-            ],
-            "frozen": false,
-            "icon": "OpenAI",
-            "output_types": [
-              "Text"
-            ],
-            "template": {
-              "_type": "CustomComponent",
-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "code",
-                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\", \"Prompt\"]},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-3.5-turbo\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n"
-              },
-              "input_value": {
-                "advanced": false,
-                "display_name": "Input",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text",
-                  "Record",
-                  "Prompt"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "input_value",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "max_tokens": {
-                "advanced": true,
-                "display_name": "Max Tokens",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "max_tokens",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "int",
-                "value": 256
-              },
-              "model_kwargs": {
-                "advanced": true,
-                "display_name": "Model Kwargs",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "model_kwargs",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "NestedDict",
-                "value": {}
-              },
-              "model_name": {
-                "advanced": false,
-                "display_name": "Model Name",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "model_name",
-                "options": [
-                  "gpt-4o",
-                  "gpt-4-turbo",
-                  "gpt-4-turbo-preview",
-                  "gpt-3.5-turbo",
-                  "gpt-3.5-turbo-0125"
-                ],
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "gpt-3.5-turbo"
-              },
-              "openai_api_base": {
-                "advanced": true,
-                "display_name": "OpenAI API Base",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "openai_api_base",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "openai_api_key": {
-                "advanced": false,
-                "display_name": "OpenAI API Key",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "The OpenAI API Key to use for the OpenAI model.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": true,
-                "multiline": false,
-                "name": "openai_api_key",
-                "password": true,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "OPENAI_API_KEY"
-              },
-              "stream": {
-                "advanced": false,
-                "display_name": "Stream",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Stream the response from the model. Streaming works only in Chat.",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "stream",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "bool",
-                "value": true
-              },
-              "system_message": {
-                "advanced": true,
-                "display_name": "System Message",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "System message to pass to the model.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "system_message",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "temperature": {
-                "advanced": false,
-                "display_name": "Temperature",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "temperature",
-                "password": false,
-                "placeholder": "",
-                "rangeSpec": {
-                  "max": 1,
-                  "min": -1,
-                  "step": 0.1,
-                  "step_type": "float"
+    "data": {
+        "edges": [
+            {
+                "className": "",
+                "data": {
+                    "sourceHandle": {
+                        "dataType": "File",
+                        "id": "File-z24tW",
+                        "name": "data",
+                        "output_types": [
+                            "Data"
+                        ]
+                    },
+                    "targetHandle": {
+                        "fieldName": "Document",
+                        "id": "Prompt-ws12t",
+                        "inputTypes": [
+                            "Document",
+                            "Message",
+                            "Data",
+                            "Text"
+                        ],
+                        "type": "str"
+                    }
                 },
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "float",
-                "value": 0.1
-              }
+                "id": "reactflow__edge-File-z24tW{œdataTypeœ:œFileœ,œidœ:œFile-z24tWœ,œnameœ:œdataœ,œoutput_typesœ:[œDataœ]}-Prompt-ws12t{œfieldNameœ:œDocumentœ,œidœ:œPrompt-ws12tœ,œinputTypesœ:[œDocumentœ,œMessageœ,œDataœ,œTextœ],œtypeœ:œstrœ}",
+                "source": "File-z24tW",
+                "sourceHandle": "{œdataTypeœ: œFileœ, œidœ: œFile-z24tWœ, œnameœ: œdataœ, œoutput_typesœ: [œDataœ]}",
+                "target": "Prompt-ws12t",
+                "targetHandle": "{œfieldNameœ: œDocumentœ, œidœ: œPrompt-ws12tœ, œinputTypesœ: [œDocumentœ, œMessageœ, œDataœ, œTextœ], œtypeœ: œstrœ}"
+            },
+            {
+                "className": "",
+                "data": {
+                    "sourceHandle": {
+                        "dataType": "ChatInput",
+                        "id": "ChatInput-YMjNE",
+                        "name": "message",
+                        "output_types": [
+                            "Message"
+                        ]
+                    },
+                    "targetHandle": {
+                        "fieldName": "Question",
+                        "id": "Prompt-ws12t",
+                        "inputTypes": [
+                            "Document",
+                            "Message",
+                            "Data",
+                            "Text"
+                        ],
+                        "type": "str"
+                    }
+                },
+                "id": "reactflow__edge-ChatInput-YMjNE{œdataTypeœ:œChatInputœ,œidœ:œChatInput-YMjNEœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Prompt-ws12t{œfieldNameœ:œQuestionœ,œidœ:œPrompt-ws12tœ,œinputTypesœ:[œDocumentœ,œMessageœ,œDataœ,œTextœ],œtypeœ:œstrœ}",
+                "source": "ChatInput-YMjNE",
+                "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-YMjNEœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
+                "target": "Prompt-ws12t",
+                "targetHandle": "{œfieldNameœ: œQuestionœ, œidœ: œPrompt-ws12tœ, œinputTypesœ: [œDocumentœ, œMessageœ, œDataœ, œTextœ], œtypeœ: œstrœ}"
+            },
+            {
+                "className": "",
+                "data": {
+                    "sourceHandle": {
+                        "dataType": "Prompt",
+                        "id": "Prompt-ws12t",
+                        "name": "prompt",
+                        "output_types": [
+                            "Message"
+                        ]
+                    },
+                    "targetHandle": {
+                        "fieldName": "input_value",
+                        "id": "OpenAIModel-AdQdh",
+                        "inputTypes": [
+                            "Message"
+                        ],
+                        "type": "str"
+                    }
+                },
+                "id": "reactflow__edge-Prompt-ws12t{œdataTypeœ:œPromptœ,œidœ:œPrompt-ws12tœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-AdQdh{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-AdQdhœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+                "source": "Prompt-ws12t",
+                "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-ws12tœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
+                "target": "OpenAIModel-AdQdh",
+                "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-AdQdhœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+            },
+            {
+                "className": "",
+                "data": {
+                    "sourceHandle": {
+                        "dataType": "OpenAIModel",
+                        "id": "OpenAIModel-AdQdh",
+                        "name": "text_output",
+                        "output_types": [
+                            "Message"
+                        ]
+                    },
+                    "targetHandle": {
+                        "fieldName": "input_value",
+                        "id": "ChatOutput-bSlkp",
+                        "inputTypes": [
+                            "Message",
+                            "str"
+                        ],
+                        "type": "str"
+                    }
+                },
+                "id": "reactflow__edge-OpenAIModel-AdQdh{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-AdQdhœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-bSlkp{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-bSlkpœ,œinputTypesœ:[œMessageœ,œstrœ],œtypeœ:œstrœ}",
+                "source": "OpenAIModel-AdQdh",
+                "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-AdQdhœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
+                "target": "ChatOutput-bSlkp",
+                "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-bSlkpœ, œinputTypesœ: [œMessageœ, œstrœ], œtypeœ: œstrœ}"
             }
-          },
-          "type": "OpenAIModel"
-        },
-        "dragging": false,
-        "height": 642,
-        "id": "OpenAIModel-Bt067",
-        "position": {
-          "x": 1137.6078582863759,
-          "y": -14.41920034020356
-        },
-        "positionAbsolute": {
-          "x": 1137.6078582863759,
-          "y": -14.41920034020356
-        },
-        "selected": false,
-        "type": "genericNode",
-        "width": 384
-      }
-    ],
-    "viewport": {
-      "x": 352.20899206064655,
-      "y": 56.054900898593075,
-      "zoom": 0.9023391400011
-    }
-  },
-  "description": "This flow integrates PDF reading with a language model to answer document-specific questions. Ideal for small-scale texts, it facilitates direct queries with immediate insights.",
-  "id": "fecbce42-6f11-454c-8ab2-db6eddbbbb0f",
-  "is_component": false,
-  "last_tested_version": "1.0.0a0",
-  "name": "Document QA"
+        ],
+        "nodes": [
+            {
+                "data": {
+                    "description": "Create a prompt template with dynamic variables.",
+                    "display_name": "Prompt",
+                    "id": "Prompt-ws12t",
+                    "node": {
+                        "base_classes": [
+                            "object",
+                            "str",
+                            "Text"
+                        ],
+                        "beta": false,
+                        "conditional_paths": [],
+                        "custom_fields": {
+                            "template": [
+                                "Document",
+                                "Question"
+                            ]
+                        },
+                        "description": "Create a prompt template with dynamic variables.",
+                        "display_name": "Prompt",
+                        "documentation": "",
+                        "error": null,
+                        "field_order": [],
+                        "frozen": false,
+                        "full_path": null,
+                        "icon": "prompts",
+                        "is_composition": null,
+                        "is_input": null,
+                        "is_output": null,
+                        "name": "",
+                        "output_types": [],
+                        "outputs": [
+                            {
+                                "cache": true,
+                                "display_name": "Prompt Message",
+                                "method": "build_prompt",
+                                "name": "prompt",
+                                "selected": "Message",
+                                "types": [
+                                    "Message"
+                                ],
+                                "value": "__UNDEFINED__"
+                            }
+                        ],
+                        "pinned": false,
+                        "template": {
+                            "Document": {
+                                "advanced": false,
+                                "display_name": "Document",
+                                "dynamic": false,
+                                "field_type": "str",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "input_types": [
+                                    "Document",
+                                    "Message",
+                                    "Data",
+                                    "Text"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "Document",
+                                "password": false,
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": ""
+                            },
+                            "Question": {
+                                "advanced": false,
+                                "display_name": "Question",
+                                "dynamic": false,
+                                "field_type": "str",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "input_types": [
+                                    "Document",
+                                    "Message",
+                                    "Data",
+                                    "Text"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "Question",
+                                "password": false,
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": ""
+                            },
+                            "_type": "Component",
+                            "code": {
+                                "advanced": true,
+                                "dynamic": true,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "code",
+                                "password": false,
+                                "placeholder": "",
+                                "required": true,
+                                "show": true,
+                                "title_case": false,
+                                "type": "code",
+                                "value": "from langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n"
+                            },
+                            "template": {
+                                "advanced": false,
+                                "display_name": "Template",
+                                "dynamic": false,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "input_types": [
+                                    "Text"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": false,
+                                "name": "template",
+                                "password": false,
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "prompt",
+                                "value": "Answer user's questions based on the document below:\n\n---\n\n{Document}\n\n---\n\nQuestion:\n{Question}\n\nAnswer:\n"
+                            }
+                        }
+                    },
+                    "type": "Prompt"
+                },
+                "dragging": false,
+                "height": 525,
+                "id": "Prompt-ws12t",
+                "position": {
+                    "x": 585.7906101139403,
+                    "y": 117.52115876762832
+                },
+                "positionAbsolute": {
+                    "x": 585.7906101139403,
+                    "y": 117.52115876762832
+                },
+                "selected": false,
+                "type": "genericNode",
+                "width": 384
+            },
+            {
+                "data": {
+                    "id": "ChatInput-YMjNE",
+                    "node": {
+                        "base_classes": [
+                            "str",
+                            "Record",
+                            "Text",
+                            "object"
+                        ],
+                        "beta": false,
+                        "custom_fields": {
+                            "input_value": null,
+                            "return_record": null,
+                            "sender": null,
+                            "sender_name": null,
+                            "session_id": null
+                        },
+                        "description": "Get chat inputs from the Playground.",
+                        "display_name": "Chat Input",
+                        "documentation": "",
+                        "field_formatters": {},
+                        "field_order": [],
+                        "frozen": false,
+                        "icon": "ChatInput",
+                        "output_types": [],
+                        "outputs": [
+                            {
+                                "cache": true,
+                                "display_name": "Message",
+                                "method": "message_response",
+                                "name": "message",
+                                "selected": "Message",
+                                "types": [
+                                    "Message"
+                                ],
+                                "value": "__UNDEFINED__"
+                            }
+                        ],
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "advanced": true,
+                                "dynamic": true,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "code",
+                                "password": false,
+                                "placeholder": "",
+                                "required": true,
+                                "show": true,
+                                "title_case": false,
+                                "type": "code",
+                                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, FileInput, MultilineInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+                            },
+                            "input_value": {
+                                "advanced": false,
+                                "display_name": "Text",
+                                "dynamic": false,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "Message to be passed as input.",
+                                "input_types": [
+                                    "Message",
+                                    "str"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "input_value",
+                                "password": false,
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": "what is this?"
+                            },
+                            "sender": {
+                                "advanced": true,
+                                "display_name": "Sender Type",
+                                "dynamic": false,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "Type of sender.",
+                                "input_types": [
+                                    "Text"
+                                ],
+                                "list": true,
+                                "load_from_db": false,
+                                "multiline": false,
+                                "name": "sender",
+                                "options": [
+                                    "Machine",
+                                    "User"
+                                ],
+                                "password": false,
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": "User"
+                            },
+                            "sender_name": {
+                                "advanced": true,
+                                "display_name": "Sender Name",
+                                "dynamic": false,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "Name of the sender.",
+                                "input_types": [
+                                    "Message",
+                                    "str"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "sender_name",
+                                "password": false,
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": "User"
+                            },
+                            "session_id": {
+                                "advanced": true,
+                                "display_name": "Session ID",
+                                "dynamic": false,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "Session ID for the message.",
+                                "input_types": [
+                                    "Message",
+                                    "str"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "session_id",
+                                "password": false,
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": ""
+                            }
+                        }
+                    },
+                    "type": "ChatInput"
+                },
+                "dragging": false,
+                "height": 309,
+                "id": "ChatInput-YMjNE",
+                "position": {
+                    "x": -38.501719080514135,
+                    "y": 379.81180230285355
+                },
+                "positionAbsolute": {
+                    "x": -38.501719080514135,
+                    "y": 379.81180230285355
+                },
+                "selected": false,
+                "type": "genericNode",
+                "width": 384
+            },
+            {
+                "data": {
+                    "id": "ChatOutput-bSlkp",
+                    "node": {
+                        "base_classes": [
+                            "str",
+                            "Record",
+                            "Text",
+                            "object"
+                        ],
+                        "beta": false,
+                        "custom_fields": {
+                            "input_value": null,
+                            "return_record": null,
+                            "sender": null,
+                            "sender_name": null,
+                            "session_id": null
+                        },
+                        "description": "Display a chat message in the Playground.",
+                        "display_name": "Chat Output",
+                        "documentation": "",
+                        "field_formatters": {},
+                        "field_order": [],
+                        "frozen": false,
+                        "icon": "ChatOutput",
+                        "output_types": [],
+                        "outputs": [
+                            {
+                                "cache": true,
+                                "display_name": "Message",
+                                "method": "message_response",
+                                "name": "message",
+                                "selected": "Message",
+                                "types": [
+                                    "Message"
+                                ],
+                                "value": "__UNDEFINED__"
+                            }
+                        ],
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "advanced": true,
+                                "dynamic": true,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "code",
+                                "password": false,
+                                "placeholder": "",
+                                "required": true,
+                                "show": true,
+                                "title_case": false,
+                                "type": "code",
+                                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+                            },
+                            "input_value": {
+                                "advanced": false,
+                                "display_name": "Text",
+                                "dynamic": false,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "Message to be passed as output.",
+                                "input_types": [
+                                    "Message",
+                                    "str"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "input_value",
+                                "password": false,
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": ""
+                            },
+                            "sender": {
+                                "advanced": true,
+                                "display_name": "Sender Type",
+                                "dynamic": false,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "Type of sender.",
+                                "input_types": [
+                                    "Text"
+                                ],
+                                "list": true,
+                                "load_from_db": false,
+                                "multiline": false,
+                                "name": "sender",
+                                "options": [
+                                    "Machine",
+                                    "User"
+                                ],
+                                "password": false,
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": "Machine"
+                            },
+                            "sender_name": {
+                                "advanced": true,
+                                "display_name": "Sender Name",
+                                "dynamic": false,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "Name of the sender.",
+                                "input_types": [
+                                    "Message",
+                                    "str"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "sender_name",
+                                "password": false,
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": "AI"
+                            },
+                            "session_id": {
+                                "advanced": true,
+                                "display_name": "Session ID",
+                                "dynamic": false,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "Session ID for the message.",
+                                "input_types": [
+                                    "Message",
+                                    "str"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "session_id",
+                                "password": false,
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": ""
+                            }
+                        }
+                    },
+                    "type": "ChatOutput"
+                },
+                "dragging": false,
+                "height": 309,
+                "id": "ChatOutput-bSlkp",
+                "position": {
+                    "x": 1733.3012915204283,
+                    "y": 168.76098809939327
+                },
+                "positionAbsolute": {
+                    "x": 1733.3012915204283,
+                    "y": 168.76098809939327
+                },
+                "selected": false,
+                "type": "genericNode",
+                "width": 384
+            },
+            {
+                "data": {
+                    "description": "A generic file loader.",
+                    "display_name": "File",
+                    "id": "File-z24tW",
+                    "node": {
+                        "base_classes": [
+                            "Data"
+                        ],
+                        "beta": false,
+                        "conditional_paths": [],
+                        "custom_fields": {},
+                        "description": "A generic file loader.",
+                        "display_name": "File",
+                        "documentation": "",
+                        "edited": false,
+                        "field_order": [
+                            "path",
+                            "silent_errors"
+                        ],
+                        "frozen": false,
+                        "icon": "file-text",
+                        "output_types": [],
+                        "outputs": [
+                            {
+                                "cache": true,
+                                "display_name": "Data",
+                                "method": "load_file",
+                                "name": "data",
+                                "selected": "Data",
+                                "types": [
+                                    "Data"
+                                ],
+                                "value": "__UNDEFINED__"
+                            }
+                        ],
+                        "pinned": false,
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "advanced": true,
+                                "dynamic": true,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "code",
+                                "password": false,
+                                "placeholder": "",
+                                "required": true,
+                                "show": true,
+                                "title_case": false,
+                                "type": "code",
+                                "value": "from pathlib import Path\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_data\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, FileInput, Output\nfrom langflow.schema import Data\n\n\nclass FileComponent(Component):\n    display_name = \"File\"\n    description = \"A generic file loader.\"\n    icon = \"file-text\"\n\n    inputs = [\n        FileInput(\n            name=\"path\",\n            display_name=\"Path\",\n            file_types=TEXT_FILE_TYPES,\n            info=f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n        ),\n        BoolInput(\n            name=\"silent_errors\",\n            display_name=\"Silent Errors\",\n            advanced=True,\n            info=\"If true, errors will not raise an exception.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"load_file\"),\n    ]\n\n    def load_file(self) -> Data:\n        if not self.path:\n            raise ValueError(\"Please, upload a file to use this component.\")\n        resolved_path = self.resolve_path(self.path)\n        silent_errors = self.silent_errors\n\n        extension = Path(resolved_path).suffix[1:].lower()\n\n        if extension == \"doc\":\n            raise ValueError(\"doc files are not supported. Please save as .docx\")\n        if extension not in TEXT_FILE_TYPES:\n            raise ValueError(f\"Unsupported file type: {extension}\")\n\n        data = parse_text_file_to_data(resolved_path, silent_errors)\n        self.status = data if data else \"No data\"\n        return data or Data()\n"
+                            },
+                            "path": {
+                                "advanced": false,
+                                "display_name": "Path",
+                                "dynamic": false,
+                                "fileTypes": [
+                                    "txt",
+                                    "md",
+                                    "mdx",
+                                    "csv",
+                                    "json",
+                                    "yaml",
+                                    "yml",
+                                    "xml",
+                                    "html",
+                                    "htm",
+                                    "pdf",
+                                    "docx",
+                                    "py",
+                                    "sh",
+                                    "sql",
+                                    "js",
+                                    "ts",
+                                    "tsx"
+                                ],
+                                "file_path": "e56e0529-7225-4f6c-9144-5ad0806f5fed/Math Router.json",
+                                "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx, py, sh, sql, js, ts, tsx",
+                                "list": false,
+                                "name": "path",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "file",
+                                "value": ""
+                            },
+                            "silent_errors": {
+                                "advanced": true,
+                                "display_name": "Silent Errors",
+                                "dynamic": false,
+                                "info": "If true, errors will not raise an exception.",
+                                "list": false,
+                                "name": "silent_errors",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "bool",
+                                "value": false
+                            }
+                        }
+                    },
+                    "type": "File"
+                },
+                "dragging": false,
+                "height": 301,
+                "id": "File-z24tW",
+                "position": {
+                    "x": -37.064128418041946,
+                    "y": 39.0475820447775
+                },
+                "positionAbsolute": {
+                    "x": -37.064128418041946,
+                    "y": 39.0475820447775
+                },
+                "selected": true,
+                "type": "genericNode",
+                "width": 384
+            },
+            {
+                "data": {
+                    "description": "Generates text using OpenAI LLMs.",
+                    "display_name": "OpenAI",
+                    "edited": false,
+                    "id": "OpenAIModel-AdQdh",
+                    "node": {
+                        "base_classes": [
+                            "LanguageModel",
+                            "Message"
+                        ],
+                        "beta": false,
+                        "conditional_paths": [],
+                        "custom_fields": {},
+                        "description": "Generates text using OpenAI LLMs.",
+                        "display_name": "OpenAI",
+                        "documentation": "",
+                        "edited": true,
+                        "field_order": [
+                            "input_value",
+                            "max_tokens",
+                            "model_kwargs",
+                            "output_schema",
+                            "model_name",
+                            "openai_api_base",
+                            "openai_api_key",
+                            "temperature",
+                            "stream",
+                            "system_message",
+                            "seed"
+                        ],
+                        "frozen": false,
+                        "icon": "OpenAI",
+                        "output_types": [],
+                        "outputs": [
+                            {
+                                "cache": true,
+                                "display_name": "Text",
+                                "method": "text_response",
+                                "name": "text_output",
+                                "selected": "Message",
+                                "types": [
+                                    "Message"
+                                ],
+                                "value": "__UNDEFINED__"
+                            },
+                            {
+                                "cache": true,
+                                "display_name": "Language Model",
+                                "method": "build_model",
+                                "name": "model_output",
+                                "selected": "LanguageModel",
+                                "types": [
+                                    "LanguageModel"
+                                ],
+                                "value": "__UNDEFINED__"
+                            }
+                        ],
+                        "pinned": false,
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "advanced": true,
+                                "dynamic": true,
+                                "fileTypes": [],
+                                "file_path": "",
+                                "info": "",
+                                "list": false,
+                                "load_from_db": false,
+                                "multiline": true,
+                                "name": "code",
+                                "password": false,
+                                "placeholder": "",
+                                "required": true,
+                                "show": true,
+                                "title_case": false,
+                                "type": "code",
+                                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    inputs = [\n        MessageInput(name=\"input_value\", display_name=\"Input\"),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:\n        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict)\n        seed = self.seed\n        model_kwargs[\"seed\"] = seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n"
+                            },
+                            "input_value": {
+                                "advanced": false,
+                                "display_name": "Input",
+                                "dynamic": false,
+                                "info": "",
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "list": false,
+                                "load_from_db": false,
+                                "name": "input_value",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": ""
+                            },
+                            "max_tokens": {
+                                "advanced": true,
+                                "display_name": "Max Tokens",
+                                "dynamic": false,
+                                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+                                "list": false,
+                                "name": "max_tokens",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "int",
+                                "value": ""
+                            },
+                            "model_kwargs": {
+                                "advanced": true,
+                                "display_name": "Model Kwargs",
+                                "dynamic": false,
+                                "info": "",
+                                "list": false,
+                                "name": "model_kwargs",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "dict",
+                                "value": {}
+                            },
+                            "model_name": {
+                                "advanced": false,
+                                "display_name": "Model Name",
+                                "dynamic": false,
+                                "info": "",
+                                "name": "model_name",
+                                "options": [
+                                    "gpt-4o",
+                                    "gpt-4-turbo",
+                                    "gpt-4-turbo-preview",
+                                    "gpt-3.5-turbo",
+                                    "gpt-3.5-turbo-0125"
+                                ],
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": "gpt-3.5-turbo"
+                            },
+                            "openai_api_base": {
+                                "advanced": true,
+                                "display_name": "OpenAI API Base",
+                                "dynamic": false,
+                                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.",
+                                "list": false,
+                                "load_from_db": false,
+                                "name": "openai_api_base",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": ""
+                            },
+                            "openai_api_key": {
+                                "advanced": false,
+                                "display_name": "OpenAI API Key",
+                                "dynamic": false,
+                                "info": "The OpenAI API Key to use for the OpenAI model.",
+                                "input_types": [],
+                                "load_from_db": true,
+                                "name": "openai_api_key",
+                                "password": true,
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": ""
+                            },
+                            "output_schema": {
+                                "advanced": true,
+                                "display_name": "Schema",
+                                "dynamic": false,
+                                "info": "The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.",
+                                "list": true,
+                                "name": "output_schema",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "dict",
+                                "value": {}
+                            },
+                            "seed": {
+                                "advanced": true,
+                                "display_name": "Seed",
+                                "dynamic": false,
+                                "info": "The seed controls the reproducibility of the job.",
+                                "list": false,
+                                "name": "seed",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "int",
+                                "value": 1
+                            },
+                            "stream": {
+                                "advanced": true,
+                                "display_name": "Stream",
+                                "dynamic": false,
+                                "info": "Stream the response from the model. Streaming works only in Chat.",
+                                "list": false,
+                                "name": "stream",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "bool",
+                                "value": false
+                            },
+                            "system_message": {
+                                "advanced": true,
+                                "display_name": "System Message",
+                                "dynamic": false,
+                                "info": "System message to pass to the model.",
+                                "list": false,
+                                "load_from_db": false,
+                                "name": "system_message",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "str",
+                                "value": ""
+                            },
+                            "temperature": {
+                                "advanced": false,
+                                "display_name": "Temperature",
+                                "dynamic": false,
+                                "info": "",
+                                "list": false,
+                                "name": "temperature",
+                                "placeholder": "",
+                                "required": false,
+                                "show": true,
+                                "title_case": false,
+                                "type": "float",
+                                "value": 0.1
+                            }
+                        }
+                    },
+                    "type": "OpenAIModel"
+                },
+                "dragging": false,
+                "height": 623,
+                "id": "OpenAIModel-AdQdh",
+                "position": {
+                    "x": 1141.7303854551026,
+                    "y": -51.19892217231286
+                },
+                "positionAbsolute": {
+                    "x": 1141.7303854551026,
+                    "y": -51.19892217231286
+                },
+                "selected": false,
+                "type": "genericNode",
+                "width": 384
+            }
+        ],
+        "viewport": {
+            "x": 91.58014849142035,
+            "y": 287.8736279905512,
+            "zoom": 0.5335671198494703
+        }
+    },
+    "description": "This flow integrates PDF reading with a language model to answer document-specific questions. Ideal for small-scale texts, it facilitates direct queries with immediate insights.",
+    "endpoint_name": null,
+    "id": "e56e0529-7225-4f6c-9144-5ad0806f5fed",
+    "is_component": false,
+    "last_tested_version": "1.0.0a61",
+    "name": "Document QA"
 }
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json
index d1bd09650..3d928b649 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json	
@@ -5,663 +5,131 @@
         "className": "",
         "data": {
           "sourceHandle": {
-            "baseClasses": [
-              "str",
-              "object",
-              "Text"
-            ],
-            "dataType": "OpenAIModel",
-            "id": "OpenAIModel-Neuec"
+            "dataType": "Memory",
+            "id": "Memory-rvcL5",
+            "name": "messages_text",
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
-            "fieldName": "input_value",
-            "id": "ChatOutput-cVR7W",
+            "fieldName": "context",
+            "id": "Prompt-VuDd0",
             "inputTypes": [
+              "Document",
+              "Message",
+              "Record",
               "Text"
             ],
             "type": "str"
           }
         },
-        "id": "reactflow__edge-OpenAIModel-Neuec{œbaseClassesœ:[œstrœ,œobjectœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-Neuecœ}-ChatOutput-cVR7W{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-cVR7Wœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
-        "source": "OpenAIModel-Neuec",
-        "sourceHandle": "{œbaseClassesœ: [œstrœ, œobjectœ, œTextœ], œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-Neuecœ}",
-        "style": {
-          "stroke": "#555"
-        },
-        "target": "ChatOutput-cVR7W",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-cVR7Wœ, œinputTypesœ: [œTextœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-Memory-rvcL5{œdataTypeœ:œMemoryœ,œidœ:œMemory-rvcL5œ,œnameœ:œmessages_textœ,œoutput_typesœ:[œMessageœ]}-Prompt-VuDd0{œfieldNameœ:œcontextœ,œidœ:œPrompt-VuDd0œ,œinputTypesœ:[œDocumentœ,œMessageœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
+        "source": "Memory-rvcL5",
+        "sourceHandle": "{œdataTypeœ: œMemoryœ, œidœ: œMemory-rvcL5œ, œnameœ: œmessages_textœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "Prompt-VuDd0",
+        "targetHandle": "{œfieldNameœ: œcontextœ, œidœ: œPrompt-VuDd0œ, œinputTypesœ: [œDocumentœ, œMessageœ, œRecordœ, œTextœ], œtypeœ: œstrœ}"
       },
       {
         "className": "",
         "data": {
           "sourceHandle": {
-            "baseClasses": [
-              "object",
-              "str",
+            "dataType": "ChatInput",
+            "id": "ChatInput-9iFsd",
+            "name": "message",
+            "output_types": [
+              "Message"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "user_message",
+            "id": "Prompt-VuDd0",
+            "inputTypes": [
+              "Document",
+              "Message",
+              "Record",
               "Text"
             ],
+            "type": "str"
+          }
+        },
+        "id": "reactflow__edge-ChatInput-9iFsd{œdataTypeœ:œChatInputœ,œidœ:œChatInput-9iFsdœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Prompt-VuDd0{œfieldNameœ:œuser_messageœ,œidœ:œPrompt-VuDd0œ,œinputTypesœ:[œDocumentœ,œMessageœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
+        "source": "ChatInput-9iFsd",
+        "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-9iFsdœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "Prompt-VuDd0",
+        "targetHandle": "{œfieldNameœ: œuser_messageœ, œidœ: œPrompt-VuDd0œ, œinputTypesœ: [œDocumentœ, œMessageœ, œRecordœ, œTextœ], œtypeœ: œstrœ}"
+      },
+      {
+        "className": "",
+        "data": {
+          "sourceHandle": {
             "dataType": "Prompt",
-            "id": "Prompt-kykM2"
+            "id": "Prompt-VuDd0",
+            "name": "prompt",
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "input_value",
-            "id": "OpenAIModel-Neuec",
+            "id": "OpenAIModel-uVOc5",
             "inputTypes": [
-              "Text",
-              "Record",
-              "Prompt"
+              "Message"
             ],
             "type": "str"
           }
         },
-        "id": "reactflow__edge-Prompt-kykM2{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-kykM2œ}-OpenAIModel-Neuec{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-Neuecœ,œinputTypesœ:[œTextœ,œRecordœ,œPromptœ],œtypeœ:œstrœ}",
-        "source": "Prompt-kykM2",
-        "sourceHandle": "{œbaseClassesœ: [œobjectœ, œstrœ, œTextœ], œdataTypeœ: œPromptœ, œidœ: œPrompt-kykM2œ}",
-        "target": "OpenAIModel-Neuec",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-Neuecœ, œinputTypesœ: [œTextœ, œRecordœ, œPromptœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-Prompt-VuDd0{œdataTypeœ:œPromptœ,œidœ:œPrompt-VuDd0œ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-uVOc5{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-uVOc5œ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+        "source": "Prompt-VuDd0",
+        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-VuDd0œ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "OpenAIModel-uVOc5",
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-uVOc5œ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
       },
       {
         "className": "",
         "data": {
           "sourceHandle": {
-            "baseClasses": [
-              "Text",
-              "object",
-              "Record",
+            "dataType": "OpenAIModel",
+            "id": "OpenAIModel-uVOc5",
+            "name": "text_output",
+            "output_types": [
+              "Message"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "input_value",
+            "id": "ChatOutput-R7jsA",
+            "inputTypes": [
+              "Message",
               "str"
             ],
-            "dataType": "ChatInput",
-            "id": "ChatInput-Z9Rn6"
-          },
-          "targetHandle": {
-            "fieldName": "UserMessage",
-            "id": "Prompt-kykM2",
-            "inputTypes": [
-              "Document",
-              "Message",
-              "Record",
-              "Text"
-            ],
             "type": "str"
           }
         },
-        "id": "reactflow__edge-ChatInput-Z9Rn6{œbaseClassesœ:[œTextœ,œobjectœ,œRecordœ,œstrœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-Z9Rn6œ}-Prompt-kykM2{œfieldNameœ:œUserMessageœ,œidœ:œPrompt-kykM2œ,œinputTypesœ:[œDocumentœ,œMessageœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
-        "source": "ChatInput-Z9Rn6",
-        "sourceHandle": "{œbaseClassesœ: [œTextœ, œobjectœ, œRecordœ, œstrœ], œdataTypeœ: œChatInputœ, œidœ: œChatInput-Z9Rn6œ}",
-        "target": "Prompt-kykM2",
-        "targetHandle": "{œfieldNameœ: œUserMessageœ, œidœ: œPrompt-kykM2œ, œinputTypesœ: [œDocumentœ, œMessageœ, œRecordœ, œTextœ], œtypeœ: œstrœ}"
-      },
-      {
-        "className": "",
-        "data": {
-          "sourceHandle": {
-            "baseClasses": [
-              "str",
-              "Text",
-              "object"
-            ],
-            "dataType": "MemoryComponent",
-            "id": "MemoryComponent-u6m5G"
-          },
-          "targetHandle": {
-            "fieldName": "Context",
-            "id": "Prompt-kykM2",
-            "inputTypes": [
-              "Document",
-              "Message",
-              "Record",
-              "Text"
-            ],
-            "type": "str"
-          }
-        },
-        "id": "reactflow__edge-MemoryComponent-u6m5G{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-u6m5Gœ}-Prompt-kykM2{œfieldNameœ:œContextœ,œidœ:œPrompt-kykM2œ,œinputTypesœ:[œDocumentœ,œMessageœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
-        "source": "MemoryComponent-u6m5G",
-        "sourceHandle": "{œbaseClassesœ: [œstrœ, œTextœ, œobjectœ], œdataTypeœ: œMemoryComponentœ, œidœ: œMemoryComponent-u6m5Gœ}",
-        "target": "Prompt-kykM2",
-        "targetHandle": "{œfieldNameœ: œContextœ, œidœ: œPrompt-kykM2œ, œinputTypesœ: [œDocumentœ, œMessageœ, œRecordœ, œTextœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-OpenAIModel-uVOc5{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uVOc5œ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-R7jsA{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-R7jsAœ,œinputTypesœ:[œMessageœ,œstrœ],œtypeœ:œstrœ}",
+        "source": "OpenAIModel-uVOc5",
+        "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-uVOc5œ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "ChatOutput-R7jsA",
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-R7jsAœ, œinputTypesœ: [œMessageœ, œstrœ], œtypeœ: œstrœ}"
       }
     ],
     "nodes": [
       {
         "data": {
-          "id": "ChatInput-Z9Rn6",
-          "node": {
-            "base_classes": [
-              "Text",
-              "object",
-              "Record",
-              "str"
-            ],
-            "beta": false,
-            "custom_fields": {
-              "input_value": null,
-              "return_record": null,
-              "sender": null,
-              "sender_name": null,
-              "session_id": null
-            },
-            "description": "Get chat inputs from the Playground.",
-            "display_name": "Chat Input",
-            "documentation": "",
-            "field_formatters": {},
-            "field_order": [],
-            "frozen": false,
-            "icon": "ChatInput",
-            "output_types": [
-              "Message",
-              "Text"
-            ],
-            "template": {
-              "_type": "CustomComponent",
-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "code",
-                "value": "from typing import Optional\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.schema.message import Message\nfrom langflow.field_typing import Text\nfrom typing import Union\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    def build_config(self):\n        build_config = super().build_config()\n        build_config[\"input_value\"] = {\n            \"input_types\": [],\n            \"display_name\": \"Text\",\n            \"multiline\": True,\n        }\n        build_config[\"return_message\"] = {\n            \"display_name\": \"Return Record\",\n            \"advanced\": True,\n        }\n\n        return build_config\n\n    def build(\n        self,\n        sender: Optional[str] = \"User\",\n        sender_name: Optional[str] = \"User\",\n        input_value: Optional[str] = None,\n        files: Optional[list[str]] = None,\n        session_id: Optional[str] = None,\n        return_message: Optional[bool] = True,\n    ) -> Union[Message, Text]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            files=files,\n            session_id=session_id,\n            return_message=return_message,\n        )\n"
-              },
-              "input_value": {
-                "advanced": false,
-                "display_name": "Text",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [],
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "input_value",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "do you know his name?"
-              },
-              "sender": {
-                "advanced": true,
-                "display_name": "Sender Type",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "sender",
-                "options": [
-                  "Machine",
-                  "User"
-                ],
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "User"
-              },
-              "sender_name": {
-                "advanced": false,
-                "display_name": "Sender Name",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "sender_name",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "User"
-              },
-              "session_id": {
-                "advanced": false,
-                "display_name": "Session ID",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "If provided, the message will be stored in the memory.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "session_id",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "MySessionID"
-              }
-            }
-          },
-          "type": "ChatInput"
-        },
-        "dragging": false,
-        "height": 477,
-        "id": "ChatInput-Z9Rn6",
-        "position": {
-          "x": 1283.2700598313072,
-          "y": 982.5953650473145
-        },
-        "positionAbsolute": {
-          "x": 1283.2700598313072,
-          "y": 982.5953650473145
-        },
-        "selected": false,
-        "type": "genericNode",
-        "width": 384
-      },
-      {
-        "data": {
-          "id": "ChatOutput-cVR7W",
-          "node": {
-            "base_classes": [
-              "Text",
-              "object",
-              "Record",
-              "str"
-            ],
-            "beta": false,
-            "custom_fields": {
-              "input_value": null,
-              "return_record": null,
-              "sender": null,
-              "sender_name": null,
-              "session_id": null
-            },
-            "description": "Display a chat message in the Playground.",
-            "display_name": "Chat Output",
-            "documentation": "",
-            "field_formatters": {},
-            "field_order": [],
-            "frozen": false,
-            "icon": "ChatOutput",
-            "output_types": [
-              "Message",
-              "Text"
-            ],
-            "template": {
-              "_type": "CustomComponent",
-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "code",
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        files: Optional[list[str]] = None,\n        return_message: Optional[bool] = False,\n    ) -> Union[Message, Text]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            files=files,\n            return_message=return_message,\n        )\n"
-              },
-              "input_value": {
-                "advanced": false,
-                "display_name": "Text",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "input_value",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "sender": {
-                "advanced": true,
-                "display_name": "Sender Type",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "sender",
-                "options": [
-                  "Machine",
-                  "User"
-                ],
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "Machine"
-              },
-              "sender_name": {
-                "advanced": false,
-                "display_name": "Sender Name",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "sender_name",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "AI"
-              },
-              "session_id": {
-                "advanced": false,
-                "display_name": "Session ID",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "If provided, the message will be stored in the memory.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "session_id",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "MySessionID"
-              }
-            }
-          },
-          "type": "ChatOutput"
-        },
-        "dragging": false,
-        "height": 485,
-        "id": "ChatOutput-cVR7W",
-        "position": {
-          "x": 3154.916355514023,
-          "y": 851.051882666333
-        },
-        "positionAbsolute": {
-          "x": 3154.916355514023,
-          "y": 851.051882666333
-        },
-        "selected": false,
-        "type": "genericNode",
-        "width": 384
-      },
-      {
-        "data": {
-          "description": "Retrieves stored chat messages given a specific Session ID.",
-          "display_name": "Chat Memory",
-          "id": "MemoryComponent-u6m5G",
-          "node": {
-            "base_classes": [
-              "str",
-              "Text",
-              "object"
-            ],
-            "beta": true,
-            "custom_fields": {
-              "n_messages": null,
-              "order": null,
-              "record_template": null,
-              "sender": null,
-              "sender_name": null,
-              "session_id": null
-            },
-            "description": "Retrieves stored chat messages given a specific Session ID.",
-            "display_name": "Chat Memory",
-            "documentation": "",
-            "field_formatters": {},
-            "field_order": [],
-            "frozen": false,
-            "icon": "history",
-            "output_types": [
-              "Text"
-            ],
-            "template": {
-              "_type": "CustomComponent",
-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "code",
-                "value": "from typing import Optional\n\nfrom langflow.base.memory.memory import BaseMemoryComponent\nfrom langflow.field_typing import Text\nfrom langflow.helpers.record import messages_to_text\nfrom langflow.memory import get_messages\nfrom langflow.schema.message import Message\n\n\nclass MemoryComponent(BaseMemoryComponent):\n    display_name = \"Chat Memory\"\n    description = \"Retrieves stored chat messages given a specific Session ID.\"\n    beta: bool = True\n    icon = \"history\"\n\n    def build_config(self):\n        return {\n            \"sender\": {\n                \"options\": [\"Machine\", \"User\", \"Machine and User\"],\n                \"display_name\": \"Sender Type\",\n            },\n            \"sender_name\": {\"display_name\": \"Sender Name\", \"advanced\": True},\n            \"n_messages\": {\n                \"display_name\": \"Number of Messages\",\n                \"info\": \"Number of messages to retrieve.\",\n            },\n            \"session_id\": {\n                \"display_name\": \"Session ID\",\n                \"info\": \"Session ID of the chat history.\",\n                \"input_types\": [\"Text\"],\n            },\n            \"order\": {\n                \"options\": [\"Ascending\", \"Descending\"],\n                \"display_name\": \"Order\",\n                \"info\": \"Order of the messages.\",\n                \"advanced\": True,\n            },\n            \"record_template\": {\n                \"display_name\": \"Record Template\",\n                \"multiline\": True,\n                \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n                \"advanced\": True,\n            },\n        }\n\n    def get_messages(self, **kwargs) -> list[Message]:\n        # Validate kwargs by checking if it contains the correct keys\n        if \"sender\" not in kwargs:\n            kwargs[\"sender\"] = None\n        if \"sender_name\" not in kwargs:\n            kwargs[\"sender_name\"] = None\n        if \"session_id\" not in kwargs:\n            kwargs[\"session_id\"] = None\n        if \"limit\" not in kwargs:\n            kwargs[\"limit\"] = 5\n        if \"order\" not in kwargs:\n            kwargs[\"order\"] = \"Descending\"\n\n        kwargs[\"order\"] = \"DESC\" if kwargs[\"order\"] == \"Descending\" else \"ASC\"\n        if kwargs[\"sender\"] == \"Machine and User\":\n            kwargs[\"sender\"] = None\n        return get_messages(**kwargs)\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine and User\",\n        sender_name: Optional[str] = None,\n        session_id: Optional[str] = None,\n        n_messages: int = 5,\n        order: Optional[str] = \"Descending\",\n        record_template: Optional[str] = \"{sender_name}: {text}\",\n    ) -> Text:\n        messages = self.get_messages(\n            sender=sender,\n            sender_name=sender_name,\n            session_id=session_id,\n            limit=n_messages,\n            order=order,\n        )\n        messages_str = messages_to_text(template=record_template or \"\", messages=messages)\n        self.status = messages_str\n        return messages_str\n"
-              },
-              "n_messages": {
-                "advanced": false,
-                "display_name": "Number of Messages",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Number of messages to retrieve.",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "n_messages",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "int",
-                "value": 5
-              },
-              "order": {
-                "advanced": true,
-                "display_name": "Order",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Order of the messages.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "order",
-                "options": [
-                  "Ascending",
-                  "Descending"
-                ],
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "Descending"
-              },
-              "record_template": {
-                "advanced": true,
-                "display_name": "Record Template",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "record_template",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "{sender_name}: {text}"
-              },
-              "sender": {
-                "advanced": false,
-                "display_name": "Sender Type",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "sender",
-                "options": [
-                  "Machine",
-                  "User",
-                  "Machine and User"
-                ],
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "Machine and User"
-              },
-              "sender_name": {
-                "advanced": true,
-                "display_name": "Sender Name",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "sender_name",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "session_id": {
-                "advanced": false,
-                "display_name": "Session ID",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Session ID of the chat history.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "session_id",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "MySessionID"
-              }
-            }
-          },
-          "type": "MemoryComponent"
-        },
-        "dragging": false,
-        "height": 505,
-        "id": "MemoryComponent-u6m5G",
-        "position": {
-          "x": 1289.9606870058817,
-          "y": 442.16804561053766
-        },
-        "positionAbsolute": {
-          "x": 1289.9606870058817,
-          "y": 442.16804561053766
-        },
-        "selected": false,
-        "type": "genericNode",
-        "width": 384
-      },
-      {
-        "data": {
-          "description": "Create a prompt template with dynamic variables.",
+          "description": "A component for creating prompt templates using dynamic variables.",
           "display_name": "Prompt",
-          "id": "Prompt-kykM2",
+          "id": "Prompt-VuDd0",
           "node": {
             "base_classes": [
-              "object",
+              "Text",
               "str",
-              "Text"
+              "object"
             ],
             "beta": false,
             "custom_fields": {
               "template": [
-                "Context",
-                "UserMessage"
+                "context",
+                "user_message"
               ]
             },
             "description": "Create a prompt template with dynamic variables.",
@@ -677,63 +145,22 @@
             "is_input": null,
             "is_output": null,
             "name": "",
-            "output_types": [
-              "Prompt"
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Prompt Message",
+                "method": "build_prompt",
+                "name": "prompt",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
             ],
             "template": {
-              "Context": {
-                "advanced": false,
-                "display_name": "Context",
-                "dynamic": false,
-                "field_type": "str",
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Document",
-                  "Message",
-                  "Record",
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "Context",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": ""
-              },
-              "UserMessage": {
-                "advanced": false,
-                "display_name": "UserMessage",
-                "dynamic": false,
-                "field_type": "str",
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Document",
-                  "Message",
-                  "Record",
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "UserMessage",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": ""
-              },
-              "_type": "CustomComponent",
+              "_type": "Component",
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -750,7 +177,33 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import CustomComponent\nfrom langflow.field_typing import TemplateField\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    async def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Prompt:\n        prompt = await Prompt.from_template_and_variables(template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
+                "value": "from langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n"
+              },
+              "context": {
+                "advanced": false,
+                "display_name": "context",
+                "dynamic": false,
+                "field_type": "str",
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "input_types": [
+                  "Document",
+                  "Message",
+                  "Record",
+                  "Text"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "context",
+                "password": false,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
               },
               "template": {
                 "advanced": false,
@@ -772,70 +225,109 @@
                 "show": true,
                 "title_case": false,
                 "type": "prompt",
-                "value": "Previous messages:\n{Context}\n\nUser: {UserMessage}\nAI: "
+                "value": "{context}\n\nUser: {user_message}\nAI: "
+              },
+              "user_message": {
+                "advanced": false,
+                "display_name": "user_message",
+                "dynamic": false,
+                "field_type": "str",
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "input_types": [
+                  "Document",
+                  "Message",
+                  "Record",
+                  "Text"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "user_message",
+                "password": false,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
               }
             }
           },
           "type": "Prompt"
         },
         "dragging": false,
-        "height": 513,
-        "id": "Prompt-kykM2",
+        "height": 525,
+        "id": "Prompt-VuDd0",
         "position": {
-          "x": 1890.2582485007167,
-          "y": 753.3797365481901
+          "x": 1900.7563740044732,
+          "y": 755.4337191022057
         },
         "positionAbsolute": {
-          "x": 1890.2582485007167,
-          "y": 753.3797365481901
+          "x": 1900.7563740044732,
+          "y": 755.4337191022057
         },
-        "selected": true,
+        "selected": false,
         "type": "genericNode",
         "width": 384
       },
       {
         "data": {
-          "id": "OpenAIModel-Neuec",
+          "description": "Retrieves stored chat messages.",
+          "display_name": "Memory",
+          "edited": false,
+          "id": "Memory-rvcL5",
           "node": {
             "base_classes": [
-              "str",
-              "object",
-              "Text"
+              "Data",
+              "Message"
             ],
             "beta": false,
-            "custom_fields": {
-              "input_value": null,
-              "max_tokens": null,
-              "model_kwargs": null,
-              "model_name": null,
-              "openai_api_base": null,
-              "openai_api_key": null,
-              "stream": null,
-              "system_message": null,
-              "temperature": null
-            },
-            "description": "Generates text using OpenAI LLMs.",
-            "display_name": "OpenAI",
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Retrieves stored chat messages.",
+            "display_name": "Memory",
             "documentation": "",
-            "field_formatters": {},
+            "edited": true,
             "field_order": [
-              "max_tokens",
-              "model_kwargs",
-              "model_name",
-              "openai_api_base",
-              "openai_api_key",
-              "temperature",
-              "input_value",
-              "system_message",
-              "stream"
+              "sender",
+              "sender_name",
+              "n_messages",
+              "session_id",
+              "order",
+              "template"
             ],
             "frozen": false,
-            "icon": "OpenAI",
-            "output_types": [
-              "Text"
+            "icon": "message-square-more",
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Chat History",
+                "method": "retrieve_messages",
+                "name": "messages",
+                "selected": "Data",
+                "types": [
+                  "Data"
+                ],
+                "value": "__UNDEFINED__"
+              },
+              {
+                "cache": true,
+                "display_name": "Messages (Text)",
+                "method": "retrieve_messages_as_text",
+                "name": "messages_text",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
             ],
+            "pinned": false,
             "template": {
-              "_type": "CustomComponent",
+              "_type": "Component",
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -852,82 +344,454 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\", \"Prompt\"]},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-3.5-turbo\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n"
+                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DropdownInput, IntInput, MultilineInput, Output, TextInput\nfrom langflow.memory import get_messages\nfrom langflow.schema import Data\nfrom langflow.schema.message import Message\n\n\nclass MemoryComponent(Component):\n    display_name = \"Chat Memory\"\n    description = \"Retrieves stored chat messages.\"\n    icon = \"message-square-more\"\n\n    inputs = [\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\", \"Machine and User\"],\n            value=\"Machine and User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"n_messages\",\n            display_name=\"Number of Messages\",\n            value=100,\n            info=\"Number of messages to retrieve.\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"session_id\",\n            display_name=\"Session ID\",\n            info=\"Session ID of the chat history.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"order\",\n            display_name=\"Order\",\n            options=[\"Ascending\", \"Descending\"],\n            value=\"Ascending\",\n            info=\"Order of the messages.\",\n            advanced=True,\n        ),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {sender} or any other key in the message data.\",\n            value=\"{sender_name}: {text}\",\n            advanced=True,\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Chat History\", name=\"messages\", method=\"retrieve_messages\"),\n        Output(display_name=\"Messages (Text)\", name=\"messages_text\", method=\"retrieve_messages_as_text\"),\n    ]\n\n    def retrieve_messages(self) -> Data:\n        sender = self.sender\n        sender_name = self.sender_name\n        session_id = self.session_id\n        n_messages = self.n_messages\n        order = \"DESC\" if self.order == \"Descending\" else \"ASC\"\n\n        if sender == \"Machine and User\":\n            sender = None\n\n        messages = get_messages(\n            sender=sender,\n            sender_name=sender_name,\n            session_id=session_id,\n            limit=n_messages,\n            order=order,\n        )\n        self.status = messages\n        return messages\n\n    def retrieve_messages_as_text(self) -> Message:\n        messages_text = data_to_text(self.template, self.retrieve_messages())\n        self.status = messages_text\n        return Message(text=messages_text)\n"
               },
-              "input_value": {
-                "advanced": false,
-                "display_name": "Input",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text",
-                  "Record",
-                  "Prompt"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "input_value",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "max_tokens": {
+              "n_messages": {
                 "advanced": true,
-                "display_name": "Max Tokens",
+                "display_name": "Number of Messages",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+                "info": "Number of messages to retrieve.",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "max_tokens",
-                "password": false,
+                "name": "n_messages",
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
                 "type": "int",
-                "value": 256
+                "value": 100
               },
-              "model_kwargs": {
+              "order": {
                 "advanced": true,
-                "display_name": "Model Kwargs",
+                "display_name": "Order",
                 "dynamic": false,
+                "info": "Order of the messages.",
+                "name": "order",
+                "options": [
+                  "Ascending",
+                  "Descending"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "Ascending"
+              },
+              "sender": {
+                "advanced": true,
+                "display_name": "Sender Type",
+                "dynamic": false,
+                "info": "Type of sender.",
+                "name": "sender",
+                "options": [
+                  "Machine",
+                  "User",
+                  "Machine and User"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "Machine and User"
+              },
+              "sender_name": {
+                "advanced": true,
+                "display_name": "Sender Name",
+                "dynamic": false,
+                "info": "Name of the sender.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "sender_name",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "session_id": {
+                "advanced": true,
+                "display_name": "Session ID",
+                "dynamic": false,
+                "info": "Session ID of the chat history.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "session_id",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "template": {
+                "advanced": true,
+                "display_name": "Template",
+                "dynamic": false,
+                "info": "The template to use for formatting the data. It can contain the keys {text}, {sender} or any other key in the message data.",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "template",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "{sender_name}: {text}"
+              }
+            }
+          },
+          "type": "Memory"
+        },
+        "dragging": false,
+        "height": 267,
+        "id": "Memory-rvcL5",
+        "position": {
+          "x": 1258.8089948698466,
+          "y": 547.1243849102437
+        },
+        "positionAbsolute": {
+          "x": 1258.8089948698466,
+          "y": 547.1243849102437
+        },
+        "selected": false,
+        "type": "genericNode",
+        "width": 384
+      },
+      {
+        "data": {
+          "id": "ChatInput-9iFsd",
+          "node": {
+            "base_classes": [
+              "Message"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Get chat inputs from the Playground.",
+            "display_name": "Chat Input",
+            "documentation": "",
+            "field_order": [
+              "input_value",
+              "sender",
+              "sender_name",
+              "session_id",
+              "files"
+            ],
+            "frozen": false,
+            "icon": "ChatInput",
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Message",
+                "method": "message_response",
+                "name": "message",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "code": {
+                "advanced": true,
+                "dynamic": true,
                 "fileTypes": [],
                 "file_path": "",
                 "info": "",
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
-                "name": "model_kwargs",
+                "multiline": true,
+                "name": "code",
                 "password": false,
                 "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, FileInput, MultilineInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+              },
+              "files": {
+                "advanced": true,
+                "display_name": "Files",
+                "dynamic": false,
+                "fileTypes": [
+                  "txt",
+                  "md",
+                  "mdx",
+                  "csv",
+                  "json",
+                  "yaml",
+                  "yml",
+                  "xml",
+                  "html",
+                  "htm",
+                  "pdf",
+                  "docx",
+                  "py",
+                  "sh",
+                  "sql",
+                  "js",
+                  "ts",
+                  "tsx",
+                  "jpg",
+                  "jpeg",
+                  "png",
+                  "bmp",
+                  "image"
+                ],
+                "file_path": "",
+                "info": "Files to be sent with the message.",
+                "list": true,
+                "name": "files",
+                "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "NestedDict",
+                "type": "file",
+                "value": ""
+              },
+              "input_value": {
+                "advanced": false,
+                "display_name": "Text",
+                "dynamic": false,
+                "info": "Message to be passed as input.",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "input_value",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "sender": {
+                "advanced": true,
+                "display_name": "Sender Type",
+                "dynamic": false,
+                "info": "Type of sender.",
+                "name": "sender",
+                "options": [
+                  "Machine",
+                  "User"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "User"
+              },
+              "sender_name": {
+                "advanced": true,
+                "display_name": "Sender Name",
+                "dynamic": false,
+                "info": "Name of the sender.",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "sender_name",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "User"
+              },
+              "session_id": {
+                "advanced": true,
+                "display_name": "Session ID",
+                "dynamic": false,
+                "info": "Session ID for the message.",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "session_id",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              }
+            }
+          },
+          "type": "ChatInput"
+        },
+        "dragging": false,
+        "height": 309,
+        "id": "ChatInput-9iFsd",
+        "position": {
+          "x": 1246.4850995457527,
+          "y": 912.733279525042
+        },
+        "positionAbsolute": {
+          "x": 1246.4850995457527,
+          "y": 912.733279525042
+        },
+        "selected": false,
+        "type": "genericNode",
+        "width": 384
+      },
+      {
+        "data": {
+          "description": "Generates text using OpenAI LLMs.",
+          "display_name": "OpenAI",
+          "edited": false,
+          "id": "OpenAIModel-uVOc5",
+          "node": {
+            "base_classes": [
+              "LanguageModel",
+              "Message"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Generates text using OpenAI LLMs.",
+            "display_name": "OpenAI",
+            "documentation": "",
+            "edited": true,
+            "field_order": [
+              "input_value",
+              "max_tokens",
+              "model_kwargs",
+              "output_schema",
+              "model_name",
+              "openai_api_base",
+              "openai_api_key",
+              "temperature",
+              "stream",
+              "system_message",
+              "seed"
+            ],
+            "frozen": false,
+            "icon": "OpenAI",
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Text",
+                "method": "text_response",
+                "name": "text_output",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              },
+              {
+                "cache": true,
+                "display_name": "Language Model",
+                "method": "build_model",
+                "name": "model_output",
+                "selected": "LanguageModel",
+                "types": [
+                  "LanguageModel"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    inputs = [\n        MessageInput(name=\"input_value\", display_name=\"Input\"),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:\n        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict)\n        seed = self.seed\n        model_kwargs[\"seed\"] = seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n"
+              },
+              "input_value": {
+                "advanced": false,
+                "display_name": "Input",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "input_value",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "max_tokens": {
+                "advanced": true,
+                "display_name": "Max Tokens",
+                "dynamic": false,
+                "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+                "list": false,
+                "name": "max_tokens",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": ""
+              },
+              "model_kwargs": {
+                "advanced": true,
+                "display_name": "Model Kwargs",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "model_kwargs",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "dict",
                 "value": {}
               },
               "model_name": {
                 "advanced": false,
                 "display_name": "Model Name",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "model_name",
                 "options": [
                   "gpt-4o",
@@ -936,69 +800,79 @@
                   "gpt-3.5-turbo",
                   "gpt-3.5-turbo-0125"
                 ],
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
                 "type": "str",
-                "value": "gpt-3.5-turbo"
+                "value": "gpt-4-turbo"
               },
               "openai_api_base": {
                 "advanced": true,
                 "display_name": "OpenAI API Base",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
-                "input_types": [
-                  "Text"
-                ],
+                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.",
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
                 "name": "openai_api_base",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "openai_api_key": {
                 "advanced": false,
                 "display_name": "OpenAI API Key",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "The OpenAI API Key to use for the OpenAI model.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
+                "input_types": [],
                 "load_from_db": true,
-                "multiline": false,
                 "name": "openai_api_key",
                 "password": true,
                 "placeholder": "",
-                "required": true,
+                "required": false,
                 "show": true,
                 "title_case": false,
                 "type": "str",
-                "value": "OPENAI_API_KEY"
+                "value": ""
+              },
+              "output_schema": {
+                "advanced": true,
+                "display_name": "Schema",
+                "dynamic": false,
+                "info": "The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.",
+                "list": true,
+                "name": "output_schema",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "dict",
+                "value": {}
+              },
+              "seed": {
+                "advanced": true,
+                "display_name": "Seed",
+                "dynamic": false,
+                "info": "The seed controls the reproducibility of the job.",
+                "list": false,
+                "name": "seed",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": 1
               },
               "stream": {
                 "advanced": true,
                 "display_name": "Stream",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "Stream the response from the model. Streaming works only in Chat.",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "stream",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -1010,62 +884,218 @@
                 "advanced": true,
                 "display_name": "System Message",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "System message to pass to the model.",
-                "input_types": [
-                  "Text"
-                ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
                 "name": "system_message",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "temperature": {
                 "advanced": false,
                 "display_name": "Temperature",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "temperature",
-                "password": false,
                 "placeholder": "",
-                "rangeSpec": {
-                  "max": 1,
-                  "min": -1,
-                  "step": 0.1,
-                  "step_type": "float"
-                },
                 "required": false,
                 "show": true,
                 "title_case": false,
                 "type": "float",
-                "value": "0.2"
+                "value": 0.1
               }
             }
           },
           "type": "OpenAIModel"
         },
         "dragging": false,
-        "height": 571,
-        "id": "OpenAIModel-Neuec",
+        "height": 623,
+        "id": "OpenAIModel-uVOc5",
         "position": {
-          "x": 2561.5850334731617,
-          "y": 553.2745131130916
+          "x": 2495.6628431453228,
+          "y": 668.0955451423632
         },
         "positionAbsolute": {
-          "x": 2561.5850334731617,
-          "y": 553.2745131130916
+          "x": 2495.6628431453228,
+          "y": 668.0955451423632
+        },
+        "selected": false,
+        "type": "genericNode",
+        "width": 384
+      },
+      {
+        "data": {
+          "id": "ChatOutput-R7jsA",
+          "node": {
+            "base_classes": [
+              "Message"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Display a chat message in the Playground.",
+            "display_name": "Chat Output",
+            "documentation": "",
+            "field_order": [
+              "input_value",
+              "sender",
+              "sender_name",
+              "session_id",
+              "data_template"
+            ],
+            "frozen": false,
+            "icon": "ChatOutput",
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Message",
+                "method": "message_response",
+                "name": "message",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
+              },
+              "data_template": {
+                "advanced": true,
+                "display_name": "Data Template",
+                "dynamic": false,
+                "info": "Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "data_template",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "{text}"
+              },
+              "input_value": {
+                "advanced": false,
+                "display_name": "Text",
+                "dynamic": false,
+                "info": "Message to be passed as output.",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "input_value",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "sender": {
+                "advanced": true,
+                "display_name": "Sender Type",
+                "dynamic": false,
+                "info": "Type of sender.",
+                "name": "sender",
+                "options": [
+                  "Machine",
+                  "User"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "Machine"
+              },
+              "sender_name": {
+                "advanced": true,
+                "display_name": "Sender Name",
+                "dynamic": false,
+                "info": "Name of the sender.",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "sender_name",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "AI"
+              },
+              "session_id": {
+                "advanced": true,
+                "display_name": "Session ID",
+                "dynamic": false,
+                "info": "Session ID for the message.",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "session_id",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              }
+            }
+          },
+          "type": "ChatOutput"
+        },
+        "dragging": false,
+        "height": 309,
+        "id": "ChatOutput-R7jsA",
+        "position": {
+          "x": 3129.987101578166,
+          "y": 888.0854888768531
+        },
+        "positionAbsolute": {
+          "x": 3129.987101578166,
+          "y": 888.0854888768531
         },
         "selected": false,
         "type": "genericNode",
@@ -1073,14 +1103,15 @@
       }
     ],
     "viewport": {
-      "x": -511.79726701119625,
-      "y": 49.514712353620894,
-      "zoom": 0.4612356948928673
+      "x": -527.2609043386433,
+      "y": 33.26280492099636,
+      "zoom": 0.48650433790103115
     }
   },
   "description": "This project can be used as a starting point for building a Chat experience with user specific memory. You can set a different Session ID to start a new message history.",
-  "id": "321b1bab-8691-42da-9689-1f12b5d2a48b",
+  "endpoint_name": null,
+  "id": "4e88f957-1541-4760-8a03-6132d4b14090",
   "is_component": false,
-  "last_tested_version": "1.0.0a54",
+  "last_tested_version": "1.0.0a61",
   "name": "Memory Chatbot"
 }
\ No newline at end of file
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json
index e1319c936..cbfbdb544 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json	
+++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json	
@@ -5,20 +5,19 @@
         "className": "stroke-gray-900 stroke-connection",
         "data": {
           "sourceHandle": {
-            "baseClasses": [
-              "str",
-              "Text",
-              "object"
-            ],
             "dataType": "TextInput",
-            "id": "TextInput-sptaH"
+            "id": "TextInput-sptaH",
+            "name": "text",
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "document",
             "id": "Prompt-amqBu",
             "inputTypes": [
               "Document",
-              "BaseOutputParser",
+              "Message",
               "Record",
               "Text"
             ],
@@ -27,24 +26,21 @@
         },
         "id": "reactflow__edge-TextInput-sptaH{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œTextInputœ,œidœ:œTextInput-sptaHœ}-Prompt-amqBu{œfieldNameœ:œdocumentœ,œidœ:œPrompt-amqBuœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
         "source": "TextInput-sptaH",
-        "sourceHandle": "{œbaseClassesœ: [œstrœ, œTextœ, œobjectœ], œdataTypeœ: œTextInputœ, œidœ: œTextInput-sptaHœ}",
+        "sourceHandle": "{œdataTypeœ: œTextInputœ, œidœ: œTextInput-sptaHœ, œoutput_typesœ: [œMessageœ], œnameœ: œtextœ}",
         "style": {
           "stroke": "#555"
         },
         "target": "Prompt-amqBu",
-        "targetHandle": "{œfieldNameœ: œdocumentœ, œidœ: œPrompt-amqBuœ, œinputTypesœ: [œDocumentœ, œBaseOutputParserœ, œRecordœ, œTextœ], œtypeœ: œstrœ}"
+        "targetHandle": "{œfieldNameœ: œdocumentœ, œidœ: œPrompt-amqBuœ, œinputTypesœ: [œDocumentœ, œMessageœ, œRecordœ, œTextœ], œtypeœ: œstrœ}"
       },
       {
         "className": "stroke-gray-900 stroke-connection",
         "data": {
           "sourceHandle": {
-            "baseClasses": [
-              "object",
-              "str",
-              "Text"
-            ],
             "dataType": "Prompt",
-            "id": "Prompt-amqBu"
+            "id": "Prompt-amqBu",
+            "name": "text",
+            "output_types": []
           },
           "targetHandle": {
             "fieldName": "input_value",
@@ -58,7 +54,7 @@
         },
         "id": "reactflow__edge-Prompt-amqBu{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}-TextOutput-2MS4a{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-2MS4aœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}",
         "source": "Prompt-amqBu",
-        "sourceHandle": "{œbaseClassesœ: [œobjectœ, œstrœ, œTextœ], œdataTypeœ: œPromptœ, œidœ: œPrompt-amqBuœ}",
+        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-amqBuœ, œoutput_typesœ: [], œnameœ: œtextœ}",
         "style": {
           "stroke": "#555"
         },
@@ -69,52 +65,48 @@
         "className": "stroke-gray-900 stroke-connection",
         "data": {
           "sourceHandle": {
-            "baseClasses": [
-              "object",
-              "str",
-              "Text"
-            ],
             "dataType": "Prompt",
-            "id": "Prompt-amqBu"
+            "id": "Prompt-amqBu",
+            "name": "prompt",
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "input_value",
             "id": "OpenAIModel-uYXZJ",
             "inputTypes": [
-              "Text",
-              "Record",
-              "Prompt"
+              "Message"
             ],
             "type": "str"
           }
         },
         "id": "reactflow__edge-Prompt-amqBu{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-amqBuœ}-OpenAIModel-uYXZJ{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-uYXZJœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
         "source": "Prompt-amqBu",
-        "sourceHandle": "{œbaseClassesœ: [œobjectœ, œstrœ, œTextœ], œdataTypeœ: œPromptœ, œidœ: œPrompt-amqBuœ}",
+        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-amqBuœ, œoutput_typesœ: [œMessageœ], œnameœ: œpromptœ}",
         "style": {
           "stroke": "#555"
         },
         "target": "OpenAIModel-uYXZJ",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-uYXZJœ, œinputTypesœ: [œTextœ, œRecordœ, œPromptœ], œtypeœ: œstrœ}"
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-uYXZJœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
       },
       {
         "className": "stroke-gray-900 stroke-connection",
         "data": {
           "sourceHandle": {
-            "baseClasses": [
-              "str",
-              "Text",
-              "object"
-            ],
             "dataType": "OpenAIModel",
-            "id": "OpenAIModel-uYXZJ"
+            "id": "OpenAIModel-uYXZJ",
+            "name": "text_output",
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "summary",
             "id": "Prompt-gTNiz",
             "inputTypes": [
               "Document",
-              "BaseOutputParser",
+              "Message",
               "Record",
               "Text"
             ],
@@ -123,54 +115,51 @@
         },
         "id": "reactflow__edge-OpenAIModel-uYXZJ{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}-Prompt-gTNiz{œfieldNameœ:œsummaryœ,œidœ:œPrompt-gTNizœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
         "source": "OpenAIModel-uYXZJ",
-        "sourceHandle": "{œbaseClassesœ: [œstrœ, œTextœ, œobjectœ], œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-uYXZJœ}",
+        "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-uYXZJœ, œoutput_typesœ: [œMessageœ], œnameœ: œtext_outputœ}",
         "style": {
           "stroke": "#555"
         },
         "target": "Prompt-gTNiz",
-        "targetHandle": "{œfieldNameœ: œsummaryœ, œidœ: œPrompt-gTNizœ, œinputTypesœ: [œDocumentœ, œBaseOutputParserœ, œRecordœ, œTextœ], œtypeœ: œstrœ}"
+        "targetHandle": "{œfieldNameœ: œsummaryœ, œidœ: œPrompt-gTNizœ, œinputTypesœ: [œDocumentœ, œMessageœ, œRecordœ, œTextœ], œtypeœ: œstrœ}"
       },
       {
         "className": "stroke-gray-900 stroke-connection",
         "data": {
           "sourceHandle": {
-            "baseClasses": [
-              "str",
-              "Text",
-              "object"
-            ],
             "dataType": "OpenAIModel",
-            "id": "OpenAIModel-uYXZJ"
+            "id": "OpenAIModel-uYXZJ",
+            "name": "text_output",
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "input_value",
             "id": "ChatOutput-EJkG3",
             "inputTypes": [
-              "Text"
+              "Message",
+              "str"
             ],
             "type": "str"
           }
         },
         "id": "reactflow__edge-OpenAIModel-uYXZJ{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-uYXZJœ}-ChatOutput-EJkG3{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-EJkG3œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
         "source": "OpenAIModel-uYXZJ",
-        "sourceHandle": "{œbaseClassesœ: [œstrœ, œTextœ, œobjectœ], œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-uYXZJœ}",
+        "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-uYXZJœ, œoutput_typesœ: [œMessageœ], œnameœ: œtext_outputœ}",
         "style": {
           "stroke": "#555"
         },
         "target": "ChatOutput-EJkG3",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-EJkG3œ, œinputTypesœ: [œTextœ], œtypeœ: œstrœ}"
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-EJkG3œ, œinputTypesœ: [œMessageœ, œstrœ], œtypeœ: œstrœ}"
       },
       {
         "className": "stroke-gray-900 stroke-connection",
         "data": {
           "sourceHandle": {
-            "baseClasses": [
-              "object",
-              "str",
-              "Text"
-            ],
             "dataType": "Prompt",
-            "id": "Prompt-gTNiz"
+            "id": "Prompt-gTNiz",
+            "name": "text",
+            "output_types": []
           },
           "targetHandle": {
             "fieldName": "input_value",
@@ -184,7 +173,7 @@
         },
         "id": "reactflow__edge-Prompt-gTNiz{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}-TextOutput-MUDOR{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-MUDORœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}",
         "source": "Prompt-gTNiz",
-        "sourceHandle": "{œbaseClassesœ: [œobjectœ, œstrœ, œTextœ], œdataTypeœ: œPromptœ, œidœ: œPrompt-gTNizœ}",
+        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-gTNizœ, œoutput_typesœ: [], œnameœ: œtextœ}",
         "style": {
           "stroke": "#555"
         },
@@ -195,63 +184,60 @@
         "className": "stroke-gray-900 stroke-connection",
         "data": {
           "sourceHandle": {
-            "baseClasses": [
-              "object",
-              "str",
-              "Text"
-            ],
             "dataType": "Prompt",
-            "id": "Prompt-gTNiz"
+            "id": "Prompt-gTNiz",
+            "name": "prompt",
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "input_value",
             "id": "OpenAIModel-XawYB",
             "inputTypes": [
-              "Text",
-              "Record",
-              "Prompt"
+              "Message"
             ],
             "type": "str"
           }
         },
         "id": "reactflow__edge-Prompt-gTNiz{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-gTNizœ}-OpenAIModel-XawYB{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-XawYBœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
         "source": "Prompt-gTNiz",
-        "sourceHandle": "{œbaseClassesœ: [œobjectœ, œstrœ, œTextœ], œdataTypeœ: œPromptœ, œidœ: œPrompt-gTNizœ}",
+        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-gTNizœ, œoutput_typesœ: [œMessageœ], œnameœ: œpromptœ}",
         "style": {
           "stroke": "#555"
         },
         "target": "OpenAIModel-XawYB",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-XawYBœ, œinputTypesœ: [œTextœ, œRecordœ, œPromptœ], œtypeœ: œstrœ}"
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-XawYBœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
       },
       {
         "className": "stroke-gray-900 stroke-connection",
         "data": {
           "sourceHandle": {
-            "baseClasses": [
-              "str",
-              "Text",
-              "object"
-            ],
             "dataType": "OpenAIModel",
-            "id": "OpenAIModel-XawYB"
+            "id": "OpenAIModel-XawYB",
+            "name": "text_output",
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "input_value",
             "id": "ChatOutput-DNmvg",
             "inputTypes": [
-              "Text"
+              "Message",
+              "str"
             ],
             "type": "str"
           }
         },
         "id": "reactflow__edge-OpenAIModel-XawYB{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-XawYBœ}-ChatOutput-DNmvg{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-DNmvgœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}",
         "source": "OpenAIModel-XawYB",
-        "sourceHandle": "{œbaseClassesœ: [œstrœ, œTextœ, œobjectœ], œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-XawYBœ}",
+        "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-XawYBœ, œoutput_typesœ: [œMessageœ], œnameœ: œtext_outputœ}",
         "style": {
           "stroke": "#555"
         },
         "target": "ChatOutput-DNmvg",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-DNmvgœ, œinputTypesœ: [œTextœ], œtypeœ: œstrœ}"
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-DNmvgœ, œinputTypesœ: [œMessageœ, œstrœ], œtypeœ: œstrœ}"
       }
     ],
     "nodes": [
@@ -285,11 +271,22 @@
             "is_input": null,
             "is_output": null,
             "name": "",
-            "output_types": [
-              "Prompt"
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Prompt Message",
+                "method": "build_prompt",
+                "name": "prompt",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
             ],
             "template": {
-              "_type": "CustomComponent",
+              "_type": "Component",
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -306,7 +303,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import CustomComponent\nfrom langflow.field_typing import TemplateField\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    async def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Prompt:\n        prompt = await Prompt.from_template_and_variables(template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
+                "value": "from langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n"
               },
               "document": {
                 "advanced": false,
@@ -318,7 +315,7 @@
                 "info": "",
                 "input_types": [
                   "Document",
-                  "BaseOutputParser",
+                  "Message",
                   "Record",
                   "Text"
                 ],
@@ -405,11 +402,22 @@
             "is_input": null,
             "is_output": null,
             "name": "",
-            "output_types": [
-              "Prompt"
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Prompt Message",
+                "method": "build_prompt",
+                "name": "prompt",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
             ],
             "template": {
-              "_type": "CustomComponent",
+              "_type": "Component",
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -426,7 +434,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import CustomComponent\nfrom langflow.field_typing import TemplateField\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    async def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Prompt:\n        prompt = await Prompt.from_template_and_variables(template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
+                "value": "from langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n"
               },
               "summary": {
                 "advanced": false,
@@ -438,7 +446,7 @@
                 "info": "",
                 "input_types": [
                   "Document",
-                  "BaseOutputParser",
+                  "Message",
                   "Record",
                   "Text"
                 ],
@@ -517,12 +525,22 @@
             "field_order": [],
             "frozen": false,
             "icon": "ChatOutput",
-            "output_types": [
-              "Message",
-              "Text"
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Message",
+                "method": "message_response",
+                "name": "message",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
             ],
             "template": {
-              "_type": "CustomComponent",
+              "_type": "Component",
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -539,7 +557,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        files: Optional[list[str]] = None,\n        return_message: Optional[bool] = False,\n    ) -> Union[Message, Text]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            files=files,\n            return_message=return_message,\n        )\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "input_value": {
                 "advanced": false,
@@ -547,9 +565,10 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "",
+                "info": "Message to be passed as output.",
                 "input_types": [
-                  "Text"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -560,7 +579,8 @@
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "sender": {
                 "advanced": true,
@@ -568,7 +588,7 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "",
+                "info": "Type of sender.",
                 "input_types": [
                   "Text"
                 ],
@@ -589,18 +609,19 @@
                 "value": "Machine"
               },
               "sender_name": {
-                "advanced": false,
+                "advanced": true,
                 "display_name": "Sender Name",
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "",
+                "info": "Name of the sender.",
                 "input_types": [
-                  "Text"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
+                "multiline": true,
                 "name": "sender_name",
                 "password": false,
                 "placeholder": "",
@@ -608,7 +629,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "str",
-                "value": "Summarizer"
+                "value": "AI"
               },
               "session_id": {
                 "advanced": true,
@@ -616,20 +637,22 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "If provided, the message will be stored in the memory.",
+                "info": "Session ID for the message.",
                 "input_types": [
-                  "Text"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
+                "multiline": true,
                 "name": "session_id",
                 "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               }
             }
           },
@@ -672,12 +695,22 @@
             "field_order": [],
             "frozen": false,
             "icon": "ChatOutput",
-            "output_types": [
-              "Message",
-              "Text"
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Message",
+                "method": "message_response",
+                "name": "message",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
             ],
             "template": {
-              "_type": "CustomComponent",
+              "_type": "Component",
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -694,7 +727,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        files: Optional[list[str]] = None,\n        return_message: Optional[bool] = False,\n    ) -> Union[Message, Text]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            files=files,\n            return_message=return_message,\n        )\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "input_value": {
                 "advanced": false,
@@ -702,9 +735,10 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "",
+                "info": "Message to be passed as output.",
                 "input_types": [
-                  "Text"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -715,7 +749,8 @@
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "sender": {
                 "advanced": true,
@@ -723,7 +758,7 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "",
+                "info": "Type of sender.",
                 "input_types": [
                   "Text"
                 ],
@@ -744,18 +779,19 @@
                 "value": "Machine"
               },
               "sender_name": {
-                "advanced": false,
+                "advanced": true,
                 "display_name": "Sender Name",
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "",
+                "info": "Name of the sender.",
                 "input_types": [
-                  "Text"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
+                "multiline": true,
                 "name": "sender_name",
                 "password": false,
                 "placeholder": "",
@@ -763,7 +799,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "str",
-                "value": "Question Generator"
+                "value": "AI"
               },
               "session_id": {
                 "advanced": true,
@@ -771,20 +807,22 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "If provided, the message will be stored in the memory.",
+                "info": "Session ID for the message.",
                 "input_types": [
-                  "Text"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
+                "multiline": true,
                 "name": "session_id",
                 "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               }
             }
           },
@@ -821,11 +859,22 @@
             "field_order": [],
             "frozen": false,
             "icon": "type",
-            "output_types": [
-              "Text"
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Text",
+                "method": "text_response",
+                "name": "text",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
             ],
             "template": {
-              "_type": "CustomComponent",
+              "_type": "Component",
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -842,7 +891,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextInput(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n\n    def build_config(self):\n        return {\n            \"input_value\": {\n                \"display_name\": \"Text\",\n                \"input_types\": [\"Record\", \"Text\"],\n                \"info\": \"Text or Record to be passed as input.\",\n            },\n            \"record_template\": {\n                \"display_name\": \"Record Template\",\n                \"multiline\": True,\n                \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Optional[Text] = \"\",\n        record_template: Optional[str] = \"\",\n    ) -> Text:\n        return super().build(input_value=input_value, record_template=record_template)\n"
+                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass TextInputComponent(TextComponent):\n    display_name = \"Text Input\"\n    description = \"Get text inputs from the Playground.\"\n    icon = \"type\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as input.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n        )\n        return message\n"
               },
               "input_value": {
                 "advanced": false,
@@ -850,37 +899,15 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "Text or Record to be passed as input.",
+                "info": "Text to be passed as input.",
                 "input_types": [
-                  "Record",
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "input_value",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "Revolutionary Nano-Battery Technology Unveiled  In a groundbreaking announcement yesterday, researchers from the fictional Tech Innovations Institute revealed the development of a new nano-battery technology that promises to revolutionize energy storage. The new battery, dubbed the \"EnerGCell\", uses advanced nanomaterials to achieve unprecedented efficiency and storage capacities.  According to lead researcher Dr. Ada Byron, the EnerGCell can store up to ten times more energy than the best lithium-ion batteries available today, while charging in just a fraction of the time. \"We're talking about charging your electric vehicle in just five minutes for a range of over 1,000 miles,\" Dr. Byron stated during the press conference.  The technology behind the EnerGCell involves a complex arrangement of nanostructured electrodes that allow for rapid ion transfer and extremely high energy density. This breakthrough was achieved after a decade of research into nanomaterials and their applications in energy storage.  The implications of this technology are vast, promising to accelerate the adoption of renewable energy by making it more practical and affordable to store wind and solar power. It could also lead to significant advancements in electric vehicles, mobile devices, and any other technology that relies on batteries.  Despite the excitement, some experts are calling for patience, noting that the EnerGCell is still in its early stages of development and may take several years before it's commercially available. However, the potential impact of such a technology on the environment and the global economy is undeniable.  Tech Innovations Institute plans to continue refining the EnerGCell and begin pilot projects with select partners in the coming year. If successful, this nano-battery technology could indeed be the breakthrough needed to usher in a new era of clean energy and technology."
-              },
-              "record_template": {
-                "advanced": true,
-                "display_name": "Record Template",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
-                "input_types": [
-                  "Text"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": true,
-                "name": "record_template",
+                "name": "input_value",
                 "password": false,
                 "placeholder": "",
                 "required": false,
@@ -1054,11 +1081,33 @@
             ],
             "frozen": false,
             "icon": "OpenAI",
-            "output_types": [
-              "Text"
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Text",
+                "method": "text_response",
+                "name": "text_output",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              },
+              {
+                "cache": true,
+                "display_name": "Language Model",
+                "method": "build_model",
+                "name": "model_output",
+                "selected": "LanguageModel",
+                "types": [
+                  "LanguageModel"
+                ],
+                "value": "__UNDEFINED__"
+              }
             ],
             "template": {
-              "_type": "CustomComponent",
+              "_type": "Component",
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -1075,7 +1124,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\", \"Prompt\"]},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-3.5-turbo\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    inputs = [\n        MessageInput(name=\"input_value\", display_name=\"Input\"),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n        Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n    ]\n\n    def text_response(self) -> Message:\n        input_value = self.input_value\n        stream = self.stream\n        system_message = self.system_message\n        output = self.build_model()\n        result = self.get_chat_result(output, stream, input_value, system_message)\n        self.status = result\n        return result\n\n    def build_model(self) -> LanguageModel:\n        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict)\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs or {},\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n\n        return output\n"
               },
               "input_value": {
                 "advanced": false,
@@ -1085,20 +1134,19 @@
                 "file_path": "",
                 "info": "",
                 "input_types": [
-                  "Text",
-                  "Record",
-                  "Prompt"
+                  "Message"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
+                "multiline": true,
                 "name": "input_value",
                 "password": false,
                 "placeholder": "",
-                "required": true,
+                "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "max_tokens": {
                 "advanced": true,
@@ -1107,6 +1155,9 @@
                 "fileTypes": [],
                 "file_path": "",
                 "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+                "input_types": [
+                  "Text"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": false,
@@ -1116,8 +1167,8 @@
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "int",
-                "value": 256
+                "type": "str",
+                "value": ""
               },
               "model_kwargs": {
                 "advanced": true,
@@ -1126,6 +1177,9 @@
                 "fileTypes": [],
                 "file_path": "",
                 "info": "",
+                "input_types": [
+                  "Text"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": false,
@@ -1135,8 +1189,8 @@
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "NestedDict",
-                "value": {}
+                "type": "str",
+                "value": ""
               },
               "model_name": {
                 "advanced": false,
@@ -1173,20 +1227,21 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
+                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.",
                 "input_types": [
                   "Text"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
+                "multiline": true,
                 "name": "openai_api_base",
                 "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "openai_api_key": {
                 "advanced": false,
@@ -1195,16 +1250,14 @@
                 "fileTypes": [],
                 "file_path": "",
                 "info": "The OpenAI API Key to use for the OpenAI model.",
-                "input_types": [
-                  "Text"
-                ],
+                "input_types": [],
                 "list": false,
                 "load_from_db": true,
                 "multiline": false,
                 "name": "openai_api_key",
                 "password": true,
                 "placeholder": "",
-                "required": true,
+                "required": false,
                 "show": true,
                 "title_case": false,
                 "type": "str",
@@ -1217,6 +1270,9 @@
                 "fileTypes": [],
                 "file_path": "",
                 "info": "Stream the response from the model. Streaming works only in Chat.",
+                "input_types": [
+                  "Text"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": false,
@@ -1226,7 +1282,7 @@
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "bool",
+                "type": "str",
                 "value": false
               },
               "system_message": {
@@ -1241,14 +1297,15 @@
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
+                "multiline": true,
                 "name": "system_message",
                 "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "temperature": {
                 "advanced": false,
@@ -1257,22 +1314,19 @@
                 "fileTypes": [],
                 "file_path": "",
                 "info": "",
+                "input_types": [
+                  "Text"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": false,
                 "name": "temperature",
                 "password": false,
                 "placeholder": "",
-                "rangeSpec": {
-                  "max": 1,
-                  "min": -1,
-                  "step": 0.1,
-                  "step_type": "float"
-                },
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "float",
+                "type": "str",
                 "value": 0.1
               }
             }
@@ -1440,11 +1494,33 @@
             ],
             "frozen": false,
             "icon": "OpenAI",
-            "output_types": [
-              "Text"
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Text",
+                "method": "text_response",
+                "name": "text_output",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              },
+              {
+                "cache": true,
+                "display_name": "Language Model",
+                "method": "build_model",
+                "name": "model_output",
+                "selected": "LanguageModel",
+                "types": [
+                  "LanguageModel"
+                ],
+                "value": "__UNDEFINED__"
+              }
             ],
             "template": {
-              "_type": "CustomComponent",
+              "_type": "Component",
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -1461,7 +1537,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\", \"Prompt\"]},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-3.5-turbo\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\nfrom langflow.schema.message import Message\nfrom langflow.template import Output\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    inputs = [\n        MessageInput(name=\"input_value\", display_name=\"Input\"),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text_output\", method=\"text_response\"),\n        Output(display_name=\"Language Model\", name=\"model_output\", method=\"build_model\"),\n    ]\n\n    def text_response(self) -> Message:\n        input_value = self.input_value\n        stream = self.stream\n        system_message = self.system_message\n        output = self.build_model()\n        result = self.get_chat_result(output, stream, input_value, system_message)\n        self.status = result\n        return result\n\n    def build_model(self) -> LanguageModel:\n        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict)\n        seed = self.seed\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs or {},\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n            seed=seed,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")\n\n        return output\n"
               },
               "input_value": {
                 "advanced": false,
@@ -1471,20 +1547,19 @@
                 "file_path": "",
                 "info": "",
                 "input_types": [
-                  "Text",
-                  "Record",
-                  "Prompt"
+                  "Message"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
+                "multiline": true,
                 "name": "input_value",
                 "password": false,
                 "placeholder": "",
-                "required": true,
+                "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "max_tokens": {
                 "advanced": true,
@@ -1493,6 +1568,9 @@
                 "fileTypes": [],
                 "file_path": "",
                 "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
+                "input_types": [
+                  "Text"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": false,
@@ -1502,8 +1580,8 @@
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "int",
-                "value": 256
+                "type": "str",
+                "value": ""
               },
               "model_kwargs": {
                 "advanced": true,
@@ -1512,6 +1590,9 @@
                 "fileTypes": [],
                 "file_path": "",
                 "info": "",
+                "input_types": [
+                  "Text"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": false,
@@ -1521,8 +1602,8 @@
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "NestedDict",
-                "value": {}
+                "type": "str",
+                "value": ""
               },
               "model_name": {
                 "advanced": false,
@@ -1551,7 +1632,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "str",
-                "value": "gpt-4-turbo-preview"
+                "value": "gpt-4o"
               },
               "openai_api_base": {
                 "advanced": true,
@@ -1559,20 +1640,21 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
+                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.",
                 "input_types": [
                   "Text"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
+                "multiline": true,
                 "name": "openai_api_base",
                 "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "openai_api_key": {
                 "advanced": false,
@@ -1581,20 +1663,18 @@
                 "fileTypes": [],
                 "file_path": "",
                 "info": "The OpenAI API Key to use for the OpenAI model.",
-                "input_types": [
-                  "Text"
-                ],
+                "input_types": [],
                 "list": false,
-                "load_from_db": false,
+                "load_from_db": true,
                 "multiline": false,
                 "name": "openai_api_key",
                 "password": true,
                 "placeholder": "",
-                "required": true,
+                "required": false,
                 "show": true,
                 "title_case": false,
                 "type": "str",
-                "value": ""
+                "value": "OPENAI_API_KEY"
               },
               "stream": {
                 "advanced": true,
@@ -1603,6 +1683,9 @@
                 "fileTypes": [],
                 "file_path": "",
                 "info": "Stream the response from the model. Streaming works only in Chat.",
+                "input_types": [
+                  "Text"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": false,
@@ -1612,7 +1695,7 @@
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "bool",
+                "type": "str",
                 "value": false
               },
               "system_message": {
@@ -1627,14 +1710,15 @@
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
+                "multiline": true,
                 "name": "system_message",
                 "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "temperature": {
                 "advanced": false,
@@ -1643,22 +1727,19 @@
                 "fileTypes": [],
                 "file_path": "",
                 "info": "",
+                "input_types": [
+                  "Text"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": false,
                 "name": "temperature",
                 "password": false,
                 "placeholder": "",
-                "rangeSpec": {
-                  "max": 1,
-                  "min": -1,
-                  "step": 0.1,
-                  "step_type": "float"
-                },
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "float",
+                "type": "str",
                 "value": 0.1
               }
             }
diff --git a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
index c9745183d..e7e90c3bf 100644
--- a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
+++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json
@@ -2,313 +2,313 @@
   "data": {
     "edges": [
       {
-        "className": "stroke-gray-900 stroke-connection",
+        "className": "",
         "data": {
           "sourceHandle": {
-            "baseClasses": [
-              "object",
-              "Text",
+            "dataType": "Prompt",
+            "id": "Prompt-jzPqb",
+            "name": "prompt",
+            "output_types": [
+              "Message"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "input_value",
+            "id": "OpenAIModel-ickkA",
+            "inputTypes": [
+              "Message"
+            ],
+            "type": "str"
+          }
+        },
+        "id": "reactflow__edge-Prompt-jzPqb{œdataTypeœ:œPromptœ,œidœ:œPrompt-jzPqbœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-ickkA{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-ickkAœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+        "selected": false,
+        "source": "Prompt-jzPqb",
+        "sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-jzPqbœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
+        "style": {
+          "stroke": "#555"
+        },
+        "target": "OpenAIModel-ickkA",
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-ickkAœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+      },
+      {
+        "className": "",
+        "data": {
+          "sourceHandle": {
+            "dataType": "OpenAIModel",
+            "id": "OpenAIModel-ickkA",
+            "name": "text_output",
+            "output_types": [
+              "Message"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "input_value",
+            "id": "ChatOutput-Zy354",
+            "inputTypes": [
+              "Message",
               "str"
             ],
+            "type": "str"
+          }
+        },
+        "id": "reactflow__edge-OpenAIModel-ickkA{œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-ickkAœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-Zy354{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-Zy354œ,œinputTypesœ:[œMessageœ,œstrœ],œtypeœ:œstrœ}",
+        "selected": false,
+        "source": "OpenAIModel-ickkA",
+        "sourceHandle": "{œdataTypeœ: œOpenAIModelœ, œidœ: œOpenAIModel-ickkAœ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
+        "style": {
+          "stroke": "#555"
+        },
+        "target": "ChatOutput-Zy354",
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-Zy354œ, œinputTypesœ: [œMessageœ, œstrœ], œtypeœ: œstrœ}"
+      },
+      {
+        "className": "",
+        "data": {
+          "sourceHandle": {
+            "dataType": "File",
+            "id": "File-28ckd",
+            "name": "data",
+            "output_types": [
+              "Data"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "data_input",
+            "id": "RecursiveCharacterTextSplitter-HVESL",
+            "inputTypes": [
+              "Document",
+              "Data"
+            ],
+            "type": "other"
+          }
+        },
+        "id": "reactflow__edge-File-28ckd{œdataTypeœ:œFileœ,œidœ:œFile-28ckdœ,œnameœ:œdataœ,œoutput_typesœ:[œDataœ]}-RecursiveCharacterTextSplitter-HVESL{œfieldNameœ:œdata_inputœ,œidœ:œRecursiveCharacterTextSplitter-HVESLœ,œinputTypesœ:[œDocumentœ,œDataœ],œtypeœ:œotherœ}",
+        "source": "File-28ckd",
+        "sourceHandle": "{œdataTypeœ: œFileœ, œidœ: œFile-28ckdœ, œnameœ: œdataœ, œoutput_typesœ: [œDataœ]}",
+        "target": "RecursiveCharacterTextSplitter-HVESL",
+        "targetHandle": "{œfieldNameœ: œdata_inputœ, œidœ: œRecursiveCharacterTextSplitter-HVESLœ, œinputTypesœ: [œDocumentœ, œDataœ], œtypeœ: œotherœ}"
+      },
+      {
+        "className": "",
+        "data": {
+          "sourceHandle": {
+            "dataType": "RecursiveCharacterTextSplitter",
+            "id": "RecursiveCharacterTextSplitter-HVESL",
+            "name": "data",
+            "output_types": [
+              "Data"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "vector_store_inputs",
+            "id": "AstraDB-irvai",
+            "inputTypes": [
+              "Document",
+              "Data"
+            ],
+            "type": "other"
+          }
+        },
+        "id": "reactflow__edge-RecursiveCharacterTextSplitter-HVESL{œdataTypeœ:œRecursiveCharacterTextSplitterœ,œidœ:œRecursiveCharacterTextSplitter-HVESLœ,œnameœ:œdataœ,œoutput_typesœ:[œDataœ]}-AstraDB-irvai{œfieldNameœ:œvector_store_inputsœ,œidœ:œAstraDB-irvaiœ,œinputTypesœ:[œDocumentœ,œDataœ],œtypeœ:œotherœ}",
+        "source": "RecursiveCharacterTextSplitter-HVESL",
+        "sourceHandle": "{œdataTypeœ: œRecursiveCharacterTextSplitterœ, œidœ: œRecursiveCharacterTextSplitter-HVESLœ, œnameœ: œdataœ, œoutput_typesœ: [œDataœ]}",
+        "target": "AstraDB-irvai",
+        "targetHandle": "{œfieldNameœ: œvector_store_inputsœ, œidœ: œAstraDB-irvaiœ, œinputTypesœ: [œDocumentœ, œDataœ], œtypeœ: œotherœ}"
+      },
+      {
+        "className": "",
+        "data": {
+          "sourceHandle": {
+            "dataType": "OpenAIEmbeddings",
+            "id": "OpenAIEmbeddings-YeYtt",
+            "name": "embeddings",
+            "output_types": [
+              "Embeddings"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "embedding",
+            "id": "AstraDB-irvai",
+            "inputTypes": [
+              "Embeddings"
+            ],
+            "type": "other"
+          }
+        },
+        "id": "reactflow__edge-OpenAIEmbeddings-YeYtt{œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-YeYttœ,œnameœ:œembeddingsœ,œoutput_typesœ:[œEmbeddingsœ]}-AstraDB-irvai{œfieldNameœ:œembeddingœ,œidœ:œAstraDB-irvaiœ,œinputTypesœ:[œEmbeddingsœ],œtypeœ:œotherœ}",
+        "source": "OpenAIEmbeddings-YeYtt",
+        "sourceHandle": "{œdataTypeœ: œOpenAIEmbeddingsœ, œidœ: œOpenAIEmbeddings-YeYttœ, œnameœ: œembeddingsœ, œoutput_typesœ: [œEmbeddingsœ]}",
+        "target": "AstraDB-irvai",
+        "targetHandle": "{œfieldNameœ: œembeddingœ, œidœ: œAstraDB-irvaiœ, œinputTypesœ: [œEmbeddingsœ], œtypeœ: œotherœ}"
+      },
+      {
+        "className": "",
+        "data": {
+          "sourceHandle": {
+            "dataType": "ChatInput",
+            "id": "ChatInput-IY8UK",
+            "name": "message",
+            "output_types": [
+              "Message"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "search_input",
+            "id": "AstraDB-wANQu",
+            "inputTypes": [
+              "Message",
+              "str"
+            ],
+            "type": "str"
+          }
+        },
+        "id": "reactflow__edge-ChatInput-IY8UK{œdataTypeœ:œChatInputœ,œidœ:œChatInput-IY8UKœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-AstraDB-wANQu{œfieldNameœ:œsearch_inputœ,œidœ:œAstraDB-wANQuœ,œinputTypesœ:[œMessageœ,œstrœ],œtypeœ:œstrœ}",
+        "selected": false,
+        "source": "ChatInput-IY8UK",
+        "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-IY8UKœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "AstraDB-wANQu",
+        "targetHandle": "{œfieldNameœ: œsearch_inputœ, œidœ: œAstraDB-wANQuœ, œinputTypesœ: [œMessageœ, œstrœ], œtypeœ: œstrœ}"
+      },
+      {
+        "className": "",
+        "data": {
+          "sourceHandle": {
+            "dataType": "OpenAIEmbeddings",
+            "id": "OpenAIEmbeddings-HoSp5",
+            "name": "embeddings",
+            "output_types": [
+              "Embeddings"
+            ]
+          },
+          "targetHandle": {
+            "fieldName": "embedding",
+            "id": "AstraDB-wANQu",
+            "inputTypes": [
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+            "id": "ParseData-C9tUn",
+            "name": "text",
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+              "Message"
+            ]
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+            "fieldName": "input_value",
+            "id": "TextOutput-IxTee",
+            "inputTypes": [
+              "Message"
+            ],
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+          }
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+        "id": "reactflow__edge-ParseData-C9tUn{œdataTypeœ:œParseDataœ,œidœ:œParseData-C9tUnœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-TextOutput-IxTee{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-IxTeeœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+        "source": "ParseData-C9tUn",
+        "sourceHandle": "{œdataTypeœ: œParseDataœ, œidœ: œParseData-C9tUnœ, œnameœ: œtextœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "TextOutput-IxTee",
+        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œTextOutput-IxTeeœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+      },
+      {
+        "className": "",
+        "data": {
+          "sourceHandle": {
             "dataType": "TextOutput",
-            "id": "TextOutput-BDknO"
+            "id": "TextOutput-IxTee",
+            "name": "text",
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "context",
-            "id": "Prompt-xeI6K",
+            "id": "Prompt-jzPqb",
             "inputTypes": [
-              "Document",
-              "BaseOutputParser",
-              "Record",
+              "Message",
               "Text"
             ],
             "type": "str"
           }
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+        "id": "reactflow__edge-TextOutput-IxTee{œdataTypeœ:œTextOutputœ,œidœ:œTextOutput-IxTeeœ,œnameœ:œtextœ,œoutput_typesœ:[œMessageœ]}-Prompt-jzPqb{œfieldNameœ:œcontextœ,œidœ:œPrompt-jzPqbœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+        "source": "TextOutput-IxTee",
+        "sourceHandle": "{œdataTypeœ: œTextOutputœ, œidœ: œTextOutput-IxTeeœ, œnameœ: œtextœ, œoutput_typesœ: [œMessageœ]}",
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       },
       {
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         "data": {
           "sourceHandle": {
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-              "Text",
-              "str",
-              "object",
-              "Record"
-            ],
             "dataType": "ChatInput",
-            "id": "ChatInput-yxMKE"
+            "id": "ChatInput-IY8UK",
+            "name": "message",
+            "output_types": [
+              "Message"
+            ]
           },
           "targetHandle": {
             "fieldName": "question",
-            "id": "Prompt-xeI6K",
+            "id": "Prompt-jzPqb",
             "inputTypes": [
-              "Document",
-              "BaseOutputParser",
-              "Record",
+              "Message",
               "Text"
             ],
             "type": "str"
           }
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-        "id": "reactflow__edge-ChatInput-yxMKE{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ,œRecordœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-yxMKEœ}-Prompt-xeI6K{œfieldNameœ:œquestionœ,œidœ:œPrompt-xeI6Kœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}",
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-            "id": "OpenAIModel-EjXlN",
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-        "style": {
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-        "source": "ChatInput-yxMKE",
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-        "style": {
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-          },
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-            "id": "AstraDB-eUCSS",
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-        "selected": false,
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-        "style": {
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-        "targetHandle": "{œfieldNameœ: œinputsœ, œidœ: œAstraDB-eUCSSœ, œinputTypesœ: null, œtypeœ: œRecordœ}"
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-            "dataType": "OpenAIEmbeddings",
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-          "targetHandle": {
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-            "id": "AstraDB-eUCSS",
-            "inputTypes": null,
-            "type": "Embeddings"
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-        "id": "reactflow__edge-OpenAIEmbeddings-9TPjc{œbaseClassesœ:[œEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-9TPjcœ}-AstraDB-eUCSS{œfieldNameœ:œembeddingœ,œidœ:œAstraDB-eUCSSœ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}",
-        "selected": false,
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-        "style": {
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-        "targetHandle": "{œfieldNameœ: œembeddingœ, œidœ: œAstraDB-eUCSSœ, œinputTypesœ: null, œtypeœ: œEmbeddingsœ}"
-      },
-      {
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-        "data": {
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-            ],
-            "dataType": "AstraDBSearch",
-            "id": "AstraDBSearch-41nRz"
-          },
-          "targetHandle": {
-            "fieldName": "input_value",
-            "id": "TextOutput-BDknO",
-            "inputTypes": [
-              "Record",
-              "Text"
-            ],
-            "type": "str"
-          }
-        },
-        "id": "reactflow__edge-AstraDBSearch-41nRz{œbaseClassesœ:[œRecordœ],œdataTypeœ:œAstraDBSearchœ,œidœ:œAstraDBSearch-41nRzœ}-TextOutput-BDknO{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-BDknOœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}",
-        "source": "AstraDBSearch-41nRz",
-        "sourceHandle": "{œbaseClassesœ: [œRecordœ], œdataTypeœ: œAstraDBSearchœ, œidœ: œAstraDBSearch-41nRzœ}",
-        "style": {
-          "stroke": "#555"
-        },
-        "target": "TextOutput-BDknO",
-        "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œTextOutput-BDknOœ, œinputTypesœ: [œRecordœ, œTextœ], œtypeœ: œstrœ}"
+        "id": "reactflow__edge-ChatInput-IY8UK{œdataTypeœ:œChatInputœ,œidœ:œChatInput-IY8UKœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Prompt-jzPqb{œfieldNameœ:œquestionœ,œidœ:œPrompt-jzPqbœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
+        "source": "ChatInput-IY8UK",
+        "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-IY8UKœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
+        "target": "Prompt-jzPqb",
+        "targetHandle": "{œfieldNameœ: œquestionœ, œidœ: œPrompt-jzPqbœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
       }
     ],
     "nodes": [
       {
         "data": {
-          "id": "ChatInput-yxMKE",
+          "id": "ChatInput-IY8UK",
           "node": {
             "base_classes": [
               "Text",
@@ -331,12 +331,23 @@
             "field_order": [],
             "frozen": false,
             "icon": "ChatInput",
-            "output_types": [
-              "Message",
-              "Text"
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Message",
+                "hidden": false,
+                "method": "message_response",
+                "name": "message",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
             ],
             "template": {
-              "_type": "CustomComponent",
+              "_type": "Component",
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -353,7 +364,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Optional\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.schema.message import Message\nfrom langflow.field_typing import Text\nfrom typing import Union\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    def build_config(self):\n        build_config = super().build_config()\n        build_config[\"input_value\"] = {\n            \"input_types\": [],\n            \"display_name\": \"Text\",\n            \"multiline\": True,\n        }\n        build_config[\"return_message\"] = {\n            \"display_name\": \"Return Record\",\n            \"advanced\": True,\n        }\n\n        return build_config\n\n    def build(\n        self,\n        sender: Optional[str] = \"User\",\n        sender_name: Optional[str] = \"User\",\n        input_value: Optional[str] = None,\n        files: Optional[list[str]] = None,\n        session_id: Optional[str] = None,\n        return_message: Optional[bool] = True,\n    ) -> Union[Message, Text]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            files=files,\n            session_id=session_id,\n            return_message=return_message,\n        )\n"
+                "value": "from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, FileInput, MultilineInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatInput(ChatComponent):\n    display_name = \"Chat Input\"\n    description = \"Get chat inputs from the Playground.\"\n    icon = \"ChatInput\"\n\n    inputs = [\n        MultilineInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            value=\"\",\n            info=\"Message to be passed as input.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"User\",\n            info=\"Type of sender.\",\n            advanced=True,\n        ),\n        TextInput(\n            name=\"sender_name\",\n            display_name=\"Sender Name\",\n            info=\"Name of the sender.\",\n            value=\"User\",\n            advanced=True,\n        ),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        FileInput(\n            name=\"files\",\n            display_name=\"Files\",\n            file_types=TEXT_FILE_TYPES + IMG_FILE_TYPES,\n            info=\"Files to be sent with the message.\",\n            advanced=True,\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n            files=self.files,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "input_value": {
                 "advanced": false,
@@ -361,8 +372,11 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "",
-                "input_types": [],
+                "info": "Message to be passed as input.",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
                 "list": false,
                 "load_from_db": false,
                 "multiline": true,
@@ -373,7 +387,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "str",
-                "value": "what is a line"
+                "value": ""
               },
               "sender": {
                 "advanced": true,
@@ -381,7 +395,7 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "",
+                "info": "Type of sender.",
                 "input_types": [
                   "Text"
                 ],
@@ -402,18 +416,19 @@
                 "value": "User"
               },
               "sender_name": {
-                "advanced": false,
+                "advanced": true,
                 "display_name": "Sender Name",
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "",
+                "info": "Name of the sender.",
                 "input_types": [
-                  "Text"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
+                "multiline": true,
                 "name": "sender_name",
                 "password": false,
                 "placeholder": "",
@@ -429,30 +444,37 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "If provided, the message will be stored in the memory.",
+                "info": "Session ID for the message.",
                 "input_types": [
-                  "Text"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
+                "multiline": true,
                 "name": "session_id",
                 "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               }
             }
           },
           "type": "ChatInput"
         },
-        "height": 383,
-        "id": "ChatInput-yxMKE",
+        "dragging": false,
+        "height": 309,
+        "id": "ChatInput-IY8UK",
         "position": {
-          "x": 1195.5276981160775,
-          "y": 209.421875
+          "x": 702.4571951501161,
+          "y": 119.7726926425525
+        },
+        "positionAbsolute": {
+          "x": 702.4571951501161,
+          "y": 119.7726926425525
         },
         "selected": false,
         "type": "genericNode",
@@ -460,30 +482,43 @@
       },
       {
         "data": {
-          "id": "TextOutput-BDknO",
+          "description": "Display a text output in the Playground.",
+          "display_name": "Extracted Chunks",
+          "edited": false,
+          "id": "TextOutput-IxTee",
           "node": {
             "base_classes": [
-              "object",
-              "Text",
-              "str"
+              "Message"
             ],
             "beta": false,
-            "custom_fields": {
-              "input_value": null,
-              "record_template": null
-            },
+            "conditional_paths": [],
+            "custom_fields": {},
             "description": "Display a text output in the Playground.",
             "display_name": "Extracted Chunks",
             "documentation": "",
-            "field_formatters": {},
-            "field_order": [],
+            "edited": true,
+            "field_order": [
+              "input_value"
+            ],
             "frozen": false,
             "icon": "type",
-            "output_types": [
-              "Text"
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Text",
+                "method": "text_response",
+                "name": "text",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
             ],
+            "pinned": false,
             "template": {
-              "_type": "CustomComponent",
+              "_type": "Component",
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -500,67 +535,40 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n    display_name = \"Text Output\"\n    description = \"Display a text output in the Playground.\"\n    icon = \"type\"\n\n    def build_config(self):\n        return {\n            \"input_value\": {\n                \"display_name\": \"Value\",\n                \"input_types\": [\"Record\", \"Text\"],\n                \"info\": \"Text or Record to be passed as output.\",\n            },\n            \"record_template\": {\n                \"display_name\": \"Record Template\",\n                \"multiline\": True,\n                \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n        return super().build(input_value=input_value, record_template=record_template)\n"
+                "value": "from langflow.base.io.text import TextComponent\nfrom langflow.io import Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass TextOutputComponent(TextComponent):\n    display_name = \"Text Output\"\n    description = \"Display a text output in the Playground.\"\n    icon = \"type\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Text to be passed as output.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"text_response\"),\n    ]\n\n    def text_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n        )\n        self.status = self.input_value\n        return message\n"
               },
               "input_value": {
                 "advanced": false,
-                "display_name": "Value",
+                "display_name": "Text",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Text or Record to be passed as output.",
+                "info": "Text to be passed as output.",
                 "input_types": [
-                  "Record",
-                  "Text"
+                  "Message"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
                 "name": "input_value",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
                 "type": "str",
                 "value": ""
-              },
-              "record_template": {
-                "advanced": true,
-                "display_name": "Record Template",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "record_template",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "{text}"
               }
             }
           },
           "type": "TextOutput"
         },
         "dragging": false,
-        "height": 289,
-        "id": "TextOutput-BDknO",
+        "height": 309,
+        "id": "TextOutput-IxTee",
         "position": {
-          "x": 2322.600672827879,
-          "y": 604.9467307442569
+          "x": 2439.792450398153,
+          "y": 661.149562774499
         },
         "positionAbsolute": {
-          "x": 2322.600672827879,
-          "y": 604.9467307442569
+          "x": 2439.792450398153,
+          "y": 661.149562774499
         },
         "selected": false,
         "type": "genericNode",
@@ -568,7 +576,7 @@
       },
       {
         "data": {
-          "id": "OpenAIEmbeddings-ZlOk1",
+          "id": "OpenAIEmbeddings-HoSp5",
           "node": {
             "base_classes": [
               "Embeddings"
@@ -604,45 +612,31 @@
             "field_formatters": {},
             "field_order": [],
             "frozen": false,
-            "output_types": [
-              "Embeddings"
+            "icon": "OpenAI",
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Embeddings",
+                "hidden": false,
+                "method": "build_embeddings",
+                "name": "embeddings",
+                "selected": "Embeddings",
+                "types": [
+                  "Embeddings"
+                ],
+                "value": "__UNDEFINED__"
+              }
             ],
             "template": {
-              "_type": "CustomComponent",
-              "allowed_special": {
-                "advanced": true,
-                "display_name": "Allowed Special",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "allowed_special",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": []
-              },
+              "_type": "Component",
               "chunk_size": {
                 "advanced": true,
                 "display_name": "Chunk Size",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "chunk_size",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -650,6 +644,25 @@
                 "type": "int",
                 "value": 1000
               },
+              "client": {
+                "advanced": true,
+                "display_name": "Client",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "client",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -666,338 +679,241 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, NestedDict\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n    display_name = \"OpenAI Embeddings\"\n    description = \"Generate embeddings using OpenAI models.\"\n\n    def build_config(self):\n        return {\n            \"allowed_special\": {\n                \"display_name\": \"Allowed Special\",\n                \"advanced\": True,\n                \"field_type\": \"str\",\n                \"is_list\": True,\n            },\n            \"default_headers\": {\n                \"display_name\": \"Default Headers\",\n                \"advanced\": True,\n                \"field_type\": \"dict\",\n            },\n            \"default_query\": {\n                \"display_name\": \"Default Query\",\n                \"advanced\": True,\n                \"field_type\": \"NestedDict\",\n            },\n            \"disallowed_special\": {\n                \"display_name\": \"Disallowed Special\",\n                \"advanced\": True,\n                \"field_type\": \"str\",\n                \"is_list\": True,\n            },\n            \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n            \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n            \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n            \"embedding_ctx_length\": {\n                \"display_name\": \"Embedding Context Length\",\n                \"advanced\": True,\n            },\n            \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n            \"model\": {\n                \"display_name\": \"Model\",\n                \"advanced\": False,\n                \"options\": [\n                    \"text-embedding-3-small\",\n                    \"text-embedding-3-large\",\n                    \"text-embedding-ada-002\",\n                ],\n            },\n            \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"password\": True,\n                \"advanced\": True,\n            },\n            \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n            \"openai_api_type\": {\n                \"display_name\": \"OpenAI API Type\",\n                \"advanced\": True,\n                \"password\": True,\n            },\n            \"openai_api_version\": {\n                \"display_name\": \"OpenAI API Version\",\n                \"advanced\": True,\n            },\n            \"openai_organization\": {\n                \"display_name\": \"OpenAI Organization\",\n                \"advanced\": True,\n            },\n            \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n            \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n            \"show_progress_bar\": {\n                \"display_name\": \"Show Progress Bar\",\n                \"advanced\": True,\n            },\n            \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n            \"tiktoken_model_name\": {\n                \"display_name\": \"TikToken Model Name\",\n                \"advanced\": True,\n            },\n            \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n            \"dimensions\": {\n                \"display_name\": \"Dimensions\",\n                \"info\": \"The number of dimensions the resulting output embeddings should have. Only supported by certain models.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        openai_api_key: str,\n        default_headers: Optional[Dict[str, str]] = None,\n        default_query: Optional[NestedDict] = {},\n        allowed_special: List[str] = [],\n        disallowed_special: List[str] = [\"all\"],\n        chunk_size: int = 1000,\n        deployment: str = \"text-embedding-ada-002\",\n        embedding_ctx_length: int = 8191,\n        max_retries: int = 6,\n        model: str = \"text-embedding-ada-002\",\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        openai_api_type: Optional[str] = None,\n        openai_api_version: Optional[str] = None,\n        openai_organization: Optional[str] = None,\n        openai_proxy: Optional[str] = None,\n        request_timeout: Optional[float] = None,\n        show_progress_bar: bool = False,\n        skip_empty: bool = False,\n        tiktoken_enable: bool = True,\n        tiktoken_model_name: Optional[str] = None,\n        dimensions: Optional[int] = None,\n    ) -> Embeddings:\n        # This is to avoid errors with Vector Stores (e.g Chroma)\n        if disallowed_special == [\"all\"]:\n            disallowed_special = \"all\"  # type: ignore\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        return OpenAIEmbeddings(\n            tiktoken_enabled=tiktoken_enable,\n            default_headers=default_headers,\n            default_query=default_query,\n            allowed_special=set(allowed_special),\n            disallowed_special=\"all\",\n            chunk_size=chunk_size,\n            deployment=deployment,\n            embedding_ctx_length=embedding_ctx_length,\n            max_retries=max_retries,\n            model=model,\n            model_kwargs=model_kwargs,\n            base_url=openai_api_base,\n            api_key=api_key,\n            openai_api_type=openai_api_type,\n            api_version=openai_api_version,\n            organization=openai_organization,\n            openai_proxy=openai_proxy,\n            timeout=request_timeout,\n            show_progress_bar=show_progress_bar,\n            skip_empty=skip_empty,\n            tiktoken_model_name=tiktoken_model_name,\n            dimensions=dimensions,\n        )\n"
+                "value": "from langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import Embeddings\nfrom langflow.io import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, Output, SecretStrInput, TextInput\n\n\nclass OpenAIEmbeddingsComponent(LCModelComponent):\n    display_name = \"OpenAI Embeddings\"\n    description = \"Generate embeddings using OpenAI models.\"\n    icon = \"OpenAI\"\n    inputs = [\n        DictInput(\n            name=\"default_headers\",\n            display_name=\"Default Headers\",\n            advanced=True,\n            info=\"Default headers to use for the API request.\",\n        ),\n        DictInput(\n            name=\"default_query\",\n            display_name=\"Default Query\",\n            advanced=True,\n            info=\"Default query parameters to use for the API request.\",\n        ),\n        IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n        TextInput(name=\"client\", display_name=\"Client\", advanced=True),\n        TextInput(name=\"deployment\", display_name=\"Deployment\", advanced=True),\n        IntInput(name=\"embedding_ctx_length\", display_name=\"Embedding Context Length\", advanced=True, value=1536),\n        IntInput(name=\"max_retries\", display_name=\"Max Retries\", value=3, advanced=True),\n        DropdownInput(\n            name=\"model\",\n            display_name=\"Model\",\n            advanced=False,\n            options=[\n                \"text-embedding-3-small\",\n                \"text-embedding-3-large\",\n                \"text-embedding-ada-002\",\n            ],\n            value=\"text-embedding-3-small\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        SecretStrInput(name=\"openai_api_base\", display_name=\"OpenAI API Base\", advanced=True),\n        SecretStrInput(name=\"openai_api_key\", display_name=\"OpenAI API Key\"),\n        SecretStrInput(name=\"openai_api_type\", display_name=\"OpenAI API Type\", advanced=True),\n        TextInput(name=\"openai_api_version\", display_name=\"OpenAI API Version\", advanced=True),\n        TextInput(\n            name=\"openai_organization\",\n            display_name=\"OpenAI Organization\",\n            advanced=True,\n        ),\n        TextInput(name=\"openai_proxy\", display_name=\"OpenAI Proxy\", advanced=True),\n        FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n        BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n        BoolInput(name=\"skip_empty\", display_name=\"Skip Empty\", advanced=True),\n        TextInput(\n            name=\"tiktoken_model_name\",\n            display_name=\"TikToken Model Name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"tiktoken_enable\",\n            display_name=\"TikToken Enable\",\n            advanced=True,\n            value=True,\n            info=\"If False, you must have transformers installed.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Embeddings\", name=\"embeddings\", method=\"build_embeddings\"),\n    ]\n\n    def build_embeddings(self) -> Embeddings:\n        return OpenAIEmbeddings(\n            tiktoken_enabled=self.tiktoken_enable,\n            default_headers=self.default_headers,\n            default_query=self.default_query,\n            allowed_special=\"all\",\n            disallowed_special=\"all\",\n            chunk_size=self.chunk_size,\n            deployment=self.deployment,\n            embedding_ctx_length=self.embedding_ctx_length,\n            max_retries=self.max_retries,\n            model=self.model,\n            model_kwargs=self.model_kwargs,\n            base_url=self.openai_api_base,\n            api_key=self.openai_api_key,\n            openai_api_type=self.openai_api_type,\n            api_version=self.openai_api_version,\n            organization=self.openai_organization,\n            openai_proxy=self.openai_proxy,\n            timeout=self.request_timeout or None,\n            show_progress_bar=self.show_progress_bar,\n            skip_empty=self.skip_empty,\n            tiktoken_model_name=self.tiktoken_model_name,\n        )\n"
               },
               "default_headers": {
                 "advanced": true,
                 "display_name": "Default Headers",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
+                "info": "Default headers to use for the API request.",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "default_headers",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "dict"
+                "type": "dict",
+                "value": {}
               },
               "default_query": {
                 "advanced": true,
                 "display_name": "Default Query",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
+                "info": "Default query parameters to use for the API request.",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "default_query",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "NestedDict",
+                "type": "dict",
                 "value": {}
               },
               "deployment": {
                 "advanced": true,
                 "display_name": "Deployment",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
                 "input_types": [
-                  "Text"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
                 "name": "deployment",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
                 "type": "str",
-                "value": "text-embedding-ada-002"
-              },
-              "disallowed_special": {
-                "advanced": true,
-                "display_name": "Disallowed Special",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "disallowed_special",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": [
-                  "all"
-                ]
+                "value": ""
               },
               "embedding_ctx_length": {
                 "advanced": true,
                 "display_name": "Embedding Context Length",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "embedding_ctx_length",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
                 "type": "int",
-                "value": 8191
+                "value": 1536
               },
               "max_retries": {
                 "advanced": true,
                 "display_name": "Max Retries",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "max_retries",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
                 "type": "int",
-                "value": 6
+                "value": 3
               },
               "model": {
                 "advanced": false,
                 "display_name": "Model",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "model",
                 "options": [
                   "text-embedding-3-small",
                   "text-embedding-3-large",
                   "text-embedding-ada-002"
                 ],
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
                 "type": "str",
-                "value": "text-embedding-ada-002"
+                "value": "text-embedding-3-small"
               },
               "model_kwargs": {
                 "advanced": true,
                 "display_name": "Model Kwargs",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "model_kwargs",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "NestedDict",
+                "type": "dict",
                 "value": {}
               },
               "openai_api_base": {
                 "advanced": true,
                 "display_name": "OpenAI API Base",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
+                "input_types": [],
+                "load_from_db": true,
                 "name": "openai_api_base",
                 "password": true,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "openai_api_key": {
                 "advanced": false,
                 "display_name": "OpenAI API Key",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
+                "input_types": [],
                 "load_from_db": true,
-                "multiline": false,
                 "name": "openai_api_key",
                 "password": true,
                 "placeholder": "",
-                "required": true,
+                "required": false,
                 "show": true,
                 "title_case": false,
                 "type": "str",
-                "value": "OPENAI_API_KEY"
+                "value": ""
               },
               "openai_api_type": {
                 "advanced": true,
                 "display_name": "OpenAI API Type",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
+                "input_types": [],
+                "load_from_db": true,
                 "name": "openai_api_type",
                 "password": true,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "openai_api_version": {
                 "advanced": true,
                 "display_name": "OpenAI API Version",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
                 "input_types": [
-                  "Text"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
                 "name": "openai_api_version",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "openai_organization": {
                 "advanced": true,
                 "display_name": "OpenAI Organization",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
                 "input_types": [
-                  "Text"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
                 "name": "openai_organization",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "openai_proxy": {
                 "advanced": true,
                 "display_name": "OpenAI Proxy",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
                 "input_types": [
-                  "Text"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
                 "name": "openai_proxy",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "request_timeout": {
                 "advanced": true,
                 "display_name": "Request Timeout",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "request_timeout",
-                "password": false,
                 "placeholder": "",
-                "rangeSpec": {
-                  "max": 1,
-                  "min": -1,
-                  "step": 0.1,
-                  "step_type": "float"
-                },
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "float"
+                "type": "float",
+                "value": ""
               },
               "show_progress_bar": {
                 "advanced": true,
                 "display_name": "Show Progress Bar",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "show_progress_bar",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -1009,14 +925,9 @@
                 "advanced": true,
                 "display_name": "Skip Empty",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "skip_empty",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -1028,14 +939,9 @@
                 "advanced": true,
                 "display_name": "TikToken Enable",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
+                "info": "If False, you must have transformers installed.",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "tiktoken_enable",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -1047,33 +953,35 @@
                 "advanced": true,
                 "display_name": "TikToken Model Name",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
                 "input_types": [
-                  "Text"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
                 "name": "tiktoken_model_name",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               }
             }
           },
           "type": "OpenAIEmbeddings"
         },
         "dragging": false,
-        "height": 383,
-        "id": "OpenAIEmbeddings-ZlOk1",
+        "height": 395,
+        "id": "OpenAIEmbeddings-HoSp5",
         "position": {
-          "x": 1183.667250865064,
-          "y": 687.3171828430261
+          "x": 690.5967478991026,
+          "y": 597.6680004855787
+        },
+        "positionAbsolute": {
+          "x": 690.5967478991026,
+          "y": 597.6680004855787
         },
         "selected": false,
         "type": "genericNode",
@@ -1081,47 +989,66 @@
       },
       {
         "data": {
-          "id": "OpenAIModel-EjXlN",
+          "description": "Generates text using OpenAI LLMs.",
+          "display_name": "OpenAI",
+          "edited": false,
+          "id": "OpenAIModel-ickkA",
           "node": {
             "base_classes": [
-              "object",
-              "Text",
-              "str"
+              "LanguageModel",
+              "Message"
             ],
             "beta": false,
-            "custom_fields": {
-              "input_value": null,
-              "max_tokens": null,
-              "model_kwargs": null,
-              "model_name": null,
-              "openai_api_base": null,
-              "openai_api_key": null,
-              "stream": null,
-              "system_message": null,
-              "temperature": null
-            },
+            "conditional_paths": [],
+            "custom_fields": {},
             "description": "Generates text using OpenAI LLMs.",
             "display_name": "OpenAI",
             "documentation": "",
-            "field_formatters": {},
+            "edited": true,
             "field_order": [
+              "input_value",
               "max_tokens",
               "model_kwargs",
+              "output_schema",
               "model_name",
               "openai_api_base",
               "openai_api_key",
               "temperature",
-              "input_value",
+              "stream",
               "system_message",
-              "stream"
+              "seed"
             ],
             "frozen": false,
             "icon": "OpenAI",
-            "output_types": [
-              "Text"
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Text",
+                "hidden": false,
+                "method": "text_response",
+                "name": "text_output",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              },
+              {
+                "cache": true,
+                "display_name": "Language Model",
+                "method": "build_model",
+                "name": "model_output",
+                "selected": "LanguageModel",
+                "types": [
+                  "LanguageModel"
+                ],
+                "value": "__UNDEFINED__"
+              }
             ],
+            "pinned": false,
             "template": {
-              "_type": "CustomComponent",
+              "_type": "Component",
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -1138,82 +1065,59 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    field_order = [\n        \"max_tokens\",\n        \"model_kwargs\",\n        \"model_name\",\n        \"openai_api_base\",\n        \"openai_api_key\",\n        \"temperature\",\n        \"input_value\",\n        \"system_message\",\n        \"stream\",\n    ]\n\n    def build_config(self):\n        return {\n            \"input_value\": {\"display_name\": \"Input\", \"input_types\": [\"Text\", \"Record\", \"Prompt\"]},\n            \"max_tokens\": {\n                \"display_name\": \"Max Tokens\",\n                \"advanced\": True,\n                \"info\": \"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n            },\n            \"model_kwargs\": {\n                \"display_name\": \"Model Kwargs\",\n                \"advanced\": True,\n            },\n            \"model_name\": {\n                \"display_name\": \"Model Name\",\n                \"advanced\": False,\n                \"options\": MODEL_NAMES,\n            },\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"advanced\": True,\n                \"info\": (\n                    \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n                    \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n                ),\n            },\n            \"openai_api_key\": {\n                \"display_name\": \"OpenAI API Key\",\n                \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n                \"advanced\": False,\n                \"password\": True,\n            },\n            \"temperature\": {\n                \"display_name\": \"Temperature\",\n                \"advanced\": False,\n                \"value\": 0.1,\n            },\n            \"stream\": {\n                \"display_name\": \"Stream\",\n                \"info\": STREAM_INFO_TEXT,\n                \"advanced\": True,\n            },\n            \"system_message\": {\n                \"display_name\": \"System Message\",\n                \"info\": \"System message to pass to the model.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        input_value: Text,\n        openai_api_key: str,\n        temperature: float = 0.1,\n        model_name: str = \"gpt-3.5-turbo\",\n        max_tokens: Optional[int] = 256,\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        stream: bool = False,\n        system_message: Optional[str] = None,\n    ) -> Text:\n        if not openai_api_base:\n            openai_api_base = \"https://api.openai.com/v1\"\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature,\n        )\n\n        return self.get_chat_result(output, stream, input_value, system_message)\n"
+                "value": "import operator\nfrom functools import reduce\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.base.models.openai_constants import MODEL_NAMES\nfrom langflow.field_typing import LanguageModel\nfrom langflow.inputs import (\n    BoolInput,\n    DictInput,\n    DropdownInput,\n    FloatInput,\n    IntInput,\n    MessageInput,\n    SecretStrInput,\n    StrInput,\n)\n\n\nclass OpenAIModelComponent(LCModelComponent):\n    display_name = \"OpenAI\"\n    description = \"Generates text using OpenAI LLMs.\"\n    icon = \"OpenAI\"\n\n    inputs = [\n        MessageInput(name=\"input_value\", display_name=\"Input\"),\n        IntInput(\n            name=\"max_tokens\",\n            display_name=\"Max Tokens\",\n            advanced=True,\n            info=\"The maximum number of tokens to generate. Set to 0 for unlimited tokens.\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        DictInput(\n            name=\"output_schema\",\n            is_list=True,\n            display_name=\"Schema\",\n            advanced=True,\n            info=\"The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.\",\n        ),\n        DropdownInput(\n            name=\"model_name\", display_name=\"Model Name\", advanced=False, options=MODEL_NAMES, value=MODEL_NAMES[0]\n        ),\n        StrInput(\n            name=\"openai_api_base\",\n            display_name=\"OpenAI API Base\",\n            advanced=True,\n            info=\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.\",\n        ),\n        SecretStrInput(\n            name=\"openai_api_key\",\n            display_name=\"OpenAI API Key\",\n            info=\"The OpenAI API Key to use for the OpenAI model.\",\n            advanced=False,\n            value=\"OPENAI_API_KEY\",\n        ),\n        FloatInput(name=\"temperature\", display_name=\"Temperature\", value=0.1),\n        BoolInput(name=\"stream\", display_name=\"Stream\", info=STREAM_INFO_TEXT, advanced=True),\n        StrInput(\n            name=\"system_message\",\n            display_name=\"System Message\",\n            info=\"System message to pass to the model.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"seed\",\n            display_name=\"Seed\",\n            info=\"The seed controls the reproducibility of the job.\",\n            advanced=True,\n            value=1,\n        ),\n    ]\n\n    def build_model(self) -> LanguageModel:\n        # self.output_schea is a list of dictionaries\n        # let's convert it to a dictionary\n        output_schema_dict: dict[str, str] = reduce(operator.ior, self.output_schema or {}, {})\n        openai_api_key = self.openai_api_key\n        temperature = self.temperature\n        model_name: str = self.model_name\n        max_tokens = self.max_tokens\n        model_kwargs = self.model_kwargs or {}\n        openai_api_base = self.openai_api_base or \"https://api.openai.com/v1\"\n        json_mode = bool(output_schema_dict)\n        seed = self.seed\n        model_kwargs[\"seed\"] = seed\n\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n        output = ChatOpenAI(\n            max_tokens=max_tokens or None,\n            model_kwargs=model_kwargs,\n            model=model_name,\n            base_url=openai_api_base,\n            api_key=api_key,\n            temperature=temperature or 0.1,\n        )\n        if json_mode:\n            output = output.with_structured_output(schema=output_schema_dict, method=\"json_mode\")  # type: ignore\n\n        return output\n\n    def _get_exception_message(self, e: Exception):\n        \"\"\"\n        Get a message from an OpenAI exception.\n\n        Args:\n            exception (Exception): The exception to get the message from.\n\n        Returns:\n            str: The message from the exception.\n        \"\"\"\n\n        try:\n            from openai import BadRequestError\n        except ImportError:\n            return\n        if isinstance(e, BadRequestError):\n            message = e.body.get(\"message\")  # type: ignore\n            if message:\n                return message\n        return\n"
               },
               "input_value": {
                 "advanced": false,
                 "display_name": "Input",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
                 "input_types": [
-                  "Text",
-                  "Record",
-                  "Prompt"
+                  "Message"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
                 "name": "input_value",
-                "password": false,
                 "placeholder": "",
-                "required": true,
+                "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "max_tokens": {
                 "advanced": true,
                 "display_name": "Max Tokens",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "max_tokens",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
                 "type": "int",
-                "value": 256
+                "value": ""
               },
               "model_kwargs": {
                 "advanced": true,
                 "display_name": "Model Kwargs",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "model_kwargs",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "NestedDict",
+                "type": "dict",
                 "value": {}
               },
               "model_name": {
                 "advanced": false,
                 "display_name": "Model Name",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "model_name",
                 "options": [
                   "gpt-4o",
@@ -1222,7 +1126,6 @@
                   "gpt-3.5-turbo",
                   "gpt-3.5-turbo-0125"
                 ],
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -1234,57 +1137,68 @@
                 "advanced": true,
                 "display_name": "OpenAI API Base",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.",
-                "input_types": [
-                  "Text"
-                ],
+                "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1. You can change this to use other APIs like JinaChat, LocalAI and Prem.",
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
                 "name": "openai_api_base",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "openai_api_key": {
                 "advanced": false,
                 "display_name": "OpenAI API Key",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "The OpenAI API Key to use for the OpenAI model.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
+                "input_types": [],
                 "load_from_db": true,
-                "multiline": false,
                 "name": "openai_api_key",
                 "password": true,
                 "placeholder": "",
-                "required": true,
+                "required": false,
                 "show": true,
                 "title_case": false,
                 "type": "str",
-                "value": "OPENAI_API_KEY"
+                "value": ""
+              },
+              "output_schema": {
+                "advanced": true,
+                "display_name": "Schema",
+                "dynamic": false,
+                "info": "The schema for the Output of the model. You must pass the word JSON in the prompt. If left blank, JSON mode will be disabled.",
+                "list": true,
+                "name": "output_schema",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "dict",
+                "value": {}
+              },
+              "seed": {
+                "advanced": true,
+                "display_name": "Seed",
+                "dynamic": false,
+                "info": "The seed controls the reproducibility of the job.",
+                "list": false,
+                "name": "seed",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": 1
               },
               "stream": {
                 "advanced": true,
                 "display_name": "Stream",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "Stream the response from the model. Streaming works only in Chat.",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "stream",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -1296,42 +1210,25 @@
                 "advanced": true,
                 "display_name": "System Message",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "System message to pass to the model.",
-                "input_types": [
-                  "Text"
-                ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
                 "name": "system_message",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "temperature": {
                 "advanced": false,
                 "display_name": "Temperature",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "temperature",
-                "password": false,
                 "placeholder": "",
-                "rangeSpec": {
-                  "max": 1,
-                  "min": -1,
-                  "step": 0.1,
-                  "step_type": "float"
-                },
                 "required": false,
                 "show": true,
                 "title_case": false,
@@ -1343,8 +1240,8 @@
           "type": "OpenAIModel"
         },
         "dragging": false,
-        "height": 563,
-        "id": "OpenAIModel-EjXlN",
+        "height": 623,
+        "id": "OpenAIModel-ickkA",
         "position": {
           "x": 3410.117202077183,
           "y": 431.2038048137648
@@ -1353,7 +1250,7 @@
           "x": 3410.117202077183,
           "y": 431.2038048137648
         },
-        "selected": true,
+        "selected": false,
         "type": "genericNode",
         "width": 384
       },
@@ -1361,14 +1258,15 @@
         "data": {
           "description": "Create a prompt template with dynamic variables.",
           "display_name": "Prompt",
-          "id": "Prompt-xeI6K",
+          "id": "Prompt-jzPqb",
           "node": {
             "base_classes": [
               "object",
-              "Text",
-              "str"
+              "str",
+              "Text"
             ],
             "beta": false,
+            "conditional_paths": [],
             "custom_fields": {
               "template": [
                 "context",
@@ -1379,7 +1277,6 @@
             "display_name": "Prompt",
             "documentation": "",
             "error": null,
-            "field_formatters": {},
             "field_order": [],
             "frozen": false,
             "full_path": null,
@@ -1388,11 +1285,24 @@
             "is_input": null,
             "is_output": null,
             "name": "",
-            "output_types": [
-              "Prompt"
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Prompt Message",
+                "hidden": false,
+                "method": "build_prompt",
+                "name": "prompt",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
             ],
+            "pinned": false,
             "template": {
-              "_type": "CustomComponent",
+              "_type": "Component",
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -1409,7 +1319,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import CustomComponent\nfrom langflow.field_typing import TemplateField\nfrom langflow.field_typing.prompt import Prompt\n\n\nclass PromptComponent(CustomComponent):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    def build_config(self):\n        return {\n            \"template\": TemplateField(display_name=\"Template\"),\n            \"code\": TemplateField(advanced=True),\n        }\n\n    async def build(\n        self,\n        template: Prompt,\n        **kwargs,\n    ) -> Prompt:\n        prompt = await Prompt.from_template_and_variables(template, kwargs)\n        self.status = prompt.format_text()\n        return prompt\n"
+                "value": "from langflow.custom import Component\nfrom langflow.io import Output, PromptInput\nfrom langflow.schema.message import Message\n\n\nclass PromptComponent(Component):\n    display_name: str = \"Prompt\"\n    description: str = \"Create a prompt template with dynamic variables.\"\n    icon = \"prompts\"\n\n    inputs = [\n        PromptInput(name=\"template\", display_name=\"Template\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Prompt Message\", name=\"prompt\", method=\"build_prompt\"),\n    ]\n\n    async def build_prompt(\n        self,\n    ) -> Message:\n        prompt = await Message.from_template_and_variables(**self._attributes)\n        self.status = prompt.text\n        return prompt\n"
               },
               "context": {
                 "advanced": false,
@@ -1420,9 +1330,7 @@
                 "file_path": "",
                 "info": "",
                 "input_types": [
-                  "Document",
-                  "BaseOutputParser",
-                  "Record",
+                  "Message",
                   "Text"
                 ],
                 "list": false,
@@ -1446,9 +1354,7 @@
                 "file_path": "",
                 "info": "",
                 "input_types": [
-                  "Document",
-                  "BaseOutputParser",
-                  "Record",
+                  "Message",
                   "Text"
                 ],
                 "list": false,
@@ -1490,15 +1396,15 @@
           "type": "Prompt"
         },
         "dragging": false,
-        "height": 477,
-        "id": "Prompt-xeI6K",
+        "height": 525,
+        "id": "Prompt-jzPqb",
         "position": {
-          "x": 2969.0261961391298,
-          "y": 442.1613649809069
+          "x": 2941.2776396951576,
+          "y": 446.43037366459487
         },
         "positionAbsolute": {
-          "x": 2969.0261961391298,
-          "y": 442.1613649809069
+          "x": 2941.2776396951576,
+          "y": 446.43037366459487
         },
         "selected": false,
         "type": "genericNode",
@@ -1506,7 +1412,7 @@
       },
       {
         "data": {
-          "id": "ChatOutput-Q39I8",
+          "id": "ChatOutput-Zy354",
           "node": {
             "base_classes": [
               "object",
@@ -1530,12 +1436,22 @@
             "field_order": [],
             "frozen": false,
             "icon": "ChatOutput",
-            "output_types": [
-              "Message",
-              "Text"
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Message",
+                "method": "message_response",
+                "name": "message",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
             ],
             "template": {
-              "_type": "CustomComponent",
+              "_type": "Component",
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -1552,7 +1468,7 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    def build(\n        self,\n        sender: Optional[str] = \"Machine\",\n        sender_name: Optional[str] = \"AI\",\n        input_value: Optional[str] = None,\n        session_id: Optional[str] = None,\n        files: Optional[list[str]] = None,\n        return_message: Optional[bool] = False,\n    ) -> Union[Message, Text]:\n        return super().build_with_record(\n            sender=sender,\n            sender_name=sender_name,\n            input_value=input_value,\n            session_id=session_id,\n            files=files,\n            return_message=return_message,\n        )\n"
+                "value": "from langflow.base.io.chat import ChatComponent\nfrom langflow.io import DropdownInput, Output, TextInput\nfrom langflow.schema.message import Message\n\n\nclass ChatOutput(ChatComponent):\n    display_name = \"Chat Output\"\n    description = \"Display a chat message in the Playground.\"\n    icon = \"ChatOutput\"\n\n    inputs = [\n        TextInput(\n            name=\"input_value\",\n            display_name=\"Text\",\n            info=\"Message to be passed as output.\",\n        ),\n        DropdownInput(\n            name=\"sender\",\n            display_name=\"Sender Type\",\n            options=[\"Machine\", \"User\"],\n            value=\"Machine\",\n            advanced=True,\n            info=\"Type of sender.\",\n        ),\n        TextInput(\n            name=\"sender_name\", display_name=\"Sender Name\", info=\"Name of the sender.\", value=\"AI\", advanced=True\n        ),\n        TextInput(name=\"session_id\", display_name=\"Session ID\", info=\"Session ID for the message.\", advanced=True),\n        TextInput(\n            name=\"data_template\",\n            display_name=\"Data Template\",\n            value=\"{text}\",\n            advanced=True,\n            info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Message\", name=\"message\", method=\"message_response\"),\n    ]\n\n    def message_response(self) -> Message:\n        message = Message(\n            text=self.input_value,\n            sender=self.sender,\n            sender_name=self.sender_name,\n            session_id=self.session_id,\n        )\n        if self.session_id and isinstance(message, Message) and isinstance(message.text, str):\n            self.store_message(message)\n            self.message.value = message\n\n        self.status = message\n        return message\n"
               },
               "input_value": {
                 "advanced": false,
@@ -1560,9 +1476,10 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "",
+                "info": "Message to be passed as output.",
                 "input_types": [
-                  "Text"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
@@ -1573,7 +1490,8 @@
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               },
               "sender": {
                 "advanced": true,
@@ -1581,7 +1499,7 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "",
+                "info": "Type of sender.",
                 "input_types": [
                   "Text"
                 ],
@@ -1602,18 +1520,19 @@
                 "value": "Machine"
               },
               "sender_name": {
-                "advanced": false,
+                "advanced": true,
                 "display_name": "Sender Name",
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "",
+                "info": "Name of the sender.",
                 "input_types": [
-                  "Text"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
+                "multiline": true,
                 "name": "sender_name",
                 "password": false,
                 "placeholder": "",
@@ -1629,35 +1548,37 @@
                 "dynamic": false,
                 "fileTypes": [],
                 "file_path": "",
-                "info": "If provided, the message will be stored in the memory.",
+                "info": "Session ID for the message.",
                 "input_types": [
-                  "Text"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
+                "multiline": true,
                 "name": "session_id",
                 "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
               }
             }
           },
           "type": "ChatOutput"
         },
         "dragging": false,
-        "height": 383,
-        "id": "ChatOutput-Q39I8",
+        "height": 309,
+        "id": "ChatOutput-Zy354",
         "position": {
-          "x": 3887.2073667611485,
-          "y": 588.4801225794856
+          "x": 3998.201592537035,
+          "y": 603.4216529723935
         },
         "positionAbsolute": {
-          "x": 3887.2073667611485,
-          "y": 588.4801225794856
+          "x": 3998.201592537035,
+          "y": 603.4216529723935
         },
         "selected": false,
         "type": "genericNode",
@@ -1665,7 +1586,7 @@
       },
       {
         "data": {
-          "id": "File-t0a6a",
+          "id": "File-28ckd",
           "node": {
             "base_classes": [
               "Record"
@@ -1682,11 +1603,23 @@
             "field_order": [],
             "frozen": false,
             "icon": "file-text",
-            "output_types": [
-              "Record"
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Data",
+                "hidden": false,
+                "method": "load_file",
+                "name": "data",
+                "selected": "Data",
+                "types": [
+                  "Data"
+                ],
+                "value": "__UNDEFINED__"
+              }
             ],
             "template": {
-              "_type": "CustomComponent",
+              "_type": "Component",
               "code": {
                 "advanced": true,
                 "dynamic": true,
@@ -1703,41 +1636,38 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n    display_name = \"File\"\n    description = \"A generic file loader.\"\n    icon = \"file-text\"\n\n    def build_config(self) -> Dict[str, Any]:\n        return {\n            \"path\": {\n                \"display_name\": \"Path\",\n                \"field_type\": \"file\",\n                \"file_types\": TEXT_FILE_TYPES,\n                \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n            },\n            \"silent_errors\": {\n                \"display_name\": \"Silent Errors\",\n                \"advanced\": True,\n                \"info\": \"If true, errors will not raise an exception.\",\n            },\n        }\n\n    def load_file(self, path: str, silent_errors: bool = False) -> Record:\n        resolved_path = self.resolve_path(path)\n        path_obj = Path(resolved_path)\n        extension = path_obj.suffix[1:].lower()\n        if extension == \"doc\":\n            raise ValueError(\"doc files are not supported. Please save as .docx\")\n        if extension not in TEXT_FILE_TYPES:\n            raise ValueError(f\"Unsupported file type: {extension}\")\n        record = parse_text_file_to_record(resolved_path, silent_errors)\n        self.status = record if record else \"No data\"\n        return record or Record()\n\n    def build(\n        self,\n        path: str,\n        silent_errors: bool = False,\n    ) -> Record:\n        record = self.load_file(path, silent_errors)\n        self.status = record\n        return record\n"
+                "value": "from pathlib import Path\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_data\nfrom langflow.custom import Component\nfrom langflow.io import BoolInput, FileInput, Output\nfrom langflow.schema import Data\n\n\nclass FileComponent(Component):\n    display_name = \"File\"\n    description = \"A generic file loader.\"\n    icon = \"file-text\"\n\n    inputs = [\n        FileInput(\n            name=\"path\",\n            display_name=\"Path\",\n            file_types=TEXT_FILE_TYPES,\n            info=f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n        ),\n        BoolInput(\n            name=\"silent_errors\",\n            display_name=\"Silent Errors\",\n            advanced=True,\n            info=\"If true, errors will not raise an exception.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"load_file\"),\n    ]\n\n    def load_file(self) -> Data:\n        if not self.path:\n            raise ValueError(\"Please, upload a file to use this component.\")\n        resolved_path = self.resolve_path(self.path)\n        silent_errors = self.silent_errors\n\n        extension = Path(resolved_path).suffix[1:].lower()\n\n        if extension == \"doc\":\n            raise ValueError(\"doc files are not supported. Please save as .docx\")\n        if extension not in TEXT_FILE_TYPES:\n            raise ValueError(f\"Unsupported file type: {extension}\")\n\n        data = parse_text_file_to_data(resolved_path, silent_errors)\n        self.status = data if data else \"No data\"\n        return data or Data()\n"
               },
               "path": {
                 "advanced": false,
                 "display_name": "Path",
                 "dynamic": false,
                 "fileTypes": [
-                  ".txt",
-                  ".md",
-                  ".mdx",
-                  ".csv",
-                  ".json",
-                  ".yaml",
-                  ".yml",
-                  ".xml",
-                  ".html",
-                  ".htm",
-                  ".pdf",
-                  ".docx",
-                  ".py",
-                  ".sh",
-                  ".sql",
-                  ".js",
-                  ".ts",
-                  ".tsx"
+                  "txt",
+                  "md",
+                  "mdx",
+                  "csv",
+                  "json",
+                  "yaml",
+                  "yml",
+                  "xml",
+                  "html",
+                  "htm",
+                  "pdf",
+                  "docx",
+                  "py",
+                  "sh",
+                  "sql",
+                  "js",
+                  "ts",
+                  "tsx"
                 ],
-                "file_path": "51e2b78a-199b-4054-9f32-e288eef6924c/Langflow conversation.pdf",
+                "file_path": "c9e0cb46-c474-451a-8496-413f58481d92/Context Once.json",
                 "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx, py, sh, sql, js, ts, tsx",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "path",
-                "password": false,
                 "placeholder": "",
-                "required": true,
+                "required": false,
                 "show": true,
                 "title_case": false,
                 "type": "file",
@@ -1747,14 +1677,9 @@
                 "advanced": true,
                 "display_name": "Silent Errors",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "If true, errors will not raise an exception.",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
                 "name": "silent_errors",
-                "password": false,
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -1767,8 +1692,8 @@
           "type": "File"
         },
         "dragging": false,
-        "height": 281,
-        "id": "File-t0a6a",
+        "height": 301,
+        "id": "File-28ckd",
         "position": {
           "x": 2257.233450682836,
           "y": 1747.5389618367233
@@ -1783,1010 +1708,7 @@
       },
       {
         "data": {
-          "id": "RecursiveCharacterTextSplitter-tR9QM",
-          "node": {
-            "base_classes": [
-              "Record"
-            ],
-            "beta": false,
-            "custom_fields": {
-              "chunk_overlap": null,
-              "chunk_size": null,
-              "inputs": null,
-              "separators": null
-            },
-            "description": "Split text into chunks of a specified length.",
-            "display_name": "Recursive Character Text Splitter",
-            "documentation": "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter",
-            "field_formatters": {},
-            "field_order": [],
-            "frozen": false,
-            "output_types": [
-              "Record"
-            ],
-            "template": {
-              "_type": "CustomComponent",
-              "chunk_overlap": {
-                "advanced": false,
-                "display_name": "Chunk Overlap",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "The amount of overlap between chunks.",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "chunk_overlap",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "int",
-                "value": 200
-              },
-              "chunk_size": {
-                "advanced": false,
-                "display_name": "Chunk Size",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "The maximum length of each chunk.",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "chunk_size",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "int",
-                "value": 1000
-              },
-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "code",
-                "value": "from typing import Optional\n\nfrom langchain_core.documents import Document\nfrom langchain_text_splitters import RecursiveCharacterTextSplitter\n\nfrom langflow.custom import CustomComponent\nfrom langflow.schema import Record\nfrom langflow.utils.util import build_loader_repr_from_records, unescape_string\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n    display_name: str = \"Recursive Character Text Splitter\"\n    description: str = \"Split text into chunks of a specified length.\"\n    documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n    def build_config(self):\n        return {\n            \"inputs\": {\n                \"display_name\": \"Input\",\n                \"info\": \"The texts to split.\",\n                \"input_types\": [\"Document\", \"Record\"],\n            },\n            \"separators\": {\n                \"display_name\": \"Separators\",\n                \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n                \"is_list\": True,\n            },\n            \"chunk_size\": {\n                \"display_name\": \"Chunk Size\",\n                \"info\": \"The maximum length of each chunk.\",\n                \"field_type\": \"int\",\n                \"value\": 1000,\n            },\n            \"chunk_overlap\": {\n                \"display_name\": \"Chunk Overlap\",\n                \"info\": \"The amount of overlap between chunks.\",\n                \"field_type\": \"int\",\n                \"value\": 200,\n            },\n            \"code\": {\"show\": False},\n        }\n\n    def build(\n        self,\n        inputs: list[Document],\n        separators: Optional[list[str]] = None,\n        chunk_size: Optional[int] = 1000,\n        chunk_overlap: Optional[int] = 200,\n    ) -> list[Record]:\n        \"\"\"\n        Split text into chunks of a specified length.\n\n        Args:\n            separators (list[str]): The characters to split on.\n            chunk_size (int): The maximum length of each chunk.\n            chunk_overlap (int): The amount of overlap between chunks.\n            length_function (function): The function to use to calculate the length of the text.\n\n        Returns:\n            list[str]: The chunks of text.\n        \"\"\"\n\n        if separators == \"\":\n            separators = None\n        elif separators:\n            # check if the separators list has escaped characters\n            # if there are escaped characters, unescape them\n            separators = [unescape_string(x) for x in separators]\n\n        # Make sure chunk_size and chunk_overlap are ints\n        if isinstance(chunk_size, str):\n            chunk_size = int(chunk_size)\n        if isinstance(chunk_overlap, str):\n            chunk_overlap = int(chunk_overlap)\n        splitter = RecursiveCharacterTextSplitter(\n            separators=separators,\n            chunk_size=chunk_size,\n            chunk_overlap=chunk_overlap,\n        )\n        documents = []\n        for _input in inputs:\n            if isinstance(_input, Record):\n                documents.append(_input.to_lc_document())\n            else:\n                documents.append(_input)\n        docs = splitter.split_documents(documents)\n        records = self.to_records(docs)\n        self.repr_value = build_loader_repr_from_records(records)\n        return records\n"
-              },
-              "inputs": {
-                "advanced": false,
-                "display_name": "Input",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "The texts to split.",
-                "input_types": [
-                  "Document",
-                  "Record"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "inputs",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "Document"
-              },
-              "separators": {
-                "advanced": false,
-                "display_name": "Separators",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "The characters to split on.\nIf left empty defaults to [\"\\n\\n\", \"\\n\", \" \", \"\"].",
-                "input_types": [
-                  "Text"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "separators",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": [
-                  ""
-                ]
-              }
-            }
-          },
-          "type": "RecursiveCharacterTextSplitter"
-        },
-        "dragging": false,
-        "height": 501,
-        "id": "RecursiveCharacterTextSplitter-tR9QM",
-        "position": {
-          "x": 2791.013514133929,
-          "y": 1462.9588953494142
-        },
-        "positionAbsolute": {
-          "x": 2791.013514133929,
-          "y": 1462.9588953494142
-        },
-        "selected": false,
-        "type": "genericNode",
-        "width": 384
-      },
-      {
-        "data": {
-          "id": "AstraDBSearch-41nRz",
-          "node": {
-            "base_classes": [
-              "Record"
-            ],
-            "beta": false,
-            "custom_fields": {
-              "api_endpoint": null,
-              "batch_size": null,
-              "bulk_delete_concurrency": null,
-              "bulk_insert_batch_concurrency": null,
-              "bulk_insert_overwrite_concurrency": null,
-              "collection_indexing_policy": null,
-              "collection_name": null,
-              "embedding": null,
-              "input_value": null,
-              "metadata_indexing_exclude": null,
-              "metadata_indexing_include": null,
-              "metric": null,
-              "namespace": null,
-              "number_of_results": null,
-              "pre_delete_collection": null,
-              "search_type": null,
-              "setup_mode": null,
-              "token": null
-            },
-            "description": "Searches an existing Astra DB Vector Store.",
-            "display_name": "Astra DB Search",
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-              "token",
-              "api_endpoint",
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-            "frozen": false,
-            "icon": "AstraDB",
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-              "_type": "CustomComponent",
-              "api_endpoint": {
-                "advanced": false,
-                "display_name": "API Endpoint",
-                "dynamic": false,
-                "fileTypes": [],
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-                "info": "API endpoint URL for the Astra DB service.",
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-                "info": "Optional number of records to process in a single batch.",
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-                "info": "Optional concurrency level for bulk delete operations.",
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-                "advanced": true,
-                "display_name": "Bulk Insert Batch Concurrency",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Optional concurrency level for bulk insert operations.",
-                "list": false,
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-                "advanced": true,
-                "display_name": "Bulk Insert Overwrite Concurrency",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Optional concurrency level for bulk insert operations that overwrite existing records.",
-                "list": false,
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-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
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-                "required": true,
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-                "value": "from typing import List, Optional\n\nfrom langflow.components.vectorstores.AstraDB import AstraDBVectorStoreComponent\nfrom langflow.components.vectorstores.base.model import LCVectorStoreComponent\nfrom langflow.field_typing import Embeddings, Text\nfrom langflow.schema import Record\n\n\nclass AstraDBSearchComponent(LCVectorStoreComponent):\n    display_name = \"Astra DB Search\"\n    description = \"Searches an existing Astra DB Vector Store.\"\n    icon = \"AstraDB\"\n    field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"input_value\", \"embedding\"]\n\n    def build_config(self):\n        return {\n            \"search_type\": {\n                \"display_name\": \"Search Type\",\n                \"options\": [\"Similarity\", \"MMR\"],\n            },\n            \"input_value\": {\n                \"display_name\": \"Input Value\",\n                \"info\": \"Input value to search\",\n            },\n            \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n            \"collection_name\": {\n                \"display_name\": \"Collection Name\",\n                \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n            },\n            \"token\": {\n                \"display_name\": \"Astra DB Application Token\",\n                \"info\": \"Authentication token for accessing Astra DB.\",\n                \"password\": True,\n            },\n            \"api_endpoint\": {\n                \"display_name\": \"API Endpoint\",\n                \"info\": \"API endpoint URL for the Astra DB service.\",\n            },\n            \"namespace\": {\n                \"display_name\": \"Namespace\",\n                \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n                \"advanced\": True,\n            },\n            \"metric\": {\n                \"display_name\": \"Metric\",\n                \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n                \"advanced\": True,\n            },\n            \"batch_size\": {\n                \"display_name\": \"Batch Size\",\n                \"info\": \"Optional number of records to process in a single batch.\",\n                \"advanced\": True,\n            },\n            \"bulk_insert_batch_concurrency\": {\n                \"display_name\": \"Bulk Insert Batch Concurrency\",\n                \"info\": \"Optional concurrency level for bulk insert operations.\",\n                \"advanced\": True,\n            },\n            \"bulk_insert_overwrite_concurrency\": {\n                \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n                \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n                \"advanced\": True,\n            },\n            \"bulk_delete_concurrency\": {\n                \"display_name\": \"Bulk Delete Concurrency\",\n                \"info\": \"Optional concurrency level for bulk delete operations.\",\n                \"advanced\": True,\n            },\n            \"setup_mode\": {\n                \"display_name\": \"Setup Mode\",\n                \"info\": \"Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.\",\n                \"options\": [\"Sync\", \"Async\", \"Off\"],\n                \"advanced\": True,\n            },\n            \"pre_delete_collection\": {\n                \"display_name\": \"Pre Delete Collection\",\n                \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n                \"advanced\": True,\n            },\n            \"metadata_indexing_include\": {\n                \"display_name\": \"Metadata Indexing Include\",\n                \"info\": \"Optional list of metadata fields to include in the indexing.\",\n                \"advanced\": True,\n            },\n            \"metadata_indexing_exclude\": {\n                \"display_name\": \"Metadata Indexing Exclude\",\n                \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n                \"advanced\": True,\n            },\n            \"collection_indexing_policy\": {\n                \"display_name\": \"Collection Indexing Policy\",\n                \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n                \"advanced\": True,\n            },\n            \"number_of_results\": {\n                \"display_name\": \"Number of Results\",\n                \"info\": \"Number of results to return.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        embedding: Embeddings,\n        collection_name: str,\n        input_value: Text,\n        token: str,\n        api_endpoint: str,\n        search_type: str = \"Similarity\",\n        number_of_results: int = 4,\n        namespace: Optional[str] = None,\n        metric: Optional[str] = None,\n        batch_size: Optional[int] = None,\n        bulk_insert_batch_concurrency: Optional[int] = None,\n        bulk_insert_overwrite_concurrency: Optional[int] = None,\n        bulk_delete_concurrency: Optional[int] = None,\n        setup_mode: str = \"Sync\",\n        pre_delete_collection: bool = False,\n        metadata_indexing_include: Optional[List[str]] = None,\n        metadata_indexing_exclude: Optional[List[str]] = None,\n        collection_indexing_policy: Optional[dict] = None,\n    ) -> List[Record]:\n        vector_store = AstraDBVectorStoreComponent().build(\n            embedding=embedding,\n            collection_name=collection_name,\n            token=token,\n            api_endpoint=api_endpoint,\n            namespace=namespace,\n            metric=metric,\n            batch_size=batch_size,\n            bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n            bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n            bulk_delete_concurrency=bulk_delete_concurrency,\n            setup_mode=setup_mode,\n            pre_delete_collection=pre_delete_collection,\n            metadata_indexing_include=metadata_indexing_include,\n            metadata_indexing_exclude=metadata_indexing_exclude,\n            collection_indexing_policy=collection_indexing_policy,\n        )\n        try:\n            return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)\n        except KeyError as e:\n            if \"content\" in str(e):\n                raise ValueError(\n                    \"You should ingest data through Langflow (or LangChain) to query it in Langflow. Your collection does not contain a field name 'content'.\"\n                )\n            else:\n                raise e\n"
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-                "fileTypes": [],
-                "file_path": "",
-                "info": "Optional dictionary defining the indexing policy for the collection.",
-                "list": false,
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-                "multiline": false,
-                "name": "collection_indexing_policy",
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-                "required": false,
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-                "advanced": false,
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-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "The name of the collection within Astra DB where the vectors will be stored.",
-                "input_types": [
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-                "required": true,
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-                "type": "str",
-                "value": "langflow"
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-              "embedding": {
-                "advanced": false,
-                "display_name": "Embedding",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Embedding to use",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "embedding",
-                "password": false,
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-                "required": true,
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-              "input_value": {
-                "advanced": false,
-                "display_name": "Input Value",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Input value to search",
-                "input_types": [
-                  "Text"
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-                "list": false,
-                "load_from_db": false,
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-                "name": "input_value",
-                "password": false,
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-                "required": true,
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-                "type": "str"
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-              "metadata_indexing_exclude": {
-                "advanced": true,
-                "display_name": "Metadata Indexing Exclude",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Optional list of metadata fields to exclude from the indexing.",
-                "input_types": [
-                  "Text"
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-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "metadata_indexing_exclude",
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-                "required": false,
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-              "metadata_indexing_include": {
-                "advanced": true,
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-                "fileTypes": [],
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-                "info": "Optional list of metadata fields to include in the indexing.",
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-              "metric": {
-                "advanced": true,
-                "display_name": "Metric",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Optional distance metric for vector comparisons in the vector store.",
-                "input_types": [
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-                "list": false,
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-              "namespace": {
-                "advanced": true,
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-                "fileTypes": [],
-                "file_path": "",
-                "info": "Optional namespace within Astra DB to use for the collection.",
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-                "info": "Number of results to return.",
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-              "pre_delete_collection": {
-                "advanced": true,
-                "display_name": "Pre Delete Collection",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Boolean flag to determine whether to delete the collection before creating a new one.",
-                "list": false,
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-                "advanced": false,
-                "display_name": "Search Type",
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-                "info": "",
-                "input_types": [
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-                "list": true,
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-              "setup_mode": {
-                "advanced": true,
-                "display_name": "Setup Mode",
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-                "advanced": false,
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-                "value": "ASTRA_DB_APPLICATION_TOKEN"
-              }
-            }
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-          "type": "AstraDBSearch"
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-        "dragging": false,
-        "height": 713,
-        "id": "AstraDBSearch-41nRz",
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-      },
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-        "data": {
-          "id": "AstraDB-eUCSS",
-          "node": {
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-            "beta": false,
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-                "advanced": false,
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-                "info": "API endpoint URL for the Astra DB service.",
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-              "batch_size": {
-                "advanced": true,
-                "display_name": "Batch Size",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Optional number of records to process in a single batch.",
-                "list": false,
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-                "multiline": false,
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-                "advanced": true,
-                "display_name": "Bulk Delete Concurrency",
-                "dynamic": false,
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-                "file_path": "",
-                "info": "Optional concurrency level for bulk delete operations.",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "bulk_delete_concurrency",
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-              "bulk_insert_batch_concurrency": {
-                "advanced": true,
-                "display_name": "Bulk Insert Batch Concurrency",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Optional concurrency level for bulk insert operations.",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "bulk_insert_batch_concurrency",
-                "password": false,
-                "placeholder": "",
-                "required": false,
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-                "title_case": false,
-                "type": "int"
-              },
-              "bulk_insert_overwrite_concurrency": {
-                "advanced": true,
-                "display_name": "Bulk Insert Overwrite Concurrency",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Optional concurrency level for bulk insert operations that overwrite existing records.",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "bulk_insert_overwrite_concurrency",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "int"
-              },
-              "code": {
-                "advanced": true,
-                "dynamic": true,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": true,
-                "name": "code",
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-                "required": true,
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-                "title_case": false,
-                "type": "code",
-                "value": "from typing import List, Optional, Union\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, VectorStore\nfrom langflow.schema import Record\nfrom langchain_core.retrievers import BaseRetriever\n\n\nclass AstraDBVectorStoreComponent(CustomComponent):\n    display_name = \"Astra DB\"\n    description = \"Builds or loads an Astra DB Vector Store.\"\n    icon = \"AstraDB\"\n    field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"inputs\", \"embedding\"]\n\n    def build_config(self):\n        return {\n            \"inputs\": {\n                \"display_name\": \"Inputs\",\n                \"info\": \"Optional list of records to be processed and stored in the vector store.\",\n            },\n            \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n            \"collection_name\": {\n                \"display_name\": \"Collection Name\",\n                \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n            },\n            \"token\": {\n                \"display_name\": \"Astra DB Application Token\",\n                \"info\": \"Authentication token for accessing Astra DB.\",\n                \"password\": True,\n            },\n            \"api_endpoint\": {\n                \"display_name\": \"API Endpoint\",\n                \"info\": \"API endpoint URL for the Astra DB service.\",\n            },\n            \"namespace\": {\n                \"display_name\": \"Namespace\",\n                \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n                \"advanced\": True,\n            },\n            \"metric\": {\n                \"display_name\": \"Metric\",\n                \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n                \"advanced\": True,\n            },\n            \"batch_size\": {\n                \"display_name\": \"Batch Size\",\n                \"info\": \"Optional number of records to process in a single batch.\",\n                \"advanced\": True,\n            },\n            \"bulk_insert_batch_concurrency\": {\n                \"display_name\": \"Bulk Insert Batch Concurrency\",\n                \"info\": \"Optional concurrency level for bulk insert operations.\",\n                \"advanced\": True,\n            },\n            \"bulk_insert_overwrite_concurrency\": {\n                \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n                \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n                \"advanced\": True,\n            },\n            \"bulk_delete_concurrency\": {\n                \"display_name\": \"Bulk Delete Concurrency\",\n                \"info\": \"Optional concurrency level for bulk delete operations.\",\n                \"advanced\": True,\n            },\n            \"setup_mode\": {\n                \"display_name\": \"Setup Mode\",\n                \"info\": \"Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.\",\n                \"options\": [\"Sync\", \"Async\", \"Off\"],\n                \"advanced\": True,\n            },\n            \"pre_delete_collection\": {\n                \"display_name\": \"Pre Delete Collection\",\n                \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n                \"advanced\": True,\n            },\n            \"metadata_indexing_include\": {\n                \"display_name\": \"Metadata Indexing Include\",\n                \"info\": \"Optional list of metadata fields to include in the indexing.\",\n                \"advanced\": True,\n            },\n            \"metadata_indexing_exclude\": {\n                \"display_name\": \"Metadata Indexing Exclude\",\n                \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n                \"advanced\": True,\n            },\n            \"collection_indexing_policy\": {\n                \"display_name\": \"Collection Indexing Policy\",\n                \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        embedding: Embeddings,\n        token: str,\n        api_endpoint: str,\n        collection_name: str,\n        inputs: Optional[List[Record]] = None,\n        namespace: Optional[str] = None,\n        metric: Optional[str] = None,\n        batch_size: Optional[int] = None,\n        bulk_insert_batch_concurrency: Optional[int] = None,\n        bulk_insert_overwrite_concurrency: Optional[int] = None,\n        bulk_delete_concurrency: Optional[int] = None,\n        setup_mode: str = \"Sync\",\n        pre_delete_collection: bool = False,\n        metadata_indexing_include: Optional[List[str]] = None,\n        metadata_indexing_exclude: Optional[List[str]] = None,\n        collection_indexing_policy: Optional[dict] = None,\n    ) -> Union[VectorStore, BaseRetriever]:\n        try:\n            from langchain_astradb import AstraDBVectorStore\n            from langchain_astradb.utils.astradb import SetupMode\n        except ImportError:\n            raise ImportError(\n                \"Could not import langchain Astra DB integration package. \"\n                \"Please install it with `pip install langchain-astradb`.\"\n            )\n\n        try:\n            setup_mode_value = SetupMode[setup_mode.upper()]\n        except KeyError:\n            raise ValueError(f\"Invalid setup mode: {setup_mode}\")\n        if inputs:\n            documents = [_input.to_lc_document() for _input in inputs]\n\n            vector_store = AstraDBVectorStore.from_documents(\n                documents=documents,\n                embedding=embedding,\n                collection_name=collection_name,\n                token=token,\n                api_endpoint=api_endpoint,\n                namespace=namespace,\n                metric=metric,\n                batch_size=batch_size,\n                bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n                bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n                bulk_delete_concurrency=bulk_delete_concurrency,\n                setup_mode=setup_mode_value,\n                pre_delete_collection=pre_delete_collection,\n                metadata_indexing_include=metadata_indexing_include,\n                metadata_indexing_exclude=metadata_indexing_exclude,\n                collection_indexing_policy=collection_indexing_policy,\n            )\n        else:\n            vector_store = AstraDBVectorStore(\n                embedding=embedding,\n                collection_name=collection_name,\n                token=token,\n                api_endpoint=api_endpoint,\n                namespace=namespace,\n                metric=metric,\n                batch_size=batch_size,\n                bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n                bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n                bulk_delete_concurrency=bulk_delete_concurrency,\n                setup_mode=setup_mode_value,\n                pre_delete_collection=pre_delete_collection,\n                metadata_indexing_include=metadata_indexing_include,\n                metadata_indexing_exclude=metadata_indexing_exclude,\n                collection_indexing_policy=collection_indexing_policy,\n            )\n\n        return vector_store\n        return vector_store\n"
-              },
-              "collection_indexing_policy": {
-                "advanced": true,
-                "display_name": "Collection Indexing Policy",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Optional dictionary defining the indexing policy for the collection.",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "collection_indexing_policy",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "dict"
-              },
-              "collection_name": {
-                "advanced": false,
-                "display_name": "Collection Name",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "The name of the collection within Astra DB where the vectors will be stored.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "collection_name",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "langflow"
-              },
-              "embedding": {
-                "advanced": false,
-                "display_name": "Embedding",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Embedding to use",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "embedding",
-                "password": false,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "Embeddings"
-              },
-              "inputs": {
-                "advanced": false,
-                "display_name": "Inputs",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Optional list of records to be processed and stored in the vector store.",
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "inputs",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "Record"
-              },
-              "metadata_indexing_exclude": {
-                "advanced": true,
-                "display_name": "Metadata Indexing Exclude",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Optional list of metadata fields to exclude from the indexing.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "metadata_indexing_exclude",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "metadata_indexing_include": {
-                "advanced": true,
-                "display_name": "Metadata Indexing Include",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Optional list of metadata fields to include in the indexing.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "metadata_indexing_include",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "metric": {
-                "advanced": true,
-                "display_name": "Metric",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Optional distance metric for vector comparisons in the vector store.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "metric",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "namespace": {
-                "advanced": true,
-                "display_name": "Namespace",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Optional namespace within Astra DB to use for the collection.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "namespace",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "pre_delete_collection": {
-                "advanced": true,
-                "display_name": "Pre Delete Collection",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Boolean flag to determine whether to delete the collection before creating a new one.",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "pre_delete_collection",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "bool",
-                "value": false
-              },
-              "setup_mode": {
-                "advanced": true,
-                "display_name": "Setup Mode",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "setup_mode",
-                "options": [
-                  "Sync",
-                  "Async",
-                  "Off"
-                ],
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "Sync"
-              },
-              "token": {
-                "advanced": false,
-                "display_name": "Astra DB Application Token",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "Authentication token for accessing Astra DB.",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": true,
-                "multiline": false,
-                "name": "token",
-                "password": true,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "ASTRA_DB_APPLICATION_TOKEN"
-              }
-            }
-          },
-          "type": "AstraDB"
-        },
-        "dragging": false,
-        "height": 573,
-        "id": "AstraDB-eUCSS",
-        "position": {
-          "x": 3372.04958055989,
-          "y": 1611.0742035495277
-        },
-        "positionAbsolute": {
-          "x": 3372.04958055989,
-          "y": 1611.0742035495277
-        },
-        "selected": false,
-        "type": "genericNode",
-        "width": 384
-      },
-      {
-        "data": {
-          "id": "OpenAIEmbeddings-9TPjc",
+          "id": "OpenAIEmbeddings-YeYtt",
           "node": {
             "base_classes": [
               "Embeddings"
@@ -2822,46 +1744,446 @@
             "field_formatters": {},
             "field_order": [],
             "frozen": false,
-            "output_types": [
-              "Embeddings"
+            "icon": "OpenAI",
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Embeddings",
+                "hidden": false,
+                "method": "build_embeddings",
+                "name": "embeddings",
+                "selected": "Embeddings",
+                "types": [
+                  "Embeddings"
+                ],
+                "value": "__UNDEFINED__"
+              }
             ],
             "template": {
-              "_type": "CustomComponent",
-              "allowed_special": {
+              "_type": "Component",
+              "chunk_size": {
                 "advanced": true,
-                "display_name": "Allowed Special",
+                "display_name": "Chunk Size",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "chunk_size",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": 1000
+              },
+              "client": {
+                "advanced": true,
+                "display_name": "Client",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
                 "info": "",
                 "input_types": [
-                  "Text"
+                  "Message",
+                  "str"
                 ],
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
-                "name": "allowed_special",
-                "password": false,
+                "name": "client",
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
                 "type": "str",
-                "value": []
+                "value": ""
               },
-              "chunk_size": {
+              "code": {
                 "advanced": true,
-                "display_name": "Chunk Size",
-                "dynamic": false,
+                "dynamic": true,
                 "fileTypes": [],
                 "file_path": "",
                 "info": "",
                 "list": false,
                 "load_from_db": false,
-                "multiline": false,
-                "name": "chunk_size",
+                "multiline": true,
+                "name": "code",
                 "password": false,
                 "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import Embeddings\nfrom langflow.io import BoolInput, DictInput, DropdownInput, FloatInput, IntInput, Output, SecretStrInput, TextInput\n\n\nclass OpenAIEmbeddingsComponent(LCModelComponent):\n    display_name = \"OpenAI Embeddings\"\n    description = \"Generate embeddings using OpenAI models.\"\n    icon = \"OpenAI\"\n    inputs = [\n        DictInput(\n            name=\"default_headers\",\n            display_name=\"Default Headers\",\n            advanced=True,\n            info=\"Default headers to use for the API request.\",\n        ),\n        DictInput(\n            name=\"default_query\",\n            display_name=\"Default Query\",\n            advanced=True,\n            info=\"Default query parameters to use for the API request.\",\n        ),\n        IntInput(name=\"chunk_size\", display_name=\"Chunk Size\", advanced=True, value=1000),\n        TextInput(name=\"client\", display_name=\"Client\", advanced=True),\n        TextInput(name=\"deployment\", display_name=\"Deployment\", advanced=True),\n        IntInput(name=\"embedding_ctx_length\", display_name=\"Embedding Context Length\", advanced=True, value=1536),\n        IntInput(name=\"max_retries\", display_name=\"Max Retries\", value=3, advanced=True),\n        DropdownInput(\n            name=\"model\",\n            display_name=\"Model\",\n            advanced=False,\n            options=[\n                \"text-embedding-3-small\",\n                \"text-embedding-3-large\",\n                \"text-embedding-ada-002\",\n            ],\n            value=\"text-embedding-3-small\",\n        ),\n        DictInput(name=\"model_kwargs\", display_name=\"Model Kwargs\", advanced=True),\n        SecretStrInput(name=\"openai_api_base\", display_name=\"OpenAI API Base\", advanced=True),\n        SecretStrInput(name=\"openai_api_key\", display_name=\"OpenAI API Key\"),\n        SecretStrInput(name=\"openai_api_type\", display_name=\"OpenAI API Type\", advanced=True),\n        TextInput(name=\"openai_api_version\", display_name=\"OpenAI API Version\", advanced=True),\n        TextInput(\n            name=\"openai_organization\",\n            display_name=\"OpenAI Organization\",\n            advanced=True,\n        ),\n        TextInput(name=\"openai_proxy\", display_name=\"OpenAI Proxy\", advanced=True),\n        FloatInput(name=\"request_timeout\", display_name=\"Request Timeout\", advanced=True),\n        BoolInput(name=\"show_progress_bar\", display_name=\"Show Progress Bar\", advanced=True),\n        BoolInput(name=\"skip_empty\", display_name=\"Skip Empty\", advanced=True),\n        TextInput(\n            name=\"tiktoken_model_name\",\n            display_name=\"TikToken Model Name\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"tiktoken_enable\",\n            display_name=\"TikToken Enable\",\n            advanced=True,\n            value=True,\n            info=\"If False, you must have transformers installed.\",\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Embeddings\", name=\"embeddings\", method=\"build_embeddings\"),\n    ]\n\n    def build_embeddings(self) -> Embeddings:\n        return OpenAIEmbeddings(\n            tiktoken_enabled=self.tiktoken_enable,\n            default_headers=self.default_headers,\n            default_query=self.default_query,\n            allowed_special=\"all\",\n            disallowed_special=\"all\",\n            chunk_size=self.chunk_size,\n            deployment=self.deployment,\n            embedding_ctx_length=self.embedding_ctx_length,\n            max_retries=self.max_retries,\n            model=self.model,\n            model_kwargs=self.model_kwargs,\n            base_url=self.openai_api_base,\n            api_key=self.openai_api_key,\n            openai_api_type=self.openai_api_type,\n            api_version=self.openai_api_version,\n            organization=self.openai_organization,\n            openai_proxy=self.openai_proxy,\n            timeout=self.request_timeout or None,\n            show_progress_bar=self.show_progress_bar,\n            skip_empty=self.skip_empty,\n            tiktoken_model_name=self.tiktoken_model_name,\n        )\n"
+              },
+              "default_headers": {
+                "advanced": true,
+                "display_name": "Default Headers",
+                "dynamic": false,
+                "info": "Default headers to use for the API request.",
+                "list": false,
+                "name": "default_headers",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "dict",
+                "value": {}
+              },
+              "default_query": {
+                "advanced": true,
+                "display_name": "Default Query",
+                "dynamic": false,
+                "info": "Default query parameters to use for the API request.",
+                "list": false,
+                "name": "default_query",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "dict",
+                "value": {}
+              },
+              "deployment": {
+                "advanced": true,
+                "display_name": "Deployment",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "deployment",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "embedding_ctx_length": {
+                "advanced": true,
+                "display_name": "Embedding Context Length",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "embedding_ctx_length",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": 1536
+              },
+              "max_retries": {
+                "advanced": true,
+                "display_name": "Max Retries",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "max_retries",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": 3
+              },
+              "model": {
+                "advanced": false,
+                "display_name": "Model",
+                "dynamic": false,
+                "info": "",
+                "name": "model",
+                "options": [
+                  "text-embedding-3-small",
+                  "text-embedding-3-large",
+                  "text-embedding-ada-002"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "text-embedding-3-small"
+              },
+              "model_kwargs": {
+                "advanced": true,
+                "display_name": "Model Kwargs",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "model_kwargs",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "dict",
+                "value": {}
+              },
+              "openai_api_base": {
+                "advanced": true,
+                "display_name": "OpenAI API Base",
+                "dynamic": false,
+                "info": "",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "openai_api_base",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "openai_api_key": {
+                "advanced": false,
+                "display_name": "OpenAI API Key",
+                "dynamic": false,
+                "info": "",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "openai_api_key",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "openai_api_type": {
+                "advanced": true,
+                "display_name": "OpenAI API Type",
+                "dynamic": false,
+                "info": "",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "openai_api_type",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "openai_api_version": {
+                "advanced": true,
+                "display_name": "OpenAI API Version",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "openai_api_version",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "openai_organization": {
+                "advanced": true,
+                "display_name": "OpenAI Organization",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "openai_organization",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "openai_proxy": {
+                "advanced": true,
+                "display_name": "OpenAI Proxy",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "openai_proxy",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "request_timeout": {
+                "advanced": true,
+                "display_name": "Request Timeout",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "request_timeout",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "float",
+                "value": ""
+              },
+              "show_progress_bar": {
+                "advanced": true,
+                "display_name": "Show Progress Bar",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "show_progress_bar",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "bool",
+                "value": false
+              },
+              "skip_empty": {
+                "advanced": true,
+                "display_name": "Skip Empty",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "name": "skip_empty",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "bool",
+                "value": false
+              },
+              "tiktoken_enable": {
+                "advanced": true,
+                "display_name": "TikToken Enable",
+                "dynamic": false,
+                "info": "If False, you must have transformers installed.",
+                "list": false,
+                "name": "tiktoken_enable",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "bool",
+                "value": true
+              },
+              "tiktoken_model_name": {
+                "advanced": true,
+                "display_name": "TikToken Model Name",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "name": "tiktoken_model_name",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              }
+            }
+          },
+          "type": "OpenAIEmbeddings"
+        },
+        "dragging": false,
+        "height": 395,
+        "id": "OpenAIEmbeddings-YeYtt",
+        "position": {
+          "x": 2781.1922529351923,
+          "y": 2206.267872396239
+        },
+        "positionAbsolute": {
+          "x": 2781.1922529351923,
+          "y": 2206.267872396239
+        },
+        "selected": false,
+        "type": "genericNode",
+        "width": 384
+      },
+      {
+        "data": {
+          "description": "Split text into chunks of a specified length.",
+          "display_name": "Recursive Character Text Splitter",
+          "id": "RecursiveCharacterTextSplitter-HVESL",
+          "node": {
+            "base_classes": [
+              "Data"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Split text into chunks of a specified length.",
+            "display_name": "Recursive Character Text Splitter",
+            "documentation": "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter",
+            "edited": false,
+            "field_order": [
+              "chunk_size",
+              "chunk_overlap",
+              "data_input",
+              "separators"
+            ],
+            "frozen": false,
+            "output_types": [
+              "Data"
+            ],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Data",
+                "hidden": false,
+                "method": "build",
+                "name": "data",
+                "selected": "Data",
+                "types": [
+                  "Data"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "chunk_overlap": {
+                "advanced": false,
+                "display_name": "Chunk Overlap",
+                "dynamic": false,
+                "info": "The amount of overlap between chunks.",
+                "list": false,
+                "name": "chunk_overlap",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": 200
+              },
+              "chunk_size": {
+                "advanced": false,
+                "display_name": "Chunk Size",
+                "dynamic": false,
+                "info": "The maximum length of each chunk.",
+                "list": false,
+                "name": "chunk_size",
+                "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
@@ -2884,376 +2206,141 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from typing import Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, NestedDict\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n    display_name = \"OpenAI Embeddings\"\n    description = \"Generate embeddings using OpenAI models.\"\n\n    def build_config(self):\n        return {\n            \"allowed_special\": {\n                \"display_name\": \"Allowed Special\",\n                \"advanced\": True,\n                \"field_type\": \"str\",\n                \"is_list\": True,\n            },\n            \"default_headers\": {\n                \"display_name\": \"Default Headers\",\n                \"advanced\": True,\n                \"field_type\": \"dict\",\n            },\n            \"default_query\": {\n                \"display_name\": \"Default Query\",\n                \"advanced\": True,\n                \"field_type\": \"NestedDict\",\n            },\n            \"disallowed_special\": {\n                \"display_name\": \"Disallowed Special\",\n                \"advanced\": True,\n                \"field_type\": \"str\",\n                \"is_list\": True,\n            },\n            \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n            \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n            \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n            \"embedding_ctx_length\": {\n                \"display_name\": \"Embedding Context Length\",\n                \"advanced\": True,\n            },\n            \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n            \"model\": {\n                \"display_name\": \"Model\",\n                \"advanced\": False,\n                \"options\": [\n                    \"text-embedding-3-small\",\n                    \"text-embedding-3-large\",\n                    \"text-embedding-ada-002\",\n                ],\n            },\n            \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n            \"openai_api_base\": {\n                \"display_name\": \"OpenAI API Base\",\n                \"password\": True,\n                \"advanced\": True,\n            },\n            \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n            \"openai_api_type\": {\n                \"display_name\": \"OpenAI API Type\",\n                \"advanced\": True,\n                \"password\": True,\n            },\n            \"openai_api_version\": {\n                \"display_name\": \"OpenAI API Version\",\n                \"advanced\": True,\n            },\n            \"openai_organization\": {\n                \"display_name\": \"OpenAI Organization\",\n                \"advanced\": True,\n            },\n            \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n            \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n            \"show_progress_bar\": {\n                \"display_name\": \"Show Progress Bar\",\n                \"advanced\": True,\n            },\n            \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n            \"tiktoken_model_name\": {\n                \"display_name\": \"TikToken Model Name\",\n                \"advanced\": True,\n            },\n            \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n            \"dimensions\": {\n                \"display_name\": \"Dimensions\",\n                \"info\": \"The number of dimensions the resulting output embeddings should have. Only supported by certain models.\",\n                \"advanced\": True,\n            },\n        }\n\n    def build(\n        self,\n        openai_api_key: str,\n        default_headers: Optional[Dict[str, str]] = None,\n        default_query: Optional[NestedDict] = {},\n        allowed_special: List[str] = [],\n        disallowed_special: List[str] = [\"all\"],\n        chunk_size: int = 1000,\n        deployment: str = \"text-embedding-ada-002\",\n        embedding_ctx_length: int = 8191,\n        max_retries: int = 6,\n        model: str = \"text-embedding-ada-002\",\n        model_kwargs: NestedDict = {},\n        openai_api_base: Optional[str] = None,\n        openai_api_type: Optional[str] = None,\n        openai_api_version: Optional[str] = None,\n        openai_organization: Optional[str] = None,\n        openai_proxy: Optional[str] = None,\n        request_timeout: Optional[float] = None,\n        show_progress_bar: bool = False,\n        skip_empty: bool = False,\n        tiktoken_enable: bool = True,\n        tiktoken_model_name: Optional[str] = None,\n        dimensions: Optional[int] = None,\n    ) -> Embeddings:\n        # This is to avoid errors with Vector Stores (e.g Chroma)\n        if disallowed_special == [\"all\"]:\n            disallowed_special = \"all\"  # type: ignore\n        if openai_api_key:\n            api_key = SecretStr(openai_api_key)\n        else:\n            api_key = None\n\n        return OpenAIEmbeddings(\n            tiktoken_enabled=tiktoken_enable,\n            default_headers=default_headers,\n            default_query=default_query,\n            allowed_special=set(allowed_special),\n            disallowed_special=\"all\",\n            chunk_size=chunk_size,\n            deployment=deployment,\n            embedding_ctx_length=embedding_ctx_length,\n            max_retries=max_retries,\n            model=model,\n            model_kwargs=model_kwargs,\n            base_url=openai_api_base,\n            api_key=api_key,\n            openai_api_type=openai_api_type,\n            api_version=openai_api_version,\n            organization=openai_organization,\n            openai_proxy=openai_proxy,\n            timeout=request_timeout,\n            show_progress_bar=show_progress_bar,\n            skip_empty=skip_empty,\n            tiktoken_model_name=tiktoken_model_name,\n            dimensions=dimensions,\n        )\n"
+                "value": "from langchain_text_splitters import RecursiveCharacterTextSplitter\n\nfrom langflow.custom import Component\nfrom langflow.inputs.inputs import DataInput, IntInput, TextInput\nfrom langflow.schema import Data\nfrom langflow.template.field.base import Output\nfrom langflow.utils.util import build_loader_repr_from_data, unescape_string\n\n\nclass RecursiveCharacterTextSplitterComponent(Component):\n    display_name: str = \"Recursive Character Text Splitter\"\n    description: str = \"Split text into chunks of a specified length.\"\n    documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n    inputs = [\n        IntInput(\n            name=\"chunk_size\",\n            display_name=\"Chunk Size\",\n            info=\"The maximum length of each chunk.\",\n            value=1000,\n        ),\n        IntInput(\n            name=\"chunk_overlap\",\n            display_name=\"Chunk Overlap\",\n            info=\"The amount of overlap between chunks.\",\n            value=200,\n        ),\n        DataInput(\n            name=\"data_input\",\n            display_name=\"Input\",\n            info=\"The texts to split.\",\n            input_types=[\"Document\", \"Data\"],\n        ),\n        TextInput(\n            name=\"separators\",\n            display_name=\"Separators\",\n            info='The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n            is_list=True,\n        ),\n    ]\n    outputs = [\n        Output(display_name=\"Data\", name=\"data\", method=\"build\"),\n    ]\n\n    def build(self) -> list[Data]:\n        \"\"\"\n        Split text into chunks of a specified length.\n\n        Args:\n            separators (list[str]): The characters to split on.\n            chunk_size (int): The maximum length of each chunk.\n            chunk_overlap (int): The amount of overlap between chunks.\n\n        Returns:\n            list[str]: The chunks of text.\n        \"\"\"\n\n        if self.separators == \"\":\n            self.separators = None\n        elif self.separators:\n            # check if the separators list has escaped characters\n            # if there are escaped characters, unescape them\n            self.separators = [unescape_string(x) for x in self.separators]\n\n        # Make sure chunk_size and chunk_overlap are ints\n        if isinstance(self.chunk_size, str):\n            self.chunk_size = int(self.chunk_size)\n        if isinstance(self.chunk_overlap, str):\n            self.chunk_overlap = int(self.chunk_overlap)\n        splitter = RecursiveCharacterTextSplitter(\n            separators=self.separators,\n            chunk_size=self.chunk_size,\n            chunk_overlap=self.chunk_overlap,\n        )\n        documents = []\n        if not isinstance(self.data_input, list):\n            self.data_input = [self.data_input]\n        for _input in self.data_input:\n            if isinstance(_input, Data):\n                documents.append(_input.to_lc_document())\n            else:\n                documents.append(_input)\n        docs = splitter.split_documents(documents)\n        data = self.to_data(docs)\n        self.repr_value = build_loader_repr_from_data(data)\n        return data\n"
               },
-              "default_headers": {
-                "advanced": true,
-                "display_name": "Default Headers",
+              "data_input": {
+                "advanced": false,
+                "display_name": "Input",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "default_headers",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "dict"
-              },
-              "default_query": {
-                "advanced": true,
-                "display_name": "Default Query",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "default_query",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "NestedDict",
-                "value": {}
-              },
-              "deployment": {
-                "advanced": true,
-                "display_name": "Deployment",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
+                "info": "The texts to split.",
                 "input_types": [
-                  "Text"
+                  "Document",
+                  "Data"
                 ],
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "deployment",
-                "password": false,
+                "name": "data_input",
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str",
-                "value": "text-embedding-ada-002"
+                "type": "other",
+                "value": ""
               },
-              "disallowed_special": {
-                "advanced": true,
-                "display_name": "Disallowed Special",
+              "separators": {
+                "advanced": false,
+                "display_name": "Separators",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
+                "info": "The characters to split on.\nIf left empty defaults to [\"\\n\\n\", \"\\n\", \" \", \"\"].",
                 "input_types": [
-                  "Text"
+                  "Message",
+                  "str"
                 ],
-                "list": false,
+                "list": true,
                 "load_from_db": false,
-                "multiline": false,
-                "name": "disallowed_special",
-                "password": false,
+                "name": "separators",
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
                 "type": "str",
                 "value": [
-                  "all"
+                  "\\n"
                 ]
+              }
+            }
+          },
+          "type": "RecursiveCharacterTextSplitter"
+        },
+        "dragging": false,
+        "height": 529,
+        "id": "RecursiveCharacterTextSplitter-HVESL",
+        "position": {
+          "x": 2726.46405760335,
+          "y": 1530.1666819162674
+        },
+        "positionAbsolute": {
+          "x": 2726.46405760335,
+          "y": 1530.1666819162674
+        },
+        "selected": false,
+        "type": "genericNode",
+        "width": 384
+      },
+      {
+        "data": {
+          "description": "Implementation of Vector Store using Astra DB with search capabilities",
+          "display_name": "Astra DB Vector Store",
+          "id": "AstraDB-irvai",
+          "node": {
+            "base_classes": [
+              "Data"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Implementation of Vector Store using Astra DB with search capabilities",
+            "display_name": "Astra DB Vector Store",
+            "documentation": "https://python.langchain.com/docs/integrations/vectorstores/astradb",
+            "edited": false,
+            "field_order": [
+              "collection_name",
+              "token",
+              "api_endpoint",
+              "vector_store_inputs",
+              "embedding",
+              "namespace",
+              "metric",
+              "batch_size",
+              "bulk_insert_batch_concurrency",
+              "bulk_insert_overwrite_concurrency",
+              "bulk_delete_concurrency",
+              "setup_mode",
+              "pre_delete_collection",
+              "metadata_indexing_include",
+              "metadata_indexing_exclude",
+              "collection_indexing_policy",
+              "add_to_vector_store",
+              "search_input",
+              "search_type",
+              "number_of_results"
+            ],
+            "frozen": false,
+            "icon": "AstraDB",
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Retriever",
+                "method": "build_base_retriever",
+                "name": "base_retriever",
+                "selected": "Data",
+                "types": [
+                  "Data"
+                ],
+                "value": "__UNDEFINED__"
               },
-              "embedding_ctx_length": {
-                "advanced": true,
-                "display_name": "Embedding Context Length",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "embedding_ctx_length",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "int",
-                "value": 8191
-              },
-              "max_retries": {
-                "advanced": true,
-                "display_name": "Max Retries",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "max_retries",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "int",
-                "value": 6
-              },
-              "model": {
+              {
+                "cache": true,
+                "display_name": "Search Results",
+                "method": "search_documents",
+                "name": "search_results",
+                "selected": "Data",
+                "types": [
+                  "Data"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "add_to_vector_store": {
                 "advanced": false,
-                "display_name": "Model",
+                "display_name": "Add to Vector Store",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": true,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "model",
-                "options": [
-                  "text-embedding-3-small",
-                  "text-embedding-3-large",
-                  "text-embedding-ada-002"
-                ],
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "text-embedding-ada-002"
-              },
-              "model_kwargs": {
-                "advanced": true,
-                "display_name": "Model Kwargs",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
+                "info": "If true, the Vector Store Inputs will be added to the Vector Store.",
                 "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "model_kwargs",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "NestedDict",
-                "value": {}
-              },
-              "openai_api_base": {
-                "advanced": true,
-                "display_name": "OpenAI API Base",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "openai_api_base",
-                "password": true,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "openai_api_key": {
-                "advanced": false,
-                "display_name": "OpenAI API Key",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": true,
-                "multiline": false,
-                "name": "openai_api_key",
-                "password": true,
-                "placeholder": "",
-                "required": true,
-                "show": true,
-                "title_case": false,
-                "type": "str",
-                "value": "OPENAI_API_KEY"
-              },
-              "openai_api_type": {
-                "advanced": true,
-                "display_name": "OpenAI API Type",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "openai_api_type",
-                "password": true,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "openai_api_version": {
-                "advanced": true,
-                "display_name": "OpenAI API Version",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "openai_api_version",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "openai_organization": {
-                "advanced": true,
-                "display_name": "OpenAI Organization",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "openai_organization",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "openai_proxy": {
-                "advanced": true,
-                "display_name": "OpenAI Proxy",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "openai_proxy",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "str"
-              },
-              "request_timeout": {
-                "advanced": true,
-                "display_name": "Request Timeout",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "request_timeout",
-                "password": false,
-                "placeholder": "",
-                "rangeSpec": {
-                  "max": 1,
-                  "min": -1,
-                  "step": 0.1,
-                  "step_type": "float"
-                },
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "float"
-              },
-              "show_progress_bar": {
-                "advanced": true,
-                "display_name": "Show Progress Bar",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "show_progress_bar",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "bool",
-                "value": false
-              },
-              "skip_empty": {
-                "advanced": true,
-                "display_name": "Skip Empty",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "skip_empty",
-                "password": false,
-                "placeholder": "",
-                "required": false,
-                "show": true,
-                "title_case": false,
-                "type": "bool",
-                "value": false
-              },
-              "tiktoken_enable": {
-                "advanced": true,
-                "display_name": "TikToken Enable",
-                "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "tiktoken_enable",
-                "password": false,
+                "name": "add_to_vector_store",
                 "placeholder": "",
                 "required": false,
                 "show": true,
@@ -3261,41 +2348,883 @@
                 "type": "bool",
                 "value": true
               },
-              "tiktoken_model_name": {
-                "advanced": true,
-                "display_name": "TikToken Model Name",
+              "api_endpoint": {
+                "advanced": false,
+                "display_name": "API Endpoint",
                 "dynamic": false,
-                "fileTypes": [],
-                "file_path": "",
-                "info": "",
-                "input_types": [
-                  "Text"
-                ],
-                "list": false,
-                "load_from_db": false,
-                "multiline": false,
-                "name": "tiktoken_model_name",
-                "password": false,
+                "info": "API endpoint URL for the Astra DB service.",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "api_endpoint",
+                "password": true,
                 "placeholder": "",
                 "required": false,
                 "show": true,
                 "title_case": false,
-                "type": "str"
+                "type": "str",
+                "value": ""
+              },
+              "batch_size": {
+                "advanced": true,
+                "display_name": "Batch Size",
+                "dynamic": false,
+                "info": "Optional number of data to process in a single batch.",
+                "list": false,
+                "name": "batch_size",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": ""
+              },
+              "bulk_delete_concurrency": {
+                "advanced": true,
+                "display_name": "Bulk Delete Concurrency",
+                "dynamic": false,
+                "info": "Optional concurrency level for bulk delete operations.",
+                "list": false,
+                "name": "bulk_delete_concurrency",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": ""
+              },
+              "bulk_insert_batch_concurrency": {
+                "advanced": true,
+                "display_name": "Bulk Insert Batch Concurrency",
+                "dynamic": false,
+                "info": "Optional concurrency level for bulk insert operations.",
+                "list": false,
+                "name": "bulk_insert_batch_concurrency",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": ""
+              },
+              "bulk_insert_overwrite_concurrency": {
+                "advanced": true,
+                "display_name": "Bulk Insert Overwrite Concurrency",
+                "dynamic": false,
+                "info": "Optional concurrency level for bulk insert operations that overwrite existing data.",
+                "list": false,
+                "name": "bulk_insert_overwrite_concurrency",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": ""
+              },
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from loguru import logger\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent\nfrom langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput\nfrom langflow.schema import Data\n\n\nclass AstraVectorStoreComponent(LCVectorStoreComponent):\n    display_name: str = \"Astra DB Vector Store\"\n    description: str = \"Implementation of Vector Store using Astra DB with search capabilities\"\n    documentation: str = \"https://python.langchain.com/docs/integrations/vectorstores/astradb\"\n    icon: str = \"AstraDB\"\n\n    inputs = [\n        StrInput(\n            name=\"collection_name\",\n            display_name=\"Collection Name\",\n            info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n        ),\n        SecretStrInput(\n            name=\"token\",\n            display_name=\"Astra DB Application Token\",\n            info=\"Authentication token for accessing Astra DB.\",\n            value=\"ASTRA_DB_APPLICATION_TOKEN\",\n        ),\n        SecretStrInput(\n            name=\"api_endpoint\",\n            display_name=\"API Endpoint\",\n            info=\"API endpoint URL for the Astra DB service.\",\n            value=\"ASTRA_DB_API_ENDPOINT\",\n        ),\n        HandleInput(\n            name=\"vector_store_inputs\",\n            display_name=\"Vector Store Inputs\",\n            input_types=[\"Document\", \"Data\"],\n            is_list=True,\n        ),\n        HandleInput(\n            name=\"embedding\",\n            display_name=\"Embedding\",\n            input_types=[\"Embeddings\"],\n        ),\n        StrInput(\n            name=\"namespace\",\n            display_name=\"Namespace\",\n            info=\"Optional namespace within Astra DB to use for the collection.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"metric\",\n            display_name=\"Metric\",\n            info=\"Optional distance metric for vector comparisons in the vector store.\",\n            options=[\"cosine\", \"dot_product\", \"euclidean\"],\n            advanced=True,\n        ),\n        IntInput(\n            name=\"batch_size\",\n            display_name=\"Batch Size\",\n            info=\"Optional number of data to process in a single batch.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_batch_concurrency\",\n            display_name=\"Bulk Insert Batch Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_overwrite_concurrency\",\n            display_name=\"Bulk Insert Overwrite Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_delete_concurrency\",\n            display_name=\"Bulk Delete Concurrency\",\n            info=\"Optional concurrency level for bulk delete operations.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"setup_mode\",\n            display_name=\"Setup Mode\",\n            info=\"Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.\",\n            options=[\"Sync\", \"Async\", \"Off\"],\n            advanced=True,\n            value=\"Sync\",\n        ),\n        BoolInput(\n            name=\"pre_delete_collection\",\n            display_name=\"Pre Delete Collection\",\n            info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_include\",\n            display_name=\"Metadata Indexing Include\",\n            info=\"Optional list of metadata fields to include in the indexing.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_exclude\",\n            display_name=\"Metadata Indexing Exclude\",\n            info=\"Optional list of metadata fields to exclude from the indexing.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"collection_indexing_policy\",\n            display_name=\"Collection Indexing Policy\",\n            info=\"Optional dictionary defining the indexing policy for the collection.\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_to_vector_store\",\n            display_name=\"Add to Vector Store\",\n            info=\"If true, the Vector Store Inputs will be added to the Vector Store.\",\n        ),\n        MultilineInput(\n            name=\"search_input\",\n            display_name=\"Search Input\",\n        ),\n        DropdownInput(\n            name=\"search_type\",\n            display_name=\"Search Type\",\n            options=[\"Similarity\", \"MMR\"],\n            value=\"Similarity\",\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Results\",\n            info=\"Number of results to return.\",\n            advanced=True,\n            value=4,\n        ),\n    ]\n\n    def build_vector_store(self):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n            from langchain_astradb.utils.astradb import SetupMode\n        except ImportError:\n            raise ImportError(\n                \"Could not import langchain Astra DB integration package. \"\n                \"Please install it with `pip install langchain-astradb`.\"\n            )\n\n        try:\n            if not self.setup_mode:\n                self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n            setup_mode_value = SetupMode[self.setup_mode.upper()]\n        except KeyError:\n            raise ValueError(f\"Invalid setup mode: {self.setup_mode}\")\n\n        vector_store_kwargs = {\n            \"embedding\": self.embedding,\n            \"collection_name\": self.collection_name,\n            \"token\": self.token,\n            \"api_endpoint\": self.api_endpoint,\n            \"namespace\": self.namespace or None,\n            \"metric\": self.metric or None,\n            \"batch_size\": self.batch_size or None,\n            \"bulk_insert_batch_concurrency\": self.bulk_insert_batch_concurrency or None,\n            \"bulk_insert_overwrite_concurrency\": self.bulk_insert_overwrite_concurrency or None,\n            \"bulk_delete_concurrency\": self.bulk_delete_concurrency or None,\n            \"setup_mode\": setup_mode_value,\n            \"pre_delete_collection\": self.pre_delete_collection or False,\n        }\n\n        if self.metadata_indexing_include:\n            vector_store_kwargs[\"metadata_indexing_include\"] = self.metadata_indexing_include\n        elif self.metadata_indexing_exclude:\n            vector_store_kwargs[\"metadata_indexing_exclude\"] = self.metadata_indexing_exclude\n        elif self.collection_indexing_policy:\n            vector_store_kwargs[\"collection_indexing_policy\"] = self.collection_indexing_policy\n\n        try:\n            vector_store = AstraDBVectorStore(**vector_store_kwargs)\n        except Exception as e:\n            raise ValueError(f\"Error initializing AstraDBVectorStore: {str(e)}\") from e\n\n        if self.add_to_vector_store:\n            self._add_documents_to_vector_store(vector_store)\n\n        return vector_store\n\n    def build_base_retriever(self):\n        vector_store = self.build_vector_store()\n        self.status = self._astradb_collection_to_data(vector_store.collection)\n        return vector_store\n\n    def _add_documents_to_vector_store(self, vector_store):\n        documents = []\n        for _input in self.vector_store_inputs or []:\n            if isinstance(_input, Data):\n                documents.append(_input.to_lc_document())\n            else:\n                raise ValueError(\"Vector Store Inputs must be Data objects.\")\n\n        if documents and self.embedding is not None:\n            logger.debug(f\"Adding {len(documents)} documents to the Vector Store.\")\n            try:\n                vector_store.add_documents(documents)\n            except Exception as e:\n                raise ValueError(f\"Error adding documents to AstraDBVectorStore: {str(e)}\") from e\n        else:\n            logger.debug(\"No documents to add to the Vector Store.\")\n\n    def search_documents(self):\n        vector_store = self.build_vector_store()\n\n        logger.debug(f\"Search input: {self.search_input}\")\n        logger.debug(f\"Search type: {self.search_type}\")\n        logger.debug(f\"Number of results: {self.number_of_results}\")\n\n        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():\n            try:\n                if self.search_type == \"Similarity\":\n                    docs = vector_store.similarity_search(\n                        query=self.search_input,\n                        k=self.number_of_results,\n                    )\n                elif self.search_type == \"MMR\":\n                    docs = vector_store.max_marginal_relevance_search(\n                        query=self.search_input,\n                        k=self.number_of_results,\n                    )\n                else:\n                    raise ValueError(f\"Invalid search type: {self.search_type}\")\n            except Exception as e:\n                raise ValueError(f\"Error performing search in AstraDBVectorStore: {str(e)}\") from e\n\n            logger.debug(f\"Retrieved documents: {len(docs)}\")\n\n            data = [Data.from_document(doc) for doc in docs]\n            logger.debug(f\"Converted documents to data: {len(data)}\")\n            self.status = data\n            return data\n        else:\n            logger.debug(\"No search input provided. Skipping search.\")\n            return []\n\n    def _astradb_collection_to_data(self, collection):\n        data = []\n        data_dict = collection.find()\n        if data_dict and \"data\" in data_dict:\n            data_dict = data_dict[\"data\"].get(\"documents\", [])\n\n        for item in data_dict:\n            data.append(Data(content=item[\"content\"]))\n        return data\n"
+              },
+              "collection_indexing_policy": {
+                "advanced": true,
+                "display_name": "Collection Indexing Policy",
+                "dynamic": false,
+                "info": "Optional dictionary defining the indexing policy for the collection.",
+                "list": false,
+                "load_from_db": false,
+                "name": "collection_indexing_policy",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "collection_name": {
+                "advanced": false,
+                "display_name": "Collection Name",
+                "dynamic": false,
+                "info": "The name of the collection within Astra DB where the vectors will be stored.",
+                "list": false,
+                "load_from_db": false,
+                "name": "collection_name",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "langflow"
+              },
+              "embedding": {
+                "advanced": false,
+                "display_name": "Embedding",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Embeddings"
+                ],
+                "list": false,
+                "name": "embedding",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "other",
+                "value": ""
+              },
+              "metadata_indexing_exclude": {
+                "advanced": true,
+                "display_name": "Metadata Indexing Exclude",
+                "dynamic": false,
+                "info": "Optional list of metadata fields to exclude from the indexing.",
+                "list": false,
+                "load_from_db": false,
+                "name": "metadata_indexing_exclude",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "metadata_indexing_include": {
+                "advanced": true,
+                "display_name": "Metadata Indexing Include",
+                "dynamic": false,
+                "info": "Optional list of metadata fields to include in the indexing.",
+                "list": false,
+                "load_from_db": false,
+                "name": "metadata_indexing_include",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "metric": {
+                "advanced": true,
+                "display_name": "Metric",
+                "dynamic": false,
+                "info": "Optional distance metric for vector comparisons in the vector store.",
+                "name": "metric",
+                "options": [
+                  "cosine",
+                  "dot_product",
+                  "euclidean"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "namespace": {
+                "advanced": true,
+                "display_name": "Namespace",
+                "dynamic": false,
+                "info": "Optional namespace within Astra DB to use for the collection.",
+                "list": false,
+                "load_from_db": false,
+                "name": "namespace",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "number_of_results": {
+                "advanced": true,
+                "display_name": "Number of Results",
+                "dynamic": false,
+                "info": "Number of results to return.",
+                "list": false,
+                "name": "number_of_results",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": 4
+              },
+              "pre_delete_collection": {
+                "advanced": true,
+                "display_name": "Pre Delete Collection",
+                "dynamic": false,
+                "info": "Boolean flag to determine whether to delete the collection before creating a new one.",
+                "list": false,
+                "name": "pre_delete_collection",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "bool",
+                "value": false
+              },
+              "search_input": {
+                "advanced": false,
+                "display_name": "Search Input",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "search_input",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "search_type": {
+                "advanced": false,
+                "display_name": "Search Type",
+                "dynamic": false,
+                "info": "",
+                "name": "search_type",
+                "options": [
+                  "Similarity",
+                  "MMR"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "Similarity"
+              },
+              "setup_mode": {
+                "advanced": true,
+                "display_name": "Setup Mode",
+                "dynamic": false,
+                "info": "Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.",
+                "name": "setup_mode",
+                "options": [
+                  "Sync",
+                  "Async",
+                  "Off"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "Sync"
+              },
+              "token": {
+                "advanced": false,
+                "display_name": "Astra DB Application Token",
+                "dynamic": false,
+                "info": "Authentication token for accessing Astra DB.",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "token",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "vector_store_inputs": {
+                "advanced": false,
+                "display_name": "Vector Store Inputs",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Document",
+                  "Data"
+                ],
+                "list": true,
+                "name": "vector_store_inputs",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "other",
+                "value": ""
               }
             }
           },
-          "type": "OpenAIEmbeddings"
+          "type": "AstraDB"
         },
         "dragging": false,
-        "height": 383,
-        "id": "OpenAIEmbeddings-9TPjc",
+        "height": 917,
+        "id": "AstraDB-irvai",
         "position": {
-          "x": 2814.0402191223047,
-          "y": 1955.9268168273086
+          "x": 3329.7211874614477,
+          "y": 1559.774393811144
         },
         "positionAbsolute": {
-          "x": 2814.0402191223047,
-          "y": 1955.9268168273086
+          "x": 3329.7211874614477,
+          "y": 1559.774393811144
+        },
+        "selected": false,
+        "type": "genericNode",
+        "width": 384
+      },
+      {
+        "data": {
+          "description": "Implementation of Vector Store using Astra DB with search capabilities",
+          "display_name": "Astra DB Vector Store",
+          "id": "AstraDB-wANQu",
+          "node": {
+            "base_classes": [
+              "Data"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Implementation of Vector Store using Astra DB with search capabilities",
+            "display_name": "Astra DB Vector Store",
+            "documentation": "https://python.langchain.com/docs/integrations/vectorstores/astradb",
+            "edited": false,
+            "field_order": [
+              "collection_name",
+              "token",
+              "api_endpoint",
+              "vector_store_inputs",
+              "embedding",
+              "namespace",
+              "metric",
+              "batch_size",
+              "bulk_insert_batch_concurrency",
+              "bulk_insert_overwrite_concurrency",
+              "bulk_delete_concurrency",
+              "setup_mode",
+              "pre_delete_collection",
+              "metadata_indexing_include",
+              "metadata_indexing_exclude",
+              "collection_indexing_policy",
+              "add_to_vector_store",
+              "search_input",
+              "search_type",
+              "number_of_results"
+            ],
+            "frozen": false,
+            "icon": "AstraDB",
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Retriever",
+                "method": "build_base_retriever",
+                "name": "base_retriever",
+                "selected": "Data",
+                "types": [
+                  "Data"
+                ],
+                "value": "__UNDEFINED__"
+              },
+              {
+                "cache": true,
+                "display_name": "Search Results",
+                "hidden": false,
+                "method": "search_documents",
+                "name": "search_results",
+                "selected": "Data",
+                "types": [
+                  "Data"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "add_to_vector_store": {
+                "advanced": false,
+                "display_name": "Add to Vector Store",
+                "dynamic": false,
+                "info": "If true, the Vector Store Inputs will be added to the Vector Store.",
+                "list": false,
+                "name": "add_to_vector_store",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "bool",
+                "value": true
+              },
+              "api_endpoint": {
+                "advanced": false,
+                "display_name": "API Endpoint",
+                "dynamic": false,
+                "info": "API endpoint URL for the Astra DB service.",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "api_endpoint",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "batch_size": {
+                "advanced": true,
+                "display_name": "Batch Size",
+                "dynamic": false,
+                "info": "Optional number of data to process in a single batch.",
+                "list": false,
+                "name": "batch_size",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": ""
+              },
+              "bulk_delete_concurrency": {
+                "advanced": true,
+                "display_name": "Bulk Delete Concurrency",
+                "dynamic": false,
+                "info": "Optional concurrency level for bulk delete operations.",
+                "list": false,
+                "name": "bulk_delete_concurrency",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": ""
+              },
+              "bulk_insert_batch_concurrency": {
+                "advanced": true,
+                "display_name": "Bulk Insert Batch Concurrency",
+                "dynamic": false,
+                "info": "Optional concurrency level for bulk insert operations.",
+                "list": false,
+                "name": "bulk_insert_batch_concurrency",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": ""
+              },
+              "bulk_insert_overwrite_concurrency": {
+                "advanced": true,
+                "display_name": "Bulk Insert Overwrite Concurrency",
+                "dynamic": false,
+                "info": "Optional concurrency level for bulk insert operations that overwrite existing data.",
+                "list": false,
+                "name": "bulk_insert_overwrite_concurrency",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": ""
+              },
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from loguru import logger\n\nfrom langflow.base.vectorstores.model import LCVectorStoreComponent\nfrom langflow.io import BoolInput, DropdownInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput\nfrom langflow.schema import Data\n\n\nclass AstraVectorStoreComponent(LCVectorStoreComponent):\n    display_name: str = \"Astra DB Vector Store\"\n    description: str = \"Implementation of Vector Store using Astra DB with search capabilities\"\n    documentation: str = \"https://python.langchain.com/docs/integrations/vectorstores/astradb\"\n    icon: str = \"AstraDB\"\n\n    inputs = [\n        StrInput(\n            name=\"collection_name\",\n            display_name=\"Collection Name\",\n            info=\"The name of the collection within Astra DB where the vectors will be stored.\",\n        ),\n        SecretStrInput(\n            name=\"token\",\n            display_name=\"Astra DB Application Token\",\n            info=\"Authentication token for accessing Astra DB.\",\n            value=\"ASTRA_DB_APPLICATION_TOKEN\",\n        ),\n        SecretStrInput(\n            name=\"api_endpoint\",\n            display_name=\"API Endpoint\",\n            info=\"API endpoint URL for the Astra DB service.\",\n            value=\"ASTRA_DB_API_ENDPOINT\",\n        ),\n        HandleInput(\n            name=\"vector_store_inputs\",\n            display_name=\"Vector Store Inputs\",\n            input_types=[\"Document\", \"Data\"],\n            is_list=True,\n        ),\n        HandleInput(\n            name=\"embedding\",\n            display_name=\"Embedding\",\n            input_types=[\"Embeddings\"],\n        ),\n        StrInput(\n            name=\"namespace\",\n            display_name=\"Namespace\",\n            info=\"Optional namespace within Astra DB to use for the collection.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"metric\",\n            display_name=\"Metric\",\n            info=\"Optional distance metric for vector comparisons in the vector store.\",\n            options=[\"cosine\", \"dot_product\", \"euclidean\"],\n            advanced=True,\n        ),\n        IntInput(\n            name=\"batch_size\",\n            display_name=\"Batch Size\",\n            info=\"Optional number of data to process in a single batch.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_batch_concurrency\",\n            display_name=\"Bulk Insert Batch Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_insert_overwrite_concurrency\",\n            display_name=\"Bulk Insert Overwrite Concurrency\",\n            info=\"Optional concurrency level for bulk insert operations that overwrite existing data.\",\n            advanced=True,\n        ),\n        IntInput(\n            name=\"bulk_delete_concurrency\",\n            display_name=\"Bulk Delete Concurrency\",\n            info=\"Optional concurrency level for bulk delete operations.\",\n            advanced=True,\n        ),\n        DropdownInput(\n            name=\"setup_mode\",\n            display_name=\"Setup Mode\",\n            info=\"Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.\",\n            options=[\"Sync\", \"Async\", \"Off\"],\n            advanced=True,\n            value=\"Sync\",\n        ),\n        BoolInput(\n            name=\"pre_delete_collection\",\n            display_name=\"Pre Delete Collection\",\n            info=\"Boolean flag to determine whether to delete the collection before creating a new one.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_include\",\n            display_name=\"Metadata Indexing Include\",\n            info=\"Optional list of metadata fields to include in the indexing.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"metadata_indexing_exclude\",\n            display_name=\"Metadata Indexing Exclude\",\n            info=\"Optional list of metadata fields to exclude from the indexing.\",\n            advanced=True,\n        ),\n        StrInput(\n            name=\"collection_indexing_policy\",\n            display_name=\"Collection Indexing Policy\",\n            info=\"Optional dictionary defining the indexing policy for the collection.\",\n            advanced=True,\n        ),\n        BoolInput(\n            name=\"add_to_vector_store\",\n            display_name=\"Add to Vector Store\",\n            info=\"If true, the Vector Store Inputs will be added to the Vector Store.\",\n        ),\n        MultilineInput(\n            name=\"search_input\",\n            display_name=\"Search Input\",\n        ),\n        DropdownInput(\n            name=\"search_type\",\n            display_name=\"Search Type\",\n            options=[\"Similarity\", \"MMR\"],\n            value=\"Similarity\",\n        ),\n        IntInput(\n            name=\"number_of_results\",\n            display_name=\"Number of Results\",\n            info=\"Number of results to return.\",\n            advanced=True,\n            value=4,\n        ),\n    ]\n\n    def build_vector_store(self):\n        try:\n            from langchain_astradb import AstraDBVectorStore\n            from langchain_astradb.utils.astradb import SetupMode\n        except ImportError:\n            raise ImportError(\n                \"Could not import langchain Astra DB integration package. \"\n                \"Please install it with `pip install langchain-astradb`.\"\n            )\n\n        try:\n            if not self.setup_mode:\n                self.setup_mode = self._inputs[\"setup_mode\"].options[0]\n\n            setup_mode_value = SetupMode[self.setup_mode.upper()]\n        except KeyError:\n            raise ValueError(f\"Invalid setup mode: {self.setup_mode}\")\n\n        vector_store_kwargs = {\n            \"embedding\": self.embedding,\n            \"collection_name\": self.collection_name,\n            \"token\": self.token,\n            \"api_endpoint\": self.api_endpoint,\n            \"namespace\": self.namespace or None,\n            \"metric\": self.metric or None,\n            \"batch_size\": self.batch_size or None,\n            \"bulk_insert_batch_concurrency\": self.bulk_insert_batch_concurrency or None,\n            \"bulk_insert_overwrite_concurrency\": self.bulk_insert_overwrite_concurrency or None,\n            \"bulk_delete_concurrency\": self.bulk_delete_concurrency or None,\n            \"setup_mode\": setup_mode_value,\n            \"pre_delete_collection\": self.pre_delete_collection or False,\n        }\n\n        if self.metadata_indexing_include:\n            vector_store_kwargs[\"metadata_indexing_include\"] = self.metadata_indexing_include\n        elif self.metadata_indexing_exclude:\n            vector_store_kwargs[\"metadata_indexing_exclude\"] = self.metadata_indexing_exclude\n        elif self.collection_indexing_policy:\n            vector_store_kwargs[\"collection_indexing_policy\"] = self.collection_indexing_policy\n\n        try:\n            vector_store = AstraDBVectorStore(**vector_store_kwargs)\n        except Exception as e:\n            raise ValueError(f\"Error initializing AstraDBVectorStore: {str(e)}\") from e\n\n        if self.add_to_vector_store:\n            self._add_documents_to_vector_store(vector_store)\n\n        return vector_store\n\n    def build_base_retriever(self):\n        vector_store = self.build_vector_store()\n        self.status = self._astradb_collection_to_data(vector_store.collection)\n        return vector_store\n\n    def _add_documents_to_vector_store(self, vector_store):\n        documents = []\n        for _input in self.vector_store_inputs or []:\n            if isinstance(_input, Data):\n                documents.append(_input.to_lc_document())\n            else:\n                raise ValueError(\"Vector Store Inputs must be Data objects.\")\n\n        if documents and self.embedding is not None:\n            logger.debug(f\"Adding {len(documents)} documents to the Vector Store.\")\n            try:\n                vector_store.add_documents(documents)\n            except Exception as e:\n                raise ValueError(f\"Error adding documents to AstraDBVectorStore: {str(e)}\") from e\n        else:\n            logger.debug(\"No documents to add to the Vector Store.\")\n\n    def search_documents(self):\n        vector_store = self.build_vector_store()\n\n        logger.debug(f\"Search input: {self.search_input}\")\n        logger.debug(f\"Search type: {self.search_type}\")\n        logger.debug(f\"Number of results: {self.number_of_results}\")\n\n        if self.search_input and isinstance(self.search_input, str) and self.search_input.strip():\n            try:\n                if self.search_type == \"Similarity\":\n                    docs = vector_store.similarity_search(\n                        query=self.search_input,\n                        k=self.number_of_results,\n                    )\n                elif self.search_type == \"MMR\":\n                    docs = vector_store.max_marginal_relevance_search(\n                        query=self.search_input,\n                        k=self.number_of_results,\n                    )\n                else:\n                    raise ValueError(f\"Invalid search type: {self.search_type}\")\n            except Exception as e:\n                raise ValueError(f\"Error performing search in AstraDBVectorStore: {str(e)}\") from e\n\n            logger.debug(f\"Retrieved documents: {len(docs)}\")\n\n            data = [Data.from_document(doc) for doc in docs]\n            logger.debug(f\"Converted documents to data: {len(data)}\")\n            self.status = data\n            return data\n        else:\n            logger.debug(\"No search input provided. Skipping search.\")\n            return []\n\n    def _astradb_collection_to_data(self, collection):\n        data = []\n        data_dict = collection.find()\n        if data_dict and \"data\" in data_dict:\n            data_dict = data_dict[\"data\"].get(\"documents\", [])\n\n        for item in data_dict:\n            data.append(Data(content=item[\"content\"]))\n        return data\n"
+              },
+              "collection_indexing_policy": {
+                "advanced": true,
+                "display_name": "Collection Indexing Policy",
+                "dynamic": false,
+                "info": "Optional dictionary defining the indexing policy for the collection.",
+                "list": false,
+                "load_from_db": false,
+                "name": "collection_indexing_policy",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "collection_name": {
+                "advanced": false,
+                "display_name": "Collection Name",
+                "dynamic": false,
+                "info": "The name of the collection within Astra DB where the vectors will be stored.",
+                "list": false,
+                "load_from_db": false,
+                "name": "collection_name",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "langflow"
+              },
+              "embedding": {
+                "advanced": false,
+                "display_name": "Embedding",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Embeddings"
+                ],
+                "list": false,
+                "name": "embedding",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "other",
+                "value": ""
+              },
+              "metadata_indexing_exclude": {
+                "advanced": true,
+                "display_name": "Metadata Indexing Exclude",
+                "dynamic": false,
+                "info": "Optional list of metadata fields to exclude from the indexing.",
+                "list": false,
+                "load_from_db": false,
+                "name": "metadata_indexing_exclude",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "metadata_indexing_include": {
+                "advanced": true,
+                "display_name": "Metadata Indexing Include",
+                "dynamic": false,
+                "info": "Optional list of metadata fields to include in the indexing.",
+                "list": false,
+                "load_from_db": false,
+                "name": "metadata_indexing_include",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "metric": {
+                "advanced": true,
+                "display_name": "Metric",
+                "dynamic": false,
+                "info": "Optional distance metric for vector comparisons in the vector store.",
+                "name": "metric",
+                "options": [
+                  "cosine",
+                  "dot_product",
+                  "euclidean"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "namespace": {
+                "advanced": true,
+                "display_name": "Namespace",
+                "dynamic": false,
+                "info": "Optional namespace within Astra DB to use for the collection.",
+                "list": false,
+                "load_from_db": false,
+                "name": "namespace",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "number_of_results": {
+                "advanced": true,
+                "display_name": "Number of Results",
+                "dynamic": false,
+                "info": "Number of results to return.",
+                "list": false,
+                "name": "number_of_results",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "int",
+                "value": 4
+              },
+              "pre_delete_collection": {
+                "advanced": true,
+                "display_name": "Pre Delete Collection",
+                "dynamic": false,
+                "info": "Boolean flag to determine whether to delete the collection before creating a new one.",
+                "list": false,
+                "name": "pre_delete_collection",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "bool",
+                "value": false
+              },
+              "search_input": {
+                "advanced": false,
+                "display_name": "Search Input",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "search_input",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "search_type": {
+                "advanced": false,
+                "display_name": "Search Type",
+                "dynamic": false,
+                "info": "",
+                "name": "search_type",
+                "options": [
+                  "Similarity",
+                  "MMR"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "Similarity"
+              },
+              "setup_mode": {
+                "advanced": true,
+                "display_name": "Setup Mode",
+                "dynamic": false,
+                "info": "Configuration mode for setting up the vector store, with options like 'Sync', 'Async', or 'Off'.",
+                "name": "setup_mode",
+                "options": [
+                  "Sync",
+                  "Async",
+                  "Off"
+                ],
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "Sync"
+              },
+              "token": {
+                "advanced": false,
+                "display_name": "Astra DB Application Token",
+                "dynamic": false,
+                "info": "Authentication token for accessing Astra DB.",
+                "input_types": [],
+                "load_from_db": true,
+                "name": "token",
+                "password": true,
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": ""
+              },
+              "vector_store_inputs": {
+                "advanced": false,
+                "display_name": "Vector Store Inputs",
+                "dynamic": false,
+                "info": "",
+                "input_types": [
+                  "Document",
+                  "Data"
+                ],
+                "list": true,
+                "name": "vector_store_inputs",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "other",
+                "value": ""
+              }
+            }
+          },
+          "type": "AstraDB"
+        },
+        "dragging": false,
+        "height": 917,
+        "id": "AstraDB-wANQu",
+        "position": {
+          "x": 1298.4611042465333,
+          "y": 160.7181472642742
+        },
+        "positionAbsolute": {
+          "x": 1298.4611042465333,
+          "y": 160.7181472642742
+        },
+        "selected": false,
+        "type": "genericNode",
+        "width": 384
+      },
+      {
+        "data": {
+          "id": "ParseData-C9tUn",
+          "node": {
+            "base_classes": [
+              "Message"
+            ],
+            "beta": false,
+            "conditional_paths": [],
+            "custom_fields": {},
+            "description": "Convert Data into plain text following a specified template.",
+            "display_name": "Parse Data",
+            "documentation": "",
+            "field_order": [
+              "data",
+              "template",
+              "sep"
+            ],
+            "frozen": false,
+            "icon": "braces",
+            "output_types": [],
+            "outputs": [
+              {
+                "cache": true,
+                "display_name": "Text",
+                "hidden": false,
+                "method": "parse_data",
+                "name": "text",
+                "selected": "Message",
+                "types": [
+                  "Message"
+                ],
+                "value": "__UNDEFINED__"
+              }
+            ],
+            "pinned": false,
+            "template": {
+              "_type": "Component",
+              "code": {
+                "advanced": true,
+                "dynamic": true,
+                "fileTypes": [],
+                "file_path": "",
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "code",
+                "password": false,
+                "placeholder": "",
+                "required": true,
+                "show": true,
+                "title_case": false,
+                "type": "code",
+                "value": "from langflow.custom import Component\nfrom langflow.helpers.data import data_to_text\nfrom langflow.io import DataInput, MultilineInput, Output, StrInput\nfrom langflow.schema.message import Message\n\n\nclass ParseDataComponent(Component):\n    display_name = \"Parse Data\"\n    description = \"Convert Data into plain text following a specified template.\"\n    icon = \"braces\"\n\n    inputs = [\n        DataInput(name=\"data\", display_name=\"Data\", info=\"The data to convert to text.\"),\n        MultilineInput(\n            name=\"template\",\n            display_name=\"Template\",\n            info=\"The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.\",\n            value=\"{text}\",\n        ),\n        StrInput(name=\"sep\", display_name=\"Separator\", advanced=True, value=\"\\n\"),\n    ]\n\n    outputs = [\n        Output(display_name=\"Text\", name=\"text\", method=\"parse_data\"),\n    ]\n\n    def parse_data(self) -> Message:\n        data = self.data if isinstance(self.data, list) else [self.data]\n        template = self.template\n\n        result_string = data_to_text(template, data, sep=self.sep)\n        self.status = result_string\n        return Message(text=result_string)\n"
+              },
+              "data": {
+                "advanced": false,
+                "display_name": "Data",
+                "dynamic": false,
+                "info": "The data to convert to text.",
+                "input_types": [
+                  "Data"
+                ],
+                "list": false,
+                "name": "data",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "other",
+                "value": ""
+              },
+              "sep": {
+                "advanced": true,
+                "display_name": "Separator",
+                "dynamic": false,
+                "info": "",
+                "list": false,
+                "load_from_db": false,
+                "name": "sep",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "---"
+              },
+              "template": {
+                "advanced": false,
+                "display_name": "Template",
+                "dynamic": false,
+                "info": "The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.",
+                "input_types": [
+                  "Message",
+                  "str"
+                ],
+                "list": false,
+                "load_from_db": false,
+                "multiline": true,
+                "name": "template",
+                "placeholder": "",
+                "required": false,
+                "show": true,
+                "title_case": false,
+                "type": "str",
+                "value": "{text}"
+              }
+            }
+          },
+          "type": "ParseData"
+        },
+        "dragging": false,
+        "height": 385,
+        "id": "ParseData-C9tUn",
+        "position": {
+          "x": 1911.4866480237615,
+          "y": 566.903831987901
+        },
+        "positionAbsolute": {
+          "x": 1911.4866480237615,
+          "y": 566.903831987901
         },
         "selected": false,
         "type": "genericNode",
@@ -3303,14 +3232,15 @@
       }
     ],
     "viewport": {
-      "x": -259.6782520315529,
-      "y": 90.3428735006047,
-      "zoom": 0.2687057134854984
+      "x": -124.92057698887993,
+      "y": 126.80996053902413,
+      "zoom": 0.370642160653555
     }
   },
   "description": "Visit https://pre-release.langflow.org/tutorials/rag-with-astradb for a detailed guide of this project.\nThis project give you both Ingestion and RAG in a single file. You'll need to visit https://astra.datastax.com/ to create an Astra DB instance, your Token and grab an API Endpoint.\nRunning this project requires you to add a file in the Files component, then define a Collection Name and click on the Play icon on the Astra DB component. \n\nAfter the ingestion ends you are ready to click on the Run button at the lower left corner and start asking questions about your data.",
-  "id": "51e2b78a-199b-4054-9f32-e288eef6924c",
+  "endpoint_name": null,
+  "id": "c9e0cb46-c474-451a-8496-413f58481d92",
   "is_component": false,
-  "last_tested_version": "1.0.0a0",
+  "last_tested_version": "1.0.0a61",
   "name": "Vector Store RAG"
 }
\ No newline at end of file
diff --git a/src/backend/base/langflow/inputs/__init__.py b/src/backend/base/langflow/inputs/__init__.py
new file mode 100644
index 000000000..41f03c9d2
--- /dev/null
+++ b/src/backend/base/langflow/inputs/__init__.py
@@ -0,0 +1,35 @@
+from .inputs import (
+    BoolInput,
+    DataInput,
+    DictInput,
+    DropdownInput,
+    FileInput,
+    FloatInput,
+    HandleInput,
+    IntInput,
+    MessageInput,
+    MultilineInput,
+    NestedDictInput,
+    PromptInput,
+    SecretStrInput,
+    StrInput,
+    TextInput,
+)
+
+__all__ = [
+    "BoolInput",
+    "DataInput",
+    "DictInput",
+    "DropdownInput",
+    "FileInput",
+    "FloatInput",
+    "HandleInput",
+    "IntInput",
+    "MessageInput",
+    "MultilineInput",
+    "NestedDictInput",
+    "PromptInput",
+    "SecretStrInput",
+    "StrInput",
+    "TextInput",
+]
diff --git a/src/backend/base/langflow/inputs/input_mixin.py b/src/backend/base/langflow/inputs/input_mixin.py
new file mode 100644
index 000000000..d9107d272
--- /dev/null
+++ b/src/backend/base/langflow/inputs/input_mixin.py
@@ -0,0 +1,129 @@
+from enum import Enum
+from typing import Annotated, Any, Optional
+
+from langflow.field_typing.range_spec import RangeSpec
+from langflow.inputs.validators import CoalesceBool
+from pydantic import BaseModel, ConfigDict, Field, PlainSerializer, field_validator, model_serializer
+
+
+class FieldTypes(str, Enum):
+    TEXT = "str"
+    INTEGER = "int"
+    PASSWORD = "str"
+    FLOAT = "float"
+    BOOLEAN = "bool"
+    DICT = "dict"
+    NESTED_DICT = "NestedDict"
+    FILE = "file"
+    PROMPT = "prompt"
+    OTHER = "other"
+
+
+SerializableFieldTypes = Annotated[FieldTypes, PlainSerializer(lambda v: v.value, return_type=str)]
+
+
+# Base mixin for common input field attributes and methods
+class BaseInputMixin(BaseModel, validate_assignment=True):
+    model_config = ConfigDict(arbitrary_types_allowed=True, extra="forbid")
+
+    field_type: Optional[SerializableFieldTypes] = Field(default=FieldTypes.TEXT)
+
+    required: bool = False
+    """Specifies if the field is required. Defaults to False."""
+
+    placeholder: str = ""
+    """A placeholder string for the field. Default is an empty string."""
+
+    show: bool = True
+    """Should the field be shown. Defaults to True."""
+
+    value: Any = ""
+    """The value of the field. Default is an empty string."""
+
+    name: Optional[str] = None
+    """Name of the field. Default is an empty string."""
+
+    display_name: Optional[str] = None
+    """Display name of the field. Defaults to None."""
+
+    advanced: bool = False
+    """Specifies if the field will an advanced parameter (hidden). Defaults to False."""
+
+    input_types: Optional[list[str]] = None
+    """List of input types for the handle when the field has more than one type. Default is an empty list."""
+
+    dynamic: bool = False
+    """Specifies if the field is dynamic. Defaults to False."""
+
+    info: Optional[str] = ""
+    """Additional information about the field to be shown in the tooltip. Defaults to an empty string."""
+
+    real_time_refresh: Optional[bool] = None
+    """Specifies if the field should have real time refresh. `refresh_button` must be False. Defaults to None."""
+
+    refresh_button: Optional[bool] = None
+    """Specifies if the field should have a refresh button. Defaults to False."""
+    refresh_button_text: Optional[str] = None
+    """Specifies the text for the refresh button. Defaults to None."""
+
+    title_case: bool = False
+    """Specifies if the field should be displayed in title case. Defaults to True."""
+
+    def to_dict(self):
+        return self.model_dump(exclude_none=True, by_alias=True)
+
+    @field_validator("field_type", mode="before")
+    @classmethod
+    def validate_field_type(cls, v):
+        if v not in FieldTypes:
+            return FieldTypes.OTHER
+        return FieldTypes(v)
+
+    @model_serializer(mode="wrap")
+    def serialize_model(self, handler):
+        dump = handler(self)
+        if "field_type" in dump:
+            dump["type"] = dump.pop("field_type")
+        return dump
+
+
+# Mixin for input fields that can be listable
+class ListableInputMixin(BaseModel):
+    is_list: bool = Field(default=False, serialization_alias="list")
+
+
+# Specific mixin for fields needing database interaction
+class DatabaseLoadMixin(BaseModel):
+    load_from_db: bool = Field(default=True)
+
+
+# Specific mixin for fields needing file interaction
+class FileMixin(BaseModel):
+    file_path: Optional[str] = Field(default="")
+    file_types: list[str] = Field(default=[], serialization_alias="fileTypes")
+
+    @field_validator("file_types")
+    @classmethod
+    def validate_file_types(cls, v):
+        if not isinstance(v, list):
+            raise ValueError("file_types must be a list")
+        # types should be a list of extensions without the dot
+        for file_type in v:
+            if not isinstance(file_type, str):
+                raise ValueError("file_types must be a list of strings")
+            if file_type.startswith("."):
+                raise ValueError("file_types should not start with a dot")
+        return v
+
+
+class RangeMixin(BaseModel):
+    range_spec: Optional[RangeSpec] = None
+
+
+class DropDownMixin(BaseModel):
+    options: Optional[list[str]] = None
+    """List of options for the field. Only used when is_list=True. Default is an empty list."""
+
+
+class MultilineMixin(BaseModel):
+    multiline: CoalesceBool = True
diff --git a/src/backend/base/langflow/inputs/inputs.py b/src/backend/base/langflow/inputs/inputs.py
new file mode 100644
index 000000000..e9c415c9a
--- /dev/null
+++ b/src/backend/base/langflow/inputs/inputs.py
@@ -0,0 +1,320 @@
+from typing import Any, AsyncIterator, Iterator, Optional, Union
+
+from loguru import logger
+from pydantic import Field, field_validator
+
+from langflow.inputs.validators import CoalesceBool
+from langflow.schema.data import Data
+from langflow.schema.message import Message
+
+from .input_mixin import (
+    BaseInputMixin,
+    DatabaseLoadMixin,
+    DropDownMixin,
+    FieldTypes,
+    FileMixin,
+    ListableInputMixin,
+    MultilineMixin,
+    RangeMixin,
+    SerializableFieldTypes,
+)
+
+
+class HandleInput(BaseInputMixin, ListableInputMixin):
+    """
+    Represents an Input that has a Handle to a specific type (e.g. BaseLanguageModel, BaseRetriever, etc.)
+
+    This class inherits from the `BaseInputMixin` and `ListableInputMixin` classes.
+
+    Attributes:
+        input_types (list[str]): A list of input types.
+        field_type (Optional[SerializableFieldTypes]): The field type of the input.
+    """
+
+    input_types: list[str] = Field(default_factory=list)
+    field_type: Optional[SerializableFieldTypes] = FieldTypes.OTHER
+
+
+class DataInput(HandleInput):
+    """
+    Represents an Input that has a Handle that receives a Data object.
+
+    Attributes:
+        input_types (list[str]): A list of input types supported by this data input.
+    """
+
+    input_types: list[str] = ["Data"]
+
+
+class PromptInput(BaseInputMixin, ListableInputMixin):
+    field_type: Optional[SerializableFieldTypes] = FieldTypes.PROMPT
+
+
+# Applying mixins to a specific input type
+class StrInput(BaseInputMixin, ListableInputMixin, DatabaseLoadMixin):
+    field_type: Optional[SerializableFieldTypes] = FieldTypes.TEXT
+    load_from_db: CoalesceBool = False
+    """Defines if the field will allow the user to open a text editor. Default is False."""
+
+    @staticmethod
+    def _validate_value(v: Any, _info):
+        """
+        Validates the given value and returns the processed value.
+
+        Args:
+            v (Any): The value to be validated.
+            _info: Additional information about the input.
+
+        Returns:
+            The processed value.
+
+        Raises:
+            ValueError: If the value is not of a valid type or if the input is missing a required key.
+        """
+        if not isinstance(v, str) and v is not None:
+            if _info.data.get("input_types") and v.__class__.__name__ not in _info.data.get("input_types"):
+                logger.warning(f"Invalid value type {type(v)}")
+        return v
+
+    @field_validator("value")
+    @classmethod
+    def validate_value(cls, v: Any, _info):
+        """
+        Validates the given value and returns the processed value.
+
+        Args:
+            v (Any): The value to be validated.
+            _info: Additional information about the input.
+
+        Returns:
+            The processed value.
+
+        Raises:
+            ValueError: If the value is not of a valid type or if the input is missing a required key.
+        """
+        is_list = _info.data["is_list"]
+        value = None
+        if is_list:
+            value = [cls._validate_value(vv, _info) for vv in v]
+        else:
+            value = cls._validate_value(v, _info)
+        return value
+
+
+class MessageInput(StrInput):
+    input_types: list[str] = ["Message"]
+
+    @staticmethod
+    def _validate_value(v: Any, _info):
+        # If v is a instance of Message, then its fine
+        if isinstance(v, Message):
+            return v
+        if isinstance(v, str):
+            return Message(text=v)
+        raise ValueError(f"Invalid value type {type(v)}")
+
+
+class TextInput(StrInput):
+    """
+    Represents a text input component for the Langflow system.
+
+    This component is used to handle text inputs in the Langflow system. It provides methods for validating and processing text values.
+
+    Attributes:
+        input_types (list[str]): A list of input types that this component supports. In this case, it supports the "Message" input type.
+    """
+
+    input_types: list[str] = ["Message"]
+
+    @staticmethod
+    def _validate_value(v: Any, _info):
+        """
+        Validates the given value and returns the processed value.
+
+        Args:
+            v (Any): The value to be validated.
+            _info: Additional information about the input.
+
+        Returns:
+            The processed value.
+
+        Raises:
+            ValueError: If the value is not of a valid type or if the input is missing a required key.
+        """
+        value: str | AsyncIterator | Iterator | None = None
+        if isinstance(v, str):
+            value = v
+        elif isinstance(v, Message):
+            value = v.text
+        elif isinstance(v, Data):
+            if v.text_key in v.data:
+                value = v.data[v.text_key]
+            else:
+                keys = ", ".join(v.data.keys())
+                input_name = _info.data["name"]
+                raise ValueError(
+                    f"The input to '{input_name}' must contain the key '{v.text_key}'."
+                    f"You can set `text_key` to one of the following keys: {keys} or set the value using another Component."
+                )
+        elif isinstance(v, (AsyncIterator, Iterator)):
+            value = v
+        else:
+            raise ValueError(f"Invalid value type {type(v)}")
+        return value
+
+
+class MultilineInput(TextInput, MultilineMixin):
+    """
+    Represents a multiline input field.
+
+    Attributes:
+        field_type (Optional[SerializableFieldTypes]): The type of the field. Defaults to FieldTypes.TEXT.
+        multiline (CoalesceBool): Indicates whether the input field should support multiple lines. Defaults to True.
+    """
+
+    field_type: Optional[SerializableFieldTypes] = FieldTypes.TEXT
+    multiline: CoalesceBool = True
+
+
+class SecretStrInput(BaseInputMixin, DatabaseLoadMixin):
+    """
+    Represents a field with password field type.
+
+    This class inherits from `BaseInputMixin` and `DatabaseLoadMixin`.
+
+    Attributes:
+        field_type (Optional[SerializableFieldTypes]): The field type of the input. Defaults to `FieldTypes.PASSWORD`.
+        password (CoalesceBool): A boolean indicating whether the input is a password. Defaults to `True`.
+        input_types (list[str]): A list of input types associated with this input. Defaults to an empty list.
+    """
+
+    field_type: Optional[SerializableFieldTypes] = FieldTypes.PASSWORD
+    password: CoalesceBool = Field(default=True)
+    input_types: list[str] = []
+    load_from_db: CoalesceBool = True
+
+
+class IntInput(BaseInputMixin, ListableInputMixin, RangeMixin):
+    """
+    Represents an integer field.
+
+    This class represents an integer input and provides functionality for handling integer values.
+    It inherits from the `BaseInputMixin`, `ListableInputMixin`, and `RangeMixin` classes.
+
+    Attributes:
+        field_type (Optional[SerializableFieldTypes]): The field type of the input. Defaults to FieldTypes.INTEGER.
+    """
+
+    field_type: Optional[SerializableFieldTypes] = FieldTypes.INTEGER
+
+
+class FloatInput(BaseInputMixin, ListableInputMixin, RangeMixin):
+    """
+    Represents a float field.
+
+    This class represents a float input and provides functionality for handling float values.
+    It inherits from the `BaseInputMixin`, `ListableInputMixin`, and `RangeMixin` classes.
+
+    Attributes:
+        field_type (Optional[SerializableFieldTypes]): The field type of the input. Defaults to FieldTypes.FLOAT.
+    """
+
+    field_type: Optional[SerializableFieldTypes] = FieldTypes.FLOAT
+
+
+class BoolInput(BaseInputMixin, ListableInputMixin):
+    """
+    Represents a boolean field.
+
+    This class represents a boolean input and provides functionality for handling boolean values.
+    It inherits from the `BaseInputMixin` and `ListableInputMixin` classes.
+
+    Attributes:
+        field_type (Optional[SerializableFieldTypes]): The field type of the input. Defaults to FieldTypes.BOOLEAN.
+        value (CoalesceBool): The value of the boolean input.
+    """
+
+    field_type: Optional[SerializableFieldTypes] = FieldTypes.BOOLEAN
+    value: CoalesceBool = False
+
+
+class NestedDictInput(BaseInputMixin, ListableInputMixin):
+    """
+    Represents a nested dictionary field.
+
+    This class represents a nested dictionary input and provides functionality for handling dictionary values.
+    It inherits from the `BaseInputMixin` and `ListableInputMixin` classes.
+
+    Attributes:
+        field_type (Optional[SerializableFieldTypes]): The field type of the input. Defaults to FieldTypes.NESTED_DICT.
+        value (Optional[dict]): The value of the input. Defaults to an empty dictionary.
+    """
+
+    field_type: Optional[SerializableFieldTypes] = FieldTypes.NESTED_DICT
+    value: Optional[dict] = {}
+
+
+class DictInput(BaseInputMixin, ListableInputMixin):
+    """
+    Represents a dictionary field.
+
+    This class represents a dictionary input and provides functionality for handling dictionary values.
+    It inherits from the `BaseInputMixin` and `ListableInputMixin` classes.
+
+    Attributes:
+        field_type (Optional[SerializableFieldTypes]): The field type of the input. Defaults to FieldTypes.DICT.
+        value (Optional[dict]): The value of the dictionary input. Defaults to an empty dictionary.
+    """
+
+    field_type: Optional[SerializableFieldTypes] = FieldTypes.DICT
+    value: Optional[dict] = {}
+
+
+class DropdownInput(BaseInputMixin, DropDownMixin):
+    """
+    Represents a dropdown input field.
+
+    This class represents a dropdown input field and provides functionality for handling dropdown values.
+    It inherits from the `BaseInputMixin` and `DropDownMixin` classes.
+
+    Attributes:
+        field_type (Optional[SerializableFieldTypes]): The field type of the input. Defaults to FieldTypes.TEXT.
+        options (Optional[Union[list[str], Callable]]): List of options for the field. Only used when is_list=True.
+            Default is None.
+    """
+
+    field_type: Optional[SerializableFieldTypes] = FieldTypes.TEXT
+    options: list[str] = Field(default_factory=list)
+
+
+class FileInput(BaseInputMixin, ListableInputMixin, FileMixin):
+    """
+    Represents a file field.
+
+    This class represents a file input and provides functionality for handling file values.
+    It inherits from the `BaseInputMixin`, `ListableInputMixin`, and `FileMixin` classes.
+
+    Attributes:
+        field_type (Optional[SerializableFieldTypes]): The field type of the input. Defaults to FieldTypes.FILE.
+    """
+
+    field_type: Optional[SerializableFieldTypes] = FieldTypes.FILE
+
+
+InputTypes = Union[
+    BoolInput,
+    DataInput,
+    DictInput,
+    DropdownInput,
+    FileInput,
+    FloatInput,
+    HandleInput,
+    IntInput,
+    MultilineInput,
+    NestedDictInput,
+    PromptInput,
+    SecretStrInput,
+    StrInput,
+    TextInput,
+    MessageInput,
+]
diff --git a/src/backend/base/langflow/inputs/validators.py b/src/backend/base/langflow/inputs/validators.py
new file mode 100644
index 000000000..7056265f8
--- /dev/null
+++ b/src/backend/base/langflow/inputs/validators.py
@@ -0,0 +1,19 @@
+from typing import Annotated
+
+from pydantic import PlainValidator
+
+
+def validate_boolean(value: bool) -> bool:
+    valid_trues = ["True", "true", "1", "yes"]
+    valid_falses = ["False", "false", "0", "no"]
+    if value in valid_trues:
+        return True
+    if value in valid_falses:
+        return False
+    if isinstance(value, bool):
+        return value
+    else:
+        raise ValueError("Value must be a boolean")
+
+
+CoalesceBool = Annotated[bool, PlainValidator(validate_boolean)]
diff --git a/src/backend/base/langflow/interface/initialize/llm.py b/src/backend/base/langflow/interface/initialize/llm.py
deleted file mode 100644
index 05219d7d3..000000000
--- a/src/backend/base/langflow/interface/initialize/llm.py
+++ /dev/null
@@ -1,7 +0,0 @@
-def initialize_vertexai(class_object, params):
-    if credentials_path := params.get("credentials"):
-        from google.oauth2 import service_account  # type: ignore
-
-        credentials_object = service_account.Credentials.from_service_account_file(filename=credentials_path)
-        params["credentials"] = credentials_object
-    return class_object(**params)
diff --git a/src/backend/base/langflow/interface/initialize/loading.py b/src/backend/base/langflow/interface/initialize/loading.py
index 67403c2fe..54aad9745 100644
--- a/src/backend/base/langflow/interface/initialize/loading.py
+++ b/src/backend/base/langflow/interface/initialize/loading.py
@@ -1,17 +1,19 @@
 import inspect
 import json
 import os
-from typing import TYPE_CHECKING, Any, Type
+import warnings
+from typing import TYPE_CHECKING, Any
 
 import orjson
 from loguru import logger
+from pydantic import PydanticDeprecatedSince20
 
+from langflow.custom import Component, CustomComponent
 from langflow.custom.eval import eval_custom_component_code
-from langflow.graph.utils import get_artifact_type, post_process_raw
-from langflow.schema import Record
+from langflow.schema import Data
+from langflow.schema.artifact import get_artifact_type, post_process_raw
 
 if TYPE_CHECKING:
-    from langflow.custom import CustomComponent
     from langflow.graph.vertex.base import Vertex
 
 
@@ -28,12 +30,29 @@ async def instantiate_class(
     params = convert_params_to_sets(params)
     params = convert_kwargs(params)
     logger.debug(f"Instantiating {vertex_type} of type {base_type}")
+
     if not base_type:
         raise ValueError("No base type provided for vertex")
-    if base_type == "custom_components":
-        return await instantiate_custom_component(params, user_id, vertex, fallback_to_env_vars=fallback_to_env_vars)
-    else:
-        raise ValueError(f"Base type {base_type} not found.")
+
+    params_copy = params.copy()
+    # Remove code from params
+    class_object = eval_custom_component_code(params_copy.pop("code"))
+    custom_component = class_object(
+        user_id=user_id,
+        parameters=params_copy,
+        vertex=vertex,
+    )
+    params_copy = update_params_with_load_from_db_fields(
+        custom_component, params_copy, vertex.load_from_db_fields, fallback_to_env_vars
+    )
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", category=PydanticDeprecatedSince20)
+        if isinstance(custom_component, Component):
+            return await build_component(params=params_copy, custom_component=custom_component, vertex=vertex)
+        elif isinstance(custom_component, CustomComponent):
+            return await build_custom_component(params=params_copy, custom_component=custom_component)
+        else:
+            raise ValueError(f"Base type {base_type} not found.")
 
 
 def convert_params_to_sets(params):
@@ -99,45 +118,58 @@ def update_params_with_load_from_db_fields(
     return params
 
 
-async def instantiate_custom_component(params, user_id, vertex, fallback_to_env_vars: bool = False):
-    params_copy = params.copy()
-    class_object: Type["CustomComponent"] = eval_custom_component_code(params_copy.pop("code"))
-    custom_component: "CustomComponent" = class_object(
-        user_id=user_id,
-        parameters=params_copy,
-        vertex=vertex,
-        selected_output_type=vertex.selected_output_type,
-    )
-    params_copy = update_params_with_load_from_db_fields(
-        custom_component, params_copy, vertex.load_from_db_fields, fallback_to_env_vars
-    )
+async def build_component(
+    params: dict,
+    custom_component: "Component",
+    vertex: "Vertex",
+):
+    # Now set the params as attributes of the custom_component
+    custom_component.set_attributes(params)
+    build_results, artifacts = await custom_component.build_results(vertex)
 
-    if "retriever" in params_copy and hasattr(params_copy["retriever"], "as_retriever"):
-        params_copy["retriever"] = params_copy["retriever"].as_retriever()
+    return custom_component, build_results, artifacts
+
+
+async def build_custom_component(params: dict, custom_component: "CustomComponent"):
+    if "retriever" in params and hasattr(params["retriever"], "as_retriever"):
+        params["retriever"] = params["retriever"].as_retriever()
 
     # Determine if the build method is asynchronous
     is_async = inspect.iscoroutinefunction(custom_component.build)
 
+    # New feature: the component has a list of outputs and we have
+    # to check the vertex.edges to see which is connected (coulb be multiple)
+    # and then we'll get the output which has the name of the method we should call.
+    # the methods don't require any params because they are already set in the custom_component
+    # so we can just call them
+
     if is_async:
         # Await the build method directly if it's async
-        build_result = await custom_component.build(**params_copy)
+        build_result = await custom_component.build(**params)
     else:
         # Call the build method directly if it's sync
-        build_result = custom_component.build(**params_copy)
+        build_result = custom_component.build(**params)
     custom_repr = custom_component.custom_repr()
-    if custom_repr is None and isinstance(build_result, (dict, Record, str)):
+    if custom_repr is None and isinstance(build_result, (dict, Data, str)):
         custom_repr = build_result
     if not isinstance(custom_repr, str):
         custom_repr = str(custom_repr)
     raw = custom_component.repr_value
     if hasattr(raw, "data") and raw is not None:
         raw = raw.data
+
     elif hasattr(raw, "model_dump") and raw is not None:
         raw = raw.model_dump()
-    if raw is None and isinstance(build_result, (dict, Record, str)):
-        raw = build_result.data if isinstance(build_result, Record) else build_result
+    if raw is None and isinstance(build_result, (dict, Data, str)):
+        raw = build_result.data if isinstance(build_result, Data) else build_result
 
     artifact_type = get_artifact_type(custom_component.repr_value or raw, build_result)
     raw = post_process_raw(raw, artifact_type)
     artifact = {"repr": custom_repr, "raw": raw, "type": artifact_type}
-    return custom_component, build_result, artifact
+
+    if custom_component.vertex is not None:
+        custom_component._artifacts = {custom_component.vertex.outputs[0].get("name"): artifact}
+        custom_component._results = {custom_component.vertex.outputs[0].get("name"): build_result}
+        return custom_component, build_result, artifact
+
+    raise ValueError("Custom component does not have a vertex")
diff --git a/src/backend/base/langflow/interface/initialize/utils.py b/src/backend/base/langflow/interface/initialize/utils.py
deleted file mode 100644
index c09525a6c..000000000
--- a/src/backend/base/langflow/interface/initialize/utils.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import contextlib
-import json
-from typing import Any, Dict, List
-
-import orjson
-from langchain.agents import ZeroShotAgent
-
-from langflow.services.database.models.base import orjson_dumps
-from langchain_core.documents import Document
-from langchain_core.output_parsers import BaseOutputParser
-
-
-def handle_node_type(node_type, class_object, params: Dict):
-    if node_type == "ZeroShotPrompt":
-        params = check_tools_in_params(params)
-        prompt = ZeroShotAgent.create_prompt(**params)
-    elif "MessagePromptTemplate" in node_type:
-        prompt = instantiate_from_template(class_object, params)
-    elif node_type == "ChatPromptTemplate":
-        prompt = class_object.from_messages(**params)
-    elif hasattr(class_object, "from_template") and params.get("template"):
-        prompt = class_object.from_template(template=params.pop("template"))
-    else:
-        prompt = class_object(**params)
-    return params, prompt
-
-
-def check_tools_in_params(params: Dict):
-    if "tools" not in params:
-        params["tools"] = []
-    return params
-
-
-def instantiate_from_template(class_object, params: Dict):
-    from_template_params = {"template": params.pop("prompt", params.pop("template", ""))}
-
-    from_template_params.update(params)
-    if not from_template_params.get("template"):
-        raise ValueError("Prompt template is required")
-    return class_object.from_template(**from_template_params)
-
-
-def handle_format_kwargs(prompt, params: Dict):
-    format_kwargs: Dict[str, Any] = {}
-    for input_variable in prompt.input_variables:
-        if input_variable in params:
-            format_kwargs = handle_variable(params, input_variable, format_kwargs)
-    return format_kwargs
-
-
-def handle_partial_variables(prompt, format_kwargs: Dict):
-    partial_variables = format_kwargs.copy()
-    partial_variables = {key: value for key, value in partial_variables.items() if value}
-    # Remove handle_keys otherwise LangChain raises an error
-    partial_variables.pop("handle_keys", None)
-    if partial_variables and hasattr(prompt, "partial"):
-        return prompt.partial(**partial_variables)
-    return prompt
-
-
-def handle_variable(params: Dict, input_variable: str, format_kwargs: Dict):
-    variable = params[input_variable]
-    if isinstance(variable, str):
-        format_kwargs[input_variable] = variable
-    elif isinstance(variable, BaseOutputParser) and hasattr(variable, "get_format_instructions"):
-        format_kwargs[input_variable] = variable.get_format_instructions()
-    elif is_instance_of_list_or_document(variable):
-        format_kwargs = format_document(variable, input_variable, format_kwargs)
-    if needs_handle_keys(variable):
-        format_kwargs = add_handle_keys(input_variable, format_kwargs)
-    return format_kwargs
-
-
-def is_instance_of_list_or_document(variable):
-    return (
-        isinstance(variable, List)
-        and all(isinstance(item, Document) for item in variable)
-        or isinstance(variable, Document)
-    )
-
-
-def format_document(variable, input_variable: str, format_kwargs: Dict):
-    variable = variable if isinstance(variable, List) else [variable]
-    content = format_content(variable)
-    format_kwargs[input_variable] = content
-    return format_kwargs
-
-
-def format_content(variable):
-    if len(variable) > 1:
-        return "\n".join([item.page_content for item in variable if item.page_content])
-    elif len(variable) == 1:
-        content = variable[0].page_content
-        return try_to_load_json(content)
-    return ""
-
-
-def try_to_load_json(content):
-    with contextlib.suppress(json.JSONDecodeError):
-        content = orjson.loads(content)
-        if isinstance(content, list):
-            content = ",".join([str(item) for item in content])
-        else:
-            content = orjson_dumps(content)
-    return content
-
-
-def needs_handle_keys(variable):
-    return is_instance_of_list_or_document(variable) or (
-        isinstance(variable, BaseOutputParser) and hasattr(variable, "get_format_instructions")
-    )
-
-
-def add_handle_keys(input_variable: str, format_kwargs: Dict):
-    if "handle_keys" not in format_kwargs:
-        format_kwargs["handle_keys"] = []
-    format_kwargs["handle_keys"].append(input_variable)
-    return format_kwargs
diff --git a/src/backend/base/langflow/interface/initialize/vector_store.py b/src/backend/base/langflow/interface/initialize/vector_store.py
deleted file mode 100644
index 8b9034e65..000000000
--- a/src/backend/base/langflow/interface/initialize/vector_store.py
+++ /dev/null
@@ -1,237 +0,0 @@
-import os
-from typing import Any, Callable, Dict, Type
-
-import orjson
-from langchain_community.vectorstores import (
-    FAISS,
-    Chroma,
-    MongoDBAtlasVectorSearch,
-    Qdrant,
-    SupabaseVectorStore,
-    Weaviate,
-)
-from langchain_core.documents import Document
-from langchain_pinecone import Pinecone
-
-
-def docs_in_params(params: dict) -> bool:
-    """Check if params has documents OR texts and one of them is not an empty list,
-    If any of them is not an empty list, return True, else return False"""
-    return ("documents" in params and params["documents"]) or ("texts" in params and params["texts"])
-
-
-def initialize_mongodb(class_object: Type[MongoDBAtlasVectorSearch], params: dict):
-    """Initialize mongodb and return the class object"""
-
-    MONGODB_ATLAS_CLUSTER_URI = params.pop("mongodb_atlas_cluster_uri")
-    if not MONGODB_ATLAS_CLUSTER_URI:
-        raise ValueError("Mongodb atlas cluster uri must be provided in the params")
-    import certifi
-    from pymongo import MongoClient
-
-    client: MongoClient = MongoClient(MONGODB_ATLAS_CLUSTER_URI, tlsCAFile=certifi.where())
-    db_name = params.pop("db_name", None)
-    collection_name = params.pop("collection_name", None)
-    if not db_name or not collection_name:
-        raise ValueError("db_name and collection_name must be provided in the params")
-
-    index_name = params.pop("index_name", None)
-    if not index_name:
-        raise ValueError("index_name must be provided in the params")
-
-    collection = client[db_name][collection_name]
-    if not docs_in_params(params):
-        # __init__ requires collection, embedding and index_name
-        init_args = {
-            "collection": collection,
-            "index_name": index_name,
-            "embedding": params.get("embedding"),
-        }
-
-        return class_object(**init_args)
-
-    if "texts" in params:
-        params["documents"] = params.pop("texts")
-
-    params["collection"] = collection
-    params["index_name"] = index_name
-
-    return class_object.from_documents(**params)
-
-
-def initialize_supabase(class_object: Type[SupabaseVectorStore], params: dict):
-    """Initialize supabase and return the class object"""
-    from supabase.client import Client, create_client
-
-    if "supabase_url" not in params or "supabase_service_key" not in params:
-        raise ValueError("Supabase url and service key must be provided in the params")
-    if "texts" in params:
-        params["documents"] = params.pop("texts")
-
-    client_kwargs = {
-        "supabase_url": params.pop("supabase_url"),
-        "supabase_key": params.pop("supabase_service_key"),
-    }
-
-    supabase: Client = create_client(**client_kwargs)
-    if not docs_in_params(params):
-        params.pop("documents", None)
-        params.pop("texts", None)
-        return class_object(client=supabase, **params)
-    # If there are docs in the params, create a new index
-
-    return class_object.from_documents(client=supabase, **params)
-
-
-def initialize_weaviate(class_object: Type[Weaviate], params: dict):
-    """Initialize weaviate and return the class object"""
-    if not docs_in_params(params):
-        import weaviate  # type: ignore
-
-        client_kwargs_json = params.get("client_kwargs", "{}")
-        client_kwargs = orjson.loads(client_kwargs_json)
-        client_params = {
-            "url": params.get("weaviate_url"),
-        }
-        client_params.update(client_kwargs)
-        weaviate_client = weaviate.Client(**client_params)
-
-        new_params = {
-            "client": weaviate_client,
-            "index_name": params.get("index_name"),
-            "text_key": params.get("text_key"),
-        }
-        return class_object(**new_params)
-    # If there are docs in the params, create a new index
-    if "texts" in params:
-        params["documents"] = params.pop("texts")
-
-    return class_object.from_documents(**params)
-
-
-def initialize_faiss(class_object: Type[FAISS], params: dict):
-    """Initialize faiss and return the class object"""
-
-    if not docs_in_params(params):
-        return class_object.load_local
-
-    save_local = params.get("save_local")
-    faiss_index = class_object(**params)
-    if save_local:
-        faiss_index.save_local(folder_path=save_local)
-    return faiss_index
-
-
-def initialize_pinecone(class_object: Type[Pinecone], params: dict):
-    """Initialize pinecone and return the class object"""
-
-    import pinecone  # type: ignore
-
-    pinecone_api_key = params.pop("pinecone_api_key")
-    pinecone_env = params.pop("pinecone_env")
-
-    if pinecone_api_key is None or pinecone_env is None:
-        if os.getenv("PINECONE_API_KEY") is not None:
-            pinecone_api_key = os.getenv("PINECONE_API_KEY")
-        if os.getenv("PINECONE_ENV") is not None:
-            pinecone_env = os.getenv("PINECONE_ENV")
-
-    if pinecone_api_key is None or pinecone_env is None:
-        raise ValueError("Pinecone API key and environment must be provided in the params")
-
-    # initialize pinecone
-    pinecone.init(
-        api_key=pinecone_api_key,  # find at app.pinecone.io
-        environment=pinecone_env,  # next to api key in console
-    )
-
-    # If there are no docs in the params, return an existing index
-    # but first remove any texts or docs keys from the params
-    if not docs_in_params(params):
-        existing_index_params = {
-            "embedding": params.pop("embedding"),
-        }
-        if "index_name" in params:
-            existing_index_params["index_name"] = params.pop("index_name")
-        if "namespace" in params:
-            existing_index_params["namespace"] = params.pop("namespace")
-
-        return class_object.from_existing_index(**existing_index_params)
-    # If there are docs in the params, create a new index
-    if "texts" in params:
-        params["documents"] = params.pop("texts")
-    return class_object.from_documents(**params)
-
-
-def initialize_chroma(class_object: Type[Chroma], params: dict):
-    """Initialize a ChromaDB object from the params"""
-    if (  # type: ignore
-        "chroma_server_host" in params or "chroma_server_http_port" in params
-    ):
-        import chromadb  # type: ignore
-
-        settings_params = {
-            key: params[key] for key, value_ in params.items() if key.startswith("chroma_server_") and value_
-        }
-        chroma_settings = chromadb.config.Settings(**settings_params)
-        params["client_settings"] = chroma_settings
-    else:
-        # remove all chroma_server_ keys from params
-        params = {key: value for key, value in params.items() if not key.startswith("chroma_server_")}
-
-    persist = params.pop("persist", False)
-    if not docs_in_params(params):
-        params.pop("documents", None)
-        params.pop("texts", None)
-        params["embedding_function"] = params.pop("embedding")
-        chromadb_instance = class_object(**params)
-    else:
-        if "texts" in params:
-            params["documents"] = params.pop("texts")
-        for doc in params["documents"]:
-            if not isinstance(doc, Document):
-                # remove any non-Document objects from the list
-                params["documents"].remove(doc)
-                continue
-            if doc.metadata is None:
-                doc.metadata = {}
-            for key, value in doc.metadata.items():
-                if value is None:
-                    doc.metadata[key] = ""
-
-        chromadb_instance = class_object.from_documents(**params)
-    if persist:
-        chromadb_instance.persist()
-    return chromadb_instance
-
-
-def initialize_qdrant(class_object: Type[Qdrant], params: dict):
-    if not docs_in_params(params):
-        if "location" not in params and "api_key" not in params:
-            raise ValueError("Location and API key must be provided in the params")
-        from qdrant_client import QdrantClient
-
-        client_params = {
-            "location": params.pop("location"),
-            "api_key": params.pop("api_key"),
-        }
-        lc_params = {
-            "collection_name": params.pop("collection_name"),
-            "embeddings": params.pop("embedding"),
-        }
-        client = QdrantClient(**client_params)
-
-        return class_object(client=client, **lc_params)
-
-    return class_object.from_documents(**params)
-
-
-vecstore_initializer: Dict[str, Callable[[Type[Any], dict], Any]] = {
-    "Pinecone": initialize_pinecone,
-    "Chroma": initialize_chroma,
-    "Qdrant": initialize_qdrant,
-    "Weaviate": initialize_weaviate,
-    "FAISS": initialize_faiss,
-    "SupabaseVectorStore": initialize_supabase,
-    "MongoDBAtlasVectorSearch": initialize_mongodb,
-}
diff --git a/src/backend/base/langflow/interface/types.py b/src/backend/base/langflow/interface/types.py
index a092a7d19..9e54ea043 100644
--- a/src/backend/base/langflow/interface/types.py
+++ b/src/backend/base/langflow/interface/types.py
@@ -1,4 +1,20 @@
-from langflow.custom.utils import build_custom_components
+import asyncio
+import json
+
+from git import TYPE_CHECKING
+from loguru import logger
+
+from langflow.custom.utils import abuild_custom_components, build_custom_components
+
+if TYPE_CHECKING:
+    from langflow.services.cache.base import CacheService
+    from langflow.services.settings.service import SettingsService
+
+
+async def aget_all_types_dict(components_paths):
+    """Get all types dictionary combining native and custom components."""
+    custom_components_from_file = await abuild_custom_components(components_paths=components_paths)
+    return custom_components_from_file
 
 
 def get_all_types_dict(components_paths):
@@ -7,6 +23,26 @@ def get_all_types_dict(components_paths):
     return custom_components_from_file
 
 
+# TypeError: unhashable type: 'list'
+def key_func(*args, **kwargs):
+    # components_paths is a list of paths
+    return json.dumps(args) + json.dumps(kwargs)
+
+
+async def aget_all_components(components_paths, as_dict=False):
+    """Get all components names combining native and custom components."""
+    all_types_dict = await aget_all_types_dict(components_paths)
+    components = {} if as_dict else []
+    for category in all_types_dict.values():
+        for component in category.values():
+            component["name"] = component["display_name"]
+            if as_dict:
+                components[component["name"]] = component
+            else:
+                components.append(component)
+    return components
+
+
 def get_all_components(components_paths, as_dict=False):
     """Get all components names combining native and custom components."""
     all_types_dict = get_all_types_dict(components_paths)
@@ -19,3 +55,17 @@ def get_all_components(components_paths, as_dict=False):
             else:
                 components.append(component)
     return components
+
+
+async def get_and_cache_all_types_dict(
+    settings_service: "SettingsService",
+    cache_service: "CacheService",
+    force_refresh: bool = False,
+    lock: asyncio.Lock | None = None,
+):
+    all_types_dict = await cache_service.get(key="all_types_dict", lock=lock)
+    if not all_types_dict or force_refresh:
+        logger.debug("Building langchain types dict")
+        all_types_dict = await aget_all_types_dict(settings_service.settings.components_path)
+    await cache_service.set(key="all_types_dict", value=all_types_dict, lock=lock)
+    return all_types_dict
diff --git a/src/backend/base/langflow/interface/utils.py b/src/backend/base/langflow/interface/utils.py
index 986352f15..dfee36021 100644
--- a/src/backend/base/langflow/interface/utils.py
+++ b/src/backend/base/langflow/interface/utils.py
@@ -3,17 +3,14 @@ import json
 import os
 import re
 from io import BytesIO
-from typing import Dict
 
 import yaml
-from docstring_parser import parse
 from langchain_core.language_models import BaseLanguageModel
 from loguru import logger
 from PIL.Image import Image
 
 from langflow.services.chat.config import ChatConfig
 from langflow.services.deps import get_settings_service
-from langflow.utils.util import format_dict, get_base_classes, get_default_factory
 
 
 def load_file_into_dict(file_path: str) -> dict:
@@ -117,51 +114,3 @@ def set_langchain_cache(settings):
             logger.warning(f"Could not import {cache_type}. ")
     else:
         logger.info("No LLM cache set.")
-
-
-def build_template_from_class(name: str, type_to_cls_dict: Dict, add_function: bool = False):
-    classes = [item.__name__ for item in type_to_cls_dict.values()]
-
-    # Raise error if name is not in chains
-    if name not in classes:
-        raise ValueError(f"{name} not found.")
-
-    for _type, v in type_to_cls_dict.items():
-        if v.__name__ == name:
-            _class = v
-
-            # Get the docstring
-            docs = parse(_class.__doc__)
-
-            variables = {"_type": _type}
-
-            if "__fields__" in _class.__dict__:
-                for class_field_items, value in _class.__fields__.items():
-                    if class_field_items in ["callback_manager"]:
-                        continue
-                    variables[class_field_items] = {}
-                    for name_, value_ in value.__repr_args__():
-                        if name_ == "default_factory":
-                            try:
-                                variables[class_field_items]["default"] = get_default_factory(
-                                    module=_class.__base__.__module__,
-                                    function=value_,
-                                )
-                            except Exception:
-                                variables[class_field_items]["default"] = None
-                        elif name_ not in ["name"]:
-                            variables[class_field_items][name_] = value_
-
-                    variables[class_field_items]["placeholder"] = (
-                        docs.params[class_field_items] if class_field_items in docs.params else ""
-                    )
-            base_classes = get_base_classes(_class)
-            # Adding function to base classes to allow
-            # the output to be a function
-            if add_function:
-                base_classes.append("Callable")
-            return {
-                "template": format_dict(variables, name),
-                "description": docs.short_description or "",
-                "base_classes": base_classes,
-            }
diff --git a/src/backend/base/langflow/io/__init__.py b/src/backend/base/langflow/io/__init__.py
new file mode 100644
index 000000000..53bafb92d
--- /dev/null
+++ b/src/backend/base/langflow/io/__init__.py
@@ -0,0 +1,37 @@
+from langflow.inputs import (
+    BoolInput,
+    DataInput,
+    DictInput,
+    DropdownInput,
+    FileInput,
+    FloatInput,
+    HandleInput,
+    IntInput,
+    MessageInput,
+    MultilineInput,
+    NestedDictInput,
+    PromptInput,
+    SecretStrInput,
+    StrInput,
+    TextInput,
+)
+from langflow.template import Output
+
+__all__ = [
+    "BoolInput",
+    "DataInput",
+    "DictInput",
+    "DropdownInput",
+    "FileInput",
+    "FloatInput",
+    "HandleInput",
+    "IntInput",
+    "MessageInput",
+    "MultilineInput",
+    "NestedDictInput",
+    "PromptInput",
+    "SecretStrInput",
+    "StrInput",
+    "TextInput",
+    "Output",
+]
diff --git a/src/backend/base/langflow/legacy_custom/customs.py b/src/backend/base/langflow/legacy_custom/customs.py
index 26e5e33fa..e4090135e 100644
--- a/src/backend/base/langflow/legacy_custom/customs.py
+++ b/src/backend/base/langflow/legacy_custom/customs.py
@@ -5,6 +5,9 @@ CUSTOM_NODES: dict[str, dict[str, frontend_node.base.FrontendNode]] = {
     "custom_components": {
         "CustomComponent": frontend_node.custom_components.CustomComponentFrontendNode(),
     },
+    "component": {
+        "Component": frontend_node.custom_components.ComponentFrontendNode(),
+    },
 }
 
 
diff --git a/src/backend/base/langflow/main.py b/src/backend/base/langflow/main.py
index 1227d4c62..4189bb3dd 100644
--- a/src/backend/base/langflow/main.py
+++ b/src/backend/base/langflow/main.py
@@ -1,3 +1,5 @@
+import asyncio
+import warnings
 from contextlib import asynccontextmanager
 from pathlib import Path
 from typing import Optional
@@ -10,6 +12,7 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import FileResponse
 from fastapi.staticfiles import StaticFiles
 from loguru import logger
+from pydantic import PydanticDeprecatedSince20
 from rich import print as rprint
 from starlette.middleware.base import BaseHTTPMiddleware
 
@@ -19,12 +22,16 @@ from langflow.initial_setup.setup import (
     initialize_super_user_if_needed,
     load_flows_from_directory,
 )
+from langflow.interface.types import get_and_cache_all_types_dict
 from langflow.interface.utils import setup_llm_caching
-from langflow.services.deps import get_settings_service
+from langflow.services.deps import get_cache_service, get_settings_service
 from langflow.services.plugins.langfuse_plugin import LangfuseInstance
 from langflow.services.utils import initialize_services, teardown_services
 from langflow.utils.logger import configure
 
+# Ignore Pydantic deprecation warnings from Langchain
+warnings.filterwarnings("ignore", category=PydanticDeprecatedSince20)
+
 
 class JavaScriptMIMETypeMiddleware(BaseHTTPMiddleware):
     async def dispatch(self, request: Request, call_next):
@@ -52,7 +59,8 @@ def get_lifespan(fix_migration=False, socketio_server=None, version=None):
             setup_llm_caching()
             LangfuseInstance.update()
             initialize_super_user_if_needed()
-            create_or_update_starter_projects()
+            task = asyncio.create_task(get_and_cache_all_types_dict(get_settings_service(), get_cache_service()))
+            await create_or_update_starter_projects(task)
             load_flows_from_directory()
             yield
         except Exception as exc:
diff --git a/src/backend/base/langflow/memory.py b/src/backend/base/langflow/memory.py
index e812f449c..e89682969 100644
--- a/src/backend/base/langflow/memory.py
+++ b/src/backend/base/langflow/memory.py
@@ -27,7 +27,7 @@ def get_messages(
         limit (Optional[int]): The maximum number of messages to retrieve.
 
     Returns:
-        List[Record]: A list of Record objects representing the retrieved messages.
+        List[Data]: A list of Data objects representing the retrieved messages.
     """
     monitor_service = get_monitor_service()
     messages_df = monitor_service.get_messages(
@@ -74,7 +74,8 @@ def add_messages(messages: Message | list[Message], flow_id: Optional[str] = Non
 
         messages_models: list[MessageModel] = []
         for msg in messages:
-            msg.timestamp = monitor_service.get_timestamp()
+            if not msg.timestamp:
+                msg.timestamp = monitor_service.get_timestamp()
             messages_models.append(MessageModel.from_message(msg, flow_id=flow_id))
 
         for message_model in messages_models:
@@ -113,7 +114,7 @@ def store_message(
         flow_id (Optional[str]): The flow ID associated with the message. When running from the CustomComponent you can access this using `self.graph.flow_id`.
 
     Returns:
-        List[Message]: A list of records containing the stored message.
+        List[Message]: A list of data containing the stored message.
 
     Raises:
         ValueError: If any of the required parameters (session_id, sender, sender_name) is not provided.
diff --git a/src/backend/base/langflow/schema/__init__.py b/src/backend/base/langflow/schema/__init__.py
index 9f7e3b384..ae65fd05a 100644
--- a/src/backend/base/langflow/schema/__init__.py
+++ b/src/backend/base/langflow/schema/__init__.py
@@ -1,4 +1,4 @@
 from .dotdict import dotdict
-from .record import Record
+from .data import Data
 
-__all__ = ["Record", "dotdict"]
+__all__ = ["Data", "dotdict"]
diff --git a/src/backend/base/langflow/schema/artifact.py b/src/backend/base/langflow/schema/artifact.py
new file mode 100644
index 000000000..75ca133da
--- /dev/null
+++ b/src/backend/base/langflow/schema/artifact.py
@@ -0,0 +1,55 @@
+from enum import Enum
+from typing import Generator
+
+from langflow.schema import Data
+from langflow.schema.message import Message
+
+
+class ArtifactType(str, Enum):
+    TEXT = "text"
+    DATA = "data"
+    OBJECT = "object"
+    ARRAY = "array"
+    STREAM = "stream"
+    UNKNOWN = "unknown"
+    MESSAGE = "message"
+
+
+def get_artifact_type(value, build_result=None) -> str:
+    result = ArtifactType.UNKNOWN
+    match value:
+        case Message():
+            if not isinstance(value.text, str):
+                enum_value = get_artifact_type(value.text)
+                result = ArtifactType(enum_value)
+            else:
+                result = ArtifactType.MESSAGE
+        case Data():
+            enum_value = get_artifact_type(value.data)
+            result = ArtifactType(enum_value)
+
+        case str():
+            result = ArtifactType.TEXT
+
+        case dict():
+            result = ArtifactType.OBJECT
+
+        case list():
+            result = ArtifactType.ARRAY
+
+    if result == ArtifactType.UNKNOWN:
+        if build_result and isinstance(build_result, Generator):
+            result = ArtifactType.STREAM
+        elif isinstance(value, Message) and isinstance(value.text, Generator):
+            result = ArtifactType.STREAM
+
+    return result.value
+
+
+def post_process_raw(raw, artifact_type: str):
+    if artifact_type == ArtifactType.STREAM.value:
+        raw = ""
+    elif artifact_type == ArtifactType.UNKNOWN.value:
+        raw = str(raw)
+
+    return raw
diff --git a/src/backend/base/langflow/schema/record.py b/src/backend/base/langflow/schema/data.py
similarity index 80%
rename from src/backend/base/langflow/schema/record.py
rename to src/backend/base/langflow/schema/data.py
index c822d5ce5..122f01507 100644
--- a/src/backend/base/langflow/schema/record.py
+++ b/src/backend/base/langflow/schema/data.py
@@ -3,13 +3,13 @@ import json
 from typing import Optional, cast
 
 from langchain_core.documents import Document
-from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
+from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
 from langchain_core.prompt_values import ImagePromptValue
 from langchain_core.prompts.image import ImagePromptTemplate
 from pydantic import BaseModel, model_serializer, model_validator
 
 
-class Record(BaseModel):
+class Data(BaseModel):
     """
     Represents a record with text and optional data.
 
@@ -49,44 +49,44 @@ class Record(BaseModel):
         return self.data.get(self.text_key, self.default_value)
 
     @classmethod
-    def from_document(cls, document: Document) -> "Record":
+    def from_document(cls, document: Document) -> "Data":
         """
-        Converts a Document to a Record.
+        Converts a Document to a Data.
 
         Args:
             document (Document): The Document to convert.
 
         Returns:
-            Record: The converted Record.
+            Data: The converted Data.
         """
         data = document.metadata
         data["text"] = document.page_content
         return cls(data=data, text_key="text")
 
     @classmethod
-    def from_lc_message(cls, message: BaseMessage) -> "Record":
+    def from_lc_message(cls, message: BaseMessage) -> "Data":
         """
-        Converts a BaseMessage to a Record.
+        Converts a BaseMessage to a Data.
 
         Args:
             message (BaseMessage): The BaseMessage to convert.
 
         Returns:
-            Record: The converted Record.
+            Data: The converted Data.
         """
         data: dict = {"text": message.content}
         data["metadata"] = cast(dict, message.to_json())
         return cls(data=data, text_key="text")
 
-    def __add__(self, other: "Record") -> "Record":
+    def __add__(self, other: "Data") -> "Data":
         """
-        Combines the data of two records by attempting to add values for overlapping keys
+        Combines the data of two data by attempting to add values for overlapping keys
         for all types that support the addition operation. Falls back to the value from 'other'
         record when addition is not supported.
         """
         combined_data = self.data.copy()
         for key, value in other.data.items():
-            # If the key exists in both records and both values support the addition operation
+            # If the key exists in both data and both values support the addition operation
             if key in combined_data:
                 try:
                     combined_data[key] += value
@@ -97,34 +97,35 @@ class Record(BaseModel):
                 # If the key is not in the first record, simply add it
                 combined_data[key] = value
 
-        return Record(data=combined_data)
+        return Data(data=combined_data)
 
     def to_lc_document(self) -> Document:
         """
-        Converts the Record to a Document.
+        Converts the Data to a Document.
 
         Returns:
             Document: The converted Document.
         """
-        text = self.data.pop(self.text_key, self.default_value)
-        return Document(page_content=text, metadata=self.data)
+        data_copy = self.data.copy()
+        text = data_copy.pop(self.text_key, self.default_value)
+        return Document(page_content=text, metadata=data_copy)
 
     def to_lc_message(
         self,
-    ) -> HumanMessage | SystemMessage:
+    ) -> BaseMessage:
         """
-        Converts the Record to a BaseMessage.
+        Converts the Data to a BaseMessage.
 
         Returns:
             BaseMessage: The converted BaseMessage.
         """
-        # The idea of this function is to be a helper to convert a Record to a BaseMessage
+        # The idea of this function is to be a helper to convert a Data to a BaseMessage
         # It will use the "sender" key to determine if the message is Human or AI
         # If the key is not present, it will default to AI
         # But first we check if all required keys are present in the data dictionary
         # they are: "text", "sender"
         if not all(key in self.data for key in ["text", "sender"]):
-            raise ValueError(f"Missing required keys ('text', 'sender') in Record: {self.data}")
+            raise ValueError(f"Missing required keys ('text', 'sender') in Data: {self.data}")
         sender = self.data.get("sender", "Machine")
         text = self.data.get("text", "")
         files = self.data.get("files", [])
@@ -154,8 +155,7 @@ class Record(BaseModel):
                 return self.__getattribute__(key)
             if key in {"data", "text_key"} or key.startswith("_"):
                 return super().__getattr__(key)
-
-            return self.data.get(key, self.default_value)
+            return self.data[key]
         except KeyError:
             # Fallback to default behavior to raise AttributeError for undefined attributes
             raise AttributeError(f"'{type(self).__name__}' object has no attribute '{key}'")
@@ -167,6 +167,9 @@ class Record(BaseModel):
         """
         if key in {"data", "text_key"} or key.startswith("_"):
             super().__setattr__(key, value)
+        elif key in self.model_fields:
+            self.data[key] = value
+            super().__setattr__(key, value)
         else:
             self.data[key] = value
 
@@ -181,17 +184,17 @@ class Record(BaseModel):
 
     def __deepcopy__(self, memo):
         """
-        Custom deepcopy implementation to handle copying of the Record object.
+        Custom deepcopy implementation to handle copying of the Data object.
         """
-        # Create a new Record object with a deep copy of the data dictionary
-        return Record(data=copy.deepcopy(self.data, memo), text_key=self.text_key, default_value=self.default_value)
+        # Create a new Data object with a deep copy of the data dictionary
+        return Data(data=copy.deepcopy(self.data, memo), text_key=self.text_key, default_value=self.default_value)
 
-    # check which attributes the Record has by checking the keys in the data dictionary
+    # check which attributes the Data has by checking the keys in the data dictionary
     def __dir__(self):
         return super().__dir__() + list(self.data.keys())
 
     def __str__(self) -> str:
-        # return a JSON string representation of the Record atributes
+        # return a JSON string representation of the Data atributes
         try:
             data = {k: v.to_json() if hasattr(v, "to_json") else v for k, v in self.data.items()}
             return json.dumps(data, indent=4)
@@ -202,4 +205,4 @@ class Record(BaseModel):
         return key in self.data
 
     def __eq__(self, other):
-        return isinstance(other, Record) and self.data == other.data
+        return isinstance(other, Data) and self.data == other.data
diff --git a/src/backend/base/langflow/schema/message.py b/src/backend/base/langflow/schema/message.py
index c8888fb24..c50dab880 100644
--- a/src/backend/base/langflow/schema/message.py
+++ b/src/backend/base/langflow/schema/message.py
@@ -1,26 +1,31 @@
 from datetime import datetime, timezone
-from typing import TYPE_CHECKING, Annotated, Any, AsyncIterator, Iterator, Optional
+from typing import Annotated, Any, AsyncIterator, Iterator, List, Optional
 
-from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
+from fastapi.encoders import jsonable_encoder
+from langchain_core.load import load
+from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage
 from langchain_core.prompt_values import ImagePromptValue
+from langchain_core.prompts import BaseChatPromptTemplate, ChatPromptTemplate, PromptTemplate
 from langchain_core.prompts.image import ImagePromptTemplate
-from langflow.schema.image import Image, get_file_paths, is_image_file
-from pydantic import BaseModel, BeforeValidator, ConfigDict, Field, field_serializer
+from loguru import logger
+from pydantic import BeforeValidator, ConfigDict, Field, field_serializer, field_validator
 
-if TYPE_CHECKING:
-    from langflow.schema.record import Record
+from langflow.base.prompts.utils import dict_values_to_string
+from langflow.schema.data import Data
+from langflow.schema.image import Image, get_file_paths, is_image_file
 
 
 def _timestamp_to_str(timestamp: datetime) -> str:
     return timestamp.strftime("%Y-%m-%d %H:%M:%S")
 
 
-class Message(BaseModel):
+class Message(Data):
     model_config = ConfigDict(arbitrary_types_allowed=True)
     # Helper class to deal with image data
+    text_key: str = "text"
     text: Optional[str | AsyncIterator | Iterator] = Field(default="")
-    sender: str
-    sender_name: str
+    sender: Optional[str] = None
+    sender_name: Optional[str] = None
     files: Optional[list[str | Image]] = Field(default=[])
     session_id: Optional[str] = Field(default="")
     timestamp: Annotated[str, BeforeValidator(_timestamp_to_str)] = Field(
@@ -28,40 +33,58 @@ class Message(BaseModel):
     )
     flow_id: Optional[str] = None
 
+    @field_validator("files", mode="before")
+    @classmethod
+    def validate_files(cls, value):
+        if not value:
+            value = []
+        elif not isinstance(value, list):
+            value = [value]
+        return value
+
     def model_post_init(self, __context: Any) -> None:
-        new_files = []
+        new_files: List[Any] = []
         for file in self.files or []:
             if is_image_file(file):
                 new_files.append(Image(path=file))
             else:
-                new_files.append(file)  # type: ignore
-        self.files = new_files  # type: ignore
+                new_files.append(file)
+        self.files = new_files
+        if "timestamp" not in self.data:
+            self.data["timestamp"] = self.timestamp
+
+    def set_flow_id(self, flow_id: str):
+        self.flow_id = flow_id
 
     def to_lc_message(
         self,
     ) -> BaseMessage:
         """
-        Converts the Record to a BaseMessage.
+        Converts the Data to a BaseMessage.
 
         Returns:
             BaseMessage: The converted BaseMessage.
         """
-        # The idea of this function is to be a helper to convert a Record to a BaseMessage
+        # The idea of this function is to be a helper to convert a Data to a BaseMessage
         # It will use the "sender" key to determine if the message is Human or AI
         # If the key is not present, it will default to AI
         # But first we check if all required keys are present in the data dictionary
         # they are: "text", "sender"
         if self.text is None or not self.sender:
-            raise ValueError("Missing required keys ('text', 'sender') in Message.")
+            logger.warning("Missing required keys ('text', 'sender') in Message, defaulting to HumanMessage.")
 
-        if self.sender == "User":
+        if self.sender == "User" or not self.sender:
             if self.files:
                 contents = [{"type": "text", "text": self.text}]
                 contents.extend(self.get_file_content_dicts())
                 human_message = HumanMessage(content=contents)  # type: ignore
             else:
+                if not isinstance(self.text, str):
+                    text = ""
+                else:
+                    text = self.text
                 human_message = HumanMessage(
-                    content=[{"type": "text", "text": self.text}],
+                    content=text,
                 )
 
             return human_message
@@ -69,25 +92,25 @@ class Message(BaseModel):
         return AIMessage(content=self.text)  # type: ignore
 
     @classmethod
-    def from_record(cls, record: "Record") -> "Message":
+    def from_data(cls, data: "Data") -> "Message":
         """
-        Converts a BaseMessage to a Record.
+        Converts a BaseMessage to a Data.
 
         Args:
             record (BaseMessage): The BaseMessage to convert.
 
         Returns:
-            Record: The converted Record.
+            Data: The converted Data.
         """
 
         return cls(
-            text=record.text,
-            sender=record.sender,
-            sender_name=record.sender_name,
-            files=record.files,
-            session_id=record.session_id,
-            timestamp=record.timestamp,
-            flow_id=record.flow_id,
+            text=data.text,
+            sender=data.sender,
+            sender_name=data.sender_name,
+            files=data.files,
+            session_id=data.session_id,
+            timestamp=data.timestamp,
+            flow_id=data.flow_id,
         )
 
     @field_serializer("text", mode="plain")
@@ -110,3 +133,56 @@ class Message(BaseModel):
                 image_prompt_value: ImagePromptValue = image_template.invoke(input={"path": file})
                 content_dicts.append({"type": "image_url", "image_url": image_prompt_value.image_url})
         return content_dicts
+
+    def load_lc_prompt(self):
+        if "prompt" not in self:
+            raise ValueError("Prompt is required.")
+        loaded_prompt = load(self.prompt)
+        # Rebuild HumanMessages if they are instance of BaseMessage
+        if isinstance(loaded_prompt, ChatPromptTemplate):
+            messages = []
+            for message in loaded_prompt.messages:
+                if isinstance(message, HumanMessage):
+                    messages.append(message)
+                elif message.type == "human":
+                    messages.append(HumanMessage(content=message.content))
+                elif message.type == "system":
+                    messages.append(SystemMessage(content=message.content))
+                elif message.type == "ai":
+                    messages.append(AIMessage(content=message.content))
+            loaded_prompt.messages = messages
+        return loaded_prompt
+
+    @classmethod
+    def from_lc_prompt(
+        cls,
+        prompt: BaseChatPromptTemplate,
+    ):
+        prompt_json = prompt.to_json()
+        return cls(prompt=prompt_json)
+
+    def format_text(self):
+        prompt_template = PromptTemplate.from_template(self.template)
+        variables_with_str_values = dict_values_to_string(self.variables)
+        formatted_prompt = prompt_template.format(**variables_with_str_values)
+        self.text = formatted_prompt
+        return formatted_prompt
+
+    @classmethod
+    async def from_template_and_variables(cls, template: str, **variables):
+        instance = cls(template=template, variables=variables)
+        text = instance.format_text()
+        # Get all Message instances from the kwargs
+        message = HumanMessage(content=text)
+        contents = []
+        for value in variables.values():
+            if isinstance(value, cls) and value.files:
+                content_dicts = await value.get_file_content_dicts()
+                contents.extend(content_dicts)
+        if contents:
+            message = HumanMessage(content=[{"type": "text", "text": text}] + contents)
+
+        prompt_template = ChatPromptTemplate.from_messages([message])  # type: ignore
+        instance.prompt = jsonable_encoder(prompt_template.to_json())
+        instance.messages = instance.prompt.get("kwargs", {}).get("messages", [])
+        return instance
diff --git a/src/backend/base/langflow/schema/schema.py b/src/backend/base/langflow/schema/schema.py
index 5153941a5..50d5ee0f6 100644
--- a/src/backend/base/langflow/schema/schema.py
+++ b/src/backend/base/langflow/schema/schema.py
@@ -1,17 +1,110 @@
-from typing import Literal
+from enum import Enum
+from typing import Generator, Literal, Union
 
+from pydantic import BaseModel
 from typing_extensions import TypedDict
 
+from langflow.schema import Data
+from langflow.schema.message import Message
+
 INPUT_FIELD_NAME = "input_value"
 
 InputType = Literal["chat", "text", "any"]
 OutputType = Literal["chat", "text", "any", "debug"]
 
 
+class LogType(str, Enum):
+    MESSAGE = "message"
+    DATA = "data"
+    STREAM = "stream"
+    OBJECT = "object"
+    ARRAY = "array"
+    TEXT = "text"
+    UNKNOWN = "unknown"
+
+
 class StreamURL(TypedDict):
     location: str
 
 
-class Log(TypedDict):
-    message: str | dict | StreamURL
+class ErrorLog(TypedDict):
+    errorMessage: str
+    stackTrace: str
+
+
+class Log(BaseModel):
+    message: Union[ErrorLog, StreamURL, dict, list, str]
     type: str
+
+
+def get_type(payload):
+    result = LogType.UNKNOWN
+    match payload:
+        case Message():
+            result = LogType.MESSAGE
+
+        case Data():
+            result = LogType.DATA
+
+        case dict():
+            result = LogType.OBJECT
+
+        case list():
+            result = LogType.ARRAY
+
+        case str():
+            result = LogType.TEXT
+
+    if result == LogType.UNKNOWN:
+        if payload and isinstance(payload, Generator):
+            result = LogType.STREAM
+
+        elif isinstance(payload, Message) and isinstance(payload.text, Generator):
+            result = LogType.STREAM
+
+    return result
+
+
+def get_message(payload):
+    message = None
+    if hasattr(payload, "data"):
+        message = payload.data
+
+    elif hasattr(payload, "model_dump"):
+        message = payload.model_dump()
+
+    if message is None and isinstance(payload, (dict, str, Data)):
+        message = payload.data if isinstance(payload, Data) else payload
+
+    return message or payload
+
+
+def build_logs(vertex, result) -> dict:
+    logs: dict[str, Log] = dict()
+    component_instance = result[0]
+    for index, output in enumerate(vertex.outputs):
+        if component_instance.status is None:
+            payload = component_instance._results
+            output_result = payload.get(output["name"])
+        else:
+            payload = component_instance._artifacts
+            output_result = payload.get(output["name"], {}).get("raw")
+        message = get_message(output_result)
+        _type = get_type(output_result)
+
+        match _type:
+            case LogType.STREAM if "stream_url" in message:
+                message = StreamURL(location=message["stream_url"])
+
+            case LogType.STREAM:
+                message = ""
+
+            case LogType.MESSAGE if hasattr(message, "message"):
+                message = message.message
+
+            case LogType.UNKNOWN:
+                message = ""
+        name = output.get("name", f"output_{index}")
+        logs |= {name: Log(message=message, type=_type).model_dump()}
+
+    return logs
diff --git a/src/backend/base/langflow/services/auth/utils.py b/src/backend/base/langflow/services/auth/utils.py
index 06836d21a..b0a35e474 100644
--- a/src/backend/base/langflow/services/auth/utils.py
+++ b/src/backend/base/langflow/services/auth/utils.py
@@ -7,14 +7,15 @@ from cryptography.fernet import Fernet
 from fastapi import Depends, HTTPException, Security, status
 from fastapi.security import APIKeyHeader, APIKeyQuery, OAuth2PasswordBearer
 from jose import JWTError, jwt
+from loguru import logger
+from sqlmodel import Session
+from starlette.websockets import WebSocket
+
 from langflow.services.database.models.api_key.crud import check_key
 from langflow.services.database.models.api_key.model import ApiKey
 from langflow.services.database.models.user.crud import get_user_by_id, get_user_by_username, update_user_last_login_at
 from langflow.services.database.models.user.model import User
 from langflow.services.deps import get_session, get_settings_service
-from loguru import logger
-from sqlmodel import Session
-from starlette.websockets import WebSocket
 
 oauth2_login = OAuth2PasswordBearer(tokenUrl="api/v1/login", auto_error=False)
 
@@ -119,7 +120,7 @@ async def get_current_user_by_jwt(
                     headers={"WWW-Authenticate": "Bearer"},
                 )
 
-        if user_id is None or token_type:
+        if user_id is None or token_type is None:
             logger.info(f"Invalid token payload. Token type: {token_type}")
             raise HTTPException(
                 status_code=status.HTTP_401_UNAUTHORIZED,
@@ -231,7 +232,7 @@ def create_user_longterm_token(db: Session = Depends(get_session)) -> tuple[UUID
         raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Super user hasn't been created")
     access_token_expires_longterm = timedelta(days=365)
     access_token = create_token(
-        data={"sub": str(super_user.id)},
+        data={"sub": str(super_user.id), "type": "access"},
         expires_delta=access_token_expires_longterm,
     )
 
@@ -247,7 +248,7 @@ def create_user_longterm_token(db: Session = Depends(get_session)) -> tuple[UUID
 
 def create_user_api_key(user_id: UUID) -> dict:
     access_token = create_token(
-        data={"sub": str(user_id), "role": "api_key"},
+        data={"sub": str(user_id), "type": "api_key"},
         expires_delta=timedelta(days=365 * 2),
     )
 
@@ -267,13 +268,13 @@ def create_user_tokens(user_id: UUID, db: Session = Depends(get_session), update
 
     access_token_expires = timedelta(seconds=settings_service.auth_settings.ACCESS_TOKEN_EXPIRE_SECONDS)
     access_token = create_token(
-        data={"sub": str(user_id)},
+        data={"sub": str(user_id), "type": "access"},
         expires_delta=access_token_expires,
     )
 
     refresh_token_expires = timedelta(seconds=settings_service.auth_settings.REFRESH_TOKEN_EXPIRE_SECONDS)
     refresh_token = create_token(
-        data={"sub": str(user_id), "type": "rf"},
+        data={"sub": str(user_id), "type": "refresh"},
         expires_delta=refresh_token_expires,
     )
 
@@ -302,13 +303,13 @@ def create_refresh_token(refresh_token: str, db: Session = Depends(get_session))
             )
         user_id: UUID = payload.get("sub")  # type: ignore
         token_type: str = payload.get("type")  # type: ignore
-
         if user_id is None or token_type is None:
             raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid refresh token")
 
         return create_user_tokens(user_id, db)
 
     except JWTError as e:
+        logger.error(f"JWT decoding error: {e}")
         raise HTTPException(
             status_code=status.HTTP_401_UNAUTHORIZED,
             detail="Invalid refresh token",
diff --git a/src/backend/base/langflow/services/cache/base.py b/src/backend/base/langflow/services/cache/base.py
index 3c484934b..02d645183 100644
--- a/src/backend/base/langflow/services/cache/base.py
+++ b/src/backend/base/langflow/services/cache/base.py
@@ -1,12 +1,15 @@
 import abc
 import asyncio
 import threading
-from typing import Optional
+from typing import Generic, Optional, TypeVar
 
 from langflow.services.base import Service
 
+LockType = TypeVar("LockType", bound=threading.Lock)
+AsyncLockType = TypeVar("AsyncLockType", bound=asyncio.Lock)
 
-class CacheService(Service):
+
+class CacheService(Service, Generic[LockType]):
     """
     Abstract base class for a cache.
     """
@@ -14,7 +17,7 @@ class CacheService(Service):
     name = "cache_service"
 
     @abc.abstractmethod
-    def get(self, key, lock: Optional[threading.Lock] = None):
+    def get(self, key, lock: Optional[LockType] = None):
         """
         Retrieve an item from the cache.
 
@@ -26,7 +29,7 @@ class CacheService(Service):
         """
 
     @abc.abstractmethod
-    def set(self, key, value, lock: Optional[threading.Lock] = None):
+    def set(self, key, value, lock: Optional[LockType] = None):
         """
         Add an item to the cache.
 
@@ -36,7 +39,7 @@ class CacheService(Service):
         """
 
     @abc.abstractmethod
-    def upsert(self, key, value, lock: Optional[threading.Lock] = None):
+    def upsert(self, key, value, lock: Optional[LockType] = None):
         """
         Add an item to the cache if it doesn't exist, or update it if it does.
 
@@ -46,7 +49,7 @@ class CacheService(Service):
         """
 
     @abc.abstractmethod
-    def delete(self, key, lock: Optional[threading.Lock] = None):
+    def delete(self, key, lock: Optional[LockType] = None):
         """
         Remove an item from the cache.
 
@@ -55,7 +58,7 @@ class CacheService(Service):
         """
 
     @abc.abstractmethod
-    def clear(self, lock: Optional[threading.Lock] = None):
+    def clear(self, lock: Optional[LockType] = None):
         """
         Clear all items from the cache.
         """
@@ -101,7 +104,7 @@ class CacheService(Service):
         """
 
 
-class AsyncBaseCacheService(Service):
+class AsyncBaseCacheService(Service, Generic[AsyncLockType]):
     """
     Abstract base class for a async cache.
     """
@@ -109,7 +112,7 @@ class AsyncBaseCacheService(Service):
     name = "cache_service"
 
     @abc.abstractmethod
-    async def get(self, key, lock: Optional[asyncio.Lock] = None):
+    async def get(self, key, lock: Optional[AsyncLockType] = None):
         """
         Retrieve an item from the cache.
 
@@ -121,7 +124,7 @@ class AsyncBaseCacheService(Service):
         """
 
     @abc.abstractmethod
-    async def set(self, key, value, lock: Optional[asyncio.Lock] = None):
+    async def set(self, key, value, lock: Optional[AsyncLockType] = None):
         """
         Add an item to the cache.
 
@@ -131,7 +134,7 @@ class AsyncBaseCacheService(Service):
         """
 
     @abc.abstractmethod
-    async def upsert(self, key, value, lock: Optional[asyncio.Lock] = None):
+    async def upsert(self, key, value, lock: Optional[AsyncLockType] = None):
         """
         Add an item to the cache if it doesn't exist, or update it if it does.
 
@@ -141,7 +144,7 @@ class AsyncBaseCacheService(Service):
         """
 
     @abc.abstractmethod
-    async def delete(self, key, lock: Optional[asyncio.Lock] = None):
+    async def delete(self, key, lock: Optional[AsyncLockType] = None):
         """
         Remove an item from the cache.
 
@@ -150,7 +153,7 @@ class AsyncBaseCacheService(Service):
         """
 
     @abc.abstractmethod
-    async def clear(self, lock: Optional[asyncio.Lock] = None):
+    async def clear(self, lock: Optional[AsyncLockType] = None):
         """
         Clear all items from the cache.
         """
diff --git a/src/backend/base/langflow/services/cache/factory.py b/src/backend/base/langflow/services/cache/factory.py
index b04eb6417..638d5ff73 100644
--- a/src/backend/base/langflow/services/cache/factory.py
+++ b/src/backend/base/langflow/services/cache/factory.py
@@ -18,7 +18,7 @@ class CacheServiceFactory(ServiceFactory):
 
         if settings_service.settings.cache_type == "redis":
             logger.debug("Creating Redis cache")
-            redis_cache = RedisCache(
+            redis_cache: RedisCache = RedisCache(
                 host=settings_service.settings.redis_host,
                 port=settings_service.settings.redis_port,
                 db=settings_service.settings.redis_db,
@@ -29,7 +29,7 @@ class CacheServiceFactory(ServiceFactory):
                 logger.debug("Redis cache is connected")
                 return redis_cache
             logger.warning("Redis cache is not connected, falling back to in-memory cache")
-            return ThreadingInMemoryCache()
+            return AsyncInMemoryCache()
 
         elif settings_service.settings.cache_type == "memory":
             return ThreadingInMemoryCache()
diff --git a/src/backend/base/langflow/services/cache/service.py b/src/backend/base/langflow/services/cache/service.py
index 72a6d29c0..b21a17d31 100644
--- a/src/backend/base/langflow/services/cache/service.py
+++ b/src/backend/base/langflow/services/cache/service.py
@@ -3,18 +3,17 @@ import pickle
 import threading
 import time
 from collections import OrderedDict
-from typing import Optional
+from typing import Generic, Optional
 
 from loguru import logger
 
-from langflow.services.base import Service
-from langflow.services.cache.base import AsyncBaseCacheService, CacheService
+from langflow.services.cache.base import AsyncBaseCacheService, AsyncLockType, CacheService, LockType
 from langflow.services.cache.utils import CacheMiss
 
 CACHE_MISS = CacheMiss()
 
 
-class ThreadingInMemoryCache(CacheService, Service):
+class ThreadingInMemoryCache(CacheService, Generic[LockType]):
     """
     A simple in-memory cache using an OrderedDict.
 
@@ -182,7 +181,7 @@ class ThreadingInMemoryCache(CacheService, Service):
         return f"InMemoryCache(max_size={self.max_size}, expiration_time={self.expiration_time})"
 
 
-class RedisCache(CacheService):
+class RedisCache(CacheService, Generic[LockType]):
     """
     A Redis-based cache implementation.
 
@@ -243,10 +242,11 @@ class RedisCache(CacheService):
         try:
             self._client.ping()
             return True
-        except redis.exceptions.ConnectionError:
+        except redis.exceptions.ConnectionError as exc:
+            logger.error(f"RedisCache could not connect to the Redis server: {exc}")
             return False
 
-    def get(self, key):
+    async def get(self, key, lock=None):
         """
         Retrieve an item from the cache.
 
@@ -256,10 +256,12 @@ class RedisCache(CacheService):
         Returns:
             The value associated with the key, or None if the key is not found.
         """
-        value = self._client.get(key)
+        if key is None:
+            return None
+        value = self._client.get(str(key))
         return pickle.loads(value) if value else None
 
-    def set(self, key, value):
+    async def set(self, key, value, lock=None):
         """
         Add an item to the cache.
 
@@ -269,13 +271,13 @@ class RedisCache(CacheService):
         """
         try:
             if pickled := pickle.dumps(value):
-                result = self._client.setex(key, self.expiration_time, pickled)
+                result = self._client.setex(str(key), self.expiration_time, pickled)
                 if not result:
                     raise ValueError("RedisCache could not set the value.")
         except TypeError as exc:
             raise TypeError("RedisCache only accepts values that can be pickled. ") from exc
 
-    def upsert(self, key, value):
+    async def upsert(self, key, value, lock=None):
         """
         Inserts or updates a value in the cache.
         If the existing value and the new value are both dictionaries, they are merged.
@@ -284,14 +286,16 @@ class RedisCache(CacheService):
             key: The key of the item.
             value: The value to insert or update.
         """
-        existing_value = self.get(key)
+        if key is None:
+            return
+        existing_value = await self.get(key)
         if existing_value is not None and isinstance(existing_value, dict) and isinstance(value, dict):
             existing_value.update(value)
             value = existing_value
 
-        self.set(key, value)
+        await self.set(key, value)
 
-    def delete(self, key):
+    async def delete(self, key, lock=None):
         """
         Remove an item from the cache.
 
@@ -300,7 +304,7 @@ class RedisCache(CacheService):
         """
         self._client.delete(key)
 
-    def clear(self):
+    async def clear(self, lock=None):
         """
         Clear all items from the cache.
         """
@@ -308,17 +312,17 @@ class RedisCache(CacheService):
 
     def __contains__(self, key):
         """Check if the key is in the cache."""
-        return False if key is None else self._client.exists(key)
+        return False if key is None else self._client.exists(str(key))
 
-    def __getitem__(self, key):
+    async def __getitem__(self, key):
         """Retrieve an item from the cache using the square bracket notation."""
         return self.get(key)
 
-    def __setitem__(self, key, value):
+    async def __setitem__(self, key, value):
         """Add an item to the cache using the square bracket notation."""
         self.set(key, value)
 
-    def __delitem__(self, key):
+    async def __delitem__(self, key):
         """Remove an item from the cache using the square bracket notation."""
         self.delete(key)
 
@@ -327,7 +331,7 @@ class RedisCache(CacheService):
         return f"RedisCache(expiration_time={self.expiration_time})"
 
 
-class AsyncInMemoryCache(AsyncBaseCacheService, Service):
+class AsyncInMemoryCache(AsyncBaseCacheService, Generic[AsyncLockType]):
     def __init__(self, max_size=None, expiration_time=3600):
         self.cache = OrderedDict()
 
diff --git a/src/backend/base/langflow/services/chat/service.py b/src/backend/base/langflow/services/chat/service.py
index 042a541a3..bb913e6e9 100644
--- a/src/backend/base/langflow/services/chat/service.py
+++ b/src/backend/base/langflow/services/chat/service.py
@@ -23,7 +23,7 @@ class ChatService(Service):
             "result": data,
             "type": type(data),
         }
-        await self.cache_service.upsert(key, result_dict, lock=lock or self._cache_locks[key])
+        await self.cache_service.upsert(str(key), result_dict, lock=lock or self._cache_locks[key])
         return key in self.cache_service
 
     async def get_cache(self, key: str, lock: Optional[asyncio.Lock] = None) -> Any:
diff --git a/src/backend/base/langflow/services/database/models/flow/model.py b/src/backend/base/langflow/services/database/models/flow/model.py
index a3f9a055d..624ea0543 100644
--- a/src/backend/base/langflow/services/database/models/flow/model.py
+++ b/src/backend/base/langflow/services/database/models/flow/model.py
@@ -13,7 +13,7 @@ from pydantic import field_serializer, field_validator
 from sqlalchemy import UniqueConstraint
 from sqlmodel import JSON, Column, Field, Relationship, SQLModel
 
-from langflow.schema import Record
+from langflow.schema import Data
 
 if TYPE_CHECKING:
     from langflow.services.database.models.folder import Folder
@@ -29,7 +29,6 @@ class FlowBase(SQLModel):
     is_component: Optional[bool] = Field(default=False, nullable=True)
     updated_at: Optional[datetime] = Field(default_factory=lambda: datetime.now(timezone.utc), nullable=True)
     webhook: Optional[bool] = Field(default=False, nullable=True, description="Can be used on the webhook endpoint")
-    folder_id: Optional[UUID] = Field(default=None, nullable=True)
     endpoint_name: Optional[str] = Field(default=None, nullable=True, index=True)
 
     @field_validator("endpoint_name")
@@ -143,7 +142,7 @@ class Flow(FlowBase, table=True):
     folder_id: Optional[UUID] = Field(default=None, foreign_key="folder.id", nullable=True, index=True)
     folder: Optional["Folder"] = Relationship(back_populates="flows")
 
-    def to_record(self):
+    def to_data(self):
         serialized = self.model_dump()
         data = {
             "id": serialized.pop("id"),
@@ -152,7 +151,7 @@ class Flow(FlowBase, table=True):
             "description": serialized.pop("description"),
             "updated_at": serialized.pop("updated_at"),
         }
-        record = Record(data=data)
+        record = Data(data=data)
         return record
 
     __table_args__ = (
diff --git a/src/backend/base/langflow/services/monitor/schema.py b/src/backend/base/langflow/services/monitor/schema.py
index 22ccb2bb4..2294678fe 100644
--- a/src/backend/base/langflow/services/monitor/schema.py
+++ b/src/backend/base/langflow/services/monitor/schema.py
@@ -1,5 +1,5 @@
 import json
-from datetime import datetime
+from datetime import datetime, timezone
 from typing import Any, Optional
 
 from pydantic import BaseModel, Field, field_serializer, field_validator
@@ -17,7 +17,7 @@ class DefaultModel(BaseModel):
 
     def json(self, **kwargs):
         # Usa a função de serialização personalizada
-        return super().json(**kwargs, encoder=self.custom_encoder)
+        return super().model_dump_json(**kwargs, encoder=self.custom_encoder)
 
     @staticmethod
     def custom_encoder(obj):
@@ -83,7 +83,7 @@ class TransactionModelResponse(DefaultModel):
 class MessageModel(DefaultModel):
     index: Optional[int] = Field(default=None)
     flow_id: Optional[str] = Field(default=None, alias="flow_id")
-    timestamp: datetime = Field(default_factory=datetime.now)
+    timestamp: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
     sender: str
     sender_name: str
     session_id: str
@@ -97,6 +97,12 @@ class MessageModel(DefaultModel):
             v = json.loads(v)
         return v
 
+    @field_serializer("timestamp")
+    @classmethod
+    def serialize_timestamp(cls, v):
+        v = v.replace(microsecond=0)
+        return v.strftime("%Y-%m-%d %H:%M:%S")
+
     @field_serializer("files")
     @classmethod
     def serialize_files(cls, v):
@@ -108,7 +114,7 @@ class MessageModel(DefaultModel):
     def from_message(cls, message: Message, flow_id: Optional[str] = None):
         # first check if the record has all the required fields
         if message.text is None or not message.sender or not message.sender_name:
-            raise ValueError("The message does not have the required fields 'sender' and 'sender_name' in the data.")
+            raise ValueError("The message does not have the required fields (text, sender, sender_name).")
         return cls(
             sender=message.sender,
             sender_name=message.sender_name,
diff --git a/src/backend/base/langflow/services/monitor/service.py b/src/backend/base/langflow/services/monitor/service.py
index d6e071881..6b99b9760 100644
--- a/src/backend/base/langflow/services/monitor/service.py
+++ b/src/backend/base/langflow/services/monitor/service.py
@@ -9,7 +9,7 @@ from loguru import logger
 from platformdirs import user_cache_dir
 
 if TYPE_CHECKING:
-    from langflow.services.settings.manager import SettingsService
+    from langflow.services.settings.service import SettingsService
     from langflow.services.monitor.schema import MessageModel, TransactionModel, VertexBuildModel
 
 
diff --git a/src/backend/base/langflow/services/monitor/utils.py b/src/backend/base/langflow/services/monitor/utils.py
index 706d62348..b3daf6a47 100644
--- a/src/backend/base/langflow/services/monitor/utils.py
+++ b/src/backend/base/langflow/services/monitor/utils.py
@@ -178,15 +178,15 @@ def build_clean_params(target: "Vertex") -> dict:
     return params
 
 
-def log_transaction(flow_id, vertex: "Vertex", status, target: Optional["Vertex"] = None, error=None):
+def log_transaction(flow_id, source: "Vertex", status, target: Optional["Vertex"] = None, error=None):
     try:
         monitor_service = get_monitor_service()
-        clean_params = build_clean_params(vertex)
+        clean_params = build_clean_params(source)
         data = {
-            "vertex_id": str(vertex.id),
+            "vertex_id": str(source.id),
             "target_id": str(target.id) if target else None,
             "inputs": clean_params,
-            "outputs": vertex.result.model_dump_json() if vertex.result else None,
+            "outputs": source.result.model_dump_json() if source.result else None,
             "timestamp": monitor_service.get_timestamp(),
             "status": status,
             "error": error,
diff --git a/src/backend/base/langflow/services/settings/base.py b/src/backend/base/langflow/services/settings/base.py
index e8c0cbf90..3df9360f9 100644
--- a/src/backend/base/langflow/services/settings/base.py
+++ b/src/backend/base/langflow/services/settings/base.py
@@ -119,8 +119,22 @@ class Settings(BaseSettings):
     """Timeout for the API calls in seconds."""
     frontend_timeout: int = 0
     """Timeout for the frontend API calls in seconds."""
+    user_agent: str = "langflow"
+    """User agent for the API calls."""
+
+    @field_validator("user_agent", mode="after")
+    @classmethod
+    def set_user_agent(cls, value):
+        if not value:
+            value = "langflow"
+        import os
+
+        os.environ["USER_AGENT"] = value
+        logger.debug(f"Setting user agent to {value}")
+        return value
 
     @field_validator("config_dir", mode="before")
+    @classmethod
     def set_langflow_dir(cls, value):
         if not value:
             from platformdirs import user_cache_dir
@@ -144,6 +158,7 @@ class Settings(BaseSettings):
         return str(value)
 
     @field_validator("database_url", mode="before")
+    @classmethod
     def set_database_url(cls, value, info):
         if not value:
             logger.debug("No database_url provided, trying LANGFLOW_DATABASE_URL env variable")
diff --git a/src/backend/base/langflow/services/settings/factory.py b/src/backend/base/langflow/services/settings/factory.py
index a94e0abbe..2393d3918 100644
--- a/src/backend/base/langflow/services/settings/factory.py
+++ b/src/backend/base/langflow/services/settings/factory.py
@@ -1,5 +1,3 @@
-from pathlib import Path
-
 from langflow.services.factory import ServiceFactory
 from langflow.services.settings.service import SettingsService
 
@@ -10,5 +8,5 @@ class SettingsServiceFactory(ServiceFactory):
 
     def create(self):
         # Here you would have logic to create and configure a SettingsService
-        langflow_dir = Path(__file__).parent.parent.parent
-        return SettingsService.load_settings_from_yaml(str(langflow_dir / "config.yaml"))
+
+        return SettingsService.initialize()
diff --git a/src/backend/base/langflow/services/settings/service.py b/src/backend/base/langflow/services/settings/service.py
index 3ecdb683d..b83b47387 100644
--- a/src/backend/base/langflow/services/settings/service.py
+++ b/src/backend/base/langflow/services/settings/service.py
@@ -1,8 +1,3 @@
-import os
-
-import yaml
-from loguru import logger
-
 from langflow.services.base import Service
 from langflow.services.settings.auth import AuthSettings
 from langflow.services.settings.base import Settings
@@ -17,23 +12,10 @@ class SettingsService(Service):
         self.auth_settings: AuthSettings = auth_settings
 
     @classmethod
-    def load_settings_from_yaml(cls, file_path: str) -> "SettingsService":
+    def initialize(cls) -> "SettingsService":
         # Check if a string is a valid path or a file name
-        if "/" not in file_path:
-            # Get current path
-            current_path = os.path.dirname(os.path.abspath(__file__))
 
-            file_path = os.path.join(current_path, file_path)
-
-        with open(file_path, "r") as f:
-            settings_dict = yaml.safe_load(f)
-
-            for key in settings_dict:
-                if key not in Settings.model_fields.keys():
-                    logger.warning(f"Key {key} not found in settings")
-                logger.debug(f"Loading {len(settings_dict[key])} {key} from {file_path}")
-
-        settings = Settings(**settings_dict)
+        settings = Settings()
         if not settings.config_dir:
             raise ValueError("CONFIG_DIR must be set in settings")
 
diff --git a/src/backend/base/langflow/services/variable/service.py b/src/backend/base/langflow/services/variable/service.py
index bab6801b1..b3bde028f 100644
--- a/src/backend/base/langflow/services/variable/service.py
+++ b/src/backend/base/langflow/services/variable/service.py
@@ -61,7 +61,7 @@ class VariableService(Service):
         # credential = session.query(Variable).filter(Variable.user_id == user_id, Variable.name == name).first()
         variable = session.exec(select(Variable).where(Variable.user_id == user_id, Variable.name == name)).first()
 
-        if variable.type == "Credential" and field == "session_id":  # type: ignore
+        if variable and variable.type == "Credential" and field == "session_id":
             raise TypeError(
                 f"variable {name} of type 'Credential' cannot be used in a Session ID field "
                 "because its purpose is to prevent the exposure of values."
diff --git a/src/backend/base/langflow/template/__init__.py b/src/backend/base/langflow/template/__init__.py
index e69de29bb..6518b9689 100644
--- a/src/backend/base/langflow/template/__init__.py
+++ b/src/backend/base/langflow/template/__init__.py
@@ -0,0 +1,11 @@
+from langflow.template.field.base import Input, Output
+from langflow.template.frontend_node.base import FrontendNode
+from langflow.template.template.base import Template
+
+
+__all__ = [
+    "Input",
+    "Output",
+    "FrontendNode",
+    "Template",
+]
diff --git a/src/backend/base/langflow/template/field/base.py b/src/backend/base/langflow/template/field/base.py
index c68a5c476..2fe1a7a9d 100644
--- a/src/backend/base/langflow/template/field/base.py
+++ b/src/backend/base/langflow/template/field/base.py
@@ -1,14 +1,24 @@
-from typing import Any, Callable, Optional, Union
+from enum import Enum
+from typing import Optional  # type: ignore
+from typing import Any, Callable, Union
 
 from pydantic import BaseModel, ConfigDict, Field, field_serializer, field_validator, model_serializer, model_validator
 
+from langflow.field_typing import Text
 from langflow.field_typing.range_spec import RangeSpec
 
 
-class TemplateField(BaseModel):
-    model_config = ConfigDict()
+class UndefinedType(Enum):
+    undefined = "__UNDEFINED__"
 
-    field_type: str = Field(default="str", serialization_alias="type")
+
+UNDEFINED = UndefinedType.undefined
+
+
+class Input(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    field_type: str | type | None = Field(default=str, serialization_alias="type")
     """The type of field this is. Default is a string."""
 
     required: bool = False
@@ -26,7 +36,7 @@ class TemplateField(BaseModel):
     multiline: bool = False
     """Defines if the field will allow the user to open a text editor. Default is False."""
 
-    value: Any = ""
+    value: Any = None
     """The value of the field. Default is None."""
 
     file_types: list[str] = Field(default=[], serialization_alias="fileTypes")
@@ -85,7 +95,7 @@ class TemplateField(BaseModel):
         if self.field_type in ["str", "Text"]:
             if "input_types" not in result:
                 result["input_types"] = ["Text"]
-        if self.field_type == "Text":
+        if self.field_type == Text:
             result["type"] = "str"
         else:
             result["type"] = self.field_type
@@ -104,7 +114,7 @@ class TemplateField(BaseModel):
 
     @field_serializer("field_type")
     def serialize_field_type(self, value, _info):
-        if value == "float" and self.range_spec is None:
+        if value == float and self.range_spec is None:
             self.range_spec = RangeSpec()
         return value
 
@@ -128,3 +138,58 @@ class TemplateField(BaseModel):
             (f".{file_type}" if isinstance(file_type, str) and not file_type.startswith(".") else file_type)
             for file_type in value
         ]
+
+
+class Output(BaseModel):
+    types: Optional[list[str]] = Field(default=[])
+    """List of output types for the field."""
+
+    selected: Optional[str] = Field(default=None)
+    """The selected output type for the field."""
+
+    name: str = Field(description="The name of the field.")
+    """The name of the field."""
+
+    hidden: Optional[bool] = Field(default=None)
+    """Dictates if the field is hidden."""
+
+    display_name: Optional[str] = Field(default=None)
+    """The display name of the field."""
+
+    method: Optional[str] = Field(default=None)
+    """The method to use for the output."""
+
+    value: Optional[Any] = Field(default=UNDEFINED)
+
+    cache: bool = Field(default=True)
+
+    def to_dict(self):
+        return self.model_dump(by_alias=True, exclude_none=True)
+
+    def add_types(self, _type: list[Any]):
+        for type_ in _type:
+            if self.types is None:
+                self.types = []
+            self.types.append(type_)
+
+    def set_selected(self):
+        if not self.selected and self.types:
+            self.selected = self.types[0]
+
+    @model_serializer(mode="wrap")
+    def serialize_model(self, handler):
+        result = handler(self)
+        if self.value == UNDEFINED:
+            result["value"] = UNDEFINED.value
+
+        return result
+
+    @model_validator(mode="after")
+    def validate_model(self):
+        if self.value == UNDEFINED.value:
+            self.value = UNDEFINED
+        if self.name is None:
+            raise ValueError("name must be set")
+        if self.display_name is None:
+            self.display_name = self.name
+        return self
diff --git a/src/backend/base/langflow/template/field/prompt.py b/src/backend/base/langflow/template/field/prompt.py
index 138b90131..6ae396cf5 100644
--- a/src/backend/base/langflow/template/field/prompt.py
+++ b/src/backend/base/langflow/template/field/prompt.py
@@ -1,14 +1,16 @@
 from typing import Optional
 
-from langflow.template.field.base import TemplateField
+from langflow.template.field.base import Input
+
+DEFAULT_PROMPT_INTUT_TYPES = ["Message", "Text"]
 
 
-class DefaultPromptField(TemplateField):
+class DefaultPromptField(Input):
     name: str
     display_name: Optional[str] = None
     field_type: str = "str"
 
     advanced: bool = False
     multiline: bool = True
-    input_types: list[str] = ["Document", "Message", "Record", "Text"]
+    input_types: list[str] = DEFAULT_PROMPT_INTUT_TYPES
     value: str = ""  # Set the value to empty string
diff --git a/src/backend/base/langflow/template/frontend_node/base.py b/src/backend/base/langflow/template/frontend_node/base.py
index 8a051058e..77b6d1f69 100644
--- a/src/backend/base/langflow/template/frontend_node/base.py
+++ b/src/backend/base/langflow/template/frontend_node/base.py
@@ -1,42 +1,10 @@
-import re
 from collections import defaultdict
-from typing import ClassVar, Dict, List, Optional, Union
+from typing import Dict, List, Optional, Union
 
-from pydantic import BaseModel, Field, field_serializer, model_serializer
+from pydantic import BaseModel, field_serializer, model_serializer
 
-from langflow.template.field.base import TemplateField
-from langflow.template.frontend_node.constants import FORCE_SHOW_FIELDS
-from langflow.template.frontend_node.formatter import field_formatters
+from langflow.template.field.base import Output
 from langflow.template.template.base import Template
-from langflow.utils import constants
-
-
-class FieldFormatters(BaseModel):
-    formatters: ClassVar[Dict] = {
-        "openai_api_key": field_formatters.OpenAIAPIKeyFormatter(),
-    }
-    base_formatters: ClassVar[Dict] = {
-        "kwargs": field_formatters.KwargsFormatter(),
-        "optional": field_formatters.RemoveOptionalFormatter(),
-        "list": field_formatters.ListTypeFormatter(),
-        "dict": field_formatters.DictTypeFormatter(),
-        "union": field_formatters.UnionTypeFormatter(),
-        "multiline": field_formatters.MultilineFieldFormatter(),
-        "show": field_formatters.ShowFieldFormatter(),
-        "password": field_formatters.PasswordFieldFormatter(),
-        "default": field_formatters.DefaultValueFormatter(),
-        "headers": field_formatters.HeadersDefaultValueFormatter(),
-        "dict_code_file": field_formatters.DictCodeFileFormatter(),
-        "model_fields": field_formatters.ModelSpecificFieldFormatter(),
-    }
-
-    def format(self, field: TemplateField, name: Optional[str] = None) -> None:
-        for key, formatter in self.base_formatters.items():
-            formatter.format(field, name)
-
-        for key, formatter in self.formatters.items():
-            if key == field.name:
-                formatter.format(field, name)
 
 
 class FrontendNode(BaseModel):
@@ -69,22 +37,23 @@ class FrontendNode(BaseModel):
     """List of output types for the frontend node."""
     full_path: Optional[str] = None
     """Full path of the frontend node."""
-    field_formatters: FieldFormatters = Field(default_factory=FieldFormatters)
-    """Field formatters for the frontend node."""
+    pinned: bool = False
+    """Whether the frontend node is pinned."""
+    conditional_paths: List[str] = []
+    """List of conditional paths for the frontend node."""
     frozen: bool = False
     """Whether the frontend node is frozen."""
+    outputs: List[Output] = []
+    """List of output fields for the frontend node."""
 
     field_order: list[str] = []
     """Order of the fields in the frontend node."""
-
     beta: bool = False
+    """Whether the frontend node is in beta."""
     error: Optional[str] = None
-
-    # field formatters is an instance attribute but it is not used in the class
-    # so we need to create a method to get it
-    @staticmethod
-    def get_field_formatters() -> FieldFormatters:
-        return FieldFormatters()
+    """Error message for the frontend node."""
+    edited: bool = False
+    """Whether the frontend node has been edited."""
 
     def set_documentation(self, documentation: str) -> None:
         """Sets the documentation of the frontend node."""
@@ -107,10 +76,17 @@ class FrontendNode(BaseModel):
     def serialize_model(self, handler):
         result = handler(self)
         if hasattr(self, "template") and hasattr(self.template, "to_dict"):
-            format_func = self.format_field if self._format_template else None
-            result["template"] = self.template.to_dict(format_func)
+            result["template"] = self.template.to_dict()
         name = result.pop("name")
 
+        # Migrate base classes to outputs
+        if "output_types" in result and not result.get("outputs"):
+            for base_class in result["output_types"]:
+                output = Output(
+                    display_name=base_class, name=base_class.lower(), types=[base_class], selected=base_class
+                )
+                result["outputs"].append(output.model_dump())
+
         return {name: result}
 
     # For backwards compatibility
@@ -127,6 +103,53 @@ class FrontendNode(BaseModel):
     def add_extra_base_classes(self) -> None:
         pass
 
+    def set_base_classes_from_outputs(self):
+        self.base_classes = [output_type for output in self.outputs for output_type in output.types]
+
+    def validate_component(self) -> None:
+        self.validate_name_overlap()
+        self.validate_attributes()
+
+    def validate_name_overlap(self) -> None:
+        # Check if any of the output names overlap with the any of the inputs
+        output_names = [output.name for output in self.outputs]
+        input_names = [input_.name for input_ in self.template.fields]
+        overlap = set(output_names).intersection(input_names)
+        if overlap:
+            overlap_str = ", ".join(map(lambda x: f"'{x}'", overlap))
+            raise ValueError(
+                f"There should be no overlap between input and output names. Names {overlap_str} are duplicated."
+            )
+
+    def validate_attributes(self) -> None:
+        # None of inputs, outputs, _artifacts, _results, logs, status, vertex, graph, display_name, description, documentation, icon
+        # should be present in outputs or input names
+        output_names = [output.name for output in self.outputs]
+        input_names = [input_.name for input_ in self.template.fields]
+        attributes = [
+            "inputs",
+            "outputs",
+            "_artifacts",
+            "_results",
+            "logs",
+            "status",
+            "vertex",
+            "graph",
+            "display_name",
+            "description",
+            "documentation",
+            "icon",
+        ]
+        output_overlap = set(output_names).intersection(attributes)
+        input_overlap = set(input_names).intersection(attributes)
+        error_message = ""
+        if output_overlap:
+            output_overlap_str = ", ".join(map(lambda x: f"'{x}'", output_overlap))
+            error_message += f"Output names {output_overlap_str} are reserved attributes.\n"
+        if input_overlap:
+            input_overlap_str = ", ".join(map(lambda x: f"'{x}'", input_overlap))
+            error_message += f"Input names {input_overlap_str} are reserved attributes."
+
     def add_base_class(self, base_class: Union[str, List[str]]) -> None:
         """Adds a base class to the frontend node."""
         if isinstance(base_class, str):
@@ -141,142 +164,12 @@ class FrontendNode(BaseModel):
         elif isinstance(output_type, list):
             self.output_types.extend(output_type)
 
-    @staticmethod
-    def format_field(field: TemplateField, name: Optional[str] = None) -> None:
-        """Formats a given field based on its attributes and value."""
-
-        FrontendNode.get_field_formatters().format(field, name)
-
-    @staticmethod
-    def remove_optional(_type: str) -> str:
-        """Removes 'Optional' wrapper from the type if present."""
-        return re.sub(r"Optional\[(.*)\]", r"\1", _type)
-
-    @staticmethod
-    def check_for_list_type(_type: str) -> tuple:
-        """Checks for list type and returns the modified type and a boolean indicating if it's a list."""
-        is_list = "List" in _type or "Sequence" in _type
-        if is_list:
-            _type = re.sub(r"(List|Sequence)\[(.*)\]", r"\2", _type)
-        return _type, is_list
-
-    @staticmethod
-    def replace_mapping_with_dict(_type: str) -> str:
-        """Replaces 'Mapping' with 'dict'."""
-        return _type.replace("Mapping", "dict")
-
-    @staticmethod
-    def handle_union_type(_type: str) -> str:
-        """Simplifies the 'Union' type to the first type in the Union."""
-        if "Union" in _type:
-            _type = _type.replace("Union[", "")[:-1]
-            _type = _type.split(",")[0]
-            _type = _type.replace("]", "").replace("[", "")
-        return _type
-
-    @staticmethod
-    def handle_special_field(field, key: str, _type: str, SPECIAL_FIELD_HANDLERS) -> str:
-        """Handles special field by using the respective handler if present."""
-        handler = SPECIAL_FIELD_HANDLERS.get(key)
-        return handler(field) if handler else _type
-
-    @staticmethod
-    def handle_dict_type(field: TemplateField, _type: str) -> str:
-        """Handles 'dict' type by replacing it with 'code' or 'file' based on the field name."""
-        if "dict" in _type.lower() and field.name == "dict_":
-            field.field_type = "file"
-            field.file_types = [".json", ".yaml", ".yml"]
-        elif _type.startswith("Dict") or _type.startswith("Mapping") or _type.startswith("dict"):
-            field.field_type = "dict"
-        return _type
-
-    @staticmethod
-    def replace_default_value(field: TemplateField, value: dict) -> None:
-        """Replaces default value with actual value if 'default' is present in value."""
-        if "default" in value:
-            field.value = value["default"]
-
-    @staticmethod
-    def handle_specific_field_values(field: TemplateField, key: str, name: Optional[str] = None) -> None:
-        """Handles specific field values for certain fields."""
-        if key == "headers":
-            field.value = """{"Authorization": "Bearer <token>"}"""
-        FrontendNode._handle_model_specific_field_values(field, key, name)
-        FrontendNode._handle_api_key_specific_field_values(field, key, name)
-
-    @staticmethod
-    def _handle_model_specific_field_values(field: TemplateField, key: str, name: Optional[str] = None) -> None:
-        """Handles specific field values related to models."""
-        model_dict = {
-            "OpenAI": constants.OPENAI_MODELS,
-            "ChatOpenAI": constants.CHAT_OPENAI_MODELS,
-            "Anthropic": constants.ANTHROPIC_MODELS,
-            "ChatAnthropic": constants.ANTHROPIC_MODELS,
-        }
-        if name in model_dict and key == "model_name":
-            field.options = model_dict[name]
-            field.is_list = True
-
-    @staticmethod
-    def _handle_api_key_specific_field_values(field: TemplateField, key: str, name: Optional[str] = None) -> None:
-        """Handles specific field values related to API keys."""
-        if "api_key" in key and "OpenAI" in str(name):
-            field.display_name = "OpenAI API Key"
-            field.required = False
-            if field.value is None:
-                field.value = ""
-
-    @staticmethod
-    def handle_kwargs_field(field: TemplateField) -> None:
-        """Handles kwargs field by setting certain attributes."""
-
-        if "kwargs" in (field.name or "").lower():
-            field.advanced = True
-            field.required = False
-            field.show = False
-
-    @staticmethod
-    def handle_api_key_field(field: TemplateField, key: str) -> None:
-        """Handles api key field by setting certain attributes."""
-        if "api" in key.lower() and "key" in key.lower():
-            field.required = False
-            field.advanced = False
-
-            field.display_name = key.replace("_", " ").title()
-            field.display_name = field.display_name.replace("Api", "API")
-
-    @staticmethod
-    def should_show_field(key: str, required: bool) -> bool:
-        """Determines whether the field should be shown."""
-        return (
-            (required and key not in ["input_variables"])
-            or key in FORCE_SHOW_FIELDS
-            or "api" in key
-            or ("key" in key and "input" not in key and "output" not in key)
-        )
-
-    @staticmethod
-    def should_be_password(key: str, show: bool) -> bool:
-        """Determines whether the field should be a password field."""
-        return any(text in key.lower() for text in {"password", "token", "api", "key"}) and show
-
-    @staticmethod
-    def should_be_multiline(key: str) -> bool:
-        """Determines whether the field should be multiline."""
-        return key in {
-            "suffix",
-            "prefix",
-            "template",
-            "examples",
-            "code",
-            "headers",
-            "description",
-        }
-
-    @staticmethod
-    def set_field_default_value(field: TemplateField, value: dict, key: str) -> None:
-        """Sets the field value with the default value if present."""
-        if "default" in value:
-            field.value = value["default"]
-        if key == "headers":
-            field.value = """{"Authorization": "Bearer <token>"}"""
+    @classmethod
+    def from_inputs(cls, **kwargs):
+        """Create a frontend node from inputs."""
+        if "inputs" not in kwargs:
+            raise ValueError("Missing 'inputs' argument.")
+        inputs = kwargs.pop("inputs")
+        template = Template(type_name="Component", fields=inputs)
+        kwargs["template"] = template
+        return cls(**kwargs)
diff --git a/src/backend/base/langflow/template/frontend_node/custom_components.py b/src/backend/base/langflow/template/frontend_node/custom_components.py
index 932d30799..2fd1e9cdf 100644
--- a/src/backend/base/langflow/template/frontend_node/custom_components.py
+++ b/src/backend/base/langflow/template/frontend_node/custom_components.py
@@ -1,6 +1,6 @@
 from typing import Optional
 
-from langflow.template.field.base import TemplateField
+from langflow.template.field.base import Input
 from langflow.template.frontend_node.base import FrontendNode
 from langflow.template.template.base import Template
 
@@ -52,7 +52,32 @@ class CustomComponentFrontendNode(FrontendNode):
     template: Template = Template(
         type_name="CustomComponent",
         fields=[
-            TemplateField(
+            Input(
+                field_type="code",
+                required=True,
+                placeholder="",
+                is_list=False,
+                show=True,
+                value=DEFAULT_CUSTOM_COMPONENT_CODE,
+                name="code",
+                advanced=False,
+                dynamic=True,
+            )
+        ],
+    )
+    description: Optional[str] = None
+    base_classes: list[str] = []
+
+
+class ComponentFrontendNode(FrontendNode):
+    _format_template: bool = False
+    name: str = "Component"
+    display_name: Optional[str] = "Component"
+    beta: bool = False
+    template: Template = Template(
+        type_name="Component",
+        fields=[
+            Input(
                 field_type="code",
                 required=True,
                 placeholder="",
diff --git a/src/backend/base/langflow/template/frontend_node/formatter/base.py b/src/backend/base/langflow/template/frontend_node/formatter/base.py
deleted file mode 100644
index 20ba64eae..000000000
--- a/src/backend/base/langflow/template/frontend_node/formatter/base.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Optional
-
-from pydantic import BaseModel
-
-from langflow.template.field.base import TemplateField
-
-
-class FieldFormatter(BaseModel, ABC):
-    @abstractmethod
-    def format(self, field: TemplateField, name: Optional[str]) -> None:
-        pass
diff --git a/src/backend/base/langflow/template/frontend_node/formatter/field_formatters.py b/src/backend/base/langflow/template/frontend_node/formatter/field_formatters.py
deleted file mode 100644
index 42d3321ff..000000000
--- a/src/backend/base/langflow/template/frontend_node/formatter/field_formatters.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import re
-from typing import ClassVar, Dict, Optional
-
-from langflow.template.field.base import TemplateField
-from langflow.template.frontend_node.constants import FORCE_SHOW_FIELDS
-from langflow.template.frontend_node.formatter.base import FieldFormatter
-from langflow.utils.constants import ANTHROPIC_MODELS, CHAT_OPENAI_MODELS, OPENAI_MODELS
-
-
-class OpenAIAPIKeyFormatter(FieldFormatter):
-    def format(self, field: TemplateField, name: Optional[str] = None) -> None:
-        if field.name and "api_key" in field.name and "OpenAI" in str(name):
-            field.display_name = "OpenAI API Key"
-            field.required = False
-            if field.value is None:
-                field.value = ""
-
-
-class ModelSpecificFieldFormatter(FieldFormatter):
-    MODEL_DICT: ClassVar[Dict] = {
-        "OpenAI": OPENAI_MODELS,
-        "ChatOpenAI": CHAT_OPENAI_MODELS,
-        "Anthropic": ANTHROPIC_MODELS,
-        "ChatAnthropic": ANTHROPIC_MODELS,
-    }
-
-    def format(self, field: TemplateField, name: Optional[str] = None) -> None:
-        if field.name and name in self.MODEL_DICT and field.name == "model_name":
-            field.options = self.MODEL_DICT[name]
-            field.is_list = True
-
-
-class KwargsFormatter(FieldFormatter):
-    def format(self, field: TemplateField, name: Optional[str] = None) -> None:
-        if field.name and "kwargs" in field.name.lower():
-            field.advanced = True
-            field.required = False
-            field.show = False
-
-
-class APIKeyFormatter(FieldFormatter):
-    def format(self, field: TemplateField, name: Optional[str] = None) -> None:
-        if field.name and "api" in field.name.lower() and "key" in field.name.lower():
-            field.required = False
-            field.advanced = False
-
-            field.display_name = (field.name or "").replace("_", " ").title()
-            field.display_name = field.display_name.replace("Api", "API")
-
-
-class RemoveOptionalFormatter(FieldFormatter):
-    def format(self, field: TemplateField, name: Optional[str] = None) -> None:
-        _type = field.field_type
-        field.field_type = re.sub(r"Optional\[(.*)\]", r"\1", _type)
-
-
-class ListTypeFormatter(FieldFormatter):
-    def format(self, field: TemplateField, name: Optional[str] = None) -> None:
-        _type = field.field_type
-        is_list = "List" in _type or "Sequence" in _type
-        if is_list:
-            _type = re.sub(r"(List|Sequence)\[(.*)\]", r"\2", _type)
-            field.is_list = True
-        field.field_type = _type
-
-
-class DictTypeFormatter(FieldFormatter):
-    def format(self, field: TemplateField, name: Optional[str] = None) -> None:
-        _type = field.field_type
-        _type = _type.replace("Mapping", "dict")
-        field.field_type = _type
-
-
-class UnionTypeFormatter(FieldFormatter):
-    def format(self, field: TemplateField, name: Optional[str] = None) -> None:
-        _type = field.field_type
-        if "Union" in _type:
-            _type = _type.replace("Union[", "")[:-1]
-            _type = _type.split(",")[0]
-            _type = _type.replace("]", "").replace("[", "")
-        field.field_type = _type
-
-
-class SpecialFieldFormatter(FieldFormatter):
-    SPECIAL_FIELD_HANDLERS: ClassVar[Dict] = {
-        "allowed_tools": lambda field: "Tool",
-        "max_value_length": lambda field: "int",
-    }
-
-    def format(self, field: TemplateField, name: Optional[str] = None) -> None:
-        handler = self.SPECIAL_FIELD_HANDLERS.get(field.name)
-        field.field_type = handler(field) if handler else field.field_type
-
-
-class ShowFieldFormatter(FieldFormatter):
-    def format(self, field: TemplateField, name: Optional[str] = None) -> None:
-        key = field.name or ""
-        required = field.required
-        field.show = (
-            (required and key not in ["input_variables"])
-            or key in FORCE_SHOW_FIELDS
-            or "api" in key
-            or ("key" in key and "input" not in key and "output" not in key)
-        )
-
-
-class PasswordFieldFormatter(FieldFormatter):
-    def format(self, field: TemplateField, name: Optional[str] = None) -> None:
-        key = field.name or ""
-        show = field.show
-        if any(text in key.lower() for text in {"password", "token", "api", "key"}) and show:
-            field.password = True
-
-
-class MultilineFieldFormatter(FieldFormatter):
-    def format(self, field: TemplateField, name: Optional[str] = None) -> None:
-        key = field.name or ""
-        if key in {
-            "suffix",
-            "prefix",
-            "template",
-            "examples",
-            "code",
-            "headers",
-            "description",
-        }:
-            field.multiline = True
-
-
-class DefaultValueFormatter(FieldFormatter):
-    def format(self, field: TemplateField, name: Optional[str] = None) -> None:
-        value = field.model_dump(by_alias=True, exclude_none=True)
-        if "default" in value:
-            field.value = value["default"]
-
-
-class HeadersDefaultValueFormatter(FieldFormatter):
-    def format(self, field: TemplateField, name: Optional[str] = None) -> None:
-        key = field.name
-        if key == "headers":
-            field.value = """{"Authorization": "Bearer <token>"}"""
-
-
-class DictCodeFileFormatter(FieldFormatter):
-    def format(self, field: TemplateField, name: Optional[str] = None) -> None:
-        key = field.name
-        value = field.model_dump(by_alias=True, exclude_none=True)
-        _type = value["type"]
-        if "dict" in _type.lower() and key == "dict_":
-            field.field_type = "file"
-            field.file_types = [".json", ".yaml", ".yml"]
-        elif _type.startswith("Dict") or _type.startswith("Mapping") or _type.startswith("dict"):
-            field.field_type = "dict"
diff --git a/src/backend/base/langflow/template/template/base.py b/src/backend/base/langflow/template/template/base.py
index d7632e239..7c0c7fa0f 100644
--- a/src/backend/base/langflow/template/template/base.py
+++ b/src/backend/base/langflow/template/template/base.py
@@ -1,14 +1,15 @@
-from typing import Callable, Union
+from typing import Callable, Union, cast
 
-from pydantic import BaseModel, model_serializer
+from pydantic import BaseModel, Field, model_serializer
 
-from langflow.template.field.base import TemplateField
+from langflow.inputs.inputs import InputTypes
+from langflow.template.field.base import Input
 from langflow.utils.constants import DIRECT_TYPES
 
 
 class Template(BaseModel):
-    type_name: str
-    fields: list[TemplateField]
+    type_name: str = Field(serialization_alias="_type")
+    fields: list[Union[Input, InputTypes]]
 
     def process_fields(
         self,
@@ -22,14 +23,16 @@ class Template(BaseModel):
         # first sort alphabetically
         # then sort fields so that fields that have .field_type in DIRECT_TYPES are first
         self.fields.sort(key=lambda x: x.name)
-        self.fields.sort(key=lambda x: x.field_type in DIRECT_TYPES, reverse=False)
+        self.fields.sort(
+            key=lambda x: x.field_type in DIRECT_TYPES if hasattr(x, "field_type") else False, reverse=False
+        )
 
     @model_serializer(mode="wrap")
     def serialize_model(self, handler):
         result = handler(self)
         for field in self.fields:
             result[field.name] = field.model_dump(by_alias=True, exclude_none=True)
-        result["_type"] = result.pop("type_name")
+
         return result
 
     # For backwards compatibility
@@ -38,17 +41,17 @@ class Template(BaseModel):
         self.sort_fields()
         return self.model_dump(by_alias=True, exclude_none=True, exclude={"fields"})
 
-    def add_field(self, field: TemplateField) -> None:
+    def add_field(self, field: Input) -> None:
         self.fields.append(field)
 
-    def get_field(self, field_name: str) -> TemplateField:
+    def get_field(self, field_name: str) -> Input:
         """Returns the field with the given name."""
         field = next((field for field in self.fields if field.name == field_name), None)
         if field is None:
             raise ValueError(f"Field {field_name} not found in template {self.type_name}")
-        return field
+        return cast(Input, field)
 
-    def update_field(self, field_name: str, field: TemplateField) -> None:
+    def update_field(self, field_name: str, field: Input) -> None:
         """Updates the field with the given name."""
         for idx, template_field in enumerate(self.fields):
             if template_field.name == field_name:
@@ -56,7 +59,7 @@ class Template(BaseModel):
                 return
         raise ValueError(f"Field {field_name} not found in template {self.type_name}")
 
-    def upsert_field(self, field_name: str, field: TemplateField) -> None:
+    def upsert_field(self, field_name: str, field: Input) -> None:
         """Updates the field with the given name or adds it if it doesn't exist."""
         try:
             self.update_field(field_name, field)
diff --git a/src/backend/base/langflow/type_extraction/__init__.py b/src/backend/base/langflow/type_extraction/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/src/backend/base/langflow/custom/code_parser/utils.py b/src/backend/base/langflow/type_extraction/type_extraction.py
similarity index 51%
rename from src/backend/base/langflow/custom/code_parser/utils.py
rename to src/backend/base/langflow/type_extraction/type_extraction.py
index 0f97b4c7b..f22a8eebe 100644
--- a/src/backend/base/langflow/custom/code_parser/utils.py
+++ b/src/backend/base/langflow/type_extraction/type_extraction.py
@@ -1,15 +1,6 @@
 import re
 from types import GenericAlias
-from typing import Any
-
-
-def extract_inner_type(return_type: str) -> str:
-    """
-    Extracts the inner type from a type hint that is a list.
-    """
-    if match := re.match(r"list\[(.*)\]", return_type, re.IGNORECASE):
-        return match[1]
-    return return_type
+from typing import Any, List, Union
 
 
 def extract_inner_type_from_generic_alias(return_type: GenericAlias) -> Any:
@@ -21,6 +12,15 @@ def extract_inner_type_from_generic_alias(return_type: GenericAlias) -> Any:
     return return_type
 
 
+def extract_inner_type(return_type: str) -> str:
+    """
+    Extracts the inner type from a type hint that is a list.
+    """
+    if match := re.match(r"list\[(.*)\]", return_type, re.IGNORECASE):
+        return match[1]
+    return return_type
+
+
 def extract_union_types(return_type: str) -> list[str]:
     """
     Extracts the inner type from a type hint that is a list.
@@ -31,6 +31,47 @@ def extract_union_types(return_type: str) -> list[str]:
     return [item.strip() for item in return_types]
 
 
+def extract_uniont_types_from_generic_alias(return_type: GenericAlias) -> list:
+    """
+    Extracts the inner type from a type hint that is a Union.
+    """
+    if isinstance(return_type, list):
+        return [
+            _inner_arg
+            for _type in return_type
+            for _inner_arg in _type.__args__
+            if _inner_arg not in set((Any, type(None), type(Any)))
+        ]
+
+    return list(return_type.__args__)
+
+
+def post_process_type(_type):
+    """
+    Process the return type of a function.
+
+    Args:
+        _type (Any): The return type of the function.
+
+    Returns:
+        Union[List[Any], Any]: The processed return type.
+
+    """
+    if hasattr(_type, "__origin__") and _type.__origin__ in [
+        list,
+        List,
+    ]:
+        _type = extract_inner_type_from_generic_alias(_type)
+
+    # If the return type is not a Union, then we just return it as a list
+    inner_type = _type[0] if isinstance(_type, list) else _type
+    if not hasattr(inner_type, "__origin__") or inner_type.__origin__ != Union:
+        return _type if isinstance(_type, list) else [_type]
+    # If the return type is a Union, then we need to parse it
+    _type = extract_union_types_from_generic_alias(_type)
+    return _type
+
+
 def extract_union_types_from_generic_alias(return_type: GenericAlias) -> list:
     """
     Extracts the inner type from a type hint that is a Union.
diff --git a/src/backend/base/langflow/utils/schemas.py b/src/backend/base/langflow/utils/schemas.py
index a17fc8aa6..344a52632 100644
--- a/src/backend/base/langflow/utils/schemas.py
+++ b/src/backend/base/langflow/utils/schemas.py
@@ -97,10 +97,10 @@ class ChatOutputResponse(BaseModel):
         return self
 
 
-class RecordOutputResponse(BaseModel):
-    """Record output response schema."""
+class DataOutputResponse(BaseModel):
+    """Data output response schema."""
 
-    records: List[Optional[Dict]]
+    data: List[Optional[Dict]]
 
 
 class ContainsEnumMeta(enum.EnumMeta):
diff --git a/src/backend/base/langflow/utils/util.py b/src/backend/base/langflow/utils/util.py
index 89b44bd0e..d58fdc4f8 100644
--- a/src/backend/base/langflow/utils/util.py
+++ b/src/backend/base/langflow/utils/util.py
@@ -7,7 +7,7 @@ from typing import Any, Dict, List, Optional, Union
 
 from docstring_parser import parse
 
-from langflow.schema import Record
+from langflow.schema import Data
 from langflow.services.deps import get_settings_service
 from langflow.template.frontend_node.constants import FORCE_SHOW_FIELDS
 from langflow.utils import constants
@@ -400,23 +400,23 @@ def add_options_to_field(value: Dict[str, Any], class_name: Optional[str], key:
         value["value"] = options_map[class_name][0]
 
 
-def build_loader_repr_from_records(records: List[Record]) -> str:
+def build_loader_repr_from_data(data: List[Data]) -> str:
     """
-    Builds a string representation of the loader based on the given records.
+    Builds a string representation of the loader based on the given data.
 
     Args:
-        records (List[Record]): A list of records.
+        data (List[Data]): A list of data.
 
     Returns:
         str: A string representation of the loader.
 
     """
-    if records:
-        avg_length = sum(len(doc.text) for doc in records) / len(records)
-        return f"""{len(records)} records
-        \nAvg. Record Length (characters): {int(avg_length)}
-        Records: {records[:3]}..."""
-    return "0 records"
+    if data:
+        avg_length = sum(len(doc.text) for doc in data) / len(data)
+        return f"""{len(data)} data
+        \nAvg. Data Length (characters): {int(avg_length)}
+        Data: {data[:3]}..."""
+    return "0 data"
 
 
 def update_settings(
@@ -449,3 +449,10 @@ def update_settings(
     if not store:
         logger.debug("Setting store to False")
         settings_service.settings.update_settings(store=False)
+
+
+def is_class_method(func, cls):
+    """
+    Check if a function is a class method.
+    """
+    return inspect.ismethod(func) and func.__self__ is cls.__class__
diff --git a/src/backend/base/langflow/utils/validate.py b/src/backend/base/langflow/utils/validate.py
index c3bbc9df8..d3edef561 100644
--- a/src/backend/base/langflow/utils/validate.py
+++ b/src/backend/base/langflow/utils/validate.py
@@ -256,6 +256,7 @@ def build_class_constructor(compiled_class, exec_globals, class_name):
                 globals()[module_name] = module
 
         instance = exec_globals[class_name](*args, **kwargs)
+
         return instance
 
     build_custom_class.__globals__.update(exec_globals)
diff --git a/src/frontend/.prettierrc.mjs b/src/frontend/.prettierrc.mjs
new file mode 100644
index 000000000..c7636f876
--- /dev/null
+++ b/src/frontend/.prettierrc.mjs
@@ -0,0 +1,7 @@
+const config = {
+  plugins: ["prettier-plugin-organize-imports", "prettier-plugin-tailwindcss"],
+  tailwindConfig: "./tailwind.config.mjs",
+  organizeImportsSkipDestructiveCodeActions: true,
+};
+
+export default config;
diff --git a/src/frontend/harFiles/langflow.har b/src/frontend/harFiles/langflow.har
index d6fef50cd..dcd0b23aa 100644
--- a/src/frontend/harFiles/langflow.har
+++ b/src/frontend/harFiles/langflow.har
@@ -19,19 +19,58 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Accept", "value": "application/json, text/plain, */*" },
-            { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" },
-            { "name": "Accept-Language", "value": "en-US,en;q=0.9" },
-            { "name": "Connection", "value": "keep-alive" },
-            { "name": "Host", "value": "localhost:3000" },
-            { "name": "Referer", "value": "http://localhost:3000/" },
-            { "name": "Sec-Fetch-Dest", "value": "empty" },
-            { "name": "Sec-Fetch-Mode", "value": "cors" },
-            { "name": "Sec-Fetch-Site", "value": "same-origin" },
-            { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" },
-            { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" },
-            { "name": "sec-ch-ua-mobile", "value": "?0" },
-            { "name": "sec-ch-ua-platform", "value": "\"Linux\"" }
+            {
+              "name": "Accept",
+              "value": "application/json, text/plain, */*"
+            },
+            {
+              "name": "Accept-Encoding",
+              "value": "gzip, deflate, br, zstd"
+            },
+            {
+              "name": "Accept-Language",
+              "value": "en-US,en;q=0.9"
+            },
+            {
+              "name": "Connection",
+              "value": "keep-alive"
+            },
+            {
+              "name": "Host",
+              "value": "localhost:3000"
+            },
+            {
+              "name": "Referer",
+              "value": "http://localhost:3000/"
+            },
+            {
+              "name": "Sec-Fetch-Dest",
+              "value": "empty"
+            },
+            {
+              "name": "Sec-Fetch-Mode",
+              "value": "cors"
+            },
+            {
+              "name": "Sec-Fetch-Site",
+              "value": "same-origin"
+            },
+            {
+              "name": "User-Agent",
+              "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36"
+            },
+            {
+              "name": "sec-ch-ua",
+              "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\""
+            },
+            {
+              "name": "sec-ch-ua-mobile",
+              "value": "?0"
+            },
+            {
+              "name": "sec-ch-ua-platform",
+              "value": "\"Linux\""
+            }
           ],
           "queryString": [],
           "headersSize": -1,
@@ -43,12 +82,30 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Access-Control-Allow-Origin", "value": "*" },
-            { "name": "connection", "value": "close" },
-            { "name": "content-length", "value": "19" },
-            { "name": "content-type", "value": "application/json" },
-            { "name": "date", "value": "Wed, 28 Feb 2024 14:32:30 GMT" },
-            { "name": "server", "value": "uvicorn" }
+            {
+              "name": "Access-Control-Allow-Origin",
+              "value": "*"
+            },
+            {
+              "name": "connection",
+              "value": "close"
+            },
+            {
+              "name": "content-length",
+              "value": "19"
+            },
+            {
+              "name": "content-type",
+              "value": "application/json"
+            },
+            {
+              "name": "date",
+              "value": "Wed, 28 Feb 2024 14:32:30 GMT"
+            },
+            {
+              "name": "server",
+              "value": "uvicorn"
+            }
           ],
           "content": {
             "size": -1,
@@ -60,7 +117,11 @@
           "redirectURL": ""
         },
         "cache": {},
-        "timings": { "send": -1, "wait": -1, "receive": 0.77 }
+        "timings": {
+          "send": -1,
+          "wait": -1,
+          "receive": 0.77
+        }
       },
       {
         "startedDateTime": "2024-02-28T14:32:30.859Z",
@@ -71,19 +132,58 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Accept", "value": "application/json, text/plain, */*" },
-            { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" },
-            { "name": "Accept-Language", "value": "en-US,en;q=0.9" },
-            { "name": "Connection", "value": "keep-alive" },
-            { "name": "Host", "value": "localhost:3000" },
-            { "name": "Referer", "value": "http://localhost:3000/" },
-            { "name": "Sec-Fetch-Dest", "value": "empty" },
-            { "name": "Sec-Fetch-Mode", "value": "cors" },
-            { "name": "Sec-Fetch-Site", "value": "same-origin" },
-            { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" },
-            { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" },
-            { "name": "sec-ch-ua-mobile", "value": "?0" },
-            { "name": "sec-ch-ua-platform", "value": "\"Linux\"" }
+            {
+              "name": "Accept",
+              "value": "application/json, text/plain, */*"
+            },
+            {
+              "name": "Accept-Encoding",
+              "value": "gzip, deflate, br, zstd"
+            },
+            {
+              "name": "Accept-Language",
+              "value": "en-US,en;q=0.9"
+            },
+            {
+              "name": "Connection",
+              "value": "keep-alive"
+            },
+            {
+              "name": "Host",
+              "value": "localhost:3000"
+            },
+            {
+              "name": "Referer",
+              "value": "http://localhost:3000/"
+            },
+            {
+              "name": "Sec-Fetch-Dest",
+              "value": "empty"
+            },
+            {
+              "name": "Sec-Fetch-Mode",
+              "value": "cors"
+            },
+            {
+              "name": "Sec-Fetch-Site",
+              "value": "same-origin"
+            },
+            {
+              "name": "User-Agent",
+              "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36"
+            },
+            {
+              "name": "sec-ch-ua",
+              "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\""
+            },
+            {
+              "name": "sec-ch-ua-mobile",
+              "value": "?0"
+            },
+            {
+              "name": "sec-ch-ua-platform",
+              "value": "\"Linux\""
+            }
           ],
           "queryString": [],
           "headersSize": -1,
@@ -95,13 +195,34 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Access-Control-Allow-Origin", "value": "*" },
-            { "name": "connection", "value": "close" },
-            { "name": "content-length", "value": "227" },
-            { "name": "content-type", "value": "application/json" },
-            { "name": "date", "value": "Wed, 28 Feb 2024 14:32:30 GMT" },
-            { "name": "server", "value": "uvicorn" },
-            { "name": "set-cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc; Path=/; SameSite=none; Secure" }
+            {
+              "name": "Access-Control-Allow-Origin",
+              "value": "*"
+            },
+            {
+              "name": "connection",
+              "value": "close"
+            },
+            {
+              "name": "content-length",
+              "value": "227"
+            },
+            {
+              "name": "content-type",
+              "value": "application/json"
+            },
+            {
+              "name": "date",
+              "value": "Wed, 28 Feb 2024 14:32:30 GMT"
+            },
+            {
+              "name": "server",
+              "value": "uvicorn"
+            },
+            {
+              "name": "set-cookie",
+              "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc; Path=/; SameSite=none; Secure"
+            }
           ],
           "content": {
             "size": -1,
@@ -113,7 +234,11 @@
           "redirectURL": ""
         },
         "cache": {},
-        "timings": { "send": -1, "wait": -1, "receive": 0.894 }
+        "timings": {
+          "send": -1,
+          "wait": -1,
+          "receive": 0.894
+        }
       },
       {
         "startedDateTime": "2024-02-28T14:32:30.937Z",
@@ -124,21 +249,66 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Accept", "value": "application/json, text/plain, */*" },
-            { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" },
-            { "name": "Accept-Language", "value": "en-US,en;q=0.9" },
-            { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
-            { "name": "Connection", "value": "keep-alive" },
-            { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
-            { "name": "Host", "value": "localhost:3000" },
-            { "name": "Referer", "value": "http://localhost:3000/" },
-            { "name": "Sec-Fetch-Dest", "value": "empty" },
-            { "name": "Sec-Fetch-Mode", "value": "cors" },
-            { "name": "Sec-Fetch-Site", "value": "same-origin" },
-            { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" },
-            { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" },
-            { "name": "sec-ch-ua-mobile", "value": "?0" },
-            { "name": "sec-ch-ua-platform", "value": "\"Linux\"" }
+            {
+              "name": "Accept",
+              "value": "application/json, text/plain, */*"
+            },
+            {
+              "name": "Accept-Encoding",
+              "value": "gzip, deflate, br, zstd"
+            },
+            {
+              "name": "Accept-Language",
+              "value": "en-US,en;q=0.9"
+            },
+            {
+              "name": "Authorization",
+              "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc"
+            },
+            {
+              "name": "Connection",
+              "value": "keep-alive"
+            },
+            {
+              "name": "Cookie",
+              "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc"
+            },
+            {
+              "name": "Host",
+              "value": "localhost:3000"
+            },
+            {
+              "name": "Referer",
+              "value": "http://localhost:3000/"
+            },
+            {
+              "name": "Sec-Fetch-Dest",
+              "value": "empty"
+            },
+            {
+              "name": "Sec-Fetch-Mode",
+              "value": "cors"
+            },
+            {
+              "name": "Sec-Fetch-Site",
+              "value": "same-origin"
+            },
+            {
+              "name": "User-Agent",
+              "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36"
+            },
+            {
+              "name": "sec-ch-ua",
+              "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\""
+            },
+            {
+              "name": "sec-ch-ua-mobile",
+              "value": "?0"
+            },
+            {
+              "name": "sec-ch-ua-platform",
+              "value": "\"Linux\""
+            }
           ],
           "queryString": [],
           "headersSize": -1,
@@ -150,12 +320,30 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Access-Control-Allow-Origin", "value": "*" },
-            { "name": "connection", "value": "close" },
-            { "name": "content-length", "value": "253" },
-            { "name": "content-type", "value": "application/json" },
-            { "name": "date", "value": "Wed, 28 Feb 2024 14:32:30 GMT" },
-            { "name": "server", "value": "uvicorn" }
+            {
+              "name": "Access-Control-Allow-Origin",
+              "value": "*"
+            },
+            {
+              "name": "connection",
+              "value": "close"
+            },
+            {
+              "name": "content-length",
+              "value": "253"
+            },
+            {
+              "name": "content-type",
+              "value": "application/json"
+            },
+            {
+              "name": "date",
+              "value": "Wed, 28 Feb 2024 14:32:30 GMT"
+            },
+            {
+              "name": "server",
+              "value": "uvicorn"
+            }
           ],
           "content": {
             "size": -1,
@@ -167,7 +355,11 @@
           "redirectURL": ""
         },
         "cache": {},
-        "timings": { "send": -1, "wait": -1, "receive": 0.944 }
+        "timings": {
+          "send": -1,
+          "wait": -1,
+          "receive": 0.944
+        }
       },
       {
         "startedDateTime": "2024-02-28T14:32:30.976Z",
@@ -178,21 +370,66 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Accept", "value": "application/json, text/plain, */*" },
-            { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" },
-            { "name": "Accept-Language", "value": "en-US,en;q=0.9" },
-            { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
-            { "name": "Connection", "value": "keep-alive" },
-            { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
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+              "value": "gzip, deflate, br, zstd"
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+              "value": "keep-alive"
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+              "name": "Host",
+              "value": "localhost:3000"
+            },
+            {
+              "name": "Referer",
+              "value": "http://localhost:3000/"
+            },
+            {
+              "name": "Sec-Fetch-Dest",
+              "value": "empty"
+            },
+            {
+              "name": "Sec-Fetch-Mode",
+              "value": "cors"
+            },
+            {
+              "name": "Sec-Fetch-Site",
+              "value": "same-origin"
+            },
+            {
+              "name": "User-Agent",
+              "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36"
+            },
+            {
+              "name": "sec-ch-ua",
+              "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\""
+            },
+            {
+              "name": "sec-ch-ua-mobile",
+              "value": "?0"
+            },
+            {
+              "name": "sec-ch-ua-platform",
+              "value": "\"Linux\""
+            }
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@@ -204,12 +441,30 @@
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-            { "name": "date", "value": "Wed, 28 Feb 2024 14:32:30 GMT" },
-            { "name": "server", "value": "uvicorn" }
+            {
+              "name": "Access-Control-Allow-Origin",
+              "value": "*"
+            },
+            {
+              "name": "connection",
+              "value": "close"
+            },
+            {
+              "name": "content-length",
+              "value": "19"
+            },
+            {
+              "name": "content-type",
+              "value": "application/json"
+            },
+            {
+              "name": "date",
+              "value": "Wed, 28 Feb 2024 14:32:30 GMT"
+            },
+            {
+              "name": "server",
+              "value": "uvicorn"
+            }
           ],
           "content": {
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@@ -221,7 +476,11 @@
           "redirectURL": ""
         },
         "cache": {},
-        "timings": { "send": -1, "wait": -1, "receive": 0.697 }
+        "timings": {
+          "send": -1,
+          "wait": -1,
+          "receive": 0.697
+        }
       },
       {
         "startedDateTime": "2024-02-28T14:32:30.976Z",
@@ -232,21 +491,66 @@
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-            { "name": "Referer", "value": "http://localhost:3000/" },
-            { "name": "Sec-Fetch-Dest", "value": "empty" },
-            { "name": "Sec-Fetch-Mode", "value": "cors" },
-            { "name": "Sec-Fetch-Site", "value": "same-origin" },
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-            { "name": "sec-ch-ua-mobile", "value": "?0" },
-            { "name": "sec-ch-ua-platform", "value": "\"Linux\"" }
+            {
+              "name": "Accept",
+              "value": "application/json, text/plain, */*"
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+              "name": "Accept-Encoding",
+              "value": "gzip, deflate, br, zstd"
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+              "value": "en-US,en;q=0.9"
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+              "value": "keep-alive"
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+            },
+            {
+              "name": "Host",
+              "value": "localhost:3000"
+            },
+            {
+              "name": "Referer",
+              "value": "http://localhost:3000/"
+            },
+            {
+              "name": "Sec-Fetch-Dest",
+              "value": "empty"
+            },
+            {
+              "name": "Sec-Fetch-Mode",
+              "value": "cors"
+            },
+            {
+              "name": "Sec-Fetch-Site",
+              "value": "same-origin"
+            },
+            {
+              "name": "User-Agent",
+              "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36"
+            },
+            {
+              "name": "sec-ch-ua",
+              "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\""
+            },
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+              "name": "sec-ch-ua-mobile",
+              "value": "?0"
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+            {
+              "name": "sec-ch-ua-platform",
+              "value": "\"Linux\""
+            }
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           "queryString": [],
           "headersSize": -1,
@@ -258,24 +562,46 @@
           "httpVersion": "HTTP/1.1",
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           "headers": [
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-            { "name": "date", "value": "Wed, 28 Feb 2024 14:32:30 GMT" },
-            { "name": "server", "value": "uvicorn" }
+            {
+              "name": "Access-Control-Allow-Origin",
+              "value": "*"
+            },
+            {
+              "name": "connection",
+              "value": "close"
+            },
+            {
+              "name": "content-length",
+              "value": "633593"
+            },
+            {
+              "name": "content-type",
+              "value": "application/json"
+            },
+            {
+              "name": "date",
+              "value": "Wed, 28 Feb 2024 14:32:30 GMT"
+            },
+            {
+              "name": "server",
+              "value": "uvicorn"
+            }
           ],
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-            "text": "{\"chains\":{\"ConversationalRetrievalChain\":{\"template\":{\"callbacks\":{\"type\":\"Callbacks\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"condense_question_llm\":{\"type\":\"BaseLanguageModel\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"condense_question_llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"condense_question_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":{\"name\":null,\"input_variables\":[\"chat_history\",\"question\"],\"input_types\":{},\"output_parser\":null,\"partial_variables\":{},\"metadata\":null,\"tags\":null,\"template\":\"Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.\\n\\nChat History:\\n{chat_history}\\nFollow Up Input: {question}\\nStandalone question:\",\"template_format\":\"f-string\",\"validate_template\":false},\"fileTypes\":[],\"password\":false,\"name\":\"condense_question_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseChatMemory\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"retriever\":{\"type\":\"BaseRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chain_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"stuff\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"stuff\",\"map_reduce\",\"map_rerank\",\"refine\"],\"name\":\"chain_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"combine_docs_chain_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"combine_docs_chain_kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"return_source_documents\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_source_documents\",\"display_name\":\"Return source documents\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationalRetrievalChain\"},\"description\":\"Convenience method to load chain from LLM and retriever.\",\"base_classes\":[\"BaseConversationalRetrievalChain\",\"Runnable\",\"Chain\",\"Generic\",\"Text\",\"RunnableSerializable\",\"Serializable\",\"object\",\"ConversationalRetrievalChain\",\"Callable\"],\"display_name\":\"ConversationalRetrievalChain\",\"documentation\":\"https://python.langchain.com/docs/modules/chains/popular/chat_vector_db\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"LLMCheckerChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain.chains import LLMCheckerChain\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, Chain\\n\\n\\nclass LLMCheckerChainComponent(CustomComponent):\\n    display_name = \\\"LLMCheckerChain\\\"\\n    description = \\\"\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/chains/additional/llm_checker\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        llm: BaseLanguageModel,\\n    ) -> Union[Chain, Callable]:\\n        return LLMCheckerChain.from_llm(llm=llm)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"LLMCheckerChain\",\"documentation\":\"https://python.langchain.com/docs/modules/chains/additional/llm_checker\",\"custom_fields\":{\"llm\":null},\"output_types\":[\"Chain\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"LLMMathChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm_chain\":{\"type\":\"LLMChain\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm_chain\",\"display_name\":\"LLM Chain\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import LLMChain, LLMMathChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, BaseMemory, Chain\\n\\n\\nclass LLMMathChainComponent(CustomComponent):\\n    display_name = \\\"LLMMathChain\\\"\\n    description = \\\"Chain that interprets a prompt and executes python code to do math.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/chains/additional/llm_math\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"llm_chain\\\": {\\\"display_name\\\": \\\"LLM Chain\\\"},\\n            \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n            \\\"input_key\\\": {\\\"display_name\\\": \\\"Input Key\\\"},\\n            \\\"output_key\\\": {\\\"display_name\\\": \\\"Output Key\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        llm: BaseLanguageModel,\\n        llm_chain: LLMChain,\\n        input_key: str = \\\"question\\\",\\n        output_key: str = \\\"answer\\\",\\n        memory: Optional[BaseMemory] = None,\\n    ) -> Union[LLMMathChain, Callable, Chain]:\\n        return LLMMathChain(llm=llm, llm_chain=llm_chain, input_key=input_key, output_key=output_key, memory=memory)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"input_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"question\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"display_name\":\"Input Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"answer\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"display_name\":\"Output Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Chain that interprets a prompt and executes python code to do math.\",\"base_classes\":[\"LLMMathChain\",\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"LLMMathChain\",\"documentation\":\"https://python.langchain.com/docs/modules/chains/additional/llm_math\",\"custom_fields\":{\"llm\":null,\"llm_chain\":null,\"input_key\":null,\"output_key\":null,\"memory\":null},\"output_types\":[\"LLMMathChain\",\"Callable\",\"Chain\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RetrievalQA\":{\"template\":{\"combine_documents_chain\":{\"type\":\"BaseCombineDocumentsChain\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"combine_documents_chain\",\"display_name\":\"Combine Documents Chain\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"retriever\":{\"type\":\"BaseRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"retriever\",\"display_name\":\"Retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains.combine_documents.base import BaseCombineDocumentsChain\\nfrom langchain.chains.retrieval_qa.base import BaseRetrievalQA, RetrievalQA\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseMemory, BaseRetriever, Text\\n\\n\\nclass RetrievalQAComponent(CustomComponent):\\n    display_name = \\\"Retrieval QA\\\"\\n    description = \\\"Chain for question-answering against an index.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"combine_documents_chain\\\": {\\\"display_name\\\": \\\"Combine Documents Chain\\\"},\\n            \\\"retriever\\\": {\\\"display_name\\\": \\\"Retriever\\\"},\\n            \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\", \\\"required\\\": False},\\n            \\\"input_key\\\": {\\\"display_name\\\": \\\"Input Key\\\", \\\"advanced\\\": True},\\n            \\\"output_key\\\": {\\\"display_name\\\": \\\"Output Key\\\", \\\"advanced\\\": True},\\n            \\\"return_source_documents\\\": {\\\"display_name\\\": \\\"Return Source Documents\\\"},\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\", \\\"input_types\\\": [\\\"Text\\\", \\\"Document\\\"]},\\n        }\\n\\n    def build(\\n        self,\\n        combine_documents_chain: BaseCombineDocumentsChain,\\n        retriever: BaseRetriever,\\n        inputs: str = \\\"\\\",\\n        memory: Optional[BaseMemory] = None,\\n        input_key: str = \\\"query\\\",\\n        output_key: str = \\\"result\\\",\\n        return_source_documents: bool = True,\\n    ) -> Union[BaseRetrievalQA, Callable, Text]:\\n        runnable = RetrievalQA(\\n            combine_documents_chain=combine_documents_chain,\\n            retriever=retriever,\\n            memory=memory,\\n            input_key=input_key,\\n            output_key=output_key,\\n            return_source_documents=return_source_documents,\\n        )\\n        if isinstance(inputs, Document):\\n            inputs = inputs.page_content\\n        self.status = runnable\\n        result = runnable.invoke({input_key: inputs})\\n        result = result.content if hasattr(result, \\\"content\\\") else result\\n        # Result is a dict with keys \\\"query\\\",  \\\"result\\\" and \\\"source_documents\\\"\\n        # for now we just return the result\\n        records = self.to_records(result.get(\\\"source_documents\\\"))\\n        references_str = \\\"\\\"\\n        if return_source_documents:\\n            references_str = self.create_references_from_records(records)\\n        result_str = result.get(\\\"result\\\")\\n        final_result = \\\"\\\\n\\\".join([result_str, references_str])\\n        self.status = final_result\\n        return final_result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"input_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"query\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"display_name\":\"Input Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"input_types\":[\"Text\",\"Document\",\"Text\"],\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"output_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"result\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"display_name\":\"Output Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_source_documents\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_source_documents\",\"display_name\":\"Return Source Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Chain for question-answering against an index.\",\"base_classes\":[\"Runnable\",\"Chain\",\"BaseRetrievalQA\",\"Generic\",\"Text\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"Retrieval QA\",\"documentation\":\"\",\"custom_fields\":{\"combine_documents_chain\":null,\"retriever\":null,\"inputs\":null,\"memory\":null,\"input_key\":null,\"output_key\":null,\"return_source_documents\":null},\"output_types\":[\"BaseRetrievalQA\",\"Callable\",\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RetrievalQAWithSourcesChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"retriever\":{\"type\":\"BaseRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"retriever\",\"display_name\":\"Retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chain_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"stuff\",\"map_reduce\",\"map_rerank\",\"refine\"],\"name\":\"chain_type\",\"display_name\":\"Chain Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"The type of chain to use to combined Documents.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.chains import RetrievalQAWithSourcesChain\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, BaseMemory, BaseRetriever, Text\\n\\n\\nclass RetrievalQAWithSourcesChainComponent(CustomComponent):\\n    display_name = \\\"RetrievalQAWithSourcesChain\\\"\\n    description = \\\"Question-answering with sources over an index.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"chain_type\\\": {\\n                \\\"display_name\\\": \\\"Chain Type\\\",\\n                \\\"options\\\": [\\\"stuff\\\", \\\"map_reduce\\\", \\\"map_rerank\\\", \\\"refine\\\"],\\n                \\\"info\\\": \\\"The type of chain to use to combined Documents.\\\",\\n            },\\n            \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n            \\\"return_source_documents\\\": {\\\"display_name\\\": \\\"Return Source Documents\\\"},\\n            \\\"retriever\\\": {\\\"display_name\\\": \\\"Retriever\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        inputs: str,\\n        retriever: BaseRetriever,\\n        llm: BaseLanguageModel,\\n        chain_type: str,\\n        memory: Optional[BaseMemory] = None,\\n        return_source_documents: Optional[bool] = True,\\n    ) -> Text:\\n        runnable = RetrievalQAWithSourcesChain.from_chain_type(\\n            llm=llm,\\n            chain_type=chain_type,\\n            memory=memory,\\n            return_source_documents=return_source_documents,\\n            retriever=retriever,\\n        )\\n        if isinstance(inputs, Document):\\n            inputs = inputs.page_content\\n        self.status = runnable\\n        input_key = runnable.input_keys[0]\\n        result = runnable.invoke({input_key: inputs})\\n        result = result.content if hasattr(result, \\\"content\\\") else result\\n        # Result is a dict with keys \\\"query\\\",  \\\"result\\\" and \\\"source_documents\\\"\\n        # for now we just return the result\\n        records = self.to_records(result.get(\\\"source_documents\\\"))\\n        references_str = \\\"\\\"\\n        if return_source_documents:\\n            references_str = self.create_references_from_records(records)\\n        result_str = result.get(\\\"answer\\\")\\n        final_result = \\\"\\\\n\\\".join([result_str, references_str])\\n        self.status = final_result\\n        return final_result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_source_documents\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_source_documents\",\"display_name\":\"Return Source Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Question-answering with sources over an index.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"RetrievalQAWithSourcesChain\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"retriever\":null,\"llm\":null,\"chain_type\":null,\"memory\":null,\"return_source_documents\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLDatabaseChain\":{\"template\":{\"db\":{\"type\":\"SQLDatabase\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"db\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prompt\":{\"type\":\"BasePromptTemplate\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"SQLDatabaseChain\"},\"description\":\"Create a SQLDatabaseChain from an LLM and a database connection.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"Text\",\"RunnableSerializable\",\"Serializable\",\"object\",\"SQLDatabaseChain\",\"Callable\"],\"display_name\":\"SQLDatabaseChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"CombineDocsChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chain_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"stuff\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"stuff\",\"map_reduce\",\"map_rerank\",\"refine\"],\"name\":\"chain_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"load_qa_chain\"},\"description\":\"Load question answering chain.\",\"base_classes\":[\"function\",\"BaseCombineDocumentsChain\"],\"display_name\":\"CombineDocsChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"SeriesCharacterChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"character\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"character\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"series\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"series\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"SeriesCharacterChain\"},\"description\":\"SeriesCharacterChain is a chain you can use to have a conversation with a character from a series.\",\"base_classes\":[\"Chain\",\"BaseCustomChain\",\"ConversationChain\",\"function\",\"SeriesCharacterChain\",\"LLMChain\"],\"display_name\":\"SeriesCharacterChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"MidJourneyPromptChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseChatMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"MidJourneyPromptChain\"},\"description\":\"MidJourneyPromptChain is a chain you can use to generate new MidJourney prompts.\",\"base_classes\":[\"Chain\",\"BaseCustomChain\",\"MidJourneyPromptChain\",\"ConversationChain\",\"LLMChain\"],\"display_name\":\"MidJourneyPromptChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"TimeTravelGuideChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseChatMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"TimeTravelGuideChain\"},\"description\":\"Time travel guide chain.\",\"base_classes\":[\"Chain\",\"BaseCustomChain\",\"ConversationChain\",\"TimeTravelGuideChain\",\"LLMChain\"],\"display_name\":\"TimeTravelGuideChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"LLMChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prompt\":{\"type\":\"BasePromptTemplate\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.chains import LLMChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n    BaseLanguageModel,\\n    BaseMemory,\\n    BasePromptTemplate,\\n    Text,\\n)\\n\\n\\nclass LLMChainComponent(CustomComponent):\\n    display_name = \\\"LLMChain\\\"\\n    description = \\\"Chain to run queries against LLMs\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        prompt: BasePromptTemplate,\\n        llm: BaseLanguageModel,\\n        memory: Optional[BaseMemory] = None,\\n    ) -> Text:\\n        runnable = LLMChain(prompt=prompt, llm=llm, memory=memory)\\n        result_dict = runnable.invoke({})\\n        output_key = runnable.output_key\\n        result = result_dict[output_key]\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Chain to run queries against LLMs\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"LLMChain\",\"documentation\":\"\",\"custom_fields\":{\"prompt\":null,\"llm\":null,\"memory\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLGenerator\":{\"template\":{\"db\":{\"type\":\"SQLDatabase\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"db\",\"display_name\":\"Database\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"Text\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prompt\":{\"type\":\"PromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"The prompt must contain `{question}`.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.chains import create_sql_query_chain\\nfrom langchain_community.utilities.sql_database import SQLDatabase\\nfrom langchain_core.prompts import PromptTemplate\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, Text\\n\\n\\nclass SQLGeneratorComponent(CustomComponent):\\n    display_name = \\\"Natural Language to SQL\\\"\\n    description = \\\"Generate SQL from natural language.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"db\\\": {\\\"display_name\\\": \\\"Database\\\"},\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"prompt\\\": {\\n                \\\"display_name\\\": \\\"Prompt\\\",\\n                \\\"info\\\": \\\"The prompt must contain `{question}`.\\\",\\n            },\\n            \\\"top_k\\\": {\\n                \\\"display_name\\\": \\\"Top K\\\",\\n                \\\"info\\\": \\\"The number of results per select statement to return. If 0, no limit.\\\",\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        inputs: Text,\\n        db: SQLDatabase,\\n        llm: BaseLanguageModel,\\n        top_k: int = 5,\\n        prompt: Optional[PromptTemplate] = None,\\n    ) -> Text:\\n        if top_k > 0:\\n            kwargs = {\\n                \\\"k\\\": top_k,\\n            }\\n        if not prompt:\\n            sql_query_chain = create_sql_query_chain(llm=llm, db=db, **kwargs)\\n        else:\\n            template = prompt.template if hasattr(prompt, \\\"template\\\") else prompt\\n            # Check if {question} is in the prompt\\n            if \\\"{question}\\\" not in template or \\\"question\\\" not in template.input_variables:\\n                raise ValueError(\\\"Prompt must contain `{question}` to be used with Natural Language to SQL.\\\")\\n            sql_query_chain = create_sql_query_chain(llm=llm, db=db, prompt=prompt, **kwargs)\\n        query_writer = sql_query_chain | {\\\"query\\\": lambda x: x.replace(\\\"SQLQuery:\\\", \\\"\\\").strip()}\\n        response = query_writer.invoke({\\\"question\\\": inputs})\\n        query = response.get(\\\"query\\\")\\n        self.status = query\\n        return query\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":false,\"dynamic\":false,\"info\":\"The number of results per select statement to return. If 0, no limit.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate SQL from natural language.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Natural Language to SQL\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"db\":null,\"llm\":null,\"top_k\":null,\"prompt\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ConversationChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"Memory to load context from. If none is provided, a ConversationBufferMemory will be used.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import ConversationChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, BaseMemory, Chain, Text\\n\\n\\nclass ConversationChainComponent(CustomComponent):\\n    display_name = \\\"ConversationChain\\\"\\n    description = \\\"Chain to have a conversation and load context from memory.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"memory\\\": {\\n                \\\"display_name\\\": \\\"Memory\\\",\\n                \\\"info\\\": \\\"Memory to load context from. If none is provided, a ConversationBufferMemory will be used.\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        inputs: str,\\n        llm: BaseLanguageModel,\\n        memory: Optional[BaseMemory] = None,\\n    ) -> Union[Chain, Callable, Text]:\\n        if memory is None:\\n            chain = ConversationChain(llm=llm)\\n        else:\\n            chain = ConversationChain(llm=llm, memory=memory)\\n        result = chain.invoke(inputs)\\n        # result is an AIMessage which is a subclass of BaseMessage\\n        # We need to check if it is a string or a BaseMessage\\n        if hasattr(result, \\\"content\\\") and isinstance(result.content, str):\\n            self.status = \\\"is message\\\"\\n            result = result.content\\n        elif isinstance(result, str):\\n            self.status = \\\"is_string\\\"\\n            result = result\\n        else:\\n            # is dict\\n            result = result.get(\\\"response\\\")\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Chain to have a conversation and load context from memory.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"Text\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"ConversationChain\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"llm\":null,\"memory\":null},\"output_types\":[\"Chain\",\"Callable\",\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"agents\":{\"ZeroShotAgent\":{\"template\":{\"callback_manager\":{\"type\":\"BaseCallbackManager\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"callback_manager\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"output_parser\":{\"type\":\"AgentOutputParser\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tools\":{\"type\":\"BaseTool\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"format_instructions\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":true,\"value\":\"Use the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [{tool_names}]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\",\"fileTypes\":[],\"password\":false,\"name\":\"format_instructions\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_variables\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"Answer the following questions as best you can. You have access to the following tools:\",\"fileTypes\":[],\"password\":false,\"name\":\"prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"suffix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"Begin!\\n\\nQuestion: {input}\\nThought:{agent_scratchpad}\",\"fileTypes\":[],\"password\":false,\"name\":\"suffix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"ZeroShotAgent\"},\"description\":\"Construct an agent from an LLM and tools.\",\"base_classes\":[\"ZeroShotAgent\",\"Callable\",\"BaseSingleActionAgent\",\"Agent\"],\"display_name\":\"ZeroShotAgent\",\"documentation\":\"https://python.langchain.com/docs/modules/agents/how_to/custom_mrkl_agent\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"JsonAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"toolkit\":{\"type\":\"JsonToolkit\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"toolkit\",\"display_name\":\"Toolkit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.agents import AgentExecutor, create_json_agent\\nfrom langflow.field_typing import (\\n    BaseLanguageModel,\\n)\\nfrom langchain_community.agent_toolkits.json.toolkit import JsonToolkit\\n\\n\\nclass JsonAgentComponent(CustomComponent):\\n    display_name = \\\"JsonAgent\\\"\\n    description = \\\"Construct a json agent from an LLM and tools.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"toolkit\\\": {\\\"display_name\\\": \\\"Toolkit\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        llm: BaseLanguageModel,\\n        toolkit: JsonToolkit,\\n    ) -> AgentExecutor:\\n        return create_json_agent(llm=llm, toolkit=toolkit)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct a json agent from an LLM and tools.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\"],\"display_name\":\"JsonAgent\",\"documentation\":\"\",\"custom_fields\":{\"llm\":null,\"toolkit\":null},\"output_types\":[\"AgentExecutor\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CSVAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".csv\"],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, AgentExecutor\\nfrom langchain_experimental.agents.agent_toolkits.csv.base import create_csv_agent\\n\\n\\nclass CSVAgentComponent(CustomComponent):\\n    display_name = \\\"CSVAgent\\\"\\n    description = \\\"Construct a CSV agent from a CSV and tools.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/agents/toolkits/csv\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\", \\\"type\\\": BaseLanguageModel},\\n            \\\"path\\\": {\\\"display_name\\\": \\\"Path\\\", \\\"field_type\\\": \\\"file\\\", \\\"suffixes\\\": [\\\".csv\\\"], \\\"file_types\\\": [\\\".csv\\\"]},\\n        }\\n\\n    def build(\\n        self,\\n        llm: BaseLanguageModel,\\n        path: str,\\n    ) -> AgentExecutor:\\n        # Instantiate and return the CSV agent class with the provided llm and path\\n        return create_csv_agent(llm=llm, path=path)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct a CSV agent from a CSV and tools.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\"],\"display_name\":\"CSVAgent\",\"documentation\":\"https://python.langchain.com/docs/modules/agents/toolkits/csv\",\"custom_fields\":{\"llm\":null,\"path\":null},\"output_types\":[\"AgentExecutor\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vector_store_toolkit\":{\"type\":\"VectorStoreToolkit\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vector_store_toolkit\",\"display_name\":\"Vector Store Info\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.agents import AgentExecutor, create_vectorstore_agent\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreToolkit\\nfrom typing import Union, Callable\\nfrom langflow.field_typing import BaseLanguageModel\\n\\n\\nclass VectorStoreAgentComponent(CustomComponent):\\n    display_name = \\\"VectorStoreAgent\\\"\\n    description = \\\"Construct an agent from a Vector Store.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"vector_store_toolkit\\\": {\\\"display_name\\\": \\\"Vector Store Info\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        llm: BaseLanguageModel,\\n        vector_store_toolkit: VectorStoreToolkit,\\n    ) -> Union[AgentExecutor, Callable]:\\n        return create_vectorstore_agent(llm=llm, toolkit=vector_store_toolkit)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct an agent from a Vector Store.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\",\"Callable\"],\"display_name\":\"VectorStoreAgent\",\"documentation\":\"\",\"custom_fields\":{\"llm\":null,\"vector_store_toolkit\":null},\"output_types\":[\"AgentExecutor\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreRouterAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vectorstoreroutertoolkit\":{\"type\":\"VectorStoreRouterToolkit\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstoreroutertoolkit\",\"display_name\":\"Vector Store Router Toolkit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain_core.language_models.base import BaseLanguageModel\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreRouterToolkit\\nfrom langchain.agents import create_vectorstore_router_agent\\nfrom typing import Callable\\n\\n\\nclass VectorStoreRouterAgentComponent(CustomComponent):\\n    display_name = \\\"VectorStoreRouterAgent\\\"\\n    description = \\\"Construct an agent from a Vector Store Router.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"vectorstoreroutertoolkit\\\": {\\\"display_name\\\": \\\"Vector Store Router Toolkit\\\"},\\n        }\\n\\n    def build(self, llm: BaseLanguageModel, vectorstoreroutertoolkit: VectorStoreRouterToolkit) -> Callable:\\n        return create_vectorstore_router_agent(llm=llm, toolkit=vectorstoreroutertoolkit)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct an agent from a Vector Store Router.\",\"base_classes\":[\"Callable\"],\"display_name\":\"VectorStoreRouterAgent\",\"documentation\":\"\",\"custom_fields\":{\"llm\":null,\"vectorstoreroutertoolkit\":null},\"output_types\":[\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Union, Callable\\nfrom langchain.agents import AgentExecutor\\nfrom langflow.field_typing import BaseLanguageModel\\nfrom langchain_community.agent_toolkits.sql.base import create_sql_agent\\nfrom langchain.sql_database import SQLDatabase\\nfrom langchain_community.agent_toolkits import SQLDatabaseToolkit\\n\\n\\nclass SQLAgentComponent(CustomComponent):\\n    display_name = \\\"SQLAgent\\\"\\n    description = \\\"Construct an SQL agent from an LLM and tools.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"database_uri\\\": {\\\"display_name\\\": \\\"Database URI\\\"},\\n            \\\"verbose\\\": {\\\"display_name\\\": \\\"Verbose\\\", \\\"value\\\": False, \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        llm: BaseLanguageModel,\\n        database_uri: str,\\n        verbose: bool = False,\\n    ) -> Union[AgentExecutor, Callable]:\\n        db = SQLDatabase.from_uri(database_uri)\\n        toolkit = SQLDatabaseToolkit(db=db, llm=llm)\\n        return create_sql_agent(llm=llm, toolkit=toolkit)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"database_uri\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"database_uri\",\"display_name\":\"Database URI\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct an SQL agent from an LLM and tools.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\",\"Callable\"],\"display_name\":\"SQLAgent\",\"documentation\":\"\",\"custom_fields\":{\"llm\":null,\"database_uri\":null,\"verbose\":null},\"output_types\":[\"AgentExecutor\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OpenAIConversationalAgent\":{\"template\":{\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"system_message\":{\"type\":\"SystemMessagePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"system_message\",\"display_name\":\"System Message\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tools\":{\"type\":\"Tool\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langchain.agents.agent import AgentExecutor\\nfrom langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import _get_default_system_message\\nfrom langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent\\nfrom langchain.memory.token_buffer import ConversationTokenBufferMemory\\nfrom langchain.prompts import SystemMessagePromptTemplate\\nfrom langchain.prompts.chat import MessagesPlaceholder\\nfrom langchain.schema.memory import BaseMemory\\nfrom langchain.tools import Tool\\nfrom langchain_community.chat_models import ChatOpenAI\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing.range_spec import RangeSpec\\n\\n\\nclass ConversationalAgent(CustomComponent):\\n    display_name: str = \\\"OpenAI Conversational Agent\\\"\\n    description: str = \\\"Conversational Agent that can use OpenAI's function calling API\\\"\\n\\n    def build_config(self):\\n        openai_function_models = [\\n            \\\"gpt-4-turbo-preview\\\",\\n            \\\"gpt-4-0125-preview\\\",\\n            \\\"gpt-4-1106-preview\\\",\\n            \\\"gpt-4-vision-preview\\\",\\n            \\\"gpt-3.5-turbo-0125\\\",\\n            \\\"gpt-3.5-turbo-1106\\\",\\n        ]\\n        return {\\n            \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n            \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n            \\\"system_message\\\": {\\\"display_name\\\": \\\"System Message\\\"},\\n            \\\"max_token_limit\\\": {\\\"display_name\\\": \\\"Max Token Limit\\\"},\\n            \\\"model_name\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"options\\\": openai_function_models,\\n                \\\"value\\\": openai_function_models[0],\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"value\\\": 0.2,\\n                \\\"range_spec\\\": RangeSpec(min=0, max=2, step=0.1),\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        model_name: str,\\n        openai_api_key: str,\\n        tools: List[Tool],\\n        openai_api_base: Optional[str] = None,\\n        memory: Optional[BaseMemory] = None,\\n        system_message: Optional[SystemMessagePromptTemplate] = None,\\n        max_token_limit: int = 2000,\\n        temperature: float = 0.9,\\n    ) -> AgentExecutor:\\n        llm = ChatOpenAI(\\n            model=model_name,\\n            api_key=openai_api_key,\\n            base_url=openai_api_base,\\n            max_tokens=max_token_limit,\\n            temperature=temperature,\\n        )\\n        if not memory:\\n            memory_key = \\\"chat_history\\\"\\n            memory = ConversationTokenBufferMemory(\\n                memory_key=memory_key,\\n                return_messages=True,\\n                output_key=\\\"output\\\",\\n                llm=llm,\\n                max_token_limit=max_token_limit,\\n            )\\n        else:\\n            memory_key = memory.memory_key  # type: ignore\\n\\n        _system_message = system_message or _get_default_system_message()\\n        prompt = OpenAIFunctionsAgent.create_prompt(\\n            system_message=_system_message,  # type: ignore\\n            extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)],\\n        )\\n        agent = OpenAIFunctionsAgent(\\n            llm=llm,\\n            tools=tools,\\n            prompt=prompt,  # type: ignore\\n        )\\n        return AgentExecutor(\\n            agent=agent,\\n            tools=tools,  # type: ignore\\n            memory=memory,\\n            verbose=True,\\n            return_intermediate_steps=True,\\n            handle_parsing_errors=True,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_token_limit\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":2000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_token_limit\",\"display_name\":\"Max Token Limit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-4-turbo-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-4-turbo-preview\",\"gpt-4-0125-preview\",\"gpt-4-1106-preview\",\"gpt-4-vision-preview\",\"gpt-3.5-turbo-0125\",\"gpt-3.5-turbo-1106\"],\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.2,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":0.0,\"max\":2.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Conversational Agent that can use OpenAI's function calling API\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\"],\"display_name\":\"OpenAI Conversational Agent\",\"documentation\":\"\",\"custom_fields\":{\"model_name\":null,\"openai_api_key\":null,\"tools\":null,\"openai_api_base\":null,\"memory\":null,\"system_message\":null,\"max_token_limit\":null,\"temperature\":null},\"output_types\":[\"AgentExecutor\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AgentInitializer\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"Language Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseChatMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tools\":{\"type\":\"Tool\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"agent\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"zero-shot-react-description\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"zero-shot-react-description\",\"react-docstore\",\"self-ask-with-search\",\"conversational-react-description\",\"chat-zero-shot-react-description\",\"chat-conversational-react-description\",\"structured-chat-zero-shot-react-description\",\"openai-functions\",\"openai-multi-functions\",\"JsonAgent\",\"CSVAgent\",\"VectorStoreAgent\",\"VectorStoreRouterAgent\",\"SQLAgent\"],\"name\":\"agent\",\"display_name\":\"Agent Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, List, Optional, Union\\n\\nfrom langchain.agents import AgentExecutor, AgentType, initialize_agent, types\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseChatMemory, BaseLanguageModel, Tool\\n\\n\\nclass AgentInitializerComponent(CustomComponent):\\n    display_name: str = \\\"Agent Initializer\\\"\\n    description: str = \\\"Initialize a Langchain Agent.\\\"\\n    documentation: str = \\\"https://python.langchain.com/docs/modules/agents/agent_types/\\\"\\n\\n    def build_config(self):\\n        agents = list(types.AGENT_TO_CLASS.keys())\\n        # field_type and required are optional\\n        return {\\n            \\\"agent\\\": {\\\"options\\\": agents, \\\"value\\\": agents[0], \\\"display_name\\\": \\\"Agent Type\\\"},\\n            \\\"max_iterations\\\": {\\\"display_name\\\": \\\"Max Iterations\\\", \\\"value\\\": 10},\\n            \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n            \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"Language Model\\\"},\\n            \\\"code\\\": {\\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        agent: str,\\n        llm: BaseLanguageModel,\\n        tools: List[Tool],\\n        max_iterations: int,\\n        memory: Optional[BaseChatMemory] = None,\\n    ) -> Union[AgentExecutor, Callable]:\\n        agent = AgentType(agent)\\n        if memory:\\n            return initialize_agent(\\n                tools=tools,\\n                llm=llm,\\n                agent=agent,\\n                memory=memory,\\n                return_intermediate_steps=True,\\n                handle_parsing_errors=True,\\n                max_iterations=max_iterations,\\n            )\\n        return initialize_agent(\\n            tools=tools,\\n            llm=llm,\\n            agent=agent,\\n            return_intermediate_steps=True,\\n            handle_parsing_errors=True,\\n            max_iterations=max_iterations,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_iterations\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_iterations\",\"display_name\":\"Max Iterations\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Initialize a Langchain Agent.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\",\"Callable\"],\"display_name\":\"Agent Initializer\",\"documentation\":\"https://python.langchain.com/docs/modules/agents/agent_types/\",\"custom_fields\":{\"agent\":null,\"llm\":null,\"tools\":null,\"max_iterations\":null,\"memory\":null},\"output_types\":[\"AgentExecutor\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"memories\":{\"ConversationBufferMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationBufferMemory\"},\"description\":\"Buffer for storing conversation memory.\",\"base_classes\":[\"BaseMemory\",\"BaseChatMemory\",\"ConversationBufferMemory\",\"Serializable\"],\"display_name\":\"ConversationBufferMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/buffer\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"ConversationBufferWindowMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationBufferWindowMemory\"},\"description\":\"Buffer for storing conversation memory inside a limited size window.\",\"base_classes\":[\"ConversationBufferWindowMemory\",\"BaseMemory\",\"BaseChatMemory\",\"Serializable\"],\"display_name\":\"ConversationBufferWindowMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/buffer_window\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"ConversationEntityMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"entity_extraction_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_extraction_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"entity_store\":{\"type\":\"BaseEntityStore\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_store\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"entity_summarization_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_summarization_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chat_history_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"history\",\"fileTypes\":[],\"password\":false,\"name\":\"chat_history_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"entity_cache\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationEntityMemory\"},\"description\":\"Entity extractor & summarizer memory.\",\"base_classes\":[\"ConversationEntityMemory\",\"BaseMemory\",\"BaseChatMemory\",\"Serializable\"],\"display_name\":\"ConversationEntityMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/integrations/entity_memory_with_sqlite\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"ConversationKGMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"entity_extraction_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_extraction_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"kg\":{\"type\":\"NetworkxEntityGraph\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"kg\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"knowledge_extraction_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"knowledge_extraction_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"summary_message_cls\":{\"type\":\"Type[langchain_core.messages.base.BaseMessage]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"summary_message_cls\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationKGMemory\"},\"description\":\"Knowledge graph conversation memory.\",\"base_classes\":[\"BaseChatMemory\",\"BaseMemory\",\"ConversationKGMemory\",\"Serializable\"],\"display_name\":\"ConversationKGMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/kg\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"ConversationSummaryMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"summary_message_cls\":{\"type\":\"Type[langchain_core.messages.base.BaseMessage]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"summary_message_cls\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"buffer\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"password\":false,\"name\":\"buffer\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationSummaryMemory\"},\"description\":\"Conversation summarizer to chat 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ed\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"requests_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"RequestsPutTool\"},\"description\":\"\",\"base_classes\":[\"RequestsPutTool\",\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"BaseRequestsTool\"],\"display_name\":\"RequestsPutTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"WikipediaQueryRun\":{\"template\":{\"api_wrapper\":{\"type\":\"WikipediaAPIWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"WikipediaQueryRun\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"WikipediaQueryRun\",\"Serializable\",\"object\"],\"display_name\":\"WikipediaQueryRun\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"WolframAlphaQueryRun\":{\"template\":{\"api_wrapper\":{\"type\":\"WolframAlphaAPIWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"WolframAlphaQueryRun\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"WolframAlphaQueryRun\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\"],\"display_name\":\"WolframAlphaQueryRun\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false}},\"toolkits\":{\"JsonToolkit\":{\"template\":{\"spec\":{\"type\":\"JsonSpec\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"spec\",\"display_name\":\"Spec\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain_community.tools.json.tool import JsonSpec\\nfrom langchain_community.agent_toolkits.json.toolkit import JsonToolkit\\n\\n\\nclass JsonToolkitComponent(CustomComponent):\\n    display_name = \\\"JsonToolkit\\\"\\n    description = \\\"Toolkit for interacting with a JSON spec.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"spec\\\": {\\\"display_name\\\": \\\"Spec\\\", \\\"type\\\": JsonSpec},\\n        }\\n\\n    def build(self, spec: JsonSpec) -> JsonToolkit:\\n        return JsonToolkit(spec=spec)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Toolkit for interacting with a JSON spec.\",\"base_classes\":[\"BaseToolkit\",\"JsonToolkit\"],\"display_name\":\"JsonToolkit\",\"documentation\":\"\",\"custom_fields\":{\"spec\":null},\"output_types\":[\"JsonToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OpenAPIToolkit\":{\"template\":{\"json_agent\":{\"type\":\"AgentExecutor\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"json_agent\",\"display_name\":\"JSON Agent\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"requests_wrapper\":{\"type\":\"TextRequestsWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"requests_wrapper\",\"display_name\":\"Text Requests Wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_community.agent_toolkits.openapi.toolkit import BaseToolkit, OpenAPIToolkit\\nfrom langchain_community.utilities.requests import TextRequestsWrapper\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import AgentExecutor\\n\\n\\nclass OpenAPIToolkitComponent(CustomComponent):\\n    display_name = \\\"OpenAPIToolkit\\\"\\n    description = \\\"Toolkit for interacting with an OpenAPI API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"json_agent\\\": {\\\"display_name\\\": \\\"JSON Agent\\\"},\\n            \\\"requests_wrapper\\\": {\\\"display_name\\\": \\\"Text Requests Wrapper\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        json_agent: AgentExecutor,\\n        requests_wrapper: TextRequestsWrapper,\\n    ) -> BaseToolkit:\\n        return OpenAPIToolkit(json_agent=json_agent, requests_wrapper=requests_wrapper)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Toolkit for interacting with an OpenAPI API.\",\"base_classes\":[\"BaseToolkit\"],\"display_name\":\"OpenAPIToolkit\",\"documentation\":\"\",\"custom_fields\":{\"json_agent\":null,\"requests_wrapper\":null},\"output_types\":[\"BaseToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreInfo\":{\"template\":{\"vectorstore\":{\"type\":\"VectorStore\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstore\",\"display_name\":\"VectorStore\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo\\nfrom langchain_community.vectorstores import VectorStore\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass VectorStoreInfoComponent(CustomComponent):\\n    display_name = \\\"VectorStoreInfo\\\"\\n    description = \\\"Information about a VectorStore\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"vectorstore\\\": {\\\"display_name\\\": \\\"VectorStore\\\"},\\n            \\\"description\\\": {\\\"display_name\\\": \\\"Description\\\", \\\"multiline\\\": True},\\n            \\\"name\\\": {\\\"display_name\\\": \\\"Name\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        vectorstore: VectorStore,\\n        description: str,\\n        name: str,\\n    ) -> Union[VectorStoreInfo, Callable]:\\n        return VectorStoreInfo(vectorstore=vectorstore, description=description, name=name)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"description\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"description\",\"display_name\":\"Description\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"name\",\"display_name\":\"Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Information about a VectorStore\",\"base_classes\":[\"Callable\",\"VectorStoreInfo\"],\"display_name\":\"VectorStoreInfo\",\"documentation\":\"\",\"custom_fields\":{\"vectorstore\":null,\"description\":null,\"name\":null},\"output_types\":[\"VectorStoreInfo\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreRouterToolkit\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vectorstores\":{\"type\":\"VectorStoreInfo\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstores\",\"display_name\":\"Vector Stores\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreRouterToolkit\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo\\nfrom langflow.field_typing import BaseLanguageModel, Tool\\n\\n\\nclass VectorStoreRouterToolkitComponent(CustomComponent):\\n    display_name = \\\"VectorStoreRouterToolkit\\\"\\n    description = \\\"Toolkit for routing between Vector Stores.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"vectorstores\\\": {\\\"display_name\\\": \\\"Vector Stores\\\"},\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n        }\\n\\n    def build(\\n        self, vectorstores: List[VectorStoreInfo], llm: BaseLanguageModel\\n    ) -> Union[Tool, VectorStoreRouterToolkit]:\\n        print(\\\"vectorstores\\\", vectorstores)\\n        print(\\\"llm\\\", llm)\\n        return VectorStoreRouterToolkit(vectorstores=vectorstores, llm=llm)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Toolkit for routing between Vector Stores.\",\"base_classes\":[\"Runnable\",\"BaseToolkit\",\"Generic\",\"VectorStoreRouterToolkit\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\"],\"display_name\":\"VectorStoreRouterToolkit\",\"documentation\":\"\",\"custom_fields\":{\"vectorstores\":null,\"llm\":null},\"output_types\":[\"Tool\",\"VectorStoreRouterToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreToolkit\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vectorstore_info\":{\"type\":\"VectorStoreInfo\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstore_info\",\"display_name\":\"Vector Store Info\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreToolkit\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo\\nfrom langflow.field_typing import (\\n    BaseLanguageModel,\\n)\\nfrom langflow.field_typing import (\\n    Tool,\\n)\\nfrom typing import Union\\n\\n\\nclass VectorStoreToolkitComponent(CustomComponent):\\n    display_name = \\\"VectorStoreToolkit\\\"\\n    description = \\\"Toolkit for interacting with a Vector Store.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"vectorstore_info\\\": {\\\"display_name\\\": \\\"Vector Store Info\\\"},\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        vectorstore_info: VectorStoreInfo,\\n        llm: BaseLanguageModel,\\n    ) -> Union[Tool, VectorStoreToolkit]:\\n        return VectorStoreToolkit(vectorstore_info=vectorstore_info, llm=llm)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Toolkit for interacting with a Vector Store.\",\"base_classes\":[\"Runnable\",\"BaseToolkit\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"VectorStoreToolkit\"],\"display_name\":\"VectorStoreToolkit\",\"documentation\":\"\",\"custom_fields\":{\"vectorstore_info\":null,\"llm\":null},\"output_types\":[\"Tool\",\"VectorStoreToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Metaphor\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Union\\n\\nfrom langchain.agents import tool\\nfrom langchain.agents.agent_toolkits.base import BaseToolkit\\nfrom langchain.tools import Tool\\nfrom metaphor_python import Metaphor  # type: ignore\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass MetaphorToolkit(CustomComponent):\\n    display_name: str = \\\"Metaphor\\\"\\n    description: str = \\\"Metaphor Toolkit\\\"\\n    documentation = \\\"https://python.langchain.com/docs/integrations/tools/metaphor_search\\\"\\n    beta: bool = True\\n    # api key should be password = True\\n    field_config = {\\n        \\\"metaphor_api_key\\\": {\\\"display_name\\\": \\\"Metaphor API Key\\\", \\\"password\\\": True},\\n        \\\"code\\\": {\\\"advanced\\\": True},\\n    }\\n\\n    def build(\\n        self,\\n        metaphor_api_key: str,\\n        use_autoprompt: bool = True,\\n        search_num_results: int = 5,\\n        similar_num_results: int = 5,\\n    ) -> Union[Tool, BaseToolkit]:\\n        # If documents, then we need to create a Vectara instance using .from_documents\\n        client = Metaphor(api_key=metaphor_api_key)\\n\\n        @tool\\n        def search(query: str):\\n            \\\"\\\"\\\"Call search engine with a query.\\\"\\\"\\\"\\n            return client.search(query, use_autoprompt=use_autoprompt, num_results=search_num_results)\\n\\n        @tool\\n        def get_contents(ids: List[str]):\\n            \\\"\\\"\\\"Get contents of a webpage.\\n\\n            The ids passed in should be a list of ids as fetched from `search`.\\n            \\\"\\\"\\\"\\n            return client.get_contents(ids)\\n\\n        @tool\\n        def find_similar(url: str):\\n            \\\"\\\"\\\"Get search results similar to a given URL.\\n\\n            The url passed in should be a URL returned from `search`\\n            \\\"\\\"\\\"\\n            return client.find_similar(url, num_results=similar_num_results)\\n\\n        return [search, get_contents, find_similar]  # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"metaphor_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"metaphor_api_key\",\"display_name\":\"Metaphor API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_num_results\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_num_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"similar_num_results\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"similar_num_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"use_autoprompt\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_autoprompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Metaphor Toolkit\",\"base_classes\":[\"Runnable\",\"BaseToolkit\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\"],\"display_name\":\"Metaphor\",\"documentation\":\"https://python.langchain.com/docs/integrations/tools/metaphor_search\",\"custom_fields\":{\"metaphor_api_key\":null,\"use_autoprompt\":null,\"search_num_results\":null,\"similar_num_results\":null},\"output_types\":[\"Tool\",\"BaseToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"wrappers\":{\"TextRequestsWrapper\":{\"template\":{\"aiosession\":{\"type\":\"ClientSession\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"aiosession\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"auth\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"auth\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"{\\\"Authorization\\\": \\\"Bearer <token>\\\"}\",\"fileTypes\":[],\"password\":false,\"name\":\"headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"response_content_type\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"text\",\"fileTypes\":[],\"password\":false,\"name\":\"response_content_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"TextRequestsWrapper\"},\"description\":\"Lightweight wrapper around requests library, with async support.\",\"base_classes\":[\"TextRequestsWrapper\",\"GenericRequestsWrapper\"],\"display_name\":\"TextRequestsWrapper\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false}},\"embeddings\":{\"OpenAIEmbeddings\":{\"template\":{\"allowed_special\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"allowed_special\",\"display_name\":\"Allowed Special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chunk_size\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"client\",\"display_name\":\"Client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Callable, Dict, List, Optional, Union\\n\\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import NestedDict\\nfrom pydantic.v1.types import SecretStr\\n\\n\\nclass OpenAIEmbeddingsComponent(CustomComponent):\\n    display_name = \\\"OpenAIEmbeddings\\\"\\n    description = \\\"OpenAI embedding models\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"allowed_special\\\": {\\n                \\\"display_name\\\": \\\"Allowed Special\\\",\\n                \\\"advanced\\\": True,\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"is_list\\\": True,\\n            },\\n            \\\"default_headers\\\": {\\n                \\\"display_name\\\": \\\"Default Headers\\\",\\n                \\\"advanced\\\": True,\\n                \\\"field_type\\\": \\\"dict\\\",\\n            },\\n            \\\"default_query\\\": {\\n                \\\"display_name\\\": \\\"Default Query\\\",\\n                \\\"advanced\\\": True,\\n                \\\"field_type\\\": \\\"NestedDict\\\",\\n            },\\n            \\\"disallowed_special\\\": {\\n                \\\"display_name\\\": \\\"Disallowed Special\\\",\\n                \\\"advanced\\\": True,\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"is_list\\\": True,\\n            },\\n            \\\"chunk_size\\\": {\\\"display_name\\\": \\\"Chunk Size\\\", \\\"advanced\\\": True},\\n            \\\"client\\\": {\\\"display_name\\\": \\\"Client\\\", \\\"advanced\\\": True},\\n            \\\"deployment\\\": {\\\"display_name\\\": \\\"Deployment\\\", \\\"advanced\\\": True},\\n            \\\"embedding_ctx_length\\\": {\\n                \\\"display_name\\\": \\\"Embedding Context Length\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"max_retries\\\": {\\\"display_name\\\": \\\"Max Retries\\\", \\\"advanced\\\": True},\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model\\\",\\n                \\\"advanced\\\": False,\\n                \\\"options\\\": [\\\"text-embedding-3-small\\\", \\\"text-embedding-3-large\\\", \\\"text-embedding-ada-002\\\"],\\n            },\\n            \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"advanced\\\": True},\\n            \\\"openai_api_base\\\": {\\\"display_name\\\": \\\"OpenAI API Base\\\", \\\"password\\\": True, \\\"advanced\\\": True},\\n            \\\"openai_api_key\\\": {\\\"display_name\\\": \\\"OpenAI API Key\\\", \\\"password\\\": True},\\n            \\\"openai_api_type\\\": {\\\"display_name\\\": \\\"OpenAI API Type\\\", \\\"advanced\\\": True, \\\"password\\\": True},\\n            \\\"openai_api_version\\\": {\\n                \\\"display_name\\\": \\\"OpenAI API Version\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"openai_organization\\\": {\\n                \\\"display_name\\\": \\\"OpenAI Organization\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"openai_proxy\\\": {\\\"display_name\\\": \\\"OpenAI Proxy\\\", \\\"advanced\\\": True},\\n            \\\"request_timeout\\\": {\\\"display_name\\\": \\\"Request Timeout\\\", \\\"advanced\\\": True},\\n            \\\"show_progress_bar\\\": {\\n                \\\"display_name\\\": \\\"Show Progress Bar\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"skip_empty\\\": {\\\"display_name\\\": \\\"Skip Empty\\\", \\\"advanced\\\": True},\\n            \\\"tiktoken_model_name\\\": {\\\"display_name\\\": \\\"TikToken Model Name\\\"},\\n            \\\"tikToken_enable\\\": {\\\"display_name\\\": \\\"TikToken Enable\\\", \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        default_headers: Optional[Dict[str, str]] = None,\\n        default_query: Optional[NestedDict] = {},\\n        allowed_special: List[str] = [],\\n        disallowed_special: List[str] = [\\\"all\\\"],\\n        chunk_size: int = 1000,\\n        client: Optional[Any] = None,\\n        deployment: str = \\\"text-embedding-3-small\\\",\\n        embedding_ctx_length: int = 8191,\\n        max_retries: int = 6,\\n        model: str = \\\"text-embedding-3-small\\\",\\n        model_kwargs: NestedDict = {},\\n        openai_api_base: Optional[str] = None,\\n        openai_api_key: Optional[str] = \\\"\\\",\\n        openai_api_type: Optional[str] = None,\\n        openai_api_version: Optional[str] = None,\\n        openai_organization: Optional[str] = None,\\n        openai_proxy: Optional[str] = None,\\n        request_timeout: Optional[float] = None,\\n        show_progress_bar: bool = False,\\n        skip_empty: bool = False,\\n        tiktoken_enable: bool = True,\\n        tiktoken_model_name: Optional[str] = None,\\n    ) -> Union[OpenAIEmbeddings, Callable]:\\n        # This is to avoid errors with Vector Stores (e.g Chroma)\\n        if disallowed_special == [\\\"all\\\"]:\\n            disallowed_special = \\\"all\\\"  # type: ignore\\n\\n        api_key = SecretStr(openai_api_key) if openai_api_key else None\\n\\n        return OpenAIEmbeddings(\\n            tiktoken_enabled=tiktoken_enable,\\n            default_headers=default_headers,\\n            default_query=default_query,\\n            allowed_special=set(allowed_special),\\n            disallowed_special=\\\"all\\\",\\n            chunk_size=chunk_size,\\n            client=client,\\n            deployment=deployment,\\n            embedding_ctx_length=embedding_ctx_length,\\n            max_retries=max_retries,\\n            model=model,\\n            model_kwargs=model_kwargs,\\n            base_url=openai_api_base,\\n            api_key=api_key,\\n            openai_api_type=openai_api_type,\\n            api_version=openai_api_version,\\n            organization=openai_organization,\\n            openai_proxy=openai_proxy,\\n            timeout=request_timeout,\\n            show_progress_bar=show_progress_bar,\\n            skip_empty=skip_empty,\\n            tiktoken_model_name=tiktoken_model_name,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"default_headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"default_headers\",\"display_name\":\"Default Headers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"default_query\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"default_query\",\"display_name\":\"Default Query\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"deployment\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"text-embedding-3-small\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"deployment\",\"display_name\":\"Deployment\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"disallowed_special\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[\"all\"],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"disallowed_special\",\"display_name\":\"Disallowed Special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"embedding_ctx_length\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":8191,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding_ctx_length\",\"display_name\":\"Embedding Context Length\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_retries\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max Retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"text-embedding-3-small\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"text-embedding-3-small\",\"text-embedding-3-large\",\"text-embedding-ada-002\"],\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"NestedDict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"openai_api_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_type\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_type\",\"display_name\":\"OpenAI API Type\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_version\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_version\",\"display_name\":\"OpenAI API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_organization\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_proxy\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"request_timeout\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"request_timeout\",\"display_name\":\"Request Timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"show_progress_bar\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"show_progress_bar\",\"display_name\":\"Show Progress Bar\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"skip_empty\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"skip_empty\",\"display_name\":\"Skip Empty\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tiktoken_enable\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tiktoken_enable\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tiktoken_model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tiktoken_model_name\",\"display_name\":\"TikToken Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"OpenAI embedding models\",\"base_classes\":[\"Embeddings\",\"OpenAIEmbeddings\",\"Callable\"],\"display_name\":\"OpenAIEmbeddings\",\"documentation\":\"\",\"custom_fields\":{\"default_headers\":null,\"default_query\":null,\"allowed_special\":null,\"disallowed_special\":null,\"chunk_size\":null,\"client\":null,\"deployment\":null,\"embedding_ctx_length\":null,\"max_retries\":null,\"model\":null,\"model_kwargs\":null,\"openai_api_base\":null,\"openai_api_key\":null,\"openai_api_type\":null,\"openai_api_version\":null,\"openai_organization\":null,\"openai_proxy\":null,\"request_timeout\":null,\"show_progress_bar\":null,\"skip_empty\":null,\"tiktoken_enable\":null,\"tiktoken_model_name\":null},\"output_types\":[\"OpenAIEmbeddings\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CohereEmbeddings\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.embeddings.cohere import CohereEmbeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass CohereEmbeddingsComponent(CustomComponent):\\n    display_name = \\\"CohereEmbeddings\\\"\\n    description = \\\"Cohere embedding models.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"cohere_api_key\\\": {\\\"display_name\\\": \\\"Cohere API Key\\\", \\\"password\\\": True},\\n            \\\"model\\\": {\\\"display_name\\\": \\\"Model\\\", \\\"default\\\": \\\"embed-english-v2.0\\\", \\\"advanced\\\": True},\\n            \\\"truncate\\\": {\\\"display_name\\\": \\\"Truncate\\\", \\\"advanced\\\": True},\\n            \\\"max_retries\\\": {\\\"display_name\\\": \\\"Max Retries\\\", \\\"advanced\\\": True},\\n            \\\"user_agent\\\": {\\\"display_name\\\": \\\"User Agent\\\", \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        request_timeout: Optional[float] = None,\\n        cohere_api_key: str = \\\"\\\",\\n        max_retries: Optional[int] = None,\\n        model: str = \\\"embed-english-v2.0\\\",\\n        truncate: Optional[str] = None,\\n        user_agent: str = \\\"langchain\\\",\\n    ) -> CohereEmbeddings:\\n        return CohereEmbeddings(  # type: ignore\\n            max_retries=max_retries,\\n            user_agent=user_agent,\\n            request_timeout=request_timeout,\\n            cohere_api_key=cohere_api_key,\\n            model=model,\\n            truncate=truncate,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"cohere_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"cohere_api_key\",\"display_name\":\"Cohere API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_retries\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max Retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"embed-english-v2.0\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"request_timeout\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"truncate\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"truncate\",\"display_name\":\"Truncate\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"user_agent\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"langchain\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"user_agent\",\"display_name\":\"User Agent\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Cohere embedding models.\",\"base_classes\":[\"Embeddings\",\"CohereEmbeddings\"],\"display_name\":\"CohereEmbeddings\",\"documentation\":\"\",\"custom_fields\":{\"request_timeout\":null,\"cohere_api_key\":null,\"max_retries\":null,\"model\":null,\"truncate\":null,\"user_agent\":null},\"output_types\":[\"CohereEmbeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"HuggingFaceEmbeddings\":{\"template\":{\"cache_folder\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache_folder\",\"display_name\":\"Cache Folder\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Optional, Dict\\nfrom langchain_community.embeddings.huggingface import HuggingFaceEmbeddings\\n\\n\\nclass HuggingFaceEmbeddingsComponent(CustomComponent):\\n    display_name = \\\"HuggingFaceEmbeddings\\\"\\n    description = \\\"HuggingFace sentence_transformers embedding models.\\\"\\n    documentation = (\\n        \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers\\\"\\n    )\\n\\n    def build_config(self):\\n        return {\\n            \\\"cache_folder\\\": {\\\"display_name\\\": \\\"Cache Folder\\\", \\\"advanced\\\": True},\\n            \\\"encode_kwargs\\\": {\\\"display_name\\\": \\\"Encode Kwargs\\\", \\\"advanced\\\": True, \\\"field_type\\\": \\\"dict\\\"},\\n            \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"field_type\\\": \\\"dict\\\", \\\"advanced\\\": True},\\n            \\\"model_name\\\": {\\\"display_name\\\": \\\"Model Name\\\"},\\n            \\\"multi_process\\\": {\\\"display_name\\\": \\\"Multi Process\\\", \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        cache_folder: Optional[str] = None,\\n        encode_kwargs: Optional[Dict] = {},\\n        model_kwargs: Optional[Dict] = {},\\n        model_name: str = \\\"sentence-transformers/all-mpnet-base-v2\\\",\\n        multi_process: bool = False,\\n    ) -> HuggingFaceEmbeddings:\\n        return HuggingFaceEmbeddings(\\n            cache_folder=cache_folder,\\n            encode_kwargs=encode_kwargs,\\n            model_kwargs=model_kwargs,\\n            model_name=model_name,\\n            multi_process=multi_process,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"encode_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"encode_kwargs\",\"display_name\":\"Encode Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"sentence-transformers/all-mpnet-base-v2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"multi_process\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"multi_process\",\"display_name\":\"Multi Process\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"HuggingFace sentence_transformers embedding models.\",\"base_classes\":[\"Embeddings\",\"HuggingFaceEmbeddings\"],\"display_name\":\"HuggingFaceEmbeddings\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers\",\"custom_fields\":{\"cache_folder\":null,\"encode_kwargs\":null,\"model_kwargs\":null,\"model_name\":null,\"multi_process\":null},\"output_types\":[\"HuggingFaceEmbeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VertexAIEmbeddings\":{\"template\":{\"credentials\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".json\"],\"file_path\":\"\",\"password\":false,\"name\":\"credentials\",\"display_name\":\"Credentials\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain_community.embeddings import VertexAIEmbeddings\\nfrom typing import Optional, List\\n\\n\\nclass VertexAIEmbeddingsComponent(CustomComponent):\\n    display_name = \\\"VertexAIEmbeddings\\\"\\n    description = \\\"Google Cloud VertexAI embedding models.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"credentials\\\": {\\n                \\\"display_name\\\": \\\"Credentials\\\",\\n                \\\"value\\\": \\\"\\\",\\n                \\\"file_types\\\": [\\\".json\\\"],\\n                \\\"field_type\\\": \\\"file\\\",\\n            },\\n            \\\"instance\\\": {\\n                \\\"display_name\\\": \\\"instance\\\",\\n                \\\"advanced\\\": True,\\n                \\\"field_type\\\": \\\"dict\\\",\\n            },\\n            \\\"location\\\": {\\n                \\\"display_name\\\": \\\"Location\\\",\\n                \\\"value\\\": \\\"us-central1\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"max_output_tokens\\\": {\\\"display_name\\\": \\\"Max Output Tokens\\\", \\\"value\\\": 128},\\n            \\\"max_retries\\\": {\\n                \\\"display_name\\\": \\\"Max Retries\\\",\\n                \\\"value\\\": 6,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"model_name\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"value\\\": \\\"textembedding-gecko\\\",\\n            },\\n            \\\"n\\\": {\\\"display_name\\\": \\\"N\\\", \\\"value\\\": 1, \\\"advanced\\\": True},\\n            \\\"project\\\": {\\\"display_name\\\": \\\"Project\\\", \\\"advanced\\\": True},\\n            \\\"request_parallelism\\\": {\\n                \\\"display_name\\\": \\\"Request Parallelism\\\",\\n                \\\"value\\\": 5,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"stop\\\": {\\\"display_name\\\": \\\"Stop\\\", \\\"advanced\\\": True},\\n            \\\"streaming\\\": {\\n                \\\"display_name\\\": \\\"Streaming\\\",\\n                \\\"value\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"temperature\\\": {\\\"display_name\\\": \\\"Temperature\\\", \\\"value\\\": 0.0},\\n            \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"value\\\": 40, \\\"advanced\\\": True},\\n            \\\"top_p\\\": {\\\"display_name\\\": \\\"Top P\\\", \\\"value\\\": 0.95, \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        instance: Optional[str] = None,\\n        credentials: Optional[str] = None,\\n        location: str = \\\"us-central1\\\",\\n        max_output_tokens: int = 128,\\n        max_retries: int = 6,\\n        model_name: str = \\\"textembedding-gecko\\\",\\n        n: int = 1,\\n        project: Optional[str] = None,\\n        request_parallelism: int = 5,\\n        stop: Optional[List[str]] = None,\\n        streaming: bool = False,\\n        temperature: float = 0.0,\\n        top_k: int = 40,\\n        top_p: float = 0.95,\\n    ) -> VertexAIEmbeddings:\\n        return VertexAIEmbeddings(\\n            instance=instance,\\n            credentials=credentials,\\n            location=location,\\n            max_output_tokens=max_output_tokens,\\n            max_retries=max_retries,\\n            model_name=model_name,\\n            n=n,\\n            project=project,\\n            request_parallelism=request_parallelism,\\n            stop=stop,\\n            streaming=streaming,\\n            temperature=temperature,\\n            top_k=top_k,\\n            top_p=top_p,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"instance\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"instance\",\"display_name\":\"instance\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"location\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"us-central1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":128,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_retries\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max Retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"textembedding-gecko\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"n\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"project\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"project\",\"display_name\":\"Project\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"request_parallelism\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"request_parallelism\",\"display_name\":\"Request Parallelism\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"stop\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Google Cloud VertexAI embedding models.\",\"base_classes\":[\"_VertexAICommon\",\"Embeddings\",\"_VertexAIBase\",\"VertexAIEmbeddings\"],\"display_name\":\"VertexAIEmbeddings\",\"documentation\":\"\",\"custom_fields\":{\"instance\":null,\"credentials\":null,\"location\":null,\"max_output_tokens\":null,\"max_retries\":null,\"model_name\":null,\"n\":null,\"project\":null,\"request_parallelism\":null,\"stop\":null,\"streaming\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null},\"output_types\":[\"VertexAIEmbeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OllamaEmbeddings\":{\"template\":{\"base_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"http://localhost:11434\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"base_url\",\"display_name\":\"Ollama Base URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langflow import CustomComponent\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain_community.embeddings import OllamaEmbeddings\\n\\n\\nclass OllamaEmbeddingsComponent(CustomComponent):\\n    \\\"\\\"\\\"\\n    A custom component for implementing an Embeddings Model using Ollama.\\n    \\\"\\\"\\\"\\n\\n    display_name: str = \\\"Ollama Embeddings\\\"\\n    description: str = \\\"Embeddings model from Ollama.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/integrations/text_embedding/ollama\\\"\\n    beta = True\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Ollama Model\\\",\\n            },\\n            \\\"base_url\\\": {\\\"display_name\\\": \\\"Ollama Base URL\\\"},\\n            \\\"temperature\\\": {\\\"display_name\\\": \\\"Model Temperature\\\"},\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        model: str = \\\"llama2\\\",\\n        base_url: str = \\\"http://localhost:11434\\\",\\n        temperature: Optional[float] = None,\\n    ) -> Embeddings:\\n        try:\\n            output = OllamaEmbeddings(model=model, base_url=base_url, temperature=temperature)  # type: ignore\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to Ollama API.\\\") from e\\n        return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"llama2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Ollama Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Model Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Embeddings model from Ollama.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Ollama Embeddings\",\"documentation\":\"https://python.langchain.com/docs/integrations/text_embedding/ollama\",\"custom_fields\":{\"model\":null,\"base_url\":null,\"temperature\":null},\"output_types\":[\"Embeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AmazonBedrockEmbeddings\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain_community.embeddings import BedrockEmbeddings\\n\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n    \\\"\\\"\\\"\\n    A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n    \\\"\\\"\\\"\\n\\n    display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n    description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n    beta = True\\n\\n    def build_config(self):\\n        return {\\n            \\\"model_id\\\": {\\n                \\\"display_name\\\": \\\"Model Id\\\",\\n                \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n            },\\n            \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n            \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n            \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n        credentials_profile_name: Optional[str] = None,\\n        endpoint_url: Optional[str] = None,\\n        region_name: Optional[str] = None,\\n    ) -> Embeddings:\\n        try:\\n            output = BedrockEmbeddings(\\n                credentials_profile_name=credentials_profile_name,\\n                model_id=model_id,\\n                endpoint_url=endpoint_url,\\n                region_name=region_name,\\n            )  # type: ignore\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n        return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"credentials_profile_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"endpoint_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"region_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"custom_fields\":{\"model_id\":null,\"credentials_profile_name\":null,\"endpoint_url\":null,\"region_name\":null},\"output_types\":[\"Embeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AzureOpenAIEmbeddings\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_version\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"2023-08-01-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"2022-12-01\",\"2023-03-15-preview\",\"2023-05-15\",\"2023-06-01-preview\",\"2023-07-01-preview\",\"2023-08-01-preview\"],\"name\":\"api_version\",\"display_name\":\"API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_deployment\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_deployment\",\"display_name\":\"Deployment Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_endpoint\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_endpoint\",\"display_name\":\"Azure Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain.embeddings.base import Embeddings\\nfrom langchain_community.embeddings import AzureOpenAIEmbeddings\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AzureOpenAIEmbeddingsComponent(CustomComponent):\\n    display_name: str = \\\"AzureOpenAIEmbeddings\\\"\\n    description: str = \\\"Embeddings model from Azure OpenAI.\\\"\\n    documentation: str = \\\"https://python.langchain.com/docs/integrations/text_embedding/azureopenai\\\"\\n    beta = False\\n\\n    API_VERSION_OPTIONS = [\\n        \\\"2022-12-01\\\",\\n        \\\"2023-03-15-preview\\\",\\n        \\\"2023-05-15\\\",\\n        \\\"2023-06-01-preview\\\",\\n        \\\"2023-07-01-preview\\\",\\n        \\\"2023-08-01-preview\\\",\\n    ]\\n\\n    def build_config(self):\\n        return {\\n            \\\"azure_endpoint\\\": {\\n                \\\"display_name\\\": \\\"Azure Endpoint\\\",\\n                \\\"required\\\": True,\\n                \\\"info\\\": \\\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\\\",\\n            },\\n            \\\"azure_deployment\\\": {\\n                \\\"display_name\\\": \\\"Deployment Name\\\",\\n                \\\"required\\\": True,\\n            },\\n            \\\"api_version\\\": {\\n                \\\"display_name\\\": \\\"API Version\\\",\\n                \\\"options\\\": self.API_VERSION_OPTIONS,\\n                \\\"value\\\": self.API_VERSION_OPTIONS[-1],\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"api_key\\\": {\\n                \\\"display_name\\\": \\\"API Key\\\",\\n                \\\"required\\\": True,\\n                \\\"password\\\": True,\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        azure_endpoint: str,\\n        azure_deployment: str,\\n        api_version: str,\\n        api_key: str,\\n    ) -> Embeddings:\\n        try:\\n            embeddings = AzureOpenAIEmbeddings(\\n                azure_endpoint=azure_endpoint,\\n                azure_deployment=azure_deployment,\\n                api_version=api_version,\\n                api_key=api_key,\\n            )\\n\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to AzureOpenAIEmbeddings API.\\\") from e\\n\\n        return embeddings\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Embeddings model from Azure OpenAI.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"AzureOpenAIEmbeddings\",\"documentation\":\"https://python.langchain.com/docs/integrations/text_embedding/azureopenai\",\"custom_fields\":{\"azure_endpoint\":null,\"azure_deployment\":null,\"api_version\":null,\"api_key\":null},\"output_types\":[\"Embeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"HuggingFaceInferenceAPIEmbeddings\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"http://localhost:8080\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"api_url\",\"display_name\":\"API URL\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"cache_folder\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache_folder\",\"display_name\":\"Cache Folder\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Dict, Optional\\n\\nfrom langchain_community.embeddings.huggingface import HuggingFaceInferenceAPIEmbeddings\\nfrom langflow import CustomComponent\\nfrom pydantic.v1.types import SecretStr\\n\\n\\nclass HuggingFaceInferenceAPIEmbeddingsComponent(CustomComponent):\\n    display_name = \\\"HuggingFaceInferenceAPIEmbeddings\\\"\\n    description = \\\"HuggingFace sentence_transformers embedding models, API version.\\\"\\n    documentation = \\\"https://github.com/huggingface/text-embeddings-inference\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"password\\\": True, \\\"advanced\\\": True},\\n            \\\"api_url\\\": {\\\"display_name\\\": \\\"API URL\\\", \\\"advanced\\\": True},\\n            \\\"model_name\\\": {\\\"display_name\\\": \\\"Model Name\\\"},\\n            \\\"cache_folder\\\": {\\\"display_name\\\": \\\"Cache Folder\\\", \\\"advanced\\\": True},\\n            \\\"encode_kwargs\\\": {\\\"display_name\\\": \\\"Encode Kwargs\\\", \\\"advanced\\\": True, \\\"field_type\\\": \\\"dict\\\"},\\n            \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"field_type\\\": \\\"dict\\\", \\\"advanced\\\": True},\\n            \\\"multi_process\\\": {\\\"display_name\\\": \\\"Multi Process\\\", \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        api_key: Optional[str] = \\\"\\\",\\n        api_url: str = \\\"http://localhost:8080\\\",\\n        model_name: str = \\\"BAAI/bge-large-en-v1.5\\\",\\n        cache_folder: Optional[str] = None,\\n        encode_kwargs: Optional[Dict] = {},\\n        model_kwargs: Optional[Dict] = {},\\n        multi_process: bool = False,\\n    ) -> HuggingFaceInferenceAPIEmbeddings:\\n        if api_key:\\n            secret_api_key = SecretStr(api_key)\\n        else:\\n            raise ValueError(\\\"API Key is required\\\")\\n        return HuggingFaceInferenceAPIEmbeddings(\\n            api_key=secret_api_key,\\n            api_url=api_url,\\n            model_name=model_name,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"encode_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"encode_kwargs\",\"display_name\":\"Encode Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"BAAI/bge-large-en-v1.5\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"multi_process\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"multi_process\",\"display_name\":\"Multi Process\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"HuggingFace sentence_transformers embedding models, API version.\",\"base_classes\":[\"Embeddings\",\"HuggingFaceInferenceAPIEmbeddings\"],\"display_name\":\"HuggingFaceInferenceAPIEmbeddings\",\"documentation\":\"https://github.com/huggingface/text-embeddings-inference\",\"custom_fields\":{\"api_key\":null,\"api_url\":null,\"model_name\":null,\"cache_folder\":null,\"encode_kwargs\":null,\"model_kwargs\":null,\"multi_process\":null},\"output_types\":[\"HuggingFaceInferenceAPIEmbeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"documentloaders\":{\"AZLyricsLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"AZLyricsLoader\"},\"description\":\"Load `AZLyrics` webpages.\",\"base_classes\":[\"Document\"],\"display_name\":\"AZLyricsLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/azlyrics\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"AirbyteJSONLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".json\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"BSHTMLLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".html\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"BSHTMLLoader\"},\"description\":\"Load `HTML` files and parse them with `beautiful soup`.\",\"base_classes\":[\"Document\"],\"display_name\":\"BSHTMLLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/html\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"CSVLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".csv\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"CoNLLULoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".csv\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CoNLLULoader\"},\"description\":\"Load `CoNLL-U` files.\",\"base_classes\":[\"Document\"],\"display_name\":\"CoNLLULoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/conll-u\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"CollegeConfidentialLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CollegeConfidentialLoader\"},\"description\":\"Load `College Confidential` webpages.\",\"base_classes\":[\"Document\"],\"display_name\":\"CollegeConfidentialLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/college_confidential\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"DirectoryLoader\":{\"template\":{\"glob\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"**/*.txt\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"glob\",\"display_name\":\"glob\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"load_hidden\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"False\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"load_hidden\",\"display_name\":\"Load hidden files\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_concurrency\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_concurrency\",\"display_name\":\"Max concurrency\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"recursive\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"True\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"recursive\",\"display_name\":\"Recursive\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"silent_errors\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"False\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"silent_errors\",\"display_name\":\"Silent errors\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"use_multithreading\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"True\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_multithreading\",\"display_name\":\"Use multithreading\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"DirectoryLoader\"},\"description\":\"Load from a directory.\",\"base_classes\":[\"Document\"],\"display_name\":\"DirectoryLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/file_directory\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"EverNoteLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".xml\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"EverNoteLoader\"},\"description\":\"Load from `EverNote`.\",\"base_classes\":[\"Document\"],\"display_name\":\"EverNoteLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/evernote\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"FacebookChatLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".json\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"FacebookChatLoader\"},\"description\":\"Load `Facebook Chat` messages directory dump.\",\"base_classes\":[\"Document\"],\"display_name\":\"FacebookChatLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/facebook_chat\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"GitLoader\":{\"template\":{\"branch\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"branch\",\"display_name\":\"Branch\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"clone_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"clone_url\",\"display_name\":\"Clone 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directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"NotionDirectoryLoader\"},\"description\":\"Load `Notion directory` dump.\",\"base_classes\":[\"Document\"],\"display_name\":\"NotionDirectoryLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/notion\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"PyPDFLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".pdf\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"PyPDFLoader\"},\"description\":\"Load PDF using pypdf into list of documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"PyPDFLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/pdf\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"PyPDFDirectoryLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"PyPDFDirectoryLoader\"},\"description\":\"Load a directory with `PDF` files using `pypdf` and chunks at character level.\",\"base_classes\":[\"Document\"],\"display_name\":\"PyPDFDirectoryLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/pdf\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"ReadTheDocsLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"ReadTheDocsLoader\"},\"description\":\"Load `ReadTheDocs` documentation directory.\",\"base_classes\":[\"Document\"],\"display_name\":\"ReadTheDocsLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/readthedocs_documentation\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"SRTLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".srt\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"SRTLoader\"},\"description\":\"Load `.srt` (subtitle) files.\",\"base_classes\":[\"Document\"],\"display_name\":\"SRTLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/subtitle\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"SlackDirectoryLoader\":{\"template\":{\"zip_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".zip\"],\"file_path\":\"\",\"password\":false,\"name\":\"zip_path\",\"display_name\":\"Path to zip file\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"workspace_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"workspace_url\",\"display_name\":\"Workspace URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"SlackDirectoryLoader\"},\"description\":\"Load from a `Slack` directory dump.\",\"base_classes\":[\"Document\"],\"display_name\":\"SlackDirectoryLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/slack\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"TextLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".txt\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"TextLoader\"},\"description\":\"Load text file.\",\"base_classes\":[\"Document\"],\"display_name\":\"TextLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredEmailLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".eml\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredEmailLoader\"},\"description\":\"Load email files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredEmailLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/email\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredHTMLLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".html\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredHTMLLoader\"},\"description\":\"Load `HTML` files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredHTMLLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/html\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredMarkdownLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".md\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredMarkdownLoader\"},\"description\":\"Load `Markdown` files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredMarkdownLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/markdown\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredPowerPointLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".pptx\",\".ppt\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredPowerPointLoader\"},\"description\":\"Load `Microsoft PowerPoint` files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredPowerPointLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/microsoft_powerpoint\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredWordDocumentLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".docx\",\".doc\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredWordDocumentLoader\"},\"description\":\"Load `Microsoft Word` file using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredWordDocumentLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/microsoft_word\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"WebBaseLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"WebBaseLoader\"},\"description\":\"Load HTML pages using `urllib` and parse them with `BeautifulSoup'.\",\"base_classes\":[\"Document\"],\"display_name\":\"WebBaseLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/web_base\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"FileLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".json\",\".txt\",\".csv\",\".jsonl\",\".html\",\".htm\",\".conllu\",\".enex\",\".msg\",\".pdf\",\".srt\",\".eml\",\".md\",\".mdx\",\".pptx\",\".docx\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"display_name\":\"File Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.utils.constants import LOADERS_INFO\\n\\n\\nclass FileLoaderComponent(CustomComponent):\\n    display_name: str = \\\"File Loader\\\"\\n    description: str = \\\"Generic File Loader\\\"\\n    beta = True\\n\\n    def build_config(self):\\n        loader_options = [\\\"Automatic\\\"] + [loader_info[\\\"name\\\"] for loader_info in LOADERS_INFO]\\n\\n        file_types = []\\n        suffixes = []\\n\\n        for loader_info in LOADERS_INFO:\\n            if \\\"allowedTypes\\\" in loader_info:\\n                file_types.extend(loader_info[\\\"allowedTypes\\\"])\\n                suffixes.extend([f\\\".{ext}\\\" for ext in loader_info[\\\"allowedTypes\\\"]])\\n\\n        return {\\n            \\\"file_path\\\": {\\n                \\\"display_name\\\": \\\"File Path\\\",\\n                \\\"required\\\": True,\\n                \\\"field_type\\\": \\\"file\\\",\\n                \\\"file_types\\\": [\\n                    \\\"json\\\",\\n                    \\\"txt\\\",\\n                    \\\"csv\\\",\\n                    \\\"jsonl\\\",\\n                    \\\"html\\\",\\n                    \\\"htm\\\",\\n                    \\\"conllu\\\",\\n                    \\\"enex\\\",\\n                    \\\"msg\\\",\\n                    \\\"pdf\\\",\\n                    \\\"srt\\\",\\n                    \\\"eml\\\",\\n                    \\\"md\\\",\\n                    \\\"mdx\\\",\\n                    \\\"pptx\\\",\\n                    \\\"docx\\\",\\n                ],\\n                \\\"suffixes\\\": [\\n                    \\\".json\\\",\\n                    \\\".txt\\\",\\n                    \\\".csv\\\",\\n                    \\\".jsonl\\\",\\n                    \\\".html\\\",\\n                    \\\".htm\\\",\\n                    \\\".conllu\\\",\\n                    \\\".enex\\\",\\n                    \\\".msg\\\",\\n                    \\\".pdf\\\",\\n                    \\\".srt\\\",\\n                    \\\".eml\\\",\\n                    \\\".md\\\",\\n                    \\\".mdx\\\",\\n                    \\\".pptx\\\",\\n                    \\\".docx\\\",\\n                ],\\n                # \\\"file_types\\\" : file_types,\\n                # \\\"suffixes\\\": suffixes,\\n            },\\n            \\\"loader\\\": {\\n                \\\"display_name\\\": \\\"Loader\\\",\\n                \\\"is_list\\\": True,\\n                \\\"required\\\": True,\\n                \\\"options\\\": loader_options,\\n                \\\"value\\\": \\\"Automatic\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(self, file_path: str, loader: str) -> Document:\\n        file_type = file_path.split(\\\".\\\")[-1]\\n\\n        # Map the loader to the correct loader class\\n        selected_loader_info = None\\n        for loader_info in LOADERS_INFO:\\n            if loader_info[\\\"name\\\"] == loader:\\n                selected_loader_info = loader_info\\n                break\\n\\n        if selected_loader_info is None and loader != \\\"Automatic\\\":\\n            raise ValueError(f\\\"Loader {loader} not found in the loader info list\\\")\\n\\n        if loader == \\\"Automatic\\\":\\n            # Determine the loader based on the file type\\n            default_loader_info = None\\n            for info in LOADERS_INFO:\\n                if \\\"defaultFor\\\" in info and file_type in info[\\\"defaultFor\\\"]:\\n                    default_loader_info = info\\n                    break\\n\\n            if default_loader_info is None:\\n                raise ValueError(f\\\"No default loader found for file type: {file_type}\\\")\\n\\n            selected_loader_info = default_loader_info\\n        if isinstance(selected_loader_info, dict):\\n            loader_import: str = selected_loader_info[\\\"import\\\"]\\n        else:\\n            raise ValueError(f\\\"Loader info for {loader} is not a dict\\\\nLoader info:\\\\n{selected_loader_info}\\\")\\n        module_name, class_name = loader_import.rsplit(\\\".\\\", 1)\\n\\n        try:\\n            # Import the loader class\\n            loader_module = __import__(module_name, fromlist=[class_name])\\n            loader_instance = getattr(loader_module, class_name)\\n        except ImportError as e:\\n            raise ValueError(f\\\"Loader {loader} could not be imported\\\\nLoader info:\\\\n{selected_loader_info}\\\") from e\\n\\n        result = loader_instance(file_path=file_path)\\n        return result.load()\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"loader\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Automatic\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Automatic\",\"Airbyte JSON (.jsonl)\",\"JSON (.json)\",\"BeautifulSoup4 HTML (.html, .htm)\",\"CSV (.csv)\",\"CoNLL-U (.conllu)\",\"EverNote (.enex)\",\"Facebook Chat (.json)\",\"Outlook Message (.msg)\",\"PyPDF (.pdf)\",\"Subtitle (.str)\",\"Text (.txt)\",\"Unstructured Email (.eml)\",\"Unstructured HTML (.html, .htm)\",\"Unstructured Markdown (.md)\",\"Unstructured PowerPoint (.pptx)\",\"Unstructured Word (.docx)\"],\"name\":\"loader\",\"display_name\":\"Loader\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generic File Loader\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"File Loader\",\"documentation\":\"\",\"custom_fields\":{\"file_path\":null,\"loader\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"UrlLoader\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List\\n\\nfrom langchain import document_loaders\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass UrlLoaderComponent(CustomComponent):\\n    display_name: str = \\\"Url Loader\\\"\\n    description: str = \\\"Generic Url Loader Component\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"web_path\\\": {\\n                \\\"display_name\\\": \\\"Url\\\",\\n                \\\"required\\\": True,\\n            },\\n            \\\"loader\\\": {\\n                \\\"display_name\\\": \\\"Loader\\\",\\n                \\\"is_list\\\": True,\\n                \\\"required\\\": True,\\n                \\\"options\\\": [\\n                    \\\"AZLyricsLoader\\\",\\n                    \\\"CollegeConfidentialLoader\\\",\\n                    \\\"GitbookLoader\\\",\\n                    \\\"HNLoader\\\",\\n                    \\\"IFixitLoader\\\",\\n                    \\\"IMSDbLoader\\\",\\n                    \\\"WebBaseLoader\\\",\\n                ],\\n                \\\"value\\\": \\\"WebBaseLoader\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(self, web_path: str, loader: str) -> List[Document]:\\n        try:\\n            loader_instance = getattr(document_loaders, loader)(web_path=web_path)\\n        except Exception as e:\\n            raise ValueError(f\\\"No loader found for: {web_path}\\\") from e\\n        docs = loader_instance.load()\\n        avg_length = sum(len(doc.page_content) for doc in docs if hasattr(doc, \\\"page_content\\\")) / len(docs)\\n        self.status = f\\\"\\\"\\\"{len(docs)} documents)\\n        \\\\nAvg. Document Length (characters): {int(avg_length)}\\n        Documents: {docs[:3]}...\\\"\\\"\\\"\\n        return docs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"loader\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"WebBaseLoader\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"AZLyricsLoader\",\"CollegeConfidentialLoader\",\"GitbookLoader\",\"HNLoader\",\"IFixitLoader\",\"IMSDbLoader\",\"WebBaseLoader\"],\"name\":\"loader\",\"display_name\":\"Loader\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generic Url Loader Component\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"Url Loader\",\"documentation\":\"\",\"custom_fields\":{\"web_path\":null,\"loader\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GatherRecords\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from concurrent import futures\\nfrom pathlib import Path\\nfrom typing import Any, Dict, List\\n\\nfrom langflow import CustomComponent\\nfrom langflow.schema import Record\\n\\n\\nclass GatherRecordsComponent(CustomComponent):\\n    display_name = \\\"Gather Records\\\"\\n    description = \\\"Gather records from a directory.\\\"\\n\\n    def build_config(self) -> Dict[str, Any]:\\n        return {\\n            \\\"load_hidden\\\": {\\n                \\\"display_name\\\": \\\"Load Hidden Files\\\",\\n                \\\"value\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"max_concurrency\\\": {\\n                \\\"display_name\\\": \\\"Max Concurrency\\\",\\n                \\\"value\\\": 10,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"path\\\": {\\\"display_name\\\": \\\"Local Directory\\\"},\\n            \\\"recursive\\\": {\\\"display_name\\\": \\\"Recursive\\\", \\\"value\\\": True, \\\"advanced\\\": True},\\n            \\\"use_multithreading\\\": {\\n                \\\"display_name\\\": \\\"Use Multithreading\\\",\\n                \\\"value\\\": True,\\n                \\\"advanced\\\": True,\\n            },\\n        }\\n\\n    def is_hidden(self, path: Path) -> bool:\\n        return path.name.startswith(\\\".\\\")\\n\\n    def retrieve_file_paths(\\n        self,\\n        path: str,\\n        types: List[str],\\n        load_hidden: bool,\\n        recursive: bool,\\n        depth: int,\\n    ) -> List[str]:\\n        path_obj = Path(path)\\n        if not path_obj.exists() or not path_obj.is_dir():\\n            raise ValueError(f\\\"Path {path} must exist and be a directory.\\\")\\n\\n        def match_types(p: Path) -> bool:\\n            return any(p.suffix == f\\\".{t}\\\" for t in types) if types else True\\n\\n        def is_not_hidden(p: Path) -> bool:\\n            return not self.is_hidden(p) or load_hidden\\n\\n        def walk_level(directory: Path, max_depth: int):\\n            directory = directory.resolve()\\n            prefix_length = len(directory.parts)\\n            for p in directory.rglob(\\\"*\\\" if recursive else \\\"[!.]*\\\"):\\n                if len(p.parts) - prefix_length <= max_depth:\\n                    yield p\\n\\n        glob = \\\"**/*\\\" if recursive else \\\"*\\\"\\n        paths = walk_level(path_obj, depth) if depth else path_obj.glob(glob)\\n        file_paths = [str(p) for p in paths if p.is_file() and match_types(p) and is_not_hidden(p)]\\n\\n        return file_paths\\n\\n    def parse_file_to_record(self, file_path: str, silent_errors: bool) -> Record:\\n        # Use the partition function to load the file\\n        from unstructured.partition.auto import partition\\n\\n        try:\\n            elements = partition(file_path)\\n        except Exception as e:\\n            if not silent_errors:\\n                raise ValueError(f\\\"Error loading file {file_path}: {e}\\\") from e\\n            return None\\n\\n        # Create a Record\\n        text = \\\"\\\\n\\\\n\\\".join([str(el) for el in elements])\\n        metadata = elements.metadata if hasattr(elements, \\\"metadata\\\") else {}\\n        metadata[\\\"file_path\\\"] = file_path\\n        record = Record(text=text, data=metadata)\\n        return record\\n\\n    def get_elements(\\n        self,\\n        file_paths: List[str],\\n        silent_errors: bool,\\n        max_concurrency: int,\\n        use_multithreading: bool,\\n    ) -> List[Record]:\\n        if use_multithreading:\\n            records = self.parallel_load_records(file_paths, silent_errors, max_concurrency)\\n        else:\\n            records = [self.parse_file_to_record(file_path, silent_errors) for file_path in file_paths]\\n        records = list(filter(None, records))\\n        return records\\n\\n    def parallel_load_records(self, file_paths: List[str], silent_errors: bool, max_concurrency: int) -> List[Record]:\\n        with futures.ThreadPoolExecutor(max_workers=max_concurrency) as executor:\\n            loaded_files = executor.map(\\n                lambda file_path: self.parse_file_to_record(file_path, silent_errors),\\n                file_paths,\\n            )\\n        return loaded_files\\n\\n    def build(\\n        self,\\n        path: str,\\n        types: List[str] = None,\\n        depth: int = 0,\\n        max_concurrency: int = 2,\\n        load_hidden: bool = False,\\n        recursive: bool = True,\\n        silent_errors: bool = False,\\n        use_multithreading: bool = True,\\n    ) -> List[Record]:\\n        resolved_path = self.resolve_path(path)\\n        file_paths = self.retrieve_file_paths(resolved_path, types, load_hidden, recursive, depth)\\n        loaded_records = []\\n\\n        if use_multithreading:\\n            loaded_records = self.parallel_load_records(file_paths, silent_errors, max_concurrency)\\n        else:\\n            loaded_records = [self.parse_file_to_record(file_path, silent_errors) for file_path in file_paths]\\n        loaded_records = list(filter(None, loaded_records))\\n        self.status = loaded_records\\n        return loaded_records\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"depth\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"depth\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"load_hidden\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"load_hidden\",\"display_name\":\"Load Hidden Files\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_concurrency\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_concurrency\",\"display_name\":\"Max Concurrency\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local Directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"recursive\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"recursive\",\"display_name\":\"Recursive\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"silent_errors\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"silent_errors\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"types\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"types\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"use_multithreading\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_multithreading\",\"display_name\":\"Use Multithreading\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Gather records from a directory.\",\"base_classes\":[\"Record\"],\"display_name\":\"Gather Records\",\"documentation\":\"\",\"custom_fields\":{\"path\":null,\"types\":null,\"depth\":null,\"max_concurrency\":null,\"load_hidden\":null,\"recursive\":null,\"silent_errors\":null,\"use_multithreading\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"textsplitters\":{\"CharacterTextSplitter\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chunk_overlap\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":200,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chunk_size\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List\\n\\nfrom langchain.text_splitter import CharacterTextSplitter\\nfrom langchain_core.documents.base import Document\\nfrom langflow import CustomComponent\\n\\n\\nclass CharacterTextSplitterComponent(CustomComponent):\\n    display_name = \\\"CharacterTextSplitter\\\"\\n    description = \\\"Splitting text that looks at characters.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n            \\\"chunk_overlap\\\": {\\\"display_name\\\": \\\"Chunk Overlap\\\", \\\"default\\\": 200},\\n            \\\"chunk_size\\\": {\\\"display_name\\\": \\\"Chunk Size\\\", \\\"default\\\": 1000},\\n            \\\"separator\\\": {\\\"display_name\\\": \\\"Separator\\\", \\\"default\\\": \\\"\\\\n\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        documents: List[Document],\\n        chunk_overlap: int = 200,\\n        chunk_size: int = 1000,\\n        separator: str = \\\"\\\\n\\\",\\n    ) -> List[Document]:\\n        # separator may come escaped from the frontend\\n        separator = separator.encode().decode(\\\"unicode_escape\\\")\\n        docs = CharacterTextSplitter(\\n            chunk_overlap=chunk_overlap,\\n            chunk_size=chunk_size,\\n            separator=separator,\\n        ).split_documents(documents)\\n        self.status = docs\\n        return docs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"separator\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\\\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"separator\",\"display_name\":\"Separator\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Splitting text that looks at characters.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"CharacterTextSplitter\",\"documentation\":\"\",\"custom_fields\":{\"documents\":null,\"chunk_overlap\":null,\"chunk_size\":null,\"separator\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RecursiveCharacterTextSplitter\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"The documents to split.\",\"title_case\":false},\"chunk_overlap\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":200,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"The amount of overlap between chunks.\",\"title_case\":false},\"chunk_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum length of each chunk.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.utils.util import build_loader_repr_from_documents\\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\\n\\n\\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\\n    display_name: str = \\\"Recursive Character Text Splitter\\\"\\n    description: str = \\\"Split text into chunks of a specified length.\\\"\\n    documentation: str = \\\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"documents\\\": {\\n                \\\"display_name\\\": \\\"Documents\\\",\\n                \\\"info\\\": \\\"The documents to split.\\\",\\n            },\\n            \\\"separators\\\": {\\n                \\\"display_name\\\": \\\"Separators\\\",\\n                \\\"info\\\": 'The characters to split on.\\\\nIf left empty defaults to [\\\"\\\\\\\\n\\\\\\\\n\\\", \\\"\\\\\\\\n\\\", \\\" \\\", \\\"\\\"].',\\n                \\\"is_list\\\": True,\\n            },\\n            \\\"chunk_size\\\": {\\n                \\\"display_name\\\": \\\"Chunk Size\\\",\\n                \\\"info\\\": \\\"The maximum length of each chunk.\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"value\\\": 1000,\\n            },\\n            \\\"chunk_overlap\\\": {\\n                \\\"display_name\\\": \\\"Chunk Overlap\\\",\\n                \\\"info\\\": \\\"The amount of overlap between chunks.\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"value\\\": 200,\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        documents: list[Document],\\n        separators: Optional[list[str]] = None,\\n        chunk_size: Optional[int] = 1000,\\n        chunk_overlap: Optional[int] = 200,\\n    ) -> list[Document]:\\n        \\\"\\\"\\\"\\n        Split text into chunks of a specified length.\\n\\n        Args:\\n            separators (list[str]): The characters to split on.\\n            chunk_size (int): The maximum length of each chunk.\\n            chunk_overlap (int): The amount of overlap between chunks.\\n            length_function (function): The function to use to calculate the length of the text.\\n\\n        Returns:\\n            list[str]: The chunks of text.\\n        \\\"\\\"\\\"\\n\\n        if separators == \\\"\\\":\\n            separators = None\\n        elif separators:\\n            # check if the separators list has escaped characters\\n            # if there are escaped characters, unescape them\\n            separators = [x.encode().decode(\\\"unicode-escape\\\") for x in separators]\\n\\n        # Make sure chunk_size and chunk_overlap are ints\\n        if isinstance(chunk_size, str):\\n            chunk_size = int(chunk_size)\\n        if isinstance(chunk_overlap, str):\\n            chunk_overlap = int(chunk_overlap)\\n        splitter = RecursiveCharacterTextSplitter(\\n            separators=separators,\\n            chunk_size=chunk_size,\\n            chunk_overlap=chunk_overlap,\\n        )\\n\\n        docs = splitter.split_documents(documents)\\n        self.repr_value = build_loader_repr_from_documents(docs)\\n        return docs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"separators\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"separators\",\"display_name\":\"Separators\",\"advanced\":false,\"dynamic\":false,\"info\":\"The characters to split on.\\nIf left empty defaults to [\\\"\\\\n\\\\n\\\", \\\"\\\\n\\\", \\\" \\\", \\\"\\\"].\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Split text into chunks of a specified length.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"Recursive Character Text Splitter\",\"documentation\":\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\",\"custom_fields\":{\"documents\":null,\"separators\":null,\"chunk_size\":null,\"chunk_overlap\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"LanguageRecursiveTextSplitter\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"The documents to split.\",\"title_case\":false},\"chunk_overlap\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":200,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"The amount of overlap between chunks.\",\"title_case\":false},\"chunk_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum length of each chunk.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.text_splitter import Language\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass LanguageRecursiveTextSplitterComponent(CustomComponent):\\n    display_name: str = \\\"Language Recursive Text Splitter\\\"\\n    description: str = \\\"Split text into chunks of a specified length based on language.\\\"\\n    documentation: str = \\\"https://docs.langflow.org/components/text-splitters#languagerecursivetextsplitter\\\"\\n\\n    def build_config(self):\\n        options = [x.value for x in Language]\\n        return {\\n            \\\"documents\\\": {\\n                \\\"display_name\\\": \\\"Documents\\\",\\n                \\\"info\\\": \\\"The documents to split.\\\",\\n            },\\n            \\\"separator_type\\\": {\\n                \\\"display_name\\\": \\\"Separator Type\\\",\\n                \\\"info\\\": \\\"The type of separator to use.\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"options\\\": options,\\n                \\\"value\\\": \\\"Python\\\",\\n            },\\n            \\\"separators\\\": {\\n                \\\"display_name\\\": \\\"Separators\\\",\\n                \\\"info\\\": \\\"The characters to split on.\\\",\\n                \\\"is_list\\\": True,\\n            },\\n            \\\"chunk_size\\\": {\\n                \\\"display_name\\\": \\\"Chunk Size\\\",\\n                \\\"info\\\": \\\"The maximum length of each chunk.\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"value\\\": 1000,\\n            },\\n            \\\"chunk_overlap\\\": {\\n                \\\"display_name\\\": \\\"Chunk Overlap\\\",\\n                \\\"info\\\": \\\"The amount of overlap between chunks.\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"value\\\": 200,\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        documents: list[Document],\\n        chunk_size: Optional[int] = 1000,\\n        chunk_overlap: Optional[int] = 200,\\n        separator_type: str = \\\"Python\\\",\\n    ) -> list[Document]:\\n        \\\"\\\"\\\"\\n        Split text into chunks of a specified length.\\n\\n        Args:\\n            separators (list[str]): The characters to split on.\\n            chunk_size (int): The maximum length of each chunk.\\n            chunk_overlap (int): The amount of overlap between chunks.\\n            length_function (function): The function to use to calculate the length of the text.\\n\\n        Returns:\\n            list[str]: The chunks of text.\\n        \\\"\\\"\\\"\\n        from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\\n        # Make sure chunk_size and chunk_overlap are ints\\n        if isinstance(chunk_size, str):\\n            chunk_size = int(chunk_size)\\n        if isinstance(chunk_overlap, str):\\n            chunk_overlap = int(chunk_overlap)\\n\\n        splitter = RecursiveCharacterTextSplitter.from_language(\\n            language=Language(separator_type),\\n            chunk_size=chunk_size,\\n            chunk_overlap=chunk_overlap,\\n        )\\n\\n        docs = splitter.split_documents(documents)\\n        return docs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"separator_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Python\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"cpp\",\"go\",\"java\",\"kotlin\",\"js\",\"ts\",\"php\",\"proto\",\"python\",\"rst\",\"ruby\",\"rust\",\"scala\",\"swift\",\"markdown\",\"latex\",\"html\",\"sol\",\"csharp\",\"cobol\",\"c\",\"lua\",\"perl\"],\"name\":\"separator_type\",\"display_name\":\"Separator Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"The type of separator to use.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Split text into chunks of a specified length based on language.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"Language Recursive Text Splitter\",\"documentation\":\"https://docs.langflow.org/components/text-splitters#languagerecursivetextsplitter\",\"custom_fields\":{\"documents\":null,\"chunk_size\":null,\"chunk_overlap\":null,\"separator_type\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"utilities\":{\"BingSearchAPIWrapper\":{\"template\":{\"bing_search_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"bing_search_url\",\"display_name\":\"Bing Search URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"bing_subscription_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"bing_subscription_key\",\"display_name\":\"Bing Subscription Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\n# Assuming `BingSearchAPIWrapper` is a class that exists in the context\\n# and has the appropriate methods and attributes.\\n# We need to make sure this class is importable from the context where this code will be running.\\nfrom langchain_community.utilities.bing_search import BingSearchAPIWrapper\\n\\n\\nclass BingSearchAPIWrapperComponent(CustomComponent):\\n    display_name = \\\"BingSearchAPIWrapper\\\"\\n    description = \\\"Wrapper for Bing Search API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"bing_search_url\\\": {\\\"display_name\\\": \\\"Bing Search URL\\\"},\\n            \\\"bing_subscription_key\\\": {\\n                \\\"display_name\\\": \\\"Bing Subscription Key\\\",\\n                \\\"password\\\": True,\\n            },\\n            \\\"k\\\": {\\\"display_name\\\": \\\"Number of results\\\", \\\"advanced\\\": True},\\n            # 'k' is not included as it is not shown (show=False)\\n        }\\n\\n    def build(\\n        self,\\n        bing_search_url: str,\\n        bing_subscription_key: str,\\n        k: int = 10,\\n    ) -> BingSearchAPIWrapper:\\n        # 'k' has a default value and is not shown (show=False), so it is hardcoded here\\n        return BingSearchAPIWrapper(bing_search_url=bing_search_url, bing_subscription_key=bing_subscription_key, k=k)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"k\",\"display_name\":\"Number of results\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper for Bing Search API.\",\"base_classes\":[\"BingSearchAPIWrapper\"],\"display_name\":\"BingSearchAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"bing_search_url\":null,\"bing_subscription_key\":null,\"k\":null},\"output_types\":[\"BingSearchAPIWrapper\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GoogleSearchAPIWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain_community.utilities.google_search import GoogleSearchAPIWrapper\\nfrom langflow import CustomComponent\\n\\n\\nclass GoogleSearchAPIWrapperComponent(CustomComponent):\\n    display_name = \\\"GoogleSearchAPIWrapper\\\"\\n    description = \\\"Wrapper for Google Search API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"google_api_key\\\": {\\\"display_name\\\": \\\"Google API Key\\\", \\\"password\\\": True},\\n            \\\"google_cse_id\\\": {\\\"display_name\\\": \\\"Google CSE ID\\\", \\\"password\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        google_api_key: str,\\n        google_cse_id: str,\\n    ) -> Union[GoogleSearchAPIWrapper, Callable]:\\n        return GoogleSearchAPIWrapper(google_api_key=google_api_key, google_cse_id=google_cse_id)  # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"google_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"google_api_key\",\"display_name\":\"Google API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"google_cse_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"google_cse_id\",\"display_name\":\"Google CSE ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper for Google Search API.\",\"base_classes\":[\"GoogleSearchAPIWrapper\",\"Callable\"],\"display_name\":\"GoogleSearchAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"google_api_key\":null,\"google_cse_id\":null},\"output_types\":[\"GoogleSearchAPIWrapper\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GoogleSerperAPIWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Dict\\n\\n# Assuming the existence of GoogleSerperAPIWrapper class in the serper module\\n# If this class does not exist, you would need to create it or import the appropriate class from another module\\nfrom langchain_community.utilities.google_serper import GoogleSerperAPIWrapper\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass GoogleSerperAPIWrapperComponent(CustomComponent):\\n    display_name = \\\"GoogleSerperAPIWrapper\\\"\\n    description = \\\"Wrapper around the Serper.dev Google Search API.\\\"\\n\\n    def build_config(self) -> Dict[str, Dict]:\\n        return {\\n            \\\"result_key_for_type\\\": {\\n                \\\"display_name\\\": \\\"Result Key for Type\\\",\\n                \\\"show\\\": True,\\n                \\\"multiline\\\": False,\\n                \\\"password\\\": False,\\n                \\\"advanced\\\": False,\\n                \\\"dynamic\\\": False,\\n                \\\"info\\\": \\\"\\\",\\n                \\\"field_type\\\": \\\"dict\\\",\\n                \\\"list\\\": False,\\n                \\\"value\\\": {\\n                    \\\"news\\\": \\\"news\\\",\\n                    \\\"places\\\": \\\"places\\\",\\n                    \\\"images\\\": \\\"images\\\",\\n                    \\\"search\\\": \\\"organic\\\",\\n                },\\n            },\\n            \\\"serper_api_key\\\": {\\n                \\\"display_name\\\": \\\"Serper API Key\\\",\\n                \\\"show\\\": True,\\n                \\\"multiline\\\": False,\\n                \\\"password\\\": True,\\n                \\\"advanced\\\": False,\\n                \\\"dynamic\\\": False,\\n                \\\"info\\\": \\\"\\\",\\n                \\\"type\\\": \\\"str\\\",\\n                \\\"list\\\": False,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        serper_api_key: str,\\n    ) -> GoogleSerperAPIWrapper:\\n        return GoogleSerperAPIWrapper(serper_api_key=serper_api_key)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"serper_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"serper_api_key\",\"display_name\":\"Serper API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper around the Serper.dev Google Search API.\",\"base_classes\":[\"GoogleSerperAPIWrapper\"],\"display_name\":\"GoogleSerperAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"serper_api_key\":null},\"output_types\":[\"GoogleSerperAPIWrapper\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SearxSearchWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Optional, Dict\\nfrom langchain_community.utilities.searx_search import SearxSearchWrapper\\n\\n\\nclass SearxSearchWrapperComponent(CustomComponent):\\n    display_name = \\\"SearxSearchWrapper\\\"\\n    description = \\\"Wrapper for Searx API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"headers\\\": {\\n                \\\"field_type\\\": \\\"dict\\\",\\n                \\\"display_name\\\": \\\"Headers\\\",\\n                \\\"multiline\\\": True,\\n                \\\"value\\\": '{\\\"Authorization\\\": \\\"Bearer <token>\\\"}',\\n            },\\n            \\\"k\\\": {\\\"display_name\\\": \\\"k\\\", \\\"advanced\\\": True, \\\"field_type\\\": \\\"int\\\", \\\"value\\\": 10},\\n            \\\"searx_host\\\": {\\n                \\\"display_name\\\": \\\"Searx Host\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"value\\\": \\\"https://searx.example.com\\\",\\n                \\\"advanced\\\": True,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        k: int = 10,\\n        headers: Optional[Dict[str, str]] = None,\\n        searx_host: str = \\\"https://searx.example.com\\\",\\n    ) -> SearxSearchWrapper:\\n        return SearxSearchWrapper(headers=headers, k=k, searx_host=searx_host)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"{\\\"Authorization\\\": \\\"Bearer <token>\\\"}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"k\",\"display_name\":\"k\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"searx_host\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"https://searx.example.com\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"searx_host\",\"display_name\":\"Searx Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper for Searx API.\",\"base_classes\":[\"SearxSearchWrapper\"],\"display_name\":\"SearxSearchWrapper\",\"documentation\":\"\",\"custom_fields\":{\"k\":null,\"headers\":null,\"searx_host\":null},\"output_types\":[\"SearxSearchWrapper\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SerpAPIWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain_community.utilities.serpapi import SerpAPIWrapper\\nfrom langflow import CustomComponent\\n\\n\\nclass SerpAPIWrapperComponent(CustomComponent):\\n    display_name = \\\"SerpAPIWrapper\\\"\\n    description = \\\"Wrapper around SerpAPI\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"serpapi_api_key\\\": {\\\"display_name\\\": \\\"SerpAPI API Key\\\", \\\"type\\\": \\\"str\\\", \\\"password\\\": True},\\n            \\\"params\\\": {\\n                \\\"display_name\\\": \\\"Parameters\\\",\\n                \\\"type\\\": \\\"dict\\\",\\n                \\\"advanced\\\": True,\\n                \\\"multiline\\\": True,\\n                \\\"value\\\": '{\\\"engine\\\": \\\"google\\\",\\\"google_domain\\\": \\\"google.com\\\",\\\"gl\\\": \\\"us\\\",\\\"hl\\\": \\\"en\\\"}',\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        serpapi_api_key: str,\\n        params: dict,\\n    ) -> Union[SerpAPIWrapper, Callable]:  # Removed quotes around SerpAPIWrapper\\n        return SerpAPIWrapper(  # type: ignore\\n            serpapi_api_key=serpapi_api_key,\\n            params=params,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"params\":{\"type\":\"dict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"{\\\"engine\\\": \\\"google\\\",\\\"google_domain\\\": \\\"google.com\\\",\\\"gl\\\": \\\"us\\\",\\\"hl\\\": \\\"en\\\"}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"params\",\"display_name\":\"Parameters\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"serpapi_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"serpapi_api_key\",\"display_name\":\"SerpAPI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper around SerpAPI\",\"base_classes\":[\"Callable\",\"SerpAPIWrapper\"],\"display_name\":\"SerpAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"serpapi_api_key\":null,\"params\":null},\"output_types\":[\"SerpAPIWrapper\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"WikipediaAPIWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain_community.utilities.wikipedia import WikipediaAPIWrapper\\nfrom langflow import CustomComponent\\n\\n# Assuming WikipediaAPIWrapper is a class that needs to be imported.\\n# The import statement is not included as it is not provided in the JSON\\n# and the actual implementation details are unknown.\\n\\n\\nclass WikipediaAPIWrapperComponent(CustomComponent):\\n    display_name = \\\"WikipediaAPIWrapper\\\"\\n    description = \\\"Wrapper around WikipediaAPI.\\\"\\n\\n    def build_config(self):\\n        return {}\\n\\n    def build(\\n        self,\\n        top_k_results: int = 3,\\n        lang: str = \\\"en\\\",\\n        load_all_available_meta: bool = False,\\n        doc_content_chars_max: int = 4000,\\n    ) -> Union[WikipediaAPIWrapper, Callable]:\\n        return WikipediaAPIWrapper(  # type: ignore\\n            top_k_results=top_k_results,\\n            lang=lang,\\n            load_all_available_meta=load_all_available_meta,\\n            doc_content_chars_max=doc_content_chars_max,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"doc_content_chars_max\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":4000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"doc_content_chars_max\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"lang\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"en\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lang\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"load_all_available_meta\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"load_all_available_meta\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_k_results\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":3,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper around WikipediaAPI.\",\"base_classes\":[\"WikipediaAPIWrapper\",\"Callable\"],\"display_name\":\"WikipediaAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"top_k_results\":null,\"lang\":null,\"load_all_available_meta\":null,\"doc_content_chars_max\":null},\"output_types\":[\"WikipediaAPIWrapper\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"WolframAlphaAPIWrapper\":{\"template\":{\"appid\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"appid\",\"display_name\":\"App ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain_community.utilities.wolfram_alpha import WolframAlphaAPIWrapper\\nfrom langflow import CustomComponent\\n\\n# Since all the fields in the JSON have show=False, we will only create a basic component\\n# without any configurable fields.\\n\\n\\nclass WolframAlphaAPIWrapperComponent(CustomComponent):\\n    display_name = \\\"WolframAlphaAPIWrapper\\\"\\n    description = \\\"Wrapper for Wolfram Alpha.\\\"\\n\\n    def build_config(self):\\n        return {\\\"appid\\\": {\\\"display_name\\\": \\\"App ID\\\", \\\"type\\\": \\\"str\\\", \\\"password\\\": True}}\\n\\n    def build(self, appid: str) -> Union[Callable, WolframAlphaAPIWrapper]:\\n        return WolframAlphaAPIWrapper(wolfram_alpha_appid=appid)  # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper for Wolfram Alpha.\",\"base_classes\":[\"WolframAlphaAPIWrapper\",\"Callable\"],\"display_name\":\"WolframAlphaAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"appid\":null},\"output_types\":[\"Callable\",\"WolframAlphaAPIWrapper\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RunnableExecutor\":{\"template\":{\"runnable\":{\"type\":\"Runnable\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"runnable\",\"display_name\":\"Runnable\",\"advanced\":false,\"dynamic\":false,\"info\":\"The runnable to execute.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_core.runnables import Runnable\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass RunnableExecComponent(CustomComponent):\\n    documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n    display_name = \\\"Runnable Executor\\\"\\n    beta = True\\n\\n    def build_config(self):\\n        return {\\n            \\\"input_key\\\": {\\n                \\\"display_name\\\": \\\"Input Key\\\",\\n                \\\"info\\\": \\\"The key to use for the input.\\\",\\n            },\\n            \\\"inputs\\\": {\\n                \\\"display_name\\\": \\\"Inputs\\\",\\n                \\\"info\\\": \\\"The inputs to pass to the runnable.\\\",\\n            },\\n            \\\"runnable\\\": {\\n                \\\"display_name\\\": \\\"Runnable\\\",\\n                \\\"info\\\": \\\"The runnable to execute.\\\",\\n            },\\n            \\\"output_key\\\": {\\n                \\\"display_name\\\": \\\"Output Key\\\",\\n                \\\"info\\\": \\\"The key to use for the output.\\\",\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        input_key: str,\\n        inputs: str,\\n        runnable: Runnable,\\n        output_key: str = \\\"output\\\",\\n    ) -> Text:\\n        result = runnable.invoke({input_key: inputs})\\n        result = result.get(output_key)\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"input_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"display_name\":\"Input Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The key to use for the input.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"The inputs to pass to the runnable.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"output\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"display_name\":\"Output Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The key to use for the output.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Runnable Executor\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"input_key\":null,\"inputs\":null,\"runnable\":null,\"output_key\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"DocumentToRecord\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List\\n\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.schema import Record\\n\\n\\nclass DocumentToRecordComponent(CustomComponent):\\n    display_name = \\\"Documents to Records\\\"\\n    description = \\\"Convert documents to records.\\\"\\n\\n    field_config = {\\n        \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n    }\\n\\n    def build(self, documents: List[Document]) -> List[Record]:\\n        if isinstance(documents, Document):\\n            documents = [documents]\\n        records = [Record.from_document(document) for document in documents]\\n        self.status = records\\n        return records\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Convert documents to records.\",\"base_classes\":[\"Record\"],\"display_name\":\"Documents to Records\",\"documentation\":\"\",\"custom_fields\":{\"documents\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GetRequest\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nimport requests\\nfrom langchain_core.documents import Document\\nfrom langflow import CustomComponent\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass GetRequest(CustomComponent):\\n    display_name: str = \\\"GET Request\\\"\\n    description: str = \\\"Make a GET request to the given URL.\\\"\\n    output_types: list[str] = [\\\"Document\\\"]\\n    documentation: str = \\\"https://docs.langflow.org/components/utilities#get-request\\\"\\n    beta: bool = True\\n    field_config = {\\n        \\\"url\\\": {\\n            \\\"display_name\\\": \\\"URL\\\",\\n            \\\"info\\\": \\\"The URL to make the request to\\\",\\n            \\\"is_list\\\": True,\\n        },\\n        \\\"headers\\\": {\\n            \\\"display_name\\\": \\\"Headers\\\",\\n            \\\"info\\\": \\\"The headers to send with the request.\\\",\\n        },\\n        \\\"code\\\": {\\\"show\\\": False},\\n        \\\"timeout\\\": {\\n            \\\"display_name\\\": \\\"Timeout\\\",\\n            \\\"field_type\\\": \\\"int\\\",\\n            \\\"info\\\": \\\"The timeout to use for the request.\\\",\\n            \\\"value\\\": 5,\\n        },\\n    }\\n\\n    def get_document(self, session: requests.Session, url: str, headers: Optional[dict], timeout: int) -> Document:\\n        try:\\n            response = session.get(url, headers=headers, timeout=int(timeout))\\n            try:\\n                response_json = response.json()\\n                result = orjson_dumps(response_json, indent_2=False)\\n            except Exception:\\n                result = response.text\\n            self.repr_value = result\\n            return Document(\\n                page_content=result,\\n                metadata={\\n                    \\\"source\\\": url,\\n                    \\\"headers\\\": headers,\\n                    \\\"status_code\\\": response.status_code,\\n                },\\n            )\\n        except requests.Timeout:\\n            return Document(\\n                page_content=\\\"Request Timed Out\\\",\\n                metadata={\\\"source\\\": url, \\\"headers\\\": headers, \\\"status_code\\\": 408},\\n            )\\n        except Exception as exc:\\n            return Document(\\n                page_content=str(exc),\\n                metadata={\\\"source\\\": url, \\\"headers\\\": headers, \\\"status_code\\\": 500},\\n            )\\n\\n    def build(\\n        self,\\n        url: str,\\n        headers: Optional[dict] = None,\\n        timeout: int = 5,\\n    ) -> list[Document]:\\n        if headers is None:\\n            headers = {}\\n        urls = url if isinstance(url, list) else [url]\\n        with requests.Session() as session:\\n            documents = [self.get_document(session, u, headers, timeout) for u in urls]\\n            self.repr_value = documents\\n        return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"title_case\":false},\"timeout\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"The timeout to use for the request.\",\"title_case\":false},\"url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Make a GET request to the given URL.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"GET Request\",\"documentation\":\"https://docs.langflow.org/components/utilities#get-request\",\"custom_fields\":{\"url\":null,\"headers\":null,\"timeout\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLExecutor\":{\"template\":{\"database\":{\"type\":\"SQLDatabase\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"database\",\"display_name\":\"Database\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"add_error\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"add_error\",\"display_name\":\"Add Error\",\"advanced\":false,\"dynamic\":false,\"info\":\"Add the error to the result.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool\\nfrom langchain_experimental.sql.base import SQLDatabase\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass SQLExecutorComponent(CustomComponent):\\n    display_name = \\\"SQL Executor\\\"\\n    description = \\\"Execute SQL query.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"database\\\": {\\\"display_name\\\": \\\"Database\\\"},\\n            \\\"include_columns\\\": {\\n                \\\"display_name\\\": \\\"Include Columns\\\",\\n                \\\"info\\\": \\\"Include columns in the result.\\\",\\n            },\\n            \\\"passthrough\\\": {\\n                \\\"display_name\\\": \\\"Passthrough\\\",\\n                \\\"info\\\": \\\"If an error occurs, return the query instead of raising an exception.\\\",\\n            },\\n            \\\"add_error\\\": {\\n                \\\"display_name\\\": \\\"Add Error\\\",\\n                \\\"info\\\": \\\"Add the error to the result.\\\",\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        query: str,\\n        database: SQLDatabase,\\n        include_columns: bool = False,\\n        passthrough: bool = False,\\n        add_error: bool = False,\\n    ) -> Text:\\n        error = None\\n        try:\\n            tool = QuerySQLDataBaseTool(db=database)\\n            result = tool.run(query, include_columns=include_columns)\\n            self.status = result\\n        except Exception as e:\\n            result = str(e)\\n            self.status = result\\n            if not passthrough:\\n                raise e\\n            error = repr(e)\\n\\n        if add_error and error is not None:\\n            result = f\\\"{result}\\\\n\\\\nError: {error}\\\\n\\\\nQuery: {query}\\\"\\n        elif error is not None:\\n            # Then we won't add the error to the result\\n            # but since we are in passthrough mode, we will return the query\\n            result = query\\n\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"include_columns\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"include_columns\",\"display_name\":\"Include Columns\",\"advanced\":false,\"dynamic\":false,\"info\":\"Include columns in the result.\",\"title_case\":false},\"passthrough\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"passthrough\",\"display_name\":\"Passthrough\",\"advanced\":false,\"dynamic\":false,\"info\":\"If an error occurs, return the query instead of raising an exception.\",\"title_case\":false},\"query\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Execute SQL query.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"SQL Executor\",\"documentation\":\"\",\"custom_fields\":{\"query\":null,\"database\":null,\"include_columns\":null,\"passthrough\":null,\"add_error\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ShouldRunNext\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"The language model to use for the decision.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"# Implement ShouldRunNext component\\nfrom langchain_core.prompts import PromptTemplate\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, Prompt\\n\\n\\nclass ShouldRunNext(CustomComponent):\\n    display_name = \\\"Should Run Next\\\"\\n    description = \\\"Decides whether to run the next component.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"prompt\\\": {\\n                \\\"display_name\\\": \\\"Prompt\\\",\\n                \\\"info\\\": \\\"The prompt to use for the decision. It should generate a boolean response (True or False).\\\",\\n            },\\n            \\\"llm\\\": {\\n                \\\"display_name\\\": \\\"LLM\\\",\\n                \\\"info\\\": \\\"The language model to use for the decision.\\\",\\n            },\\n        }\\n\\n    def build(self, template: Prompt, llm: BaseLanguageModel, **kwargs) -> dict:\\n        # This is a simple component that always returns True\\n        prompt_template = PromptTemplate.from_template(template)\\n\\n        attributes_to_check = [\\\"text\\\", \\\"page_content\\\"]\\n        for key, value in kwargs.items():\\n            for attribute in attributes_to_check:\\n                if hasattr(value, attribute):\\n                    kwargs[key] = getattr(value, attribute)\\n\\n        chain = prompt_template | llm\\n        result = chain.invoke(kwargs)\\n        if hasattr(result, \\\"content\\\") and isinstance(result.content, str):\\n            result = result.content\\n        elif isinstance(result, str):\\n            result = result\\n        else:\\n            result = result.get(\\\"response\\\")\\n\\n        if result.lower() not in [\\\"true\\\", \\\"false\\\"]:\\n            raise ValueError(\\\"The prompt should generate a boolean response (True or False).\\\")\\n        # The string should be the words true or false\\n        # if not raise an error\\n        bool_result = result.lower() == \\\"true\\\"\\n        return {\\\"condition\\\": bool_result, \\\"result\\\": kwargs}\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"template\":{\"type\":\"prompt\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Decides whether to run the next component.\",\"base_classes\":[\"object\",\"dict\"],\"display_name\":\"Should Run Next\",\"documentation\":\"\",\"custom_fields\":{\"template\":null,\"llm\":null},\"output_types\":[\"dict\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"PythonFunction\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Code\\nfrom langflow.interface.custom.utils import get_function\\n\\n\\nclass PythonFunctionComponent(CustomComponent):\\n    display_name = \\\"Python Function\\\"\\n    description = \\\"Define a Python function.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"function_code\\\": {\\n                \\\"display_name\\\": \\\"Code\\\",\\n                \\\"info\\\": \\\"The code for the function.\\\",\\n                \\\"show\\\": True,\\n            },\\n        }\\n\\n    def build(self, function_code: Code) -> Callable:\\n        self.status = function_code\\n        func = get_function(function_code)\\n        return func\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"function_code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"function_code\",\"display_name\":\"Code\",\"advanced\":false,\"dynamic\":false,\"info\":\"The code for the function.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Define a Python function.\",\"base_classes\":[\"Callable\"],\"display_name\":\"Python Function\",\"documentation\":\"\",\"custom_fields\":{\"function_code\":null},\"output_types\":[\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"PostRequest\":{\"template\":{\"document\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nimport requests\\nfrom langchain_core.documents import Document\\nfrom langflow import CustomComponent\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass PostRequest(CustomComponent):\\n    display_name: str = \\\"POST Request\\\"\\n    description: str = \\\"Make a POST request to the given URL.\\\"\\n    output_types: list[str] = [\\\"Document\\\"]\\n    documentation: str = \\\"https://docs.langflow.org/components/utilities#post-request\\\"\\n    beta: bool = True\\n    field_config = {\\n        \\\"url\\\": {\\\"display_name\\\": \\\"URL\\\", \\\"info\\\": \\\"The URL to make the request to.\\\"},\\n        \\\"headers\\\": {\\n            \\\"display_name\\\": \\\"Headers\\\",\\n            \\\"info\\\": \\\"The headers to send with the request.\\\",\\n        },\\n        \\\"code\\\": {\\\"show\\\": False},\\n        \\\"document\\\": {\\\"display_name\\\": \\\"Document\\\"},\\n    }\\n\\n    def post_document(\\n        self,\\n        session: requests.Session,\\n        document: Document,\\n        url: str,\\n        headers: Optional[dict] = None,\\n    ) -> Document:\\n        try:\\n            response = session.post(url, headers=headers, data=document.page_content)\\n            try:\\n                response_json = response.json()\\n                result = orjson_dumps(response_json, indent_2=False)\\n            except Exception:\\n                result = response.text\\n            self.repr_value = result\\n            return Document(\\n                page_content=result,\\n                metadata={\\n                    \\\"source\\\": url,\\n                    \\\"headers\\\": headers,\\n                    \\\"status_code\\\": response,\\n                },\\n            )\\n        except Exception as exc:\\n            return Document(\\n                page_content=str(exc),\\n                metadata={\\n                    \\\"source\\\": url,\\n                    \\\"headers\\\": headers,\\n                    \\\"status_code\\\": 500,\\n                },\\n            )\\n\\n    def build(\\n        self,\\n        document: Document,\\n        url: str,\\n        headers: Optional[dict] = None,\\n    ) -> list[Document]:\\n        if headers is None:\\n            headers = {}\\n\\n        if not isinstance(document, list) and isinstance(document, Document):\\n            documents: list[Document] = [document]\\n        elif isinstance(document, list) and all(isinstance(doc, Document) for doc in document):\\n            documents = document\\n        else:\\n            raise ValueError(\\\"document must be a Document or a list of Documents\\\")\\n\\n        with requests.Session() as session:\\n            documents = [self.post_document(session, doc, url, headers) for doc in documents]\\n            self.repr_value = documents\\n        return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"title_case\":false},\"url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Make a POST request to the given URL.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"POST Request\",\"documentation\":\"https://docs.langflow.org/components/utilities#post-request\",\"custom_fields\":{\"document\":null,\"url\":null,\"headers\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"IDGenerator\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"import uuid\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass UUIDGeneratorComponent(CustomComponent):\\n    documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n    display_name = \\\"Unique ID Generator\\\"\\n    description = \\\"Generates a unique ID.\\\"\\n\\n    def generate(self, *args, **kwargs):\\n        return str(uuid.uuid4().hex)\\n\\n    def build_config(self):\\n        return {\\\"unique_id\\\": {\\\"display_name\\\": \\\"Value\\\", \\\"value\\\": self.generate}}\\n\\n    def build(self, unique_id: str) -> str:\\n        return unique_id\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"unique_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"a62d43140aba4c799af4ddc400295790\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"unique_id\",\"display_name\":\"Value\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"refresh\":true,\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generates a unique ID.\",\"base_classes\":[\"object\",\"str\"],\"display_name\":\"Unique ID Generator\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"unique_id\":null},\"output_types\":[\"str\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLDatabase\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_experimental.sql.base import SQLDatabase\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass SQLDatabaseComponent(CustomComponent):\\n    display_name = \\\"SQLDatabase\\\"\\n    description = \\\"SQL Database\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"uri\\\": {\\\"display_name\\\": \\\"URI\\\", \\\"info\\\": \\\"URI to the database.\\\"},\\n        }\\n\\n    def clean_up_uri(self, uri: str) -> str:\\n        if uri.startswith(\\\"postgresql://\\\"):\\n            uri = uri.replace(\\\"postgresql://\\\", \\\"postgres://\\\")\\n        return uri.strip()\\n\\n    def build(self, uri: str) -> SQLDatabase:\\n        uri = self.clean_up_uri(uri)\\n        return SQLDatabase.from_uri(uri)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"uri\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"uri\",\"display_name\":\"URI\",\"advanced\":false,\"dynamic\":false,\"info\":\"URI to the database.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"SQL Database\",\"base_classes\":[\"object\",\"SQLDatabase\"],\"display_name\":\"SQLDatabase\",\"documentation\":\"\",\"custom_fields\":{\"uri\":null},\"output_types\":[\"SQLDatabase\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RecordsAsText\":{\"template\":{\"records\":{\"type\":\"Record\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"records\",\"display_name\":\"Records\",\"advanced\":false,\"dynamic\":false,\"info\":\"The records to convert to text.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Text\\nfrom langflow.schema import Record\\n\\n\\nclass RecordsAsTextComponent(CustomComponent):\\n    display_name = \\\"Records to Text\\\"\\n    description = \\\"Converts Records a list of Records to text using a template.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"records\\\": {\\n                \\\"display_name\\\": \\\"Records\\\",\\n                \\\"info\\\": \\\"The records to convert to text.\\\",\\n            },\\n            \\\"template\\\": {\\n                \\\"display_name\\\": \\\"Template\\\",\\n                \\\"info\\\": \\\"The template to use for formatting the records. It must contain the keys {text} and {data}.\\\",\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        records: list[Record],\\n        template: str = \\\"Text: {text}\\\\nData: {data}\\\",\\n    ) -> Text:\\n        if isinstance(records, Record):\\n            records = [records]\\n\\n        formated_records = [\\n            template.format(text=record.text, data=record.data, **record.data)\\n            for record in records\\n        ]\\n        result_string = \\\"\\\\n\\\".join(formated_records)\\n        self.status = result_string\\n        return result_string\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"template\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"Text: {text}\\\\nData: {data}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"display_name\":\"Template\",\"advanced\":false,\"dynamic\":false,\"info\":\"The template to use for formatting the records. It must contain the keys {text} and {data}.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Converts Records a list of Records to text using a template.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Records to Text\",\"documentation\":\"\",\"custom_fields\":{\"records\":null,\"template\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"UpdateRequest\":{\"template\":{\"document\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nimport requests\\nfrom langchain_core.documents import Document\\nfrom langflow import CustomComponent\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass UpdateRequest(CustomComponent):\\n    display_name: str = \\\"Update Request\\\"\\n    description: str = \\\"Make a PATCH request to the given URL.\\\"\\n    output_types: list[str] = [\\\"Document\\\"]\\n    documentation: str = \\\"https://docs.langflow.org/components/utilities#update-request\\\"\\n    beta: bool = True\\n    field_config = {\\n        \\\"url\\\": {\\\"display_name\\\": \\\"URL\\\", \\\"info\\\": \\\"The URL to make the request to.\\\"},\\n        \\\"headers\\\": {\\n            \\\"display_name\\\": \\\"Headers\\\",\\n            \\\"field_type\\\": \\\"NestedDict\\\",\\n            \\\"info\\\": \\\"The headers to send with the request.\\\",\\n        },\\n        \\\"code\\\": {\\\"show\\\": False},\\n        \\\"document\\\": {\\\"display_name\\\": \\\"Document\\\"},\\n        \\\"method\\\": {\\n            \\\"display_name\\\": \\\"Method\\\",\\n            \\\"field_type\\\": \\\"str\\\",\\n            \\\"info\\\": \\\"The HTTP method to use.\\\",\\n            \\\"options\\\": [\\\"PATCH\\\", \\\"PUT\\\"],\\n            \\\"value\\\": \\\"PATCH\\\",\\n        },\\n    }\\n\\n    def update_document(\\n        self,\\n        session: requests.Session,\\n        document: Document,\\n        url: str,\\n        headers: Optional[dict] = None,\\n        method: str = \\\"PATCH\\\",\\n    ) -> Document:\\n        try:\\n            if method == \\\"PATCH\\\":\\n                response = session.patch(url, headers=headers, data=document.page_content)\\n            elif method == \\\"PUT\\\":\\n                response = session.put(url, headers=headers, data=document.page_content)\\n            else:\\n                raise ValueError(f\\\"Unsupported method: {method}\\\")\\n            try:\\n                response_json = response.json()\\n                result = orjson_dumps(response_json, indent_2=False)\\n            except Exception:\\n                result = response.text\\n            self.repr_value = result\\n            return Document(\\n                page_content=result,\\n                metadata={\\n                    \\\"source\\\": url,\\n                    \\\"headers\\\": headers,\\n                    \\\"status_code\\\": response.status_code,\\n                },\\n            )\\n        except Exception as exc:\\n            return Document(\\n                page_content=str(exc),\\n                metadata={\\\"source\\\": url, \\\"headers\\\": headers, \\\"status_code\\\": 500},\\n            )\\n\\n    def build(\\n        self,\\n        method: str,\\n        document: Document,\\n        url: str,\\n        headers: Optional[dict] = None,\\n    ) -> List[Document]:\\n        if headers is None:\\n            headers = {}\\n\\n        if not isinstance(document, list) and isinstance(document, Document):\\n            documents: list[Document] = [document]\\n        elif isinstance(document, list) and all(isinstance(doc, Document) for doc in document):\\n            documents = document\\n        else:\\n            raise ValueError(\\\"document must be a Document or a list of Documents\\\")\\n\\n        with requests.Session() as session:\\n            documents = [self.update_document(session, doc, url, headers, method) for doc in documents]\\n            self.repr_value = documents\\n        return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"title_case\":false},\"method\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"PATCH\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"PATCH\",\"PUT\"],\"name\":\"method\",\"display_name\":\"Method\",\"advanced\":false,\"dynamic\":false,\"info\":\"The HTTP method to use.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Make a PATCH request to the given URL.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"Update Request\",\"documentation\":\"https://docs.langflow.org/components/utilities#update-request\",\"custom_fields\":{\"method\":null,\"document\":null,\"url\":null,\"headers\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"JSONDocumentBuilder\":{\"template\":{\"document\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"### JSON Document Builder\\n\\n# Build a Document containing a JSON object using a key and another Document page content.\\n\\n# **Params**\\n\\n# - **Key:** The key to use for the JSON object.\\n# - **Document:** The Document page to use for the JSON object.\\n\\n# **Output**\\n\\n# - **Document:** The Document containing the JSON object.\\n\\nfrom langchain_core.documents import Document\\nfrom langflow import CustomComponent\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass JSONDocumentBuilder(CustomComponent):\\n    display_name: str = \\\"JSON Document Builder\\\"\\n    description: str = \\\"Build a Document containing a JSON object using a key and another Document page content.\\\"\\n    output_types: list[str] = [\\\"Document\\\"]\\n    beta = True\\n    documentation: str = \\\"https://docs.langflow.org/components/utilities#json-document-builder\\\"\\n\\n    field_config = {\\n        \\\"key\\\": {\\\"display_name\\\": \\\"Key\\\"},\\n        \\\"document\\\": {\\\"display_name\\\": \\\"Document\\\"},\\n    }\\n\\n    def build(\\n        self,\\n        key: str,\\n        document: Document,\\n    ) -> Document:\\n        documents = None\\n        if isinstance(document, list):\\n            documents = [\\n                Document(page_content=orjson_dumps({key: doc.page_content}, indent_2=False)) for doc in document\\n            ]\\n        elif isinstance(document, Document):\\n            documents = Document(page_content=orjson_dumps({key: document.page_content}, indent_2=False))\\n        else:\\n            raise TypeError(f\\\"Expected Document or list of Documents, got {type(document)}\\\")\\n        self.repr_value = documents\\n        return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"key\",\"display_name\":\"Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Build a Document containing a JSON object using a key and another Document page content.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"JSON Document Builder\",\"documentation\":\"https://docs.langflow.org/components/utilities#json-document-builder\",\"custom_fields\":{\"key\":null,\"document\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"output_parsers\":{\"ResponseSchema\":{\"template\":{\"description\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"fileTypes\":[],\"password\":false,\"name\":\"description\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"type\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"string\",\"fileTypes\":[],\"password\":false,\"name\":\"type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"ResponseSchema\"},\"description\":\"A schema for a response from a structured output parser.\",\"base_classes\":[\"ResponseSchema\"],\"display_name\":\"ResponseSchema\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/output_parsers/structured\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"StructuredOutputParser\":{\"template\":{\"response_schemas\":{\"type\":\"ResponseSchema\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"response_schemas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"StructuredOutputParser\"},\"description\":\"\",\"base_classes\":[\"BaseOutputParser\",\"Runnable\",\"BaseLLMOutputParser\",\"Generic\",\"RunnableSerializable\",\"StructuredOutputParser\",\"Serializable\",\"object\"],\"display_name\":\"StructuredOutputParser\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/output_parsers/structured\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false}},\"retrievers\":{\"AmazonKendra\":{\"template\":{\"attribute_filter\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"attribute_filter\",\"display_name\":\"Attribute Filter\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.retrievers import AmazonKendraRetriever\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonKendraRetrieverComponent(CustomComponent):\\n    display_name: str = \\\"Amazon Kendra Retriever\\\"\\n    description: str = \\\"Retriever that uses the Amazon Kendra API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"index_id\\\": {\\\"display_name\\\": \\\"Index ID\\\"},\\n            \\\"region_name\\\": {\\\"display_name\\\": \\\"Region Name\\\"},\\n            \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n            \\\"attribute_filter\\\": {\\n                \\\"display_name\\\": \\\"Attribute Filter\\\",\\n                \\\"field_type\\\": \\\"code\\\",\\n            },\\n            \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"field_type\\\": \\\"int\\\"},\\n            \\\"user_context\\\": {\\n                \\\"display_name\\\": \\\"User Context\\\",\\n                \\\"field_type\\\": \\\"code\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        index_id: str,\\n        top_k: int = 3,\\n        region_name: Optional[str] = None,\\n        credentials_profile_name: Optional[str] = None,\\n        attribute_filter: Optional[dict] = None,\\n        user_context: Optional[dict] = None,\\n    ) -> BaseRetriever:\\n        try:\\n            output = AmazonKendraRetriever(\\n                index_id=index_id,\\n                top_k=top_k,\\n                region_name=region_name,\\n                credentials_profile_name=credentials_profile_name,\\n                attribute_filter=attribute_filter,\\n                user_context=user_context,\\n            )  # type: ignore\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to AmazonKendra API.\\\") from e\\n        return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"credentials_profile_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"index_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_id\",\"display_name\":\"Index ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"region_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"region_name\",\"display_name\":\"Region Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":3,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"user_context\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"user_context\",\"display_name\":\"User Context\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Retriever that uses the Amazon Kendra API.\",\"base_classes\":[\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Amazon Kendra Retriever\",\"documentation\":\"\",\"custom_fields\":{\"index_id\":null,\"top_k\":null,\"region_name\":null,\"credentials_profile_name\":null,\"attribute_filter\":null,\"user_context\":null},\"output_types\":[\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectaraSelfQueryRetriver\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"For self query retriever\",\"title_case\":false},\"vectorstore\":{\"type\":\"VectorStore\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstore\",\"display_name\":\"Vector Store\",\"advanced\":false,\"dynamic\":false,\"info\":\"Input Vectara Vectore Store\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List\\nfrom langflow import CustomComponent\\nimport json\\nfrom langchain.schema import BaseRetriever\\nfrom langchain.schema.vectorstore import VectorStore\\nfrom langchain.base_language import BaseLanguageModel\\nfrom langchain.retrievers.self_query.base import SelfQueryRetriever\\nfrom langchain.chains.query_constructor.base import AttributeInfo\\n\\n\\nclass VectaraSelfQueryRetriverComponent(CustomComponent):\\n    \\\"\\\"\\\"\\n    A custom component for implementing Vectara Self Query Retriever using a vector store.\\n    \\\"\\\"\\\"\\n\\n    display_name: str = \\\"Vectara Self Query Retriever for Vectara Vector Store\\\"\\n    description: str = \\\"Implementation of Vectara Self Query Retriever\\\"\\n    documentation = \\\"https://python.langchain.com/docs/integrations/retrievers/self_query/vectara_self_query\\\"\\n    beta = True\\n\\n    field_config = {\\n        \\\"code\\\": {\\\"show\\\": True},\\n        \\\"vectorstore\\\": {\\\"display_name\\\": \\\"Vector Store\\\", \\\"info\\\": \\\"Input Vectara Vectore Store\\\"},\\n        \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\", \\\"info\\\": \\\"For self query retriever\\\"},\\n        \\\"document_content_description\\\": {\\n            \\\"display_name\\\": \\\"Document Content Description\\\",\\n            \\\"info\\\": \\\"For self query retriever\\\",\\n        },\\n        \\\"metadata_field_info\\\": {\\n            \\\"display_name\\\": \\\"Metadata Field Info\\\",\\n            \\\"info\\\": 'Each metadata field info is a string in the form of key value pair dictionary containing additional search metadata.\\\\nExample input: {\\\"name\\\":\\\"speech\\\",\\\"description\\\":\\\"what name of the speech\\\",\\\"type\\\":\\\"string or list[string]\\\"}.\\\\nThe keys should remain constant(name, description, type)',\\n        },\\n    }\\n\\n    def build(\\n        self,\\n        vectorstore: VectorStore,\\n        document_content_description: str,\\n        llm: BaseLanguageModel,\\n        metadata_field_info: List[str],\\n    ) -> BaseRetriever:\\n        metadata_field_obj = []\\n\\n        for meta in metadata_field_info:\\n            meta_obj = json.loads(meta)\\n            if \\\"name\\\" not in meta_obj or \\\"description\\\" not in meta_obj or \\\"type\\\" not in meta_obj:\\n                raise Exception(\\\"Incorrect metadata field info format.\\\")\\n            attribute_info = AttributeInfo(\\n                name=meta_obj[\\\"name\\\"],\\n                description=meta_obj[\\\"description\\\"],\\n                type=meta_obj[\\\"type\\\"],\\n            )\\n            metadata_field_obj.append(attribute_info)\\n\\n        return SelfQueryRetriever.from_llm(\\n            llm, vectorstore, document_content_description, metadata_field_obj, verbose=True\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"document_content_description\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"document_content_description\",\"display_name\":\"Document Content Description\",\"advanced\":false,\"dynamic\":false,\"info\":\"For self query retriever\",\"title_case\":false,\"input_types\":[\"Text\"]},\"metadata_field_info\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata_field_info\",\"display_name\":\"Metadata Field Info\",\"advanced\":false,\"dynamic\":false,\"info\":\"Each metadata field info is a string in the form of key value pair dictionary containing additional search metadata.\\nExample input: {\\\"name\\\":\\\"speech\\\",\\\"description\\\":\\\"what name of the speech\\\",\\\"type\\\":\\\"string or list[string]\\\"}.\\nThe keys should remain constant(name, description, type)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vectara Self Query Retriever\",\"base_classes\":[\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Vectara Self Query Retriever for Vectara Vector Store\",\"documentation\":\"https://python.langchain.com/docs/integrations/retrievers/self_query/vectara_self_query\",\"custom_fields\":{\"vectorstore\":null,\"document_content_description\":null,\"llm\":null,\"metadata_field_info\":null},\"output_types\":[\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"MultiQueryRetriever\":{\"template\":{\"llm\":{\"type\":\"BaseLLM\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prompt\":{\"type\":\"PromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"retriever\":{\"type\":\"BaseRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"retriever\",\"display_name\":\"Retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.retrievers import MultiQueryRetriever\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLLM, BaseRetriever, PromptTemplate\\n\\n\\nclass MultiQueryRetrieverComponent(CustomComponent):\\n    display_name = \\\"MultiQueryRetriever\\\"\\n    description = \\\"Initialize from llm using default template.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/data_connection/retrievers/how_to/MultiQueryRetriever\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"prompt\\\": {\\n                \\\"display_name\\\": \\\"Prompt\\\",\\n                \\\"default\\\": {\\n                    \\\"input_variables\\\": [\\\"question\\\"],\\n                    \\\"input_types\\\": {},\\n                    \\\"output_parser\\\": None,\\n                    \\\"partial_variables\\\": {},\\n                    \\\"template\\\": \\\"You are an AI language model assistant. Your task is \\\\n\\\"\\n                    \\\"to generate 3 different versions of the given user \\\\n\\\"\\n                    \\\"question to retrieve relevant documents from a vector database. \\\\n\\\"\\n                    \\\"By generating multiple perspectives on the user question, \\\\n\\\"\\n                    \\\"your goal is to help the user overcome some of the limitations \\\\n\\\"\\n                    \\\"of distance-based similarity search. Provide these alternative \\\\n\\\"\\n                    \\\"questions separated by newlines. Original question: {question}\\\",\\n                    \\\"template_format\\\": \\\"f-string\\\",\\n                    \\\"validate_template\\\": False,\\n                    \\\"_type\\\": \\\"prompt\\\",\\n                },\\n            },\\n            \\\"retriever\\\": {\\\"display_name\\\": \\\"Retriever\\\"},\\n            \\\"parser_key\\\": {\\\"display_name\\\": \\\"Parser Key\\\", \\\"default\\\": \\\"lines\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        llm: BaseLLM,\\n        retriever: BaseRetriever,\\n        prompt: Optional[PromptTemplate] = None,\\n        parser_key: str = \\\"lines\\\",\\n    ) -> Union[Callable, MultiQueryRetriever]:\\n        if not prompt:\\n            return MultiQueryRetriever.from_llm(llm=llm, retriever=retriever, parser_key=parser_key)\\n        else:\\n            return MultiQueryRetriever.from_llm(llm=llm, retriever=retriever, prompt=prompt, parser_key=parser_key)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"parser_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"lines\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"parser_key\",\"display_name\":\"Parser Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Initialize from llm using default template.\",\"base_classes\":[],\"display_name\":\"MultiQueryRetriever\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/retrievers/how_to/MultiQueryRetriever\",\"custom_fields\":{\"llm\":null,\"retriever\":null,\"prompt\":null,\"parser_key\":null},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"MetalRetriever\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"client_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"client_id\",\"display_name\":\"Client ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.retrievers import MetalRetriever\\nfrom metal_sdk.metal import Metal  # type: ignore\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass MetalRetrieverComponent(CustomComponent):\\n    display_name: str = \\\"Metal Retriever\\\"\\n    description: str = \\\"Retriever that uses the Metal API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"password\\\": True},\\n            \\\"client_id\\\": {\\\"display_name\\\": \\\"Client ID\\\", \\\"password\\\": True},\\n            \\\"index_id\\\": {\\\"display_name\\\": \\\"Index ID\\\"},\\n            \\\"params\\\": {\\\"display_name\\\": \\\"Parameters\\\"},\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(self, api_key: str, client_id: str, index_id: str, params: Optional[dict] = None) -> BaseRetriever:\\n        try:\\n            metal = Metal(api_key=api_key, client_id=client_id, index_id=index_id)\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to Metal API.\\\") from e\\n        return MetalRetriever(client=metal, params=params or {})\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"index_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_id\",\"display_name\":\"Index ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"params\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"params\",\"display_name\":\"Parameters\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Retriever that uses the Metal API.\",\"base_classes\":[\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Metal Retriever\",\"documentation\":\"\",\"custom_fields\":{\"api_key\":null,\"client_id\":null,\"index_id\":null,\"params\":null},\"output_types\":[\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"custom_components\":{\"CustomComponent\":{\"template\":{\"param\":{\"type\":\"Data\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"param\",\"display_name\":\"Parameter\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\n\\nclass Component(CustomComponent):\\n    documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n\\n    def build_config(self):\\n        return {\\\"param\\\": {\\\"display_name\\\": \\\"Parameter\\\"}}\\n\\n    def build(self, param: Data) -> Data:\\n        return param\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"base_classes\":[\"object\",\"Data\"],\"display_name\":\"CustomComponent\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"param\":null},\"output_types\":[\"Data\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"vectorstores\":{\"Weaviate\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"attributes\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"attributes\",\"display_name\":\"Attributes\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nimport weaviate  # type: ignore\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain.schema import BaseRetriever, Document\\nfrom langchain_community.vectorstores import VectorStore, Weaviate\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass WeaviateVectorStore(CustomComponent):\\n    display_name: str = \\\"Weaviate\\\"\\n    description: str = \\\"Implementation of Vector Store using Weaviate\\\"\\n    documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/weaviate\\\"\\n    beta = True\\n    field_config = {\\n        \\\"url\\\": {\\\"display_name\\\": \\\"Weaviate URL\\\", \\\"value\\\": \\\"http://localhost:8080\\\"},\\n        \\\"api_key\\\": {\\n            \\\"display_name\\\": \\\"API Key\\\",\\n            \\\"password\\\": True,\\n            \\\"required\\\": False,\\n        },\\n        \\\"index_name\\\": {\\n            \\\"display_name\\\": \\\"Index name\\\",\\n            \\\"required\\\": False,\\n        },\\n        \\\"text_key\\\": {\\\"display_name\\\": \\\"Text Key\\\", \\\"required\\\": False, \\\"advanced\\\": True, \\\"value\\\": \\\"text\\\"},\\n        \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n        \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n        \\\"attributes\\\": {\\n            \\\"display_name\\\": \\\"Attributes\\\",\\n            \\\"required\\\": False,\\n            \\\"is_list\\\": True,\\n            \\\"field_type\\\": \\\"str\\\",\\n            \\\"advanced\\\": True,\\n        },\\n        \\\"search_by_text\\\": {\\\"display_name\\\": \\\"Search By Text\\\", \\\"field_type\\\": \\\"bool\\\", \\\"advanced\\\": True},\\n        \\\"code\\\": {\\\"show\\\": False},\\n    }\\n\\n    def build(\\n        self,\\n        url: str,\\n        search_by_text: bool = False,\\n        api_key: Optional[str] = None,\\n        index_name: Optional[str] = None,\\n        text_key: str = \\\"text\\\",\\n        embedding: Optional[Embeddings] = None,\\n        documents: Optional[Document] = None,\\n        attributes: Optional[list] = None,\\n    ) -> Union[VectorStore, BaseRetriever]:\\n        if api_key:\\n            auth_config = weaviate.AuthApiKey(api_key=api_key)\\n            client = weaviate.Client(url=url, auth_client_secret=auth_config)\\n        else:\\n            client = weaviate.Client(url=url)\\n\\n        def _to_pascal_case(word: str):\\n            if word and not word[0].isupper():\\n                word = word.capitalize()\\n\\n            if word.isidentifier():\\n                return word\\n\\n            word = word.replace(\\\"-\\\", \\\" \\\").replace(\\\"_\\\", \\\" \\\")\\n            parts = word.split()\\n            pascal_case_word = \\\"\\\".join([part.capitalize() for part in parts])\\n\\n            return pascal_case_word\\n\\n        index_name = _to_pascal_case(index_name) if index_name else None\\n\\n        if documents is not None and embedding is not None:\\n            return Weaviate.from_documents(\\n                client=client,\\n                index_name=index_name,\\n                documents=documents,\\n                embedding=embedding,\\n                by_text=search_by_text,\\n            )\\n\\n        return Weaviate(\\n            client=client,\\n            index_name=index_name,\\n            text_key=text_key,\\n            embedding=embedding,\\n            by_text=search_by_text,\\n            attributes=attributes if attributes is not None else [],\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"index_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_name\",\"display_name\":\"Index name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_by_text\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_by_text\",\"display_name\":\"Search By Text\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"text_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"text\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"text_key\",\"display_name\":\"Text Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"http://localhost:8080\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"Weaviate URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using Weaviate\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Weaviate\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/weaviate\",\"custom_fields\":{\"url\":null,\"search_by_text\":null,\"api_key\":null,\"index_name\":null,\"text_key\":null,\"embedding\":null,\"documents\":null,\"attributes\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Vectara\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"If provided, will be upserted to corpus (optional)\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"import tempfile\\nimport urllib\\nimport urllib.request\\nfrom typing import List, Optional, Union\\n\\nfrom langchain_community.embeddings import FakeEmbeddings\\nfrom langchain_community.vectorstores.vectara import Vectara\\nfrom langchain_core.vectorstores import VectorStore\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseRetriever, Document\\n\\n\\nclass VectaraComponent(CustomComponent):\\n    display_name: str = \\\"Vectara\\\"\\n    description: str = \\\"Implementation of Vector Store using Vectara\\\"\\n    documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/vectara\\\"\\n    beta = True\\n    field_config = {\\n        \\\"vectara_customer_id\\\": {\\n            \\\"display_name\\\": \\\"Vectara Customer ID\\\",\\n        },\\n        \\\"vectara_corpus_id\\\": {\\n            \\\"display_name\\\": \\\"Vectara Corpus ID\\\",\\n        },\\n        \\\"vectara_api_key\\\": {\\n            \\\"display_name\\\": \\\"Vectara API Key\\\",\\n            \\\"password\\\": True,\\n        },\\n        \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"info\\\": \\\"If provided, will be upserted to corpus (optional)\\\"},\\n        \\\"files_url\\\": {\\n            \\\"display_name\\\": \\\"Files Url\\\",\\n            \\\"info\\\": \\\"Make vectara object using url of files (optional)\\\",\\n        },\\n    }\\n\\n    def build(\\n        self,\\n        vectara_customer_id: str,\\n        vectara_corpus_id: str,\\n        vectara_api_key: str,\\n        files_url: Optional[List[str]] = None,\\n        documents: Optional[Document] = None,\\n    ) -> Union[VectorStore, BaseRetriever]:\\n        source = \\\"Langflow\\\"\\n\\n        if documents is not None:\\n            return Vectara.from_documents(\\n                documents=documents,  # type: ignore\\n                embedding=FakeEmbeddings(size=768),\\n                vectara_customer_id=vectara_customer_id,\\n                vectara_corpus_id=vectara_corpus_id,\\n                vectara_api_key=vectara_api_key,\\n                source=source,\\n            )\\n\\n        if files_url is not None:\\n            files_list = []\\n            for url in files_url:\\n                name = tempfile.NamedTemporaryFile().name\\n                urllib.request.urlretrieve(url, name)\\n                files_list.append(name)\\n\\n            return Vectara.from_files(\\n                files=files_list,\\n                embedding=FakeEmbeddings(size=768),\\n                vectara_customer_id=vectara_customer_id,\\n                vectara_corpus_id=vectara_corpus_id,\\n                vectara_api_key=vectara_api_key,\\n                source=source,\\n            )\\n\\n        return Vectara(\\n            vectara_customer_id=vectara_customer_id,\\n            vectara_corpus_id=vectara_corpus_id,\\n            vectara_api_key=vectara_api_key,\\n            source=source,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"files_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"files_url\",\"display_name\":\"Files Url\",\"advanced\":false,\"dynamic\":false,\"info\":\"Make vectara object using url of files (optional)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"vectara_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"vectara_api_key\",\"display_name\":\"Vectara API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"vectara_corpus_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectara_corpus_id\",\"display_name\":\"Vectara Corpus ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"vectara_customer_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectara_customer_id\",\"display_name\":\"Vectara Customer ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using Vectara\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Vectara\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/vectara\",\"custom_fields\":{\"vectara_customer_id\":null,\"vectara_corpus_id\":null,\"vectara_api_key\":null,\"files_url\":null,\"documents\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Chroma\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_cors_allow_origins\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_cors_allow_origins\",\"display_name\":\"Server CORS Allow Origins\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chroma_server_grpc_port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_grpc_port\",\"display_name\":\"Server gRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_host\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_host\",\"display_name\":\"Server Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chroma_server_port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_port\",\"display_name\":\"Server Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_ssl_enabled\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_ssl_enabled\",\"display_name\":\"Server SSL Enabled\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional, Union\\n\\nimport chromadb  # type: ignore\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain.schema import BaseRetriever, Document\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.chroma import Chroma\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass ChromaComponent(CustomComponent):\\n    \\\"\\\"\\\"\\n    A custom component for implementing a Vector Store using Chroma.\\n    \\\"\\\"\\\"\\n\\n    display_name: str = \\\"Chroma\\\"\\n    description: str = \\\"Implementation of Vector Store using Chroma\\\"\\n    documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/chroma\\\"\\n    beta: bool = True\\n    icon = \\\"Chroma\\\"\\n\\n    def build_config(self):\\n        \\\"\\\"\\\"\\n        Builds the configuration for the component.\\n\\n        Returns:\\n        - dict: A dictionary containing the configuration options for the component.\\n        \\\"\\\"\\\"\\n        return {\\n            \\\"collection_name\\\": {\\\"display_name\\\": \\\"Collection Name\\\", \\\"value\\\": \\\"langflow\\\"},\\n            \\\"index_directory\\\": {\\\"display_name\\\": \\\"Persist Directory\\\"},\\n            \\\"code\\\": {\\\"advanced\\\": True, \\\"display_name\\\": \\\"Code\\\"},\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n            \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n            \\\"chroma_server_cors_allow_origins\\\": {\\n                \\\"display_name\\\": \\\"Server CORS Allow Origins\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"chroma_server_host\\\": {\\\"display_name\\\": \\\"Server Host\\\", \\\"advanced\\\": True},\\n            \\\"chroma_server_port\\\": {\\\"display_name\\\": \\\"Server Port\\\", \\\"advanced\\\": True},\\n            \\\"chroma_server_grpc_port\\\": {\\n                \\\"display_name\\\": \\\"Server gRPC Port\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"chroma_server_ssl_enabled\\\": {\\n                \\\"display_name\\\": \\\"Server SSL Enabled\\\",\\n                \\\"advanced\\\": True,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        collection_name: str,\\n        embedding: Embeddings,\\n        chroma_server_ssl_enabled: bool,\\n        index_directory: Optional[str] = None,\\n        documents: Optional[List[Document]] = None,\\n        chroma_server_cors_allow_origins: Optional[str] = None,\\n        chroma_server_host: Optional[str] = None,\\n        chroma_server_port: Optional[int] = None,\\n        chroma_server_grpc_port: Optional[int] = None,\\n    ) -> Union[VectorStore, BaseRetriever]:\\n        \\\"\\\"\\\"\\n        Builds the Vector Store or BaseRetriever object.\\n\\n        Args:\\n        - collection_name (str): The name of the collection.\\n        - index_directory (Optional[str]): The directory to persist the Vector Store to.\\n        - chroma_server_ssl_enabled (bool): Whether to enable SSL for the Chroma server.\\n        - embedding (Optional[Embeddings]): The embeddings to use for the Vector Store.\\n        - documents (Optional[Document]): The documents to use for the Vector Store.\\n        - chroma_server_cors_allow_origins (Optional[str]): The CORS allow origins for the Chroma server.\\n        - chroma_server_host (Optional[str]): The host for the Chroma server.\\n        - chroma_server_port (Optional[int]): The port for the Chroma server.\\n        - chroma_server_grpc_port (Optional[int]): The gRPC port for the Chroma server.\\n\\n        Returns:\\n        - Union[VectorStore, BaseRetriever]: The Vector Store or BaseRetriever object.\\n        \\\"\\\"\\\"\\n\\n        # Chroma settings\\n        chroma_settings = None\\n\\n        if chroma_server_host is not None:\\n            chroma_settings = chromadb.config.Settings(\\n                chroma_server_cors_allow_origins=chroma_server_cors_allow_origins\\n                or None,\\n                chroma_server_host=chroma_server_host,\\n                chroma_server_port=chroma_server_port or None,\\n                chroma_server_grpc_port=chroma_server_grpc_port or None,\\n                chroma_server_ssl_enabled=chroma_server_ssl_enabled,\\n            )\\n\\n        # If documents, then we need to create a Chroma instance using .from_documents\\n\\n        # Check index_directory and expand it if it is a relative path\\n\\n        index_directory = self.resolve_path(index_directory)\\n\\n        if documents is not None and embedding is not None:\\n            if len(documents) == 0:\\n                raise ValueError(\\n                    \\\"If documents are provided, there must be at least one document.\\\"\\n                )\\n            chroma = Chroma.from_documents(\\n                documents=documents,  # type: ignore\\n                persist_directory=index_directory,\\n                collection_name=collection_name,\\n                embedding=embedding,\\n                client_settings=chroma_settings,\\n            )\\n        else:\\n            chroma = Chroma(\\n                persist_directory=index_directory,\\n                client_settings=chroma_settings,\\n                embedding_function=embedding,\\n            )\\n        return chroma\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"langflow\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"index_directory\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_directory\",\"display_name\":\"Persist Directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using Chroma\",\"icon\":\"Chroma\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Chroma\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/chroma\",\"custom_fields\":{\"collection_name\":null,\"embedding\":null,\"chroma_server_ssl_enabled\":null,\"index_directory\":null,\"documents\":null,\"chroma_server_cors_allow_origins\":null,\"chroma_server_host\":null,\"chroma_server_port\":null,\"chroma_server_grpc_port\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SupabaseVectorStore\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Union\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.supabase import SupabaseVectorStore\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Document, Embeddings, NestedDict\\nfrom supabase.client import Client, create_client\\n\\n\\nclass SupabaseComponent(CustomComponent):\\n    display_name = \\\"Supabase\\\"\\n    description = \\\"Return VectorStore initialized from texts and embeddings.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n            \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n            \\\"query_name\\\": {\\\"display_name\\\": \\\"Query Name\\\"},\\n            \\\"search_kwargs\\\": {\\\"display_name\\\": \\\"Search Kwargs\\\", \\\"advanced\\\": True},\\n            \\\"supabase_service_key\\\": {\\\"display_name\\\": \\\"Supabase Service Key\\\"},\\n            \\\"supabase_url\\\": {\\\"display_name\\\": \\\"Supabase URL\\\"},\\n            \\\"table_name\\\": {\\\"display_name\\\": \\\"Table Name\\\", \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        embedding: Embeddings,\\n        documents: List[Document],\\n        query_name: str = \\\"\\\",\\n        search_kwargs: NestedDict = {},\\n        supabase_service_key: str = \\\"\\\",\\n        supabase_url: str = \\\"\\\",\\n        table_name: str = \\\"\\\",\\n    ) -> Union[VectorStore, SupabaseVectorStore, BaseRetriever]:\\n        supabase: Client = create_client(supabase_url, supabase_key=supabase_service_key)\\n        return SupabaseVectorStore.from_documents(\\n            documents=documents,\\n            embedding=embedding,\\n            query_name=query_name,\\n            search_kwargs=search_kwargs,\\n            client=supabase,\\n            table_name=table_name,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"query_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"query_name\",\"display_name\":\"Query Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_kwargs\":{\"type\":\"NestedDict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_kwargs\",\"display_name\":\"Search Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"supabase_service_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"supabase_service_key\",\"display_name\":\"Supabase Service Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"supabase_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"supabase_url\",\"display_name\":\"Supabase URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"table_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"table_name\",\"display_name\":\"Table Name\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Return VectorStore initialized from texts and embeddings.\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"SupabaseVectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Supabase\",\"documentation\":\"\",\"custom_fields\":{\"embedding\":null,\"documents\":null,\"query_name\":null,\"search_kwargs\":null,\"supabase_service_key\":null,\"supabase_url\":null,\"table_name\":null},\"output_types\":[\"VectorStore\",\"SupabaseVectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Redis\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.redis import Redis\\nfrom langchain_core.documents import Document\\nfrom langchain_core.retrievers import BaseRetriever\\nfrom langflow import CustomComponent\\n\\n\\nclass RedisComponent(CustomComponent):\\n    \\\"\\\"\\\"\\n    A custom component for implementing a Vector Store using Redis.\\n    \\\"\\\"\\\"\\n\\n    display_name: str = \\\"Redis\\\"\\n    description: str = \\\"Implementation of Vector Store using Redis\\\"\\n    documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/redis\\\"\\n    beta = True\\n\\n    def build_config(self):\\n        \\\"\\\"\\\"\\n        Builds the configuration for the component.\\n\\n        Returns:\\n        - dict: A dictionary containing the configuration options for the component.\\n        \\\"\\\"\\\"\\n        return {\\n            \\\"index_name\\\": {\\\"display_name\\\": \\\"Index Name\\\", \\\"value\\\": \\\"your_index\\\"},\\n            \\\"code\\\": {\\\"show\\\": False, \\\"display_name\\\": \\\"Code\\\"},\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n            \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n            \\\"schema\\\": {\\\"display_name\\\": \\\"Schema\\\", \\\"file_types\\\": [\\\".yaml\\\"]},\\n            \\\"redis_server_url\\\": {\\n                \\\"display_name\\\": \\\"Redis Server Connection String\\\",\\n                \\\"advanced\\\": False,\\n            },\\n            \\\"redis_index_name\\\": {\\\"display_name\\\": \\\"Redis Index\\\", \\\"advanced\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        embedding: Embeddings,\\n        redis_server_url: str,\\n        redis_index_name: str,\\n        schema: Optional[str] = None,\\n        documents: Optional[Document] = None,\\n    ) -> Union[VectorStore, BaseRetriever]:\\n        \\\"\\\"\\\"\\n        Builds the Vector Store or BaseRetriever object.\\n\\n        Args:\\n        - embedding (Embeddings): The embeddings to use for the Vector Store.\\n        - documents (Optional[Document]): The documents to use for the Vector Store.\\n        - redis_index_name (str): The name of the Redis index.\\n        - redis_server_url (str): The URL for the Redis server.\\n\\n        Returns:\\n        - VectorStore: The Vector Store object.\\n        \\\"\\\"\\\"\\n        if documents is None:\\n            if schema is None:\\n                raise ValueError(\\\"If no documents are provided, a schema must be provided.\\\")\\n            redis_vs = Redis.from_existing_index(\\n                embedding=embedding,\\n                index_name=redis_index_name,\\n                schema=schema,\\n                key_prefix=None,\\n                redis_url=redis_server_url,\\n            )\\n        else:\\n            redis_vs = Redis.from_documents(\\n                documents=documents,  # type: ignore\\n                embedding=embedding,\\n                redis_url=redis_server_url,\\n                index_name=redis_index_name,\\n            )\\n        return redis_vs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"redis_index_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"redis_index_name\",\"display_name\":\"Redis Index\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"redis_server_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"redis_server_url\",\"display_name\":\"Redis Server Connection String\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"schema\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".yaml\"],\"file_path\":\"\",\"password\":false,\"name\":\"schema\",\"display_name\":\"Schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using Redis\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Redis\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/redis\",\"custom_fields\":{\"embedding\":null,\"redis_server_url\":null,\"redis_index_name\":null,\"schema\":null,\"documents\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"pgvector\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.pgvector import PGVector\\nfrom langchain_core.documents import Document\\nfrom langchain_core.retrievers import BaseRetriever\\nfrom langflow import CustomComponent\\n\\n\\nclass PGVectorComponent(CustomComponent):\\n    \\\"\\\"\\\"\\n    A custom component for implementing a Vector Store using PostgreSQL.\\n    \\\"\\\"\\\"\\n\\n    display_name: str = \\\"PGVector\\\"\\n    description: str = \\\"Implementation of Vector Store using PostgreSQL\\\"\\n    documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/pgvector\\\"\\n\\n    def build_config(self):\\n        \\\"\\\"\\\"\\n        Builds the configuration for the component.\\n\\n        Returns:\\n        - dict: A dictionary containing the configuration options for the component.\\n        \\\"\\\"\\\"\\n        return {\\n            \\\"code\\\": {\\\"show\\\": False},\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n            \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n            \\\"pg_server_url\\\": {\\n                \\\"display_name\\\": \\\"PostgreSQL Server Connection String\\\",\\n                \\\"advanced\\\": False,\\n            },\\n            \\\"collection_name\\\": {\\\"display_name\\\": \\\"Table\\\", \\\"advanced\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        embedding: Embeddings,\\n        pg_server_url: str,\\n        collection_name: str,\\n        documents: Optional[Document] = None,\\n    ) -> Union[VectorStore, BaseRetriever]:\\n        \\\"\\\"\\\"\\n        Builds the Vector Store or BaseRetriever object.\\n\\n        Args:\\n        - embedding (Embeddings): The embeddings to use for the Vector Store.\\n        - documents (Optional[Document]): The documents to use for the Vector Store.\\n        - collection_name (str): The name of the PG table.\\n        - pg_server_url (str): The URL for the PG server.\\n\\n        Returns:\\n        - VectorStore: The Vector Store object.\\n        \\\"\\\"\\\"\\n\\n        try:\\n            if documents is None:\\n                vector_store = PGVector.from_existing_index(\\n                    embedding=embedding,\\n                    collection_name=collection_name,\\n                    connection_string=pg_server_url,\\n                )\\n            else:\\n                vector_store = PGVector.from_documents(\\n                    embedding=embedding,\\n                    documents=documents,  # type: ignore\\n                    collection_name=collection_name,\\n                    connection_string=pg_server_url,\\n                )\\n        except Exception as e:\\n            raise RuntimeError(f\\\"Failed to build PGVector: {e}\\\")\\n        return vector_store\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Table\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"pg_server_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"pg_server_url\",\"display_name\":\"PostgreSQL Server Connection String\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using PostgreSQL\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"PGVector\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/pgvector\",\"custom_fields\":{\"embedding\":null,\"pg_server_url\":null,\"collection_name\":null,\"documents\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Pinecone\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"import os\\nfrom typing import List, Optional, Union\\n\\nimport pinecone  # type: ignore\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.pinecone import Pinecone\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Document, Embeddings\\n\\n\\nclass PineconeComponent(CustomComponent):\\n    display_name = \\\"Pinecone\\\"\\n    description = \\\"Construct Pinecone wrapper from raw documents.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n            \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n            \\\"index_name\\\": {\\\"display_name\\\": \\\"Index Name\\\"},\\n            \\\"namespace\\\": {\\\"display_name\\\": \\\"Namespace\\\"},\\n            \\\"pinecone_api_key\\\": {\\\"display_name\\\": \\\"Pinecone API Key\\\", \\\"default\\\": \\\"\\\", \\\"password\\\": True, \\\"required\\\": True},\\n            \\\"pinecone_env\\\": {\\\"display_name\\\": \\\"Pinecone Environment\\\", \\\"default\\\": \\\"\\\", \\\"required\\\": True},\\n            \\\"search_kwargs\\\": {\\\"display_name\\\": \\\"Search Kwargs\\\", \\\"default\\\": \\\"{}\\\"},\\n            \\\"pool_threads\\\": {\\\"display_name\\\": \\\"Pool Threads\\\", \\\"default\\\": 1, \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        embedding: Embeddings,\\n        pinecone_env: str,\\n        documents: List[Document],\\n        text_key: str = \\\"text\\\",\\n        pool_threads: int = 4,\\n        index_name: Optional[str] = None,\\n        pinecone_api_key: Optional[str] = None,\\n        namespace: Optional[str] = \\\"default\\\",\\n    ) -> Union[VectorStore, Pinecone, BaseRetriever]:\\n        if pinecone_api_key is None or pinecone_env is None:\\n            raise ValueError(\\\"Pinecone API Key and Environment are required.\\\")\\n        if os.getenv(\\\"PINECONE_API_KEY\\\") is None and pinecone_api_key is None:\\n            raise ValueError(\\\"Pinecone API Key is required.\\\")\\n\\n        pinecone.init(api_key=pinecone_api_key, environment=pinecone_env)  # type: ignore\\n        if not index_name:\\n            raise ValueError(\\\"Index Name is required.\\\")\\n        if documents:\\n            return Pinecone.from_documents(\\n                documents=documents,\\n                embedding=embedding,\\n                index_name=index_name,\\n                pool_threads=pool_threads,\\n                namespace=namespace,\\n                text_key=text_key,\\n            )\\n\\n        return Pinecone.from_existing_index(\\n            index_name=index_name,\\n            embedding=embedding,\\n            text_key=text_key,\\n            namespace=namespace,\\n            pool_threads=pool_threads,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"index_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_name\",\"display_name\":\"Index Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"namespace\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"default\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"namespace\",\"display_name\":\"Namespace\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"pinecone_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"pinecone_api_key\",\"display_name\":\"Pinecone API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"pinecone_env\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"pinecone_env\",\"display_name\":\"Pinecone Environment\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"pool_threads\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":4,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"pool_threads\",\"display_name\":\"Pool Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"text_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"text\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"text_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Construct Pinecone wrapper from raw documents.\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"Pinecone\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Pinecone\",\"documentation\":\"\",\"custom_fields\":{\"embedding\":null,\"pinecone_env\":null,\"documents\":null,\"text_key\":null,\"pool_threads\":null,\"index_name\":null,\"pinecone_api_key\":null,\"namespace\":null},\"output_types\":[\"VectorStore\",\"Pinecone\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Qdrant\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.qdrant import Qdrant\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Document, Embeddings, NestedDict\\n\\n\\nclass QdrantComponent(CustomComponent):\\n    display_name = \\\"Qdrant\\\"\\n    description = \\\"Construct Qdrant wrapper from a list of texts.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n            \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n            \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"password\\\": True, \\\"advanced\\\": True},\\n            \\\"collection_name\\\": {\\\"display_name\\\": \\\"Collection Name\\\"},\\n            \\\"content_payload_key\\\": {\\\"display_name\\\": \\\"Content Payload Key\\\", \\\"advanced\\\": True},\\n            \\\"distance_func\\\": {\\\"display_name\\\": \\\"Distance Function\\\", \\\"advanced\\\": True},\\n            \\\"grpc_port\\\": {\\\"display_name\\\": \\\"gRPC Port\\\", \\\"advanced\\\": True},\\n            \\\"host\\\": {\\\"display_name\\\": \\\"Host\\\", \\\"advanced\\\": True},\\n            \\\"https\\\": {\\\"display_name\\\": \\\"HTTPS\\\", \\\"advanced\\\": True},\\n            \\\"location\\\": {\\\"display_name\\\": \\\"Location\\\", \\\"advanced\\\": True},\\n            \\\"metadata_payload_key\\\": {\\\"display_name\\\": \\\"Metadata Payload Key\\\", \\\"advanced\\\": True},\\n            \\\"path\\\": {\\\"display_name\\\": \\\"Path\\\", \\\"advanced\\\": True},\\n            \\\"port\\\": {\\\"display_name\\\": \\\"Port\\\", \\\"advanced\\\": True},\\n            \\\"prefer_grpc\\\": {\\\"display_name\\\": \\\"Prefer gRPC\\\", \\\"advanced\\\": True},\\n            \\\"prefix\\\": {\\\"display_name\\\": \\\"Prefix\\\", \\\"advanced\\\": True},\\n            \\\"search_kwargs\\\": {\\\"display_name\\\": \\\"Search Kwargs\\\", \\\"advanced\\\": True},\\n            \\\"timeout\\\": {\\\"display_name\\\": \\\"Timeout\\\", \\\"advanced\\\": True},\\n            \\\"url\\\": {\\\"display_name\\\": \\\"URL\\\", \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        embedding: Embeddings,\\n        collection_name: str,\\n        documents: Optional[Document] = None,\\n        api_key: Optional[str] = None,\\n        content_payload_key: str = \\\"page_content\\\",\\n        distance_func: str = \\\"Cosine\\\",\\n        grpc_port: int = 6334,\\n        https: bool = False,\\n        host: Optional[str] = None,\\n        location: Optional[str] = None,\\n        metadata_payload_key: str = \\\"metadata\\\",\\n        path: Optional[str] = None,\\n        port: Optional[int] = 6333,\\n        prefer_grpc: bool = False,\\n        prefix: Optional[str] = None,\\n        search_kwargs: Optional[NestedDict] = None,\\n        timeout: Optional[int] = None,\\n        url: Optional[str] = None,\\n    ) -> Union[VectorStore, Qdrant, BaseRetriever]:\\n        if documents is None:\\n            from qdrant_client import QdrantClient\\n\\n            client = QdrantClient(\\n                location=location,\\n                url=host,\\n                port=port,\\n                grpc_port=grpc_port,\\n                https=https,\\n                prefix=prefix,\\n                timeout=timeout,\\n                prefer_grpc=prefer_grpc,\\n                metadata_payload_key=metadata_payload_key,\\n                content_payload_key=content_payload_key,\\n                api_key=api_key,\\n                collection_name=collection_name,\\n                host=host,\\n                path=path,\\n            )\\n            vs = Qdrant(\\n                client=client,\\n                collection_name=collection_name,\\n                embeddings=embedding,\\n            )\\n            return vs\\n        else:\\n            vs = Qdrant.from_documents(\\n                documents=documents,  # type: ignore\\n                embedding=embedding,\\n                api_key=api_key,\\n                collection_name=collection_name,\\n                content_payload_key=content_payload_key,\\n                distance_func=distance_func,\\n                grpc_port=grpc_port,\\n                host=host,\\n                https=https,\\n                location=location,\\n                metadata_payload_key=metadata_payload_key,\\n                path=path,\\n                port=port,\\n                prefer_grpc=prefer_grpc,\\n                prefix=prefix,\\n                search_kwargs=search_kwargs,\\n                timeout=timeout,\\n                url=url,\\n            )\\n        return vs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"content_payload_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"page_content\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"content_payload_key\",\"display_name\":\"Content Payload Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"distance_func\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"Cosine\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"distance_func\",\"display_name\":\"Distance Function\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"grpc_port\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6334,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grpc_port\",\"display_name\":\"gRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"host\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"host\",\"display_name\":\"Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"https\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"https\",\"display_name\":\"HTTPS\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"location\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"metadata_payload_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"metadata\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata_payload_key\",\"display_name\":\"Metadata Payload Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6333,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"port\",\"display_name\":\"Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prefer_grpc\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prefer_grpc\",\"display_name\":\"Prefer gRPC\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prefix\",\"display_name\":\"Prefix\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_kwargs\",\"display_name\":\"Search Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"timeout\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Construct Qdrant wrapper from a list of texts.\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"Qdrant\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Qdrant\",\"documentation\":\"\",\"custom_fields\":{\"embedding\":null,\"collection_name\":null,\"documents\":null,\"api_key\":null,\"content_payload_key\":null,\"distance_func\":null,\"grpc_port\":null,\"https\":null,\"host\":null,\"location\":null,\"metadata_payload_key\":null,\"path\":null,\"port\":null,\"prefer_grpc\":null,\"prefix\":null,\"search_kwargs\":null,\"timeout\":null,\"url\":null},\"output_types\":[\"VectorStore\",\"Qdrant\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"MongoDBAtlasVectorSearch\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langchain_community.vectorstores import MongoDBAtlasVectorSearch\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n    Document,\\n    Embeddings,\\n    NestedDict,\\n)\\n\\n\\nclass MongoDBAtlasComponent(CustomComponent):\\n    display_name = \\\"MongoDB Atlas\\\"\\n    description = \\\"Construct a `MongoDB Atlas Vector Search` vector store from raw documents.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n            \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n            \\\"collection_name\\\": {\\\"display_name\\\": \\\"Collection Name\\\"},\\n            \\\"db_name\\\": {\\\"display_name\\\": \\\"Database Name\\\"},\\n            \\\"index_name\\\": {\\\"display_name\\\": \\\"Index Name\\\"},\\n            \\\"mongodb_atlas_cluster_uri\\\": {\\\"display_name\\\": \\\"MongoDB Atlas Cluster URI\\\"},\\n            \\\"search_kwargs\\\": {\\\"display_name\\\": \\\"Search Kwargs\\\", \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        documents: List[Document],\\n        embedding: Embeddings,\\n        collection_name: str = \\\"\\\",\\n        db_name: str = \\\"\\\",\\n        index_name: str = \\\"\\\",\\n        mongodb_atlas_cluster_uri: str = \\\"\\\",\\n        search_kwargs: Optional[NestedDict] = None,\\n    ) -> MongoDBAtlasVectorSearch:\\n        search_kwargs = search_kwargs or {}\\n        return MongoDBAtlasVectorSearch(\\n            documents=documents,\\n            embedding=embedding,\\n            collection_name=collection_name,\\n            db_name=db_name,\\n            index_name=index_name,\\n            mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri,\\n            search_kwargs=search_kwargs,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"db_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"db_name\",\"display_name\":\"Database Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"index_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_name\",\"display_name\":\"Index Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mongodb_atlas_cluster_uri\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mongodb_atlas_cluster_uri\",\"display_name\":\"MongoDB Atlas Cluster URI\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_kwargs\",\"display_name\":\"Search Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct a `MongoDB Atlas Vector Search` vector store from raw documents.\",\"base_classes\":[\"VectorStore\",\"MongoDBAtlasVectorSearch\"],\"display_name\":\"MongoDB Atlas\",\"documentation\":\"\",\"custom_fields\":{\"documents\":null,\"embedding\":null,\"collection_name\":null,\"db_name\":null,\"index_name\":null,\"mongodb_atlas_cluster_uri\":null,\"search_kwargs\":null},\"output_types\":[\"MongoDBAtlasVectorSearch\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChromaSearch\":{\"template\":{\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"Embedding model to vectorize inputs (make sure to use same as index)\",\"title_case\":false},\"inputs\":{\"type\":\"Text\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_cors_allow_origins\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_cors_allow_origins\",\"display_name\":\"Server CORS Allow Origins\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chroma_server_grpc_port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_grpc_port\",\"display_name\":\"Server gRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_host\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_host\",\"display_name\":\"Server Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chroma_server_port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_port\",\"display_name\":\"Server Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_ssl_enabled\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_ssl_enabled\",\"display_name\":\"Server SSL Enabled\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nimport chromadb  # type: ignore\\nfrom langchain_community.vectorstores.chroma import Chroma\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Embeddings, Text\\nfrom langflow.schema import Record, docs_to_records\\n\\n\\nclass ChromaSearchComponent(CustomComponent):\\n    \\\"\\\"\\\"\\n    A custom component for implementing a Vector Store using Chroma.\\n    \\\"\\\"\\\"\\n\\n    display_name: str = \\\"Chroma Search\\\"\\n    description: str = \\\"Search a Chroma collection for similar documents.\\\"\\n    beta: bool = True\\n    icon = \\\"Chroma\\\"\\n\\n    def build_config(self):\\n        \\\"\\\"\\\"\\n        Builds the configuration for the component.\\n\\n        Returns:\\n        - dict: A dictionary containing the configuration options for the component.\\n        \\\"\\\"\\\"\\n        return {\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n            \\\"search_type\\\": {\\n                \\\"display_name\\\": \\\"Search Type\\\",\\n                \\\"options\\\": [\\\"Similarity\\\", \\\"MMR\\\"],\\n            },\\n            \\\"collection_name\\\": {\\\"display_name\\\": \\\"Collection Name\\\", \\\"value\\\": \\\"langflow\\\"},\\n            # \\\"persist\\\": {\\\"display_name\\\": \\\"Persist\\\"},\\n            \\\"index_directory\\\": {\\\"display_name\\\": \\\"Index Directory\\\"},\\n            \\\"code\\\": {\\\"show\\\": False, \\\"display_name\\\": \\\"Code\\\"},\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n            \\\"embedding\\\": {\\n                \\\"display_name\\\": \\\"Embedding\\\",\\n                \\\"info\\\": \\\"Embedding model to vectorize inputs (make sure to use same as index)\\\",\\n            },\\n            \\\"chroma_server_cors_allow_origins\\\": {\\n                \\\"display_name\\\": \\\"Server CORS Allow Origins\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"chroma_server_host\\\": {\\\"display_name\\\": \\\"Server Host\\\", \\\"advanced\\\": True},\\n            \\\"chroma_server_port\\\": {\\\"display_name\\\": \\\"Server Port\\\", \\\"advanced\\\": True},\\n            \\\"chroma_server_grpc_port\\\": {\\n                \\\"display_name\\\": \\\"Server gRPC Port\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"chroma_server_ssl_enabled\\\": {\\n                \\\"display_name\\\": \\\"Server SSL Enabled\\\",\\n                \\\"advanced\\\": True,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        inputs: Text,\\n        search_type: str,\\n        collection_name: str,\\n        embedding: Embeddings,\\n        chroma_server_ssl_enabled: bool,\\n        index_directory: Optional[str] = None,\\n        chroma_server_cors_allow_origins: Optional[str] = None,\\n        chroma_server_host: Optional[str] = None,\\n        chroma_server_port: Optional[int] = None,\\n        chroma_server_grpc_port: Optional[int] = None,\\n    ) -> List[Record]:\\n        \\\"\\\"\\\"\\n        Builds the Vector Store or BaseRetriever object.\\n\\n        Args:\\n        - collection_name (str): The name of the collection.\\n        - persist_directory (Optional[str]): The directory to persist the Vector Store to.\\n        - chroma_server_ssl_enabled (bool): Whether to enable SSL for the Chroma server.\\n        - persist (bool): Whether to persist the Vector Store or not.\\n        - embedding (Optional[Embeddings]): The embeddings to use for the Vector Store.\\n        - documents (Optional[Document]): The documents to use for the Vector Store.\\n        - chroma_server_cors_allow_origins (Optional[str]): The CORS allow origins for the Chroma server.\\n        - chroma_server_host (Optional[str]): The host for the Chroma server.\\n        - chroma_server_port (Optional[int]): The port for the Chroma server.\\n        - chroma_server_grpc_port (Optional[int]): The gRPC port for the Chroma server.\\n\\n        Returns:\\n        - Union[VectorStore, BaseRetriever]: The Vector Store or BaseRetriever object.\\n        \\\"\\\"\\\"\\n\\n        # Chroma settings\\n        chroma_settings = None\\n\\n        if chroma_server_host is not None:\\n            chroma_settings = chromadb.config.Settings(\\n                chroma_server_cors_allow_origins=chroma_server_cors_allow_origins or None,\\n                chroma_server_host=chroma_server_host,\\n                chroma_server_port=chroma_server_port or None,\\n                chroma_server_grpc_port=chroma_server_grpc_port or None,\\n                chroma_server_ssl_enabled=chroma_server_ssl_enabled,\\n            )\\n        index_directory = self.resolve_path(index_directory)\\n        chroma = Chroma(\\n            embedding_function=embedding,\\n            collection_name=collection_name,\\n            persist_directory=index_directory,\\n            client_settings=chroma_settings,\\n        )\\n\\n        # Validate the inputs\\n        docs = []\\n        if inputs and isinstance(inputs, str):\\n            docs = chroma.search(query=inputs, search_type=search_type.lower())\\n        else:\\n            raise ValueError(\\\"Invalid inputs provided.\\\")\\n        return docs_to_records(docs)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"langflow\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"index_directory\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_directory\",\"display_name\":\"Index Directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Similarity\",\"MMR\"],\"name\":\"search_type\",\"display_name\":\"Search Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Search a Chroma collection for similar documents.\",\"icon\":\"Chroma\",\"base_classes\":[\"Record\"],\"display_name\":\"Chroma Search\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"search_type\":null,\"collection_name\":null,\"embedding\":null,\"chroma_server_ssl_enabled\":null,\"index_directory\":null,\"chroma_server_cors_allow_origins\":null,\"chroma_server_host\":null,\"chroma_server_port\":null,\"chroma_server_grpc_port\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"FAISS\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Union\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.faiss import FAISS\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Document, Embeddings\\n\\n\\nclass FAISSComponent(CustomComponent):\\n    display_name = \\\"FAISS\\\"\\n    description = \\\"Construct FAISS wrapper from raw documents.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n            \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        embedding: Embeddings,\\n        documents: List[Document],\\n    ) -> Union[VectorStore, FAISS, BaseRetriever]:\\n        return FAISS.from_documents(documents=documents, embedding=embedding)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct FAISS wrapper from raw documents.\",\"base_classes\":[\"Runnable\",\"FAISS\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"FAISS\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss\",\"custom_fields\":{\"embedding\":null,\"documents\":null},\"output_types\":[\"VectorStore\",\"FAISS\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"models\":{\"LlamaCppModel\":{\"template\":{\"metadata\":{\"type\":\"Dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"Dict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".bin\"],\"file_path\":\"\",\"password\":false,\"name\":\"model_path\",\"display_name\":\"Model Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"client\",\"display_name\":\"Client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Dict, List, Optional\\n\\nfrom langchain_community.llms.llamacpp import LlamaCpp\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass LlamaCppComponent(CustomComponent):\\n    display_name = \\\"LlamaCppModel\\\"\\n    description = \\\"Generate text using llama.cpp model.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"grammar\\\": {\\\"display_name\\\": \\\"Grammar\\\", \\\"advanced\\\": True},\\n            \\\"cache\\\": {\\\"display_name\\\": \\\"Cache\\\", \\\"advanced\\\": True},\\n            \\\"client\\\": {\\\"display_name\\\": \\\"Client\\\", \\\"advanced\\\": True},\\n            \\\"echo\\\": {\\\"display_name\\\": \\\"Echo\\\", \\\"advanced\\\": True},\\n            \\\"f16_kv\\\": {\\\"display_name\\\": \\\"F16 KV\\\", \\\"advanced\\\": True},\\n            \\\"grammar_path\\\": {\\\"display_name\\\": \\\"Grammar Path\\\", \\\"advanced\\\": True},\\n            \\\"last_n_tokens_size\\\": {\\n                \\\"display_name\\\": \\\"Last N Tokens Size\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"logits_all\\\": {\\\"display_name\\\": \\\"Logits All\\\", \\\"advanced\\\": True},\\n            \\\"logprobs\\\": {\\\"display_name\\\": \\\"Logprobs\\\", \\\"advanced\\\": True},\\n            \\\"lora_base\\\": {\\\"display_name\\\": \\\"Lora Base\\\", \\\"advanced\\\": True},\\n            \\\"lora_path\\\": {\\\"display_name\\\": \\\"Lora Path\\\", \\\"advanced\\\": True},\\n            \\\"max_tokens\\\": {\\\"display_name\\\": \\\"Max Tokens\\\", \\\"advanced\\\": True},\\n            \\\"metadata\\\": {\\\"display_name\\\": \\\"Metadata\\\", \\\"advanced\\\": True},\\n            \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"advanced\\\": True},\\n            \\\"model_path\\\": {\\n                \\\"display_name\\\": \\\"Model Path\\\",\\n                \\\"field_type\\\": \\\"file\\\",\\n                \\\"file_types\\\": [\\\".bin\\\"],\\n                \\\"required\\\": True,\\n            },\\n            \\\"n_batch\\\": {\\\"display_name\\\": \\\"N Batch\\\", \\\"advanced\\\": True},\\n            \\\"n_ctx\\\": {\\\"display_name\\\": \\\"N Ctx\\\", \\\"advanced\\\": True},\\n            \\\"n_gpu_layers\\\": {\\\"display_name\\\": \\\"N GPU Layers\\\", \\\"advanced\\\": True},\\n            \\\"n_parts\\\": {\\\"display_name\\\": \\\"N Parts\\\", \\\"advanced\\\": True},\\n            \\\"n_threads\\\": {\\\"display_name\\\": \\\"N Threads\\\", \\\"advanced\\\": True},\\n            \\\"repeat_penalty\\\": {\\\"display_name\\\": \\\"Repeat Penalty\\\", \\\"advanced\\\": True},\\n            \\\"rope_freq_base\\\": {\\\"display_name\\\": \\\"Rope Freq Base\\\", \\\"advanced\\\": True},\\n            \\\"rope_freq_scale\\\": {\\\"display_name\\\": \\\"Rope Freq Scale\\\", \\\"advanced\\\": True},\\n            \\\"seed\\\": {\\\"display_name\\\": \\\"Seed\\\", \\\"advanced\\\": True},\\n            \\\"stop\\\": {\\\"display_name\\\": \\\"Stop\\\", \\\"advanced\\\": True},\\n            \\\"streaming\\\": {\\\"display_name\\\": \\\"Streaming\\\", \\\"advanced\\\": True},\\n            \\\"suffix\\\": {\\\"display_name\\\": \\\"Suffix\\\", \\\"advanced\\\": True},\\n            \\\"tags\\\": {\\\"display_name\\\": \\\"Tags\\\", \\\"advanced\\\": True},\\n            \\\"temperature\\\": {\\\"display_name\\\": \\\"Temperature\\\"},\\n            \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"advanced\\\": True},\\n            \\\"top_p\\\": {\\\"display_name\\\": \\\"Top P\\\", \\\"advanced\\\": True},\\n            \\\"use_mlock\\\": {\\\"display_name\\\": \\\"Use Mlock\\\", \\\"advanced\\\": True},\\n            \\\"use_mmap\\\": {\\\"display_name\\\": \\\"Use Mmap\\\", \\\"advanced\\\": True},\\n            \\\"verbose\\\": {\\\"display_name\\\": \\\"Verbose\\\", \\\"advanced\\\": True},\\n            \\\"vocab_only\\\": {\\\"display_name\\\": \\\"Vocab Only\\\", \\\"advanced\\\": True},\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        model_path: str,\\n        inputs: str,\\n        grammar: Optional[str] = None,\\n        cache: Optional[bool] = None,\\n        client: Optional[Any] = None,\\n        echo: Optional[bool] = False,\\n        f16_kv: bool = True,\\n        grammar_path: Optional[str] = None,\\n        last_n_tokens_size: Optional[int] = 64,\\n        logits_all: bool = False,\\n        logprobs: Optional[int] = None,\\n        lora_base: Optional[str] = None,\\n        lora_path: Optional[str] = None,\\n        max_tokens: Optional[int] = 256,\\n        metadata: Optional[Dict] = None,\\n        model_kwargs: Dict = {},\\n        n_batch: Optional[int] = 8,\\n        n_ctx: int = 512,\\n        n_gpu_layers: Optional[int] = 1,\\n        n_parts: int = -1,\\n        n_threads: Optional[int] = 1,\\n        repeat_penalty: Optional[float] = 1.1,\\n        rope_freq_base: float = 10000.0,\\n        rope_freq_scale: float = 1.0,\\n        seed: int = -1,\\n        stop: Optional[List[str]] = [],\\n        streaming: bool = True,\\n        suffix: Optional[str] = \\\"\\\",\\n        tags: Optional[List[str]] = [],\\n        temperature: Optional[float] = 0.8,\\n        top_k: Optional[int] = 40,\\n        top_p: Optional[float] = 0.95,\\n        use_mlock: bool = False,\\n        use_mmap: Optional[bool] = True,\\n        verbose: bool = True,\\n        vocab_only: bool = False,\\n    ) -> Text:\\n        output = LlamaCpp(\\n            model_path=model_path,\\n            grammar=grammar,\\n            cache=cache,\\n            client=client,\\n            echo=echo,\\n            f16_kv=f16_kv,\\n            grammar_path=grammar_path,\\n            last_n_tokens_size=last_n_tokens_size,\\n            logits_all=logits_all,\\n            logprobs=logprobs,\\n            lora_base=lora_base,\\n            lora_path=lora_path,\\n            max_tokens=max_tokens,\\n            metadata=metadata,\\n            model_kwargs=model_kwargs,\\n            n_batch=n_batch,\\n            n_ctx=n_ctx,\\n            n_gpu_layers=n_gpu_layers,\\n            n_parts=n_parts,\\n            n_threads=n_threads,\\n            repeat_penalty=repeat_penalty,\\n            rope_freq_base=rope_freq_base,\\n            rope_freq_scale=rope_freq_scale,\\n            seed=seed,\\n            stop=stop,\\n            streaming=streaming,\\n            suffix=suffix,\\n            tags=tags,\\n            temperature=temperature,\\n            top_k=top_k,\\n            top_p=top_p,\\n            use_mlock=use_mlock,\\n            use_mmap=use_mmap,\\n            verbose=verbose,\\n            vocab_only=vocab_only,\\n        )\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"echo\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"echo\",\"display_name\":\"Echo\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"f16_kv\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"f16_kv\",\"display_name\":\"F16 KV\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"grammar\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grammar\",\"display_name\":\"Grammar\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"grammar_path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grammar_path\",\"display_name\":\"Grammar Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"last_n_tokens_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":64,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"last_n_tokens_size\",\"display_name\":\"Last N Tokens Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"logits_all\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"logits_all\",\"display_name\":\"Logits All\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"logprobs\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"logprobs\",\"display_name\":\"Logprobs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"lora_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lora_base\",\"display_name\":\"Lora Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"lora_path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lora_path\",\"display_name\":\"Lora Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_batch\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_batch\",\"display_name\":\"N Batch\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_ctx\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":512,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_ctx\",\"display_name\":\"N Ctx\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_gpu_layers\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_gpu_layers\",\"display_name\":\"N GPU Layers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_parts\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":-1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_parts\",\"display_name\":\"N Parts\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_threads\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_threads\",\"display_name\":\"N Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"rope_freq_base\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10000.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"rope_freq_base\",\"display_name\":\"Rope Freq Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"rope_freq_scale\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"rope_freq_scale\",\"display_name\":\"Rope Freq Scale\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"seed\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":-1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"seed\",\"display_name\":\"Seed\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"stop\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"suffix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"suffix\",\"display_name\":\"Suffix\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"display_name\":\"Tags\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"use_mlock\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_mlock\",\"display_name\":\"Use Mlock\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"use_mmap\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_mmap\",\"display_name\":\"Use Mmap\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vocab_only\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vocab_only\",\"display_name\":\"Vocab Only\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using llama.cpp model.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"LlamaCppModel\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp\",\"custom_fields\":{\"model_path\":null,\"inputs\":null,\"grammar\":null,\"cache\":null,\"client\":null,\"echo\":null,\"f16_kv\":null,\"grammar_path\":null,\"last_n_tokens_size\":null,\"logits_all\":null,\"logprobs\":null,\"lora_base\":null,\"lora_path\":null,\"max_tokens\":null,\"metadata\":null,\"model_kwargs\":null,\"n_batch\":null,\"n_ctx\":null,\"n_gpu_layers\":null,\"n_parts\":null,\"n_threads\":null,\"repeat_penalty\":null,\"rope_freq_base\":null,\"rope_freq_scale\":null,\"seed\":null,\"stop\":null,\"streaming\":null,\"suffix\":null,\"tags\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"use_mlock\":null,\"use_mmap\":null,\"verbose\":null,\"vocab_only\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"BaiduQianfanChatModel\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass QianfanChatEndpointComponent(CustomComponent):\\n    display_name: str = \\\"QianfanChat Model\\\"\\n    description: str = (\\n        \\\"Generate text using Baidu Qianfan chat models. Get more detail from \\\"\\n        \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\\\"\\n    )\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"options\\\": [\\n                    \\\"ERNIE-Bot\\\",\\n                    \\\"ERNIE-Bot-turbo\\\",\\n                    \\\"BLOOMZ-7B\\\",\\n                    \\\"Llama-2-7b-chat\\\",\\n                    \\\"Llama-2-13b-chat\\\",\\n                    \\\"Llama-2-70b-chat\\\",\\n                    \\\"Qianfan-BLOOMZ-7B-compressed\\\",\\n                    \\\"Qianfan-Chinese-Llama-2-7B\\\",\\n                    \\\"ChatGLM2-6B-32K\\\",\\n                    \\\"AquilaChat-7B\\\",\\n                ],\\n                \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\",\\n                \\\"required\\\": True,\\n            },\\n            \\\"qianfan_ak\\\": {\\n                \\\"display_name\\\": \\\"Qianfan Ak\\\",\\n                \\\"required\\\": True,\\n                \\\"password\\\": True,\\n                \\\"info\\\": \\\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\\\",\\n            },\\n            \\\"qianfan_sk\\\": {\\n                \\\"display_name\\\": \\\"Qianfan Sk\\\",\\n                \\\"required\\\": True,\\n                \\\"password\\\": True,\\n                \\\"info\\\": \\\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\\\",\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top p\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n                \\\"value\\\": 0.8,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n                \\\"value\\\": 0.95,\\n            },\\n            \\\"penalty_score\\\": {\\n                \\\"display_name\\\": \\\"Penalty Score\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n                \\\"value\\\": 1.0,\\n            },\\n            \\\"endpoint\\\": {\\n                \\\"display_name\\\": \\\"Endpoint\\\",\\n                \\\"info\\\": \\\"Endpoint of the Qianfan LLM, required if custom model used.\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        inputs: str,\\n        model: str = \\\"ERNIE-Bot-turbo\\\",\\n        qianfan_ak: Optional[str] = None,\\n        qianfan_sk: Optional[str] = None,\\n        top_p: Optional[float] = None,\\n        temperature: Optional[float] = None,\\n        penalty_score: Optional[float] = None,\\n        endpoint: Optional[str] = None,\\n    ) -> Text:\\n        try:\\n            output = QianfanChatEndpoint(  # type: ignore\\n                model=model,\\n                qianfan_ak=SecretStr(qianfan_ak) if qianfan_ak else None,\\n                qianfan_sk=SecretStr(qianfan_sk) if qianfan_sk else None,\\n                top_p=top_p,\\n                temperature=temperature,\\n                penalty_score=penalty_score,\\n                endpoint=endpoint,\\n            )\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to Baidu Qianfan API.\\\") from e\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint\",\"display_name\":\"Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Qianfan LLM, required if custom model used.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"ERNIE-Bot-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ERNIE-Bot\",\"ERNIE-Bot-turbo\",\"BLOOMZ-7B\",\"Llama-2-7b-chat\",\"Llama-2-13b-chat\",\"Llama-2-70b-chat\",\"Qianfan-BLOOMZ-7B-compressed\",\"Qianfan-Chinese-Llama-2-7B\",\"ChatGLM2-6B-32K\",\"AquilaChat-7B\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"title_case\":false,\"input_types\":[\"Text\"]},\"penalty_score\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"penalty_score\",\"display_name\":\"Penalty Score\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"qianfan_ak\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_ak\",\"display_name\":\"Qianfan Ak\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"qianfan_sk\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_sk\",\"display_name\":\"Qianfan Sk\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Baidu Qianfan chat models. Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"QianfanChat Model\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"model\":null,\"qianfan_ak\":null,\"qianfan_sk\":null,\"top_p\":null,\"temperature\":null,\"penalty_score\":null,\"endpoint\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GoogleGenerativeAIModel\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_google_genai import ChatGoogleGenerativeAI  # type: ignore\\nfrom pydantic.v1.types import SecretStr\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import RangeSpec, Text\\n\\n\\nclass GoogleGenerativeAIComponent(CustomComponent):\\n    display_name: str = \\\"Google Generative AIModel\\\"\\n    description: str = \\\"Generate text using Google Generative AI to generate text.\\\"\\n    documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"google_api_key\\\": {\\n                \\\"display_name\\\": \\\"Google API Key\\\",\\n                \\\"info\\\": \\\"The Google API Key to use for the Google Generative AI.\\\",\\n            },\\n            \\\"max_output_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n                \\\"info\\\": \\\"The maximum number of tokens to generate.\\\",\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"info\\\": \\\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\\\",\\n            },\\n            \\\"top_k\\\": {\\n                \\\"display_name\\\": \\\"Top K\\\",\\n                \\\"info\\\": \\\"Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.\\\",\\n                \\\"range_spec\\\": RangeSpec(min=0, max=2, step=0.1),\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top P\\\",\\n                \\\"info\\\": \\\"The maximum cumulative probability of tokens to consider when sampling.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"n\\\": {\\n                \\\"display_name\\\": \\\"N\\\",\\n                \\\"info\\\": \\\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model\\\",\\n                \\\"info\\\": \\\"The name of the model to use. Supported examples: gemini-pro\\\",\\n                \\\"options\\\": [\\\"gemini-pro\\\", \\\"gemini-pro-vision\\\"],\\n            },\\n            \\\"code\\\": {\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        google_api_key: str,\\n        model: str,\\n        inputs: str,\\n        max_output_tokens: Optional[int] = None,\\n        temperature: float = 0.1,\\n        top_k: Optional[int] = None,\\n        top_p: Optional[float] = None,\\n        n: Optional[int] = 1,\\n    ) -> Text:\\n        output = ChatGoogleGenerativeAI(\\n            model=model,\\n            max_output_tokens=max_output_tokens or None,  # type: ignore\\n            temperature=temperature,\\n            top_k=top_k or None,\\n            top_p=top_p or None,  # type: ignore\\n            n=n or 1,\\n            google_api_key=SecretStr(google_api_key),\\n        )\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"google_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"google_api_key\",\"display_name\":\"Google API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The Google API Key to use for the Google Generative AI.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum number of tokens to generate.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gemini-pro\",\"gemini-pro-vision\"],\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"The name of the model to use. Supported examples: gemini-pro\",\"title_case\":false,\"input_types\":[\"Text\"]},\"n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.\",\"rangeSpec\":{\"min\":0.0,\"max\":2.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"The maximum cumulative probability of tokens to consider when sampling.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Google Generative AI to generate text.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Google Generative AIModel\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"google_api_key\":null,\"model\":null,\"inputs\":null,\"max_output_tokens\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"n\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CTransformersModel\":{\"template\":{\"model_file\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".bin\"],\"file_path\":\"\",\"password\":false,\"name\":\"model_file\",\"display_name\":\"Model File\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Dict, Optional\\n\\nfrom langchain_community.llms.ctransformers import CTransformers\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass CTransformersComponent(CustomComponent):\\n    display_name = \\\"CTransformersModel\\\"\\n    description = \\\"Generate text using CTransformers LLM models\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\\"display_name\\\": \\\"Model\\\", \\\"required\\\": True},\\n            \\\"model_file\\\": {\\n                \\\"display_name\\\": \\\"Model File\\\",\\n                \\\"required\\\": False,\\n                \\\"field_type\\\": \\\"file\\\",\\n                \\\"file_types\\\": [\\\".bin\\\"],\\n            },\\n            \\\"model_type\\\": {\\\"display_name\\\": \\\"Model Type\\\", \\\"required\\\": True},\\n            \\\"config\\\": {\\n                \\\"display_name\\\": \\\"Config\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n                \\\"field_type\\\": \\\"dict\\\",\\n                \\\"value\\\": '{\\\"top_k\\\":40,\\\"top_p\\\":0.95,\\\"temperature\\\":0.8,\\\"repetition_penalty\\\":1.1,\\\"last_n_tokens\\\":64,\\\"seed\\\":-1,\\\"max_new_tokens\\\":256,\\\"stop\\\":\\\"\\\",\\\"stream\\\":\\\"False\\\",\\\"reset\\\":\\\"True\\\",\\\"batch_size\\\":8,\\\"threads\\\":-1,\\\"context_length\\\":-1,\\\"gpu_layers\\\":0}',\\n            },\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        model: str,\\n        model_file: str,\\n        inputs: str,\\n        model_type: str,\\n        config: Optional[Dict] = None,\\n    ) -> Text:\\n        output = CTransformers(model=model, model_file=model_file, model_type=model_type, config=config)\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"config\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"{\\\"top_k\\\":40,\\\"top_p\\\":0.95,\\\"temperature\\\":0.8,\\\"repetition_penalty\\\":1.1,\\\"last_n_tokens\\\":64,\\\"seed\\\":-1,\\\"max_new_tokens\\\":256,\\\"stop\\\":\\\"\\\",\\\"stream\\\":\\\"False\\\",\\\"reset\\\":\\\"True\\\",\\\"batch_size\\\":8,\\\"threads\\\":-1,\\\"context_length\\\":-1,\\\"gpu_layers\\\":0}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"config\",\"display_name\":\"Config\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_type\",\"display_name\":\"Model Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using CTransformers LLM models\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"CTransformersModel\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers\",\"custom_fields\":{\"model\":null,\"model_file\":null,\"inputs\":null,\"model_type\":null,\"config\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VertexAiModel\":{\"template\":{\"credentials\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".json\"],\"password\":false,\"name\":\"credentials\",\"display_name\":\"Credentials\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"examples\":{\"type\":\"BaseMessage\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":true,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"examples\",\"display_name\":\"Examples\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langchain_core.messages.base import BaseMessage\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass ChatVertexAIComponent(CustomComponent):\\n    display_name = \\\"ChatVertexAIModel\\\"\\n    description = \\\"Generate text using Vertex AI Chat large language models API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"credentials\\\": {\\n                \\\"display_name\\\": \\\"Credentials\\\",\\n                \\\"field_type\\\": \\\"file\\\",\\n                \\\"file_types\\\": [\\\".json\\\"],\\n                \\\"file_path\\\": None,\\n            },\\n            \\\"examples\\\": {\\n                \\\"display_name\\\": \\\"Examples\\\",\\n                \\\"multiline\\\": True,\\n            },\\n            \\\"location\\\": {\\n                \\\"display_name\\\": \\\"Location\\\",\\n                \\\"value\\\": \\\"us-central1\\\",\\n            },\\n            \\\"max_output_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n                \\\"value\\\": 128,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"model_name\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"value\\\": \\\"chat-bison\\\",\\n            },\\n            \\\"project\\\": {\\n                \\\"display_name\\\": \\\"Project\\\",\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"value\\\": 0.0,\\n            },\\n            \\\"top_k\\\": {\\n                \\\"display_name\\\": \\\"Top K\\\",\\n                \\\"value\\\": 40,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top P\\\",\\n                \\\"value\\\": 0.95,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"verbose\\\": {\\n                \\\"display_name\\\": \\\"Verbose\\\",\\n                \\\"value\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        inputs: str,\\n        credentials: Optional[str],\\n        project: str,\\n        examples: Optional[List[BaseMessage]] = [],\\n        location: str = \\\"us-central1\\\",\\n        max_output_tokens: int = 128,\\n        model_name: str = \\\"chat-bison\\\",\\n        temperature: float = 0.0,\\n        top_k: int = 40,\\n        top_p: float = 0.95,\\n        verbose: bool = False,\\n    ) -> Text:\\n        try:\\n            from langchain_google_vertexai import ChatVertexAI\\n        except ImportError:\\n            raise ImportError(\\n                \\\"To use the ChatVertexAI model, you need to install the langchain-google-vertexai package.\\\"\\n            )\\n        output = ChatVertexAI(\\n            credentials=credentials,\\n            examples=examples,\\n            location=location,\\n            max_output_tokens=max_output_tokens,\\n            model_name=model_name,\\n            project=project,\\n            temperature=temperature,\\n            top_k=top_k,\\n            top_p=top_p,\\n            verbose=verbose,\\n        )\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"location\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"us-central1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":128,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat-bison\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"project\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"project\",\"display_name\":\"Project\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Vertex AI Chat large language models API.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"ChatVertexAIModel\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"credentials\":null,\"project\":null,\"examples\":null,\"location\":null,\"max_output_tokens\":null,\"model_name\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"verbose\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OllamaModel\":{\"template\":{\"metadata\":{\"type\":\"Dict[str, Any]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":true,\"dynamic\":false,\"info\":\"Metadata to add to the run trace.\",\"title_case\":false},\"stop\":{\"type\":\"list\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"List of tokens to signal the model to stop generating text.\",\"title_case\":false},\"tags\":{\"type\":\"list\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"display_name\":\"Tags\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tags to add to the run trace.\",\"title_case\":false},\"base_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"base_url\",\"display_name\":\"Base URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable or disable caching.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Dict, List, Optional\\n\\n# from langchain_community.chat_models import ChatOllama\\nfrom langchain_community.chat_models import ChatOllama\\n\\n# from langchain.chat_models import ChatOllama\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n# whe When a callback component is added to Langflow, the comment must be uncommented.\\n# from langchain.callbacks.manager import CallbackManager\\n\\n\\nclass ChatOllamaComponent(CustomComponent):\\n    display_name = \\\"ChatOllamaModel\\\"\\n    description = \\\"Generate text using Local LLM for chat with Ollama.\\\"\\n\\n    def build_config(self) -> dict:\\n        return {\\n            \\\"base_url\\\": {\\n                \\\"display_name\\\": \\\"Base URL\\\",\\n                \\\"info\\\": \\\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\\\",\\n            },\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"value\\\": \\\"llama2\\\",\\n                \\\"info\\\": \\\"Refer to https://ollama.ai/library for more models.\\\",\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"value\\\": 0.8,\\n                \\\"info\\\": \\\"Controls the creativity of model responses.\\\",\\n            },\\n            \\\"cache\\\": {\\n                \\\"display_name\\\": \\\"Cache\\\",\\n                \\\"field_type\\\": \\\"bool\\\",\\n                \\\"info\\\": \\\"Enable or disable caching.\\\",\\n                \\\"advanced\\\": True,\\n                \\\"value\\\": False,\\n            },\\n            ### When a callback component is added to Langflow, the comment must be uncommented. ###\\n            # \\\"callback_manager\\\": {\\n            #     \\\"display_name\\\": \\\"Callback Manager\\\",\\n            #     \\\"info\\\": \\\"Optional callback manager for additional functionality.\\\",\\n            #     \\\"advanced\\\": True,\\n            # },\\n            # \\\"callbacks\\\": {\\n            #     \\\"display_name\\\": \\\"Callbacks\\\",\\n            #     \\\"info\\\": \\\"Callbacks to execute during model runtime.\\\",\\n            #     \\\"advanced\\\": True,\\n            # },\\n            ########################################################################################\\n            \\\"format\\\": {\\n                \\\"display_name\\\": \\\"Format\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"info\\\": \\\"Specify the format of the output (e.g., json).\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"metadata\\\": {\\n                \\\"display_name\\\": \\\"Metadata\\\",\\n                \\\"info\\\": \\\"Metadata to add to the run trace.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"mirostat\\\": {\\n                \\\"display_name\\\": \\\"Mirostat\\\",\\n                \\\"options\\\": [\\\"Disabled\\\", \\\"Mirostat\\\", \\\"Mirostat 2.0\\\"],\\n                \\\"info\\\": \\\"Enable/disable Mirostat sampling for controlling perplexity.\\\",\\n                \\\"value\\\": \\\"Disabled\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"mirostat_eta\\\": {\\n                \\\"display_name\\\": \\\"Mirostat Eta\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Learning rate for Mirostat algorithm. (Default: 0.1)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"mirostat_tau\\\": {\\n                \\\"display_name\\\": \\\"Mirostat Tau\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"num_ctx\\\": {\\n                \\\"display_name\\\": \\\"Context Window Size\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Size of the context window for generating tokens. (Default: 2048)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"num_gpu\\\": {\\n                \\\"display_name\\\": \\\"Number of GPUs\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"num_thread\\\": {\\n                \\\"display_name\\\": \\\"Number of Threads\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Number of threads to use during computation. (Default: detected for optimal performance)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"repeat_last_n\\\": {\\n                \\\"display_name\\\": \\\"Repeat Last N\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"repeat_penalty\\\": {\\n                \\\"display_name\\\": \\\"Repeat Penalty\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Penalty for repetitions in generated text. (Default: 1.1)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"tfs_z\\\": {\\n                \\\"display_name\\\": \\\"TFS Z\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Tail free sampling value. (Default: 1)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"timeout\\\": {\\n                \\\"display_name\\\": \\\"Timeout\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Timeout for the request stream.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_k\\\": {\\n                \\\"display_name\\\": \\\"Top K\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Limits token selection to top K. (Default: 40)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top P\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Works together with top-k. (Default: 0.9)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"verbose\\\": {\\n                \\\"display_name\\\": \\\"Verbose\\\",\\n                \\\"field_type\\\": \\\"bool\\\",\\n                \\\"info\\\": \\\"Whether to print out response text.\\\",\\n            },\\n            \\\"tags\\\": {\\n                \\\"display_name\\\": \\\"Tags\\\",\\n                \\\"field_type\\\": \\\"list\\\",\\n                \\\"info\\\": \\\"Tags to add to the run trace.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"stop\\\": {\\n                \\\"display_name\\\": \\\"Stop Tokens\\\",\\n                \\\"field_type\\\": \\\"list\\\",\\n                \\\"info\\\": \\\"List of tokens to signal the model to stop generating text.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"system\\\": {\\n                \\\"display_name\\\": \\\"System\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"info\\\": \\\"System to use for generating text.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"template\\\": {\\n                \\\"display_name\\\": \\\"Template\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"info\\\": \\\"Template to use for generating text.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        base_url: Optional[str],\\n        model: str,\\n        inputs: str,\\n        mirostat: Optional[str],\\n        mirostat_eta: Optional[float] = None,\\n        mirostat_tau: Optional[float] = None,\\n        ### When a callback component is added to Langflow, the comment must be uncommented.###\\n        # callback_manager: Optional[CallbackManager] = None,\\n        # callbacks: Optional[List[Callbacks]] = None,\\n        #######################################################################################\\n        repeat_last_n: Optional[int] = None,\\n        verbose: Optional[bool] = None,\\n        cache: Optional[bool] = None,\\n        num_ctx: Optional[int] = None,\\n        num_gpu: Optional[int] = None,\\n        format: Optional[str] = None,\\n        metadata: Optional[Dict[str, Any]] = None,\\n        num_thread: Optional[int] = None,\\n        repeat_penalty: Optional[float] = None,\\n        stop: Optional[List[str]] = None,\\n        system: Optional[str] = None,\\n        tags: Optional[List[str]] = None,\\n        temperature: Optional[float] = None,\\n        template: Optional[str] = None,\\n        tfs_z: Optional[float] = None,\\n        timeout: Optional[int] = None,\\n        top_k: Optional[int] = None,\\n        top_p: Optional[int] = None,\\n    ) -> Text:\\n        if not base_url:\\n            base_url = \\\"http://localhost:11434\\\"\\n\\n        # Mapping mirostat settings to their corresponding values\\n        mirostat_options = {\\\"Mirostat\\\": 1, \\\"Mirostat 2.0\\\": 2}\\n\\n        # Default to 0 for 'Disabled'\\n        mirostat_value = mirostat_options.get(mirostat, 0)  # type: ignore\\n\\n        # Set mirostat_eta and mirostat_tau to None if mirostat is disabled\\n        if mirostat_value == 0:\\n            mirostat_eta = None\\n            mirostat_tau = None\\n\\n        # Mapping system settings to their corresponding values\\n        llm_params = {\\n            \\\"base_url\\\": base_url,\\n            \\\"cache\\\": cache,\\n            \\\"model\\\": model,\\n            \\\"mirostat\\\": mirostat_value,\\n            \\\"format\\\": format,\\n            \\\"metadata\\\": metadata,\\n            \\\"tags\\\": tags,\\n            ## When a callback component is added to Langflow, the comment must be uncommented.##\\n            # \\\"callback_manager\\\": callback_manager,\\n            # \\\"callbacks\\\": callbacks,\\n            #####################################################################################\\n            \\\"mirostat_eta\\\": mirostat_eta,\\n            \\\"mirostat_tau\\\": mirostat_tau,\\n            \\\"num_ctx\\\": num_ctx,\\n            \\\"num_gpu\\\": num_gpu,\\n            \\\"num_thread\\\": num_thread,\\n            \\\"repeat_last_n\\\": repeat_last_n,\\n            \\\"repeat_penalty\\\": repeat_penalty,\\n            \\\"temperature\\\": temperature,\\n            \\\"stop\\\": stop,\\n            \\\"system\\\": system,\\n            \\\"template\\\": template,\\n            \\\"tfs_z\\\": tfs_z,\\n            \\\"timeout\\\": timeout,\\n            \\\"top_k\\\": top_k,\\n            \\\"top_p\\\": top_p,\\n            \\\"verbose\\\": verbose,\\n        }\\n\\n        # None Value remove\\n        llm_params = {k: v for k, v in llm_params.items() if v is not None}\\n\\n        try:\\n            output = ChatOllama(**llm_params)  # type: ignore\\n        except Exception as e:\\n            raise ValueError(\\\"Could not initialize Ollama LLM.\\\") from e\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"format\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"format\",\"display_name\":\"Format\",\"advanced\":true,\"dynamic\":false,\"info\":\"Specify the format of the output (e.g., json).\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Disabled\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Disabled\",\"Mirostat\",\"Mirostat 2.0\"],\"name\":\"mirostat\",\"display_name\":\"Mirostat\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable/disable Mirostat sampling for controlling perplexity.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat_eta\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_eta\",\"display_name\":\"Mirostat Eta\",\"advanced\":true,\"dynamic\":false,\"info\":\"Learning rate for Mirostat algorithm. (Default: 0.1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"mirostat_tau\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_tau\",\"display_name\":\"Mirostat Tau\",\"advanced\":true,\"dynamic\":false,\"info\":\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"llama2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"Refer to https://ollama.ai/library for more models.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"num_ctx\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_ctx\",\"display_name\":\"Context Window Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"Size of the context window for generating tokens. (Default: 2048)\",\"title_case\":false},\"num_gpu\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_gpu\",\"display_name\":\"Number of GPUs\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\",\"title_case\":false},\"num_thread\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_thread\",\"display_name\":\"Number of Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of threads to use during computation. (Default: detected for optimal performance)\",\"title_case\":false},\"repeat_last_n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_last_n\",\"display_name\":\"Repeat Last N\",\"advanced\":true,\"dynamic\":false,\"info\":\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"Penalty for repetitions in generated text. (Default: 1.1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"system\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"system\",\"display_name\":\"System\",\"advanced\":true,\"dynamic\":false,\"info\":\"System to use for generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Controls the creativity of model responses.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"template\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"display_name\":\"Template\",\"advanced\":true,\"dynamic\":false,\"info\":\"Template to use for generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"tfs_z\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tfs_z\",\"display_name\":\"TFS Z\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tail free sampling value. (Default: 1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"timeout\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"Timeout for the request stream.\",\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Limits token selection to top K. (Default: 40)\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"Works together with top-k. (Default: 0.9)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"Whether to print out response text.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Local LLM for chat with Ollama.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"ChatOllamaModel\",\"documentation\":\"\",\"custom_fields\":{\"base_url\":null,\"model\":null,\"inputs\":null,\"mirostat\":null,\"mirostat_eta\":null,\"mirostat_tau\":null,\"repeat_last_n\":null,\"verbose\":null,\"cache\":null,\"num_ctx\":null,\"num_gpu\":null,\"format\":null,\"metadata\":null,\"num_thread\":null,\"repeat_penalty\":null,\"stop\":null,\"system\":null,\"tags\":null,\"temperature\":null,\"template\":null,\"tfs_z\":null,\"timeout\":null,\"top_k\":null,\"top_p\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AnthropicModel\":{\"template\":{\"anthropic_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"anthropic_api_key\",\"display_name\":\"Anthropic API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Anthropic API key.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"api_endpoint\",\"display_name\":\"API Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.anthropic import ChatAnthropic\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass AnthropicLLM(CustomComponent):\\n    display_name: str = \\\"AnthropicModel\\\"\\n    description: str = \\\"Generate text using Anthropic Chat&Completion large language models.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"options\\\": [\\n                    \\\"claude-2.1\\\",\\n                    \\\"claude-2.0\\\",\\n                    \\\"claude-instant-1.2\\\",\\n                    \\\"claude-instant-1\\\",\\n                    # Add more models as needed\\n                ],\\n                \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/anthropic\\\",\\n                \\\"required\\\": True,\\n                \\\"value\\\": \\\"claude-2.1\\\",\\n            },\\n            \\\"anthropic_api_key\\\": {\\n                \\\"display_name\\\": \\\"Anthropic API Key\\\",\\n                \\\"required\\\": True,\\n                \\\"password\\\": True,\\n                \\\"info\\\": \\\"Your Anthropic API key.\\\",\\n            },\\n            \\\"max_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Tokens\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"value\\\": 256,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"value\\\": 0.7,\\n            },\\n            \\\"api_endpoint\\\": {\\n                \\\"display_name\\\": \\\"API Endpoint\\\",\\n                \\\"info\\\": \\\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        model: str,\\n        inputs: str,\\n        anthropic_api_key: Optional[str] = None,\\n        max_tokens: Optional[int] = None,\\n        temperature: Optional[float] = None,\\n        api_endpoint: Optional[str] = None,\\n    ) -> Text:\\n        # Set default API endpoint if not provided\\n        if not api_endpoint:\\n            api_endpoint = \\\"https://api.anthropic.com\\\"\\n\\n        try:\\n            output = ChatAnthropic(\\n                model_name=model,\\n                anthropic_api_key=(SecretStr(anthropic_api_key) if anthropic_api_key else None),\\n                max_tokens_to_sample=max_tokens,  # type: ignore\\n                temperature=temperature,\\n                anthropic_api_url=api_endpoint,\\n            )\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to Anthropic API.\\\") from e\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"claude-2.1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"claude-2.1\",\"claude-2.0\",\"claude-instant-1.2\",\"claude-instant-1\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/anthropic\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Anthropic Chat&Completion large language models.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"AnthropicModel\",\"documentation\":\"\",\"custom_fields\":{\"model\":null,\"inputs\":null,\"anthropic_api_key\":null,\"max_tokens\":null,\"temperature\":null,\"api_endpoint\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OpenAIModel\":{\"template\":{\"inputs\":{\"type\":\"Text\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_openai import ChatOpenAI\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import NestedDict, Text\\n\\n\\nclass OpenAIModelComponent(CustomComponent):\\n    display_name = \\\"OpenAI Model\\\"\\n    description = \\\"Generates text using OpenAI's models.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n            \\\"max_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Tokens\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n            },\\n            \\\"model_kwargs\\\": {\\n                \\\"display_name\\\": \\\"Model Kwargs\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n            },\\n            \\\"model_name\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"options\\\": [\\n                    \\\"gpt-4-turbo-preview\\\",\\n                    \\\"gpt-4-0125-preview\\\",\\n                    \\\"gpt-4-1106-preview\\\",\\n                    \\\"gpt-4-vision-preview\\\",\\n                    \\\"gpt-3.5-turbo-0125\\\",\\n                    \\\"gpt-3.5-turbo-1106\\\",\\n                ],\\n            },\\n            \\\"openai_api_base\\\": {\\n                \\\"display_name\\\": \\\"OpenAI API Base\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"info\\\": (\\n                    \\\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\\\n\\\\n\\\"\\n                    \\\"You can change this to use other APIs like JinaChat, LocalAI and Prem.\\\"\\n                ),\\n            },\\n            \\\"openai_api_key\\\": {\\n                \\\"display_name\\\": \\\"OpenAI API Key\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"password\\\": True,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"value\\\": 0.7,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        inputs: Text,\\n        max_tokens: Optional[int] = 256,\\n        model_kwargs: NestedDict = {},\\n        model_name: str = \\\"gpt-4-1106-preview\\\",\\n        openai_api_base: Optional[str] = None,\\n        openai_api_key: Optional[str] = None,\\n        temperature: float = 0.7,\\n    ) -> Text:\\n        if not openai_api_base:\\n            openai_api_base = \\\"https://api.openai.com/v1\\\"\\n        model = ChatOpenAI(\\n            max_tokens=max_tokens,\\n            model_kwargs=model_kwargs,\\n            model=model_name,\\n            base_url=openai_api_base,\\n            api_key=openai_api_key,\\n            temperature=temperature,\\n        )\\n\\n        message = model.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-4-turbo-preview\",\"gpt-4-0125-preview\",\"gpt-4-1106-preview\",\"gpt-4-vision-preview\",\"gpt-3.5-turbo-0125\",\"gpt-3.5-turbo-1106\"],\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generates text using OpenAI's models.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"OpenAI Model\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"max_tokens\":null,\"model_kwargs\":null,\"model_name\":null,\"openai_api_base\":null,\"openai_api_key\":null,\"temperature\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"HuggingFaceModel\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.huggingface import ChatHuggingFace\\nfrom langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint\\n\\nfrom langflow import CustomComponent\\n\\nfrom langflow.field_typing import Text\\n\\n\\nclass HuggingFaceEndpointsComponent(CustomComponent):\\n    display_name: str = \\\"Hugging Face Inference API models\\\"\\n    description: str = \\\"Generate text using LLM model from Hugging Face Inference API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Endpoint URL\\\", \\\"password\\\": True},\\n            \\\"task\\\": {\\n                \\\"display_name\\\": \\\"Task\\\",\\n                \\\"options\\\": [\\\"text2text-generation\\\", \\\"text-generation\\\", \\\"summarization\\\"],\\n            },\\n            \\\"huggingfacehub_api_token\\\": {\\\"display_name\\\": \\\"API token\\\", \\\"password\\\": True},\\n            \\\"model_kwargs\\\": {\\n                \\\"display_name\\\": \\\"Model Keyword Arguments\\\",\\n                \\\"field_type\\\": \\\"code\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        inputs: str,\\n        endpoint_url: str,\\n        task: str = \\\"text2text-generation\\\",\\n        huggingfacehub_api_token: Optional[str] = None,\\n        model_kwargs: Optional[dict] = None,\\n    ) -> Text:\\n        try:\\n            llm = HuggingFaceEndpoint(\\n                endpoint_url=endpoint_url,\\n                task=task,\\n                huggingfacehub_api_token=huggingfacehub_api_token,\\n                model_kwargs=model_kwargs,\\n            )\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to HuggingFace Endpoints API.\\\") from e\\n        output = ChatHuggingFace(llm=llm)\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"endpoint_url\",\"display_name\":\"Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"huggingfacehub_api_token\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"huggingfacehub_api_token\",\"display_name\":\"API token\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Keyword Arguments\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"task\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"text2text-generation\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"text2text-generation\",\"text-generation\",\"summarization\"],\"name\":\"task\",\"display_name\":\"Task\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using LLM model from Hugging Face Inference API.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Hugging Face Inference API models\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"endpoint_url\":null,\"task\":null,\"huggingfacehub_api_token\":null,\"model_kwargs\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AzureOpenAIModel\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_version\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"2023-12-01-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"2023-03-15-preview\",\"2023-05-15\",\"2023-06-01-preview\",\"2023-07-01-preview\",\"2023-08-01-preview\",\"2023-09-01-preview\",\"2023-12-01-preview\"],\"name\":\"api_version\",\"display_name\":\"API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_deployment\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_deployment\",\"display_name\":\"Deployment Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_endpoint\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_endpoint\",\"display_name\":\"Azure Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.llms.base import BaseLanguageModel\\nfrom langchain_openai import AzureChatOpenAI\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AzureChatOpenAIComponent(CustomComponent):\\n    display_name: str = \\\"AzureOpenAI Model\\\"\\n    description: str = \\\"Generate text using LLM model from Azure OpenAI.\\\"\\n    documentation: str = \\\"https://python.langchain.com/docs/integrations/llms/azure_openai\\\"\\n    beta = False\\n\\n    AZURE_OPENAI_MODELS = [\\n        \\\"gpt-35-turbo\\\",\\n        \\\"gpt-35-turbo-16k\\\",\\n        \\\"gpt-35-turbo-instruct\\\",\\n        \\\"gpt-4\\\",\\n        \\\"gpt-4-32k\\\",\\n        \\\"gpt-4-vision\\\",\\n    ]\\n\\n    AZURE_OPENAI_API_VERSIONS = [\\n        \\\"2023-03-15-preview\\\",\\n        \\\"2023-05-15\\\",\\n        \\\"2023-06-01-preview\\\",\\n        \\\"2023-07-01-preview\\\",\\n        \\\"2023-08-01-preview\\\",\\n        \\\"2023-09-01-preview\\\",\\n        \\\"2023-12-01-preview\\\",\\n    ]\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"value\\\": self.AZURE_OPENAI_MODELS[0],\\n                \\\"options\\\": self.AZURE_OPENAI_MODELS,\\n                \\\"required\\\": True,\\n            },\\n            \\\"azure_endpoint\\\": {\\n                \\\"display_name\\\": \\\"Azure Endpoint\\\",\\n                \\\"required\\\": True,\\n                \\\"info\\\": \\\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\\\",\\n            },\\n            \\\"azure_deployment\\\": {\\n                \\\"display_name\\\": \\\"Deployment Name\\\",\\n                \\\"required\\\": True,\\n            },\\n            \\\"api_version\\\": {\\n                \\\"display_name\\\": \\\"API Version\\\",\\n                \\\"options\\\": self.AZURE_OPENAI_API_VERSIONS,\\n                \\\"value\\\": self.AZURE_OPENAI_API_VERSIONS[-1],\\n                \\\"required\\\": True,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"required\\\": True, \\\"password\\\": True},\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"value\\\": 0.7,\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"required\\\": False,\\n            },\\n            \\\"max_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Tokens\\\",\\n                \\\"value\\\": 1000,\\n                \\\"required\\\": False,\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"advanced\\\": True,\\n                \\\"info\\\": \\\"Maximum number of tokens to generate.\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        model: str,\\n        azure_endpoint: str,\\n        inputs: str,\\n        azure_deployment: str,\\n        api_key: str,\\n        api_version: str,\\n        temperature: float = 0.7,\\n        max_tokens: Optional[int] = 1000,\\n    ) -> BaseLanguageModel:\\n        try:\\n            output = AzureChatOpenAI(\\n                model=model,\\n                azure_endpoint=azure_endpoint,\\n                azure_deployment=azure_deployment,\\n                api_version=api_version,\\n                api_key=api_key,\\n                temperature=temperature,\\n                max_tokens=max_tokens,\\n            )\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to AzureOpenAI API.\\\") from e\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"Maximum number of tokens to generate.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-35-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-35-turbo\",\"gpt-35-turbo-16k\",\"gpt-35-turbo-instruct\",\"gpt-4\",\"gpt-4-32k\",\"gpt-4-vision\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using LLM model from Azure OpenAI.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"AzureOpenAI Model\",\"documentation\":\"https://python.langchain.com/docs/integrations/llms/azure_openai\",\"custom_fields\":{\"model\":null,\"azure_endpoint\":null,\"inputs\":null,\"azure_deployment\":null,\"api_key\":null,\"api_version\":null,\"temperature\":null,\"max_tokens\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"AmazonBedrockModel\":{\"template\":{\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.bedrock import BedrockChat\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass AmazonBedrockComponent(CustomComponent):\\n    display_name: str = \\\"Amazon Bedrock Model\\\"\\n    description: str = \\\"Generate text using LLM model from Amazon Bedrock.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"model_id\\\": {\\n                \\\"display_name\\\": \\\"Model Id\\\",\\n                \\\"options\\\": [\\n                    \\\"ai21.j2-grande-instruct\\\",\\n                    \\\"ai21.j2-jumbo-instruct\\\",\\n                    \\\"ai21.j2-mid\\\",\\n                    \\\"ai21.j2-mid-v1\\\",\\n                    \\\"ai21.j2-ultra\\\",\\n                    \\\"ai21.j2-ultra-v1\\\",\\n                    \\\"anthropic.claude-instant-v1\\\",\\n                    \\\"anthropic.claude-v1\\\",\\n                    \\\"anthropic.claude-v2\\\",\\n                    \\\"cohere.command-text-v14\\\",\\n                ],\\n            },\\n            \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n            \\\"streaming\\\": {\\\"display_name\\\": \\\"Streaming\\\", \\\"field_type\\\": \\\"bool\\\"},\\n            \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Endpoint URL\\\"},\\n            \\\"region_name\\\": {\\\"display_name\\\": \\\"Region Name\\\"},\\n            \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\"},\\n            \\\"cache\\\": {\\\"display_name\\\": \\\"Cache\\\"},\\n            \\\"code\\\": {\\\"advanced\\\": True},\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        inputs: str,\\n        model_id: str = \\\"anthropic.claude-instant-v1\\\",\\n        credentials_profile_name: Optional[str] = None,\\n        region_name: Optional[str] = None,\\n        model_kwargs: Optional[dict] = None,\\n        endpoint_url: Optional[str] = None,\\n        streaming: bool = False,\\n        cache: Optional[bool] = None,\\n    ) -> Text:\\n        try:\\n            output = BedrockChat(\\n                credentials_profile_name=credentials_profile_name,\\n                model_id=model_id,\\n                region_name=region_name,\\n                model_kwargs=model_kwargs,\\n                endpoint_url=endpoint_url,\\n                streaming=streaming,\\n                cache=cache,\\n            )  # type: ignore\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"credentials_profile_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"endpoint_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"anthropic.claude-instant-v1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ai21.j2-grande-instruct\",\"ai21.j2-jumbo-instruct\",\"ai21.j2-mid\",\"ai21.j2-mid-v1\",\"ai21.j2-ultra\",\"ai21.j2-ultra-v1\",\"anthropic.claude-instant-v1\",\"anthropic.claude-v1\",\"anthropic.claude-v2\",\"cohere.command-text-v14\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"region_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"region_name\",\"display_name\":\"Region Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using LLM model from Amazon Bedrock.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Amazon Bedrock Model\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"model_id\":null,\"credentials_profile_name\":null,\"region_name\":null,\"model_kwargs\":null,\"endpoint_url\":null,\"streaming\":null,\"cache\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CohereModel\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_community.chat_models.cohere import ChatCohere\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass CohereComponent(CustomComponent):\\n    display_name = \\\"CohereModel\\\"\\n    description = \\\"Generate text using Cohere large language models.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"cohere_api_key\\\": {\\n                \\\"display_name\\\": \\\"Cohere API Key\\\",\\n                \\\"type\\\": \\\"password\\\",\\n                \\\"password\\\": True,\\n            },\\n            \\\"max_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Tokens\\\",\\n                \\\"default\\\": 256,\\n                \\\"type\\\": \\\"int\\\",\\n                \\\"show\\\": True,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"default\\\": 0.75,\\n                \\\"type\\\": \\\"float\\\",\\n                \\\"show\\\": True,\\n            },\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        cohere_api_key: str,\\n        inputs: str,\\n        max_tokens: int = 256,\\n        temperature: float = 0.75,\\n    ) -> Text:\\n        output = ChatCohere(\\n            cohere_api_key=cohere_api_key,\\n            max_tokens=max_tokens,\\n            temperature=temperature,\\n        )\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"cohere_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"cohere_api_key\",\"display_name\":\"Cohere API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.75,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Cohere large language models.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"CohereModel\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere\",\"custom_fields\":{\"cohere_api_key\":null,\"inputs\":null,\"max_tokens\":null,\"temperature\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"model_specs\":{\"AmazonBedrockSpecs\":{\"template\":{\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain_community.llms.bedrock import Bedrock\\n\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockComponent(CustomComponent):\\n    display_name: str = \\\"Amazon Bedrock\\\"\\n    description: str = \\\"LLM model from Amazon Bedrock.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"model_id\\\": {\\n                \\\"display_name\\\": \\\"Model Id\\\",\\n                \\\"options\\\": [\\n                    \\\"ai21.j2-grande-instruct\\\",\\n                    \\\"ai21.j2-jumbo-instruct\\\",\\n                    \\\"ai21.j2-mid\\\",\\n                    \\\"ai21.j2-mid-v1\\\",\\n                    \\\"ai21.j2-ultra\\\",\\n                    \\\"ai21.j2-ultra-v1\\\",\\n                    \\\"anthropic.claude-instant-v1\\\",\\n                    \\\"anthropic.claude-v1\\\",\\n                    \\\"anthropic.claude-v2\\\",\\n                    \\\"cohere.command-text-v14\\\",\\n                ],\\n            },\\n            \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n            \\\"streaming\\\": {\\\"display_name\\\": \\\"Streaming\\\", \\\"field_type\\\": \\\"bool\\\"},\\n            \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Endpoint URL\\\"},\\n            \\\"region_name\\\": {\\\"display_name\\\": \\\"Region Name\\\"},\\n            \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\"},\\n            \\\"cache\\\": {\\\"display_name\\\": \\\"Cache\\\"},\\n            \\\"code\\\": {\\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        model_id: str = \\\"anthropic.claude-instant-v1\\\",\\n        credentials_profile_name: Optional[str] = None,\\n        region_name: Optional[str] = None,\\n        model_kwargs: Optional[dict] = None,\\n        endpoint_url: Optional[str] = None,\\n        streaming: bool = False,\\n        cache: Optional[bool] = None,\\n    ) -> BaseLLM:\\n        try:\\n            output = Bedrock(\\n                credentials_profile_name=credentials_profile_name,\\n                model_id=model_id,\\n                region_name=region_name,\\n                model_kwargs=model_kwargs,\\n                endpoint_url=endpoint_url,\\n                streaming=streaming,\\n                cache=cache,\\n            )  # type: ignore\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n        return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"credentials_profile_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"endpoint_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"anthropic.claude-instant-v1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ai21.j2-grande-instruct\",\"ai21.j2-jumbo-instruct\",\"ai21.j2-mid\",\"ai21.j2-mid-v1\",\"ai21.j2-ultra\",\"ai21.j2-ultra-v1\",\"anthropic.claude-instant-v1\",\"anthropic.claude-v1\",\"anthropic.claude-v2\",\"cohere.command-text-v14\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"region_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"region_name\",\"display_name\":\"Region Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"LLM model from Amazon Bedrock.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"Amazon Bedrock\",\"documentation\":\"\",\"custom_fields\":{\"model_id\":null,\"credentials_profile_name\":null,\"region_name\":null,\"model_kwargs\":null,\"endpoint_url\":null,\"streaming\":null,\"cache\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatVertexAISpecs\":{\"template\":{\"credentials\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".json\"],\"password\":false,\"name\":\"credentials\",\"display_name\":\"Credentials\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"examples\":{\"type\":\"BaseMessage\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":true,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"examples\",\"display_name\":\"Examples\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional, Union\\n\\nfrom langchain.llms import BaseLLM\\nfrom langchain_community.chat_models.vertexai import ChatVertexAI\\nfrom langchain_core.messages.base import BaseMessage\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel\\n\\n\\nclass ChatVertexAIComponent(CustomComponent):\\n    display_name = \\\"ChatVertexAI\\\"\\n    description = \\\"`Vertex AI` Chat large language models API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"credentials\\\": {\\n                \\\"display_name\\\": \\\"Credentials\\\",\\n                \\\"field_type\\\": \\\"file\\\",\\n                \\\"file_types\\\": [\\\".json\\\"],\\n                \\\"file_path\\\": None,\\n            },\\n            \\\"examples\\\": {\\n                \\\"display_name\\\": \\\"Examples\\\",\\n                \\\"multiline\\\": True,\\n            },\\n            \\\"location\\\": {\\n                \\\"display_name\\\": \\\"Location\\\",\\n                \\\"value\\\": \\\"us-central1\\\",\\n            },\\n            \\\"max_output_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n                \\\"value\\\": 128,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"model_name\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"value\\\": \\\"chat-bison\\\",\\n            },\\n            \\\"project\\\": {\\n                \\\"display_name\\\": \\\"Project\\\",\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"value\\\": 0.0,\\n            },\\n            \\\"top_k\\\": {\\n                \\\"display_name\\\": \\\"Top K\\\",\\n                \\\"value\\\": 40,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top P\\\",\\n                \\\"value\\\": 0.95,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"verbose\\\": {\\n                \\\"display_name\\\": \\\"Verbose\\\",\\n                \\\"value\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        credentials: Optional[str],\\n        project: str,\\n        examples: Optional[List[BaseMessage]] = [],\\n        location: str = \\\"us-central1\\\",\\n        max_output_tokens: int = 128,\\n        model_name: str = \\\"chat-bison\\\",\\n        temperature: float = 0.0,\\n        top_k: int = 40,\\n        top_p: float = 0.95,\\n        verbose: bool = False,\\n    ) -> Union[BaseLanguageModel, BaseLLM]:\\n        return ChatVertexAI(\\n            credentials=credentials,\\n            examples=examples,\\n            location=location,\\n            max_output_tokens=max_output_tokens,\\n            model_name=model_name,\\n            project=project,\\n            temperature=temperature,\\n            top_k=top_k,\\n            top_p=top_p,\\n            verbose=verbose,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"location\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"us-central1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":128,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat-bison\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"project\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"project\",\"display_name\":\"Project\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"`Vertex AI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"ChatVertexAI\",\"documentation\":\"\",\"custom_fields\":{\"credentials\":null,\"project\":null,\"examples\":null,\"location\":null,\"max_output_tokens\":null,\"model_name\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"verbose\":null},\"output_types\":[\"BaseLanguageModel\",\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VertexAISpecs\":{\"template\":{\"credentials\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".json\"],\"file_path\":\"\",\"password\":false,\"name\":\"credentials\",\"display_name\":\"Credentials\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.llms import BaseLLM\\nfrom typing import Optional, Union, Callable, Dict\\nfrom langchain_community.llms.vertexai import VertexAI\\n\\n\\nclass VertexAIComponent(CustomComponent):\\n    display_name = \\\"VertexAI\\\"\\n    description = \\\"Google Vertex AI large language models\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"credentials\\\": {\\n                \\\"display_name\\\": \\\"Credentials\\\",\\n                \\\"field_type\\\": \\\"file\\\",\\n                \\\"file_types\\\": [\\\".json\\\"],\\n                \\\"required\\\": False,\\n                \\\"value\\\": None,\\n            },\\n            \\\"location\\\": {\\n                \\\"display_name\\\": \\\"Location\\\",\\n                \\\"type\\\": \\\"str\\\",\\n                \\\"advanced\\\": True,\\n                \\\"value\\\": \\\"us-central1\\\",\\n                \\\"required\\\": False,\\n            },\\n            \\\"max_output_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"value\\\": 128,\\n                \\\"required\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"max_retries\\\": {\\n                \\\"display_name\\\": \\\"Max Retries\\\",\\n                \\\"type\\\": \\\"int\\\",\\n                \\\"value\\\": 6,\\n                \\\"required\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"metadata\\\": {\\n                \\\"display_name\\\": \\\"Metadata\\\",\\n                \\\"field_type\\\": \\\"dict\\\",\\n                \\\"required\\\": False,\\n                \\\"default\\\": {},\\n            },\\n            \\\"model_name\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"type\\\": \\\"str\\\",\\n                \\\"value\\\": \\\"text-bison\\\",\\n                \\\"required\\\": False,\\n            },\\n            \\\"n\\\": {\\n                \\\"advanced\\\": True,\\n                \\\"display_name\\\": \\\"N\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"value\\\": 1,\\n                \\\"required\\\": False,\\n            },\\n            \\\"project\\\": {\\n                \\\"display_name\\\": \\\"Project\\\",\\n                \\\"type\\\": \\\"str\\\",\\n                \\\"required\\\": False,\\n                \\\"default\\\": None,\\n            },\\n            \\\"request_parallelism\\\": {\\n                \\\"display_name\\\": \\\"Request Parallelism\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"value\\\": 5,\\n                \\\"required\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"streaming\\\": {\\n                \\\"display_name\\\": \\\"Streaming\\\",\\n                \\\"field_type\\\": \\\"bool\\\",\\n                \\\"value\\\": False,\\n                \\\"required\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"value\\\": 0.0,\\n                \\\"required\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"type\\\": \\\"int\\\", \\\"default\\\": 40, \\\"required\\\": False, \\\"advanced\\\": True},\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top P\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"value\\\": 0.95,\\n                \\\"required\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"tuned_model_name\\\": {\\n                \\\"display_name\\\": \\\"Tuned Model Name\\\",\\n                \\\"type\\\": \\\"str\\\",\\n                \\\"required\\\": False,\\n                \\\"value\\\": None,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"verbose\\\": {\\n                \\\"display_name\\\": \\\"Verbose\\\",\\n                \\\"field_type\\\": \\\"bool\\\",\\n                \\\"value\\\": False,\\n                \\\"required\\\": False,\\n            },\\n            \\\"name\\\": {\\\"display_name\\\": \\\"Name\\\", \\\"field_type\\\": \\\"str\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        credentials: Optional[str] = None,\\n        location: str = \\\"us-central1\\\",\\n        max_output_tokens: int = 128,\\n        max_retries: int = 6,\\n        metadata: Dict = {},\\n        model_name: str = \\\"text-bison\\\",\\n        n: int = 1,\\n        name: Optional[str] = None,\\n        project: Optional[str] = None,\\n        request_parallelism: int = 5,\\n        streaming: bool = False,\\n        temperature: float = 0.0,\\n        top_k: int = 40,\\n        top_p: float = 0.95,\\n        tuned_model_name: Optional[str] = None,\\n        verbose: bool = False,\\n    ) -> Union[BaseLLM, Callable]:\\n        return VertexAI(\\n            credentials=credentials,\\n            location=location,\\n            max_output_tokens=max_output_tokens,\\n            max_retries=max_retries,\\n            metadata=metadata,\\n            model_name=model_name,\\n            n=n,\\n            name=name,\\n            project=project,\\n            request_parallelism=request_parallelism,\\n            streaming=streaming,\\n            temperature=temperature,\\n            top_k=top_k,\\n            top_p=top_p,\\n            tuned_model_name=tuned_model_name,\\n            verbose=verbose,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"location\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"us-central1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":128,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_retries\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max Retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"text-bison\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"name\",\"display_name\":\"Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"project\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"project\",\"display_name\":\"Project\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"request_parallelism\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"request_parallelism\",\"display_name\":\"Request Parallelism\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"streaming\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"tuned_model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tuned_model_name\",\"display_name\":\"Tuned Model Name\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Google Vertex AI large language models\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\",\"Callable\"],\"display_name\":\"VertexAI\",\"documentation\":\"\",\"custom_fields\":{\"credentials\":null,\"location\":null,\"max_output_tokens\":null,\"max_retries\":null,\"metadata\":null,\"model_name\":null,\"n\":null,\"name\":null,\"project\":null,\"request_parallelism\":null,\"streaming\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"tuned_model_name\":null,\"verbose\":null},\"output_types\":[\"BaseLLM\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatAnthropicSpecs\":{\"template\":{\"anthropic_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"anthropic_api_key\",\"display_name\":\"Anthropic API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"anthropic_api_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"anthropic_api_url\",\"display_name\":\"Anthropic API URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from pydantic.v1.types import SecretStr\\nfrom langflow import CustomComponent\\nfrom typing import Optional, Union, Callable\\nfrom langflow.field_typing import BaseLanguageModel\\nfrom langchain_community.chat_models.anthropic import ChatAnthropic\\n\\n\\nclass ChatAnthropicComponent(CustomComponent):\\n    display_name = \\\"ChatAnthropic\\\"\\n    description = \\\"`Anthropic` chat large language models.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/anthropic\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"anthropic_api_key\\\": {\\n                \\\"display_name\\\": \\\"Anthropic API Key\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"password\\\": True,\\n            },\\n            \\\"anthropic_api_url\\\": {\\n                \\\"display_name\\\": \\\"Anthropic API URL\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n            },\\n            \\\"model_kwargs\\\": {\\n                \\\"display_name\\\": \\\"Model Kwargs\\\",\\n                \\\"field_type\\\": \\\"dict\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        anthropic_api_key: str,\\n        anthropic_api_url: Optional[str] = None,\\n        model_kwargs: dict = {},\\n        temperature: Optional[float] = None,\\n    ) -> Union[BaseLanguageModel, Callable]:\\n        return ChatAnthropic(\\n            anthropic_api_key=SecretStr(anthropic_api_key),\\n            anthropic_api_url=anthropic_api_url,\\n            model_kwargs=model_kwargs,\\n            temperature=temperature,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"dict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"`Anthropic` chat large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"ChatAnthropic\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/anthropic\",\"custom_fields\":{\"anthropic_api_key\":null,\"anthropic_api_url\":null,\"model_kwargs\":null,\"temperature\":null},\"output_types\":[\"BaseLanguageModel\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AzureChatOpenAISpecs\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_version\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"2023-12-01-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"2023-03-15-preview\",\"2023-05-15\",\"2023-06-01-preview\",\"2023-07-01-preview\",\"2023-08-01-preview\",\"2023-09-01-preview\",\"2023-12-01-preview\"],\"name\":\"api_version\",\"display_name\":\"API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_deployment\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_deployment\",\"display_name\":\"Deployment Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_endpoint\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_endpoint\",\"display_name\":\"Azure Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.llms.base import BaseLanguageModel\\nfrom langchain_community.chat_models.azure_openai import AzureChatOpenAI\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AzureChatOpenAISpecsComponent(CustomComponent):\\n    display_name: str = \\\"AzureChatOpenAI\\\"\\n    description: str = \\\"LLM model from Azure OpenAI.\\\"\\n    documentation: str = \\\"https://python.langchain.com/docs/integrations/llms/azure_openai\\\"\\n    beta = False\\n\\n    AZURE_OPENAI_MODELS = [\\n        \\\"gpt-35-turbo\\\",\\n        \\\"gpt-35-turbo-16k\\\",\\n        \\\"gpt-35-turbo-instruct\\\",\\n        \\\"gpt-4\\\",\\n        \\\"gpt-4-32k\\\",\\n        \\\"gpt-4-vision\\\",\\n    ]\\n\\n    AZURE_OPENAI_API_VERSIONS = [\\n        \\\"2023-03-15-preview\\\",\\n        \\\"2023-05-15\\\",\\n        \\\"2023-06-01-preview\\\",\\n        \\\"2023-07-01-preview\\\",\\n        \\\"2023-08-01-preview\\\",\\n        \\\"2023-09-01-preview\\\",\\n        \\\"2023-12-01-preview\\\",\\n    ]\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"value\\\": self.AZURE_OPENAI_MODELS[0],\\n                \\\"options\\\": self.AZURE_OPENAI_MODELS,\\n                \\\"required\\\": True,\\n            },\\n            \\\"azure_endpoint\\\": {\\n                \\\"display_name\\\": \\\"Azure Endpoint\\\",\\n                \\\"required\\\": True,\\n                \\\"info\\\": \\\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\\\",\\n            },\\n            \\\"azure_deployment\\\": {\\n                \\\"display_name\\\": \\\"Deployment Name\\\",\\n                \\\"required\\\": True,\\n            },\\n            \\\"api_version\\\": {\\n                \\\"display_name\\\": \\\"API Version\\\",\\n                \\\"options\\\": self.AZURE_OPENAI_API_VERSIONS,\\n                \\\"value\\\": self.AZURE_OPENAI_API_VERSIONS[-1],\\n                \\\"required\\\": True,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"required\\\": True, \\\"password\\\": True},\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"value\\\": 0.7,\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"required\\\": False,\\n            },\\n            \\\"max_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Tokens\\\",\\n                \\\"value\\\": 1000,\\n                \\\"required\\\": False,\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"advanced\\\": True,\\n                \\\"info\\\": \\\"Maximum number of tokens to generate.\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        model: str,\\n        azure_endpoint: str,\\n        azure_deployment: str,\\n        api_key: str,\\n        api_version: str,\\n        temperature: float = 0.7,\\n        max_tokens: Optional[int] = 1000,\\n    ) -> BaseLanguageModel:\\n        try:\\n            llm = AzureChatOpenAI(\\n                model=model,\\n                azure_endpoint=azure_endpoint,\\n                azure_deployment=azure_deployment,\\n                api_version=api_version,\\n                api_key=api_key,\\n                temperature=temperature,\\n                max_tokens=max_tokens,\\n            )\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to AzureOpenAI API.\\\") from e\\n        return llm\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"Maximum number of tokens to generate.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-35-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-35-turbo\",\"gpt-35-turbo-16k\",\"gpt-35-turbo-instruct\",\"gpt-4\",\"gpt-4-32k\",\"gpt-4-vision\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"LLM model from Azure OpenAI.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"AzureChatOpenAI\",\"documentation\":\"https://python.langchain.com/docs/integrations/llms/azure_openai\",\"custom_fields\":{\"model\":null,\"azure_endpoint\":null,\"azure_deployment\":null,\"api_key\":null,\"api_version\":null,\"temperature\":null,\"max_tokens\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"ChatOllamaEndpointSpecs\":{\"template\":{\"metadata\":{\"type\":\"Dict[str, Any]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":true,\"dynamic\":false,\"info\":\"Metadata to add to the run trace.\",\"title_case\":false},\"stop\":{\"type\":\"list\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"List of tokens to signal the model to stop generating text.\",\"title_case\":false},\"tags\":{\"type\":\"list\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"display_name\":\"Tags\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tags to add to the run trace.\",\"title_case\":false},\"base_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"base_url\",\"display_name\":\"Base URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable or disable caching.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Dict, List, Optional\\n\\n# from langchain_community.chat_models import ChatOllama\\nfrom langchain_community.chat_models import ChatOllama\\nfrom langchain_core.language_models.chat_models import BaseChatModel\\n\\n# from langchain.chat_models import ChatOllama\\nfrom langflow import CustomComponent\\n\\n# whe When a callback component is added to Langflow, the comment must be uncommented.\\n# from langchain.callbacks.manager import CallbackManager\\n\\n\\nclass ChatOllamaComponent(CustomComponent):\\n    display_name = \\\"ChatOllama\\\"\\n    description = \\\"Local LLM for chat with Ollama.\\\"\\n\\n    def build_config(self) -> dict:\\n        return {\\n            \\\"base_url\\\": {\\n                \\\"display_name\\\": \\\"Base URL\\\",\\n                \\\"info\\\": \\\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\\\",\\n            },\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"value\\\": \\\"llama2\\\",\\n                \\\"info\\\": \\\"Refer to https://ollama.ai/library for more models.\\\",\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"value\\\": 0.8,\\n                \\\"info\\\": \\\"Controls the creativity of model responses.\\\",\\n            },\\n            \\\"cache\\\": {\\n                \\\"display_name\\\": \\\"Cache\\\",\\n                \\\"field_type\\\": \\\"bool\\\",\\n                \\\"info\\\": \\\"Enable or disable caching.\\\",\\n                \\\"advanced\\\": True,\\n                \\\"value\\\": False,\\n            },\\n            ### When a callback component is added to Langflow, the comment must be uncommented. ###\\n            # \\\"callback_manager\\\": {\\n            #     \\\"display_name\\\": \\\"Callback Manager\\\",\\n            #     \\\"info\\\": \\\"Optional callback manager for additional functionality.\\\",\\n            #     \\\"advanced\\\": True,\\n            # },\\n            # \\\"callbacks\\\": {\\n            #     \\\"display_name\\\": \\\"Callbacks\\\",\\n            #     \\\"info\\\": \\\"Callbacks to execute during model runtime.\\\",\\n            #     \\\"advanced\\\": True,\\n            # },\\n            ########################################################################################\\n            \\\"format\\\": {\\n                \\\"display_name\\\": \\\"Format\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"info\\\": \\\"Specify the format of the output (e.g., json).\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"metadata\\\": {\\n                \\\"display_name\\\": \\\"Metadata\\\",\\n                \\\"info\\\": \\\"Metadata to add to the run trace.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"mirostat\\\": {\\n                \\\"display_name\\\": \\\"Mirostat\\\",\\n                \\\"options\\\": [\\\"Disabled\\\", \\\"Mirostat\\\", \\\"Mirostat 2.0\\\"],\\n                \\\"info\\\": \\\"Enable/disable Mirostat sampling for controlling perplexity.\\\",\\n                \\\"value\\\": \\\"Disabled\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"mirostat_eta\\\": {\\n                \\\"display_name\\\": \\\"Mirostat Eta\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Learning rate for Mirostat algorithm. (Default: 0.1)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"mirostat_tau\\\": {\\n                \\\"display_name\\\": \\\"Mirostat Tau\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"num_ctx\\\": {\\n                \\\"display_name\\\": \\\"Context Window Size\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Size of the context window for generating tokens. (Default: 2048)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"num_gpu\\\": {\\n                \\\"display_name\\\": \\\"Number of GPUs\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"num_thread\\\": {\\n                \\\"display_name\\\": \\\"Number of Threads\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Number of threads to use during computation. (Default: detected for optimal performance)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"repeat_last_n\\\": {\\n                \\\"display_name\\\": \\\"Repeat Last N\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"repeat_penalty\\\": {\\n                \\\"display_name\\\": \\\"Repeat Penalty\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Penalty for repetitions in generated text. (Default: 1.1)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"tfs_z\\\": {\\n                \\\"display_name\\\": \\\"TFS Z\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Tail free sampling value. (Default: 1)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"timeout\\\": {\\n                \\\"display_name\\\": \\\"Timeout\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Timeout for the request stream.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_k\\\": {\\n                \\\"display_name\\\": \\\"Top K\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Limits token selection to top K. (Default: 40)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top P\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Works together with top-k. (Default: 0.9)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"verbose\\\": {\\n                \\\"display_name\\\": \\\"Verbose\\\",\\n                \\\"field_type\\\": \\\"bool\\\",\\n                \\\"info\\\": \\\"Whether to print out response text.\\\",\\n            },\\n            \\\"tags\\\": {\\n                \\\"display_name\\\": \\\"Tags\\\",\\n                \\\"field_type\\\": \\\"list\\\",\\n                \\\"info\\\": \\\"Tags to add to the run trace.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"stop\\\": {\\n                \\\"display_name\\\": \\\"Stop Tokens\\\",\\n                \\\"field_type\\\": \\\"list\\\",\\n                \\\"info\\\": \\\"List of tokens to signal the model to stop generating text.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"system\\\": {\\n                \\\"display_name\\\": \\\"System\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"info\\\": \\\"System to use for generating text.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"template\\\": {\\n                \\\"display_name\\\": \\\"Template\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"info\\\": \\\"Template to use for generating text.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        base_url: Optional[str],\\n        model: str,\\n        mirostat: Optional[str],\\n        mirostat_eta: Optional[float] = None,\\n        mirostat_tau: Optional[float] = None,\\n        ### When a callback component is added to Langflow, the comment must be uncommented.###\\n        # callback_manager: Optional[CallbackManager] = None,\\n        # callbacks: Optional[List[Callbacks]] = None,\\n        #######################################################################################\\n        repeat_last_n: Optional[int] = None,\\n        verbose: Optional[bool] = None,\\n        cache: Optional[bool] = None,\\n        num_ctx: Optional[int] = None,\\n        num_gpu: Optional[int] = None,\\n        format: Optional[str] = None,\\n        metadata: Optional[Dict[str, Any]] = None,\\n        num_thread: Optional[int] = None,\\n        repeat_penalty: Optional[float] = None,\\n        stop: Optional[List[str]] = None,\\n        system: Optional[str] = None,\\n        tags: Optional[List[str]] = None,\\n        temperature: Optional[float] = None,\\n        template: Optional[str] = None,\\n        tfs_z: Optional[float] = None,\\n        timeout: Optional[int] = None,\\n        top_k: Optional[int] = None,\\n        top_p: Optional[int] = None,\\n    ) -> BaseChatModel:\\n        if not base_url:\\n            base_url = \\\"http://localhost:11434\\\"\\n\\n        # Mapping mirostat settings to their corresponding values\\n        mirostat_options = {\\\"Mirostat\\\": 1, \\\"Mirostat 2.0\\\": 2}\\n\\n        # Default to 0 for 'Disabled'\\n        mirostat_value = mirostat_options.get(mirostat, 0)  # type: ignore\\n\\n        # Set mirostat_eta and mirostat_tau to None if mirostat is disabled\\n        if mirostat_value == 0:\\n            mirostat_eta = None\\n            mirostat_tau = None\\n\\n        # Mapping system settings to their corresponding values\\n        llm_params = {\\n            \\\"base_url\\\": base_url,\\n            \\\"cache\\\": cache,\\n            \\\"model\\\": model,\\n            \\\"mirostat\\\": mirostat_value,\\n            \\\"format\\\": format,\\n            \\\"metadata\\\": metadata,\\n            \\\"tags\\\": tags,\\n            ## When a callback component is added to Langflow, the comment must be uncommented.##\\n            # \\\"callback_manager\\\": callback_manager,\\n            # \\\"callbacks\\\": callbacks,\\n            #####################################################################################\\n            \\\"mirostat_eta\\\": mirostat_eta,\\n            \\\"mirostat_tau\\\": mirostat_tau,\\n            \\\"num_ctx\\\": num_ctx,\\n            \\\"num_gpu\\\": num_gpu,\\n            \\\"num_thread\\\": num_thread,\\n            \\\"repeat_last_n\\\": repeat_last_n,\\n            \\\"repeat_penalty\\\": repeat_penalty,\\n            \\\"temperature\\\": temperature,\\n            \\\"stop\\\": stop,\\n            \\\"system\\\": system,\\n            \\\"template\\\": template,\\n            \\\"tfs_z\\\": tfs_z,\\n            \\\"timeout\\\": timeout,\\n            \\\"top_k\\\": top_k,\\n            \\\"top_p\\\": top_p,\\n            \\\"verbose\\\": verbose,\\n        }\\n\\n        # None Value remove\\n        llm_params = {k: v for k, v in llm_params.items() if v is not None}\\n\\n        try:\\n            output = ChatOllama(**llm_params)  # type: ignore\\n        except Exception as e:\\n            raise ValueError(\\\"Could not initialize Ollama LLM.\\\") from e\\n\\n        return output  # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"format\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"format\",\"display_name\":\"Format\",\"advanced\":true,\"dynamic\":false,\"info\":\"Specify the format of the output (e.g., json).\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Disabled\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Disabled\",\"Mirostat\",\"Mirostat 2.0\"],\"name\":\"mirostat\",\"display_name\":\"Mirostat\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable/disable Mirostat sampling for controlling perplexity.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat_eta\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_eta\",\"display_name\":\"Mirostat Eta\",\"advanced\":true,\"dynamic\":false,\"info\":\"Learning rate for Mirostat algorithm. (Default: 0.1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"mirostat_tau\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_tau\",\"display_name\":\"Mirostat Tau\",\"advanced\":true,\"dynamic\":false,\"info\":\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"llama2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"Refer to https://ollama.ai/library for more models.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"num_ctx\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_ctx\",\"display_name\":\"Context Window Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"Size of the context window for generating tokens. (Default: 2048)\",\"title_case\":false},\"num_gpu\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_gpu\",\"display_name\":\"Number of GPUs\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\",\"title_case\":false},\"num_thread\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_thread\",\"display_name\":\"Number of Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of threads to use during computation. (Default: detected for optimal performance)\",\"title_case\":false},\"repeat_last_n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_last_n\",\"display_name\":\"Repeat Last N\",\"advanced\":true,\"dynamic\":false,\"info\":\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"Penalty for repetitions in generated text. (Default: 1.1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"system\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"system\",\"display_name\":\"System\",\"advanced\":true,\"dynamic\":false,\"info\":\"System to use for generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Controls the creativity of model responses.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"template\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"display_name\":\"Template\",\"advanced\":true,\"dynamic\":false,\"info\":\"Template to use for generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"tfs_z\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tfs_z\",\"display_name\":\"TFS Z\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tail free sampling value. (Default: 1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"timeout\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"Timeout for the request stream.\",\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Limits token selection to top K. (Default: 40)\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"Works together with top-k. (Default: 0.9)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"Whether to print out response text.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Local LLM for chat with Ollama.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseChatModel\"],\"display_name\":\"ChatOllama\",\"documentation\":\"\",\"custom_fields\":{\"base_url\":null,\"model\":null,\"mirostat\":null,\"mirostat_eta\":null,\"mirostat_tau\":null,\"repeat_last_n\":null,\"verbose\":null,\"cache\":null,\"num_ctx\":null,\"num_gpu\":null,\"format\":null,\"metadata\":null,\"num_thread\":null,\"repeat_penalty\":null,\"stop\":null,\"system\":null,\"tags\":null,\"temperature\":null,\"template\":null,\"tfs_z\":null,\"timeout\":null,\"top_k\":null,\"top_p\":null},\"output_types\":[\"BaseChatModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"BaiduQianfanChatEndpointsSpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint\\nfrom langchain.llms.base import BaseLLM\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass QianfanChatEndpointComponent(CustomComponent):\\n    display_name: str = \\\"QianfanChatEndpoint\\\"\\n    description: str = (\\n        \\\"Baidu Qianfan chat models. Get more detail from \\\"\\n        \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\\\"\\n    )\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"options\\\": [\\n                    \\\"ERNIE-Bot\\\",\\n                    \\\"ERNIE-Bot-turbo\\\",\\n                    \\\"BLOOMZ-7B\\\",\\n                    \\\"Llama-2-7b-chat\\\",\\n                    \\\"Llama-2-13b-chat\\\",\\n                    \\\"Llama-2-70b-chat\\\",\\n                    \\\"Qianfan-BLOOMZ-7B-compressed\\\",\\n                    \\\"Qianfan-Chinese-Llama-2-7B\\\",\\n                    \\\"ChatGLM2-6B-32K\\\",\\n                    \\\"AquilaChat-7B\\\",\\n                ],\\n                \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\",\\n                \\\"required\\\": True,\\n            },\\n            \\\"qianfan_ak\\\": {\\n                \\\"display_name\\\": \\\"Qianfan Ak\\\",\\n                \\\"required\\\": True,\\n                \\\"password\\\": True,\\n                \\\"info\\\": \\\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\\\",\\n            },\\n            \\\"qianfan_sk\\\": {\\n                \\\"display_name\\\": \\\"Qianfan Sk\\\",\\n                \\\"required\\\": True,\\n                \\\"password\\\": True,\\n                \\\"info\\\": \\\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\\\",\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top p\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n                \\\"value\\\": 0.8,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n                \\\"value\\\": 0.95,\\n            },\\n            \\\"penalty_score\\\": {\\n                \\\"display_name\\\": \\\"Penalty Score\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n                \\\"value\\\": 1.0,\\n            },\\n            \\\"endpoint\\\": {\\n                \\\"display_name\\\": \\\"Endpoint\\\",\\n                \\\"info\\\": \\\"Endpoint of the Qianfan LLM, required if custom model used.\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        model: str = \\\"ERNIE-Bot-turbo\\\",\\n        qianfan_ak: Optional[str] = None,\\n        qianfan_sk: Optional[str] = None,\\n        top_p: Optional[float] = None,\\n        temperature: Optional[float] = None,\\n        penalty_score: Optional[float] = None,\\n        endpoint: Optional[str] = None,\\n    ) -> BaseLLM:\\n        try:\\n            output = QianfanChatEndpoint(  # type: ignore\\n                model=model,\\n                qianfan_ak=SecretStr(qianfan_ak) if qianfan_ak else None,\\n                qianfan_sk=SecretStr(qianfan_sk) if qianfan_sk else None,\\n                top_p=top_p,\\n                temperature=temperature,\\n                penalty_score=penalty_score,\\n                endpoint=endpoint,\\n            )\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to Baidu Qianfan API.\\\") from e\\n        return output  # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint\",\"display_name\":\"Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Qianfan LLM, required if custom model used.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"ERNIE-Bot-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ERNIE-Bot\",\"ERNIE-Bot-turbo\",\"BLOOMZ-7B\",\"Llama-2-7b-chat\",\"Llama-2-13b-chat\",\"Llama-2-70b-chat\",\"Qianfan-BLOOMZ-7B-compressed\",\"Qianfan-Chinese-Llama-2-7B\",\"ChatGLM2-6B-32K\",\"AquilaChat-7B\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"title_case\":false,\"input_types\":[\"Text\"]},\"penalty_score\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"penalty_score\",\"display_name\":\"Penalty Score\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"qianfan_ak\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_ak\",\"display_name\":\"Qianfan Ak\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"qianfan_sk\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_sk\",\"display_name\":\"Qianfan Sk\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Baidu Qianfan chat models. Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"QianfanChatEndpoint\",\"documentation\":\"\",\"custom_fields\":{\"model\":null,\"qianfan_ak\":null,\"qianfan_sk\":null,\"top_p\":null,\"temperature\":null,\"penalty_score\":null,\"endpoint\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"LlamaCppSpecs\":{\"template\":{\"metadata\":{\"type\":\"Dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"Dict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".bin\"],\"file_path\":\"\",\"password\":false,\"name\":\"model_path\",\"display_name\":\"Model Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"client\",\"display_name\":\"Client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, List, Dict, Any\\nfrom langflow import CustomComponent\\nfrom langchain_community.llms.llamacpp import LlamaCpp\\n\\n\\nclass LlamaCppComponent(CustomComponent):\\n    display_name = \\\"LlamaCpp\\\"\\n    description = \\\"llama.cpp model.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"grammar\\\": {\\\"display_name\\\": \\\"Grammar\\\", \\\"advanced\\\": True},\\n            \\\"cache\\\": {\\\"display_name\\\": \\\"Cache\\\", \\\"advanced\\\": True},\\n            \\\"client\\\": {\\\"display_name\\\": \\\"Client\\\", \\\"advanced\\\": True},\\n            \\\"echo\\\": {\\\"display_name\\\": \\\"Echo\\\", \\\"advanced\\\": True},\\n            \\\"f16_kv\\\": {\\\"display_name\\\": \\\"F16 KV\\\", \\\"advanced\\\": True},\\n            \\\"grammar_path\\\": {\\\"display_name\\\": \\\"Grammar Path\\\", \\\"advanced\\\": True},\\n            \\\"last_n_tokens_size\\\": {\\\"display_name\\\": \\\"Last N Tokens Size\\\", \\\"advanced\\\": True},\\n            \\\"logits_all\\\": {\\\"display_name\\\": \\\"Logits All\\\", \\\"advanced\\\": True},\\n            \\\"logprobs\\\": {\\\"display_name\\\": \\\"Logprobs\\\", \\\"advanced\\\": True},\\n            \\\"lora_base\\\": {\\\"display_name\\\": \\\"Lora Base\\\", \\\"advanced\\\": True},\\n            \\\"lora_path\\\": {\\\"display_name\\\": \\\"Lora Path\\\", \\\"advanced\\\": True},\\n            \\\"max_tokens\\\": {\\\"display_name\\\": \\\"Max Tokens\\\", \\\"advanced\\\": True},\\n            \\\"metadata\\\": {\\\"display_name\\\": \\\"Metadata\\\", \\\"advanced\\\": True},\\n            \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"advanced\\\": True},\\n            \\\"model_path\\\": {\\n                \\\"display_name\\\": \\\"Model Path\\\",\\n                \\\"field_type\\\": \\\"file\\\",\\n                \\\"file_types\\\": [\\\".bin\\\"],\\n                \\\"required\\\": True,\\n            },\\n            \\\"n_batch\\\": {\\\"display_name\\\": \\\"N Batch\\\", \\\"advanced\\\": True},\\n            \\\"n_ctx\\\": {\\\"display_name\\\": \\\"N Ctx\\\", \\\"advanced\\\": True},\\n            \\\"n_gpu_layers\\\": {\\\"display_name\\\": \\\"N GPU Layers\\\", \\\"advanced\\\": True},\\n            \\\"n_parts\\\": {\\\"display_name\\\": \\\"N Parts\\\", \\\"advanced\\\": True},\\n            \\\"n_threads\\\": {\\\"display_name\\\": \\\"N Threads\\\", \\\"advanced\\\": True},\\n            \\\"repeat_penalty\\\": {\\\"display_name\\\": \\\"Repeat Penalty\\\", \\\"advanced\\\": True},\\n            \\\"rope_freq_base\\\": {\\\"display_name\\\": \\\"Rope Freq Base\\\", \\\"advanced\\\": True},\\n            \\\"rope_freq_scale\\\": {\\\"display_name\\\": \\\"Rope Freq Scale\\\", \\\"advanced\\\": True},\\n            \\\"seed\\\": {\\\"display_name\\\": \\\"Seed\\\", \\\"advanced\\\": True},\\n            \\\"stop\\\": {\\\"display_name\\\": \\\"Stop\\\", \\\"advanced\\\": True},\\n            \\\"streaming\\\": {\\\"display_name\\\": \\\"Streaming\\\", \\\"advanced\\\": True},\\n            \\\"suffix\\\": {\\\"display_name\\\": \\\"Suffix\\\", \\\"advanced\\\": True},\\n            \\\"tags\\\": {\\\"display_name\\\": \\\"Tags\\\", \\\"advanced\\\": True},\\n            \\\"temperature\\\": {\\\"display_name\\\": \\\"Temperature\\\"},\\n            \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"advanced\\\": True},\\n            \\\"top_p\\\": {\\\"display_name\\\": \\\"Top P\\\", \\\"advanced\\\": True},\\n            \\\"use_mlock\\\": {\\\"display_name\\\": \\\"Use Mlock\\\", \\\"advanced\\\": True},\\n            \\\"use_mmap\\\": {\\\"display_name\\\": \\\"Use Mmap\\\", \\\"advanced\\\": True},\\n            \\\"verbose\\\": {\\\"display_name\\\": \\\"Verbose\\\", \\\"advanced\\\": True},\\n            \\\"vocab_only\\\": {\\\"display_name\\\": \\\"Vocab Only\\\", \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        model_path: str,\\n        grammar: Optional[str] = None,\\n        cache: Optional[bool] = None,\\n        client: Optional[Any] = None,\\n        echo: Optional[bool] = False,\\n        f16_kv: bool = True,\\n        grammar_path: Optional[str] = None,\\n        last_n_tokens_size: Optional[int] = 64,\\n        logits_all: bool = False,\\n        logprobs: Optional[int] = None,\\n        lora_base: Optional[str] = None,\\n        lora_path: Optional[str] = None,\\n        max_tokens: Optional[int] = 256,\\n        metadata: Optional[Dict] = None,\\n        model_kwargs: Dict = {},\\n        n_batch: Optional[int] = 8,\\n        n_ctx: int = 512,\\n        n_gpu_layers: Optional[int] = 1,\\n        n_parts: int = -1,\\n        n_threads: Optional[int] = 1,\\n        repeat_penalty: Optional[float] = 1.1,\\n        rope_freq_base: float = 10000.0,\\n        rope_freq_scale: float = 1.0,\\n        seed: int = -1,\\n        stop: Optional[List[str]] = [],\\n        streaming: bool = True,\\n        suffix: Optional[str] = \\\"\\\",\\n        tags: Optional[List[str]] = [],\\n        temperature: Optional[float] = 0.8,\\n        top_k: Optional[int] = 40,\\n        top_p: Optional[float] = 0.95,\\n        use_mlock: bool = False,\\n        use_mmap: Optional[bool] = True,\\n        verbose: bool = True,\\n        vocab_only: bool = False,\\n    ) -> LlamaCpp:\\n        return LlamaCpp(\\n            model_path=model_path,\\n            grammar=grammar,\\n            cache=cache,\\n            client=client,\\n            echo=echo,\\n            f16_kv=f16_kv,\\n            grammar_path=grammar_path,\\n            last_n_tokens_size=last_n_tokens_size,\\n            logits_all=logits_all,\\n            logprobs=logprobs,\\n            lora_base=lora_base,\\n            lora_path=lora_path,\\n            max_tokens=max_tokens,\\n            metadata=metadata,\\n            model_kwargs=model_kwargs,\\n            n_batch=n_batch,\\n            n_ctx=n_ctx,\\n            n_gpu_layers=n_gpu_layers,\\n            n_parts=n_parts,\\n            n_threads=n_threads,\\n            repeat_penalty=repeat_penalty,\\n            rope_freq_base=rope_freq_base,\\n            rope_freq_scale=rope_freq_scale,\\n            seed=seed,\\n            stop=stop,\\n            streaming=streaming,\\n            suffix=suffix,\\n            tags=tags,\\n            temperature=temperature,\\n            top_k=top_k,\\n            top_p=top_p,\\n            use_mlock=use_mlock,\\n            use_mmap=use_mmap,\\n            verbose=verbose,\\n            vocab_only=vocab_only,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"echo\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"echo\",\"display_name\":\"Echo\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"f16_kv\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"f16_kv\",\"display_name\":\"F16 KV\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"grammar\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grammar\",\"display_name\":\"Grammar\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"grammar_path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grammar_path\",\"display_name\":\"Grammar Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"last_n_tokens_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":64,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"last_n_tokens_size\",\"display_name\":\"Last N Tokens Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"logits_all\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"logits_all\",\"display_name\":\"Logits All\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"logprobs\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"logprobs\",\"display_name\":\"Logprobs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"lora_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lora_base\",\"display_name\":\"Lora Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"lora_path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lora_path\",\"display_name\":\"Lora Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_batch\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_batch\",\"display_name\":\"N Batch\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_ctx\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":512,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_ctx\",\"display_name\":\"N Ctx\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_gpu_layers\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_gpu_layers\",\"display_name\":\"N GPU Layers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_parts\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":-1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_parts\",\"display_name\":\"N Parts\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_threads\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_threads\",\"display_name\":\"N Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"rope_freq_base\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10000.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"rope_freq_base\",\"display_name\":\"Rope Freq Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"rope_freq_scale\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"rope_freq_scale\",\"display_name\":\"Rope Freq Scale\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"seed\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":-1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"seed\",\"display_name\":\"Seed\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"stop\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"suffix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"suffix\",\"display_name\":\"Suffix\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"display_name\":\"Tags\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"use_mlock\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_mlock\",\"display_name\":\"Use Mlock\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"use_mmap\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_mmap\",\"display_name\":\"Use Mmap\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vocab_only\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vocab_only\",\"display_name\":\"Vocab Only\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"llama.cpp model.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"LlamaCpp\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\",\"LLM\"],\"display_name\":\"LlamaCpp\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp\",\"custom_fields\":{\"model_path\":null,\"grammar\":null,\"cache\":null,\"client\":null,\"echo\":null,\"f16_kv\":null,\"grammar_path\":null,\"last_n_tokens_size\":null,\"logits_all\":null,\"logprobs\":null,\"lora_base\":null,\"lora_path\":null,\"max_tokens\":null,\"metadata\":null,\"model_kwargs\":null,\"n_batch\":null,\"n_ctx\":null,\"n_gpu_layers\":null,\"n_parts\":null,\"n_threads\":null,\"repeat_penalty\":null,\"rope_freq_base\":null,\"rope_freq_scale\":null,\"seed\":null,\"stop\":null,\"streaming\":null,\"suffix\":null,\"tags\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"use_mlock\":null,\"use_mmap\":null,\"verbose\":null,\"vocab_only\":null},\"output_types\":[\"LlamaCpp\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AnthropicSpecs\":{\"template\":{\"anthropic_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"anthropic_api_key\",\"display_name\":\"Anthropic API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"anthropic_api_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"anthropic_api_url\",\"display_name\":\"Anthropic API URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.llms.anthropic import Anthropic\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, NestedDict\\n\\n\\nclass AnthropicComponent(CustomComponent):\\n    display_name = \\\"Anthropic\\\"\\n    description = \\\"Anthropic large language models.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"anthropic_api_key\\\": {\\n                \\\"display_name\\\": \\\"Anthropic API Key\\\",\\n                \\\"type\\\": str,\\n                \\\"password\\\": True,\\n            },\\n            \\\"anthropic_api_url\\\": {\\n                \\\"display_name\\\": \\\"Anthropic API URL\\\",\\n                \\\"type\\\": str,\\n            },\\n            \\\"model_kwargs\\\": {\\n                \\\"display_name\\\": \\\"Model Kwargs\\\",\\n                \\\"field_type\\\": \\\"NestedDict\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        anthropic_api_key: str,\\n        anthropic_api_url: str,\\n        model_kwargs: NestedDict = {},\\n        temperature: Optional[float] = None,\\n    ) -> BaseLanguageModel:\\n        return Anthropic(\\n            anthropic_api_key=SecretStr(anthropic_api_key),\\n            anthropic_api_url=anthropic_api_url,\\n            model_kwargs=model_kwargs,\\n            temperature=temperature,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"NestedDict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Anthropic large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"Anthropic\",\"documentation\":\"\",\"custom_fields\":{\"anthropic_api_key\":null,\"anthropic_api_url\":null,\"model_kwargs\":null,\"temperature\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AnthropicLLMSpecs\":{\"template\":{\"anthropic_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"anthropic_api_key\",\"display_name\":\"Anthropic API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Anthropic API key.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"api_endpoint\",\"display_name\":\"API Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.anthropic import ChatAnthropic\\nfrom langchain.llms.base import BaseLanguageModel\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AnthropicLLM(CustomComponent):\\n    display_name: str = \\\"AnthropicLLM\\\"\\n    description: str = \\\"Anthropic Chat&Completion large language models.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"options\\\": [\\n                    \\\"claude-2.1\\\",\\n                    \\\"claude-2.0\\\",\\n                    \\\"claude-instant-1.2\\\",\\n                    \\\"claude-instant-1\\\",\\n                    # Add more models as needed\\n                ],\\n                \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/anthropic\\\",\\n                \\\"required\\\": True,\\n                \\\"value\\\": \\\"claude-2.1\\\",\\n            },\\n            \\\"anthropic_api_key\\\": {\\n                \\\"display_name\\\": \\\"Anthropic API Key\\\",\\n                \\\"required\\\": True,\\n                \\\"password\\\": True,\\n                \\\"info\\\": \\\"Your Anthropic API key.\\\",\\n            },\\n            \\\"max_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Tokens\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"value\\\": 256,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"value\\\": 0.7,\\n            },\\n            \\\"api_endpoint\\\": {\\n                \\\"display_name\\\": \\\"API Endpoint\\\",\\n                \\\"info\\\": \\\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        model: str,\\n        anthropic_api_key: Optional[str] = None,\\n        max_tokens: Optional[int] = None,\\n        temperature: Optional[float] = None,\\n        api_endpoint: Optional[str] = None,\\n    ) -> BaseLanguageModel:\\n        # Set default API endpoint if not provided\\n        if not api_endpoint:\\n            api_endpoint = \\\"https://api.anthropic.com\\\"\\n\\n        try:\\n            output = ChatAnthropic(\\n                model_name=model,\\n                anthropic_api_key=SecretStr(anthropic_api_key) if anthropic_api_key else None,\\n                max_tokens_to_sample=max_tokens,  # type: ignore\\n                temperature=temperature,\\n                anthropic_api_url=api_endpoint,\\n            )\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to Anthropic API.\\\") from e\\n        return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"claude-2.1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"claude-2.1\",\"claude-2.0\",\"claude-instant-1.2\",\"claude-instant-1\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/anthropic\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Anthropic Chat&Completion large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"AnthropicLLM\",\"documentation\":\"\",\"custom_fields\":{\"model\":null,\"anthropic_api_key\":null,\"max_tokens\":null,\"temperature\":null,\"api_endpoint\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CohereSpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_community.llms.cohere import Cohere\\nfrom langchain_core.language_models.base import BaseLanguageModel\\nfrom langflow import CustomComponent\\n\\n\\nclass CohereComponent(CustomComponent):\\n    display_name = \\\"Cohere\\\"\\n    description = \\\"Cohere large language models.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"cohere_api_key\\\": {\\\"display_name\\\": \\\"Cohere API Key\\\", \\\"type\\\": \\\"password\\\", \\\"password\\\": True},\\n            \\\"max_tokens\\\": {\\\"display_name\\\": \\\"Max Tokens\\\", \\\"default\\\": 256, \\\"type\\\": \\\"int\\\", \\\"show\\\": True},\\n            \\\"temperature\\\": {\\\"display_name\\\": \\\"Temperature\\\", \\\"default\\\": 0.75, \\\"type\\\": \\\"float\\\", \\\"show\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        cohere_api_key: str,\\n        max_tokens: int = 256,\\n        temperature: float = 0.75,\\n    ) -> BaseLanguageModel:\\n        return Cohere(cohere_api_key=cohere_api_key, max_tokens=max_tokens, temperature=temperature)  # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"cohere_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"cohere_api_key\",\"display_name\":\"Cohere API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.75,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Cohere large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"Cohere\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere\",\"custom_fields\":{\"cohere_api_key\":null,\"max_tokens\":null,\"temperature\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GoogleGenerativeAISpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_google_genai import ChatGoogleGenerativeAI  # type: ignore\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, RangeSpec\\nfrom pydantic.v1.types import SecretStr\\n\\n\\nclass GoogleGenerativeAIComponent(CustomComponent):\\n    display_name: str = \\\"Google Generative AI\\\"\\n    description: str = \\\"A component that uses Google Generative AI to generate text.\\\"\\n    documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"google_api_key\\\": {\\n                \\\"display_name\\\": \\\"Google API Key\\\",\\n                \\\"info\\\": \\\"The Google API Key to use for the Google Generative AI.\\\",\\n            },\\n            \\\"max_output_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n                \\\"info\\\": \\\"The maximum number of tokens to generate.\\\",\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"info\\\": \\\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\\\",\\n            },\\n            \\\"top_k\\\": {\\n                \\\"display_name\\\": \\\"Top K\\\",\\n                \\\"info\\\": \\\"Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.\\\",\\n                \\\"range_spec\\\": RangeSpec(min=0, max=2, step=0.1),\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top P\\\",\\n                \\\"info\\\": \\\"The maximum cumulative probability of tokens to consider when sampling.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"n\\\": {\\n                \\\"display_name\\\": \\\"N\\\",\\n                \\\"info\\\": \\\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model\\\",\\n                \\\"info\\\": \\\"The name of the model to use. Supported examples: gemini-pro\\\",\\n                \\\"options\\\": [\\\"gemini-pro\\\", \\\"gemini-pro-vision\\\"],\\n            },\\n            \\\"code\\\": {\\n                \\\"advanced\\\": True,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        google_api_key: str,\\n        model: str,\\n        max_output_tokens: Optional[int] = None,\\n        temperature: float = 0.1,\\n        top_k: Optional[int] = None,\\n        top_p: Optional[float] = None,\\n        n: Optional[int] = 1,\\n    ) -> BaseLanguageModel:\\n        return ChatGoogleGenerativeAI(\\n            model=model,\\n            max_output_tokens=max_output_tokens or None,  # type: ignore\\n            temperature=temperature,\\n            top_k=top_k or None,\\n            top_p=top_p or None,  # type: ignore\\n            n=n or 1,\\n            google_api_key=SecretStr(google_api_key),\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"google_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"google_api_key\",\"display_name\":\"Google API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The Google API Key to use for the Google Generative AI.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum number of tokens to generate.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gemini-pro\",\"gemini-pro-vision\"],\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"The name of the model to use. Supported examples: gemini-pro\",\"title_case\":false,\"input_types\":[\"Text\"]},\"n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.\",\"rangeSpec\":{\"min\":0.0,\"max\":2.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"The maximum cumulative probability of tokens to consider when sampling.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"A component that uses Google Generative AI to generate text.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"Google Generative AI\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"google_api_key\":null,\"model\":null,\"max_output_tokens\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"n\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"BaiduQianfanLLMEndpointsSpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint\\nfrom langchain.llms.base import BaseLLM\\n\\n\\nclass QianfanLLMEndpointComponent(CustomComponent):\\n    display_name: str = \\\"QianfanLLMEndpoint\\\"\\n    description: str = (\\n        \\\"Baidu Qianfan hosted open source or customized models. \\\"\\n        \\\"Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\"\\n    )\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"options\\\": [\\n                    \\\"ERNIE-Bot\\\",\\n                    \\\"ERNIE-Bot-turbo\\\",\\n                    \\\"BLOOMZ-7B\\\",\\n                    \\\"Llama-2-7b-chat\\\",\\n                    \\\"Llama-2-13b-chat\\\",\\n                    \\\"Llama-2-70b-chat\\\",\\n                    \\\"Qianfan-BLOOMZ-7B-compressed\\\",\\n                    \\\"Qianfan-Chinese-Llama-2-7B\\\",\\n                    \\\"ChatGLM2-6B-32K\\\",\\n                    \\\"AquilaChat-7B\\\",\\n                ],\\n                \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\",\\n                \\\"required\\\": True,\\n            },\\n            \\\"qianfan_ak\\\": {\\n                \\\"display_name\\\": \\\"Qianfan Ak\\\",\\n                \\\"required\\\": True,\\n                \\\"password\\\": True,\\n                \\\"info\\\": \\\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\\\",\\n            },\\n            \\\"qianfan_sk\\\": {\\n                \\\"display_name\\\": \\\"Qianfan Sk\\\",\\n                \\\"required\\\": True,\\n                \\\"password\\\": True,\\n                \\\"info\\\": \\\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\\\",\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top p\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n                \\\"value\\\": 0.8,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n                \\\"value\\\": 0.95,\\n            },\\n            \\\"penalty_score\\\": {\\n                \\\"display_name\\\": \\\"Penalty Score\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n                \\\"value\\\": 1.0,\\n            },\\n            \\\"endpoint\\\": {\\n                \\\"display_name\\\": \\\"Endpoint\\\",\\n                \\\"info\\\": \\\"Endpoint of the Qianfan LLM, required if custom model used.\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        model: str = \\\"ERNIE-Bot-turbo\\\",\\n        qianfan_ak: Optional[str] = None,\\n        qianfan_sk: Optional[str] = None,\\n        top_p: Optional[float] = None,\\n        temperature: Optional[float] = None,\\n        penalty_score: Optional[float] = None,\\n        endpoint: Optional[str] = None,\\n    ) -> BaseLLM:\\n        try:\\n            output = QianfanLLMEndpoint(  # type: ignore\\n                model=model,\\n                qianfan_ak=qianfan_ak,\\n                qianfan_sk=qianfan_sk,\\n                top_p=top_p,\\n                temperature=temperature,\\n                penalty_score=penalty_score,\\n                endpoint=endpoint,\\n            )\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to Baidu Qianfan API.\\\") from e\\n        return output  # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint\",\"display_name\":\"Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Qianfan LLM, required if custom model used.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"ERNIE-Bot-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ERNIE-Bot\",\"ERNIE-Bot-turbo\",\"BLOOMZ-7B\",\"Llama-2-7b-chat\",\"Llama-2-13b-chat\",\"Llama-2-70b-chat\",\"Qianfan-BLOOMZ-7B-compressed\",\"Qianfan-Chinese-Llama-2-7B\",\"ChatGLM2-6B-32K\",\"AquilaChat-7B\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"title_case\":false,\"input_types\":[\"Text\"]},\"penalty_score\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"penalty_score\",\"display_name\":\"Penalty Score\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"qianfan_ak\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_ak\",\"display_name\":\"Qianfan Ak\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"qianfan_sk\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_sk\",\"display_name\":\"Qianfan Sk\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Baidu Qianfan hosted open source or customized models. Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"QianfanLLMEndpoint\",\"documentation\":\"\",\"custom_fields\":{\"model\":null,\"qianfan_ak\":null,\"qianfan_sk\":null,\"top_p\":null,\"temperature\":null,\"penalty_score\":null,\"endpoint\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatLiteLLMSpecs\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Callable, Dict, Optional, Union\\n\\nfrom langchain_community.chat_models.litellm import ChatLiteLLM, ChatLiteLLMException\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel\\n\\n\\nclass ChatLiteLLMComponent(CustomComponent):\\n    display_name = \\\"ChatLiteLLM\\\"\\n    description = \\\"`LiteLLM` collection of large language models.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/integrations/chat/litellm\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model name\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": True,\\n                \\\"info\\\": \\\"The name of the model to use. For example, `gpt-3.5-turbo`.\\\",\\n            },\\n            \\\"api_key\\\": {\\n                \\\"display_name\\\": \\\"API key\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"password\\\": True,\\n            },\\n            \\\"provider\\\": {\\n                \\\"display_name\\\": \\\"Provider\\\",\\n                \\\"info\\\": \\\"The provider of the API key.\\\",\\n                \\\"options\\\": [\\n                    \\\"OpenAI\\\",\\n                    \\\"Azure\\\",\\n                    \\\"Anthropic\\\",\\n                    \\\"Replicate\\\",\\n                    \\\"Cohere\\\",\\n                    \\\"OpenRouter\\\",\\n                ],\\n            },\\n            \\\"streaming\\\": {\\n                \\\"display_name\\\": \\\"Streaming\\\",\\n                \\\"field_type\\\": \\\"bool\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n                \\\"default\\\": True,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"default\\\": 0.7,\\n            },\\n            \\\"model_kwargs\\\": {\\n                \\\"display_name\\\": \\\"Model kwargs\\\",\\n                \\\"field_type\\\": \\\"dict\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n                \\\"default\\\": {},\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top p\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n            },\\n            \\\"top_k\\\": {\\n                \\\"display_name\\\": \\\"Top k\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n            },\\n            \\\"n\\\": {\\n                \\\"display_name\\\": \\\"N\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n                \\\"info\\\": \\\"Number of chat completions to generate for each prompt. \\\"\\n                \\\"Note that the API may not return the full n completions if duplicates are generated.\\\",\\n                \\\"default\\\": 1,\\n            },\\n            \\\"max_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max tokens\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"default\\\": 256,\\n                \\\"info\\\": \\\"The maximum number of tokens to generate for each chat completion.\\\",\\n            },\\n            \\\"max_retries\\\": {\\n                \\\"display_name\\\": \\\"Max retries\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n                \\\"default\\\": 6,\\n            },\\n            \\\"verbose\\\": {\\n                \\\"display_name\\\": \\\"Verbose\\\",\\n                \\\"field_type\\\": \\\"bool\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n                \\\"default\\\": False,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        model: str,\\n        provider: str,\\n        api_key: Optional[str] = None,\\n        streaming: bool = True,\\n        temperature: Optional[float] = 0.7,\\n        model_kwargs: Optional[Dict[str, Any]] = {},\\n        top_p: Optional[float] = None,\\n        top_k: Optional[int] = None,\\n        n: int = 1,\\n        max_tokens: int = 256,\\n        max_retries: int = 6,\\n        verbose: bool = False,\\n    ) -> Union[BaseLanguageModel, Callable]:\\n        try:\\n            import litellm  # type: ignore\\n\\n            litellm.drop_params = True\\n            litellm.set_verbose = verbose\\n        except ImportError:\\n            raise ChatLiteLLMException(\\n                \\\"Could not import litellm python package. \\\" \\\"Please install it with `pip install litellm`\\\"\\n            )\\n        provider_map = {\\n            \\\"OpenAI\\\": \\\"openai_api_key\\\",\\n            \\\"Azure\\\": \\\"azure_api_key\\\",\\n            \\\"Anthropic\\\": \\\"anthropic_api_key\\\",\\n            \\\"Replicate\\\": \\\"replicate_api_key\\\",\\n            \\\"Cohere\\\": \\\"cohere_api_key\\\",\\n            \\\"OpenRouter\\\": \\\"openrouter_api_key\\\",\\n        }\\n        # Set the API key based on the provider\\n        kwarg = {provider_map[provider]: api_key}\\n\\n        LLM = ChatLiteLLM(\\n            model=model,\\n            client=None,\\n            streaming=streaming,\\n            temperature=temperature,\\n            model_kwargs=model_kwargs if model_kwargs is not None else {},\\n            top_p=top_p,\\n            top_k=top_k,\\n            n=n,\\n            max_tokens=max_tokens,\\n            max_retries=max_retries,\\n            **kwarg,\\n        )\\n        return LLM\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_retries\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum number of tokens to generate for each chat completion.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model name\",\"advanced\":false,\"dynamic\":false,\"info\":\"The name of the model to use. For example, `gpt-3.5-turbo`.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\",\"title_case\":false},\"provider\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"OpenAI\",\"Azure\",\"Anthropic\",\"Replicate\",\"Cohere\",\"OpenRouter\"],\"name\":\"provider\",\"display_name\":\"Provider\",\"advanced\":false,\"dynamic\":false,\"info\":\"The provider of the API key.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top k\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"`LiteLLM` collection of large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"ChatLiteLLM\",\"documentation\":\"https://python.langchain.com/docs/integrations/chat/litellm\",\"custom_fields\":{\"model\":null,\"provider\":null,\"api_key\":null,\"streaming\":null,\"temperature\":null,\"model_kwargs\":null,\"top_p\":null,\"top_k\":null,\"n\":null,\"max_tokens\":null,\"max_retries\":null,\"verbose\":null},\"output_types\":[\"BaseLanguageModel\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CTransformersSpecs\":{\"template\":{\"model_file\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".bin\"],\"file_path\":\"\",\"password\":false,\"name\":\"model_file\",\"display_name\":\"Model File\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Dict, Optional\\n\\nfrom langchain_community.llms.ctransformers import CTransformers\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass CTransformersComponent(CustomComponent):\\n    display_name = \\\"CTransformers\\\"\\n    description = \\\"C Transformers LLM models\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\\"display_name\\\": \\\"Model\\\", \\\"required\\\": True},\\n            \\\"model_file\\\": {\\n                \\\"display_name\\\": \\\"Model File\\\",\\n                \\\"required\\\": False,\\n                \\\"field_type\\\": \\\"file\\\",\\n                \\\"file_types\\\": [\\\".bin\\\"],\\n            },\\n            \\\"model_type\\\": {\\\"display_name\\\": \\\"Model Type\\\", \\\"required\\\": True},\\n            \\\"config\\\": {\\n                \\\"display_name\\\": \\\"Config\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n                \\\"field_type\\\": \\\"dict\\\",\\n                \\\"value\\\": '{\\\"top_k\\\":40,\\\"top_p\\\":0.95,\\\"temperature\\\":0.8,\\\"repetition_penalty\\\":1.1,\\\"last_n_tokens\\\":64,\\\"seed\\\":-1,\\\"max_new_tokens\\\":256,\\\"stop\\\":\\\"\\\",\\\"stream\\\":\\\"False\\\",\\\"reset\\\":\\\"True\\\",\\\"batch_size\\\":8,\\\"threads\\\":-1,\\\"context_length\\\":-1,\\\"gpu_layers\\\":0}',\\n            },\\n        }\\n\\n    def build(self, model: str, model_file: str, model_type: str, config: Optional[Dict] = None) -> CTransformers:\\n        return CTransformers(model=model, model_file=model_file, model_type=model_type, config=config)  # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"config\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"{\\\"top_k\\\":40,\\\"top_p\\\":0.95,\\\"temperature\\\":0.8,\\\"repetition_penalty\\\":1.1,\\\"last_n_tokens\\\":64,\\\"seed\\\":-1,\\\"max_new_tokens\\\":256,\\\"stop\\\":\\\"\\\",\\\"stream\\\":\\\"False\\\",\\\"reset\\\":\\\"True\\\",\\\"batch_size\\\":8,\\\"threads\\\":-1,\\\"context_length\\\":-1,\\\"gpu_layers\\\":0}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"config\",\"display_name\":\"Config\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_type\",\"display_name\":\"Model Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"C Transformers LLM models\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"CTransformers\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\",\"LLM\"],\"display_name\":\"CTransformers\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers\",\"custom_fields\":{\"model\":null,\"model_file\":null,\"model_type\":null,\"config\":null},\"output_types\":[\"CTransformers\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"HuggingFaceEndpointsSpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.llms.huggingface_endpoint import HuggingFaceEndpoint\\nfrom langflow import CustomComponent\\n\\n\\nclass HuggingFaceEndpointsComponent(CustomComponent):\\n    display_name: str = \\\"Hugging Face Inference API\\\"\\n    description: str = \\\"LLM model from Hugging Face Inference API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Endpoint URL\\\", \\\"password\\\": True},\\n            \\\"task\\\": {\\n                \\\"display_name\\\": \\\"Task\\\",\\n                \\\"options\\\": [\\\"text2text-generation\\\", \\\"text-generation\\\", \\\"summarization\\\"],\\n            },\\n            \\\"huggingfacehub_api_token\\\": {\\\"display_name\\\": \\\"API token\\\", \\\"password\\\": True},\\n            \\\"model_kwargs\\\": {\\n                \\\"display_name\\\": \\\"Model Keyword Arguments\\\",\\n                \\\"field_type\\\": \\\"code\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        endpoint_url: str,\\n        task: str = \\\"text2text-generation\\\",\\n        huggingfacehub_api_token: Optional[str] = None,\\n        model_kwargs: Optional[dict] = None,\\n    ) -> BaseLLM:\\n        try:\\n            output = HuggingFaceEndpoint(  # type: ignore\\n                endpoint_url=endpoint_url,\\n                task=task,\\n                huggingfacehub_api_token=huggingfacehub_api_token,\\n                model_kwargs=model_kwargs or {},\\n            )\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to HuggingFace Endpoints API.\\\") from e\\n        return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"endpoint_url\",\"display_name\":\"Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"huggingfacehub_api_token\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"huggingfacehub_api_token\",\"display_name\":\"API token\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Keyword Arguments\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"task\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"text2text-generation\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"text2text-generation\",\"text-generation\",\"summarization\"],\"name\":\"task\",\"display_name\":\"Task\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"LLM model from Hugging Face Inference API.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"Hugging Face Inference API\",\"documentation\":\"\",\"custom_fields\":{\"endpoint_url\":null,\"task\":null,\"huggingfacehub_api_token\":null,\"model_kwargs\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatOpenAISpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langchain.llms import BaseLLM\\nfrom langchain_community.chat_models.openai import ChatOpenAI\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, NestedDict\\n\\n\\nclass ChatOpenAIComponent(CustomComponent):\\n    display_name = \\\"ChatOpenAI\\\"\\n    description = \\\"`OpenAI` Chat large language models API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"max_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Tokens\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n            },\\n            \\\"model_kwargs\\\": {\\n                \\\"display_name\\\": \\\"Model Kwargs\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n            },\\n            \\\"model_name\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"options\\\": [\\n                    \\\"gpt-4-turbo-preview\\\",\\n                    \\\"gpt-4-0125-preview\\\",\\n                    \\\"gpt-4-1106-preview\\\",\\n                    \\\"gpt-4-vision-preview\\\",\\n                    \\\"gpt-3.5-turbo-0125\\\",\\n                    \\\"gpt-3.5-turbo-1106\\\",\\n                ],\\n            },\\n            \\\"openai_api_base\\\": {\\n                \\\"display_name\\\": \\\"OpenAI API Base\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"info\\\": (\\n                    \\\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\\\n\\\\n\\\"\\n                    \\\"You can change this to use other APIs like JinaChat, LocalAI and Prem.\\\"\\n                ),\\n            },\\n            \\\"openai_api_key\\\": {\\n                \\\"display_name\\\": \\\"OpenAI API Key\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"password\\\": True,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"value\\\": 0.7,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        max_tokens: Optional[int] = 256,\\n        model_kwargs: NestedDict = {},\\n        model_name: str = \\\"gpt-4-1106-preview\\\",\\n        openai_api_base: Optional[str] = None,\\n        openai_api_key: Optional[str] = None,\\n        temperature: float = 0.7,\\n    ) -> Union[BaseLanguageModel, BaseLLM]:\\n        if not openai_api_base:\\n            openai_api_base = \\\"https://api.openai.com/v1\\\"\\n        return ChatOpenAI(\\n            max_tokens=max_tokens,\\n            model_kwargs=model_kwargs,\\n            model=model_name,\\n            base_url=openai_api_base,\\n            api_key=openai_api_key,\\n            temperature=temperature,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-4-turbo-preview\",\"gpt-4-0125-preview\",\"gpt-4-1106-preview\",\"gpt-4-vision-preview\",\"gpt-3.5-turbo-0125\",\"gpt-3.5-turbo-1106\"],\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"documentation\":\"\",\"custom_fields\":{\"max_tokens\":null,\"model_kwargs\":null,\"model_name\":null,\"openai_api_base\":null,\"openai_api_key\":null,\"temperature\":null},\"output_types\":[\"BaseLanguageModel\",\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OllamaLLMSpecs\":{\"template\":{\"base_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"base_url\",\"display_name\":\"Base URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain_community.llms.ollama import Ollama\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass OllamaLLM(CustomComponent):\\n    display_name = \\\"Ollama\\\"\\n    description = \\\"Local LLM with Ollama.\\\"\\n\\n    def build_config(self) -> dict:\\n        return {\\n            \\\"base_url\\\": {\\n                \\\"display_name\\\": \\\"Base URL\\\",\\n                \\\"info\\\": \\\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\\\",\\n            },\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"value\\\": \\\"llama2\\\",\\n                \\\"info\\\": \\\"Refer to https://ollama.ai/library for more models.\\\",\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"value\\\": 0.8,\\n                \\\"info\\\": \\\"Controls the creativity of model responses.\\\",\\n            },\\n            \\\"mirostat\\\": {\\n                \\\"display_name\\\": \\\"Mirostat\\\",\\n                \\\"options\\\": [\\\"Disabled\\\", \\\"Mirostat\\\", \\\"Mirostat 2.0\\\"],\\n                \\\"info\\\": \\\"Enable/disable Mirostat sampling for controlling perplexity.\\\",\\n                \\\"value\\\": \\\"Disabled\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"mirostat_eta\\\": {\\n                \\\"display_name\\\": \\\"Mirostat Eta\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Learning rate influencing the algorithm's response to feedback.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"mirostat_tau\\\": {\\n                \\\"display_name\\\": \\\"Mirostat Tau\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Controls balance between coherence and diversity.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"num_ctx\\\": {\\n                \\\"display_name\\\": \\\"Context Window Size\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Size of the context window for generating the next token.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"num_gpu\\\": {\\n                \\\"display_name\\\": \\\"Number of GPUs\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Number of GPUs to use for computation.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"num_thread\\\": {\\n                \\\"display_name\\\": \\\"Number of Threads\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Number of threads to use during computation.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"repeat_last_n\\\": {\\n                \\\"display_name\\\": \\\"Repeat Last N\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Sets how far back the model looks to prevent repetition.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"repeat_penalty\\\": {\\n                \\\"display_name\\\": \\\"Repeat Penalty\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Penalty for repetitions in generated text.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"stop\\\": {\\n                \\\"display_name\\\": \\\"Stop Tokens\\\",\\n                \\\"info\\\": \\\"List of tokens to signal the model to stop generating text.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"tfs_z\\\": {\\n                \\\"display_name\\\": \\\"TFS Z\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Tail free sampling to reduce impact of less probable tokens.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_k\\\": {\\n                \\\"display_name\\\": \\\"Top K\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Limits token selection to top K for reducing nonsense generation.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top P\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Works with top-k to control diversity of generated text.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        base_url: Optional[str],\\n        model: str,\\n        temperature: Optional[float],\\n        mirostat: Optional[str],\\n        mirostat_eta: Optional[float] = None,\\n        mirostat_tau: Optional[float] = None,\\n        num_ctx: Optional[int] = None,\\n        num_gpu: Optional[int] = None,\\n        num_thread: Optional[int] = None,\\n        repeat_last_n: Optional[int] = None,\\n        repeat_penalty: Optional[float] = None,\\n        stop: Optional[List[str]] = None,\\n        tfs_z: Optional[float] = None,\\n        top_k: Optional[int] = None,\\n        top_p: Optional[int] = None,\\n    ) -> BaseLLM:\\n        if not base_url:\\n            base_url = \\\"http://localhost:11434\\\"\\n\\n        # Mapping mirostat settings to their corresponding values\\n        mirostat_options = {\\\"Mirostat\\\": 1, \\\"Mirostat 2.0\\\": 2}\\n\\n        # Default to 0 for 'Disabled'\\n        mirostat_value = mirostat_options.get(mirostat, 0)  # type: ignore\\n\\n        # Set mirostat_eta and mirostat_tau to None if mirostat is disabled\\n        if mirostat_value == 0:\\n            mirostat_eta = None\\n            mirostat_tau = None\\n\\n        try:\\n            llm = Ollama(\\n                base_url=base_url,\\n                model=model,\\n                mirostat=mirostat_value,\\n                mirostat_eta=mirostat_eta,\\n                mirostat_tau=mirostat_tau,\\n                num_ctx=num_ctx,\\n                num_gpu=num_gpu,\\n                num_thread=num_thread,\\n                repeat_last_n=repeat_last_n,\\n                repeat_penalty=repeat_penalty,\\n                temperature=temperature,\\n                stop=stop,\\n                tfs_z=tfs_z,\\n                top_k=top_k,\\n                top_p=top_p,\\n            )\\n\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to Ollama.\\\") from e\\n\\n        return llm\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"mirostat\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Disabled\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Disabled\",\"Mirostat\",\"Mirostat 2.0\"],\"name\":\"mirostat\",\"display_name\":\"Mirostat\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable/disable Mirostat sampling for controlling perplexity.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat_eta\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_eta\",\"display_name\":\"Mirostat Eta\",\"advanced\":true,\"dynamic\":false,\"info\":\"Learning rate influencing the algorithm's response to feedback.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"mirostat_tau\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_tau\",\"display_name\":\"Mirostat Tau\",\"advanced\":true,\"dynamic\":false,\"info\":\"Controls balance between coherence and diversity.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"llama2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"Refer to https://ollama.ai/library for more models.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"num_ctx\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_ctx\",\"display_name\":\"Context Window Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"Size of the context window for generating the next token.\",\"title_case\":false},\"num_gpu\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_gpu\",\"display_name\":\"Number of GPUs\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of GPUs to use for computation.\",\"title_case\":false},\"num_thread\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_thread\",\"display_name\":\"Number of Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of threads to use during computation.\",\"title_case\":false},\"repeat_last_n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_last_n\",\"display_name\":\"Repeat Last N\",\"advanced\":true,\"dynamic\":false,\"info\":\"Sets how far back the model looks to prevent repetition.\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"Penalty for repetitions in generated text.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"stop\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"List of tokens to signal the model to stop generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Controls the creativity of model responses.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"tfs_z\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tfs_z\",\"display_name\":\"TFS Z\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tail free sampling to reduce impact of less probable tokens.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Limits token selection to top K for reducing nonsense generation.\",\"title_case\":false},\"top_p\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"Works with top-k to control diversity of generated text.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Local LLM with Ollama.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"Ollama\",\"documentation\":\"\",\"custom_fields\":{\"base_url\":null,\"model\":null,\"temperature\":null,\"mirostat\":null,\"mirostat_eta\":null,\"mirostat_tau\":null,\"num_ctx\":null,\"num_gpu\":null,\"num_thread\":null,\"repeat_last_n\":null,\"repeat_penalty\":null,\"stop\":null,\"tfs_z\":null,\"top_k\":null,\"top_p\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"io\":{\"ChatOutput\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\nfrom langflow.schema import Record\\n\\n\\nclass ChatOutput(CustomComponent):\\n    display_name = \\\"Chat Output\\\"\\n    description = \\\"Used to send a message to the chat.\\\"\\n\\n    field_config = {\\n        \\\"code\\\": {\\n            \\\"show\\\": True,\\n        }\\n    }\\n\\n    def build_config(self):\\n        return {\\n            \\\"message\\\": {\\\"input_types\\\": [\\\"Text\\\"], \\\"display_name\\\": \\\"Message\\\"},\\n            \\\"sender\\\": {\\n                \\\"options\\\": [\\\"Machine\\\", \\\"User\\\"],\\n                \\\"display_name\\\": \\\"Sender Type\\\",\\n            },\\n            \\\"sender_name\\\": {\\\"display_name\\\": \\\"Sender Name\\\"},\\n            \\\"session_id\\\": {\\n                \\\"display_name\\\": \\\"Session ID\\\",\\n                \\\"info\\\": \\\"Session ID of the chat history.\\\",\\n                \\\"input_types\\\": [\\\"Text\\\"],\\n            },\\n            \\\"return_record\\\": {\\n                \\\"display_name\\\": \\\"Return Record\\\",\\n                \\\"info\\\": \\\"Return the message as a record containing the sender, sender_name, and session_id.\\\",\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        sender: Optional[str] = \\\"Machine\\\",\\n        sender_name: Optional[str] = \\\"AI\\\",\\n        session_id: Optional[str] = None,\\n        message: Optional[str] = None,\\n        return_record: Optional[bool] = False,\\n    ) -> Union[Text, Record]:\\n        if return_record:\\n            if isinstance(message, Record):\\n                # Update the data of the record\\n                message.data[\\\"sender\\\"] = sender\\n                message.data[\\\"sender_name\\\"] = sender_name\\n                message.data[\\\"session_id\\\"] = session_id\\n            else:\\n                message = Record(\\n                    text=message,\\n                    data={\\n                        \\\"sender\\\": sender,\\n                        \\\"sender_name\\\": sender_name,\\n                        \\\"session_id\\\": session_id,\\n                    },\\n                )\\n        if not message:\\n            message = \\\"\\\"\\n        self.status = message\\n        return message\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"message\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"message\",\"display_name\":\"Message\",\"advanced\":false,\"input_types\":[\"Text\",\"Text\"],\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"return_record\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_record\",\"display_name\":\"Return Record\",\"advanced\":false,\"dynamic\":false,\"info\":\"Return the message as a record containing the sender, sender_name, and session_id.\",\"title_case\":false},\"sender\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Machine\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Machine\",\"User\"],\"name\":\"sender\",\"display_name\":\"Sender Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"sender_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender_name\",\"display_name\":\"Sender Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"display_name\":\"Session ID\",\"advanced\":false,\"input_types\":[\"Text\",\"Text\"],\"dynamic\":false,\"info\":\"Session ID of the chat history.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Used to send a message to the chat.\",\"base_classes\":[\"Text\",\"object\",\"Record\"],\"display_name\":\"Chat Output\",\"documentation\":\"\",\"custom_fields\":{\"sender\":null,\"sender_name\":null,\"session_id\":null,\"message\":null,\"return_record\":null},\"output_types\":[\"Text\",\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"MessageHistory\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.memory import get_messages\\nfrom langflow.schema import Record\\n\\n\\nclass MessageHistoryComponent(CustomComponent):\\n    display_name = \\\"Message History\\\"\\n    description = \\\"Used to retrieve stored messages.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"sender\\\": {\\n                \\\"options\\\": [\\\"Machine\\\", \\\"User\\\"],\\n                \\\"display_name\\\": \\\"Sender Type\\\",\\n            },\\n            \\\"sender_name\\\": {\\\"display_name\\\": \\\"Sender Name\\\"},\\n            \\\"file_path\\\": {\\n                \\\"display_name\\\": \\\"File Path\\\",\\n                \\\"info\\\": \\\"Path of the local JSON file to store the messages. It should be a unique path for each chat history.\\\",\\n            },\\n            \\\"n_messages\\\": {\\n                \\\"display_name\\\": \\\"Number of Messages\\\",\\n                \\\"info\\\": \\\"Number of messages to retrieve.\\\",\\n            },\\n            \\\"session_id\\\": {\\n                \\\"display_name\\\": \\\"Session ID\\\",\\n                \\\"info\\\": \\\"Session ID of the chat history.\\\",\\n                \\\"input_types\\\": [\\\"Text\\\"],\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        sender: Optional[str] = None,\\n        sender_name: Optional[str] = None,\\n        session_id: Optional[str] = None,\\n        n_messages: int = 5,\\n    ) -> List[Record]:\\n        messages = get_messages(\\n            sender=sender,\\n            sender_name=sender_name,\\n            session_id=session_id,\\n            limit=n_messages,\\n        )\\n        self.status = messages\\n        return messages\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"n_messages\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_messages\",\"display_name\":\"Number of Messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"Number of messages to retrieve.\",\"title_case\":false},\"sender\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Machine\",\"User\"],\"name\":\"sender\",\"display_name\":\"Sender Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"sender_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender_name\",\"display_name\":\"Sender Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"display_name\":\"Session ID\",\"advanced\":false,\"input_types\":[\"Text\",\"Text\"],\"dynamic\":false,\"info\":\"Session ID of the chat history.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Used to retrieve stored messages.\",\"base_classes\":[\"Record\"],\"display_name\":\"Message History\",\"documentation\":\"\",\"custom_fields\":{\"sender\":null,\"sender_name\":null,\"session_id\":null,\"n_messages\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"TextOutput\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass TextOutput(CustomComponent):\\n    display_name = \\\"Text Output\\\"\\n    description = \\\"Used to pass text output to the next component.\\\"\\n\\n    field_config = {\\n        \\\"value\\\": {\\\"display_name\\\": \\\"Value\\\"},\\n    }\\n\\n    def build(self, value: Optional[str] = \\\"\\\") -> Text:\\n        self.status = value\\n        if not value:\\n            value = \\\"\\\"\\n        return value\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"value\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"value\",\"display_name\":\"Value\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Used to pass text output to the next component.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Text Output\",\"documentation\":\"\",\"custom_fields\":{\"value\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"StoreMessages\":{\"template\":{\"records\":{\"type\":\"Record\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"records\",\"display_name\":\"Records\",\"advanced\":false,\"dynamic\":false,\"info\":\"The list of records to store. Each record should contain the keys 'sender', 'sender_name', and 'session_id'.\",\"title_case\":false},\"texts\":{\"type\":\"Text\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"texts\",\"display_name\":\"Texts\",\"advanced\":false,\"dynamic\":false,\"info\":\"The list of texts to store. If records is not provided, texts must be provided.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\nfrom langflow.memory import add_messages\\nfrom langflow.schema import Record\\n\\n\\nclass StoreMessages(CustomComponent):\\n    display_name = \\\"Store Messages\\\"\\n    description = \\\"Used to store messages.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"records\\\": {\\n                \\\"display_name\\\": \\\"Records\\\",\\n                \\\"info\\\": \\\"The list of records to store. Each record should contain the keys 'sender', 'sender_name', and 'session_id'.\\\",\\n            },\\n            \\\"texts\\\": {\\n                \\\"display_name\\\": \\\"Texts\\\",\\n                \\\"info\\\": \\\"The list of texts to store. If records is not provided, texts must be provided.\\\",\\n            },\\n            \\\"session_id\\\": {\\n                \\\"display_name\\\": \\\"Session ID\\\",\\n                \\\"info\\\": \\\"The session ID to store.\\\",\\n            },\\n            \\\"sender\\\": {\\n                \\\"display_name\\\": \\\"Sender\\\",\\n                \\\"info\\\": \\\"The sender to store.\\\",\\n            },\\n            \\\"sender_name\\\": {\\n                \\\"display_name\\\": \\\"Sender Name\\\",\\n                \\\"info\\\": \\\"The sender name to store.\\\",\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        records: Optional[List[Record]] = None,\\n        texts: Optional[List[Text]] = None,\\n        session_id: Optional[str] = None,\\n        sender: Optional[str] = None,\\n        sender_name: Optional[str] = None,\\n    ) -> List[Record]:\\n        # Records is the main way to store messages\\n        # If records is not provided, we can use texts\\n        # but we need to create the records from the texts\\n        # and the other parameters\\n        if not texts and not records:\\n            raise ValueError(\\\"Either texts or records must be provided.\\\")\\n\\n        if not records:\\n            records = []\\n            if not session_id or not sender or not sender_name:\\n                raise ValueError(\\\"If passing texts, session_id, sender, and sender_name must be provided.\\\")\\n            for text in texts:\\n                record = Record(\\n                    text=text,\\n                    data={\\n                        \\\"session_id\\\": session_id,\\n                        \\\"sender\\\": sender,\\n                        \\\"sender_name\\\": sender_name,\\n                    },\\n                )\\n                records.append(record)\\n        elif isinstance(records, Record):\\n            records = [records]\\n\\n        self.status = records\\n        records = add_messages(records)\\n        return records\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"sender\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender\",\"display_name\":\"Sender\",\"advanced\":false,\"dynamic\":false,\"info\":\"The sender to store.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"sender_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender_name\",\"display_name\":\"Sender Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"The sender name to store.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"display_name\":\"Session ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"The session ID to store.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Used to store messages.\",\"base_classes\":[\"Record\"],\"display_name\":\"Store Messages\",\"documentation\":\"\",\"custom_fields\":{\"records\":null,\"texts\":null,\"session_id\":null,\"sender\":null,\"sender_name\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatInput\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.schema import Record\\n\\n\\nclass ChatInput(CustomComponent):\\n    display_name = \\\"Chat Input\\\"\\n    description = \\\"Used to get user input from the chat.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"message\\\": {\\n                \\\"input_types\\\": [\\\"Text\\\"],\\n                \\\"display_name\\\": \\\"Message\\\",\\n                \\\"multiline\\\": True,\\n            },\\n            \\\"sender\\\": {\\n                \\\"options\\\": [\\\"Machine\\\", \\\"User\\\"],\\n                \\\"display_name\\\": \\\"Sender Type\\\",\\n            },\\n            \\\"sender_name\\\": {\\\"display_name\\\": \\\"Sender Name\\\"},\\n            \\\"session_id\\\": {\\n                \\\"display_name\\\": \\\"Session ID\\\",\\n                \\\"info\\\": \\\"Session ID of the chat history.\\\",\\n            },\\n            \\\"return_record\\\": {\\n                \\\"display_name\\\": \\\"Return Record\\\",\\n                \\\"info\\\": \\\"Return the message as a record containing the sender, sender_name, and session_id.\\\",\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        sender: Optional[str] = \\\"User\\\",\\n        sender_name: Optional[str] = \\\"User\\\",\\n        message: Optional[str] = None,\\n        session_id: Optional[str] = None,\\n        return_record: Optional[bool] = False,\\n    ) -> Record:\\n        if return_record:\\n            if isinstance(message, Record):\\n                # Update the data of the record\\n                message.data[\\\"sender\\\"] = sender\\n                message.data[\\\"sender_name\\\"] = sender_name\\n                message.data[\\\"session_id\\\"] = session_id\\n            else:\\n                message = Record(\\n                    text=message,\\n                    data={\\n                        \\\"sender\\\": sender,\\n                        \\\"sender_name\\\": sender_name,\\n                        \\\"session_id\\\": session_id,\\n                    },\\n                )\\n        if not message:\\n            message = \\\"\\\"\\n        self.status = message\\n        return message\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"message\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"message\",\"display_name\":\"Message\",\"advanced\":false,\"input_types\":[\"Text\",\"Text\"],\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"return_record\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_record\",\"display_name\":\"Return Record\",\"advanced\":false,\"dynamic\":false,\"info\":\"Return the message as a record containing the sender, sender_name, and session_id.\",\"title_case\":false},\"sender\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"User\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Machine\",\"User\"],\"name\":\"sender\",\"display_name\":\"Sender Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"sender_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"User\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender_name\",\"display_name\":\"Sender Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"display_name\":\"Session ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"Session ID of the chat history.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Used to get user input from the chat.\",\"base_classes\":[\"Record\"],\"display_name\":\"Chat Input\",\"documentation\":\"\",\"custom_fields\":{\"sender\":null,\"sender_name\":null,\"message\":null,\"session_id\":null,\"return_record\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"TextInput\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass TextInput(CustomComponent):\\n    display_name = \\\"Text Input\\\"\\n    description = \\\"Used to pass text input to the next component.\\\"\\n\\n    field_config = {\\n        \\\"value\\\": {\\\"display_name\\\": \\\"Value\\\"},\\n    }\\n\\n    def build(self, value: Optional[str] = \\\"\\\") -> Text:\\n        self.status = value\\n        if not value:\\n            value = \\\"\\\"\\n        return value\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"value\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"value\",\"display_name\":\"Value\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Used to pass text input to the next component.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Text Input\",\"documentation\":\"\",\"custom_fields\":{\"value\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"prompts\":{\"Prompt\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_core.prompts import PromptTemplate\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Prompt, TemplateField, Text\\n\\n\\nclass PromptComponent(CustomComponent):\\n    display_name: str = \\\"Prompt\\\"\\n    description: str = \\\"A component for creating prompts using templates\\\"\\n    beta = True\\n\\n    def build_config(self):\\n        return {\\n            \\\"template\\\": TemplateField(display_name=\\\"Template\\\"),\\n            \\\"code\\\": TemplateField(advanced=True),\\n        }\\n\\n    def build(\\n        self,\\n        template: Prompt,\\n        **kwargs,\\n    ) -> Text:\\n        prompt_template = PromptTemplate.from_template(template)\\n\\n        attributes_to_check = [\\\"text\\\", \\\"page_content\\\"]\\n        for key, value in kwargs.items():\\n            for attribute in attributes_to_check:\\n                if hasattr(value, attribute):\\n                    kwargs[key] = getattr(value, attribute)\\n\\n        try:\\n            formated_prompt = prompt_template.format(**kwargs)\\n        except Exception as exc:\\n            raise ValueError(f\\\"Error formatting prompt: {exc}\\\") from exc\\n        self.status = f'Prompt: \\\"{formated_prompt}\\\"'\\n        return formated_prompt\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"template\":{\"type\":\"prompt\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"display_name\":\"Template\",\"advanced\":false,\"input_types\":[\"Text\"],\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"A component for creating prompts using templates\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Prompt\",\"documentation\":\"\",\"custom_fields\":{\"template\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}}}"
+            "text": "{\"chains\":{\"ConversationalRetrievalChain\":{\"template\":{\"callbacks\":{\"type\":\"Callbacks\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"condense_question_llm\":{\"type\":\"BaseLanguageModel\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"condense_question_llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"condense_question_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":{\"name\":null,\"input_variables\":[\"chat_history\",\"question\"],\"input_types\":{},\"output_parser\":null,\"partial_variables\":{},\"metadata\":null,\"tags\":null,\"template\":\"Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.\\n\\nChat History:\\n{chat_history}\\nFollow Up Input: {question}\\nStandalone question:\",\"template_format\":\"f-string\",\"validate_template\":false},\"fileTypes\":[],\"password\":false,\"name\":\"condense_question_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseChatMemory\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"retriever\":{\"type\":\"BaseRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chain_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"stuff\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"stuff\",\"map_reduce\",\"map_rerank\",\"refine\"],\"name\":\"chain_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"combine_docs_chain_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"combine_docs_chain_kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"return_source_documents\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_source_documents\",\"display_name\":\"Return source documents\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationalRetrievalChain\"},\"description\":\"Convenience method to load chain from LLM and retriever.\",\"base_classes\":[\"BaseConversationalRetrievalChain\",\"Runnable\",\"Chain\",\"Generic\",\"Text\",\"RunnableSerializable\",\"Serializable\",\"object\",\"ConversationalRetrievalChain\",\"Callable\"],\"display_name\":\"ConversationalRetrievalChain\",\"documentation\":\"https://python.langchain.com/docs/modules/chains/popular/chat_vector_db\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"LLMCheckerChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain.chains import LLMCheckerChain\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, Chain\\n\\n\\nclass LLMCheckerChainComponent(CustomComponent):\\n    display_name = \\\"LLMCheckerChain\\\"\\n    description = \\\"\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/chains/additional/llm_checker\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        llm: BaseLanguageModel,\\n    ) -> Union[Chain, Callable]:\\n        return LLMCheckerChain.from_llm(llm=llm)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"LLMCheckerChain\",\"documentation\":\"https://python.langchain.com/docs/modules/chains/additional/llm_checker\",\"custom_fields\":{\"llm\":null},\"output_types\":[\"Chain\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"LLMMathChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm_chain\":{\"type\":\"LLMChain\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm_chain\",\"display_name\":\"LLM Chain\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import LLMChain, LLMMathChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, BaseMemory, Chain\\n\\n\\nclass LLMMathChainComponent(CustomComponent):\\n    display_name = \\\"LLMMathChain\\\"\\n    description = \\\"Chain that interprets a prompt and executes python code to do math.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/chains/additional/llm_math\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"llm_chain\\\": {\\\"display_name\\\": \\\"LLM Chain\\\"},\\n            \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n            \\\"input_key\\\": {\\\"display_name\\\": \\\"Input Key\\\"},\\n            \\\"output_key\\\": {\\\"display_name\\\": \\\"Output Key\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        llm: BaseLanguageModel,\\n        llm_chain: LLMChain,\\n        input_key: str = \\\"question\\\",\\n        output_key: str = \\\"answer\\\",\\n        memory: Optional[BaseMemory] = None,\\n    ) -> Union[LLMMathChain, Callable, Chain]:\\n        return LLMMathChain(llm=llm, llm_chain=llm_chain, input_key=input_key, output_key=output_key, memory=memory)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"input_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"question\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"display_name\":\"Input Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"answer\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"display_name\":\"Output Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Chain that interprets a prompt and executes python code to do math.\",\"base_classes\":[\"LLMMathChain\",\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"LLMMathChain\",\"documentation\":\"https://python.langchain.com/docs/modules/chains/additional/llm_math\",\"custom_fields\":{\"llm\":null,\"llm_chain\":null,\"input_key\":null,\"output_key\":null,\"memory\":null},\"output_types\":[\"LLMMathChain\",\"Callable\",\"Chain\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RetrievalQA\":{\"template\":{\"combine_documents_chain\":{\"type\":\"BaseCombineDocumentsChain\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"combine_documents_chain\",\"display_name\":\"Combine Documents Chain\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"retriever\":{\"type\":\"BaseRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"retriever\",\"display_name\":\"Retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains.combine_documents.base import BaseCombineDocumentsChain\\nfrom langchain.chains.retrieval_qa.base import BaseRetrievalQA, RetrievalQA\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseMemory, BaseRetriever, Text\\n\\n\\nclass RetrievalQAComponent(CustomComponent):\\n    display_name = \\\"Retrieval QA\\\"\\n    description = \\\"Chain for question-answering against an index.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"combine_documents_chain\\\": {\\\"display_name\\\": \\\"Combine Documents Chain\\\"},\\n            \\\"retriever\\\": {\\\"display_name\\\": \\\"Retriever\\\"},\\n            \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\", \\\"required\\\": False},\\n            \\\"input_key\\\": {\\\"display_name\\\": \\\"Input Key\\\", \\\"advanced\\\": True},\\n            \\\"output_key\\\": {\\\"display_name\\\": \\\"Output Key\\\", \\\"advanced\\\": True},\\n            \\\"return_source_documents\\\": {\\\"display_name\\\": \\\"Return Source Documents\\\"},\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\", \\\"input_types\\\": [\\\"Text\\\", \\\"Document\\\"]},\\n        }\\n\\n    def build(\\n        self,\\n        combine_documents_chain: BaseCombineDocumentsChain,\\n        retriever: BaseRetriever,\\n        inputs: str = \\\"\\\",\\n        memory: Optional[BaseMemory] = None,\\n        input_key: str = \\\"query\\\",\\n        output_key: str = \\\"result\\\",\\n        return_source_documents: bool = True,\\n    ) -> Union[BaseRetrievalQA, Callable, Text]:\\n        runnable = RetrievalQA(\\n            combine_documents_chain=combine_documents_chain,\\n            retriever=retriever,\\n            memory=memory,\\n            input_key=input_key,\\n            output_key=output_key,\\n            return_source_documents=return_source_documents,\\n        )\\n        if isinstance(inputs, Document):\\n            inputs = inputs.page_content\\n        self.status = runnable\\n        result = runnable.invoke({input_key: inputs})\\n        result = result.content if hasattr(result, \\\"content\\\") else result\\n        # Result is a dict with keys \\\"query\\\",  \\\"result\\\" and \\\"source_documents\\\"\\n        # for now we just return the result\\n        records = self.to_records(result.get(\\\"source_documents\\\"))\\n        references_str = \\\"\\\"\\n        if return_source_documents:\\n            references_str = self.create_references_from_records(records)\\n        result_str = result.get(\\\"result\\\")\\n        final_result = \\\"\\\\n\\\".join([result_str, references_str])\\n        self.status = final_result\\n        return final_result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"input_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"query\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"display_name\":\"Input Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"input_types\":[\"Text\",\"Document\",\"Text\"],\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"output_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"result\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"display_name\":\"Output Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_source_documents\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_source_documents\",\"display_name\":\"Return Source Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Chain for question-answering against an index.\",\"base_classes\":[\"Runnable\",\"Chain\",\"BaseRetrievalQA\",\"Generic\",\"Text\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"Retrieval QA\",\"documentation\":\"\",\"custom_fields\":{\"combine_documents_chain\":null,\"retriever\":null,\"inputs\":null,\"memory\":null,\"input_key\":null,\"output_key\":null,\"return_source_documents\":null},\"output_types\":[\"BaseRetrievalQA\",\"Callable\",\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RetrievalQAWithSourcesChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"retriever\":{\"type\":\"BaseRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"retriever\",\"display_name\":\"Retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chain_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"stuff\",\"map_reduce\",\"map_rerank\",\"refine\"],\"name\":\"chain_type\",\"display_name\":\"Chain Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"The type of chain to use to combined Documents.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.chains import RetrievalQAWithSourcesChain\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, BaseMemory, BaseRetriever, Text\\n\\n\\nclass RetrievalQAWithSourcesChainComponent(CustomComponent):\\n    display_name = \\\"RetrievalQAWithSourcesChain\\\"\\n    description = \\\"Question-answering with sources over an index.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"chain_type\\\": {\\n                \\\"display_name\\\": \\\"Chain Type\\\",\\n                \\\"options\\\": [\\\"stuff\\\", \\\"map_reduce\\\", \\\"map_rerank\\\", \\\"refine\\\"],\\n                \\\"info\\\": \\\"The type of chain to use to combined Documents.\\\",\\n            },\\n            \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n            \\\"return_source_documents\\\": {\\\"display_name\\\": \\\"Return Source Documents\\\"},\\n            \\\"retriever\\\": {\\\"display_name\\\": \\\"Retriever\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        inputs: str,\\n        retriever: BaseRetriever,\\n        llm: BaseLanguageModel,\\n        chain_type: str,\\n        memory: Optional[BaseMemory] = None,\\n        return_source_documents: Optional[bool] = True,\\n    ) -> Text:\\n        runnable = RetrievalQAWithSourcesChain.from_chain_type(\\n            llm=llm,\\n            chain_type=chain_type,\\n            memory=memory,\\n            return_source_documents=return_source_documents,\\n            retriever=retriever,\\n        )\\n        if isinstance(inputs, Document):\\n            inputs = inputs.page_content\\n        self.status = runnable\\n        input_key = runnable.input_keys[0]\\n        result = runnable.invoke({input_key: inputs})\\n        result = result.content if hasattr(result, \\\"content\\\") else result\\n        # Result is a dict with keys \\\"query\\\",  \\\"result\\\" and \\\"source_documents\\\"\\n        # for now we just return the result\\n        records = self.to_records(result.get(\\\"source_documents\\\"))\\n        references_str = \\\"\\\"\\n        if return_source_documents:\\n            references_str = self.create_references_from_records(records)\\n        result_str = result.get(\\\"answer\\\")\\n        final_result = \\\"\\\\n\\\".join([result_str, references_str])\\n        self.status = final_result\\n        return final_result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_source_documents\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_source_documents\",\"display_name\":\"Return Source Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Question-answering with sources over an index.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"RetrievalQAWithSourcesChain\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"retriever\":null,\"llm\":null,\"chain_type\":null,\"memory\":null,\"return_source_documents\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLDatabaseChain\":{\"template\":{\"db\":{\"type\":\"SQLDatabase\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"db\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prompt\":{\"type\":\"BasePromptTemplate\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"SQLDatabaseChain\"},\"description\":\"Create a SQLDatabaseChain from an LLM and a database connection.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"Text\",\"RunnableSerializable\",\"Serializable\",\"object\",\"SQLDatabaseChain\",\"Callable\"],\"display_name\":\"SQLDatabaseChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"CombineDocsChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chain_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"stuff\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"stuff\",\"map_reduce\",\"map_rerank\",\"refine\"],\"name\":\"chain_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"load_qa_chain\"},\"description\":\"Load question answering chain.\",\"base_classes\":[\"function\",\"BaseCombineDocumentsChain\"],\"display_name\":\"CombineDocsChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"SeriesCharacterChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"character\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"character\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"series\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"series\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"SeriesCharacterChain\"},\"description\":\"SeriesCharacterChain is a chain you can use to have a conversation with a character from a series.\",\"base_classes\":[\"Chain\",\"BaseCustomChain\",\"ConversationChain\",\"function\",\"SeriesCharacterChain\",\"LLMChain\"],\"display_name\":\"SeriesCharacterChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"MidJourneyPromptChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseChatMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"MidJourneyPromptChain\"},\"description\":\"MidJourneyPromptChain is a chain you can use to generate new MidJourney prompts.\",\"base_classes\":[\"Chain\",\"BaseCustomChain\",\"MidJourneyPromptChain\",\"ConversationChain\",\"LLMChain\"],\"display_name\":\"MidJourneyPromptChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"TimeTravelGuideChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseChatMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"TimeTravelGuideChain\"},\"description\":\"Time travel guide chain.\",\"base_classes\":[\"Chain\",\"BaseCustomChain\",\"ConversationChain\",\"TimeTravelGuideChain\",\"LLMChain\"],\"display_name\":\"TimeTravelGuideChain\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false,\"output_type\":\"Chain\"},\"LLMChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prompt\":{\"type\":\"BasePromptTemplate\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.chains import LLMChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n    BaseLanguageModel,\\n    BaseMemory,\\n    BasePromptTemplate,\\n    Text,\\n)\\n\\n\\nclass LLMChainComponent(CustomComponent):\\n    display_name = \\\"LLMChain\\\"\\n    description = \\\"Chain to run queries against LLMs\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        prompt: BasePromptTemplate,\\n        llm: BaseLanguageModel,\\n        memory: Optional[BaseMemory] = None,\\n    ) -> Text:\\n        runnable = LLMChain(prompt=prompt, llm=llm, memory=memory)\\n        result_dict = runnable.invoke({})\\n        output_key = runnable.output_key\\n        result = result_dict[output_key]\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Chain to run queries against LLMs\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"LLMChain\",\"documentation\":\"\",\"custom_fields\":{\"prompt\":null,\"llm\":null,\"memory\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLGenerator\":{\"template\":{\"db\":{\"type\":\"SQLDatabase\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"db\",\"display_name\":\"Database\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"Text\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prompt\":{\"type\":\"PromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"The prompt must contain `{question}`.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.chains import create_sql_query_chain\\nfrom langchain_community.utilities.sql_database import SQLDatabase\\nfrom langchain_core.prompts import PromptTemplate\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, Text\\n\\n\\nclass SQLGeneratorComponent(CustomComponent):\\n    display_name = \\\"Natural Language to SQL\\\"\\n    description = \\\"Generate SQL from natural language.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"db\\\": {\\\"display_name\\\": \\\"Database\\\"},\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"prompt\\\": {\\n                \\\"display_name\\\": \\\"Prompt\\\",\\n                \\\"info\\\": \\\"The prompt must contain `{question}`.\\\",\\n            },\\n            \\\"top_k\\\": {\\n                \\\"display_name\\\": \\\"Top K\\\",\\n                \\\"info\\\": \\\"The number of results per select statement to return. If 0, no limit.\\\",\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        inputs: Text,\\n        db: SQLDatabase,\\n        llm: BaseLanguageModel,\\n        top_k: int = 5,\\n        prompt: Optional[PromptTemplate] = None,\\n    ) -> Text:\\n        if top_k > 0:\\n            kwargs = {\\n                \\\"k\\\": top_k,\\n            }\\n        if not prompt:\\n            sql_query_chain = create_sql_query_chain(llm=llm, db=db, **kwargs)\\n        else:\\n            template = prompt.template if hasattr(prompt, \\\"template\\\") else prompt\\n            # Check if {question} is in the prompt\\n            if \\\"{question}\\\" not in template or \\\"question\\\" not in template.input_variables:\\n                raise ValueError(\\\"Prompt must contain `{question}` to be used with Natural Language to SQL.\\\")\\n            sql_query_chain = create_sql_query_chain(llm=llm, db=db, prompt=prompt, **kwargs)\\n        query_writer = sql_query_chain | {\\\"query\\\": lambda x: x.replace(\\\"SQLQuery:\\\", \\\"\\\").strip()}\\n        response = query_writer.invoke({\\\"question\\\": inputs})\\n        query = response.get(\\\"query\\\")\\n        self.status = query\\n        return query\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":false,\"dynamic\":false,\"info\":\"The number of results per select statement to return. If 0, no limit.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate SQL from natural language.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Natural Language to SQL\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"db\":null,\"llm\":null,\"top_k\":null,\"prompt\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ConversationChain\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"Memory to load context from. If none is provided, a ConversationBufferMemory will be used.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.chains import ConversationChain\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, BaseMemory, Chain, Text\\n\\n\\nclass ConversationChainComponent(CustomComponent):\\n    display_name = \\\"ConversationChain\\\"\\n    description = \\\"Chain to have a conversation and load context from memory.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"prompt\\\": {\\\"display_name\\\": \\\"Prompt\\\"},\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"memory\\\": {\\n                \\\"display_name\\\": \\\"Memory\\\",\\n                \\\"info\\\": \\\"Memory to load context from. If none is provided, a ConversationBufferMemory will be used.\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        inputs: str,\\n        llm: BaseLanguageModel,\\n        memory: Optional[BaseMemory] = None,\\n    ) -> Union[Chain, Callable, Text]:\\n        if memory is None:\\n            chain = ConversationChain(llm=llm)\\n        else:\\n            chain = ConversationChain(llm=llm, memory=memory)\\n        result = chain.invoke(inputs)\\n        # result is an AIMessage which is a subclass of BaseMessage\\n        # We need to check if it is a string or a BaseMessage\\n        if hasattr(result, \\\"content\\\") and isinstance(result.content, str):\\n            self.status = \\\"is message\\\"\\n            result = result.content\\n        elif isinstance(result, str):\\n            self.status = \\\"is_string\\\"\\n            result = result\\n        else:\\n            # is dict\\n            result = result.get(\\\"response\\\")\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Chain to have a conversation and load context from memory.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"Text\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"ConversationChain\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"llm\":null,\"memory\":null},\"output_types\":[\"Chain\",\"Callable\",\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"agents\":{\"ZeroShotAgent\":{\"template\":{\"callback_manager\":{\"type\":\"BaseCallbackManager\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"callback_manager\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"output_parser\":{\"type\":\"AgentOutputParser\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tools\":{\"type\":\"BaseTool\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"format_instructions\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":true,\"value\":\"Use the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [{tool_names}]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\",\"fileTypes\":[],\"password\":false,\"name\":\"format_instructions\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_variables\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"Answer the following questions as best you can. You have access to the following tools:\",\"fileTypes\":[],\"password\":false,\"name\":\"prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"suffix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"Begin!\\n\\nQuestion: {input}\\nThought:{agent_scratchpad}\",\"fileTypes\":[],\"password\":false,\"name\":\"suffix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"ZeroShotAgent\"},\"description\":\"Construct an agent from an LLM and tools.\",\"base_classes\":[\"ZeroShotAgent\",\"Callable\",\"BaseSingleActionAgent\",\"Agent\"],\"display_name\":\"ZeroShotAgent\",\"documentation\":\"https://python.langchain.com/docs/modules/agents/how_to/custom_mrkl_agent\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"JsonAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"toolkit\":{\"type\":\"JsonToolkit\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"toolkit\",\"display_name\":\"Toolkit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.agents import AgentExecutor, create_json_agent\\nfrom langflow.field_typing import (\\n    BaseLanguageModel,\\n)\\nfrom langchain_community.agent_toolkits.json.toolkit import JsonToolkit\\n\\n\\nclass JsonAgentComponent(CustomComponent):\\n    display_name = \\\"JsonAgent\\\"\\n    description = \\\"Construct a json agent from an LLM and tools.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"toolkit\\\": {\\\"display_name\\\": \\\"Toolkit\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        llm: BaseLanguageModel,\\n        toolkit: JsonToolkit,\\n    ) -> AgentExecutor:\\n        return create_json_agent(llm=llm, toolkit=toolkit)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct a json agent from an LLM and tools.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\"],\"display_name\":\"JsonAgent\",\"documentation\":\"\",\"custom_fields\":{\"llm\":null,\"toolkit\":null},\"output_types\":[\"AgentExecutor\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CSVAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".csv\"],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, AgentExecutor\\nfrom langchain_experimental.agents.agent_toolkits.csv.base import create_csv_agent\\n\\n\\nclass CSVAgentComponent(CustomComponent):\\n    display_name = \\\"CSVAgent\\\"\\n    description = \\\"Construct a CSV agent from a CSV and tools.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/agents/toolkits/csv\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\", \\\"type\\\": BaseLanguageModel},\\n            \\\"path\\\": {\\\"display_name\\\": \\\"Path\\\", \\\"field_type\\\": \\\"file\\\", \\\"suffixes\\\": [\\\".csv\\\"], \\\"file_types\\\": [\\\".csv\\\"]},\\n        }\\n\\n    def build(\\n        self,\\n        llm: BaseLanguageModel,\\n        path: str,\\n    ) -> AgentExecutor:\\n        # Instantiate and return the CSV agent class with the provided llm and path\\n        return create_csv_agent(llm=llm, path=path)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct a CSV agent from a CSV and tools.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\"],\"display_name\":\"CSVAgent\",\"documentation\":\"https://python.langchain.com/docs/modules/agents/toolkits/csv\",\"custom_fields\":{\"llm\":null,\"path\":null},\"output_types\":[\"AgentExecutor\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vector_store_toolkit\":{\"type\":\"VectorStoreToolkit\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vector_store_toolkit\",\"display_name\":\"Vector Store Info\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.agents import AgentExecutor, create_vectorstore_agent\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreToolkit\\nfrom typing import Union, Callable\\nfrom langflow.field_typing import BaseLanguageModel\\n\\n\\nclass VectorStoreAgentComponent(CustomComponent):\\n    display_name = \\\"VectorStoreAgent\\\"\\n    description = \\\"Construct an agent from a Vector Store.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"vector_store_toolkit\\\": {\\\"display_name\\\": \\\"Vector Store Info\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        llm: BaseLanguageModel,\\n        vector_store_toolkit: VectorStoreToolkit,\\n    ) -> Union[AgentExecutor, Callable]:\\n        return create_vectorstore_agent(llm=llm, toolkit=vector_store_toolkit)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct an agent from a Vector Store.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\",\"Callable\"],\"display_name\":\"VectorStoreAgent\",\"documentation\":\"\",\"custom_fields\":{\"llm\":null,\"vector_store_toolkit\":null},\"output_types\":[\"AgentExecutor\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreRouterAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vectorstoreroutertoolkit\":{\"type\":\"VectorStoreRouterToolkit\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstoreroutertoolkit\",\"display_name\":\"Vector Store Router Toolkit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain_core.language_models.base import BaseLanguageModel\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreRouterToolkit\\nfrom langchain.agents import create_vectorstore_router_agent\\nfrom typing import Callable\\n\\n\\nclass VectorStoreRouterAgentComponent(CustomComponent):\\n    display_name = \\\"VectorStoreRouterAgent\\\"\\n    description = \\\"Construct an agent from a Vector Store Router.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"vectorstoreroutertoolkit\\\": {\\\"display_name\\\": \\\"Vector Store Router Toolkit\\\"},\\n        }\\n\\n    def build(self, llm: BaseLanguageModel, vectorstoreroutertoolkit: VectorStoreRouterToolkit) -> Callable:\\n        return create_vectorstore_router_agent(llm=llm, toolkit=vectorstoreroutertoolkit)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct an agent from a Vector Store Router.\",\"base_classes\":[\"Callable\"],\"display_name\":\"VectorStoreRouterAgent\",\"documentation\":\"\",\"custom_fields\":{\"llm\":null,\"vectorstoreroutertoolkit\":null},\"output_types\":[\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLAgent\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Union, Callable\\nfrom langchain.agents import AgentExecutor\\nfrom langflow.field_typing import BaseLanguageModel\\nfrom langchain_community.agent_toolkits.sql.base import create_sql_agent\\nfrom langchain.sql_database import SQLDatabase\\nfrom langchain_community.agent_toolkits import SQLDatabaseToolkit\\n\\n\\nclass SQLAgentComponent(CustomComponent):\\n    display_name = \\\"SQLAgent\\\"\\n    description = \\\"Construct an SQL agent from an LLM and tools.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"database_uri\\\": {\\\"display_name\\\": \\\"Database URI\\\"},\\n            \\\"verbose\\\": {\\\"display_name\\\": \\\"Verbose\\\", \\\"value\\\": False, \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        llm: BaseLanguageModel,\\n        database_uri: str,\\n        verbose: bool = False,\\n    ) -> Union[AgentExecutor, Callable]:\\n        db = SQLDatabase.from_uri(database_uri)\\n        toolkit = SQLDatabaseToolkit(db=db, llm=llm)\\n        return create_sql_agent(llm=llm, toolkit=toolkit)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"database_uri\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"database_uri\",\"display_name\":\"Database URI\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct an SQL agent from an LLM and tools.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\",\"Callable\"],\"display_name\":\"SQLAgent\",\"documentation\":\"\",\"custom_fields\":{\"llm\":null,\"database_uri\":null,\"verbose\":null},\"output_types\":[\"AgentExecutor\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OpenAIConversationalAgent\":{\"template\":{\"memory\":{\"type\":\"BaseMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"system_message\":{\"type\":\"SystemMessagePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"system_message\",\"display_name\":\"System Message\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tools\":{\"type\":\"Tool\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langchain.agents.agent import AgentExecutor\\nfrom langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import _get_default_system_message\\nfrom langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent\\nfrom langchain.memory.token_buffer import ConversationTokenBufferMemory\\nfrom langchain.prompts import SystemMessagePromptTemplate\\nfrom langchain.prompts.chat import MessagesPlaceholder\\nfrom langchain.schema.memory import BaseMemory\\nfrom langchain.tools import Tool\\nfrom langchain_community.chat_models import ChatOpenAI\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing.range_spec import RangeSpec\\n\\n\\nclass ConversationalAgent(CustomComponent):\\n    display_name: str = \\\"OpenAI Conversational Agent\\\"\\n    description: str = \\\"Conversational Agent that can use OpenAI's function calling API\\\"\\n\\n    def build_config(self):\\n        openai_function_models = [\\n            \\\"gpt-4-turbo-preview\\\",\\n            \\\"gpt-4-0125-preview\\\",\\n            \\\"gpt-4-1106-preview\\\",\\n            \\\"gpt-4-vision-preview\\\",\\n            \\\"gpt-3.5-turbo-0125\\\",\\n            \\\"gpt-3.5-turbo-1106\\\",\\n        ]\\n        return {\\n            \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n            \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n            \\\"system_message\\\": {\\\"display_name\\\": \\\"System Message\\\"},\\n            \\\"max_token_limit\\\": {\\\"display_name\\\": \\\"Max Token Limit\\\"},\\n            \\\"model_name\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"options\\\": openai_function_models,\\n                \\\"value\\\": openai_function_models[0],\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"value\\\": 0.2,\\n                \\\"range_spec\\\": RangeSpec(min=0, max=2, step=0.1),\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        model_name: str,\\n        openai_api_key: str,\\n        tools: List[Tool],\\n        openai_api_base: Optional[str] = None,\\n        memory: Optional[BaseMemory] = None,\\n        system_message: Optional[SystemMessagePromptTemplate] = None,\\n        max_token_limit: int = 2000,\\n        temperature: float = 0.9,\\n    ) -> AgentExecutor:\\n        llm = ChatOpenAI(\\n            model=model_name,\\n            api_key=openai_api_key,\\n            base_url=openai_api_base,\\n            max_tokens=max_token_limit,\\n            temperature=temperature,\\n        )\\n        if not memory:\\n            memory_key = \\\"chat_history\\\"\\n            memory = ConversationTokenBufferMemory(\\n                memory_key=memory_key,\\n                return_messages=True,\\n                output_key=\\\"output\\\",\\n                llm=llm,\\n                max_token_limit=max_token_limit,\\n            )\\n        else:\\n            memory_key = memory.memory_key  # type: ignore\\n\\n        _system_message = system_message or _get_default_system_message()\\n        prompt = OpenAIFunctionsAgent.create_prompt(\\n            system_message=_system_message,  # type: ignore\\n            extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)],\\n        )\\n        agent = OpenAIFunctionsAgent(\\n            llm=llm,\\n            tools=tools,\\n            prompt=prompt,  # type: ignore\\n        )\\n        return AgentExecutor(\\n            agent=agent,\\n            tools=tools,  # type: ignore\\n            memory=memory,\\n            verbose=True,\\n            return_intermediate_steps=True,\\n            handle_parsing_errors=True,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_token_limit\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":2000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_token_limit\",\"display_name\":\"Max Token Limit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-4-turbo-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-4-turbo-preview\",\"gpt-4-0125-preview\",\"gpt-4-1106-preview\",\"gpt-4-vision-preview\",\"gpt-3.5-turbo-0125\",\"gpt-3.5-turbo-1106\"],\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.2,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":0.0,\"max\":2.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Conversational Agent that can use OpenAI's function calling API\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\"],\"display_name\":\"OpenAI Conversational Agent\",\"documentation\":\"\",\"custom_fields\":{\"model_name\":null,\"openai_api_key\":null,\"tools\":null,\"openai_api_base\":null,\"memory\":null,\"system_message\":null,\"max_token_limit\":null,\"temperature\":null},\"output_types\":[\"AgentExecutor\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AgentInitializer\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"Language Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory\":{\"type\":\"BaseChatMemory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tools\":{\"type\":\"Tool\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"agent\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"zero-shot-react-description\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"zero-shot-react-description\",\"react-docstore\",\"self-ask-with-search\",\"conversational-react-description\",\"chat-zero-shot-react-description\",\"chat-conversational-react-description\",\"structured-chat-zero-shot-react-description\",\"openai-functions\",\"openai-multi-functions\",\"JsonAgent\",\"CSVAgent\",\"VectorStoreAgent\",\"VectorStoreRouterAgent\",\"SQLAgent\"],\"name\":\"agent\",\"display_name\":\"Agent Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, List, Optional, Union\\n\\nfrom langchain.agents import AgentExecutor, AgentType, initialize_agent, types\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseChatMemory, BaseLanguageModel, Tool\\n\\n\\nclass AgentInitializerComponent(CustomComponent):\\n    display_name: str = \\\"Agent Initializer\\\"\\n    description: str = \\\"Initialize a Langchain Agent.\\\"\\n    documentation: str = \\\"https://python.langchain.com/docs/modules/agents/agent_types/\\\"\\n\\n    def build_config(self):\\n        agents = list(types.AGENT_TO_CLASS.keys())\\n        # field_type and required are optional\\n        return {\\n            \\\"agent\\\": {\\\"options\\\": agents, \\\"value\\\": agents[0], \\\"display_name\\\": \\\"Agent Type\\\"},\\n            \\\"max_iterations\\\": {\\\"display_name\\\": \\\"Max Iterations\\\", \\\"value\\\": 10},\\n            \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n            \\\"tools\\\": {\\\"display_name\\\": \\\"Tools\\\"},\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"Language Model\\\"},\\n            \\\"code\\\": {\\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        agent: str,\\n        llm: BaseLanguageModel,\\n        tools: List[Tool],\\n        max_iterations: int,\\n        memory: Optional[BaseChatMemory] = None,\\n    ) -> Union[AgentExecutor, Callable]:\\n        agent = AgentType(agent)\\n        if memory:\\n            return initialize_agent(\\n                tools=tools,\\n                llm=llm,\\n                agent=agent,\\n                memory=memory,\\n                return_intermediate_steps=True,\\n                handle_parsing_errors=True,\\n                max_iterations=max_iterations,\\n            )\\n        return initialize_agent(\\n            tools=tools,\\n            llm=llm,\\n            agent=agent,\\n            return_intermediate_steps=True,\\n            handle_parsing_errors=True,\\n            max_iterations=max_iterations,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_iterations\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_iterations\",\"display_name\":\"Max Iterations\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Initialize a Langchain Agent.\",\"base_classes\":[\"Runnable\",\"Chain\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"AgentExecutor\",\"object\",\"Callable\"],\"display_name\":\"Agent Initializer\",\"documentation\":\"https://python.langchain.com/docs/modules/agents/agent_types/\",\"custom_fields\":{\"agent\":null,\"llm\":null,\"tools\":null,\"max_iterations\":null,\"memory\":null},\"output_types\":[\"AgentExecutor\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"memories\":{\"ConversationBufferMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationBufferMemory\"},\"description\":\"Buffer for storing conversation memory.\",\"base_classes\":[\"BaseMemory\",\"BaseChatMemory\",\"ConversationBufferMemory\",\"Serializable\"],\"display_name\":\"ConversationBufferMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/buffer\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"ConversationBufferWindowMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"memory_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"fileTypes\":[],\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationBufferWindowMemory\"},\"description\":\"Buffer for storing conversation memory inside a limited size window.\",\"base_classes\":[\"ConversationBufferWindowMemory\",\"BaseMemory\",\"BaseChatMemory\",\"Serializable\"],\"display_name\":\"ConversationBufferWindowMemory\",\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/buffer_window\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":true,\"beta\":false},\"ConversationEntityMemory\":{\"template\":{\"chat_memory\":{\"type\":\"BaseChatMessageHistory\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"entity_extraction_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_extraction_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"entity_store\":{\"type\":\"BaseEntityStore\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_store\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"entity_summarization_prompt\":{\"type\":\"BasePromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_summarization_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"ai_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chat_history_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"history\",\"fileTypes\":[],\"password\":false,\"name\":\"chat_history_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"entity_cache\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"entity_cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"human_prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"Human\",\"fileTypes\":[],\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"input_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"output_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"return_messages\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"ConversationEntityMemory\"},\"description\":\"Entity extractor & summarizer 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ed\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"requests_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"RequestsPutTool\"},\"description\":\"\",\"base_classes\":[\"RequestsPutTool\",\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"BaseRequestsTool\"],\"display_name\":\"RequestsPutTool\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"WikipediaQueryRun\":{\"template\":{\"api_wrapper\":{\"type\":\"WikipediaAPIWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"WikipediaQueryRun\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"WikipediaQueryRun\",\"Serializable\",\"object\"],\"display_name\":\"WikipediaQueryRun\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"WolframAlphaQueryRun\":{\"template\":{\"api_wrapper\":{\"type\":\"WolframAlphaAPIWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"args_schema\":{\"type\":\"Type[pydantic.v1.main.BaseModel]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"args_schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"callbacks\":{\"type\":\"langchain_core.callbacks.base.BaseCallbackHandler\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_tool_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_tool_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"handle_validation_error\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"handle_validation_error\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"WolframAlphaQueryRun\"},\"description\":\"\",\"base_classes\":[\"Runnable\",\"WolframAlphaQueryRun\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\"],\"display_name\":\"WolframAlphaQueryRun\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false}},\"toolkits\":{\"JsonToolkit\":{\"template\":{\"spec\":{\"type\":\"JsonSpec\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"spec\",\"display_name\":\"Spec\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain_community.tools.json.tool import JsonSpec\\nfrom langchain_community.agent_toolkits.json.toolkit import JsonToolkit\\n\\n\\nclass JsonToolkitComponent(CustomComponent):\\n    display_name = \\\"JsonToolkit\\\"\\n    description = \\\"Toolkit for interacting with a JSON spec.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"spec\\\": {\\\"display_name\\\": \\\"Spec\\\", \\\"type\\\": JsonSpec},\\n        }\\n\\n    def build(self, spec: JsonSpec) -> JsonToolkit:\\n        return JsonToolkit(spec=spec)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Toolkit for interacting with a JSON spec.\",\"base_classes\":[\"BaseToolkit\",\"JsonToolkit\"],\"display_name\":\"JsonToolkit\",\"documentation\":\"\",\"custom_fields\":{\"spec\":null},\"output_types\":[\"JsonToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OpenAPIToolkit\":{\"template\":{\"json_agent\":{\"type\":\"AgentExecutor\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"json_agent\",\"display_name\":\"JSON Agent\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"requests_wrapper\":{\"type\":\"TextRequestsWrapper\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"requests_wrapper\",\"display_name\":\"Text Requests Wrapper\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_community.agent_toolkits.openapi.toolkit import BaseToolkit, OpenAPIToolkit\\nfrom langchain_community.utilities.requests import TextRequestsWrapper\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import AgentExecutor\\n\\n\\nclass OpenAPIToolkitComponent(CustomComponent):\\n    display_name = \\\"OpenAPIToolkit\\\"\\n    description = \\\"Toolkit for interacting with an OpenAPI API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"json_agent\\\": {\\\"display_name\\\": \\\"JSON Agent\\\"},\\n            \\\"requests_wrapper\\\": {\\\"display_name\\\": \\\"Text Requests Wrapper\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        json_agent: AgentExecutor,\\n        requests_wrapper: TextRequestsWrapper,\\n    ) -> BaseToolkit:\\n        return OpenAPIToolkit(json_agent=json_agent, requests_wrapper=requests_wrapper)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Toolkit for interacting with an OpenAPI API.\",\"base_classes\":[\"BaseToolkit\"],\"display_name\":\"OpenAPIToolkit\",\"documentation\":\"\",\"custom_fields\":{\"json_agent\":null,\"requests_wrapper\":null},\"output_types\":[\"BaseToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreInfo\":{\"template\":{\"vectorstore\":{\"type\":\"VectorStore\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstore\",\"display_name\":\"VectorStore\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo\\nfrom langchain_community.vectorstores import VectorStore\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass VectorStoreInfoComponent(CustomComponent):\\n    display_name = \\\"VectorStoreInfo\\\"\\n    description = \\\"Information about a VectorStore\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"vectorstore\\\": {\\\"display_name\\\": \\\"VectorStore\\\"},\\n            \\\"description\\\": {\\\"display_name\\\": \\\"Description\\\", \\\"multiline\\\": True},\\n            \\\"name\\\": {\\\"display_name\\\": \\\"Name\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        vectorstore: VectorStore,\\n        description: str,\\n        name: str,\\n    ) -> Union[VectorStoreInfo, Callable]:\\n        return VectorStoreInfo(vectorstore=vectorstore, description=description, name=name)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"description\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"description\",\"display_name\":\"Description\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"name\",\"display_name\":\"Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Information about a VectorStore\",\"base_classes\":[\"Callable\",\"VectorStoreInfo\"],\"display_name\":\"VectorStoreInfo\",\"documentation\":\"\",\"custom_fields\":{\"vectorstore\":null,\"description\":null,\"name\":null},\"output_types\":[\"VectorStoreInfo\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreRouterToolkit\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vectorstores\":{\"type\":\"VectorStoreInfo\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstores\",\"display_name\":\"Vector Stores\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import List, Union\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreRouterToolkit\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo\\nfrom langflow.field_typing import BaseLanguageModel, Tool\\n\\n\\nclass VectorStoreRouterToolkitComponent(CustomComponent):\\n    display_name = \\\"VectorStoreRouterToolkit\\\"\\n    description = \\\"Toolkit for routing between Vector Stores.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"vectorstores\\\": {\\\"display_name\\\": \\\"Vector Stores\\\"},\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n        }\\n\\n    def build(\\n        self, vectorstores: List[VectorStoreInfo], llm: BaseLanguageModel\\n    ) -> Union[Tool, VectorStoreRouterToolkit]:\\n        print(\\\"vectorstores\\\", vectorstores)\\n        print(\\\"llm\\\", llm)\\n        return VectorStoreRouterToolkit(vectorstores=vectorstores, llm=llm)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Toolkit for routing between Vector Stores.\",\"base_classes\":[\"Runnable\",\"BaseToolkit\",\"Generic\",\"VectorStoreRouterToolkit\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\"],\"display_name\":\"VectorStoreRouterToolkit\",\"documentation\":\"\",\"custom_fields\":{\"vectorstores\":null,\"llm\":null},\"output_types\":[\"Tool\",\"VectorStoreRouterToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectorStoreToolkit\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vectorstore_info\":{\"type\":\"VectorStoreInfo\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstore_info\",\"display_name\":\"Vector Store Info\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreToolkit\\nfrom langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo\\nfrom langflow.field_typing import (\\n    BaseLanguageModel,\\n)\\nfrom langflow.field_typing import (\\n    Tool,\\n)\\nfrom typing import Union\\n\\n\\nclass VectorStoreToolkitComponent(CustomComponent):\\n    display_name = \\\"VectorStoreToolkit\\\"\\n    description = \\\"Toolkit for interacting with a Vector Store.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"vectorstore_info\\\": {\\\"display_name\\\": \\\"Vector Store Info\\\"},\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        vectorstore_info: VectorStoreInfo,\\n        llm: BaseLanguageModel,\\n    ) -> Union[Tool, VectorStoreToolkit]:\\n        return VectorStoreToolkit(vectorstore_info=vectorstore_info, llm=llm)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Toolkit for interacting with a Vector Store.\",\"base_classes\":[\"Runnable\",\"BaseToolkit\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\",\"VectorStoreToolkit\"],\"display_name\":\"VectorStoreToolkit\",\"documentation\":\"\",\"custom_fields\":{\"vectorstore_info\":null,\"llm\":null},\"output_types\":[\"Tool\",\"VectorStoreToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Metaphor\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Union\\n\\nfrom langchain.agents import tool\\nfrom langchain.agents.agent_toolkits.base import BaseToolkit\\nfrom langchain.tools import Tool\\nfrom metaphor_python import Metaphor  # type: ignore\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass MetaphorToolkit(CustomComponent):\\n    display_name: str = \\\"Metaphor\\\"\\n    description: str = \\\"Metaphor Toolkit\\\"\\n    documentation = \\\"https://python.langchain.com/docs/integrations/tools/metaphor_search\\\"\\n    beta: bool = True\\n    # api key should be password = True\\n    field_config = {\\n        \\\"metaphor_api_key\\\": {\\\"display_name\\\": \\\"Metaphor API Key\\\", \\\"password\\\": True},\\n        \\\"code\\\": {\\\"advanced\\\": True},\\n    }\\n\\n    def build(\\n        self,\\n        metaphor_api_key: str,\\n        use_autoprompt: bool = True,\\n        search_num_results: int = 5,\\n        similar_num_results: int = 5,\\n    ) -> Union[Tool, BaseToolkit]:\\n        # If documents, then we need to create a Vectara instance using .from_documents\\n        client = Metaphor(api_key=metaphor_api_key)\\n\\n        @tool\\n        def search(query: str):\\n            \\\"\\\"\\\"Call search engine with a query.\\\"\\\"\\\"\\n            return client.search(query, use_autoprompt=use_autoprompt, num_results=search_num_results)\\n\\n        @tool\\n        def get_contents(ids: List[str]):\\n            \\\"\\\"\\\"Get contents of a webpage.\\n\\n            The ids passed in should be a list of ids as fetched from `search`.\\n            \\\"\\\"\\\"\\n            return client.get_contents(ids)\\n\\n        @tool\\n        def find_similar(url: str):\\n            \\\"\\\"\\\"Get search results similar to a given URL.\\n\\n            The url passed in should be a URL returned from `search`\\n            \\\"\\\"\\\"\\n            return client.find_similar(url, num_results=similar_num_results)\\n\\n        return [search, get_contents, find_similar]  # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"metaphor_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"metaphor_api_key\",\"display_name\":\"Metaphor API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_num_results\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_num_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"similar_num_results\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"similar_num_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"use_autoprompt\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_autoprompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Metaphor Toolkit\",\"base_classes\":[\"Runnable\",\"BaseToolkit\",\"Generic\",\"BaseTool\",\"RunnableSerializable\",\"Tool\",\"Serializable\",\"object\"],\"display_name\":\"Metaphor\",\"documentation\":\"https://python.langchain.com/docs/integrations/tools/metaphor_search\",\"custom_fields\":{\"metaphor_api_key\":null,\"use_autoprompt\":null,\"search_num_results\":null,\"similar_num_results\":null},\"output_types\":[\"Tool\",\"BaseToolkit\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"wrappers\":{\"TextRequestsWrapper\":{\"template\":{\"aiosession\":{\"type\":\"ClientSession\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"aiosession\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"auth\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"auth\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"{\\\"Authorization\\\": \\\"Bearer <token>\\\"}\",\"fileTypes\":[],\"password\":false,\"name\":\"headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"response_content_type\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":false,\"multiline\":false,\"value\":\"text\",\"fileTypes\":[],\"password\":false,\"name\":\"response_content_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"TextRequestsWrapper\"},\"description\":\"Lightweight wrapper around requests library, with async support.\",\"base_classes\":[\"TextRequestsWrapper\",\"GenericRequestsWrapper\"],\"display_name\":\"TextRequestsWrapper\",\"documentation\":\"\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false}},\"embeddings\":{\"OpenAIEmbeddings\":{\"template\":{\"allowed_special\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"allowed_special\",\"display_name\":\"Allowed Special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chunk_size\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"client\",\"display_name\":\"Client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Callable, Dict, List, Optional, Union\\n\\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import NestedDict\\nfrom pydantic.v1.types import SecretStr\\n\\n\\nclass OpenAIEmbeddingsComponent(CustomComponent):\\n    display_name = \\\"OpenAIEmbeddings\\\"\\n    description = \\\"OpenAI embedding models\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"allowed_special\\\": {\\n                \\\"display_name\\\": \\\"Allowed Special\\\",\\n                \\\"advanced\\\": True,\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"is_list\\\": True,\\n            },\\n            \\\"default_headers\\\": {\\n                \\\"display_name\\\": \\\"Default Headers\\\",\\n                \\\"advanced\\\": True,\\n                \\\"field_type\\\": \\\"dict\\\",\\n            },\\n            \\\"default_query\\\": {\\n                \\\"display_name\\\": \\\"Default Query\\\",\\n                \\\"advanced\\\": True,\\n                \\\"field_type\\\": \\\"NestedDict\\\",\\n            },\\n            \\\"disallowed_special\\\": {\\n                \\\"display_name\\\": \\\"Disallowed Special\\\",\\n                \\\"advanced\\\": True,\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"is_list\\\": True,\\n            },\\n            \\\"chunk_size\\\": {\\\"display_name\\\": \\\"Chunk Size\\\", \\\"advanced\\\": True},\\n            \\\"client\\\": {\\\"display_name\\\": \\\"Client\\\", \\\"advanced\\\": True},\\n            \\\"deployment\\\": {\\\"display_name\\\": \\\"Deployment\\\", \\\"advanced\\\": True},\\n            \\\"embedding_ctx_length\\\": {\\n                \\\"display_name\\\": \\\"Embedding Context Length\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"max_retries\\\": {\\\"display_name\\\": \\\"Max Retries\\\", \\\"advanced\\\": True},\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model\\\",\\n                \\\"advanced\\\": False,\\n                \\\"options\\\": [\\\"text-embedding-3-small\\\", \\\"text-embedding-3-large\\\", \\\"text-embedding-ada-002\\\"],\\n            },\\n            \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"advanced\\\": True},\\n            \\\"openai_api_base\\\": {\\\"display_name\\\": \\\"OpenAI API Base\\\", \\\"password\\\": True, \\\"advanced\\\": True},\\n            \\\"openai_api_key\\\": {\\\"display_name\\\": \\\"OpenAI API Key\\\", \\\"password\\\": True},\\n            \\\"openai_api_type\\\": {\\\"display_name\\\": \\\"OpenAI API Type\\\", \\\"advanced\\\": True, \\\"password\\\": True},\\n            \\\"openai_api_version\\\": {\\n                \\\"display_name\\\": \\\"OpenAI API Version\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"openai_organization\\\": {\\n                \\\"display_name\\\": \\\"OpenAI Organization\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"openai_proxy\\\": {\\\"display_name\\\": \\\"OpenAI Proxy\\\", \\\"advanced\\\": True},\\n            \\\"request_timeout\\\": {\\\"display_name\\\": \\\"Request Timeout\\\", \\\"advanced\\\": True},\\n            \\\"show_progress_bar\\\": {\\n                \\\"display_name\\\": \\\"Show Progress Bar\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"skip_empty\\\": {\\\"display_name\\\": \\\"Skip Empty\\\", \\\"advanced\\\": True},\\n            \\\"tiktoken_model_name\\\": {\\\"display_name\\\": \\\"TikToken Model Name\\\"},\\n            \\\"tikToken_enable\\\": {\\\"display_name\\\": \\\"TikToken Enable\\\", \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        default_headers: Optional[Dict[str, str]] = None,\\n        default_query: Optional[NestedDict] = {},\\n        allowed_special: List[str] = [],\\n        disallowed_special: List[str] = [\\\"all\\\"],\\n        chunk_size: int = 1000,\\n        client: Optional[Any] = None,\\n        deployment: str = \\\"text-embedding-3-small\\\",\\n        embedding_ctx_length: int = 8191,\\n        max_retries: int = 6,\\n        model: str = \\\"text-embedding-3-small\\\",\\n        model_kwargs: NestedDict = {},\\n        openai_api_base: Optional[str] = None,\\n        openai_api_key: Optional[str] = \\\"\\\",\\n        openai_api_type: Optional[str] = None,\\n        openai_api_version: Optional[str] = None,\\n        openai_organization: Optional[str] = None,\\n        openai_proxy: Optional[str] = None,\\n        request_timeout: Optional[float] = None,\\n        show_progress_bar: bool = False,\\n        skip_empty: bool = False,\\n        tiktoken_enable: bool = True,\\n        tiktoken_model_name: Optional[str] = None,\\n    ) -> Union[OpenAIEmbeddings, Callable]:\\n        # This is to avoid errors with Vector Stores (e.g Chroma)\\n        if disallowed_special == [\\\"all\\\"]:\\n            disallowed_special = \\\"all\\\"  # type: ignore\\n\\n        api_key = SecretStr(openai_api_key) if openai_api_key else None\\n\\n        return OpenAIEmbeddings(\\n            tiktoken_enabled=tiktoken_enable,\\n            default_headers=default_headers,\\n            default_query=default_query,\\n            allowed_special=set(allowed_special),\\n            disallowed_special=\\\"all\\\",\\n            chunk_size=chunk_size,\\n            client=client,\\n            deployment=deployment,\\n            embedding_ctx_length=embedding_ctx_length,\\n            max_retries=max_retries,\\n            model=model,\\n            model_kwargs=model_kwargs,\\n            base_url=openai_api_base,\\n            api_key=api_key,\\n            openai_api_type=openai_api_type,\\n            api_version=openai_api_version,\\n            organization=openai_organization,\\n            openai_proxy=openai_proxy,\\n            timeout=request_timeout,\\n            show_progress_bar=show_progress_bar,\\n            skip_empty=skip_empty,\\n            tiktoken_model_name=tiktoken_model_name,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"default_headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"default_headers\",\"display_name\":\"Default Headers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"default_query\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"default_query\",\"display_name\":\"Default Query\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"deployment\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"text-embedding-3-small\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"deployment\",\"display_name\":\"Deployment\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"disallowed_special\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[\"all\"],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"disallowed_special\",\"display_name\":\"Disallowed Special\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"embedding_ctx_length\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":8191,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding_ctx_length\",\"display_name\":\"Embedding Context Length\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_retries\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max Retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"text-embedding-3-small\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"text-embedding-3-small\",\"text-embedding-3-large\",\"text-embedding-ada-002\"],\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"NestedDict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"openai_api_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_type\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_type\",\"display_name\":\"OpenAI API Type\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_version\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_version\",\"display_name\":\"OpenAI API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_organization\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_proxy\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"request_timeout\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"request_timeout\",\"display_name\":\"Request Timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"show_progress_bar\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"show_progress_bar\",\"display_name\":\"Show Progress Bar\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"skip_empty\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"skip_empty\",\"display_name\":\"Skip Empty\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tiktoken_enable\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tiktoken_enable\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"tiktoken_model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tiktoken_model_name\",\"display_name\":\"TikToken Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"OpenAI embedding models\",\"base_classes\":[\"Embeddings\",\"OpenAIEmbeddings\",\"Callable\"],\"display_name\":\"OpenAIEmbeddings\",\"documentation\":\"\",\"custom_fields\":{\"default_headers\":null,\"default_query\":null,\"allowed_special\":null,\"disallowed_special\":null,\"chunk_size\":null,\"client\":null,\"deployment\":null,\"embedding_ctx_length\":null,\"max_retries\":null,\"model\":null,\"model_kwargs\":null,\"openai_api_base\":null,\"openai_api_key\":null,\"openai_api_type\":null,\"openai_api_version\":null,\"openai_organization\":null,\"openai_proxy\":null,\"request_timeout\":null,\"show_progress_bar\":null,\"skip_empty\":null,\"tiktoken_enable\":null,\"tiktoken_model_name\":null},\"output_types\":[\"OpenAIEmbeddings\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CohereEmbeddings\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.embeddings.cohere import CohereEmbeddings\\nfrom langflow import CustomComponent\\n\\n\\nclass CohereEmbeddingsComponent(CustomComponent):\\n    display_name = \\\"CohereEmbeddings\\\"\\n    description = \\\"Cohere embedding models.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"cohere_api_key\\\": {\\\"display_name\\\": \\\"Cohere API Key\\\", \\\"password\\\": True},\\n            \\\"model\\\": {\\\"display_name\\\": \\\"Model\\\", \\\"default\\\": \\\"embed-english-v2.0\\\", \\\"advanced\\\": True},\\n            \\\"truncate\\\": {\\\"display_name\\\": \\\"Truncate\\\", \\\"advanced\\\": True},\\n            \\\"max_retries\\\": {\\\"display_name\\\": \\\"Max Retries\\\", \\\"advanced\\\": True},\\n            \\\"user_agent\\\": {\\\"display_name\\\": \\\"User Agent\\\", \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        request_timeout: Optional[float] = None,\\n        cohere_api_key: str = \\\"\\\",\\n        max_retries: Optional[int] = None,\\n        model: str = \\\"embed-english-v2.0\\\",\\n        truncate: Optional[str] = None,\\n        user_agent: str = \\\"langchain\\\",\\n    ) -> CohereEmbeddings:\\n        return CohereEmbeddings(  # type: ignore\\n            max_retries=max_retries,\\n            user_agent=user_agent,\\n            request_timeout=request_timeout,\\n            cohere_api_key=cohere_api_key,\\n            model=model,\\n            truncate=truncate,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"cohere_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"cohere_api_key\",\"display_name\":\"Cohere API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_retries\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max Retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"embed-english-v2.0\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"request_timeout\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"truncate\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"truncate\",\"display_name\":\"Truncate\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"user_agent\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"langchain\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"user_agent\",\"display_name\":\"User Agent\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Cohere embedding models.\",\"base_classes\":[\"Embeddings\",\"CohereEmbeddings\"],\"display_name\":\"CohereEmbeddings\",\"documentation\":\"\",\"custom_fields\":{\"request_timeout\":null,\"cohere_api_key\":null,\"max_retries\":null,\"model\":null,\"truncate\":null,\"user_agent\":null},\"output_types\":[\"CohereEmbeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"HuggingFaceEmbeddings\":{\"template\":{\"cache_folder\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache_folder\",\"display_name\":\"Cache Folder\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Optional, Dict\\nfrom langchain_community.embeddings.huggingface import HuggingFaceEmbeddings\\n\\n\\nclass HuggingFaceEmbeddingsComponent(CustomComponent):\\n    display_name = \\\"HuggingFaceEmbeddings\\\"\\n    description = \\\"HuggingFace sentence_transformers embedding models.\\\"\\n    documentation = (\\n        \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers\\\"\\n    )\\n\\n    def build_config(self):\\n        return {\\n            \\\"cache_folder\\\": {\\\"display_name\\\": \\\"Cache Folder\\\", \\\"advanced\\\": True},\\n            \\\"encode_kwargs\\\": {\\\"display_name\\\": \\\"Encode Kwargs\\\", \\\"advanced\\\": True, \\\"field_type\\\": \\\"dict\\\"},\\n            \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"field_type\\\": \\\"dict\\\", \\\"advanced\\\": True},\\n            \\\"model_name\\\": {\\\"display_name\\\": \\\"Model Name\\\"},\\n            \\\"multi_process\\\": {\\\"display_name\\\": \\\"Multi Process\\\", \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        cache_folder: Optional[str] = None,\\n        encode_kwargs: Optional[Dict] = {},\\n        model_kwargs: Optional[Dict] = {},\\n        model_name: str = \\\"sentence-transformers/all-mpnet-base-v2\\\",\\n        multi_process: bool = False,\\n    ) -> HuggingFaceEmbeddings:\\n        return HuggingFaceEmbeddings(\\n            cache_folder=cache_folder,\\n            encode_kwargs=encode_kwargs,\\n            model_kwargs=model_kwargs,\\n            model_name=model_name,\\n            multi_process=multi_process,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"encode_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"encode_kwargs\",\"display_name\":\"Encode Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"sentence-transformers/all-mpnet-base-v2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"multi_process\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"multi_process\",\"display_name\":\"Multi Process\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"HuggingFace sentence_transformers embedding models.\",\"base_classes\":[\"Embeddings\",\"HuggingFaceEmbeddings\"],\"display_name\":\"HuggingFaceEmbeddings\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers\",\"custom_fields\":{\"cache_folder\":null,\"encode_kwargs\":null,\"model_kwargs\":null,\"model_name\":null,\"multi_process\":null},\"output_types\":[\"HuggingFaceEmbeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VertexAIEmbeddings\":{\"template\":{\"credentials\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".json\"],\"file_path\":\"\",\"password\":false,\"name\":\"credentials\",\"display_name\":\"Credentials\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain_community.embeddings import VertexAIEmbeddings\\nfrom typing import Optional, List\\n\\n\\nclass VertexAIEmbeddingsComponent(CustomComponent):\\n    display_name = \\\"VertexAIEmbeddings\\\"\\n    description = \\\"Google Cloud VertexAI embedding models.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"credentials\\\": {\\n                \\\"display_name\\\": \\\"Credentials\\\",\\n                \\\"value\\\": \\\"\\\",\\n                \\\"file_types\\\": [\\\".json\\\"],\\n                \\\"field_type\\\": \\\"file\\\",\\n            },\\n            \\\"instance\\\": {\\n                \\\"display_name\\\": \\\"instance\\\",\\n                \\\"advanced\\\": True,\\n                \\\"field_type\\\": \\\"dict\\\",\\n            },\\n            \\\"location\\\": {\\n                \\\"display_name\\\": \\\"Location\\\",\\n                \\\"value\\\": \\\"us-central1\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"max_output_tokens\\\": {\\\"display_name\\\": \\\"Max Output Tokens\\\", \\\"value\\\": 128},\\n            \\\"max_retries\\\": {\\n                \\\"display_name\\\": \\\"Max Retries\\\",\\n                \\\"value\\\": 6,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"model_name\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"value\\\": \\\"textembedding-gecko\\\",\\n            },\\n            \\\"n\\\": {\\\"display_name\\\": \\\"N\\\", \\\"value\\\": 1, \\\"advanced\\\": True},\\n            \\\"project\\\": {\\\"display_name\\\": \\\"Project\\\", \\\"advanced\\\": True},\\n            \\\"request_parallelism\\\": {\\n                \\\"display_name\\\": \\\"Request Parallelism\\\",\\n                \\\"value\\\": 5,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"stop\\\": {\\\"display_name\\\": \\\"Stop\\\", \\\"advanced\\\": True},\\n            \\\"streaming\\\": {\\n                \\\"display_name\\\": \\\"Streaming\\\",\\n                \\\"value\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"temperature\\\": {\\\"display_name\\\": \\\"Temperature\\\", \\\"value\\\": 0.0},\\n            \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"value\\\": 40, \\\"advanced\\\": True},\\n            \\\"top_p\\\": {\\\"display_name\\\": \\\"Top P\\\", \\\"value\\\": 0.95, \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        instance: Optional[str] = None,\\n        credentials: Optional[str] = None,\\n        location: str = \\\"us-central1\\\",\\n        max_output_tokens: int = 128,\\n        max_retries: int = 6,\\n        model_name: str = \\\"textembedding-gecko\\\",\\n        n: int = 1,\\n        project: Optional[str] = None,\\n        request_parallelism: int = 5,\\n        stop: Optional[List[str]] = None,\\n        streaming: bool = False,\\n        temperature: float = 0.0,\\n        top_k: int = 40,\\n        top_p: float = 0.95,\\n    ) -> VertexAIEmbeddings:\\n        return VertexAIEmbeddings(\\n            instance=instance,\\n            credentials=credentials,\\n            location=location,\\n            max_output_tokens=max_output_tokens,\\n            max_retries=max_retries,\\n            model_name=model_name,\\n            n=n,\\n            project=project,\\n            request_parallelism=request_parallelism,\\n            stop=stop,\\n            streaming=streaming,\\n            temperature=temperature,\\n            top_k=top_k,\\n            top_p=top_p,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"instance\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"instance\",\"display_name\":\"instance\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"location\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"us-central1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":128,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_retries\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max Retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"textembedding-gecko\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"n\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"project\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"project\",\"display_name\":\"Project\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"request_parallelism\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"request_parallelism\",\"display_name\":\"Request Parallelism\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"stop\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Google Cloud VertexAI embedding models.\",\"base_classes\":[\"_VertexAICommon\",\"Embeddings\",\"_VertexAIBase\",\"VertexAIEmbeddings\"],\"display_name\":\"VertexAIEmbeddings\",\"documentation\":\"\",\"custom_fields\":{\"instance\":null,\"credentials\":null,\"location\":null,\"max_output_tokens\":null,\"max_retries\":null,\"model_name\":null,\"n\":null,\"project\":null,\"request_parallelism\":null,\"stop\":null,\"streaming\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null},\"output_types\":[\"VertexAIEmbeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OllamaEmbeddings\":{\"template\":{\"base_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"http://localhost:11434\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"base_url\",\"display_name\":\"Ollama Base URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langflow import CustomComponent\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain_community.embeddings import OllamaEmbeddings\\n\\n\\nclass OllamaEmbeddingsComponent(CustomComponent):\\n    \\\"\\\"\\\"\\n    A custom component for implementing an Embeddings Model using Ollama.\\n    \\\"\\\"\\\"\\n\\n    display_name: str = \\\"Ollama Embeddings\\\"\\n    description: str = \\\"Embeddings model from Ollama.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/integrations/text_embedding/ollama\\\"\\n    beta = True\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Ollama Model\\\",\\n            },\\n            \\\"base_url\\\": {\\\"display_name\\\": \\\"Ollama Base URL\\\"},\\n            \\\"temperature\\\": {\\\"display_name\\\": \\\"Model Temperature\\\"},\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        model: str = \\\"llama2\\\",\\n        base_url: str = \\\"http://localhost:11434\\\",\\n        temperature: Optional[float] = None,\\n    ) -> Embeddings:\\n        try:\\n            output = OllamaEmbeddings(model=model, base_url=base_url, temperature=temperature)  # type: ignore\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to Ollama API.\\\") from e\\n        return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"llama2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Ollama Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Model Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Embeddings model from Ollama.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Ollama Embeddings\",\"documentation\":\"https://python.langchain.com/docs/integrations/text_embedding/ollama\",\"custom_fields\":{\"model\":null,\"base_url\":null,\"temperature\":null},\"output_types\":[\"Embeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AmazonBedrockEmbeddings\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain_community.embeddings import BedrockEmbeddings\\n\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockEmeddingsComponent(CustomComponent):\\n    \\\"\\\"\\\"\\n    A custom component for implementing an Embeddings Model using Amazon Bedrock.\\n    \\\"\\\"\\\"\\n\\n    display_name: str = \\\"Amazon Bedrock Embeddings\\\"\\n    description: str = \\\"Embeddings model from Amazon Bedrock.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\\\"\\n    beta = True\\n\\n    def build_config(self):\\n        return {\\n            \\\"model_id\\\": {\\n                \\\"display_name\\\": \\\"Model Id\\\",\\n                \\\"options\\\": [\\\"amazon.titan-embed-text-v1\\\"],\\n            },\\n            \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n            \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Bedrock Endpoint URL\\\"},\\n            \\\"region_name\\\": {\\\"display_name\\\": \\\"AWS Region\\\"},\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        model_id: str = \\\"amazon.titan-embed-text-v1\\\",\\n        credentials_profile_name: Optional[str] = None,\\n        endpoint_url: Optional[str] = None,\\n        region_name: Optional[str] = None,\\n    ) -> Embeddings:\\n        try:\\n            output = BedrockEmbeddings(\\n                credentials_profile_name=credentials_profile_name,\\n                model_id=model_id,\\n                endpoint_url=endpoint_url,\\n                region_name=region_name,\\n            )  # type: ignore\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n        return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"credentials_profile_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"endpoint_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Bedrock Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"amazon.titan-embed-text-v1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"amazon.titan-embed-text-v1\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"region_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"region_name\",\"display_name\":\"AWS Region\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Embeddings model from Amazon Bedrock.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"Amazon Bedrock Embeddings\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/bedrock\",\"custom_fields\":{\"model_id\":null,\"credentials_profile_name\":null,\"endpoint_url\":null,\"region_name\":null},\"output_types\":[\"Embeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AzureOpenAIEmbeddings\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_version\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"2023-08-01-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"2022-12-01\",\"2023-03-15-preview\",\"2023-05-15\",\"2023-06-01-preview\",\"2023-07-01-preview\",\"2023-08-01-preview\"],\"name\":\"api_version\",\"display_name\":\"API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_deployment\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_deployment\",\"display_name\":\"Deployment Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_endpoint\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_endpoint\",\"display_name\":\"Azure Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain.embeddings.base import Embeddings\\nfrom langchain_community.embeddings import AzureOpenAIEmbeddings\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AzureOpenAIEmbeddingsComponent(CustomComponent):\\n    display_name: str = \\\"AzureOpenAIEmbeddings\\\"\\n    description: str = \\\"Embeddings model from Azure OpenAI.\\\"\\n    documentation: str = \\\"https://python.langchain.com/docs/integrations/text_embedding/azureopenai\\\"\\n    beta = False\\n\\n    API_VERSION_OPTIONS = [\\n        \\\"2022-12-01\\\",\\n        \\\"2023-03-15-preview\\\",\\n        \\\"2023-05-15\\\",\\n        \\\"2023-06-01-preview\\\",\\n        \\\"2023-07-01-preview\\\",\\n        \\\"2023-08-01-preview\\\",\\n    ]\\n\\n    def build_config(self):\\n        return {\\n            \\\"azure_endpoint\\\": {\\n                \\\"display_name\\\": \\\"Azure Endpoint\\\",\\n                \\\"required\\\": True,\\n                \\\"info\\\": \\\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\\\",\\n            },\\n            \\\"azure_deployment\\\": {\\n                \\\"display_name\\\": \\\"Deployment Name\\\",\\n                \\\"required\\\": True,\\n            },\\n            \\\"api_version\\\": {\\n                \\\"display_name\\\": \\\"API Version\\\",\\n                \\\"options\\\": self.API_VERSION_OPTIONS,\\n                \\\"value\\\": self.API_VERSION_OPTIONS[-1],\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"api_key\\\": {\\n                \\\"display_name\\\": \\\"API Key\\\",\\n                \\\"required\\\": True,\\n                \\\"password\\\": True,\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        azure_endpoint: str,\\n        azure_deployment: str,\\n        api_version: str,\\n        api_key: str,\\n    ) -> Embeddings:\\n        try:\\n            embeddings = AzureOpenAIEmbeddings(\\n                azure_endpoint=azure_endpoint,\\n                azure_deployment=azure_deployment,\\n                api_version=api_version,\\n                api_key=api_key,\\n            )\\n\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to AzureOpenAIEmbeddings API.\\\") from e\\n\\n        return embeddings\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Embeddings model from Azure OpenAI.\",\"base_classes\":[\"Embeddings\"],\"display_name\":\"AzureOpenAIEmbeddings\",\"documentation\":\"https://python.langchain.com/docs/integrations/text_embedding/azureopenai\",\"custom_fields\":{\"azure_endpoint\":null,\"azure_deployment\":null,\"api_version\":null,\"api_key\":null},\"output_types\":[\"Embeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"HuggingFaceInferenceAPIEmbeddings\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"http://localhost:8080\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"api_url\",\"display_name\":\"API URL\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"cache_folder\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache_folder\",\"display_name\":\"Cache Folder\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Dict, Optional\\n\\nfrom langchain_community.embeddings.huggingface import HuggingFaceInferenceAPIEmbeddings\\nfrom langflow import CustomComponent\\nfrom pydantic.v1.types import SecretStr\\n\\n\\nclass HuggingFaceInferenceAPIEmbeddingsComponent(CustomComponent):\\n    display_name = \\\"HuggingFaceInferenceAPIEmbeddings\\\"\\n    description = \\\"HuggingFace sentence_transformers embedding models, API version.\\\"\\n    documentation = \\\"https://github.com/huggingface/text-embeddings-inference\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"password\\\": True, \\\"advanced\\\": True},\\n            \\\"api_url\\\": {\\\"display_name\\\": \\\"API URL\\\", \\\"advanced\\\": True},\\n            \\\"model_name\\\": {\\\"display_name\\\": \\\"Model Name\\\"},\\n            \\\"cache_folder\\\": {\\\"display_name\\\": \\\"Cache Folder\\\", \\\"advanced\\\": True},\\n            \\\"encode_kwargs\\\": {\\\"display_name\\\": \\\"Encode Kwargs\\\", \\\"advanced\\\": True, \\\"field_type\\\": \\\"dict\\\"},\\n            \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"field_type\\\": \\\"dict\\\", \\\"advanced\\\": True},\\n            \\\"multi_process\\\": {\\\"display_name\\\": \\\"Multi Process\\\", \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        api_key: Optional[str] = \\\"\\\",\\n        api_url: str = \\\"http://localhost:8080\\\",\\n        model_name: str = \\\"BAAI/bge-large-en-v1.5\\\",\\n        cache_folder: Optional[str] = None,\\n        encode_kwargs: Optional[Dict] = {},\\n        model_kwargs: Optional[Dict] = {},\\n        multi_process: bool = False,\\n    ) -> HuggingFaceInferenceAPIEmbeddings:\\n        if api_key:\\n            secret_api_key = SecretStr(api_key)\\n        else:\\n            raise ValueError(\\\"API Key is required\\\")\\n        return HuggingFaceInferenceAPIEmbeddings(\\n            api_key=secret_api_key,\\n            api_url=api_url,\\n            model_name=model_name,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"encode_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"encode_kwargs\",\"display_name\":\"Encode Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"BAAI/bge-large-en-v1.5\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"multi_process\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"multi_process\",\"display_name\":\"Multi Process\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"HuggingFace sentence_transformers embedding models, API version.\",\"base_classes\":[\"Embeddings\",\"HuggingFaceInferenceAPIEmbeddings\"],\"display_name\":\"HuggingFaceInferenceAPIEmbeddings\",\"documentation\":\"https://github.com/huggingface/text-embeddings-inference\",\"custom_fields\":{\"api_key\":null,\"api_url\":null,\"model_name\":null,\"cache_folder\":null,\"encode_kwargs\":null,\"model_kwargs\":null,\"multi_process\":null},\"output_types\":[\"HuggingFaceInferenceAPIEmbeddings\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"documentloaders\":{\"AZLyricsLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"AZLyricsLoader\"},\"description\":\"Load `AZLyrics` webpages.\",\"base_classes\":[\"Document\"],\"display_name\":\"AZLyricsLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/azlyrics\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"AirbyteJSONLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".json\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"BSHTMLLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".html\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"BSHTMLLoader\"},\"description\":\"Load `HTML` files and parse them with `beautiful soup`.\",\"base_classes\":[\"Document\"],\"display_name\":\"BSHTMLLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/html\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"CSVLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".csv\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"CoNLLULoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".csv\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CoNLLULoader\"},\"description\":\"Load `CoNLL-U` files.\",\"base_classes\":[\"Document\"],\"display_name\":\"CoNLLULoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/conll-u\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"CollegeConfidentialLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CollegeConfidentialLoader\"},\"description\":\"Load `College Confidential` webpages.\",\"base_classes\":[\"Document\"],\"display_name\":\"CollegeConfidentialLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/college_confidential\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"DirectoryLoader\":{\"template\":{\"glob\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"**/*.txt\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"glob\",\"display_name\":\"glob\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"load_hidden\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"False\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"load_hidden\",\"display_name\":\"Load hidden files\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_concurrency\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_concurrency\",\"display_name\":\"Max concurrency\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"recursive\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"True\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"recursive\",\"display_name\":\"Recursive\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"silent_errors\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"False\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"silent_errors\",\"display_name\":\"Silent errors\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"use_multithreading\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"True\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_multithreading\",\"display_name\":\"Use multithreading\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"DirectoryLoader\"},\"description\":\"Load from a directory.\",\"base_classes\":[\"Document\"],\"display_name\":\"DirectoryLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/file_directory\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"EverNoteLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".xml\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"EverNoteLoader\"},\"description\":\"Load from `EverNote`.\",\"base_classes\":[\"Document\"],\"display_name\":\"EverNoteLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/evernote\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"FacebookChatLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".json\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"FacebookChatLoader\"},\"description\":\"Load `Facebook Chat` messages directory dump.\",\"base_classes\":[\"Document\"],\"display_name\":\"FacebookChatLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/facebook_chat\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"GitLoader\":{\"template\":{\"branch\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"branch\",\"display_name\":\"Branch\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"clone_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"clone_url\",\"display_name\":\"Clone URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"file_filter\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"file_filter\",\"display_name\":\"File extensions (comma-separated)\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"repo_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repo_path\",\"display_name\":\"Path to repository\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"GitLoader\"},\"description\":\"Load `Git` repository files.\",\"base_classes\":[\"Document\"],\"display_name\":\"GitLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/git\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"GitbookLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_page\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_page\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"GitbookLoader\"},\"description\":\"Load `GitBook` data.\",\"base_classes\":[\"Document\"],\"display_name\":\"GitbookLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/gitbook\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"GutenbergLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"GutenbergLoader\"},\"description\":\"Load from `Gutenberg.org`.\",\"base_classes\":[\"Document\"],\"display_name\":\"GutenbergLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/gutenberg\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"HNLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"HNLoader\"},\"description\":\"Load `Hacker News` data.\",\"base_classes\":[\"Document\"],\"display_name\":\"HNLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/hacker_news\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"IFixitLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"IFixitLoader\"},\"description\":\"Load `iFixit` repair guides, device wikis and answers.\",\"base_classes\":[\"Document\"],\"display_name\":\"IFixitLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/ifixit\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"IMSDbLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"IMSDbLoader\"},\"description\":\"Load `IMSDb` webpages.\",\"base_classes\":[\"Document\"],\"display_name\":\"IMSDbLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/imsdb\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"NotionDirectoryLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"NotionDirectoryLoader\"},\"description\":\"Load `Notion directory` dump.\",\"base_classes\":[\"Document\"],\"display_name\":\"NotionDirectoryLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/notion\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"PyPDFLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".pdf\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"PyPDFLoader\"},\"description\":\"Load PDF using pypdf into list of documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"PyPDFLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/pdf\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"PyPDFDirectoryLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"PyPDFDirectoryLoader\"},\"description\":\"Load a directory with `PDF` files using `pypdf` and chunks at character level.\",\"base_classes\":[\"Document\"],\"display_name\":\"PyPDFDirectoryLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/pdf\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"ReadTheDocsLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"ReadTheDocsLoader\"},\"description\":\"Load `ReadTheDocs` documentation directory.\",\"base_classes\":[\"Document\"],\"display_name\":\"ReadTheDocsLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/readthedocs_documentation\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"SRTLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".srt\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"SRTLoader\"},\"description\":\"Load `.srt` (subtitle) files.\",\"base_classes\":[\"Document\"],\"display_name\":\"SRTLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/subtitle\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"SlackDirectoryLoader\":{\"template\":{\"zip_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".zip\"],\"file_path\":\"\",\"password\":false,\"name\":\"zip_path\",\"display_name\":\"Path to zip file\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"workspace_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"workspace_url\",\"display_name\":\"Workspace URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"SlackDirectoryLoader\"},\"description\":\"Load from a `Slack` directory dump.\",\"base_classes\":[\"Document\"],\"display_name\":\"SlackDirectoryLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/slack\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"TextLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".txt\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"TextLoader\"},\"description\":\"Load text file.\",\"base_classes\":[\"Document\"],\"display_name\":\"TextLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredEmailLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".eml\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredEmailLoader\"},\"description\":\"Load email files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredEmailLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/email\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredHTMLLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".html\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredHTMLLoader\"},\"description\":\"Load `HTML` files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredHTMLLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/html\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredMarkdownLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".md\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredMarkdownLoader\"},\"description\":\"Load `Markdown` files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredMarkdownLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/markdown\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredPowerPointLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".pptx\",\".ppt\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredPowerPointLoader\"},\"description\":\"Load `Microsoft PowerPoint` files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredPowerPointLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/microsoft_powerpoint\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"UnstructuredWordDocumentLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[\".docx\",\".doc\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"UnstructuredWordDocumentLoader\"},\"description\":\"Load `Microsoft Word` file using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredWordDocumentLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/microsoft_word\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"WebBaseLoader\":{\"template\":{\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"WebBaseLoader\"},\"description\":\"Load HTML pages using `urllib` and parse them with `BeautifulSoup'.\",\"base_classes\":[\"Document\"],\"display_name\":\"WebBaseLoader\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/web_base\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"FileLoader\":{\"template\":{\"file_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".json\",\".txt\",\".csv\",\".jsonl\",\".html\",\".htm\",\".conllu\",\".enex\",\".msg\",\".pdf\",\".srt\",\".eml\",\".md\",\".mdx\",\".pptx\",\".docx\"],\"file_path\":\"\",\"password\":false,\"name\":\"file_path\",\"display_name\":\"File Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.utils.constants import LOADERS_INFO\\n\\n\\nclass FileLoaderComponent(CustomComponent):\\n    display_name: str = \\\"File Loader\\\"\\n    description: str = \\\"Generic File Loader\\\"\\n    beta = True\\n\\n    def build_config(self):\\n        loader_options = [\\\"Automatic\\\"] + [loader_info[\\\"name\\\"] for loader_info in LOADERS_INFO]\\n\\n        file_types = []\\n        suffixes = []\\n\\n        for loader_info in LOADERS_INFO:\\n            if \\\"allowedTypes\\\" in loader_info:\\n                file_types.extend(loader_info[\\\"allowedTypes\\\"])\\n                suffixes.extend([f\\\".{ext}\\\" for ext in loader_info[\\\"allowedTypes\\\"]])\\n\\n        return {\\n            \\\"file_path\\\": {\\n                \\\"display_name\\\": \\\"File Path\\\",\\n                \\\"required\\\": True,\\n                \\\"field_type\\\": \\\"file\\\",\\n                \\\"file_types\\\": [\\n                    \\\"json\\\",\\n                    \\\"txt\\\",\\n                    \\\"csv\\\",\\n                    \\\"jsonl\\\",\\n                    \\\"html\\\",\\n                    \\\"htm\\\",\\n                    \\\"conllu\\\",\\n                    \\\"enex\\\",\\n                    \\\"msg\\\",\\n                    \\\"pdf\\\",\\n                    \\\"srt\\\",\\n                    \\\"eml\\\",\\n                    \\\"md\\\",\\n                    \\\"mdx\\\",\\n                    \\\"pptx\\\",\\n                    \\\"docx\\\",\\n                ],\\n                \\\"suffixes\\\": [\\n                    \\\".json\\\",\\n                    \\\".txt\\\",\\n                    \\\".csv\\\",\\n                    \\\".jsonl\\\",\\n                    \\\".html\\\",\\n                    \\\".htm\\\",\\n                    \\\".conllu\\\",\\n                    \\\".enex\\\",\\n                    \\\".msg\\\",\\n                    \\\".pdf\\\",\\n                    \\\".srt\\\",\\n                    \\\".eml\\\",\\n                    \\\".md\\\",\\n                    \\\".mdx\\\",\\n                    \\\".pptx\\\",\\n                    \\\".docx\\\",\\n                ],\\n                # \\\"file_types\\\" : file_types,\\n                # \\\"suffixes\\\": suffixes,\\n            },\\n            \\\"loader\\\": {\\n                \\\"display_name\\\": \\\"Loader\\\",\\n                \\\"is_list\\\": True,\\n                \\\"required\\\": True,\\n                \\\"options\\\": loader_options,\\n                \\\"value\\\": \\\"Automatic\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(self, file_path: str, loader: str) -> Document:\\n        file_type = file_path.split(\\\".\\\")[-1]\\n\\n        # Map the loader to the correct loader class\\n        selected_loader_info = None\\n        for loader_info in LOADERS_INFO:\\n            if loader_info[\\\"name\\\"] == loader:\\n                selected_loader_info = loader_info\\n                break\\n\\n        if selected_loader_info is None and loader != \\\"Automatic\\\":\\n            raise ValueError(f\\\"Loader {loader} not found in the loader info list\\\")\\n\\n        if loader == \\\"Automatic\\\":\\n            # Determine the loader based on the file type\\n            default_loader_info = None\\n            for info in LOADERS_INFO:\\n                if \\\"defaultFor\\\" in info and file_type in info[\\\"defaultFor\\\"]:\\n                    default_loader_info = info\\n                    break\\n\\n            if default_loader_info is None:\\n                raise ValueError(f\\\"No default loader found for file type: {file_type}\\\")\\n\\n            selected_loader_info = default_loader_info\\n        if isinstance(selected_loader_info, dict):\\n            loader_import: str = selected_loader_info[\\\"import\\\"]\\n        else:\\n            raise ValueError(f\\\"Loader info for {loader} is not a dict\\\\nLoader info:\\\\n{selected_loader_info}\\\")\\n        module_name, class_name = loader_import.rsplit(\\\".\\\", 1)\\n\\n        try:\\n            # Import the loader class\\n            loader_module = __import__(module_name, fromlist=[class_name])\\n            loader_instance = getattr(loader_module, class_name)\\n        except ImportError as e:\\n            raise ValueError(f\\\"Loader {loader} could not be imported\\\\nLoader info:\\\\n{selected_loader_info}\\\") from e\\n\\n        result = loader_instance(file_path=file_path)\\n        return result.load()\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"loader\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Automatic\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Automatic\",\"Airbyte JSON (.jsonl)\",\"JSON (.json)\",\"BeautifulSoup4 HTML (.html, .htm)\",\"CSV (.csv)\",\"CoNLL-U (.conllu)\",\"EverNote (.enex)\",\"Facebook Chat (.json)\",\"Outlook Message (.msg)\",\"PyPDF (.pdf)\",\"Subtitle (.str)\",\"Text (.txt)\",\"Unstructured Email (.eml)\",\"Unstructured HTML (.html, .htm)\",\"Unstructured Markdown (.md)\",\"Unstructured PowerPoint (.pptx)\",\"Unstructured Word (.docx)\"],\"name\":\"loader\",\"display_name\":\"Loader\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generic File Loader\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"File Loader\",\"documentation\":\"\",\"custom_fields\":{\"file_path\":null,\"loader\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"UrlLoader\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List\\n\\nfrom langchain import document_loaders\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass UrlLoaderComponent(CustomComponent):\\n    display_name: str = \\\"Url Loader\\\"\\n    description: str = \\\"Generic Url Loader Component\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"web_path\\\": {\\n                \\\"display_name\\\": \\\"Url\\\",\\n                \\\"required\\\": True,\\n            },\\n            \\\"loader\\\": {\\n                \\\"display_name\\\": \\\"Loader\\\",\\n                \\\"is_list\\\": True,\\n                \\\"required\\\": True,\\n                \\\"options\\\": [\\n                    \\\"AZLyricsLoader\\\",\\n                    \\\"CollegeConfidentialLoader\\\",\\n                    \\\"GitbookLoader\\\",\\n                    \\\"HNLoader\\\",\\n                    \\\"IFixitLoader\\\",\\n                    \\\"IMSDbLoader\\\",\\n                    \\\"WebBaseLoader\\\",\\n                ],\\n                \\\"value\\\": \\\"WebBaseLoader\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(self, web_path: str, loader: str) -> List[Document]:\\n        try:\\n            loader_instance = getattr(document_loaders, loader)(web_path=web_path)\\n        except Exception as e:\\n            raise ValueError(f\\\"No loader found for: {web_path}\\\") from e\\n        docs = loader_instance.load()\\n        avg_length = sum(len(doc.page_content) for doc in docs if hasattr(doc, \\\"page_content\\\")) / len(docs)\\n        self.status = f\\\"\\\"\\\"{len(docs)} documents)\\n        \\\\nAvg. Document Length (characters): {int(avg_length)}\\n        Documents: {docs[:3]}...\\\"\\\"\\\"\\n        return docs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"loader\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"WebBaseLoader\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"AZLyricsLoader\",\"CollegeConfidentialLoader\",\"GitbookLoader\",\"HNLoader\",\"IFixitLoader\",\"IMSDbLoader\",\"WebBaseLoader\"],\"name\":\"loader\",\"display_name\":\"Loader\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"web_path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generic Url Loader Component\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"Url Loader\",\"documentation\":\"\",\"custom_fields\":{\"web_path\":null,\"loader\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GatherRecords\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from concurrent import futures\\nfrom pathlib import Path\\nfrom typing import Any, Dict, List\\n\\nfrom langflow import CustomComponent\\nfrom langflow.schema import Record\\n\\n\\nclass GatherRecordsComponent(CustomComponent):\\n    display_name = \\\"Gather Records\\\"\\n    description = \\\"Gather records from a directory.\\\"\\n\\n    def build_config(self) -> Dict[str, Any]:\\n        return {\\n            \\\"load_hidden\\\": {\\n                \\\"display_name\\\": \\\"Load Hidden Files\\\",\\n                \\\"value\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"max_concurrency\\\": {\\n                \\\"display_name\\\": \\\"Max Concurrency\\\",\\n                \\\"value\\\": 10,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"path\\\": {\\\"display_name\\\": \\\"Local Directory\\\"},\\n            \\\"recursive\\\": {\\\"display_name\\\": \\\"Recursive\\\", \\\"value\\\": True, \\\"advanced\\\": True},\\n            \\\"use_multithreading\\\": {\\n                \\\"display_name\\\": \\\"Use Multithreading\\\",\\n                \\\"value\\\": True,\\n                \\\"advanced\\\": True,\\n            },\\n        }\\n\\n    def is_hidden(self, path: Path) -> bool:\\n        return path.name.startswith(\\\".\\\")\\n\\n    def retrieve_file_paths(\\n        self,\\n        path: str,\\n        types: List[str],\\n        load_hidden: bool,\\n        recursive: bool,\\n        depth: int,\\n    ) -> List[str]:\\n        path_obj = Path(path)\\n        if not path_obj.exists() or not path_obj.is_dir():\\n            raise ValueError(f\\\"Path {path} must exist and be a directory.\\\")\\n\\n        def match_types(p: Path) -> bool:\\n            return any(p.suffix == f\\\".{t}\\\" for t in types) if types else True\\n\\n        def is_not_hidden(p: Path) -> bool:\\n            return not self.is_hidden(p) or load_hidden\\n\\n        def walk_level(directory: Path, max_depth: int):\\n            directory = directory.resolve()\\n            prefix_length = len(directory.parts)\\n            for p in directory.rglob(\\\"*\\\" if recursive else \\\"[!.]*\\\"):\\n                if len(p.parts) - prefix_length <= max_depth:\\n                    yield p\\n\\n        glob = \\\"**/*\\\" if recursive else \\\"*\\\"\\n        paths = walk_level(path_obj, depth) if depth else path_obj.glob(glob)\\n        file_paths = [str(p) for p in paths if p.is_file() and match_types(p) and is_not_hidden(p)]\\n\\n        return file_paths\\n\\n    def parse_file_to_record(self, file_path: str, silent_errors: bool) -> Record:\\n        # Use the partition function to load the file\\n        from unstructured.partition.auto import partition\\n\\n        try:\\n            elements = partition(file_path)\\n        except Exception as e:\\n            if not silent_errors:\\n                raise ValueError(f\\\"Error loading file {file_path}: {e}\\\") from e\\n            return None\\n\\n        # Create a Record\\n        text = \\\"\\\\n\\\\n\\\".join([str(el) for el in elements])\\n        metadata = elements.metadata if hasattr(elements, \\\"metadata\\\") else {}\\n        metadata[\\\"file_path\\\"] = file_path\\n        record = Record(text=text, data=metadata)\\n        return record\\n\\n    def get_elements(\\n        self,\\n        file_paths: List[str],\\n        silent_errors: bool,\\n        max_concurrency: int,\\n        use_multithreading: bool,\\n    ) -> List[Record]:\\n        if use_multithreading:\\n            records = self.parallel_load_records(file_paths, silent_errors, max_concurrency)\\n        else:\\n            records = [self.parse_file_to_record(file_path, silent_errors) for file_path in file_paths]\\n        records = list(filter(None, records))\\n        return records\\n\\n    def parallel_load_records(self, file_paths: List[str], silent_errors: bool, max_concurrency: int) -> List[Record]:\\n        with futures.ThreadPoolExecutor(max_workers=max_concurrency) as executor:\\n            loaded_files = executor.map(\\n                lambda file_path: self.parse_file_to_record(file_path, silent_errors),\\n                file_paths,\\n            )\\n        return loaded_files\\n\\n    def build(\\n        self,\\n        path: str,\\n        types: List[str] = None,\\n        depth: int = 0,\\n        max_concurrency: int = 2,\\n        load_hidden: bool = False,\\n        recursive: bool = True,\\n        silent_errors: bool = False,\\n        use_multithreading: bool = True,\\n    ) -> List[Record]:\\n        resolved_path = self.resolve_path(path)\\n        file_paths = self.retrieve_file_paths(resolved_path, types, load_hidden, recursive, depth)\\n        loaded_records = []\\n\\n        if use_multithreading:\\n            loaded_records = self.parallel_load_records(file_paths, silent_errors, max_concurrency)\\n        else:\\n            loaded_records = [self.parse_file_to_record(file_path, silent_errors) for file_path in file_paths]\\n        loaded_records = list(filter(None, loaded_records))\\n        self.status = loaded_records\\n        return loaded_records\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"depth\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"depth\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"load_hidden\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"load_hidden\",\"display_name\":\"Load Hidden Files\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_concurrency\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_concurrency\",\"display_name\":\"Max Concurrency\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"path\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local Directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"recursive\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"recursive\",\"display_name\":\"Recursive\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"silent_errors\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"silent_errors\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"types\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"types\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"use_multithreading\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_multithreading\",\"display_name\":\"Use Multithreading\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Gather records from a directory.\",\"base_classes\":[\"Record\"],\"display_name\":\"Gather Records\",\"documentation\":\"\",\"custom_fields\":{\"path\":null,\"types\":null,\"depth\":null,\"max_concurrency\":null,\"load_hidden\":null,\"recursive\":null,\"silent_errors\":null,\"use_multithreading\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"textsplitters\":{\"CharacterTextSplitter\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chunk_overlap\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":200,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chunk_size\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List\\n\\nfrom langchain.text_splitter import CharacterTextSplitter\\nfrom langchain_core.documents.base import Document\\nfrom langflow import CustomComponent\\n\\n\\nclass CharacterTextSplitterComponent(CustomComponent):\\n    display_name = \\\"CharacterTextSplitter\\\"\\n    description = \\\"Splitting text that looks at characters.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n            \\\"chunk_overlap\\\": {\\\"display_name\\\": \\\"Chunk Overlap\\\", \\\"default\\\": 200},\\n            \\\"chunk_size\\\": {\\\"display_name\\\": \\\"Chunk Size\\\", \\\"default\\\": 1000},\\n            \\\"separator\\\": {\\\"display_name\\\": \\\"Separator\\\", \\\"default\\\": \\\"\\\\n\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        documents: List[Document],\\n        chunk_overlap: int = 200,\\n        chunk_size: int = 1000,\\n        separator: str = \\\"\\\\n\\\",\\n    ) -> List[Document]:\\n        # separator may come escaped from the frontend\\n        separator = separator.encode().decode(\\\"unicode_escape\\\")\\n        docs = CharacterTextSplitter(\\n            chunk_overlap=chunk_overlap,\\n            chunk_size=chunk_size,\\n            separator=separator,\\n        ).split_documents(documents)\\n        self.status = docs\\n        return docs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"separator\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\\\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"separator\",\"display_name\":\"Separator\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Splitting text that looks at characters.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"CharacterTextSplitter\",\"documentation\":\"\",\"custom_fields\":{\"documents\":null,\"chunk_overlap\":null,\"chunk_size\":null,\"separator\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RecursiveCharacterTextSplitter\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"The documents to split.\",\"title_case\":false},\"chunk_overlap\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":200,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"The amount of overlap between chunks.\",\"title_case\":false},\"chunk_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum length of each chunk.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.utils.util import build_loader_repr_from_documents\\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\\n\\n\\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\\n    display_name: str = \\\"Recursive Character Text Splitter\\\"\\n    description: str = \\\"Split text into chunks of a specified length.\\\"\\n    documentation: str = \\\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"documents\\\": {\\n                \\\"display_name\\\": \\\"Documents\\\",\\n                \\\"info\\\": \\\"The documents to split.\\\",\\n            },\\n            \\\"separators\\\": {\\n                \\\"display_name\\\": \\\"Separators\\\",\\n                \\\"info\\\": 'The characters to split on.\\\\nIf left empty defaults to [\\\"\\\\\\\\n\\\\\\\\n\\\", \\\"\\\\\\\\n\\\", \\\" \\\", \\\"\\\"].',\\n                \\\"is_list\\\": True,\\n            },\\n            \\\"chunk_size\\\": {\\n                \\\"display_name\\\": \\\"Chunk Size\\\",\\n                \\\"info\\\": \\\"The maximum length of each chunk.\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"value\\\": 1000,\\n            },\\n            \\\"chunk_overlap\\\": {\\n                \\\"display_name\\\": \\\"Chunk Overlap\\\",\\n                \\\"info\\\": \\\"The amount of overlap between chunks.\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"value\\\": 200,\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        documents: list[Document],\\n        separators: Optional[list[str]] = None,\\n        chunk_size: Optional[int] = 1000,\\n        chunk_overlap: Optional[int] = 200,\\n    ) -> list[Document]:\\n        \\\"\\\"\\\"\\n        Split text into chunks of a specified length.\\n\\n        Args:\\n            separators (list[str]): The characters to split on.\\n            chunk_size (int): The maximum length of each chunk.\\n            chunk_overlap (int): The amount of overlap between chunks.\\n            length_function (function): The function to use to calculate the length of the text.\\n\\n        Returns:\\n            list[str]: The chunks of text.\\n        \\\"\\\"\\\"\\n\\n        if separators == \\\"\\\":\\n            separators = None\\n        elif separators:\\n            # check if the separators list has escaped characters\\n            # if there are escaped characters, unescape them\\n            separators = [x.encode().decode(\\\"unicode-escape\\\") for x in separators]\\n\\n        # Make sure chunk_size and chunk_overlap are ints\\n        if isinstance(chunk_size, str):\\n            chunk_size = int(chunk_size)\\n        if isinstance(chunk_overlap, str):\\n            chunk_overlap = int(chunk_overlap)\\n        splitter = RecursiveCharacterTextSplitter(\\n            separators=separators,\\n            chunk_size=chunk_size,\\n            chunk_overlap=chunk_overlap,\\n        )\\n\\n        docs = splitter.split_documents(documents)\\n        self.repr_value = build_loader_repr_from_documents(docs)\\n        return docs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"separators\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"separators\",\"display_name\":\"Separators\",\"advanced\":false,\"dynamic\":false,\"info\":\"The characters to split on.\\nIf left empty defaults to [\\\"\\\\n\\\\n\\\", \\\"\\\\n\\\", \\\" \\\", \\\"\\\"].\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Split text into chunks of a specified length.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"Recursive Character Text Splitter\",\"documentation\":\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\",\"custom_fields\":{\"documents\":null,\"separators\":null,\"chunk_size\":null,\"chunk_overlap\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"LanguageRecursiveTextSplitter\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"The documents to split.\",\"title_case\":false},\"chunk_overlap\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":200,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"The amount of overlap between chunks.\",\"title_case\":false},\"chunk_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum length of each chunk.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.text_splitter import Language\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass LanguageRecursiveTextSplitterComponent(CustomComponent):\\n    display_name: str = \\\"Language Recursive Text Splitter\\\"\\n    description: str = \\\"Split text into chunks of a specified length based on language.\\\"\\n    documentation: str = \\\"https://docs.langflow.org/components/text-splitters#languagerecursivetextsplitter\\\"\\n\\n    def build_config(self):\\n        options = [x.value for x in Language]\\n        return {\\n            \\\"documents\\\": {\\n                \\\"display_name\\\": \\\"Documents\\\",\\n                \\\"info\\\": \\\"The documents to split.\\\",\\n            },\\n            \\\"separator_type\\\": {\\n                \\\"display_name\\\": \\\"Separator Type\\\",\\n                \\\"info\\\": \\\"The type of separator to use.\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"options\\\": options,\\n                \\\"value\\\": \\\"Python\\\",\\n            },\\n            \\\"separators\\\": {\\n                \\\"display_name\\\": \\\"Separators\\\",\\n                \\\"info\\\": \\\"The characters to split on.\\\",\\n                \\\"is_list\\\": True,\\n            },\\n            \\\"chunk_size\\\": {\\n                \\\"display_name\\\": \\\"Chunk Size\\\",\\n                \\\"info\\\": \\\"The maximum length of each chunk.\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"value\\\": 1000,\\n            },\\n            \\\"chunk_overlap\\\": {\\n                \\\"display_name\\\": \\\"Chunk Overlap\\\",\\n                \\\"info\\\": \\\"The amount of overlap between chunks.\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"value\\\": 200,\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        documents: list[Document],\\n        chunk_size: Optional[int] = 1000,\\n        chunk_overlap: Optional[int] = 200,\\n        separator_type: str = \\\"Python\\\",\\n    ) -> list[Document]:\\n        \\\"\\\"\\\"\\n        Split text into chunks of a specified length.\\n\\n        Args:\\n            separators (list[str]): The characters to split on.\\n            chunk_size (int): The maximum length of each chunk.\\n            chunk_overlap (int): The amount of overlap between chunks.\\n            length_function (function): The function to use to calculate the length of the text.\\n\\n        Returns:\\n            list[str]: The chunks of text.\\n        \\\"\\\"\\\"\\n        from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\\n        # Make sure chunk_size and chunk_overlap are ints\\n        if isinstance(chunk_size, str):\\n            chunk_size = int(chunk_size)\\n        if isinstance(chunk_overlap, str):\\n            chunk_overlap = int(chunk_overlap)\\n\\n        splitter = RecursiveCharacterTextSplitter.from_language(\\n            language=Language(separator_type),\\n            chunk_size=chunk_size,\\n            chunk_overlap=chunk_overlap,\\n        )\\n\\n        docs = splitter.split_documents(documents)\\n        return docs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"separator_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Python\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"cpp\",\"go\",\"java\",\"kotlin\",\"js\",\"ts\",\"php\",\"proto\",\"python\",\"rst\",\"ruby\",\"rust\",\"scala\",\"swift\",\"markdown\",\"latex\",\"html\",\"sol\",\"csharp\",\"cobol\",\"c\",\"lua\",\"perl\"],\"name\":\"separator_type\",\"display_name\":\"Separator Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"The type of separator to use.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Split text into chunks of a specified length based on language.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"Language Recursive Text Splitter\",\"documentation\":\"https://docs.langflow.org/components/text-splitters#languagerecursivetextsplitter\",\"custom_fields\":{\"documents\":null,\"chunk_size\":null,\"chunk_overlap\":null,\"separator_type\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"utilities\":{\"BingSearchAPIWrapper\":{\"template\":{\"bing_search_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"bing_search_url\",\"display_name\":\"Bing Search URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"bing_subscription_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"bing_subscription_key\",\"display_name\":\"Bing Subscription Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\n# Assuming `BingSearchAPIWrapper` is a class that exists in the context\\n# and has the appropriate methods and attributes.\\n# We need to make sure this class is importable from the context where this code will be running.\\nfrom langchain_community.utilities.bing_search import BingSearchAPIWrapper\\n\\n\\nclass BingSearchAPIWrapperComponent(CustomComponent):\\n    display_name = \\\"BingSearchAPIWrapper\\\"\\n    description = \\\"Wrapper for Bing Search API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"bing_search_url\\\": {\\\"display_name\\\": \\\"Bing Search URL\\\"},\\n            \\\"bing_subscription_key\\\": {\\n                \\\"display_name\\\": \\\"Bing Subscription Key\\\",\\n                \\\"password\\\": True,\\n            },\\n            \\\"k\\\": {\\\"display_name\\\": \\\"Number of results\\\", \\\"advanced\\\": True},\\n            # 'k' is not included as it is not shown (show=False)\\n        }\\n\\n    def build(\\n        self,\\n        bing_search_url: str,\\n        bing_subscription_key: str,\\n        k: int = 10,\\n    ) -> BingSearchAPIWrapper:\\n        # 'k' has a default value and is not shown (show=False), so it is hardcoded here\\n        return BingSearchAPIWrapper(bing_search_url=bing_search_url, bing_subscription_key=bing_subscription_key, k=k)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"k\",\"display_name\":\"Number of results\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper for Bing Search API.\",\"base_classes\":[\"BingSearchAPIWrapper\"],\"display_name\":\"BingSearchAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"bing_search_url\":null,\"bing_subscription_key\":null,\"k\":null},\"output_types\":[\"BingSearchAPIWrapper\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GoogleSearchAPIWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain_community.utilities.google_search import GoogleSearchAPIWrapper\\nfrom langflow import CustomComponent\\n\\n\\nclass GoogleSearchAPIWrapperComponent(CustomComponent):\\n    display_name = \\\"GoogleSearchAPIWrapper\\\"\\n    description = \\\"Wrapper for Google Search API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"google_api_key\\\": {\\\"display_name\\\": \\\"Google API Key\\\", \\\"password\\\": True},\\n            \\\"google_cse_id\\\": {\\\"display_name\\\": \\\"Google CSE ID\\\", \\\"password\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        google_api_key: str,\\n        google_cse_id: str,\\n    ) -> Union[GoogleSearchAPIWrapper, Callable]:\\n        return GoogleSearchAPIWrapper(google_api_key=google_api_key, google_cse_id=google_cse_id)  # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"google_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"google_api_key\",\"display_name\":\"Google API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"google_cse_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"google_cse_id\",\"display_name\":\"Google CSE ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper for Google Search API.\",\"base_classes\":[\"GoogleSearchAPIWrapper\",\"Callable\"],\"display_name\":\"GoogleSearchAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"google_api_key\":null,\"google_cse_id\":null},\"output_types\":[\"GoogleSearchAPIWrapper\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GoogleSerperAPIWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Dict\\n\\n# Assuming the existence of GoogleSerperAPIWrapper class in the serper module\\n# If this class does not exist, you would need to create it or import the appropriate class from another module\\nfrom langchain_community.utilities.google_serper import GoogleSerperAPIWrapper\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass GoogleSerperAPIWrapperComponent(CustomComponent):\\n    display_name = \\\"GoogleSerperAPIWrapper\\\"\\n    description = \\\"Wrapper around the Serper.dev Google Search API.\\\"\\n\\n    def build_config(self) -> Dict[str, Dict]:\\n        return {\\n            \\\"result_key_for_type\\\": {\\n                \\\"display_name\\\": \\\"Result Key for Type\\\",\\n                \\\"show\\\": True,\\n                \\\"multiline\\\": False,\\n                \\\"password\\\": False,\\n                \\\"advanced\\\": False,\\n                \\\"dynamic\\\": False,\\n                \\\"info\\\": \\\"\\\",\\n                \\\"field_type\\\": \\\"dict\\\",\\n                \\\"list\\\": False,\\n                \\\"value\\\": {\\n                    \\\"news\\\": \\\"news\\\",\\n                    \\\"places\\\": \\\"places\\\",\\n                    \\\"images\\\": \\\"images\\\",\\n                    \\\"search\\\": \\\"organic\\\",\\n                },\\n            },\\n            \\\"serper_api_key\\\": {\\n                \\\"display_name\\\": \\\"Serper API Key\\\",\\n                \\\"show\\\": True,\\n                \\\"multiline\\\": False,\\n                \\\"password\\\": True,\\n                \\\"advanced\\\": False,\\n                \\\"dynamic\\\": False,\\n                \\\"info\\\": \\\"\\\",\\n                \\\"type\\\": \\\"str\\\",\\n                \\\"list\\\": False,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        serper_api_key: str,\\n    ) -> GoogleSerperAPIWrapper:\\n        return GoogleSerperAPIWrapper(serper_api_key=serper_api_key)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"serper_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"serper_api_key\",\"display_name\":\"Serper API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper around the Serper.dev Google Search API.\",\"base_classes\":[\"GoogleSerperAPIWrapper\"],\"display_name\":\"GoogleSerperAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"serper_api_key\":null},\"output_types\":[\"GoogleSerperAPIWrapper\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SearxSearchWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Optional, Dict\\nfrom langchain_community.utilities.searx_search import SearxSearchWrapper\\n\\n\\nclass SearxSearchWrapperComponent(CustomComponent):\\n    display_name = \\\"SearxSearchWrapper\\\"\\n    description = \\\"Wrapper for Searx API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"headers\\\": {\\n                \\\"field_type\\\": \\\"dict\\\",\\n                \\\"display_name\\\": \\\"Headers\\\",\\n                \\\"multiline\\\": True,\\n                \\\"value\\\": '{\\\"Authorization\\\": \\\"Bearer <token>\\\"}',\\n            },\\n            \\\"k\\\": {\\\"display_name\\\": \\\"k\\\", \\\"advanced\\\": True, \\\"field_type\\\": \\\"int\\\", \\\"value\\\": 10},\\n            \\\"searx_host\\\": {\\n                \\\"display_name\\\": \\\"Searx Host\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"value\\\": \\\"https://searx.example.com\\\",\\n                \\\"advanced\\\": True,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        k: int = 10,\\n        headers: Optional[Dict[str, str]] = None,\\n        searx_host: str = \\\"https://searx.example.com\\\",\\n    ) -> SearxSearchWrapper:\\n        return SearxSearchWrapper(headers=headers, k=k, searx_host=searx_host)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"{\\\"Authorization\\\": \\\"Bearer <token>\\\"}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"k\",\"display_name\":\"k\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"searx_host\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"https://searx.example.com\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"searx_host\",\"display_name\":\"Searx Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper for Searx API.\",\"base_classes\":[\"SearxSearchWrapper\"],\"display_name\":\"SearxSearchWrapper\",\"documentation\":\"\",\"custom_fields\":{\"k\":null,\"headers\":null,\"searx_host\":null},\"output_types\":[\"SearxSearchWrapper\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SerpAPIWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain_community.utilities.serpapi import SerpAPIWrapper\\nfrom langflow import CustomComponent\\n\\n\\nclass SerpAPIWrapperComponent(CustomComponent):\\n    display_name = \\\"SerpAPIWrapper\\\"\\n    description = \\\"Wrapper around SerpAPI\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"serpapi_api_key\\\": {\\\"display_name\\\": \\\"SerpAPI API Key\\\", \\\"type\\\": \\\"str\\\", \\\"password\\\": True},\\n            \\\"params\\\": {\\n                \\\"display_name\\\": \\\"Parameters\\\",\\n                \\\"type\\\": \\\"dict\\\",\\n                \\\"advanced\\\": True,\\n                \\\"multiline\\\": True,\\n                \\\"value\\\": '{\\\"engine\\\": \\\"google\\\",\\\"google_domain\\\": \\\"google.com\\\",\\\"gl\\\": \\\"us\\\",\\\"hl\\\": \\\"en\\\"}',\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        serpapi_api_key: str,\\n        params: dict,\\n    ) -> Union[SerpAPIWrapper, Callable]:  # Removed quotes around SerpAPIWrapper\\n        return SerpAPIWrapper(  # type: ignore\\n            serpapi_api_key=serpapi_api_key,\\n            params=params,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"params\":{\"type\":\"dict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"{\\\"engine\\\": \\\"google\\\",\\\"google_domain\\\": \\\"google.com\\\",\\\"gl\\\": \\\"us\\\",\\\"hl\\\": \\\"en\\\"}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"params\",\"display_name\":\"Parameters\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"serpapi_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"serpapi_api_key\",\"display_name\":\"SerpAPI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper around SerpAPI\",\"base_classes\":[\"Callable\",\"SerpAPIWrapper\"],\"display_name\":\"SerpAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"serpapi_api_key\":null,\"params\":null},\"output_types\":[\"SerpAPIWrapper\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"WikipediaAPIWrapper\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain_community.utilities.wikipedia import WikipediaAPIWrapper\\nfrom langflow import CustomComponent\\n\\n# Assuming WikipediaAPIWrapper is a class that needs to be imported.\\n# The import statement is not included as it is not provided in the JSON\\n# and the actual implementation details are unknown.\\n\\n\\nclass WikipediaAPIWrapperComponent(CustomComponent):\\n    display_name = \\\"WikipediaAPIWrapper\\\"\\n    description = \\\"Wrapper around WikipediaAPI.\\\"\\n\\n    def build_config(self):\\n        return {}\\n\\n    def build(\\n        self,\\n        top_k_results: int = 3,\\n        lang: str = \\\"en\\\",\\n        load_all_available_meta: bool = False,\\n        doc_content_chars_max: int = 4000,\\n    ) -> Union[WikipediaAPIWrapper, Callable]:\\n        return WikipediaAPIWrapper(  # type: ignore\\n            top_k_results=top_k_results,\\n            lang=lang,\\n            load_all_available_meta=load_all_available_meta,\\n            doc_content_chars_max=doc_content_chars_max,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"doc_content_chars_max\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":4000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"doc_content_chars_max\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"lang\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"en\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lang\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"load_all_available_meta\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"load_all_available_meta\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_k_results\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":3,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper around WikipediaAPI.\",\"base_classes\":[\"WikipediaAPIWrapper\",\"Callable\"],\"display_name\":\"WikipediaAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"top_k_results\":null,\"lang\":null,\"load_all_available_meta\":null,\"doc_content_chars_max\":null},\"output_types\":[\"WikipediaAPIWrapper\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"WolframAlphaAPIWrapper\":{\"template\":{\"appid\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"appid\",\"display_name\":\"App ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Union\\n\\nfrom langchain_community.utilities.wolfram_alpha import WolframAlphaAPIWrapper\\nfrom langflow import CustomComponent\\n\\n# Since all the fields in the JSON have show=False, we will only create a basic component\\n# without any configurable fields.\\n\\n\\nclass WolframAlphaAPIWrapperComponent(CustomComponent):\\n    display_name = \\\"WolframAlphaAPIWrapper\\\"\\n    description = \\\"Wrapper for Wolfram Alpha.\\\"\\n\\n    def build_config(self):\\n        return {\\\"appid\\\": {\\\"display_name\\\": \\\"App ID\\\", \\\"type\\\": \\\"str\\\", \\\"password\\\": True}}\\n\\n    def build(self, appid: str) -> Union[Callable, WolframAlphaAPIWrapper]:\\n        return WolframAlphaAPIWrapper(wolfram_alpha_appid=appid)  # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Wrapper for Wolfram Alpha.\",\"base_classes\":[\"WolframAlphaAPIWrapper\",\"Callable\"],\"display_name\":\"WolframAlphaAPIWrapper\",\"documentation\":\"\",\"custom_fields\":{\"appid\":null},\"output_types\":[\"Callable\",\"WolframAlphaAPIWrapper\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RunnableExecutor\":{\"template\":{\"runnable\":{\"type\":\"Runnable\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"runnable\",\"display_name\":\"Runnable\",\"advanced\":false,\"dynamic\":false,\"info\":\"The runnable to execute.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_core.runnables import Runnable\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass RunnableExecComponent(CustomComponent):\\n    documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n    display_name = \\\"Runnable Executor\\\"\\n    beta = True\\n\\n    def build_config(self):\\n        return {\\n            \\\"input_key\\\": {\\n                \\\"display_name\\\": \\\"Input Key\\\",\\n                \\\"info\\\": \\\"The key to use for the input.\\\",\\n            },\\n            \\\"inputs\\\": {\\n                \\\"display_name\\\": \\\"Inputs\\\",\\n                \\\"info\\\": \\\"The inputs to pass to the runnable.\\\",\\n            },\\n            \\\"runnable\\\": {\\n                \\\"display_name\\\": \\\"Runnable\\\",\\n                \\\"info\\\": \\\"The runnable to execute.\\\",\\n            },\\n            \\\"output_key\\\": {\\n                \\\"display_name\\\": \\\"Output Key\\\",\\n                \\\"info\\\": \\\"The key to use for the output.\\\",\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        input_key: str,\\n        inputs: str,\\n        runnable: Runnable,\\n        output_key: str = \\\"output\\\",\\n    ) -> Text:\\n        result = runnable.invoke({input_key: inputs})\\n        result = result.get(output_key)\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"input_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"input_key\",\"display_name\":\"Input Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The key to use for the input.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"The inputs to pass to the runnable.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"output_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"output\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"output_key\",\"display_name\":\"Output Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The key to use for the output.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Runnable Executor\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"input_key\":null,\"inputs\":null,\"runnable\":null,\"output_key\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"DocumentToRecord\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List\\n\\nfrom langchain_core.documents import Document\\n\\nfrom langflow import CustomComponent\\nfrom langflow.schema import Record\\n\\n\\nclass DocumentToRecordComponent(CustomComponent):\\n    display_name = \\\"Documents to Records\\\"\\n    description = \\\"Convert documents to records.\\\"\\n\\n    field_config = {\\n        \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n    }\\n\\n    def build(self, documents: List[Document]) -> List[Record]:\\n        if isinstance(documents, Document):\\n            documents = [documents]\\n        records = [Record.from_document(document) for document in documents]\\n        self.status = records\\n        return records\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Convert documents to records.\",\"base_classes\":[\"Record\"],\"display_name\":\"Documents to Records\",\"documentation\":\"\",\"custom_fields\":{\"documents\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GetRequest\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nimport requests\\nfrom langchain_core.documents import Document\\nfrom langflow import CustomComponent\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass GetRequest(CustomComponent):\\n    display_name: str = \\\"GET Request\\\"\\n    description: str = \\\"Make a GET request to the given URL.\\\"\\n    output_types: list[str] = [\\\"Document\\\"]\\n    documentation: str = \\\"https://docs.langflow.org/components/utilities#get-request\\\"\\n    beta: bool = True\\n    field_config = {\\n        \\\"url\\\": {\\n            \\\"display_name\\\": \\\"URL\\\",\\n            \\\"info\\\": \\\"The URL to make the request to\\\",\\n            \\\"is_list\\\": True,\\n        },\\n        \\\"headers\\\": {\\n            \\\"display_name\\\": \\\"Headers\\\",\\n            \\\"info\\\": \\\"The headers to send with the request.\\\",\\n        },\\n        \\\"code\\\": {\\\"show\\\": False},\\n        \\\"timeout\\\": {\\n            \\\"display_name\\\": \\\"Timeout\\\",\\n            \\\"field_type\\\": \\\"int\\\",\\n            \\\"info\\\": \\\"The timeout to use for the request.\\\",\\n            \\\"value\\\": 5,\\n        },\\n    }\\n\\n    def get_document(self, session: requests.Session, url: str, headers: Optional[dict], timeout: int) -> Document:\\n        try:\\n            response = session.get(url, headers=headers, timeout=int(timeout))\\n            try:\\n                response_json = response.json()\\n                result = orjson_dumps(response_json, indent_2=False)\\n            except Exception:\\n                result = response.text\\n            self.repr_value = result\\n            return Document(\\n                page_content=result,\\n                metadata={\\n                    \\\"source\\\": url,\\n                    \\\"headers\\\": headers,\\n                    \\\"status_code\\\": response.status_code,\\n                },\\n            )\\n        except requests.Timeout:\\n            return Document(\\n                page_content=\\\"Request Timed Out\\\",\\n                metadata={\\\"source\\\": url, \\\"headers\\\": headers, \\\"status_code\\\": 408},\\n            )\\n        except Exception as exc:\\n            return Document(\\n                page_content=str(exc),\\n                metadata={\\\"source\\\": url, \\\"headers\\\": headers, \\\"status_code\\\": 500},\\n            )\\n\\n    def build(\\n        self,\\n        url: str,\\n        headers: Optional[dict] = None,\\n        timeout: int = 5,\\n    ) -> list[Document]:\\n        if headers is None:\\n            headers = {}\\n        urls = url if isinstance(url, list) else [url]\\n        with requests.Session() as session:\\n            documents = [self.get_document(session, u, headers, timeout) for u in urls]\\n            self.repr_value = documents\\n        return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"title_case\":false},\"timeout\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"The timeout to use for the request.\",\"title_case\":false},\"url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Make a GET request to the given URL.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"GET Request\",\"documentation\":\"https://docs.langflow.org/components/utilities#get-request\",\"custom_fields\":{\"url\":null,\"headers\":null,\"timeout\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLExecutor\":{\"template\":{\"database\":{\"type\":\"SQLDatabase\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"database\",\"display_name\":\"Database\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"add_error\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"add_error\",\"display_name\":\"Add Error\",\"advanced\":false,\"dynamic\":false,\"info\":\"Add the error to the result.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool\\nfrom langchain_experimental.sql.base import SQLDatabase\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass SQLExecutorComponent(CustomComponent):\\n    display_name = \\\"SQL Executor\\\"\\n    description = \\\"Execute SQL query.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"database\\\": {\\\"display_name\\\": \\\"Database\\\"},\\n            \\\"include_columns\\\": {\\n                \\\"display_name\\\": \\\"Include Columns\\\",\\n                \\\"info\\\": \\\"Include columns in the result.\\\",\\n            },\\n            \\\"passthrough\\\": {\\n                \\\"display_name\\\": \\\"Passthrough\\\",\\n                \\\"info\\\": \\\"If an error occurs, return the query instead of raising an exception.\\\",\\n            },\\n            \\\"add_error\\\": {\\n                \\\"display_name\\\": \\\"Add Error\\\",\\n                \\\"info\\\": \\\"Add the error to the result.\\\",\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        query: str,\\n        database: SQLDatabase,\\n        include_columns: bool = False,\\n        passthrough: bool = False,\\n        add_error: bool = False,\\n    ) -> Text:\\n        error = None\\n        try:\\n            tool = QuerySQLDataBaseTool(db=database)\\n            result = tool.run(query, include_columns=include_columns)\\n            self.status = result\\n        except Exception as e:\\n            result = str(e)\\n            self.status = result\\n            if not passthrough:\\n                raise e\\n            error = repr(e)\\n\\n        if add_error and error is not None:\\n            result = f\\\"{result}\\\\n\\\\nError: {error}\\\\n\\\\nQuery: {query}\\\"\\n        elif error is not None:\\n            # Then we won't add the error to the result\\n            # but since we are in passthrough mode, we will return the query\\n            result = query\\n\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"include_columns\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"include_columns\",\"display_name\":\"Include Columns\",\"advanced\":false,\"dynamic\":false,\"info\":\"Include columns in the result.\",\"title_case\":false},\"passthrough\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"passthrough\",\"display_name\":\"Passthrough\",\"advanced\":false,\"dynamic\":false,\"info\":\"If an error occurs, return the query instead of raising an exception.\",\"title_case\":false},\"query\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"query\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Execute SQL query.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"SQL Executor\",\"documentation\":\"\",\"custom_fields\":{\"query\":null,\"database\":null,\"include_columns\":null,\"passthrough\":null,\"add_error\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ShouldRunNext\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"The language model to use for the decision.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"# Implement ShouldRunNext component\\nfrom langchain_core.prompts import PromptTemplate\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, Prompt\\n\\n\\nclass ShouldRunNext(CustomComponent):\\n    display_name = \\\"Should Run Next\\\"\\n    description = \\\"Decides whether to run the next component.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"prompt\\\": {\\n                \\\"display_name\\\": \\\"Prompt\\\",\\n                \\\"info\\\": \\\"The prompt to use for the decision. It should generate a boolean response (True or False).\\\",\\n            },\\n            \\\"llm\\\": {\\n                \\\"display_name\\\": \\\"LLM\\\",\\n                \\\"info\\\": \\\"The language model to use for the decision.\\\",\\n            },\\n        }\\n\\n    def build(self, template: Prompt, llm: BaseLanguageModel, **kwargs) -> dict:\\n        # This is a simple component that always returns True\\n        prompt_template = PromptTemplate.from_template(template)\\n\\n        attributes_to_check = [\\\"text\\\", \\\"page_content\\\"]\\n        for key, value in kwargs.items():\\n            for attribute in attributes_to_check:\\n                if hasattr(value, attribute):\\n                    kwargs[key] = getattr(value, attribute)\\n\\n        chain = prompt_template | llm\\n        result = chain.invoke(kwargs)\\n        if hasattr(result, \\\"content\\\") and isinstance(result.content, str):\\n            result = result.content\\n        elif isinstance(result, str):\\n            result = result\\n        else:\\n            result = result.get(\\\"response\\\")\\n\\n        if result.lower() not in [\\\"true\\\", \\\"false\\\"]:\\n            raise ValueError(\\\"The prompt should generate a boolean response (True or False).\\\")\\n        # The string should be the words true or false\\n        # if not raise an error\\n        bool_result = result.lower() == \\\"true\\\"\\n        return {\\\"condition\\\": bool_result, \\\"result\\\": kwargs}\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"template\":{\"type\":\"prompt\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Decides whether to run the next component.\",\"base_classes\":[\"object\",\"dict\"],\"display_name\":\"Should Run Next\",\"documentation\":\"\",\"custom_fields\":{\"template\":null,\"llm\":null},\"output_types\":[\"dict\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"PythonFunction\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Code\\nfrom langflow.interface.custom.utils import get_function\\n\\n\\nclass PythonFunctionComponent(CustomComponent):\\n    display_name = \\\"Python Function\\\"\\n    description = \\\"Define a Python function.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"function_code\\\": {\\n                \\\"display_name\\\": \\\"Code\\\",\\n                \\\"info\\\": \\\"The code for the function.\\\",\\n                \\\"show\\\": True,\\n            },\\n        }\\n\\n    def build(self, function_code: Code) -> Callable:\\n        self.status = function_code\\n        func = get_function(function_code)\\n        return func\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"function_code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"function_code\",\"display_name\":\"Code\",\"advanced\":false,\"dynamic\":false,\"info\":\"The code for the function.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Define a Python function.\",\"base_classes\":[\"Callable\"],\"display_name\":\"Python Function\",\"documentation\":\"\",\"custom_fields\":{\"function_code\":null},\"output_types\":[\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"PostRequest\":{\"template\":{\"document\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nimport requests\\nfrom langchain_core.documents import Document\\nfrom langflow import CustomComponent\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass PostRequest(CustomComponent):\\n    display_name: str = \\\"POST Request\\\"\\n    description: str = \\\"Make a POST request to the given URL.\\\"\\n    output_types: list[str] = [\\\"Document\\\"]\\n    documentation: str = \\\"https://docs.langflow.org/components/utilities#post-request\\\"\\n    beta: bool = True\\n    field_config = {\\n        \\\"url\\\": {\\\"display_name\\\": \\\"URL\\\", \\\"info\\\": \\\"The URL to make the request to.\\\"},\\n        \\\"headers\\\": {\\n            \\\"display_name\\\": \\\"Headers\\\",\\n            \\\"info\\\": \\\"The headers to send with the request.\\\",\\n        },\\n        \\\"code\\\": {\\\"show\\\": False},\\n        \\\"document\\\": {\\\"display_name\\\": \\\"Document\\\"},\\n    }\\n\\n    def post_document(\\n        self,\\n        session: requests.Session,\\n        document: Document,\\n        url: str,\\n        headers: Optional[dict] = None,\\n    ) -> Document:\\n        try:\\n            response = session.post(url, headers=headers, data=document.page_content)\\n            try:\\n                response_json = response.json()\\n                result = orjson_dumps(response_json, indent_2=False)\\n            except Exception:\\n                result = response.text\\n            self.repr_value = result\\n            return Document(\\n                page_content=result,\\n                metadata={\\n                    \\\"source\\\": url,\\n                    \\\"headers\\\": headers,\\n                    \\\"status_code\\\": response,\\n                },\\n            )\\n        except Exception as exc:\\n            return Document(\\n                page_content=str(exc),\\n                metadata={\\n                    \\\"source\\\": url,\\n                    \\\"headers\\\": headers,\\n                    \\\"status_code\\\": 500,\\n                },\\n            )\\n\\n    def build(\\n        self,\\n        document: Document,\\n        url: str,\\n        headers: Optional[dict] = None,\\n    ) -> list[Document]:\\n        if headers is None:\\n            headers = {}\\n\\n        if not isinstance(document, list) and isinstance(document, Document):\\n            documents: list[Document] = [document]\\n        elif isinstance(document, list) and all(isinstance(doc, Document) for doc in document):\\n            documents = document\\n        else:\\n            raise ValueError(\\\"document must be a Document or a list of Documents\\\")\\n\\n        with requests.Session() as session:\\n            documents = [self.post_document(session, doc, url, headers) for doc in documents]\\n            self.repr_value = documents\\n        return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"title_case\":false},\"url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Make a POST request to the given URL.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"POST Request\",\"documentation\":\"https://docs.langflow.org/components/utilities#post-request\",\"custom_fields\":{\"document\":null,\"url\":null,\"headers\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"IDGenerator\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"import uuid\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass UUIDGeneratorComponent(CustomComponent):\\n    documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n    display_name = \\\"Unique ID Generator\\\"\\n    description = \\\"Generates a unique ID.\\\"\\n\\n    def generate(self, *args, **kwargs):\\n        return str(uuid.uuid4().hex)\\n\\n    def build_config(self):\\n        return {\\\"unique_id\\\": {\\\"display_name\\\": \\\"Value\\\", \\\"value\\\": self.generate}}\\n\\n    def build(self, unique_id: str) -> str:\\n        return unique_id\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"unique_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"a62d43140aba4c799af4ddc400295790\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"unique_id\",\"display_name\":\"Value\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"refresh\":true,\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generates a unique ID.\",\"base_classes\":[\"object\",\"str\"],\"display_name\":\"Unique ID Generator\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"unique_id\":null},\"output_types\":[\"str\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SQLDatabase\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_experimental.sql.base import SQLDatabase\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass SQLDatabaseComponent(CustomComponent):\\n    display_name = \\\"SQLDatabase\\\"\\n    description = \\\"SQL Database\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"uri\\\": {\\\"display_name\\\": \\\"URI\\\", \\\"info\\\": \\\"URI to the database.\\\"},\\n        }\\n\\n    def clean_up_uri(self, uri: str) -> str:\\n        if uri.startswith(\\\"postgresql://\\\"):\\n            uri = uri.replace(\\\"postgresql://\\\", \\\"postgres://\\\")\\n        return uri.strip()\\n\\n    def build(self, uri: str) -> SQLDatabase:\\n        uri = self.clean_up_uri(uri)\\n        return SQLDatabase.from_uri(uri)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"uri\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"uri\",\"display_name\":\"URI\",\"advanced\":false,\"dynamic\":false,\"info\":\"URI to the database.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"SQL Database\",\"base_classes\":[\"object\",\"SQLDatabase\"],\"display_name\":\"SQLDatabase\",\"documentation\":\"\",\"custom_fields\":{\"uri\":null},\"output_types\":[\"SQLDatabase\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"RecordsAsText\":{\"template\":{\"records\":{\"type\":\"Record\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"records\",\"display_name\":\"Records\",\"advanced\":false,\"dynamic\":false,\"info\":\"The records to convert to text.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Text\\nfrom langflow.schema import Record\\n\\n\\nclass RecordsAsTextComponent(CustomComponent):\\n    display_name = \\\"Records to Text\\\"\\n    description = \\\"Converts Records a list of Records to text using a template.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"records\\\": {\\n                \\\"display_name\\\": \\\"Records\\\",\\n                \\\"info\\\": \\\"The records to convert to text.\\\",\\n            },\\n            \\\"template\\\": {\\n                \\\"display_name\\\": \\\"Template\\\",\\n                \\\"info\\\": \\\"The template to use for formatting the records. It must contain the keys {text} and {data}.\\\",\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        records: list[Record],\\n        template: str = \\\"Text: {text}\\\\nData: {data}\\\",\\n    ) -> Text:\\n        if isinstance(records, Record):\\n            records = [records]\\n\\n        formated_records = [\\n            template.format(text=record.text, data=record.data, **record.data)\\n            for record in records\\n        ]\\n        result_string = \\\"\\\\n\\\".join(formated_records)\\n        self.status = result_string\\n        return result_string\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"template\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"Text: {text}\\\\nData: {data}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"display_name\":\"Template\",\"advanced\":false,\"dynamic\":false,\"info\":\"The template to use for formatting the records. It must contain the keys {text} and {data}.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Converts Records a list of Records to text using a template.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Records to Text\",\"documentation\":\"\",\"custom_fields\":{\"records\":null,\"template\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"UpdateRequest\":{\"template\":{\"document\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nimport requests\\nfrom langchain_core.documents import Document\\nfrom langflow import CustomComponent\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass UpdateRequest(CustomComponent):\\n    display_name: str = \\\"Update Request\\\"\\n    description: str = \\\"Make a PATCH request to the given URL.\\\"\\n    output_types: list[str] = [\\\"Document\\\"]\\n    documentation: str = \\\"https://docs.langflow.org/components/utilities#update-request\\\"\\n    beta: bool = True\\n    field_config = {\\n        \\\"url\\\": {\\\"display_name\\\": \\\"URL\\\", \\\"info\\\": \\\"The URL to make the request to.\\\"},\\n        \\\"headers\\\": {\\n            \\\"display_name\\\": \\\"Headers\\\",\\n            \\\"field_type\\\": \\\"NestedDict\\\",\\n            \\\"info\\\": \\\"The headers to send with the request.\\\",\\n        },\\n        \\\"code\\\": {\\\"show\\\": False},\\n        \\\"document\\\": {\\\"display_name\\\": \\\"Document\\\"},\\n        \\\"method\\\": {\\n            \\\"display_name\\\": \\\"Method\\\",\\n            \\\"field_type\\\": \\\"str\\\",\\n            \\\"info\\\": \\\"The HTTP method to use.\\\",\\n            \\\"options\\\": [\\\"PATCH\\\", \\\"PUT\\\"],\\n            \\\"value\\\": \\\"PATCH\\\",\\n        },\\n    }\\n\\n    def update_document(\\n        self,\\n        session: requests.Session,\\n        document: Document,\\n        url: str,\\n        headers: Optional[dict] = None,\\n        method: str = \\\"PATCH\\\",\\n    ) -> Document:\\n        try:\\n            if method == \\\"PATCH\\\":\\n                response = session.patch(url, headers=headers, data=document.page_content)\\n            elif method == \\\"PUT\\\":\\n                response = session.put(url, headers=headers, data=document.page_content)\\n            else:\\n                raise ValueError(f\\\"Unsupported method: {method}\\\")\\n            try:\\n                response_json = response.json()\\n                result = orjson_dumps(response_json, indent_2=False)\\n            except Exception:\\n                result = response.text\\n            self.repr_value = result\\n            return Document(\\n                page_content=result,\\n                metadata={\\n                    \\\"source\\\": url,\\n                    \\\"headers\\\": headers,\\n                    \\\"status_code\\\": response.status_code,\\n                },\\n            )\\n        except Exception as exc:\\n            return Document(\\n                page_content=str(exc),\\n                metadata={\\\"source\\\": url, \\\"headers\\\": headers, \\\"status_code\\\": 500},\\n            )\\n\\n    def build(\\n        self,\\n        method: str,\\n        document: Document,\\n        url: str,\\n        headers: Optional[dict] = None,\\n    ) -> List[Document]:\\n        if headers is None:\\n            headers = {}\\n\\n        if not isinstance(document, list) and isinstance(document, Document):\\n            documents: list[Document] = [document]\\n        elif isinstance(document, list) and all(isinstance(doc, Document) for doc in document):\\n            documents = document\\n        else:\\n            raise ValueError(\\\"document must be a Document or a list of Documents\\\")\\n\\n        with requests.Session() as session:\\n            documents = [self.update_document(session, doc, url, headers, method) for doc in documents]\\n            self.repr_value = documents\\n        return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"headers\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"title_case\":false},\"method\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"PATCH\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"PATCH\",\"PUT\"],\"name\":\"method\",\"display_name\":\"Method\",\"advanced\":false,\"dynamic\":false,\"info\":\"The HTTP method to use.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Make a PATCH request to the given URL.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"Update Request\",\"documentation\":\"https://docs.langflow.org/components/utilities#update-request\",\"custom_fields\":{\"method\":null,\"document\":null,\"url\":null,\"headers\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"JSONDocumentBuilder\":{\"template\":{\"document\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"### JSON Document Builder\\n\\n# Build a Document containing a JSON object using a key and another Document page content.\\n\\n# **Params**\\n\\n# - **Key:** The key to use for the JSON object.\\n# - **Document:** The Document page to use for the JSON object.\\n\\n# **Output**\\n\\n# - **Document:** The Document containing the JSON object.\\n\\nfrom langchain_core.documents import Document\\nfrom langflow import CustomComponent\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass JSONDocumentBuilder(CustomComponent):\\n    display_name: str = \\\"JSON Document Builder\\\"\\n    description: str = \\\"Build a Document containing a JSON object using a key and another Document page content.\\\"\\n    output_types: list[str] = [\\\"Document\\\"]\\n    beta = True\\n    documentation: str = \\\"https://docs.langflow.org/components/utilities#json-document-builder\\\"\\n\\n    field_config = {\\n        \\\"key\\\": {\\\"display_name\\\": \\\"Key\\\"},\\n        \\\"document\\\": {\\\"display_name\\\": \\\"Document\\\"},\\n    }\\n\\n    def build(\\n        self,\\n        key: str,\\n        document: Document,\\n    ) -> Document:\\n        documents = None\\n        if isinstance(document, list):\\n            documents = [\\n                Document(page_content=orjson_dumps({key: doc.page_content}, indent_2=False)) for doc in document\\n            ]\\n        elif isinstance(document, Document):\\n            documents = Document(page_content=orjson_dumps({key: document.page_content}, indent_2=False))\\n        else:\\n            raise TypeError(f\\\"Expected Document or list of Documents, got {type(document)}\\\")\\n        self.repr_value = documents\\n        return documents\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"key\",\"display_name\":\"Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Build a Document containing a JSON object using a key and another Document page content.\",\"base_classes\":[\"Serializable\",\"Document\"],\"display_name\":\"JSON Document Builder\",\"documentation\":\"https://docs.langflow.org/components/utilities#json-document-builder\",\"custom_fields\":{\"key\":null,\"document\":null},\"output_types\":[\"Document\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"output_parsers\":{\"ResponseSchema\":{\"template\":{\"description\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"fileTypes\":[],\"password\":false,\"name\":\"description\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"type\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"string\",\"fileTypes\":[],\"password\":false,\"name\":\"type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"ResponseSchema\"},\"description\":\"A schema for a response from a structured output parser.\",\"base_classes\":[\"ResponseSchema\"],\"display_name\":\"ResponseSchema\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/output_parsers/structured\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"StructuredOutputParser\":{\"template\":{\"response_schemas\":{\"type\":\"ResponseSchema\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"password\":false,\"name\":\"response_schemas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"StructuredOutputParser\"},\"description\":\"\",\"base_classes\":[\"BaseOutputParser\",\"Runnable\",\"BaseLLMOutputParser\",\"Generic\",\"RunnableSerializable\",\"StructuredOutputParser\",\"Serializable\",\"object\"],\"display_name\":\"StructuredOutputParser\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/output_parsers/structured\",\"custom_fields\":{},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":false}},\"retrievers\":{\"AmazonKendra\":{\"template\":{\"attribute_filter\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"attribute_filter\",\"display_name\":\"Attribute Filter\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.retrievers import AmazonKendraRetriever\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonKendraRetrieverComponent(CustomComponent):\\n    display_name: str = \\\"Amazon Kendra Retriever\\\"\\n    description: str = \\\"Retriever that uses the Amazon Kendra API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"index_id\\\": {\\\"display_name\\\": \\\"Index ID\\\"},\\n            \\\"region_name\\\": {\\\"display_name\\\": \\\"Region Name\\\"},\\n            \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n            \\\"attribute_filter\\\": {\\n                \\\"display_name\\\": \\\"Attribute Filter\\\",\\n                \\\"field_type\\\": \\\"code\\\",\\n            },\\n            \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"field_type\\\": \\\"int\\\"},\\n            \\\"user_context\\\": {\\n                \\\"display_name\\\": \\\"User Context\\\",\\n                \\\"field_type\\\": \\\"code\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        index_id: str,\\n        top_k: int = 3,\\n        region_name: Optional[str] = None,\\n        credentials_profile_name: Optional[str] = None,\\n        attribute_filter: Optional[dict] = None,\\n        user_context: Optional[dict] = None,\\n    ) -> BaseRetriever:\\n        try:\\n            output = AmazonKendraRetriever(\\n                index_id=index_id,\\n                top_k=top_k,\\n                region_name=region_name,\\n                credentials_profile_name=credentials_profile_name,\\n                attribute_filter=attribute_filter,\\n                user_context=user_context,\\n            )  # type: ignore\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to AmazonKendra API.\\\") from e\\n        return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"credentials_profile_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"index_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_id\",\"display_name\":\"Index ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"region_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"region_name\",\"display_name\":\"Region Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":3,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"user_context\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"user_context\",\"display_name\":\"User Context\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Retriever that uses the Amazon Kendra API.\",\"base_classes\":[\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Amazon Kendra Retriever\",\"documentation\":\"\",\"custom_fields\":{\"index_id\":null,\"top_k\":null,\"region_name\":null,\"credentials_profile_name\":null,\"attribute_filter\":null,\"user_context\":null},\"output_types\":[\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VectaraSelfQueryRetriver\":{\"template\":{\"llm\":{\"type\":\"BaseLanguageModel\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"For self query retriever\",\"title_case\":false},\"vectorstore\":{\"type\":\"VectorStore\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectorstore\",\"display_name\":\"Vector Store\",\"advanced\":false,\"dynamic\":false,\"info\":\"Input Vectara Vectore Store\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List\\nfrom langflow import CustomComponent\\nimport json\\nfrom langchain.schema import BaseRetriever\\nfrom langchain.schema.vectorstore import VectorStore\\nfrom langchain.base_language import BaseLanguageModel\\nfrom langchain.retrievers.self_query.base import SelfQueryRetriever\\nfrom langchain.chains.query_constructor.base import AttributeInfo\\n\\n\\nclass VectaraSelfQueryRetriverComponent(CustomComponent):\\n    \\\"\\\"\\\"\\n    A custom component for implementing Vectara Self Query Retriever using a vector store.\\n    \\\"\\\"\\\"\\n\\n    display_name: str = \\\"Vectara Self Query Retriever for Vectara Vector Store\\\"\\n    description: str = \\\"Implementation of Vectara Self Query Retriever\\\"\\n    documentation = \\\"https://python.langchain.com/docs/integrations/retrievers/self_query/vectara_self_query\\\"\\n    beta = True\\n\\n    field_config = {\\n        \\\"code\\\": {\\\"show\\\": True},\\n        \\\"vectorstore\\\": {\\\"display_name\\\": \\\"Vector Store\\\", \\\"info\\\": \\\"Input Vectara Vectore Store\\\"},\\n        \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\", \\\"info\\\": \\\"For self query retriever\\\"},\\n        \\\"document_content_description\\\": {\\n            \\\"display_name\\\": \\\"Document Content Description\\\",\\n            \\\"info\\\": \\\"For self query retriever\\\",\\n        },\\n        \\\"metadata_field_info\\\": {\\n            \\\"display_name\\\": \\\"Metadata Field Info\\\",\\n            \\\"info\\\": 'Each metadata field info is a string in the form of key value pair dictionary containing additional search metadata.\\\\nExample input: {\\\"name\\\":\\\"speech\\\",\\\"description\\\":\\\"what name of the speech\\\",\\\"type\\\":\\\"string or list[string]\\\"}.\\\\nThe keys should remain constant(name, description, type)',\\n        },\\n    }\\n\\n    def build(\\n        self,\\n        vectorstore: VectorStore,\\n        document_content_description: str,\\n        llm: BaseLanguageModel,\\n        metadata_field_info: List[str],\\n    ) -> BaseRetriever:\\n        metadata_field_obj = []\\n\\n        for meta in metadata_field_info:\\n            meta_obj = json.loads(meta)\\n            if \\\"name\\\" not in meta_obj or \\\"description\\\" not in meta_obj or \\\"type\\\" not in meta_obj:\\n                raise Exception(\\\"Incorrect metadata field info format.\\\")\\n            attribute_info = AttributeInfo(\\n                name=meta_obj[\\\"name\\\"],\\n                description=meta_obj[\\\"description\\\"],\\n                type=meta_obj[\\\"type\\\"],\\n            )\\n            metadata_field_obj.append(attribute_info)\\n\\n        return SelfQueryRetriever.from_llm(\\n            llm, vectorstore, document_content_description, metadata_field_obj, verbose=True\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"document_content_description\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"document_content_description\",\"display_name\":\"Document Content Description\",\"advanced\":false,\"dynamic\":false,\"info\":\"For self query retriever\",\"title_case\":false,\"input_types\":[\"Text\"]},\"metadata_field_info\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata_field_info\",\"display_name\":\"Metadata Field Info\",\"advanced\":false,\"dynamic\":false,\"info\":\"Each metadata field info is a string in the form of key value pair dictionary containing additional search metadata.\\nExample input: {\\\"name\\\":\\\"speech\\\",\\\"description\\\":\\\"what name of the speech\\\",\\\"type\\\":\\\"string or list[string]\\\"}.\\nThe keys should remain constant(name, description, type)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vectara Self Query Retriever\",\"base_classes\":[\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Vectara Self Query Retriever for Vectara Vector Store\",\"documentation\":\"https://python.langchain.com/docs/integrations/retrievers/self_query/vectara_self_query\",\"custom_fields\":{\"vectorstore\":null,\"document_content_description\":null,\"llm\":null,\"metadata_field_info\":null},\"output_types\":[\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"MultiQueryRetriever\":{\"template\":{\"llm\":{\"type\":\"BaseLLM\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prompt\":{\"type\":\"PromptTemplate\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"retriever\":{\"type\":\"BaseRetriever\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"retriever\",\"display_name\":\"Retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Callable, Optional, Union\\n\\nfrom langchain.retrievers import MultiQueryRetriever\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLLM, BaseRetriever, PromptTemplate\\n\\n\\nclass MultiQueryRetrieverComponent(CustomComponent):\\n    display_name = \\\"MultiQueryRetriever\\\"\\n    description = \\\"Initialize from llm using default template.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/data_connection/retrievers/how_to/MultiQueryRetriever\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n            \\\"prompt\\\": {\\n                \\\"display_name\\\": \\\"Prompt\\\",\\n                \\\"default\\\": {\\n                    \\\"input_variables\\\": [\\\"question\\\"],\\n                    \\\"input_types\\\": {},\\n                    \\\"output_parser\\\": None,\\n                    \\\"partial_variables\\\": {},\\n                    \\\"template\\\": \\\"You are an AI language model assistant. Your task is \\\\n\\\"\\n                    \\\"to generate 3 different versions of the given user \\\\n\\\"\\n                    \\\"question to retrieve relevant documents from a vector database. \\\\n\\\"\\n                    \\\"By generating multiple perspectives on the user question, \\\\n\\\"\\n                    \\\"your goal is to help the user overcome some of the limitations \\\\n\\\"\\n                    \\\"of distance-based similarity search. Provide these alternative \\\\n\\\"\\n                    \\\"questions separated by newlines. Original question: {question}\\\",\\n                    \\\"template_format\\\": \\\"f-string\\\",\\n                    \\\"validate_template\\\": False,\\n                    \\\"_type\\\": \\\"prompt\\\",\\n                },\\n            },\\n            \\\"retriever\\\": {\\\"display_name\\\": \\\"Retriever\\\"},\\n            \\\"parser_key\\\": {\\\"display_name\\\": \\\"Parser Key\\\", \\\"default\\\": \\\"lines\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        llm: BaseLLM,\\n        retriever: BaseRetriever,\\n        prompt: Optional[PromptTemplate] = None,\\n        parser_key: str = \\\"lines\\\",\\n    ) -> Union[Callable, MultiQueryRetriever]:\\n        if not prompt:\\n            return MultiQueryRetriever.from_llm(llm=llm, retriever=retriever, parser_key=parser_key)\\n        else:\\n            return MultiQueryRetriever.from_llm(llm=llm, retriever=retriever, prompt=prompt, parser_key=parser_key)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"parser_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"lines\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"parser_key\",\"display_name\":\"Parser Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Initialize from llm using default template.\",\"base_classes\":[],\"display_name\":\"MultiQueryRetriever\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/retrievers/how_to/MultiQueryRetriever\",\"custom_fields\":{\"llm\":null,\"retriever\":null,\"prompt\":null,\"parser_key\":null},\"output_types\":[],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"MetalRetriever\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"client_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"client_id\",\"display_name\":\"Client ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.retrievers import MetalRetriever\\nfrom metal_sdk.metal import Metal  # type: ignore\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass MetalRetrieverComponent(CustomComponent):\\n    display_name: str = \\\"Metal Retriever\\\"\\n    description: str = \\\"Retriever that uses the Metal API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"password\\\": True},\\n            \\\"client_id\\\": {\\\"display_name\\\": \\\"Client ID\\\", \\\"password\\\": True},\\n            \\\"index_id\\\": {\\\"display_name\\\": \\\"Index ID\\\"},\\n            \\\"params\\\": {\\\"display_name\\\": \\\"Parameters\\\"},\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(self, api_key: str, client_id: str, index_id: str, params: Optional[dict] = None) -> BaseRetriever:\\n        try:\\n            metal = Metal(api_key=api_key, client_id=client_id, index_id=index_id)\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to Metal API.\\\") from e\\n        return MetalRetriever(client=metal, params=params or {})\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"index_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_id\",\"display_name\":\"Index ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"params\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"params\",\"display_name\":\"Parameters\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Retriever that uses the Metal API.\",\"base_classes\":[\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Metal Retriever\",\"documentation\":\"\",\"custom_fields\":{\"api_key\":null,\"client_id\":null,\"index_id\":null,\"params\":null},\"output_types\":[\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"custom_components\":{\"CustomComponent\":{\"template\":{\"param\":{\"type\":\"Data\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"param\",\"display_name\":\"Parameter\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langflow.field_typing import Data\\n\\n\\nclass Component(CustomComponent):\\n    documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n\\n    def build_config(self):\\n        return {\\\"param\\\": {\\\"display_name\\\": \\\"Parameter\\\"}}\\n\\n    def build(self, param: Data) -> Data:\\n        return param\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"base_classes\":[\"object\",\"Data\"],\"display_name\":\"CustomComponent\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"param\":null},\"output_types\":[\"Data\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"vectorstores\":{\"Weaviate\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"attributes\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"attributes\",\"display_name\":\"Attributes\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nimport weaviate  # type: ignore\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain.schema import BaseRetriever, Document\\nfrom langchain_community.vectorstores import VectorStore, Weaviate\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass WeaviateVectorStore(CustomComponent):\\n    display_name: str = \\\"Weaviate\\\"\\n    description: str = \\\"Implementation of Vector Store using Weaviate\\\"\\n    documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/weaviate\\\"\\n    beta = True\\n    field_config = {\\n        \\\"url\\\": {\\\"display_name\\\": \\\"Weaviate URL\\\", \\\"value\\\": \\\"http://localhost:8080\\\"},\\n        \\\"api_key\\\": {\\n            \\\"display_name\\\": \\\"API Key\\\",\\n            \\\"password\\\": True,\\n            \\\"required\\\": False,\\n        },\\n        \\\"index_name\\\": {\\n            \\\"display_name\\\": \\\"Index name\\\",\\n            \\\"required\\\": False,\\n        },\\n        \\\"text_key\\\": {\\\"display_name\\\": \\\"Text Key\\\", \\\"required\\\": False, \\\"advanced\\\": True, \\\"value\\\": \\\"text\\\"},\\n        \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n        \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n        \\\"attributes\\\": {\\n            \\\"display_name\\\": \\\"Attributes\\\",\\n            \\\"required\\\": False,\\n            \\\"is_list\\\": True,\\n            \\\"field_type\\\": \\\"str\\\",\\n            \\\"advanced\\\": True,\\n        },\\n        \\\"search_by_text\\\": {\\\"display_name\\\": \\\"Search By Text\\\", \\\"field_type\\\": \\\"bool\\\", \\\"advanced\\\": True},\\n        \\\"code\\\": {\\\"show\\\": False},\\n    }\\n\\n    def build(\\n        self,\\n        url: str,\\n        search_by_text: bool = False,\\n        api_key: Optional[str] = None,\\n        index_name: Optional[str] = None,\\n        text_key: str = \\\"text\\\",\\n        embedding: Optional[Embeddings] = None,\\n        documents: Optional[Document] = None,\\n        attributes: Optional[list] = None,\\n    ) -> Union[VectorStore, BaseRetriever]:\\n        if api_key:\\n            auth_config = weaviate.AuthApiKey(api_key=api_key)\\n            client = weaviate.Client(url=url, auth_client_secret=auth_config)\\n        else:\\n            client = weaviate.Client(url=url)\\n\\n        def _to_pascal_case(word: str):\\n            if word and not word[0].isupper():\\n                word = word.capitalize()\\n\\n            if word.isidentifier():\\n                return word\\n\\n            word = word.replace(\\\"-\\\", \\\" \\\").replace(\\\"_\\\", \\\" \\\")\\n            parts = word.split()\\n            pascal_case_word = \\\"\\\".join([part.capitalize() for part in parts])\\n\\n            return pascal_case_word\\n\\n        index_name = _to_pascal_case(index_name) if index_name else None\\n\\n        if documents is not None and embedding is not None:\\n            return Weaviate.from_documents(\\n                client=client,\\n                index_name=index_name,\\n                documents=documents,\\n                embedding=embedding,\\n                by_text=search_by_text,\\n            )\\n\\n        return Weaviate(\\n            client=client,\\n            index_name=index_name,\\n            text_key=text_key,\\n            embedding=embedding,\\n            by_text=search_by_text,\\n            attributes=attributes if attributes is not None else [],\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"index_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_name\",\"display_name\":\"Index name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_by_text\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_by_text\",\"display_name\":\"Search By Text\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"text_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"text\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"text_key\",\"display_name\":\"Text Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"http://localhost:8080\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"Weaviate URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using Weaviate\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Weaviate\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/weaviate\",\"custom_fields\":{\"url\":null,\"search_by_text\":null,\"api_key\":null,\"index_name\":null,\"text_key\":null,\"embedding\":null,\"documents\":null,\"attributes\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Vectara\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"If provided, will be upserted to corpus (optional)\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"import tempfile\\nimport urllib\\nimport urllib.request\\nfrom typing import List, Optional, Union\\n\\nfrom langchain_community.embeddings import FakeEmbeddings\\nfrom langchain_community.vectorstores.vectara import Vectara\\nfrom langchain_core.vectorstores import VectorStore\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseRetriever, Document\\n\\n\\nclass VectaraComponent(CustomComponent):\\n    display_name: str = \\\"Vectara\\\"\\n    description: str = \\\"Implementation of Vector Store using Vectara\\\"\\n    documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/vectara\\\"\\n    beta = True\\n    field_config = {\\n        \\\"vectara_customer_id\\\": {\\n            \\\"display_name\\\": \\\"Vectara Customer ID\\\",\\n        },\\n        \\\"vectara_corpus_id\\\": {\\n            \\\"display_name\\\": \\\"Vectara Corpus ID\\\",\\n        },\\n        \\\"vectara_api_key\\\": {\\n            \\\"display_name\\\": \\\"Vectara API Key\\\",\\n            \\\"password\\\": True,\\n        },\\n        \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"info\\\": \\\"If provided, will be upserted to corpus (optional)\\\"},\\n        \\\"files_url\\\": {\\n            \\\"display_name\\\": \\\"Files Url\\\",\\n            \\\"info\\\": \\\"Make vectara object using url of files (optional)\\\",\\n        },\\n    }\\n\\n    def build(\\n        self,\\n        vectara_customer_id: str,\\n        vectara_corpus_id: str,\\n        vectara_api_key: str,\\n        files_url: Optional[List[str]] = None,\\n        documents: Optional[Document] = None,\\n    ) -> Union[VectorStore, BaseRetriever]:\\n        source = \\\"Langflow\\\"\\n\\n        if documents is not None:\\n            return Vectara.from_documents(\\n                documents=documents,  # type: ignore\\n                embedding=FakeEmbeddings(size=768),\\n                vectara_customer_id=vectara_customer_id,\\n                vectara_corpus_id=vectara_corpus_id,\\n                vectara_api_key=vectara_api_key,\\n                source=source,\\n            )\\n\\n        if files_url is not None:\\n            files_list = []\\n            for url in files_url:\\n                name = tempfile.NamedTemporaryFile().name\\n                urllib.request.urlretrieve(url, name)\\n                files_list.append(name)\\n\\n            return Vectara.from_files(\\n                files=files_list,\\n                embedding=FakeEmbeddings(size=768),\\n                vectara_customer_id=vectara_customer_id,\\n                vectara_corpus_id=vectara_corpus_id,\\n                vectara_api_key=vectara_api_key,\\n                source=source,\\n            )\\n\\n        return Vectara(\\n            vectara_customer_id=vectara_customer_id,\\n            vectara_corpus_id=vectara_corpus_id,\\n            vectara_api_key=vectara_api_key,\\n            source=source,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"files_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"files_url\",\"display_name\":\"Files Url\",\"advanced\":false,\"dynamic\":false,\"info\":\"Make vectara object using url of files (optional)\",\"title_case\":false,\"input_types\":[\"Text\"]},\"vectara_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"vectara_api_key\",\"display_name\":\"Vectara API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"vectara_corpus_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectara_corpus_id\",\"display_name\":\"Vectara Corpus ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"vectara_customer_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vectara_customer_id\",\"display_name\":\"Vectara Customer ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using Vectara\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Vectara\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/vectara\",\"custom_fields\":{\"vectara_customer_id\":null,\"vectara_corpus_id\":null,\"vectara_api_key\":null,\"files_url\":null,\"documents\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Chroma\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_cors_allow_origins\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_cors_allow_origins\",\"display_name\":\"Server CORS Allow Origins\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chroma_server_grpc_port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_grpc_port\",\"display_name\":\"Server gRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_host\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_host\",\"display_name\":\"Server Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chroma_server_port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_port\",\"display_name\":\"Server Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_ssl_enabled\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_ssl_enabled\",\"display_name\":\"Server SSL Enabled\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional, Union\\n\\nimport chromadb  # type: ignore\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain.schema import BaseRetriever, Document\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.chroma import Chroma\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass ChromaComponent(CustomComponent):\\n    \\\"\\\"\\\"\\n    A custom component for implementing a Vector Store using Chroma.\\n    \\\"\\\"\\\"\\n\\n    display_name: str = \\\"Chroma\\\"\\n    description: str = \\\"Implementation of Vector Store using Chroma\\\"\\n    documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/chroma\\\"\\n    beta: bool = True\\n    icon = \\\"Chroma\\\"\\n\\n    def build_config(self):\\n        \\\"\\\"\\\"\\n        Builds the configuration for the component.\\n\\n        Returns:\\n        - dict: A dictionary containing the configuration options for the component.\\n        \\\"\\\"\\\"\\n        return {\\n            \\\"collection_name\\\": {\\\"display_name\\\": \\\"Collection Name\\\", \\\"value\\\": \\\"langflow\\\"},\\n            \\\"index_directory\\\": {\\\"display_name\\\": \\\"Persist Directory\\\"},\\n            \\\"code\\\": {\\\"advanced\\\": True, \\\"display_name\\\": \\\"Code\\\"},\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n            \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n            \\\"chroma_server_cors_allow_origins\\\": {\\n                \\\"display_name\\\": \\\"Server CORS Allow Origins\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"chroma_server_host\\\": {\\\"display_name\\\": \\\"Server Host\\\", \\\"advanced\\\": True},\\n            \\\"chroma_server_port\\\": {\\\"display_name\\\": \\\"Server Port\\\", \\\"advanced\\\": True},\\n            \\\"chroma_server_grpc_port\\\": {\\n                \\\"display_name\\\": \\\"Server gRPC Port\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"chroma_server_ssl_enabled\\\": {\\n                \\\"display_name\\\": \\\"Server SSL Enabled\\\",\\n                \\\"advanced\\\": True,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        collection_name: str,\\n        embedding: Embeddings,\\n        chroma_server_ssl_enabled: bool,\\n        index_directory: Optional[str] = None,\\n        documents: Optional[List[Document]] = None,\\n        chroma_server_cors_allow_origins: Optional[str] = None,\\n        chroma_server_host: Optional[str] = None,\\n        chroma_server_port: Optional[int] = None,\\n        chroma_server_grpc_port: Optional[int] = None,\\n    ) -> Union[VectorStore, BaseRetriever]:\\n        \\\"\\\"\\\"\\n        Builds the Vector Store or BaseRetriever object.\\n\\n        Args:\\n        - collection_name (str): The name of the collection.\\n        - index_directory (Optional[str]): The directory to persist the Vector Store to.\\n        - chroma_server_ssl_enabled (bool): Whether to enable SSL for the Chroma server.\\n        - embedding (Optional[Embeddings]): The embeddings to use for the Vector Store.\\n        - documents (Optional[Document]): The documents to use for the Vector Store.\\n        - chroma_server_cors_allow_origins (Optional[str]): The CORS allow origins for the Chroma server.\\n        - chroma_server_host (Optional[str]): The host for the Chroma server.\\n        - chroma_server_port (Optional[int]): The port for the Chroma server.\\n        - chroma_server_grpc_port (Optional[int]): The gRPC port for the Chroma server.\\n\\n        Returns:\\n        - Union[VectorStore, BaseRetriever]: The Vector Store or BaseRetriever object.\\n        \\\"\\\"\\\"\\n\\n        # Chroma settings\\n        chroma_settings = None\\n\\n        if chroma_server_host is not None:\\n            chroma_settings = chromadb.config.Settings(\\n                chroma_server_cors_allow_origins=chroma_server_cors_allow_origins\\n                or None,\\n                chroma_server_host=chroma_server_host,\\n                chroma_server_port=chroma_server_port or None,\\n                chroma_server_grpc_port=chroma_server_grpc_port or None,\\n                chroma_server_ssl_enabled=chroma_server_ssl_enabled,\\n            )\\n\\n        # If documents, then we need to create a Chroma instance using .from_documents\\n\\n        # Check index_directory and expand it if it is a relative path\\n\\n        index_directory = self.resolve_path(index_directory)\\n\\n        if documents is not None and embedding is not None:\\n            if len(documents) == 0:\\n                raise ValueError(\\n                    \\\"If documents are provided, there must be at least one document.\\\"\\n                )\\n            chroma = Chroma.from_documents(\\n                documents=documents,  # type: ignore\\n                persist_directory=index_directory,\\n                collection_name=collection_name,\\n                embedding=embedding,\\n                client_settings=chroma_settings,\\n            )\\n        else:\\n            chroma = Chroma(\\n                persist_directory=index_directory,\\n                client_settings=chroma_settings,\\n                embedding_function=embedding,\\n            )\\n        return chroma\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"langflow\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"index_directory\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_directory\",\"display_name\":\"Persist Directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using Chroma\",\"icon\":\"Chroma\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Chroma\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/chroma\",\"custom_fields\":{\"collection_name\":null,\"embedding\":null,\"chroma_server_ssl_enabled\":null,\"index_directory\":null,\"documents\":null,\"chroma_server_cors_allow_origins\":null,\"chroma_server_host\":null,\"chroma_server_port\":null,\"chroma_server_grpc_port\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"SupabaseVectorStore\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Union\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.supabase import SupabaseVectorStore\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Document, Embeddings, NestedDict\\nfrom supabase.client import Client, create_client\\n\\n\\nclass SupabaseComponent(CustomComponent):\\n    display_name = \\\"Supabase\\\"\\n    description = \\\"Return VectorStore initialized from texts and embeddings.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n            \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n            \\\"query_name\\\": {\\\"display_name\\\": \\\"Query Name\\\"},\\n            \\\"search_kwargs\\\": {\\\"display_name\\\": \\\"Search Kwargs\\\", \\\"advanced\\\": True},\\n            \\\"supabase_service_key\\\": {\\\"display_name\\\": \\\"Supabase Service Key\\\"},\\n            \\\"supabase_url\\\": {\\\"display_name\\\": \\\"Supabase URL\\\"},\\n            \\\"table_name\\\": {\\\"display_name\\\": \\\"Table Name\\\", \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        embedding: Embeddings,\\n        documents: List[Document],\\n        query_name: str = \\\"\\\",\\n        search_kwargs: NestedDict = {},\\n        supabase_service_key: str = \\\"\\\",\\n        supabase_url: str = \\\"\\\",\\n        table_name: str = \\\"\\\",\\n    ) -> Union[VectorStore, SupabaseVectorStore, BaseRetriever]:\\n        supabase: Client = create_client(supabase_url, supabase_key=supabase_service_key)\\n        return SupabaseVectorStore.from_documents(\\n            documents=documents,\\n            embedding=embedding,\\n            query_name=query_name,\\n            search_kwargs=search_kwargs,\\n            client=supabase,\\n            table_name=table_name,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"query_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"query_name\",\"display_name\":\"Query Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_kwargs\":{\"type\":\"NestedDict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_kwargs\",\"display_name\":\"Search Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"supabase_service_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"supabase_service_key\",\"display_name\":\"Supabase Service Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"supabase_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"supabase_url\",\"display_name\":\"Supabase URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"table_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"table_name\",\"display_name\":\"Table Name\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Return VectorStore initialized from texts and embeddings.\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"SupabaseVectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Supabase\",\"documentation\":\"\",\"custom_fields\":{\"embedding\":null,\"documents\":null,\"query_name\":null,\"search_kwargs\":null,\"supabase_service_key\":null,\"supabase_url\":null,\"table_name\":null},\"output_types\":[\"VectorStore\",\"SupabaseVectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Redis\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.redis import Redis\\nfrom langchain_core.documents import Document\\nfrom langchain_core.retrievers import BaseRetriever\\nfrom langflow import CustomComponent\\n\\n\\nclass RedisComponent(CustomComponent):\\n    \\\"\\\"\\\"\\n    A custom component for implementing a Vector Store using Redis.\\n    \\\"\\\"\\\"\\n\\n    display_name: str = \\\"Redis\\\"\\n    description: str = \\\"Implementation of Vector Store using Redis\\\"\\n    documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/redis\\\"\\n    beta = True\\n\\n    def build_config(self):\\n        \\\"\\\"\\\"\\n        Builds the configuration for the component.\\n\\n        Returns:\\n        - dict: A dictionary containing the configuration options for the component.\\n        \\\"\\\"\\\"\\n        return {\\n            \\\"index_name\\\": {\\\"display_name\\\": \\\"Index Name\\\", \\\"value\\\": \\\"your_index\\\"},\\n            \\\"code\\\": {\\\"show\\\": False, \\\"display_name\\\": \\\"Code\\\"},\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n            \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n            \\\"schema\\\": {\\\"display_name\\\": \\\"Schema\\\", \\\"file_types\\\": [\\\".yaml\\\"]},\\n            \\\"redis_server_url\\\": {\\n                \\\"display_name\\\": \\\"Redis Server Connection String\\\",\\n                \\\"advanced\\\": False,\\n            },\\n            \\\"redis_index_name\\\": {\\\"display_name\\\": \\\"Redis Index\\\", \\\"advanced\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        embedding: Embeddings,\\n        redis_server_url: str,\\n        redis_index_name: str,\\n        schema: Optional[str] = None,\\n        documents: Optional[Document] = None,\\n    ) -> Union[VectorStore, BaseRetriever]:\\n        \\\"\\\"\\\"\\n        Builds the Vector Store or BaseRetriever object.\\n\\n        Args:\\n        - embedding (Embeddings): The embeddings to use for the Vector Store.\\n        - documents (Optional[Document]): The documents to use for the Vector Store.\\n        - redis_index_name (str): The name of the Redis index.\\n        - redis_server_url (str): The URL for the Redis server.\\n\\n        Returns:\\n        - VectorStore: The Vector Store object.\\n        \\\"\\\"\\\"\\n        if documents is None:\\n            if schema is None:\\n                raise ValueError(\\\"If no documents are provided, a schema must be provided.\\\")\\n            redis_vs = Redis.from_existing_index(\\n                embedding=embedding,\\n                index_name=redis_index_name,\\n                schema=schema,\\n                key_prefix=None,\\n                redis_url=redis_server_url,\\n            )\\n        else:\\n            redis_vs = Redis.from_documents(\\n                documents=documents,  # type: ignore\\n                embedding=embedding,\\n                redis_url=redis_server_url,\\n                index_name=redis_index_name,\\n            )\\n        return redis_vs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"redis_index_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"redis_index_name\",\"display_name\":\"Redis Index\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"redis_server_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"redis_server_url\",\"display_name\":\"Redis Server Connection String\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"schema\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".yaml\"],\"file_path\":\"\",\"password\":false,\"name\":\"schema\",\"display_name\":\"Schema\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using Redis\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Redis\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/redis\",\"custom_fields\":{\"embedding\":null,\"redis_server_url\":null,\"redis_index_name\":null,\"schema\":null,\"documents\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"pgvector\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langchain.embeddings.base import Embeddings\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.pgvector import PGVector\\nfrom langchain_core.documents import Document\\nfrom langchain_core.retrievers import BaseRetriever\\nfrom langflow import CustomComponent\\n\\n\\nclass PGVectorComponent(CustomComponent):\\n    \\\"\\\"\\\"\\n    A custom component for implementing a Vector Store using PostgreSQL.\\n    \\\"\\\"\\\"\\n\\n    display_name: str = \\\"PGVector\\\"\\n    description: str = \\\"Implementation of Vector Store using PostgreSQL\\\"\\n    documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/pgvector\\\"\\n\\n    def build_config(self):\\n        \\\"\\\"\\\"\\n        Builds the configuration for the component.\\n\\n        Returns:\\n        - dict: A dictionary containing the configuration options for the component.\\n        \\\"\\\"\\\"\\n        return {\\n            \\\"code\\\": {\\\"show\\\": False},\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n            \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n            \\\"pg_server_url\\\": {\\n                \\\"display_name\\\": \\\"PostgreSQL Server Connection String\\\",\\n                \\\"advanced\\\": False,\\n            },\\n            \\\"collection_name\\\": {\\\"display_name\\\": \\\"Table\\\", \\\"advanced\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        embedding: Embeddings,\\n        pg_server_url: str,\\n        collection_name: str,\\n        documents: Optional[Document] = None,\\n    ) -> Union[VectorStore, BaseRetriever]:\\n        \\\"\\\"\\\"\\n        Builds the Vector Store or BaseRetriever object.\\n\\n        Args:\\n        - embedding (Embeddings): The embeddings to use for the Vector Store.\\n        - documents (Optional[Document]): The documents to use for the Vector Store.\\n        - collection_name (str): The name of the PG table.\\n        - pg_server_url (str): The URL for the PG server.\\n\\n        Returns:\\n        - VectorStore: The Vector Store object.\\n        \\\"\\\"\\\"\\n\\n        try:\\n            if documents is None:\\n                vector_store = PGVector.from_existing_index(\\n                    embedding=embedding,\\n                    collection_name=collection_name,\\n                    connection_string=pg_server_url,\\n                )\\n            else:\\n                vector_store = PGVector.from_documents(\\n                    embedding=embedding,\\n                    documents=documents,  # type: ignore\\n                    collection_name=collection_name,\\n                    connection_string=pg_server_url,\\n                )\\n        except Exception as e:\\n            raise RuntimeError(f\\\"Failed to build PGVector: {e}\\\")\\n        return vector_store\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Table\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"pg_server_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"pg_server_url\",\"display_name\":\"PostgreSQL Server Connection String\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Implementation of Vector Store using PostgreSQL\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"PGVector\",\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/pgvector\",\"custom_fields\":{\"embedding\":null,\"pg_server_url\":null,\"collection_name\":null,\"documents\":null},\"output_types\":[\"VectorStore\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Pinecone\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"import os\\nfrom typing import List, Optional, Union\\n\\nimport pinecone  # type: ignore\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.pinecone import Pinecone\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Document, Embeddings\\n\\n\\nclass PineconeComponent(CustomComponent):\\n    display_name = \\\"Pinecone\\\"\\n    description = \\\"Construct Pinecone wrapper from raw documents.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n            \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n            \\\"index_name\\\": {\\\"display_name\\\": \\\"Index Name\\\"},\\n            \\\"namespace\\\": {\\\"display_name\\\": \\\"Namespace\\\"},\\n            \\\"pinecone_api_key\\\": {\\\"display_name\\\": \\\"Pinecone API Key\\\", \\\"default\\\": \\\"\\\", \\\"password\\\": True, \\\"required\\\": True},\\n            \\\"pinecone_env\\\": {\\\"display_name\\\": \\\"Pinecone Environment\\\", \\\"default\\\": \\\"\\\", \\\"required\\\": True},\\n            \\\"search_kwargs\\\": {\\\"display_name\\\": \\\"Search Kwargs\\\", \\\"default\\\": \\\"{}\\\"},\\n            \\\"pool_threads\\\": {\\\"display_name\\\": \\\"Pool Threads\\\", \\\"default\\\": 1, \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        embedding: Embeddings,\\n        pinecone_env: str,\\n        documents: List[Document],\\n        text_key: str = \\\"text\\\",\\n        pool_threads: int = 4,\\n        index_name: Optional[str] = None,\\n        pinecone_api_key: Optional[str] = None,\\n        namespace: Optional[str] = \\\"default\\\",\\n    ) -> Union[VectorStore, Pinecone, BaseRetriever]:\\n        if pinecone_api_key is None or pinecone_env is None:\\n            raise ValueError(\\\"Pinecone API Key and Environment are required.\\\")\\n        if os.getenv(\\\"PINECONE_API_KEY\\\") is None and pinecone_api_key is None:\\n            raise ValueError(\\\"Pinecone API Key is required.\\\")\\n\\n        pinecone.init(api_key=pinecone_api_key, environment=pinecone_env)  # type: ignore\\n        if not index_name:\\n            raise ValueError(\\\"Index Name is required.\\\")\\n        if documents:\\n            return Pinecone.from_documents(\\n                documents=documents,\\n                embedding=embedding,\\n                index_name=index_name,\\n                pool_threads=pool_threads,\\n                namespace=namespace,\\n                text_key=text_key,\\n            )\\n\\n        return Pinecone.from_existing_index(\\n            index_name=index_name,\\n            embedding=embedding,\\n            text_key=text_key,\\n            namespace=namespace,\\n            pool_threads=pool_threads,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"index_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_name\",\"display_name\":\"Index Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"namespace\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"default\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"namespace\",\"display_name\":\"Namespace\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"pinecone_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"pinecone_api_key\",\"display_name\":\"Pinecone API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"pinecone_env\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"pinecone_env\",\"display_name\":\"Pinecone Environment\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"pool_threads\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":4,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"pool_threads\",\"display_name\":\"Pool Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"text_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"text\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"text_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Construct Pinecone wrapper from raw documents.\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"Pinecone\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Pinecone\",\"documentation\":\"\",\"custom_fields\":{\"embedding\":null,\"pinecone_env\":null,\"documents\":null,\"text_key\":null,\"pool_threads\":null,\"index_name\":null,\"pinecone_api_key\":null,\"namespace\":null},\"output_types\":[\"VectorStore\",\"Pinecone\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"Qdrant\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.qdrant import Qdrant\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Document, Embeddings, NestedDict\\n\\n\\nclass QdrantComponent(CustomComponent):\\n    display_name = \\\"Qdrant\\\"\\n    description = \\\"Construct Qdrant wrapper from a list of texts.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n            \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n            \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"password\\\": True, \\\"advanced\\\": True},\\n            \\\"collection_name\\\": {\\\"display_name\\\": \\\"Collection Name\\\"},\\n            \\\"content_payload_key\\\": {\\\"display_name\\\": \\\"Content Payload Key\\\", \\\"advanced\\\": True},\\n            \\\"distance_func\\\": {\\\"display_name\\\": \\\"Distance Function\\\", \\\"advanced\\\": True},\\n            \\\"grpc_port\\\": {\\\"display_name\\\": \\\"gRPC Port\\\", \\\"advanced\\\": True},\\n            \\\"host\\\": {\\\"display_name\\\": \\\"Host\\\", \\\"advanced\\\": True},\\n            \\\"https\\\": {\\\"display_name\\\": \\\"HTTPS\\\", \\\"advanced\\\": True},\\n            \\\"location\\\": {\\\"display_name\\\": \\\"Location\\\", \\\"advanced\\\": True},\\n            \\\"metadata_payload_key\\\": {\\\"display_name\\\": \\\"Metadata Payload Key\\\", \\\"advanced\\\": True},\\n            \\\"path\\\": {\\\"display_name\\\": \\\"Path\\\", \\\"advanced\\\": True},\\n            \\\"port\\\": {\\\"display_name\\\": \\\"Port\\\", \\\"advanced\\\": True},\\n            \\\"prefer_grpc\\\": {\\\"display_name\\\": \\\"Prefer gRPC\\\", \\\"advanced\\\": True},\\n            \\\"prefix\\\": {\\\"display_name\\\": \\\"Prefix\\\", \\\"advanced\\\": True},\\n            \\\"search_kwargs\\\": {\\\"display_name\\\": \\\"Search Kwargs\\\", \\\"advanced\\\": True},\\n            \\\"timeout\\\": {\\\"display_name\\\": \\\"Timeout\\\", \\\"advanced\\\": True},\\n            \\\"url\\\": {\\\"display_name\\\": \\\"URL\\\", \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        embedding: Embeddings,\\n        collection_name: str,\\n        documents: Optional[Document] = None,\\n        api_key: Optional[str] = None,\\n        content_payload_key: str = \\\"page_content\\\",\\n        distance_func: str = \\\"Cosine\\\",\\n        grpc_port: int = 6334,\\n        https: bool = False,\\n        host: Optional[str] = None,\\n        location: Optional[str] = None,\\n        metadata_payload_key: str = \\\"metadata\\\",\\n        path: Optional[str] = None,\\n        port: Optional[int] = 6333,\\n        prefer_grpc: bool = False,\\n        prefix: Optional[str] = None,\\n        search_kwargs: Optional[NestedDict] = None,\\n        timeout: Optional[int] = None,\\n        url: Optional[str] = None,\\n    ) -> Union[VectorStore, Qdrant, BaseRetriever]:\\n        if documents is None:\\n            from qdrant_client import QdrantClient\\n\\n            client = QdrantClient(\\n                location=location,\\n                url=host,\\n                port=port,\\n                grpc_port=grpc_port,\\n                https=https,\\n                prefix=prefix,\\n                timeout=timeout,\\n                prefer_grpc=prefer_grpc,\\n                metadata_payload_key=metadata_payload_key,\\n                content_payload_key=content_payload_key,\\n                api_key=api_key,\\n                collection_name=collection_name,\\n                host=host,\\n                path=path,\\n            )\\n            vs = Qdrant(\\n                client=client,\\n                collection_name=collection_name,\\n                embeddings=embedding,\\n            )\\n            return vs\\n        else:\\n            vs = Qdrant.from_documents(\\n                documents=documents,  # type: ignore\\n                embedding=embedding,\\n                api_key=api_key,\\n                collection_name=collection_name,\\n                content_payload_key=content_payload_key,\\n                distance_func=distance_func,\\n                grpc_port=grpc_port,\\n                host=host,\\n                https=https,\\n                location=location,\\n                metadata_payload_key=metadata_payload_key,\\n                path=path,\\n                port=port,\\n                prefer_grpc=prefer_grpc,\\n                prefix=prefix,\\n                search_kwargs=search_kwargs,\\n                timeout=timeout,\\n                url=url,\\n            )\\n        return vs\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"content_payload_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"page_content\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"content_payload_key\",\"display_name\":\"Content Payload Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"distance_func\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"Cosine\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"distance_func\",\"display_name\":\"Distance Function\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"grpc_port\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6334,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grpc_port\",\"display_name\":\"gRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"host\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"host\",\"display_name\":\"Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"https\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"https\",\"display_name\":\"HTTPS\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"location\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"metadata_payload_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"metadata\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata_payload_key\",\"display_name\":\"Metadata Payload Key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6333,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"port\",\"display_name\":\"Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prefer_grpc\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prefer_grpc\",\"display_name\":\"Prefer gRPC\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"prefix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"prefix\",\"display_name\":\"Prefix\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_kwargs\",\"display_name\":\"Search Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"timeout\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Construct Qdrant wrapper from a list of texts.\",\"base_classes\":[\"Runnable\",\"Generic\",\"VectorStore\",\"Qdrant\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"Qdrant\",\"documentation\":\"\",\"custom_fields\":{\"embedding\":null,\"collection_name\":null,\"documents\":null,\"api_key\":null,\"content_payload_key\":null,\"distance_func\":null,\"grpc_port\":null,\"https\":null,\"host\":null,\"location\":null,\"metadata_payload_key\":null,\"path\":null,\"port\":null,\"prefer_grpc\":null,\"prefix\":null,\"search_kwargs\":null,\"timeout\":null,\"url\":null},\"output_types\":[\"VectorStore\",\"Qdrant\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"MongoDBAtlasVectorSearch\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langchain_community.vectorstores import MongoDBAtlasVectorSearch\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import (\\n    Document,\\n    Embeddings,\\n    NestedDict,\\n)\\n\\n\\nclass MongoDBAtlasComponent(CustomComponent):\\n    display_name = \\\"MongoDB Atlas\\\"\\n    description = \\\"Construct a `MongoDB Atlas Vector Search` vector store from raw documents.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n            \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n            \\\"collection_name\\\": {\\\"display_name\\\": \\\"Collection Name\\\"},\\n            \\\"db_name\\\": {\\\"display_name\\\": \\\"Database Name\\\"},\\n            \\\"index_name\\\": {\\\"display_name\\\": \\\"Index Name\\\"},\\n            \\\"mongodb_atlas_cluster_uri\\\": {\\\"display_name\\\": \\\"MongoDB Atlas Cluster URI\\\"},\\n            \\\"search_kwargs\\\": {\\\"display_name\\\": \\\"Search Kwargs\\\", \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        documents: List[Document],\\n        embedding: Embeddings,\\n        collection_name: str = \\\"\\\",\\n        db_name: str = \\\"\\\",\\n        index_name: str = \\\"\\\",\\n        mongodb_atlas_cluster_uri: str = \\\"\\\",\\n        search_kwargs: Optional[NestedDict] = None,\\n    ) -> MongoDBAtlasVectorSearch:\\n        search_kwargs = search_kwargs or {}\\n        return MongoDBAtlasVectorSearch(\\n            documents=documents,\\n            embedding=embedding,\\n            collection_name=collection_name,\\n            db_name=db_name,\\n            index_name=index_name,\\n            mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri,\\n            search_kwargs=search_kwargs,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"db_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"db_name\",\"display_name\":\"Database Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"index_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_name\",\"display_name\":\"Index Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mongodb_atlas_cluster_uri\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mongodb_atlas_cluster_uri\",\"display_name\":\"MongoDB Atlas Cluster URI\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"search_kwargs\",\"display_name\":\"Search Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct a `MongoDB Atlas Vector Search` vector store from raw documents.\",\"base_classes\":[\"VectorStore\",\"MongoDBAtlasVectorSearch\"],\"display_name\":\"MongoDB Atlas\",\"documentation\":\"\",\"custom_fields\":{\"documents\":null,\"embedding\":null,\"collection_name\":null,\"db_name\":null,\"index_name\":null,\"mongodb_atlas_cluster_uri\":null,\"search_kwargs\":null},\"output_types\":[\"MongoDBAtlasVectorSearch\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChromaSearch\":{\"template\":{\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"Embedding model to vectorize inputs (make sure to use same as index)\",\"title_case\":false},\"inputs\":{\"type\":\"Text\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_cors_allow_origins\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_cors_allow_origins\",\"display_name\":\"Server CORS Allow Origins\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chroma_server_grpc_port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_grpc_port\",\"display_name\":\"Server gRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_host\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_host\",\"display_name\":\"Server Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"chroma_server_port\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_port\",\"display_name\":\"Server Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"chroma_server_ssl_enabled\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"chroma_server_ssl_enabled\",\"display_name\":\"Server SSL Enabled\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nimport chromadb  # type: ignore\\nfrom langchain_community.vectorstores.chroma import Chroma\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Embeddings, Text\\nfrom langflow.schema import Record, docs_to_records\\n\\n\\nclass ChromaSearchComponent(CustomComponent):\\n    \\\"\\\"\\\"\\n    A custom component for implementing a Vector Store using Chroma.\\n    \\\"\\\"\\\"\\n\\n    display_name: str = \\\"Chroma Search\\\"\\n    description: str = \\\"Search a Chroma collection for similar documents.\\\"\\n    beta: bool = True\\n    icon = \\\"Chroma\\\"\\n\\n    def build_config(self):\\n        \\\"\\\"\\\"\\n        Builds the configuration for the component.\\n\\n        Returns:\\n        - dict: A dictionary containing the configuration options for the component.\\n        \\\"\\\"\\\"\\n        return {\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n            \\\"search_type\\\": {\\n                \\\"display_name\\\": \\\"Search Type\\\",\\n                \\\"options\\\": [\\\"Similarity\\\", \\\"MMR\\\"],\\n            },\\n            \\\"collection_name\\\": {\\\"display_name\\\": \\\"Collection Name\\\", \\\"value\\\": \\\"langflow\\\"},\\n            # \\\"persist\\\": {\\\"display_name\\\": \\\"Persist\\\"},\\n            \\\"index_directory\\\": {\\\"display_name\\\": \\\"Index Directory\\\"},\\n            \\\"code\\\": {\\\"show\\\": False, \\\"display_name\\\": \\\"Code\\\"},\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n            \\\"embedding\\\": {\\n                \\\"display_name\\\": \\\"Embedding\\\",\\n                \\\"info\\\": \\\"Embedding model to vectorize inputs (make sure to use same as index)\\\",\\n            },\\n            \\\"chroma_server_cors_allow_origins\\\": {\\n                \\\"display_name\\\": \\\"Server CORS Allow Origins\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"chroma_server_host\\\": {\\\"display_name\\\": \\\"Server Host\\\", \\\"advanced\\\": True},\\n            \\\"chroma_server_port\\\": {\\\"display_name\\\": \\\"Server Port\\\", \\\"advanced\\\": True},\\n            \\\"chroma_server_grpc_port\\\": {\\n                \\\"display_name\\\": \\\"Server gRPC Port\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"chroma_server_ssl_enabled\\\": {\\n                \\\"display_name\\\": \\\"Server SSL Enabled\\\",\\n                \\\"advanced\\\": True,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        inputs: Text,\\n        search_type: str,\\n        collection_name: str,\\n        embedding: Embeddings,\\n        chroma_server_ssl_enabled: bool,\\n        index_directory: Optional[str] = None,\\n        chroma_server_cors_allow_origins: Optional[str] = None,\\n        chroma_server_host: Optional[str] = None,\\n        chroma_server_port: Optional[int] = None,\\n        chroma_server_grpc_port: Optional[int] = None,\\n    ) -> List[Record]:\\n        \\\"\\\"\\\"\\n        Builds the Vector Store or BaseRetriever object.\\n\\n        Args:\\n        - collection_name (str): The name of the collection.\\n        - persist_directory (Optional[str]): The directory to persist the Vector Store to.\\n        - chroma_server_ssl_enabled (bool): Whether to enable SSL for the Chroma server.\\n        - persist (bool): Whether to persist the Vector Store or not.\\n        - embedding (Optional[Embeddings]): The embeddings to use for the Vector Store.\\n        - documents (Optional[Document]): The documents to use for the Vector Store.\\n        - chroma_server_cors_allow_origins (Optional[str]): The CORS allow origins for the Chroma server.\\n        - chroma_server_host (Optional[str]): The host for the Chroma server.\\n        - chroma_server_port (Optional[int]): The port for the Chroma server.\\n        - chroma_server_grpc_port (Optional[int]): The gRPC port for the Chroma server.\\n\\n        Returns:\\n        - Union[VectorStore, BaseRetriever]: The Vector Store or BaseRetriever object.\\n        \\\"\\\"\\\"\\n\\n        # Chroma settings\\n        chroma_settings = None\\n\\n        if chroma_server_host is not None:\\n            chroma_settings = chromadb.config.Settings(\\n                chroma_server_cors_allow_origins=chroma_server_cors_allow_origins or None,\\n                chroma_server_host=chroma_server_host,\\n                chroma_server_port=chroma_server_port or None,\\n                chroma_server_grpc_port=chroma_server_grpc_port or None,\\n                chroma_server_ssl_enabled=chroma_server_ssl_enabled,\\n            )\\n        index_directory = self.resolve_path(index_directory)\\n        chroma = Chroma(\\n            embedding_function=embedding,\\n            collection_name=collection_name,\\n            persist_directory=index_directory,\\n            client_settings=chroma_settings,\\n        )\\n\\n        # Validate the inputs\\n        docs = []\\n        if inputs and isinstance(inputs, str):\\n            docs = chroma.search(query=inputs, search_type=search_type.lower())\\n        else:\\n            raise ValueError(\\\"Invalid inputs provided.\\\")\\n        return docs_to_records(docs)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"collection_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"langflow\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"index_directory\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"index_directory\",\"display_name\":\"Index Directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"search_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Similarity\",\"MMR\"],\"name\":\"search_type\",\"display_name\":\"Search Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Search a Chroma collection for similar documents.\",\"icon\":\"Chroma\",\"base_classes\":[\"Record\"],\"display_name\":\"Chroma Search\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"search_type\":null,\"collection_name\":null,\"embedding\":null,\"chroma_server_ssl_enabled\":null,\"index_directory\":null,\"chroma_server_cors_allow_origins\":null,\"chroma_server_host\":null,\"chroma_server_port\":null,\"chroma_server_grpc_port\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"FAISS\":{\"template\":{\"documents\":{\"type\":\"Document\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"embedding\":{\"type\":\"Embeddings\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Union\\n\\nfrom langchain.schema import BaseRetriever\\nfrom langchain_community.vectorstores import VectorStore\\nfrom langchain_community.vectorstores.faiss import FAISS\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Document, Embeddings\\n\\n\\nclass FAISSComponent(CustomComponent):\\n    display_name = \\\"FAISS\\\"\\n    description = \\\"Construct FAISS wrapper from raw documents.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n            \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        embedding: Embeddings,\\n        documents: List[Document],\\n    ) -> Union[VectorStore, FAISS, BaseRetriever]:\\n        return FAISS.from_documents(documents=documents, embedding=embedding)\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Construct FAISS wrapper from raw documents.\",\"base_classes\":[\"Runnable\",\"FAISS\",\"Generic\",\"VectorStore\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseRetriever\"],\"display_name\":\"FAISS\",\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/faiss\",\"custom_fields\":{\"embedding\":null,\"documents\":null},\"output_types\":[\"VectorStore\",\"FAISS\",\"BaseRetriever\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"models\":{\"LlamaCppModel\":{\"template\":{\"metadata\":{\"type\":\"Dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"Dict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".bin\"],\"file_path\":\"\",\"password\":false,\"name\":\"model_path\",\"display_name\":\"Model Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"client\",\"display_name\":\"Client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Dict, List, Optional\\n\\nfrom langchain_community.llms.llamacpp import LlamaCpp\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass LlamaCppComponent(CustomComponent):\\n    display_name = \\\"LlamaCppModel\\\"\\n    description = \\\"Generate text using llama.cpp model.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"grammar\\\": {\\\"display_name\\\": \\\"Grammar\\\", \\\"advanced\\\": True},\\n            \\\"cache\\\": {\\\"display_name\\\": \\\"Cache\\\", \\\"advanced\\\": True},\\n            \\\"client\\\": {\\\"display_name\\\": \\\"Client\\\", \\\"advanced\\\": True},\\n            \\\"echo\\\": {\\\"display_name\\\": \\\"Echo\\\", \\\"advanced\\\": True},\\n            \\\"f16_kv\\\": {\\\"display_name\\\": \\\"F16 KV\\\", \\\"advanced\\\": True},\\n            \\\"grammar_path\\\": {\\\"display_name\\\": \\\"Grammar Path\\\", \\\"advanced\\\": True},\\n            \\\"last_n_tokens_size\\\": {\\n                \\\"display_name\\\": \\\"Last N Tokens Size\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"logits_all\\\": {\\\"display_name\\\": \\\"Logits All\\\", \\\"advanced\\\": True},\\n            \\\"logprobs\\\": {\\\"display_name\\\": \\\"Logprobs\\\", \\\"advanced\\\": True},\\n            \\\"lora_base\\\": {\\\"display_name\\\": \\\"Lora Base\\\", \\\"advanced\\\": True},\\n            \\\"lora_path\\\": {\\\"display_name\\\": \\\"Lora Path\\\", \\\"advanced\\\": True},\\n            \\\"max_tokens\\\": {\\\"display_name\\\": \\\"Max Tokens\\\", \\\"advanced\\\": True},\\n            \\\"metadata\\\": {\\\"display_name\\\": \\\"Metadata\\\", \\\"advanced\\\": True},\\n            \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"advanced\\\": True},\\n            \\\"model_path\\\": {\\n                \\\"display_name\\\": \\\"Model Path\\\",\\n                \\\"field_type\\\": \\\"file\\\",\\n                \\\"file_types\\\": [\\\".bin\\\"],\\n                \\\"required\\\": True,\\n            },\\n            \\\"n_batch\\\": {\\\"display_name\\\": \\\"N Batch\\\", \\\"advanced\\\": True},\\n            \\\"n_ctx\\\": {\\\"display_name\\\": \\\"N Ctx\\\", \\\"advanced\\\": True},\\n            \\\"n_gpu_layers\\\": {\\\"display_name\\\": \\\"N GPU Layers\\\", \\\"advanced\\\": True},\\n            \\\"n_parts\\\": {\\\"display_name\\\": \\\"N Parts\\\", \\\"advanced\\\": True},\\n            \\\"n_threads\\\": {\\\"display_name\\\": \\\"N Threads\\\", \\\"advanced\\\": True},\\n            \\\"repeat_penalty\\\": {\\\"display_name\\\": \\\"Repeat Penalty\\\", \\\"advanced\\\": True},\\n            \\\"rope_freq_base\\\": {\\\"display_name\\\": \\\"Rope Freq Base\\\", \\\"advanced\\\": True},\\n            \\\"rope_freq_scale\\\": {\\\"display_name\\\": \\\"Rope Freq Scale\\\", \\\"advanced\\\": True},\\n            \\\"seed\\\": {\\\"display_name\\\": \\\"Seed\\\", \\\"advanced\\\": True},\\n            \\\"stop\\\": {\\\"display_name\\\": \\\"Stop\\\", \\\"advanced\\\": True},\\n            \\\"streaming\\\": {\\\"display_name\\\": \\\"Streaming\\\", \\\"advanced\\\": True},\\n            \\\"suffix\\\": {\\\"display_name\\\": \\\"Suffix\\\", \\\"advanced\\\": True},\\n            \\\"tags\\\": {\\\"display_name\\\": \\\"Tags\\\", \\\"advanced\\\": True},\\n            \\\"temperature\\\": {\\\"display_name\\\": \\\"Temperature\\\"},\\n            \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"advanced\\\": True},\\n            \\\"top_p\\\": {\\\"display_name\\\": \\\"Top P\\\", \\\"advanced\\\": True},\\n            \\\"use_mlock\\\": {\\\"display_name\\\": \\\"Use Mlock\\\", \\\"advanced\\\": True},\\n            \\\"use_mmap\\\": {\\\"display_name\\\": \\\"Use Mmap\\\", \\\"advanced\\\": True},\\n            \\\"verbose\\\": {\\\"display_name\\\": \\\"Verbose\\\", \\\"advanced\\\": True},\\n            \\\"vocab_only\\\": {\\\"display_name\\\": \\\"Vocab Only\\\", \\\"advanced\\\": True},\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        model_path: str,\\n        inputs: str,\\n        grammar: Optional[str] = None,\\n        cache: Optional[bool] = None,\\n        client: Optional[Any] = None,\\n        echo: Optional[bool] = False,\\n        f16_kv: bool = True,\\n        grammar_path: Optional[str] = None,\\n        last_n_tokens_size: Optional[int] = 64,\\n        logits_all: bool = False,\\n        logprobs: Optional[int] = None,\\n        lora_base: Optional[str] = None,\\n        lora_path: Optional[str] = None,\\n        max_tokens: Optional[int] = 256,\\n        metadata: Optional[Dict] = None,\\n        model_kwargs: Dict = {},\\n        n_batch: Optional[int] = 8,\\n        n_ctx: int = 512,\\n        n_gpu_layers: Optional[int] = 1,\\n        n_parts: int = -1,\\n        n_threads: Optional[int] = 1,\\n        repeat_penalty: Optional[float] = 1.1,\\n        rope_freq_base: float = 10000.0,\\n        rope_freq_scale: float = 1.0,\\n        seed: int = -1,\\n        stop: Optional[List[str]] = [],\\n        streaming: bool = True,\\n        suffix: Optional[str] = \\\"\\\",\\n        tags: Optional[List[str]] = [],\\n        temperature: Optional[float] = 0.8,\\n        top_k: Optional[int] = 40,\\n        top_p: Optional[float] = 0.95,\\n        use_mlock: bool = False,\\n        use_mmap: Optional[bool] = True,\\n        verbose: bool = True,\\n        vocab_only: bool = False,\\n    ) -> Text:\\n        output = LlamaCpp(\\n            model_path=model_path,\\n            grammar=grammar,\\n            cache=cache,\\n            client=client,\\n            echo=echo,\\n            f16_kv=f16_kv,\\n            grammar_path=grammar_path,\\n            last_n_tokens_size=last_n_tokens_size,\\n            logits_all=logits_all,\\n            logprobs=logprobs,\\n            lora_base=lora_base,\\n            lora_path=lora_path,\\n            max_tokens=max_tokens,\\n            metadata=metadata,\\n            model_kwargs=model_kwargs,\\n            n_batch=n_batch,\\n            n_ctx=n_ctx,\\n            n_gpu_layers=n_gpu_layers,\\n            n_parts=n_parts,\\n            n_threads=n_threads,\\n            repeat_penalty=repeat_penalty,\\n            rope_freq_base=rope_freq_base,\\n            rope_freq_scale=rope_freq_scale,\\n            seed=seed,\\n            stop=stop,\\n            streaming=streaming,\\n            suffix=suffix,\\n            tags=tags,\\n            temperature=temperature,\\n            top_k=top_k,\\n            top_p=top_p,\\n            use_mlock=use_mlock,\\n            use_mmap=use_mmap,\\n            verbose=verbose,\\n            vocab_only=vocab_only,\\n        )\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"echo\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"echo\",\"display_name\":\"Echo\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"f16_kv\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"f16_kv\",\"display_name\":\"F16 KV\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"grammar\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grammar\",\"display_name\":\"Grammar\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"grammar_path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grammar_path\",\"display_name\":\"Grammar Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"last_n_tokens_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":64,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"last_n_tokens_size\",\"display_name\":\"Last N Tokens Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"logits_all\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"logits_all\",\"display_name\":\"Logits All\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"logprobs\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"logprobs\",\"display_name\":\"Logprobs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"lora_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lora_base\",\"display_name\":\"Lora Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"lora_path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lora_path\",\"display_name\":\"Lora Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_batch\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_batch\",\"display_name\":\"N Batch\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_ctx\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":512,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_ctx\",\"display_name\":\"N Ctx\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_gpu_layers\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_gpu_layers\",\"display_name\":\"N GPU Layers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_parts\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":-1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_parts\",\"display_name\":\"N Parts\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_threads\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_threads\",\"display_name\":\"N Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"rope_freq_base\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10000.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"rope_freq_base\",\"display_name\":\"Rope Freq Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"rope_freq_scale\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"rope_freq_scale\",\"display_name\":\"Rope Freq Scale\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"seed\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":-1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"seed\",\"display_name\":\"Seed\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"stop\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"suffix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"suffix\",\"display_name\":\"Suffix\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"display_name\":\"Tags\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"use_mlock\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_mlock\",\"display_name\":\"Use Mlock\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"use_mmap\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_mmap\",\"display_name\":\"Use Mmap\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vocab_only\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vocab_only\",\"display_name\":\"Vocab Only\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using llama.cpp model.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"LlamaCppModel\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp\",\"custom_fields\":{\"model_path\":null,\"inputs\":null,\"grammar\":null,\"cache\":null,\"client\":null,\"echo\":null,\"f16_kv\":null,\"grammar_path\":null,\"last_n_tokens_size\":null,\"logits_all\":null,\"logprobs\":null,\"lora_base\":null,\"lora_path\":null,\"max_tokens\":null,\"metadata\":null,\"model_kwargs\":null,\"n_batch\":null,\"n_ctx\":null,\"n_gpu_layers\":null,\"n_parts\":null,\"n_threads\":null,\"repeat_penalty\":null,\"rope_freq_base\":null,\"rope_freq_scale\":null,\"seed\":null,\"stop\":null,\"streaming\":null,\"suffix\":null,\"tags\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"use_mlock\":null,\"use_mmap\":null,\"verbose\":null,\"vocab_only\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"BaiduQianfanChatModel\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass QianfanChatEndpointComponent(CustomComponent):\\n    display_name: str = \\\"QianfanChat Model\\\"\\n    description: str = (\\n        \\\"Generate text using Baidu Qianfan chat models. Get more detail from \\\"\\n        \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\\\"\\n    )\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"options\\\": [\\n                    \\\"ERNIE-Bot\\\",\\n                    \\\"ERNIE-Bot-turbo\\\",\\n                    \\\"BLOOMZ-7B\\\",\\n                    \\\"Llama-2-7b-chat\\\",\\n                    \\\"Llama-2-13b-chat\\\",\\n                    \\\"Llama-2-70b-chat\\\",\\n                    \\\"Qianfan-BLOOMZ-7B-compressed\\\",\\n                    \\\"Qianfan-Chinese-Llama-2-7B\\\",\\n                    \\\"ChatGLM2-6B-32K\\\",\\n                    \\\"AquilaChat-7B\\\",\\n                ],\\n                \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\",\\n                \\\"required\\\": True,\\n            },\\n            \\\"qianfan_ak\\\": {\\n                \\\"display_name\\\": \\\"Qianfan Ak\\\",\\n                \\\"required\\\": True,\\n                \\\"password\\\": True,\\n                \\\"info\\\": \\\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\\\",\\n            },\\n            \\\"qianfan_sk\\\": {\\n                \\\"display_name\\\": \\\"Qianfan Sk\\\",\\n                \\\"required\\\": True,\\n                \\\"password\\\": True,\\n                \\\"info\\\": \\\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\\\",\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top p\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n                \\\"value\\\": 0.8,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n                \\\"value\\\": 0.95,\\n            },\\n            \\\"penalty_score\\\": {\\n                \\\"display_name\\\": \\\"Penalty Score\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n                \\\"value\\\": 1.0,\\n            },\\n            \\\"endpoint\\\": {\\n                \\\"display_name\\\": \\\"Endpoint\\\",\\n                \\\"info\\\": \\\"Endpoint of the Qianfan LLM, required if custom model used.\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        inputs: str,\\n        model: str = \\\"ERNIE-Bot-turbo\\\",\\n        qianfan_ak: Optional[str] = None,\\n        qianfan_sk: Optional[str] = None,\\n        top_p: Optional[float] = None,\\n        temperature: Optional[float] = None,\\n        penalty_score: Optional[float] = None,\\n        endpoint: Optional[str] = None,\\n    ) -> Text:\\n        try:\\n            output = QianfanChatEndpoint(  # type: ignore\\n                model=model,\\n                qianfan_ak=SecretStr(qianfan_ak) if qianfan_ak else None,\\n                qianfan_sk=SecretStr(qianfan_sk) if qianfan_sk else None,\\n                top_p=top_p,\\n                temperature=temperature,\\n                penalty_score=penalty_score,\\n                endpoint=endpoint,\\n            )\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to Baidu Qianfan API.\\\") from e\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint\",\"display_name\":\"Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Qianfan LLM, required if custom model used.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"ERNIE-Bot-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ERNIE-Bot\",\"ERNIE-Bot-turbo\",\"BLOOMZ-7B\",\"Llama-2-7b-chat\",\"Llama-2-13b-chat\",\"Llama-2-70b-chat\",\"Qianfan-BLOOMZ-7B-compressed\",\"Qianfan-Chinese-Llama-2-7B\",\"ChatGLM2-6B-32K\",\"AquilaChat-7B\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"title_case\":false,\"input_types\":[\"Text\"]},\"penalty_score\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"penalty_score\",\"display_name\":\"Penalty Score\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"qianfan_ak\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_ak\",\"display_name\":\"Qianfan Ak\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"qianfan_sk\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_sk\",\"display_name\":\"Qianfan Sk\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Baidu Qianfan chat models. Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"QianfanChat Model\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"model\":null,\"qianfan_ak\":null,\"qianfan_sk\":null,\"top_p\":null,\"temperature\":null,\"penalty_score\":null,\"endpoint\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GoogleGenerativeAIModel\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_google_genai import ChatGoogleGenerativeAI  # type: ignore\\nfrom pydantic.v1.types import SecretStr\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import RangeSpec, Text\\n\\n\\nclass GoogleGenerativeAIComponent(CustomComponent):\\n    display_name: str = \\\"Google Generative AIModel\\\"\\n    description: str = \\\"Generate text using Google Generative AI to generate text.\\\"\\n    documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"google_api_key\\\": {\\n                \\\"display_name\\\": \\\"Google API Key\\\",\\n                \\\"info\\\": \\\"The Google API Key to use for the Google Generative AI.\\\",\\n            },\\n            \\\"max_output_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n                \\\"info\\\": \\\"The maximum number of tokens to generate.\\\",\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"info\\\": \\\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\\\",\\n            },\\n            \\\"top_k\\\": {\\n                \\\"display_name\\\": \\\"Top K\\\",\\n                \\\"info\\\": \\\"Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.\\\",\\n                \\\"range_spec\\\": RangeSpec(min=0, max=2, step=0.1),\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top P\\\",\\n                \\\"info\\\": \\\"The maximum cumulative probability of tokens to consider when sampling.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"n\\\": {\\n                \\\"display_name\\\": \\\"N\\\",\\n                \\\"info\\\": \\\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model\\\",\\n                \\\"info\\\": \\\"The name of the model to use. Supported examples: gemini-pro\\\",\\n                \\\"options\\\": [\\\"gemini-pro\\\", \\\"gemini-pro-vision\\\"],\\n            },\\n            \\\"code\\\": {\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        google_api_key: str,\\n        model: str,\\n        inputs: str,\\n        max_output_tokens: Optional[int] = None,\\n        temperature: float = 0.1,\\n        top_k: Optional[int] = None,\\n        top_p: Optional[float] = None,\\n        n: Optional[int] = 1,\\n    ) -> Text:\\n        output = ChatGoogleGenerativeAI(\\n            model=model,\\n            max_output_tokens=max_output_tokens or None,  # type: ignore\\n            temperature=temperature,\\n            top_k=top_k or None,\\n            top_p=top_p or None,  # type: ignore\\n            n=n or 1,\\n            google_api_key=SecretStr(google_api_key),\\n        )\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"google_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"google_api_key\",\"display_name\":\"Google API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The Google API Key to use for the Google Generative AI.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum number of tokens to generate.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gemini-pro\",\"gemini-pro-vision\"],\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"The name of the model to use. Supported examples: gemini-pro\",\"title_case\":false,\"input_types\":[\"Text\"]},\"n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.\",\"rangeSpec\":{\"min\":0.0,\"max\":2.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"The maximum cumulative probability of tokens to consider when sampling.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Google Generative AI to generate text.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Google Generative AIModel\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"google_api_key\":null,\"model\":null,\"inputs\":null,\"max_output_tokens\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"n\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CTransformersModel\":{\"template\":{\"model_file\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".bin\"],\"file_path\":\"\",\"password\":false,\"name\":\"model_file\",\"display_name\":\"Model File\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Dict, Optional\\n\\nfrom langchain_community.llms.ctransformers import CTransformers\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass CTransformersComponent(CustomComponent):\\n    display_name = \\\"CTransformersModel\\\"\\n    description = \\\"Generate text using CTransformers LLM models\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\\"display_name\\\": \\\"Model\\\", \\\"required\\\": True},\\n            \\\"model_file\\\": {\\n                \\\"display_name\\\": \\\"Model File\\\",\\n                \\\"required\\\": False,\\n                \\\"field_type\\\": \\\"file\\\",\\n                \\\"file_types\\\": [\\\".bin\\\"],\\n            },\\n            \\\"model_type\\\": {\\\"display_name\\\": \\\"Model Type\\\", \\\"required\\\": True},\\n            \\\"config\\\": {\\n                \\\"display_name\\\": \\\"Config\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n                \\\"field_type\\\": \\\"dict\\\",\\n                \\\"value\\\": '{\\\"top_k\\\":40,\\\"top_p\\\":0.95,\\\"temperature\\\":0.8,\\\"repetition_penalty\\\":1.1,\\\"last_n_tokens\\\":64,\\\"seed\\\":-1,\\\"max_new_tokens\\\":256,\\\"stop\\\":\\\"\\\",\\\"stream\\\":\\\"False\\\",\\\"reset\\\":\\\"True\\\",\\\"batch_size\\\":8,\\\"threads\\\":-1,\\\"context_length\\\":-1,\\\"gpu_layers\\\":0}',\\n            },\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        model: str,\\n        model_file: str,\\n        inputs: str,\\n        model_type: str,\\n        config: Optional[Dict] = None,\\n    ) -> Text:\\n        output = CTransformers(model=model, model_file=model_file, model_type=model_type, config=config)\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"config\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"{\\\"top_k\\\":40,\\\"top_p\\\":0.95,\\\"temperature\\\":0.8,\\\"repetition_penalty\\\":1.1,\\\"last_n_tokens\\\":64,\\\"seed\\\":-1,\\\"max_new_tokens\\\":256,\\\"stop\\\":\\\"\\\",\\\"stream\\\":\\\"False\\\",\\\"reset\\\":\\\"True\\\",\\\"batch_size\\\":8,\\\"threads\\\":-1,\\\"context_length\\\":-1,\\\"gpu_layers\\\":0}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"config\",\"display_name\":\"Config\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_type\",\"display_name\":\"Model Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using CTransformers LLM models\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"CTransformersModel\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers\",\"custom_fields\":{\"model\":null,\"model_file\":null,\"inputs\":null,\"model_type\":null,\"config\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VertexAiModel\":{\"template\":{\"credentials\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".json\"],\"password\":false,\"name\":\"credentials\",\"display_name\":\"Credentials\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"examples\":{\"type\":\"BaseMessage\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":true,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"examples\",\"display_name\":\"Examples\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langchain_core.messages.base import BaseMessage\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass ChatVertexAIComponent(CustomComponent):\\n    display_name = \\\"ChatVertexAIModel\\\"\\n    description = \\\"Generate text using Vertex AI Chat large language models API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"credentials\\\": {\\n                \\\"display_name\\\": \\\"Credentials\\\",\\n                \\\"field_type\\\": \\\"file\\\",\\n                \\\"file_types\\\": [\\\".json\\\"],\\n                \\\"file_path\\\": None,\\n            },\\n            \\\"examples\\\": {\\n                \\\"display_name\\\": \\\"Examples\\\",\\n                \\\"multiline\\\": True,\\n            },\\n            \\\"location\\\": {\\n                \\\"display_name\\\": \\\"Location\\\",\\n                \\\"value\\\": \\\"us-central1\\\",\\n            },\\n            \\\"max_output_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n                \\\"value\\\": 128,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"model_name\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"value\\\": \\\"chat-bison\\\",\\n            },\\n            \\\"project\\\": {\\n                \\\"display_name\\\": \\\"Project\\\",\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"value\\\": 0.0,\\n            },\\n            \\\"top_k\\\": {\\n                \\\"display_name\\\": \\\"Top K\\\",\\n                \\\"value\\\": 40,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top P\\\",\\n                \\\"value\\\": 0.95,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"verbose\\\": {\\n                \\\"display_name\\\": \\\"Verbose\\\",\\n                \\\"value\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        inputs: str,\\n        credentials: Optional[str],\\n        project: str,\\n        examples: Optional[List[BaseMessage]] = [],\\n        location: str = \\\"us-central1\\\",\\n        max_output_tokens: int = 128,\\n        model_name: str = \\\"chat-bison\\\",\\n        temperature: float = 0.0,\\n        top_k: int = 40,\\n        top_p: float = 0.95,\\n        verbose: bool = False,\\n    ) -> Text:\\n        try:\\n            from langchain_google_vertexai import ChatVertexAI\\n        except ImportError:\\n            raise ImportError(\\n                \\\"To use the ChatVertexAI model, you need to install the langchain-google-vertexai package.\\\"\\n            )\\n        output = ChatVertexAI(\\n            credentials=credentials,\\n            examples=examples,\\n            location=location,\\n            max_output_tokens=max_output_tokens,\\n            model_name=model_name,\\n            project=project,\\n            temperature=temperature,\\n            top_k=top_k,\\n            top_p=top_p,\\n            verbose=verbose,\\n        )\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"location\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"us-central1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":128,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat-bison\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"project\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"project\",\"display_name\":\"Project\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Vertex AI Chat large language models API.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"ChatVertexAIModel\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"credentials\":null,\"project\":null,\"examples\":null,\"location\":null,\"max_output_tokens\":null,\"model_name\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"verbose\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OllamaModel\":{\"template\":{\"metadata\":{\"type\":\"Dict[str, Any]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":true,\"dynamic\":false,\"info\":\"Metadata to add to the run trace.\",\"title_case\":false},\"stop\":{\"type\":\"list\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"List of tokens to signal the model to stop generating text.\",\"title_case\":false},\"tags\":{\"type\":\"list\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"display_name\":\"Tags\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tags to add to the run trace.\",\"title_case\":false},\"base_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"base_url\",\"display_name\":\"Base URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable or disable caching.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Dict, List, Optional\\n\\n# from langchain_community.chat_models import ChatOllama\\nfrom langchain_community.chat_models import ChatOllama\\n\\n# from langchain.chat_models import ChatOllama\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n# whe When a callback component is added to Langflow, the comment must be uncommented.\\n# from langchain.callbacks.manager import CallbackManager\\n\\n\\nclass ChatOllamaComponent(CustomComponent):\\n    display_name = \\\"ChatOllamaModel\\\"\\n    description = \\\"Generate text using Local LLM for chat with Ollama.\\\"\\n\\n    def build_config(self) -> dict:\\n        return {\\n            \\\"base_url\\\": {\\n                \\\"display_name\\\": \\\"Base URL\\\",\\n                \\\"info\\\": \\\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\\\",\\n            },\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"value\\\": \\\"llama2\\\",\\n                \\\"info\\\": \\\"Refer to https://ollama.ai/library for more models.\\\",\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"value\\\": 0.8,\\n                \\\"info\\\": \\\"Controls the creativity of model responses.\\\",\\n            },\\n            \\\"cache\\\": {\\n                \\\"display_name\\\": \\\"Cache\\\",\\n                \\\"field_type\\\": \\\"bool\\\",\\n                \\\"info\\\": \\\"Enable or disable caching.\\\",\\n                \\\"advanced\\\": True,\\n                \\\"value\\\": False,\\n            },\\n            ### When a callback component is added to Langflow, the comment must be uncommented. ###\\n            # \\\"callback_manager\\\": {\\n            #     \\\"display_name\\\": \\\"Callback Manager\\\",\\n            #     \\\"info\\\": \\\"Optional callback manager for additional functionality.\\\",\\n            #     \\\"advanced\\\": True,\\n            # },\\n            # \\\"callbacks\\\": {\\n            #     \\\"display_name\\\": \\\"Callbacks\\\",\\n            #     \\\"info\\\": \\\"Callbacks to execute during model runtime.\\\",\\n            #     \\\"advanced\\\": True,\\n            # },\\n            ########################################################################################\\n            \\\"format\\\": {\\n                \\\"display_name\\\": \\\"Format\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"info\\\": \\\"Specify the format of the output (e.g., json).\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"metadata\\\": {\\n                \\\"display_name\\\": \\\"Metadata\\\",\\n                \\\"info\\\": \\\"Metadata to add to the run trace.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"mirostat\\\": {\\n                \\\"display_name\\\": \\\"Mirostat\\\",\\n                \\\"options\\\": [\\\"Disabled\\\", \\\"Mirostat\\\", \\\"Mirostat 2.0\\\"],\\n                \\\"info\\\": \\\"Enable/disable Mirostat sampling for controlling perplexity.\\\",\\n                \\\"value\\\": \\\"Disabled\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"mirostat_eta\\\": {\\n                \\\"display_name\\\": \\\"Mirostat Eta\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Learning rate for Mirostat algorithm. (Default: 0.1)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"mirostat_tau\\\": {\\n                \\\"display_name\\\": \\\"Mirostat Tau\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"num_ctx\\\": {\\n                \\\"display_name\\\": \\\"Context Window Size\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Size of the context window for generating tokens. (Default: 2048)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"num_gpu\\\": {\\n                \\\"display_name\\\": \\\"Number of GPUs\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"num_thread\\\": {\\n                \\\"display_name\\\": \\\"Number of Threads\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Number of threads to use during computation. (Default: detected for optimal performance)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"repeat_last_n\\\": {\\n                \\\"display_name\\\": \\\"Repeat Last N\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"repeat_penalty\\\": {\\n                \\\"display_name\\\": \\\"Repeat Penalty\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Penalty for repetitions in generated text. (Default: 1.1)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"tfs_z\\\": {\\n                \\\"display_name\\\": \\\"TFS Z\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Tail free sampling value. (Default: 1)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"timeout\\\": {\\n                \\\"display_name\\\": \\\"Timeout\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Timeout for the request stream.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_k\\\": {\\n                \\\"display_name\\\": \\\"Top K\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Limits token selection to top K. (Default: 40)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top P\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Works together with top-k. (Default: 0.9)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"verbose\\\": {\\n                \\\"display_name\\\": \\\"Verbose\\\",\\n                \\\"field_type\\\": \\\"bool\\\",\\n                \\\"info\\\": \\\"Whether to print out response text.\\\",\\n            },\\n            \\\"tags\\\": {\\n                \\\"display_name\\\": \\\"Tags\\\",\\n                \\\"field_type\\\": \\\"list\\\",\\n                \\\"info\\\": \\\"Tags to add to the run trace.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"stop\\\": {\\n                \\\"display_name\\\": \\\"Stop Tokens\\\",\\n                \\\"field_type\\\": \\\"list\\\",\\n                \\\"info\\\": \\\"List of tokens to signal the model to stop generating text.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"system\\\": {\\n                \\\"display_name\\\": \\\"System\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"info\\\": \\\"System to use for generating text.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"template\\\": {\\n                \\\"display_name\\\": \\\"Template\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"info\\\": \\\"Template to use for generating text.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        base_url: Optional[str],\\n        model: str,\\n        inputs: str,\\n        mirostat: Optional[str],\\n        mirostat_eta: Optional[float] = None,\\n        mirostat_tau: Optional[float] = None,\\n        ### When a callback component is added to Langflow, the comment must be uncommented.###\\n        # callback_manager: Optional[CallbackManager] = None,\\n        # callbacks: Optional[List[Callbacks]] = None,\\n        #######################################################################################\\n        repeat_last_n: Optional[int] = None,\\n        verbose: Optional[bool] = None,\\n        cache: Optional[bool] = None,\\n        num_ctx: Optional[int] = None,\\n        num_gpu: Optional[int] = None,\\n        format: Optional[str] = None,\\n        metadata: Optional[Dict[str, Any]] = None,\\n        num_thread: Optional[int] = None,\\n        repeat_penalty: Optional[float] = None,\\n        stop: Optional[List[str]] = None,\\n        system: Optional[str] = None,\\n        tags: Optional[List[str]] = None,\\n        temperature: Optional[float] = None,\\n        template: Optional[str] = None,\\n        tfs_z: Optional[float] = None,\\n        timeout: Optional[int] = None,\\n        top_k: Optional[int] = None,\\n        top_p: Optional[int] = None,\\n    ) -> Text:\\n        if not base_url:\\n            base_url = \\\"http://localhost:11434\\\"\\n\\n        # Mapping mirostat settings to their corresponding values\\n        mirostat_options = {\\\"Mirostat\\\": 1, \\\"Mirostat 2.0\\\": 2}\\n\\n        # Default to 0 for 'Disabled'\\n        mirostat_value = mirostat_options.get(mirostat, 0)  # type: ignore\\n\\n        # Set mirostat_eta and mirostat_tau to None if mirostat is disabled\\n        if mirostat_value == 0:\\n            mirostat_eta = None\\n            mirostat_tau = None\\n\\n        # Mapping system settings to their corresponding values\\n        llm_params = {\\n            \\\"base_url\\\": base_url,\\n            \\\"cache\\\": cache,\\n            \\\"model\\\": model,\\n            \\\"mirostat\\\": mirostat_value,\\n            \\\"format\\\": format,\\n            \\\"metadata\\\": metadata,\\n            \\\"tags\\\": tags,\\n            ## When a callback component is added to Langflow, the comment must be uncommented.##\\n            # \\\"callback_manager\\\": callback_manager,\\n            # \\\"callbacks\\\": callbacks,\\n            #####################################################################################\\n            \\\"mirostat_eta\\\": mirostat_eta,\\n            \\\"mirostat_tau\\\": mirostat_tau,\\n            \\\"num_ctx\\\": num_ctx,\\n            \\\"num_gpu\\\": num_gpu,\\n            \\\"num_thread\\\": num_thread,\\n            \\\"repeat_last_n\\\": repeat_last_n,\\n            \\\"repeat_penalty\\\": repeat_penalty,\\n            \\\"temperature\\\": temperature,\\n            \\\"stop\\\": stop,\\n            \\\"system\\\": system,\\n            \\\"template\\\": template,\\n            \\\"tfs_z\\\": tfs_z,\\n            \\\"timeout\\\": timeout,\\n            \\\"top_k\\\": top_k,\\n            \\\"top_p\\\": top_p,\\n            \\\"verbose\\\": verbose,\\n        }\\n\\n        # None Value remove\\n        llm_params = {k: v for k, v in llm_params.items() if v is not None}\\n\\n        try:\\n            output = ChatOllama(**llm_params)  # type: ignore\\n        except Exception as e:\\n            raise ValueError(\\\"Could not initialize Ollama LLM.\\\") from e\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"format\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"format\",\"display_name\":\"Format\",\"advanced\":true,\"dynamic\":false,\"info\":\"Specify the format of the output (e.g., json).\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Disabled\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Disabled\",\"Mirostat\",\"Mirostat 2.0\"],\"name\":\"mirostat\",\"display_name\":\"Mirostat\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable/disable Mirostat sampling for controlling perplexity.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat_eta\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_eta\",\"display_name\":\"Mirostat Eta\",\"advanced\":true,\"dynamic\":false,\"info\":\"Learning rate for Mirostat algorithm. (Default: 0.1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"mirostat_tau\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_tau\",\"display_name\":\"Mirostat Tau\",\"advanced\":true,\"dynamic\":false,\"info\":\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"llama2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"Refer to https://ollama.ai/library for more models.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"num_ctx\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_ctx\",\"display_name\":\"Context Window Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"Size of the context window for generating tokens. (Default: 2048)\",\"title_case\":false},\"num_gpu\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_gpu\",\"display_name\":\"Number of GPUs\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\",\"title_case\":false},\"num_thread\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_thread\",\"display_name\":\"Number of Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of threads to use during computation. (Default: detected for optimal performance)\",\"title_case\":false},\"repeat_last_n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_last_n\",\"display_name\":\"Repeat Last N\",\"advanced\":true,\"dynamic\":false,\"info\":\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"Penalty for repetitions in generated text. (Default: 1.1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"system\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"system\",\"display_name\":\"System\",\"advanced\":true,\"dynamic\":false,\"info\":\"System to use for generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Controls the creativity of model responses.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"template\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"display_name\":\"Template\",\"advanced\":true,\"dynamic\":false,\"info\":\"Template to use for generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"tfs_z\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tfs_z\",\"display_name\":\"TFS Z\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tail free sampling value. (Default: 1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"timeout\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"Timeout for the request stream.\",\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Limits token selection to top K. (Default: 40)\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"Works together with top-k. (Default: 0.9)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"Whether to print out response text.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Local LLM for chat with Ollama.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"ChatOllamaModel\",\"documentation\":\"\",\"custom_fields\":{\"base_url\":null,\"model\":null,\"inputs\":null,\"mirostat\":null,\"mirostat_eta\":null,\"mirostat_tau\":null,\"repeat_last_n\":null,\"verbose\":null,\"cache\":null,\"num_ctx\":null,\"num_gpu\":null,\"format\":null,\"metadata\":null,\"num_thread\":null,\"repeat_penalty\":null,\"stop\":null,\"system\":null,\"tags\":null,\"temperature\":null,\"template\":null,\"tfs_z\":null,\"timeout\":null,\"top_k\":null,\"top_p\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AnthropicModel\":{\"template\":{\"anthropic_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"anthropic_api_key\",\"display_name\":\"Anthropic API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Anthropic API key.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"api_endpoint\",\"display_name\":\"API Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.anthropic import ChatAnthropic\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass AnthropicLLM(CustomComponent):\\n    display_name: str = \\\"AnthropicModel\\\"\\n    description: str = \\\"Generate text using Anthropic Chat&Completion large language models.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"options\\\": [\\n                    \\\"claude-2.1\\\",\\n                    \\\"claude-2.0\\\",\\n                    \\\"claude-instant-1.2\\\",\\n                    \\\"claude-instant-1\\\",\\n                    # Add more models as needed\\n                ],\\n                \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/anthropic\\\",\\n                \\\"required\\\": True,\\n                \\\"value\\\": \\\"claude-2.1\\\",\\n            },\\n            \\\"anthropic_api_key\\\": {\\n                \\\"display_name\\\": \\\"Anthropic API Key\\\",\\n                \\\"required\\\": True,\\n                \\\"password\\\": True,\\n                \\\"info\\\": \\\"Your Anthropic API key.\\\",\\n            },\\n            \\\"max_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Tokens\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"value\\\": 256,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"value\\\": 0.7,\\n            },\\n            \\\"api_endpoint\\\": {\\n                \\\"display_name\\\": \\\"API Endpoint\\\",\\n                \\\"info\\\": \\\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        model: str,\\n        inputs: str,\\n        anthropic_api_key: Optional[str] = None,\\n        max_tokens: Optional[int] = None,\\n        temperature: Optional[float] = None,\\n        api_endpoint: Optional[str] = None,\\n    ) -> Text:\\n        # Set default API endpoint if not provided\\n        if not api_endpoint:\\n            api_endpoint = \\\"https://api.anthropic.com\\\"\\n\\n        try:\\n            output = ChatAnthropic(\\n                model_name=model,\\n                anthropic_api_key=(SecretStr(anthropic_api_key) if anthropic_api_key else None),\\n                max_tokens_to_sample=max_tokens,  # type: ignore\\n                temperature=temperature,\\n                anthropic_api_url=api_endpoint,\\n            )\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to Anthropic API.\\\") from e\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"claude-2.1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"claude-2.1\",\"claude-2.0\",\"claude-instant-1.2\",\"claude-instant-1\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/anthropic\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Anthropic Chat&Completion large language models.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"AnthropicModel\",\"documentation\":\"\",\"custom_fields\":{\"model\":null,\"inputs\":null,\"anthropic_api_key\":null,\"max_tokens\":null,\"temperature\":null,\"api_endpoint\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OpenAIModel\":{\"template\":{\"inputs\":{\"type\":\"Text\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_openai import ChatOpenAI\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import NestedDict, Text\\n\\n\\nclass OpenAIModelComponent(CustomComponent):\\n    display_name = \\\"OpenAI Model\\\"\\n    description = \\\"Generates text using OpenAI's models.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n            \\\"max_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Tokens\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n            },\\n            \\\"model_kwargs\\\": {\\n                \\\"display_name\\\": \\\"Model Kwargs\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n            },\\n            \\\"model_name\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"options\\\": [\\n                    \\\"gpt-4-turbo-preview\\\",\\n                    \\\"gpt-4-0125-preview\\\",\\n                    \\\"gpt-4-1106-preview\\\",\\n                    \\\"gpt-4-vision-preview\\\",\\n                    \\\"gpt-3.5-turbo-0125\\\",\\n                    \\\"gpt-3.5-turbo-1106\\\",\\n                ],\\n            },\\n            \\\"openai_api_base\\\": {\\n                \\\"display_name\\\": \\\"OpenAI API Base\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"info\\\": (\\n                    \\\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\\\n\\\\n\\\"\\n                    \\\"You can change this to use other APIs like JinaChat, LocalAI and Prem.\\\"\\n                ),\\n            },\\n            \\\"openai_api_key\\\": {\\n                \\\"display_name\\\": \\\"OpenAI API Key\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"password\\\": True,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"value\\\": 0.7,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        inputs: Text,\\n        max_tokens: Optional[int] = 256,\\n        model_kwargs: NestedDict = {},\\n        model_name: str = \\\"gpt-4-1106-preview\\\",\\n        openai_api_base: Optional[str] = None,\\n        openai_api_key: Optional[str] = None,\\n        temperature: float = 0.7,\\n    ) -> Text:\\n        if not openai_api_base:\\n            openai_api_base = \\\"https://api.openai.com/v1\\\"\\n        model = ChatOpenAI(\\n            max_tokens=max_tokens,\\n            model_kwargs=model_kwargs,\\n            model=model_name,\\n            base_url=openai_api_base,\\n            api_key=openai_api_key,\\n            temperature=temperature,\\n        )\\n\\n        message = model.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-4-turbo-preview\",\"gpt-4-0125-preview\",\"gpt-4-1106-preview\",\"gpt-4-vision-preview\",\"gpt-3.5-turbo-0125\",\"gpt-3.5-turbo-1106\"],\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generates text using OpenAI's models.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"OpenAI Model\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"max_tokens\":null,\"model_kwargs\":null,\"model_name\":null,\"openai_api_base\":null,\"openai_api_key\":null,\"temperature\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"HuggingFaceModel\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.huggingface import ChatHuggingFace\\nfrom langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint\\n\\nfrom langflow import CustomComponent\\n\\nfrom langflow.field_typing import Text\\n\\n\\nclass HuggingFaceEndpointsComponent(CustomComponent):\\n    display_name: str = \\\"Hugging Face Inference API models\\\"\\n    description: str = \\\"Generate text using LLM model from Hugging Face Inference API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Endpoint URL\\\", \\\"password\\\": True},\\n            \\\"task\\\": {\\n                \\\"display_name\\\": \\\"Task\\\",\\n                \\\"options\\\": [\\\"text2text-generation\\\", \\\"text-generation\\\", \\\"summarization\\\"],\\n            },\\n            \\\"huggingfacehub_api_token\\\": {\\\"display_name\\\": \\\"API token\\\", \\\"password\\\": True},\\n            \\\"model_kwargs\\\": {\\n                \\\"display_name\\\": \\\"Model Keyword Arguments\\\",\\n                \\\"field_type\\\": \\\"code\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        inputs: str,\\n        endpoint_url: str,\\n        task: str = \\\"text2text-generation\\\",\\n        huggingfacehub_api_token: Optional[str] = None,\\n        model_kwargs: Optional[dict] = None,\\n    ) -> Text:\\n        try:\\n            llm = HuggingFaceEndpoint(\\n                endpoint_url=endpoint_url,\\n                task=task,\\n                huggingfacehub_api_token=huggingfacehub_api_token,\\n                model_kwargs=model_kwargs,\\n            )\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to HuggingFace Endpoints API.\\\") from e\\n        output = ChatHuggingFace(llm=llm)\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"endpoint_url\",\"display_name\":\"Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"huggingfacehub_api_token\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"huggingfacehub_api_token\",\"display_name\":\"API token\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Keyword Arguments\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"task\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"text2text-generation\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"text2text-generation\",\"text-generation\",\"summarization\"],\"name\":\"task\",\"display_name\":\"Task\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using LLM model from Hugging Face Inference API.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Hugging Face Inference API models\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"endpoint_url\":null,\"task\":null,\"huggingfacehub_api_token\":null,\"model_kwargs\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AzureOpenAIModel\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_version\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"2023-12-01-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"2023-03-15-preview\",\"2023-05-15\",\"2023-06-01-preview\",\"2023-07-01-preview\",\"2023-08-01-preview\",\"2023-09-01-preview\",\"2023-12-01-preview\"],\"name\":\"api_version\",\"display_name\":\"API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_deployment\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_deployment\",\"display_name\":\"Deployment Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_endpoint\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_endpoint\",\"display_name\":\"Azure Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.llms.base import BaseLanguageModel\\nfrom langchain_openai import AzureChatOpenAI\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AzureChatOpenAIComponent(CustomComponent):\\n    display_name: str = \\\"AzureOpenAI Model\\\"\\n    description: str = \\\"Generate text using LLM model from Azure OpenAI.\\\"\\n    documentation: str = \\\"https://python.langchain.com/docs/integrations/llms/azure_openai\\\"\\n    beta = False\\n\\n    AZURE_OPENAI_MODELS = [\\n        \\\"gpt-35-turbo\\\",\\n        \\\"gpt-35-turbo-16k\\\",\\n        \\\"gpt-35-turbo-instruct\\\",\\n        \\\"gpt-4\\\",\\n        \\\"gpt-4-32k\\\",\\n        \\\"gpt-4-vision\\\",\\n    ]\\n\\n    AZURE_OPENAI_API_VERSIONS = [\\n        \\\"2023-03-15-preview\\\",\\n        \\\"2023-05-15\\\",\\n        \\\"2023-06-01-preview\\\",\\n        \\\"2023-07-01-preview\\\",\\n        \\\"2023-08-01-preview\\\",\\n        \\\"2023-09-01-preview\\\",\\n        \\\"2023-12-01-preview\\\",\\n    ]\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"value\\\": self.AZURE_OPENAI_MODELS[0],\\n                \\\"options\\\": self.AZURE_OPENAI_MODELS,\\n                \\\"required\\\": True,\\n            },\\n            \\\"azure_endpoint\\\": {\\n                \\\"display_name\\\": \\\"Azure Endpoint\\\",\\n                \\\"required\\\": True,\\n                \\\"info\\\": \\\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\\\",\\n            },\\n            \\\"azure_deployment\\\": {\\n                \\\"display_name\\\": \\\"Deployment Name\\\",\\n                \\\"required\\\": True,\\n            },\\n            \\\"api_version\\\": {\\n                \\\"display_name\\\": \\\"API Version\\\",\\n                \\\"options\\\": self.AZURE_OPENAI_API_VERSIONS,\\n                \\\"value\\\": self.AZURE_OPENAI_API_VERSIONS[-1],\\n                \\\"required\\\": True,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"required\\\": True, \\\"password\\\": True},\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"value\\\": 0.7,\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"required\\\": False,\\n            },\\n            \\\"max_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Tokens\\\",\\n                \\\"value\\\": 1000,\\n                \\\"required\\\": False,\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"advanced\\\": True,\\n                \\\"info\\\": \\\"Maximum number of tokens to generate.\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        model: str,\\n        azure_endpoint: str,\\n        inputs: str,\\n        azure_deployment: str,\\n        api_key: str,\\n        api_version: str,\\n        temperature: float = 0.7,\\n        max_tokens: Optional[int] = 1000,\\n    ) -> BaseLanguageModel:\\n        try:\\n            output = AzureChatOpenAI(\\n                model=model,\\n                azure_endpoint=azure_endpoint,\\n                azure_deployment=azure_deployment,\\n                api_version=api_version,\\n                api_key=api_key,\\n                temperature=temperature,\\n                max_tokens=max_tokens,\\n            )\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to AzureOpenAI API.\\\") from e\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"Maximum number of tokens to generate.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-35-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-35-turbo\",\"gpt-35-turbo-16k\",\"gpt-35-turbo-instruct\",\"gpt-4\",\"gpt-4-32k\",\"gpt-4-vision\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using LLM model from Azure OpenAI.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"AzureOpenAI Model\",\"documentation\":\"https://python.langchain.com/docs/integrations/llms/azure_openai\",\"custom_fields\":{\"model\":null,\"azure_endpoint\":null,\"inputs\":null,\"azure_deployment\":null,\"api_key\":null,\"api_version\":null,\"temperature\":null,\"max_tokens\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"AmazonBedrockModel\":{\"template\":{\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.bedrock import BedrockChat\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass AmazonBedrockComponent(CustomComponent):\\n    display_name: str = \\\"Amazon Bedrock Model\\\"\\n    description: str = \\\"Generate text using LLM model from Amazon Bedrock.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"model_id\\\": {\\n                \\\"display_name\\\": \\\"Model Id\\\",\\n                \\\"options\\\": [\\n                    \\\"ai21.j2-grande-instruct\\\",\\n                    \\\"ai21.j2-jumbo-instruct\\\",\\n                    \\\"ai21.j2-mid\\\",\\n                    \\\"ai21.j2-mid-v1\\\",\\n                    \\\"ai21.j2-ultra\\\",\\n                    \\\"ai21.j2-ultra-v1\\\",\\n                    \\\"anthropic.claude-instant-v1\\\",\\n                    \\\"anthropic.claude-v1\\\",\\n                    \\\"anthropic.claude-v2\\\",\\n                    \\\"cohere.command-text-v14\\\",\\n                ],\\n            },\\n            \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n            \\\"streaming\\\": {\\\"display_name\\\": \\\"Streaming\\\", \\\"field_type\\\": \\\"bool\\\"},\\n            \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Endpoint URL\\\"},\\n            \\\"region_name\\\": {\\\"display_name\\\": \\\"Region Name\\\"},\\n            \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\"},\\n            \\\"cache\\\": {\\\"display_name\\\": \\\"Cache\\\"},\\n            \\\"code\\\": {\\\"advanced\\\": True},\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        inputs: str,\\n        model_id: str = \\\"anthropic.claude-instant-v1\\\",\\n        credentials_profile_name: Optional[str] = None,\\n        region_name: Optional[str] = None,\\n        model_kwargs: Optional[dict] = None,\\n        endpoint_url: Optional[str] = None,\\n        streaming: bool = False,\\n        cache: Optional[bool] = None,\\n    ) -> Text:\\n        try:\\n            output = BedrockChat(\\n                credentials_profile_name=credentials_profile_name,\\n                model_id=model_id,\\n                region_name=region_name,\\n                model_kwargs=model_kwargs,\\n                endpoint_url=endpoint_url,\\n                streaming=streaming,\\n                cache=cache,\\n            )  # type: ignore\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"credentials_profile_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"endpoint_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"anthropic.claude-instant-v1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ai21.j2-grande-instruct\",\"ai21.j2-jumbo-instruct\",\"ai21.j2-mid\",\"ai21.j2-mid-v1\",\"ai21.j2-ultra\",\"ai21.j2-ultra-v1\",\"anthropic.claude-instant-v1\",\"anthropic.claude-v1\",\"anthropic.claude-v2\",\"cohere.command-text-v14\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"region_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"region_name\",\"display_name\":\"Region Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using LLM model from Amazon Bedrock.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Amazon Bedrock Model\",\"documentation\":\"\",\"custom_fields\":{\"inputs\":null,\"model_id\":null,\"credentials_profile_name\":null,\"region_name\":null,\"model_kwargs\":null,\"endpoint_url\":null,\"streaming\":null,\"cache\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CohereModel\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_community.chat_models.cohere import ChatCohere\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass CohereComponent(CustomComponent):\\n    display_name = \\\"CohereModel\\\"\\n    description = \\\"Generate text using Cohere large language models.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"cohere_api_key\\\": {\\n                \\\"display_name\\\": \\\"Cohere API Key\\\",\\n                \\\"type\\\": \\\"password\\\",\\n                \\\"password\\\": True,\\n            },\\n            \\\"max_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Tokens\\\",\\n                \\\"default\\\": 256,\\n                \\\"type\\\": \\\"int\\\",\\n                \\\"show\\\": True,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"default\\\": 0.75,\\n                \\\"type\\\": \\\"float\\\",\\n                \\\"show\\\": True,\\n            },\\n            \\\"inputs\\\": {\\\"display_name\\\": \\\"Input\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        cohere_api_key: str,\\n        inputs: str,\\n        max_tokens: int = 256,\\n        temperature: float = 0.75,\\n    ) -> Text:\\n        output = ChatCohere(\\n            cohere_api_key=cohere_api_key,\\n            max_tokens=max_tokens,\\n            temperature=temperature,\\n        )\\n        message = output.invoke(inputs)\\n        result = message.content if hasattr(message, \\\"content\\\") else message\\n        self.status = result\\n        return result\\n        return result\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"cohere_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"cohere_api_key\",\"display_name\":\"Cohere API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"inputs\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"inputs\",\"display_name\":\"Input\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.75,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Generate text using Cohere large language models.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"CohereModel\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere\",\"custom_fields\":{\"cohere_api_key\":null,\"inputs\":null,\"max_tokens\":null,\"temperature\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"model_specs\":{\"AmazonBedrockSpecs\":{\"template\":{\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain_community.llms.bedrock import Bedrock\\n\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AmazonBedrockComponent(CustomComponent):\\n    display_name: str = \\\"Amazon Bedrock\\\"\\n    description: str = \\\"LLM model from Amazon Bedrock.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"model_id\\\": {\\n                \\\"display_name\\\": \\\"Model Id\\\",\\n                \\\"options\\\": [\\n                    \\\"ai21.j2-grande-instruct\\\",\\n                    \\\"ai21.j2-jumbo-instruct\\\",\\n                    \\\"ai21.j2-mid\\\",\\n                    \\\"ai21.j2-mid-v1\\\",\\n                    \\\"ai21.j2-ultra\\\",\\n                    \\\"ai21.j2-ultra-v1\\\",\\n                    \\\"anthropic.claude-instant-v1\\\",\\n                    \\\"anthropic.claude-v1\\\",\\n                    \\\"anthropic.claude-v2\\\",\\n                    \\\"cohere.command-text-v14\\\",\\n                ],\\n            },\\n            \\\"credentials_profile_name\\\": {\\\"display_name\\\": \\\"Credentials Profile Name\\\"},\\n            \\\"streaming\\\": {\\\"display_name\\\": \\\"Streaming\\\", \\\"field_type\\\": \\\"bool\\\"},\\n            \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Endpoint URL\\\"},\\n            \\\"region_name\\\": {\\\"display_name\\\": \\\"Region Name\\\"},\\n            \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\"},\\n            \\\"cache\\\": {\\\"display_name\\\": \\\"Cache\\\"},\\n            \\\"code\\\": {\\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        model_id: str = \\\"anthropic.claude-instant-v1\\\",\\n        credentials_profile_name: Optional[str] = None,\\n        region_name: Optional[str] = None,\\n        model_kwargs: Optional[dict] = None,\\n        endpoint_url: Optional[str] = None,\\n        streaming: bool = False,\\n        cache: Optional[bool] = None,\\n    ) -> BaseLLM:\\n        try:\\n            output = Bedrock(\\n                credentials_profile_name=credentials_profile_name,\\n                model_id=model_id,\\n                region_name=region_name,\\n                model_kwargs=model_kwargs,\\n                endpoint_url=endpoint_url,\\n                streaming=streaming,\\n                cache=cache,\\n            )  # type: ignore\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to AmazonBedrock API.\\\") from e\\n        return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"credentials_profile_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"credentials_profile_name\",\"display_name\":\"Credentials Profile Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"endpoint_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint_url\",\"display_name\":\"Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_id\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"anthropic.claude-instant-v1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ai21.j2-grande-instruct\",\"ai21.j2-jumbo-instruct\",\"ai21.j2-mid\",\"ai21.j2-mid-v1\",\"ai21.j2-ultra\",\"ai21.j2-ultra-v1\",\"anthropic.claude-instant-v1\",\"anthropic.claude-v1\",\"anthropic.claude-v2\",\"cohere.command-text-v14\"],\"name\":\"model_id\",\"display_name\":\"Model Id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"region_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"region_name\",\"display_name\":\"Region Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"LLM model from Amazon Bedrock.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"Amazon Bedrock\",\"documentation\":\"\",\"custom_fields\":{\"model_id\":null,\"credentials_profile_name\":null,\"region_name\":null,\"model_kwargs\":null,\"endpoint_url\":null,\"streaming\":null,\"cache\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatVertexAISpecs\":{\"template\":{\"credentials\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".json\"],\"password\":false,\"name\":\"credentials\",\"display_name\":\"Credentials\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"examples\":{\"type\":\"BaseMessage\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":true,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"examples\",\"display_name\":\"Examples\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional, Union\\n\\nfrom langchain.llms import BaseLLM\\nfrom langchain_community.chat_models.vertexai import ChatVertexAI\\nfrom langchain_core.messages.base import BaseMessage\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel\\n\\n\\nclass ChatVertexAIComponent(CustomComponent):\\n    display_name = \\\"ChatVertexAI\\\"\\n    description = \\\"`Vertex AI` Chat large language models API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"credentials\\\": {\\n                \\\"display_name\\\": \\\"Credentials\\\",\\n                \\\"field_type\\\": \\\"file\\\",\\n                \\\"file_types\\\": [\\\".json\\\"],\\n                \\\"file_path\\\": None,\\n            },\\n            \\\"examples\\\": {\\n                \\\"display_name\\\": \\\"Examples\\\",\\n                \\\"multiline\\\": True,\\n            },\\n            \\\"location\\\": {\\n                \\\"display_name\\\": \\\"Location\\\",\\n                \\\"value\\\": \\\"us-central1\\\",\\n            },\\n            \\\"max_output_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n                \\\"value\\\": 128,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"model_name\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"value\\\": \\\"chat-bison\\\",\\n            },\\n            \\\"project\\\": {\\n                \\\"display_name\\\": \\\"Project\\\",\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"value\\\": 0.0,\\n            },\\n            \\\"top_k\\\": {\\n                \\\"display_name\\\": \\\"Top K\\\",\\n                \\\"value\\\": 40,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top P\\\",\\n                \\\"value\\\": 0.95,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"verbose\\\": {\\n                \\\"display_name\\\": \\\"Verbose\\\",\\n                \\\"value\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        credentials: Optional[str],\\n        project: str,\\n        examples: Optional[List[BaseMessage]] = [],\\n        location: str = \\\"us-central1\\\",\\n        max_output_tokens: int = 128,\\n        model_name: str = \\\"chat-bison\\\",\\n        temperature: float = 0.0,\\n        top_k: int = 40,\\n        top_p: float = 0.95,\\n        verbose: bool = False,\\n    ) -> Union[BaseLanguageModel, BaseLLM]:\\n        return ChatVertexAI(\\n            credentials=credentials,\\n            examples=examples,\\n            location=location,\\n            max_output_tokens=max_output_tokens,\\n            model_name=model_name,\\n            project=project,\\n            temperature=temperature,\\n            top_k=top_k,\\n            top_p=top_p,\\n            verbose=verbose,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"location\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"us-central1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":128,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"chat-bison\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"project\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"project\",\"display_name\":\"Project\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"`Vertex AI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"ChatVertexAI\",\"documentation\":\"\",\"custom_fields\":{\"credentials\":null,\"project\":null,\"examples\":null,\"location\":null,\"max_output_tokens\":null,\"model_name\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"verbose\":null},\"output_types\":[\"BaseLanguageModel\",\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"VertexAISpecs\":{\"template\":{\"credentials\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".json\"],\"file_path\":\"\",\"password\":false,\"name\":\"credentials\",\"display_name\":\"Credentials\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.llms import BaseLLM\\nfrom typing import Optional, Union, Callable, Dict\\nfrom langchain_community.llms.vertexai import VertexAI\\n\\n\\nclass VertexAIComponent(CustomComponent):\\n    display_name = \\\"VertexAI\\\"\\n    description = \\\"Google Vertex AI large language models\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"credentials\\\": {\\n                \\\"display_name\\\": \\\"Credentials\\\",\\n                \\\"field_type\\\": \\\"file\\\",\\n                \\\"file_types\\\": [\\\".json\\\"],\\n                \\\"required\\\": False,\\n                \\\"value\\\": None,\\n            },\\n            \\\"location\\\": {\\n                \\\"display_name\\\": \\\"Location\\\",\\n                \\\"type\\\": \\\"str\\\",\\n                \\\"advanced\\\": True,\\n                \\\"value\\\": \\\"us-central1\\\",\\n                \\\"required\\\": False,\\n            },\\n            \\\"max_output_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"value\\\": 128,\\n                \\\"required\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"max_retries\\\": {\\n                \\\"display_name\\\": \\\"Max Retries\\\",\\n                \\\"type\\\": \\\"int\\\",\\n                \\\"value\\\": 6,\\n                \\\"required\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"metadata\\\": {\\n                \\\"display_name\\\": \\\"Metadata\\\",\\n                \\\"field_type\\\": \\\"dict\\\",\\n                \\\"required\\\": False,\\n                \\\"default\\\": {},\\n            },\\n            \\\"model_name\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"type\\\": \\\"str\\\",\\n                \\\"value\\\": \\\"text-bison\\\",\\n                \\\"required\\\": False,\\n            },\\n            \\\"n\\\": {\\n                \\\"advanced\\\": True,\\n                \\\"display_name\\\": \\\"N\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"value\\\": 1,\\n                \\\"required\\\": False,\\n            },\\n            \\\"project\\\": {\\n                \\\"display_name\\\": \\\"Project\\\",\\n                \\\"type\\\": \\\"str\\\",\\n                \\\"required\\\": False,\\n                \\\"default\\\": None,\\n            },\\n            \\\"request_parallelism\\\": {\\n                \\\"display_name\\\": \\\"Request Parallelism\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"value\\\": 5,\\n                \\\"required\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"streaming\\\": {\\n                \\\"display_name\\\": \\\"Streaming\\\",\\n                \\\"field_type\\\": \\\"bool\\\",\\n                \\\"value\\\": False,\\n                \\\"required\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"value\\\": 0.0,\\n                \\\"required\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"type\\\": \\\"int\\\", \\\"default\\\": 40, \\\"required\\\": False, \\\"advanced\\\": True},\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top P\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"value\\\": 0.95,\\n                \\\"required\\\": False,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"tuned_model_name\\\": {\\n                \\\"display_name\\\": \\\"Tuned Model Name\\\",\\n                \\\"type\\\": \\\"str\\\",\\n                \\\"required\\\": False,\\n                \\\"value\\\": None,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"verbose\\\": {\\n                \\\"display_name\\\": \\\"Verbose\\\",\\n                \\\"field_type\\\": \\\"bool\\\",\\n                \\\"value\\\": False,\\n                \\\"required\\\": False,\\n            },\\n            \\\"name\\\": {\\\"display_name\\\": \\\"Name\\\", \\\"field_type\\\": \\\"str\\\"},\\n        }\\n\\n    def build(\\n        self,\\n        credentials: Optional[str] = None,\\n        location: str = \\\"us-central1\\\",\\n        max_output_tokens: int = 128,\\n        max_retries: int = 6,\\n        metadata: Dict = {},\\n        model_name: str = \\\"text-bison\\\",\\n        n: int = 1,\\n        name: Optional[str] = None,\\n        project: Optional[str] = None,\\n        request_parallelism: int = 5,\\n        streaming: bool = False,\\n        temperature: float = 0.0,\\n        top_k: int = 40,\\n        top_p: float = 0.95,\\n        tuned_model_name: Optional[str] = None,\\n        verbose: bool = False,\\n    ) -> Union[BaseLLM, Callable]:\\n        return VertexAI(\\n            credentials=credentials,\\n            location=location,\\n            max_output_tokens=max_output_tokens,\\n            max_retries=max_retries,\\n            metadata=metadata,\\n            model_name=model_name,\\n            n=n,\\n            name=name,\\n            project=project,\\n            request_parallelism=request_parallelism,\\n            streaming=streaming,\\n            temperature=temperature,\\n            top_k=top_k,\\n            top_p=top_p,\\n            tuned_model_name=tuned_model_name,\\n            verbose=verbose,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"location\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"us-central1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"location\",\"display_name\":\"Location\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":128,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_retries\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max Retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"metadata\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"text-bison\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"name\",\"display_name\":\"Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"project\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"project\",\"display_name\":\"Project\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"request_parallelism\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"request_parallelism\",\"display_name\":\"Request Parallelism\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"streaming\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"tuned_model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tuned_model_name\",\"display_name\":\"Tuned Model Name\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Google Vertex AI large language models\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\",\"Callable\"],\"display_name\":\"VertexAI\",\"documentation\":\"\",\"custom_fields\":{\"credentials\":null,\"location\":null,\"max_output_tokens\":null,\"max_retries\":null,\"metadata\":null,\"model_name\":null,\"n\":null,\"name\":null,\"project\":null,\"request_parallelism\":null,\"streaming\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"tuned_model_name\":null,\"verbose\":null},\"output_types\":[\"BaseLLM\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatAnthropicSpecs\":{\"template\":{\"anthropic_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"anthropic_api_key\",\"display_name\":\"Anthropic API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"anthropic_api_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"anthropic_api_url\",\"display_name\":\"Anthropic API URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from pydantic.v1.types import SecretStr\\nfrom langflow import CustomComponent\\nfrom typing import Optional, Union, Callable\\nfrom langflow.field_typing import BaseLanguageModel\\nfrom langchain_community.chat_models.anthropic import ChatAnthropic\\n\\n\\nclass ChatAnthropicComponent(CustomComponent):\\n    display_name = \\\"ChatAnthropic\\\"\\n    description = \\\"`Anthropic` chat large language models.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/anthropic\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"anthropic_api_key\\\": {\\n                \\\"display_name\\\": \\\"Anthropic API Key\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"password\\\": True,\\n            },\\n            \\\"anthropic_api_url\\\": {\\n                \\\"display_name\\\": \\\"Anthropic API URL\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n            },\\n            \\\"model_kwargs\\\": {\\n                \\\"display_name\\\": \\\"Model Kwargs\\\",\\n                \\\"field_type\\\": \\\"dict\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        anthropic_api_key: str,\\n        anthropic_api_url: Optional[str] = None,\\n        model_kwargs: dict = {},\\n        temperature: Optional[float] = None,\\n    ) -> Union[BaseLanguageModel, Callable]:\\n        return ChatAnthropic(\\n            anthropic_api_key=SecretStr(anthropic_api_key),\\n            anthropic_api_url=anthropic_api_url,\\n            model_kwargs=model_kwargs,\\n            temperature=temperature,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"dict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"`Anthropic` chat large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"ChatAnthropic\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/anthropic\",\"custom_fields\":{\"anthropic_api_key\":null,\"anthropic_api_url\":null,\"model_kwargs\":null,\"temperature\":null},\"output_types\":[\"BaseLanguageModel\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AzureChatOpenAISpecs\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_version\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"2023-12-01-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"2023-03-15-preview\",\"2023-05-15\",\"2023-06-01-preview\",\"2023-07-01-preview\",\"2023-08-01-preview\",\"2023-09-01-preview\",\"2023-12-01-preview\"],\"name\":\"api_version\",\"display_name\":\"API Version\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_deployment\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_deployment\",\"display_name\":\"Deployment Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"azure_endpoint\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"azure_endpoint\",\"display_name\":\"Azure Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.llms.base import BaseLanguageModel\\nfrom langchain_community.chat_models.azure_openai import AzureChatOpenAI\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AzureChatOpenAISpecsComponent(CustomComponent):\\n    display_name: str = \\\"AzureChatOpenAI\\\"\\n    description: str = \\\"LLM model from Azure OpenAI.\\\"\\n    documentation: str = \\\"https://python.langchain.com/docs/integrations/llms/azure_openai\\\"\\n    beta = False\\n\\n    AZURE_OPENAI_MODELS = [\\n        \\\"gpt-35-turbo\\\",\\n        \\\"gpt-35-turbo-16k\\\",\\n        \\\"gpt-35-turbo-instruct\\\",\\n        \\\"gpt-4\\\",\\n        \\\"gpt-4-32k\\\",\\n        \\\"gpt-4-vision\\\",\\n    ]\\n\\n    AZURE_OPENAI_API_VERSIONS = [\\n        \\\"2023-03-15-preview\\\",\\n        \\\"2023-05-15\\\",\\n        \\\"2023-06-01-preview\\\",\\n        \\\"2023-07-01-preview\\\",\\n        \\\"2023-08-01-preview\\\",\\n        \\\"2023-09-01-preview\\\",\\n        \\\"2023-12-01-preview\\\",\\n    ]\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"value\\\": self.AZURE_OPENAI_MODELS[0],\\n                \\\"options\\\": self.AZURE_OPENAI_MODELS,\\n                \\\"required\\\": True,\\n            },\\n            \\\"azure_endpoint\\\": {\\n                \\\"display_name\\\": \\\"Azure Endpoint\\\",\\n                \\\"required\\\": True,\\n                \\\"info\\\": \\\"Your Azure endpoint, including the resource.. Example: `https://example-resource.azure.openai.com/`\\\",\\n            },\\n            \\\"azure_deployment\\\": {\\n                \\\"display_name\\\": \\\"Deployment Name\\\",\\n                \\\"required\\\": True,\\n            },\\n            \\\"api_version\\\": {\\n                \\\"display_name\\\": \\\"API Version\\\",\\n                \\\"options\\\": self.AZURE_OPENAI_API_VERSIONS,\\n                \\\"value\\\": self.AZURE_OPENAI_API_VERSIONS[-1],\\n                \\\"required\\\": True,\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"required\\\": True, \\\"password\\\": True},\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"value\\\": 0.7,\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"required\\\": False,\\n            },\\n            \\\"max_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Tokens\\\",\\n                \\\"value\\\": 1000,\\n                \\\"required\\\": False,\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"advanced\\\": True,\\n                \\\"info\\\": \\\"Maximum number of tokens to generate.\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        model: str,\\n        azure_endpoint: str,\\n        azure_deployment: str,\\n        api_key: str,\\n        api_version: str,\\n        temperature: float = 0.7,\\n        max_tokens: Optional[int] = 1000,\\n    ) -> BaseLanguageModel:\\n        try:\\n            llm = AzureChatOpenAI(\\n                model=model,\\n                azure_endpoint=azure_endpoint,\\n                azure_deployment=azure_deployment,\\n                api_version=api_version,\\n                api_key=api_key,\\n                temperature=temperature,\\n                max_tokens=max_tokens,\\n            )\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to AzureOpenAI API.\\\") from e\\n        return llm\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1000,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"Maximum number of tokens to generate.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-35-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-35-turbo\",\"gpt-35-turbo-16k\",\"gpt-35-turbo-instruct\",\"gpt-4\",\"gpt-4-32k\",\"gpt-4-vision\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"LLM model from Azure OpenAI.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"AzureChatOpenAI\",\"documentation\":\"https://python.langchain.com/docs/integrations/llms/azure_openai\",\"custom_fields\":{\"model\":null,\"azure_endpoint\":null,\"azure_deployment\":null,\"api_key\":null,\"api_version\":null,\"temperature\":null,\"max_tokens\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":false},\"ChatOllamaEndpointSpecs\":{\"template\":{\"metadata\":{\"type\":\"Dict[str, Any]\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":true,\"dynamic\":false,\"info\":\"Metadata to add to the run trace.\",\"title_case\":false},\"stop\":{\"type\":\"list\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"List of tokens to signal the model to stop generating text.\",\"title_case\":false},\"tags\":{\"type\":\"list\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"display_name\":\"Tags\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tags to add to the run trace.\",\"title_case\":false},\"base_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"base_url\",\"display_name\":\"Base URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable or disable caching.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Dict, List, Optional\\n\\n# from langchain_community.chat_models import ChatOllama\\nfrom langchain_community.chat_models import ChatOllama\\nfrom langchain_core.language_models.chat_models import BaseChatModel\\n\\n# from langchain.chat_models import ChatOllama\\nfrom langflow import CustomComponent\\n\\n# whe When a callback component is added to Langflow, the comment must be uncommented.\\n# from langchain.callbacks.manager import CallbackManager\\n\\n\\nclass ChatOllamaComponent(CustomComponent):\\n    display_name = \\\"ChatOllama\\\"\\n    description = \\\"Local LLM for chat with Ollama.\\\"\\n\\n    def build_config(self) -> dict:\\n        return {\\n            \\\"base_url\\\": {\\n                \\\"display_name\\\": \\\"Base URL\\\",\\n                \\\"info\\\": \\\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\\\",\\n            },\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"value\\\": \\\"llama2\\\",\\n                \\\"info\\\": \\\"Refer to https://ollama.ai/library for more models.\\\",\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"value\\\": 0.8,\\n                \\\"info\\\": \\\"Controls the creativity of model responses.\\\",\\n            },\\n            \\\"cache\\\": {\\n                \\\"display_name\\\": \\\"Cache\\\",\\n                \\\"field_type\\\": \\\"bool\\\",\\n                \\\"info\\\": \\\"Enable or disable caching.\\\",\\n                \\\"advanced\\\": True,\\n                \\\"value\\\": False,\\n            },\\n            ### When a callback component is added to Langflow, the comment must be uncommented. ###\\n            # \\\"callback_manager\\\": {\\n            #     \\\"display_name\\\": \\\"Callback Manager\\\",\\n            #     \\\"info\\\": \\\"Optional callback manager for additional functionality.\\\",\\n            #     \\\"advanced\\\": True,\\n            # },\\n            # \\\"callbacks\\\": {\\n            #     \\\"display_name\\\": \\\"Callbacks\\\",\\n            #     \\\"info\\\": \\\"Callbacks to execute during model runtime.\\\",\\n            #     \\\"advanced\\\": True,\\n            # },\\n            ########################################################################################\\n            \\\"format\\\": {\\n                \\\"display_name\\\": \\\"Format\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"info\\\": \\\"Specify the format of the output (e.g., json).\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"metadata\\\": {\\n                \\\"display_name\\\": \\\"Metadata\\\",\\n                \\\"info\\\": \\\"Metadata to add to the run trace.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"mirostat\\\": {\\n                \\\"display_name\\\": \\\"Mirostat\\\",\\n                \\\"options\\\": [\\\"Disabled\\\", \\\"Mirostat\\\", \\\"Mirostat 2.0\\\"],\\n                \\\"info\\\": \\\"Enable/disable Mirostat sampling for controlling perplexity.\\\",\\n                \\\"value\\\": \\\"Disabled\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"mirostat_eta\\\": {\\n                \\\"display_name\\\": \\\"Mirostat Eta\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Learning rate for Mirostat algorithm. (Default: 0.1)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"mirostat_tau\\\": {\\n                \\\"display_name\\\": \\\"Mirostat Tau\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"num_ctx\\\": {\\n                \\\"display_name\\\": \\\"Context Window Size\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Size of the context window for generating tokens. (Default: 2048)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"num_gpu\\\": {\\n                \\\"display_name\\\": \\\"Number of GPUs\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"num_thread\\\": {\\n                \\\"display_name\\\": \\\"Number of Threads\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Number of threads to use during computation. (Default: detected for optimal performance)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"repeat_last_n\\\": {\\n                \\\"display_name\\\": \\\"Repeat Last N\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"repeat_penalty\\\": {\\n                \\\"display_name\\\": \\\"Repeat Penalty\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Penalty for repetitions in generated text. (Default: 1.1)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"tfs_z\\\": {\\n                \\\"display_name\\\": \\\"TFS Z\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Tail free sampling value. (Default: 1)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"timeout\\\": {\\n                \\\"display_name\\\": \\\"Timeout\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Timeout for the request stream.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_k\\\": {\\n                \\\"display_name\\\": \\\"Top K\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Limits token selection to top K. (Default: 40)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top P\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Works together with top-k. (Default: 0.9)\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"verbose\\\": {\\n                \\\"display_name\\\": \\\"Verbose\\\",\\n                \\\"field_type\\\": \\\"bool\\\",\\n                \\\"info\\\": \\\"Whether to print out response text.\\\",\\n            },\\n            \\\"tags\\\": {\\n                \\\"display_name\\\": \\\"Tags\\\",\\n                \\\"field_type\\\": \\\"list\\\",\\n                \\\"info\\\": \\\"Tags to add to the run trace.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"stop\\\": {\\n                \\\"display_name\\\": \\\"Stop Tokens\\\",\\n                \\\"field_type\\\": \\\"list\\\",\\n                \\\"info\\\": \\\"List of tokens to signal the model to stop generating text.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"system\\\": {\\n                \\\"display_name\\\": \\\"System\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"info\\\": \\\"System to use for generating text.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"template\\\": {\\n                \\\"display_name\\\": \\\"Template\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"info\\\": \\\"Template to use for generating text.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        base_url: Optional[str],\\n        model: str,\\n        mirostat: Optional[str],\\n        mirostat_eta: Optional[float] = None,\\n        mirostat_tau: Optional[float] = None,\\n        ### When a callback component is added to Langflow, the comment must be uncommented.###\\n        # callback_manager: Optional[CallbackManager] = None,\\n        # callbacks: Optional[List[Callbacks]] = None,\\n        #######################################################################################\\n        repeat_last_n: Optional[int] = None,\\n        verbose: Optional[bool] = None,\\n        cache: Optional[bool] = None,\\n        num_ctx: Optional[int] = None,\\n        num_gpu: Optional[int] = None,\\n        format: Optional[str] = None,\\n        metadata: Optional[Dict[str, Any]] = None,\\n        num_thread: Optional[int] = None,\\n        repeat_penalty: Optional[float] = None,\\n        stop: Optional[List[str]] = None,\\n        system: Optional[str] = None,\\n        tags: Optional[List[str]] = None,\\n        temperature: Optional[float] = None,\\n        template: Optional[str] = None,\\n        tfs_z: Optional[float] = None,\\n        timeout: Optional[int] = None,\\n        top_k: Optional[int] = None,\\n        top_p: Optional[int] = None,\\n    ) -> BaseChatModel:\\n        if not base_url:\\n            base_url = \\\"http://localhost:11434\\\"\\n\\n        # Mapping mirostat settings to their corresponding values\\n        mirostat_options = {\\\"Mirostat\\\": 1, \\\"Mirostat 2.0\\\": 2}\\n\\n        # Default to 0 for 'Disabled'\\n        mirostat_value = mirostat_options.get(mirostat, 0)  # type: ignore\\n\\n        # Set mirostat_eta and mirostat_tau to None if mirostat is disabled\\n        if mirostat_value == 0:\\n            mirostat_eta = None\\n            mirostat_tau = None\\n\\n        # Mapping system settings to their corresponding values\\n        llm_params = {\\n            \\\"base_url\\\": base_url,\\n            \\\"cache\\\": cache,\\n            \\\"model\\\": model,\\n            \\\"mirostat\\\": mirostat_value,\\n            \\\"format\\\": format,\\n            \\\"metadata\\\": metadata,\\n            \\\"tags\\\": tags,\\n            ## When a callback component is added to Langflow, the comment must be uncommented.##\\n            # \\\"callback_manager\\\": callback_manager,\\n            # \\\"callbacks\\\": callbacks,\\n            #####################################################################################\\n            \\\"mirostat_eta\\\": mirostat_eta,\\n            \\\"mirostat_tau\\\": mirostat_tau,\\n            \\\"num_ctx\\\": num_ctx,\\n            \\\"num_gpu\\\": num_gpu,\\n            \\\"num_thread\\\": num_thread,\\n            \\\"repeat_last_n\\\": repeat_last_n,\\n            \\\"repeat_penalty\\\": repeat_penalty,\\n            \\\"temperature\\\": temperature,\\n            \\\"stop\\\": stop,\\n            \\\"system\\\": system,\\n            \\\"template\\\": template,\\n            \\\"tfs_z\\\": tfs_z,\\n            \\\"timeout\\\": timeout,\\n            \\\"top_k\\\": top_k,\\n            \\\"top_p\\\": top_p,\\n            \\\"verbose\\\": verbose,\\n        }\\n\\n        # None Value remove\\n        llm_params = {k: v for k, v in llm_params.items() if v is not None}\\n\\n        try:\\n            output = ChatOllama(**llm_params)  # type: ignore\\n        except Exception as e:\\n            raise ValueError(\\\"Could not initialize Ollama LLM.\\\") from e\\n\\n        return output  # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"format\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"format\",\"display_name\":\"Format\",\"advanced\":true,\"dynamic\":false,\"info\":\"Specify the format of the output (e.g., json).\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Disabled\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Disabled\",\"Mirostat\",\"Mirostat 2.0\"],\"name\":\"mirostat\",\"display_name\":\"Mirostat\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable/disable Mirostat sampling for controlling perplexity.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat_eta\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_eta\",\"display_name\":\"Mirostat Eta\",\"advanced\":true,\"dynamic\":false,\"info\":\"Learning rate for Mirostat algorithm. (Default: 0.1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"mirostat_tau\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_tau\",\"display_name\":\"Mirostat Tau\",\"advanced\":true,\"dynamic\":false,\"info\":\"Controls the balance between coherence and diversity of the output. (Default: 5.0)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"llama2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"Refer to https://ollama.ai/library for more models.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"num_ctx\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_ctx\",\"display_name\":\"Context Window Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"Size of the context window for generating tokens. (Default: 2048)\",\"title_case\":false},\"num_gpu\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_gpu\",\"display_name\":\"Number of GPUs\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of GPUs to use for computation. (Default: 1 on macOS, 0 to disable)\",\"title_case\":false},\"num_thread\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_thread\",\"display_name\":\"Number of Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of threads to use during computation. (Default: detected for optimal performance)\",\"title_case\":false},\"repeat_last_n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_last_n\",\"display_name\":\"Repeat Last N\",\"advanced\":true,\"dynamic\":false,\"info\":\"How far back the model looks to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"Penalty for repetitions in generated text. (Default: 1.1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"system\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"system\",\"display_name\":\"System\",\"advanced\":true,\"dynamic\":false,\"info\":\"System to use for generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Controls the creativity of model responses.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"template\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"display_name\":\"Template\",\"advanced\":true,\"dynamic\":false,\"info\":\"Template to use for generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"tfs_z\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tfs_z\",\"display_name\":\"TFS Z\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tail free sampling value. (Default: 1)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"timeout\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"Timeout for the request stream.\",\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Limits token selection to top K. (Default: 40)\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"Works together with top-k. (Default: 0.9)\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"Whether to print out response text.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Local LLM for chat with Ollama.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseChatModel\"],\"display_name\":\"ChatOllama\",\"documentation\":\"\",\"custom_fields\":{\"base_url\":null,\"model\":null,\"mirostat\":null,\"mirostat_eta\":null,\"mirostat_tau\":null,\"repeat_last_n\":null,\"verbose\":null,\"cache\":null,\"num_ctx\":null,\"num_gpu\":null,\"format\":null,\"metadata\":null,\"num_thread\":null,\"repeat_penalty\":null,\"stop\":null,\"system\":null,\"tags\":null,\"temperature\":null,\"template\":null,\"tfs_z\":null,\"timeout\":null,\"top_k\":null,\"top_p\":null},\"output_types\":[\"BaseChatModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"BaiduQianfanChatEndpointsSpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint\\nfrom langchain.llms.base import BaseLLM\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass QianfanChatEndpointComponent(CustomComponent):\\n    display_name: str = \\\"QianfanChatEndpoint\\\"\\n    description: str = (\\n        \\\"Baidu Qianfan chat models. Get more detail from \\\"\\n        \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\\\"\\n    )\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"options\\\": [\\n                    \\\"ERNIE-Bot\\\",\\n                    \\\"ERNIE-Bot-turbo\\\",\\n                    \\\"BLOOMZ-7B\\\",\\n                    \\\"Llama-2-7b-chat\\\",\\n                    \\\"Llama-2-13b-chat\\\",\\n                    \\\"Llama-2-70b-chat\\\",\\n                    \\\"Qianfan-BLOOMZ-7B-compressed\\\",\\n                    \\\"Qianfan-Chinese-Llama-2-7B\\\",\\n                    \\\"ChatGLM2-6B-32K\\\",\\n                    \\\"AquilaChat-7B\\\",\\n                ],\\n                \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\",\\n                \\\"required\\\": True,\\n            },\\n            \\\"qianfan_ak\\\": {\\n                \\\"display_name\\\": \\\"Qianfan Ak\\\",\\n                \\\"required\\\": True,\\n                \\\"password\\\": True,\\n                \\\"info\\\": \\\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\\\",\\n            },\\n            \\\"qianfan_sk\\\": {\\n                \\\"display_name\\\": \\\"Qianfan Sk\\\",\\n                \\\"required\\\": True,\\n                \\\"password\\\": True,\\n                \\\"info\\\": \\\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\\\",\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top p\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n                \\\"value\\\": 0.8,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n                \\\"value\\\": 0.95,\\n            },\\n            \\\"penalty_score\\\": {\\n                \\\"display_name\\\": \\\"Penalty Score\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n                \\\"value\\\": 1.0,\\n            },\\n            \\\"endpoint\\\": {\\n                \\\"display_name\\\": \\\"Endpoint\\\",\\n                \\\"info\\\": \\\"Endpoint of the Qianfan LLM, required if custom model used.\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        model: str = \\\"ERNIE-Bot-turbo\\\",\\n        qianfan_ak: Optional[str] = None,\\n        qianfan_sk: Optional[str] = None,\\n        top_p: Optional[float] = None,\\n        temperature: Optional[float] = None,\\n        penalty_score: Optional[float] = None,\\n        endpoint: Optional[str] = None,\\n    ) -> BaseLLM:\\n        try:\\n            output = QianfanChatEndpoint(  # type: ignore\\n                model=model,\\n                qianfan_ak=SecretStr(qianfan_ak) if qianfan_ak else None,\\n                qianfan_sk=SecretStr(qianfan_sk) if qianfan_sk else None,\\n                top_p=top_p,\\n                temperature=temperature,\\n                penalty_score=penalty_score,\\n                endpoint=endpoint,\\n            )\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to Baidu Qianfan API.\\\") from e\\n        return output  # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint\",\"display_name\":\"Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Qianfan LLM, required if custom model used.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"ERNIE-Bot-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ERNIE-Bot\",\"ERNIE-Bot-turbo\",\"BLOOMZ-7B\",\"Llama-2-7b-chat\",\"Llama-2-13b-chat\",\"Llama-2-70b-chat\",\"Qianfan-BLOOMZ-7B-compressed\",\"Qianfan-Chinese-Llama-2-7B\",\"ChatGLM2-6B-32K\",\"AquilaChat-7B\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"title_case\":false,\"input_types\":[\"Text\"]},\"penalty_score\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"penalty_score\",\"display_name\":\"Penalty Score\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"qianfan_ak\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_ak\",\"display_name\":\"Qianfan Ak\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"qianfan_sk\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_sk\",\"display_name\":\"Qianfan Sk\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Baidu Qianfan chat models. Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"QianfanChatEndpoint\",\"documentation\":\"\",\"custom_fields\":{\"model\":null,\"qianfan_ak\":null,\"qianfan_sk\":null,\"top_p\":null,\"temperature\":null,\"penalty_score\":null,\"endpoint\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"LlamaCppSpecs\":{\"template\":{\"metadata\":{\"type\":\"Dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"Dict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_path\":{\"type\":\"file\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".bin\"],\"file_path\":\"\",\"password\":false,\"name\":\"model_path\",\"display_name\":\"Model Path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"cache\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"cache\",\"display_name\":\"Cache\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"client\":{\"type\":\"Any\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"client\",\"display_name\":\"Client\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, List, Dict, Any\\nfrom langflow import CustomComponent\\nfrom langchain_community.llms.llamacpp import LlamaCpp\\n\\n\\nclass LlamaCppComponent(CustomComponent):\\n    display_name = \\\"LlamaCpp\\\"\\n    description = \\\"llama.cpp model.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"grammar\\\": {\\\"display_name\\\": \\\"Grammar\\\", \\\"advanced\\\": True},\\n            \\\"cache\\\": {\\\"display_name\\\": \\\"Cache\\\", \\\"advanced\\\": True},\\n            \\\"client\\\": {\\\"display_name\\\": \\\"Client\\\", \\\"advanced\\\": True},\\n            \\\"echo\\\": {\\\"display_name\\\": \\\"Echo\\\", \\\"advanced\\\": True},\\n            \\\"f16_kv\\\": {\\\"display_name\\\": \\\"F16 KV\\\", \\\"advanced\\\": True},\\n            \\\"grammar_path\\\": {\\\"display_name\\\": \\\"Grammar Path\\\", \\\"advanced\\\": True},\\n            \\\"last_n_tokens_size\\\": {\\\"display_name\\\": \\\"Last N Tokens Size\\\", \\\"advanced\\\": True},\\n            \\\"logits_all\\\": {\\\"display_name\\\": \\\"Logits All\\\", \\\"advanced\\\": True},\\n            \\\"logprobs\\\": {\\\"display_name\\\": \\\"Logprobs\\\", \\\"advanced\\\": True},\\n            \\\"lora_base\\\": {\\\"display_name\\\": \\\"Lora Base\\\", \\\"advanced\\\": True},\\n            \\\"lora_path\\\": {\\\"display_name\\\": \\\"Lora Path\\\", \\\"advanced\\\": True},\\n            \\\"max_tokens\\\": {\\\"display_name\\\": \\\"Max Tokens\\\", \\\"advanced\\\": True},\\n            \\\"metadata\\\": {\\\"display_name\\\": \\\"Metadata\\\", \\\"advanced\\\": True},\\n            \\\"model_kwargs\\\": {\\\"display_name\\\": \\\"Model Kwargs\\\", \\\"advanced\\\": True},\\n            \\\"model_path\\\": {\\n                \\\"display_name\\\": \\\"Model Path\\\",\\n                \\\"field_type\\\": \\\"file\\\",\\n                \\\"file_types\\\": [\\\".bin\\\"],\\n                \\\"required\\\": True,\\n            },\\n            \\\"n_batch\\\": {\\\"display_name\\\": \\\"N Batch\\\", \\\"advanced\\\": True},\\n            \\\"n_ctx\\\": {\\\"display_name\\\": \\\"N Ctx\\\", \\\"advanced\\\": True},\\n            \\\"n_gpu_layers\\\": {\\\"display_name\\\": \\\"N GPU Layers\\\", \\\"advanced\\\": True},\\n            \\\"n_parts\\\": {\\\"display_name\\\": \\\"N Parts\\\", \\\"advanced\\\": True},\\n            \\\"n_threads\\\": {\\\"display_name\\\": \\\"N Threads\\\", \\\"advanced\\\": True},\\n            \\\"repeat_penalty\\\": {\\\"display_name\\\": \\\"Repeat Penalty\\\", \\\"advanced\\\": True},\\n            \\\"rope_freq_base\\\": {\\\"display_name\\\": \\\"Rope Freq Base\\\", \\\"advanced\\\": True},\\n            \\\"rope_freq_scale\\\": {\\\"display_name\\\": \\\"Rope Freq Scale\\\", \\\"advanced\\\": True},\\n            \\\"seed\\\": {\\\"display_name\\\": \\\"Seed\\\", \\\"advanced\\\": True},\\n            \\\"stop\\\": {\\\"display_name\\\": \\\"Stop\\\", \\\"advanced\\\": True},\\n            \\\"streaming\\\": {\\\"display_name\\\": \\\"Streaming\\\", \\\"advanced\\\": True},\\n            \\\"suffix\\\": {\\\"display_name\\\": \\\"Suffix\\\", \\\"advanced\\\": True},\\n            \\\"tags\\\": {\\\"display_name\\\": \\\"Tags\\\", \\\"advanced\\\": True},\\n            \\\"temperature\\\": {\\\"display_name\\\": \\\"Temperature\\\"},\\n            \\\"top_k\\\": {\\\"display_name\\\": \\\"Top K\\\", \\\"advanced\\\": True},\\n            \\\"top_p\\\": {\\\"display_name\\\": \\\"Top P\\\", \\\"advanced\\\": True},\\n            \\\"use_mlock\\\": {\\\"display_name\\\": \\\"Use Mlock\\\", \\\"advanced\\\": True},\\n            \\\"use_mmap\\\": {\\\"display_name\\\": \\\"Use Mmap\\\", \\\"advanced\\\": True},\\n            \\\"verbose\\\": {\\\"display_name\\\": \\\"Verbose\\\", \\\"advanced\\\": True},\\n            \\\"vocab_only\\\": {\\\"display_name\\\": \\\"Vocab Only\\\", \\\"advanced\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        model_path: str,\\n        grammar: Optional[str] = None,\\n        cache: Optional[bool] = None,\\n        client: Optional[Any] = None,\\n        echo: Optional[bool] = False,\\n        f16_kv: bool = True,\\n        grammar_path: Optional[str] = None,\\n        last_n_tokens_size: Optional[int] = 64,\\n        logits_all: bool = False,\\n        logprobs: Optional[int] = None,\\n        lora_base: Optional[str] = None,\\n        lora_path: Optional[str] = None,\\n        max_tokens: Optional[int] = 256,\\n        metadata: Optional[Dict] = None,\\n        model_kwargs: Dict = {},\\n        n_batch: Optional[int] = 8,\\n        n_ctx: int = 512,\\n        n_gpu_layers: Optional[int] = 1,\\n        n_parts: int = -1,\\n        n_threads: Optional[int] = 1,\\n        repeat_penalty: Optional[float] = 1.1,\\n        rope_freq_base: float = 10000.0,\\n        rope_freq_scale: float = 1.0,\\n        seed: int = -1,\\n        stop: Optional[List[str]] = [],\\n        streaming: bool = True,\\n        suffix: Optional[str] = \\\"\\\",\\n        tags: Optional[List[str]] = [],\\n        temperature: Optional[float] = 0.8,\\n        top_k: Optional[int] = 40,\\n        top_p: Optional[float] = 0.95,\\n        use_mlock: bool = False,\\n        use_mmap: Optional[bool] = True,\\n        verbose: bool = True,\\n        vocab_only: bool = False,\\n    ) -> LlamaCpp:\\n        return LlamaCpp(\\n            model_path=model_path,\\n            grammar=grammar,\\n            cache=cache,\\n            client=client,\\n            echo=echo,\\n            f16_kv=f16_kv,\\n            grammar_path=grammar_path,\\n            last_n_tokens_size=last_n_tokens_size,\\n            logits_all=logits_all,\\n            logprobs=logprobs,\\n            lora_base=lora_base,\\n            lora_path=lora_path,\\n            max_tokens=max_tokens,\\n            metadata=metadata,\\n            model_kwargs=model_kwargs,\\n            n_batch=n_batch,\\n            n_ctx=n_ctx,\\n            n_gpu_layers=n_gpu_layers,\\n            n_parts=n_parts,\\n            n_threads=n_threads,\\n            repeat_penalty=repeat_penalty,\\n            rope_freq_base=rope_freq_base,\\n            rope_freq_scale=rope_freq_scale,\\n            seed=seed,\\n            stop=stop,\\n            streaming=streaming,\\n            suffix=suffix,\\n            tags=tags,\\n            temperature=temperature,\\n            top_k=top_k,\\n            top_p=top_p,\\n            use_mlock=use_mlock,\\n            use_mmap=use_mmap,\\n            verbose=verbose,\\n            vocab_only=vocab_only,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"echo\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"echo\",\"display_name\":\"Echo\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"f16_kv\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"f16_kv\",\"display_name\":\"F16 KV\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"grammar\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grammar\",\"display_name\":\"Grammar\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"grammar_path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"grammar_path\",\"display_name\":\"Grammar Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"last_n_tokens_size\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":64,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"last_n_tokens_size\",\"display_name\":\"Last N Tokens Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"logits_all\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"logits_all\",\"display_name\":\"Logits All\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"logprobs\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"logprobs\",\"display_name\":\"Logprobs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"lora_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lora_base\",\"display_name\":\"Lora Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"lora_path\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"lora_path\",\"display_name\":\"Lora Path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_batch\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_batch\",\"display_name\":\"N Batch\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_ctx\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":512,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_ctx\",\"display_name\":\"N Ctx\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_gpu_layers\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_gpu_layers\",\"display_name\":\"N GPU Layers\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_parts\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":-1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_parts\",\"display_name\":\"N Parts\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n_threads\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_threads\",\"display_name\":\"N Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"rope_freq_base\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":10000.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"rope_freq_base\",\"display_name\":\"Rope Freq Base\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"rope_freq_scale\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"rope_freq_scale\",\"display_name\":\"Rope Freq Scale\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"seed\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":-1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"seed\",\"display_name\":\"Seed\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"stop\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"suffix\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"suffix\",\"display_name\":\"Suffix\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"tags\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":[],\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tags\",\"display_name\":\"Tags\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":40,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"use_mlock\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_mlock\",\"display_name\":\"Use Mlock\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"use_mmap\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"use_mmap\",\"display_name\":\"Use Mmap\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"vocab_only\":{\"type\":\"bool\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"vocab_only\",\"display_name\":\"Vocab Only\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"llama.cpp model.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"LlamaCpp\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\",\"LLM\"],\"display_name\":\"LlamaCpp\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp\",\"custom_fields\":{\"model_path\":null,\"grammar\":null,\"cache\":null,\"client\":null,\"echo\":null,\"f16_kv\":null,\"grammar_path\":null,\"last_n_tokens_size\":null,\"logits_all\":null,\"logprobs\":null,\"lora_base\":null,\"lora_path\":null,\"max_tokens\":null,\"metadata\":null,\"model_kwargs\":null,\"n_batch\":null,\"n_ctx\":null,\"n_gpu_layers\":null,\"n_parts\":null,\"n_threads\":null,\"repeat_penalty\":null,\"rope_freq_base\":null,\"rope_freq_scale\":null,\"seed\":null,\"stop\":null,\"streaming\":null,\"suffix\":null,\"tags\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"use_mlock\":null,\"use_mmap\":null,\"verbose\":null,\"vocab_only\":null},\"output_types\":[\"LlamaCpp\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AnthropicSpecs\":{\"template\":{\"anthropic_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"anthropic_api_key\",\"display_name\":\"Anthropic API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"anthropic_api_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"anthropic_api_url\",\"display_name\":\"Anthropic API URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.llms.anthropic import Anthropic\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, NestedDict\\n\\n\\nclass AnthropicComponent(CustomComponent):\\n    display_name = \\\"Anthropic\\\"\\n    description = \\\"Anthropic large language models.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"anthropic_api_key\\\": {\\n                \\\"display_name\\\": \\\"Anthropic API Key\\\",\\n                \\\"type\\\": str,\\n                \\\"password\\\": True,\\n            },\\n            \\\"anthropic_api_url\\\": {\\n                \\\"display_name\\\": \\\"Anthropic API URL\\\",\\n                \\\"type\\\": str,\\n            },\\n            \\\"model_kwargs\\\": {\\n                \\\"display_name\\\": \\\"Model Kwargs\\\",\\n                \\\"field_type\\\": \\\"NestedDict\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        anthropic_api_key: str,\\n        anthropic_api_url: str,\\n        model_kwargs: NestedDict = {},\\n        temperature: Optional[float] = None,\\n    ) -> BaseLanguageModel:\\n        return Anthropic(\\n            anthropic_api_key=SecretStr(anthropic_api_key),\\n            anthropic_api_url=anthropic_api_url,\\n            model_kwargs=model_kwargs,\\n            temperature=temperature,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"NestedDict\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Anthropic large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"Anthropic\",\"documentation\":\"\",\"custom_fields\":{\"anthropic_api_key\":null,\"anthropic_api_url\":null,\"model_kwargs\":null,\"temperature\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"AnthropicLLMSpecs\":{\"template\":{\"anthropic_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"anthropic_api_key\",\"display_name\":\"Anthropic API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"Your Anthropic API key.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"api_endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"api_endpoint\",\"display_name\":\"API Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_community.chat_models.anthropic import ChatAnthropic\\nfrom langchain.llms.base import BaseLanguageModel\\nfrom pydantic.v1 import SecretStr\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass AnthropicLLM(CustomComponent):\\n    display_name: str = \\\"AnthropicLLM\\\"\\n    description: str = \\\"Anthropic Chat&Completion large language models.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"options\\\": [\\n                    \\\"claude-2.1\\\",\\n                    \\\"claude-2.0\\\",\\n                    \\\"claude-instant-1.2\\\",\\n                    \\\"claude-instant-1\\\",\\n                    # Add more models as needed\\n                ],\\n                \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/anthropic\\\",\\n                \\\"required\\\": True,\\n                \\\"value\\\": \\\"claude-2.1\\\",\\n            },\\n            \\\"anthropic_api_key\\\": {\\n                \\\"display_name\\\": \\\"Anthropic API Key\\\",\\n                \\\"required\\\": True,\\n                \\\"password\\\": True,\\n                \\\"info\\\": \\\"Your Anthropic API key.\\\",\\n            },\\n            \\\"max_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Tokens\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"value\\\": 256,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"value\\\": 0.7,\\n            },\\n            \\\"api_endpoint\\\": {\\n                \\\"display_name\\\": \\\"API Endpoint\\\",\\n                \\\"info\\\": \\\"Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        model: str,\\n        anthropic_api_key: Optional[str] = None,\\n        max_tokens: Optional[int] = None,\\n        temperature: Optional[float] = None,\\n        api_endpoint: Optional[str] = None,\\n    ) -> BaseLanguageModel:\\n        # Set default API endpoint if not provided\\n        if not api_endpoint:\\n            api_endpoint = \\\"https://api.anthropic.com\\\"\\n\\n        try:\\n            output = ChatAnthropic(\\n                model_name=model,\\n                anthropic_api_key=SecretStr(anthropic_api_key) if anthropic_api_key else None,\\n                max_tokens_to_sample=max_tokens,  # type: ignore\\n                temperature=temperature,\\n                anthropic_api_url=api_endpoint,\\n            )\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to Anthropic API.\\\") from e\\n        return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"claude-2.1\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"claude-2.1\",\"claude-2.0\",\"claude-instant-1.2\",\"claude-instant-1\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/anthropic\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Anthropic Chat&Completion large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"AnthropicLLM\",\"documentation\":\"\",\"custom_fields\":{\"model\":null,\"anthropic_api_key\":null,\"max_tokens\":null,\"temperature\":null,\"api_endpoint\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CohereSpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_community.llms.cohere import Cohere\\nfrom langchain_core.language_models.base import BaseLanguageModel\\nfrom langflow import CustomComponent\\n\\n\\nclass CohereComponent(CustomComponent):\\n    display_name = \\\"Cohere\\\"\\n    description = \\\"Cohere large language models.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"cohere_api_key\\\": {\\\"display_name\\\": \\\"Cohere API Key\\\", \\\"type\\\": \\\"password\\\", \\\"password\\\": True},\\n            \\\"max_tokens\\\": {\\\"display_name\\\": \\\"Max Tokens\\\", \\\"default\\\": 256, \\\"type\\\": \\\"int\\\", \\\"show\\\": True},\\n            \\\"temperature\\\": {\\\"display_name\\\": \\\"Temperature\\\", \\\"default\\\": 0.75, \\\"type\\\": \\\"float\\\", \\\"show\\\": True},\\n        }\\n\\n    def build(\\n        self,\\n        cohere_api_key: str,\\n        max_tokens: int = 256,\\n        temperature: float = 0.75,\\n    ) -> BaseLanguageModel:\\n        return Cohere(cohere_api_key=cohere_api_key, max_tokens=max_tokens, temperature=temperature)  # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"cohere_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"cohere_api_key\",\"display_name\":\"Cohere API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_tokens\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.75,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Cohere large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"Cohere\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/cohere\",\"custom_fields\":{\"cohere_api_key\":null,\"max_tokens\":null,\"temperature\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"GoogleGenerativeAISpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain_google_genai import ChatGoogleGenerativeAI  # type: ignore\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, RangeSpec\\nfrom pydantic.v1.types import SecretStr\\n\\n\\nclass GoogleGenerativeAIComponent(CustomComponent):\\n    display_name: str = \\\"Google Generative AI\\\"\\n    description: str = \\\"A component that uses Google Generative AI to generate text.\\\"\\n    documentation: str = \\\"http://docs.langflow.org/components/custom\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"google_api_key\\\": {\\n                \\\"display_name\\\": \\\"Google API Key\\\",\\n                \\\"info\\\": \\\"The Google API Key to use for the Google Generative AI.\\\",\\n            },\\n            \\\"max_output_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Output Tokens\\\",\\n                \\\"info\\\": \\\"The maximum number of tokens to generate.\\\",\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"info\\\": \\\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\\\",\\n            },\\n            \\\"top_k\\\": {\\n                \\\"display_name\\\": \\\"Top K\\\",\\n                \\\"info\\\": \\\"Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.\\\",\\n                \\\"range_spec\\\": RangeSpec(min=0, max=2, step=0.1),\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top P\\\",\\n                \\\"info\\\": \\\"The maximum cumulative probability of tokens to consider when sampling.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"n\\\": {\\n                \\\"display_name\\\": \\\"N\\\",\\n                \\\"info\\\": \\\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model\\\",\\n                \\\"info\\\": \\\"The name of the model to use. Supported examples: gemini-pro\\\",\\n                \\\"options\\\": [\\\"gemini-pro\\\", \\\"gemini-pro-vision\\\"],\\n            },\\n            \\\"code\\\": {\\n                \\\"advanced\\\": True,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        google_api_key: str,\\n        model: str,\\n        max_output_tokens: Optional[int] = None,\\n        temperature: float = 0.1,\\n        top_k: Optional[int] = None,\\n        top_p: Optional[float] = None,\\n        n: Optional[int] = 1,\\n    ) -> BaseLanguageModel:\\n        return ChatGoogleGenerativeAI(\\n            model=model,\\n            max_output_tokens=max_output_tokens or None,  # type: ignore\\n            temperature=temperature,\\n            top_k=top_k or None,\\n            top_p=top_p or None,  # type: ignore\\n            n=n or 1,\\n            google_api_key=SecretStr(google_api_key),\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"google_api_key\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"google_api_key\",\"display_name\":\"Google API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The Google API Key to use for the Google Generative AI.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"max_output_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_output_tokens\",\"display_name\":\"Max Output Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum number of tokens to generate.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gemini-pro\",\"gemini-pro-vision\"],\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"The name of the model to use. Supported examples: gemini-pro\",\"title_case\":false,\"input_types\":[\"Text\"]},\"n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Run inference with this temperature. Must by in the closed interval [0.0, 1.0].\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Decode using top-k sampling: consider the set of top_k most probable tokens. Must be positive.\",\"rangeSpec\":{\"min\":0.0,\"max\":2.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"The maximum cumulative probability of tokens to consider when sampling.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"A component that uses Google Generative AI to generate text.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\"],\"display_name\":\"Google Generative AI\",\"documentation\":\"http://docs.langflow.org/components/custom\",\"custom_fields\":{\"google_api_key\":null,\"model\":null,\"max_output_tokens\":null,\"temperature\":null,\"top_k\":null,\"top_p\":null,\"n\":null},\"output_types\":[\"BaseLanguageModel\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"BaiduQianfanLLMEndpointsSpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint\\nfrom langchain.llms.base import BaseLLM\\n\\n\\nclass QianfanLLMEndpointComponent(CustomComponent):\\n    display_name: str = \\\"QianfanLLMEndpoint\\\"\\n    description: str = (\\n        \\\"Baidu Qianfan hosted open source or customized models. \\\"\\n        \\\"Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\"\\n    )\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"options\\\": [\\n                    \\\"ERNIE-Bot\\\",\\n                    \\\"ERNIE-Bot-turbo\\\",\\n                    \\\"BLOOMZ-7B\\\",\\n                    \\\"Llama-2-7b-chat\\\",\\n                    \\\"Llama-2-13b-chat\\\",\\n                    \\\"Llama-2-70b-chat\\\",\\n                    \\\"Qianfan-BLOOMZ-7B-compressed\\\",\\n                    \\\"Qianfan-Chinese-Llama-2-7B\\\",\\n                    \\\"ChatGLM2-6B-32K\\\",\\n                    \\\"AquilaChat-7B\\\",\\n                ],\\n                \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\",\\n                \\\"required\\\": True,\\n            },\\n            \\\"qianfan_ak\\\": {\\n                \\\"display_name\\\": \\\"Qianfan Ak\\\",\\n                \\\"required\\\": True,\\n                \\\"password\\\": True,\\n                \\\"info\\\": \\\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\\\",\\n            },\\n            \\\"qianfan_sk\\\": {\\n                \\\"display_name\\\": \\\"Qianfan Sk\\\",\\n                \\\"required\\\": True,\\n                \\\"password\\\": True,\\n                \\\"info\\\": \\\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\\\",\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top p\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n                \\\"value\\\": 0.8,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n                \\\"value\\\": 0.95,\\n            },\\n            \\\"penalty_score\\\": {\\n                \\\"display_name\\\": \\\"Penalty Score\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n                \\\"value\\\": 1.0,\\n            },\\n            \\\"endpoint\\\": {\\n                \\\"display_name\\\": \\\"Endpoint\\\",\\n                \\\"info\\\": \\\"Endpoint of the Qianfan LLM, required if custom model used.\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        model: str = \\\"ERNIE-Bot-turbo\\\",\\n        qianfan_ak: Optional[str] = None,\\n        qianfan_sk: Optional[str] = None,\\n        top_p: Optional[float] = None,\\n        temperature: Optional[float] = None,\\n        penalty_score: Optional[float] = None,\\n        endpoint: Optional[str] = None,\\n    ) -> BaseLLM:\\n        try:\\n            output = QianfanLLMEndpoint(  # type: ignore\\n                model=model,\\n                qianfan_ak=qianfan_ak,\\n                qianfan_sk=qianfan_sk,\\n                top_p=top_p,\\n                temperature=temperature,\\n                penalty_score=penalty_score,\\n                endpoint=endpoint,\\n            )\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to Baidu Qianfan API.\\\") from e\\n        return output  # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"endpoint\",\"display_name\":\"Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Qianfan LLM, required if custom model used.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"ERNIE-Bot-turbo\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"ERNIE-Bot\",\"ERNIE-Bot-turbo\",\"BLOOMZ-7B\",\"Llama-2-7b-chat\",\"Llama-2-13b-chat\",\"Llama-2-70b-chat\",\"Qianfan-BLOOMZ-7B-compressed\",\"Qianfan-Chinese-Llama-2-7B\",\"ChatGLM2-6B-32K\",\"AquilaChat-7B\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"title_case\":false,\"input_types\":[\"Text\"]},\"penalty_score\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1.0,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"penalty_score\",\"display_name\":\"Penalty Score\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"qianfan_ak\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_ak\",\"display_name\":\"Qianfan Ak\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"qianfan_sk\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"qianfan_sk\",\"display_name\":\"Qianfan Sk\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from  https://cloud.baidu.com/product/wenxinworkshop\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.95,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Baidu Qianfan hosted open source or customized models. Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"QianfanLLMEndpoint\",\"documentation\":\"\",\"custom_fields\":{\"model\":null,\"qianfan_ak\":null,\"qianfan_sk\":null,\"top_p\":null,\"temperature\":null,\"penalty_score\":null,\"endpoint\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatLiteLLMSpecs\":{\"template\":{\"api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"api_key\",\"display_name\":\"API key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Any, Callable, Dict, Optional, Union\\n\\nfrom langchain_community.chat_models.litellm import ChatLiteLLM, ChatLiteLLMException\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel\\n\\n\\nclass ChatLiteLLMComponent(CustomComponent):\\n    display_name = \\\"ChatLiteLLM\\\"\\n    description = \\\"`LiteLLM` collection of large language models.\\\"\\n    documentation = \\\"https://python.langchain.com/docs/integrations/chat/litellm\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model name\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": True,\\n                \\\"info\\\": \\\"The name of the model to use. For example, `gpt-3.5-turbo`.\\\",\\n            },\\n            \\\"api_key\\\": {\\n                \\\"display_name\\\": \\\"API key\\\",\\n                \\\"field_type\\\": \\\"str\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"password\\\": True,\\n            },\\n            \\\"provider\\\": {\\n                \\\"display_name\\\": \\\"Provider\\\",\\n                \\\"info\\\": \\\"The provider of the API key.\\\",\\n                \\\"options\\\": [\\n                    \\\"OpenAI\\\",\\n                    \\\"Azure\\\",\\n                    \\\"Anthropic\\\",\\n                    \\\"Replicate\\\",\\n                    \\\"Cohere\\\",\\n                    \\\"OpenRouter\\\",\\n                ],\\n            },\\n            \\\"streaming\\\": {\\n                \\\"display_name\\\": \\\"Streaming\\\",\\n                \\\"field_type\\\": \\\"bool\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n                \\\"default\\\": True,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"default\\\": 0.7,\\n            },\\n            \\\"model_kwargs\\\": {\\n                \\\"display_name\\\": \\\"Model kwargs\\\",\\n                \\\"field_type\\\": \\\"dict\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n                \\\"default\\\": {},\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top p\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n            },\\n            \\\"top_k\\\": {\\n                \\\"display_name\\\": \\\"Top k\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n            },\\n            \\\"n\\\": {\\n                \\\"display_name\\\": \\\"N\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n                \\\"info\\\": \\\"Number of chat completions to generate for each prompt. \\\"\\n                \\\"Note that the API may not return the full n completions if duplicates are generated.\\\",\\n                \\\"default\\\": 1,\\n            },\\n            \\\"max_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max tokens\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"default\\\": 256,\\n                \\\"info\\\": \\\"The maximum number of tokens to generate for each chat completion.\\\",\\n            },\\n            \\\"max_retries\\\": {\\n                \\\"display_name\\\": \\\"Max retries\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n                \\\"default\\\": 6,\\n            },\\n            \\\"verbose\\\": {\\n                \\\"display_name\\\": \\\"Verbose\\\",\\n                \\\"field_type\\\": \\\"bool\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n                \\\"default\\\": False,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        model: str,\\n        provider: str,\\n        api_key: Optional[str] = None,\\n        streaming: bool = True,\\n        temperature: Optional[float] = 0.7,\\n        model_kwargs: Optional[Dict[str, Any]] = {},\\n        top_p: Optional[float] = None,\\n        top_k: Optional[int] = None,\\n        n: int = 1,\\n        max_tokens: int = 256,\\n        max_retries: int = 6,\\n        verbose: bool = False,\\n    ) -> Union[BaseLanguageModel, Callable]:\\n        try:\\n            import litellm  # type: ignore\\n\\n            litellm.drop_params = True\\n            litellm.set_verbose = verbose\\n        except ImportError:\\n            raise ChatLiteLLMException(\\n                \\\"Could not import litellm python package. \\\" \\\"Please install it with `pip install litellm`\\\"\\n            )\\n        provider_map = {\\n            \\\"OpenAI\\\": \\\"openai_api_key\\\",\\n            \\\"Azure\\\": \\\"azure_api_key\\\",\\n            \\\"Anthropic\\\": \\\"anthropic_api_key\\\",\\n            \\\"Replicate\\\": \\\"replicate_api_key\\\",\\n            \\\"Cohere\\\": \\\"cohere_api_key\\\",\\n            \\\"OpenRouter\\\": \\\"openrouter_api_key\\\",\\n        }\\n        # Set the API key based on the provider\\n        kwarg = {provider_map[provider]: api_key}\\n\\n        LLM = ChatLiteLLM(\\n            model=model,\\n            client=None,\\n            streaming=streaming,\\n            temperature=temperature,\\n            model_kwargs=model_kwargs if model_kwargs is not None else {},\\n            top_p=top_p,\\n            top_k=top_k,\\n            n=n,\\n            max_tokens=max_tokens,\\n            max_retries=max_retries,\\n            **kwarg,\\n        )\\n        return LLM\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_retries\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":6,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_retries\",\"display_name\":\"Max retries\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum number of tokens to generate for each chat completion.\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model name\",\"advanced\":false,\"dynamic\":false,\"info\":\"The name of the model to use. For example, `gpt-3.5-turbo`.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":1,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n\",\"display_name\":\"N\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of chat completions to generate for each prompt. Note that the API may not return the full n completions if duplicates are generated.\",\"title_case\":false},\"provider\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"OpenAI\",\"Azure\",\"Anthropic\",\"Replicate\",\"Cohere\",\"OpenRouter\"],\"name\":\"provider\",\"display_name\":\"Provider\",\"advanced\":false,\"dynamic\":false,\"info\":\"The provider of the API key.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"streaming\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"streaming\",\"display_name\":\"Streaming\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top k\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"top_p\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"verbose\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"verbose\",\"display_name\":\"Verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"`LiteLLM` collection of large language models.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"Callable\"],\"display_name\":\"ChatLiteLLM\",\"documentation\":\"https://python.langchain.com/docs/integrations/chat/litellm\",\"custom_fields\":{\"model\":null,\"provider\":null,\"api_key\":null,\"streaming\":null,\"temperature\":null,\"model_kwargs\":null,\"top_p\":null,\"top_k\":null,\"n\":null,\"max_tokens\":null,\"max_retries\":null,\"verbose\":null},\"output_types\":[\"BaseLanguageModel\",\"Callable\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"CTransformersSpecs\":{\"template\":{\"model_file\":{\"type\":\"file\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[\".bin\"],\"file_path\":\"\",\"password\":false,\"name\":\"model_file\",\"display_name\":\"Model File\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Dict, Optional\\n\\nfrom langchain_community.llms.ctransformers import CTransformers\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass CTransformersComponent(CustomComponent):\\n    display_name = \\\"CTransformers\\\"\\n    description = \\\"C Transformers LLM models\\\"\\n    documentation = \\\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"model\\\": {\\\"display_name\\\": \\\"Model\\\", \\\"required\\\": True},\\n            \\\"model_file\\\": {\\n                \\\"display_name\\\": \\\"Model File\\\",\\n                \\\"required\\\": False,\\n                \\\"field_type\\\": \\\"file\\\",\\n                \\\"file_types\\\": [\\\".bin\\\"],\\n            },\\n            \\\"model_type\\\": {\\\"display_name\\\": \\\"Model Type\\\", \\\"required\\\": True},\\n            \\\"config\\\": {\\n                \\\"display_name\\\": \\\"Config\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n                \\\"field_type\\\": \\\"dict\\\",\\n                \\\"value\\\": '{\\\"top_k\\\":40,\\\"top_p\\\":0.95,\\\"temperature\\\":0.8,\\\"repetition_penalty\\\":1.1,\\\"last_n_tokens\\\":64,\\\"seed\\\":-1,\\\"max_new_tokens\\\":256,\\\"stop\\\":\\\"\\\",\\\"stream\\\":\\\"False\\\",\\\"reset\\\":\\\"True\\\",\\\"batch_size\\\":8,\\\"threads\\\":-1,\\\"context_length\\\":-1,\\\"gpu_layers\\\":0}',\\n            },\\n        }\\n\\n    def build(self, model: str, model_file: str, model_type: str, config: Optional[Dict] = None) -> CTransformers:\\n        return CTransformers(model=model, model_file=model_file, model_type=model_type, config=config)  # type: ignore\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"config\":{\"type\":\"dict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"{\\\"top_k\\\":40,\\\"top_p\\\":0.95,\\\"temperature\\\":0.8,\\\"repetition_penalty\\\":1.1,\\\"last_n_tokens\\\":64,\\\"seed\\\":-1,\\\"max_new_tokens\\\":256,\\\"stop\\\":\\\"\\\",\\\"stream\\\":\\\"False\\\",\\\"reset\\\":\\\"True\\\",\\\"batch_size\\\":8,\\\"threads\\\":-1,\\\"context_length\\\":-1,\\\"gpu_layers\\\":0}\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"config\",\"display_name\":\"Config\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_type\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_type\",\"display_name\":\"Model Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"C Transformers LLM models\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"CTransformers\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\",\"LLM\"],\"display_name\":\"CTransformers\",\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/llms/integrations/ctransformers\",\"custom_fields\":{\"model\":null,\"model_file\":null,\"model_type\":null,\"config\":null},\"output_types\":[\"CTransformers\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"HuggingFaceEndpointsSpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.llms.huggingface_endpoint import HuggingFaceEndpoint\\nfrom langflow import CustomComponent\\n\\n\\nclass HuggingFaceEndpointsComponent(CustomComponent):\\n    display_name: str = \\\"Hugging Face Inference API\\\"\\n    description: str = \\\"LLM model from Hugging Face Inference API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Endpoint URL\\\", \\\"password\\\": True},\\n            \\\"task\\\": {\\n                \\\"display_name\\\": \\\"Task\\\",\\n                \\\"options\\\": [\\\"text2text-generation\\\", \\\"text-generation\\\", \\\"summarization\\\"],\\n            },\\n            \\\"huggingfacehub_api_token\\\": {\\\"display_name\\\": \\\"API token\\\", \\\"password\\\": True},\\n            \\\"model_kwargs\\\": {\\n                \\\"display_name\\\": \\\"Model Keyword Arguments\\\",\\n                \\\"field_type\\\": \\\"code\\\",\\n            },\\n            \\\"code\\\": {\\\"show\\\": False},\\n        }\\n\\n    def build(\\n        self,\\n        endpoint_url: str,\\n        task: str = \\\"text2text-generation\\\",\\n        huggingfacehub_api_token: Optional[str] = None,\\n        model_kwargs: Optional[dict] = None,\\n    ) -> BaseLLM:\\n        try:\\n            output = HuggingFaceEndpoint(  # type: ignore\\n                endpoint_url=endpoint_url,\\n                task=task,\\n                huggingfacehub_api_token=huggingfacehub_api_token,\\n                model_kwargs=model_kwargs or {},\\n            )\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to HuggingFace Endpoints API.\\\") from e\\n        return output\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"endpoint_url\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"endpoint_url\",\"display_name\":\"Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"huggingfacehub_api_token\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"huggingfacehub_api_token\",\"display_name\":\"API token\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"model_kwargs\":{\"type\":\"code\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Keyword Arguments\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"task\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"text2text-generation\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"text2text-generation\",\"text-generation\",\"summarization\"],\"name\":\"task\",\"display_name\":\"Task\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"LLM model from Hugging Face Inference API.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"Hugging Face Inference API\",\"documentation\":\"\",\"custom_fields\":{\"endpoint_url\":null,\"task\":null,\"huggingfacehub_api_token\":null,\"model_kwargs\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatOpenAISpecs\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langchain.llms import BaseLLM\\nfrom langchain_community.chat_models.openai import ChatOpenAI\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import BaseLanguageModel, NestedDict\\n\\n\\nclass ChatOpenAIComponent(CustomComponent):\\n    display_name = \\\"ChatOpenAI\\\"\\n    description = \\\"`OpenAI` Chat large language models API.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"max_tokens\\\": {\\n                \\\"display_name\\\": \\\"Max Tokens\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n            },\\n            \\\"model_kwargs\\\": {\\n                \\\"display_name\\\": \\\"Model Kwargs\\\",\\n                \\\"advanced\\\": True,\\n                \\\"required\\\": False,\\n            },\\n            \\\"model_name\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"options\\\": [\\n                    \\\"gpt-4-turbo-preview\\\",\\n                    \\\"gpt-4-0125-preview\\\",\\n                    \\\"gpt-4-1106-preview\\\",\\n                    \\\"gpt-4-vision-preview\\\",\\n                    \\\"gpt-3.5-turbo-0125\\\",\\n                    \\\"gpt-3.5-turbo-1106\\\",\\n                ],\\n            },\\n            \\\"openai_api_base\\\": {\\n                \\\"display_name\\\": \\\"OpenAI API Base\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"info\\\": (\\n                    \\\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\\\n\\\\n\\\"\\n                    \\\"You can change this to use other APIs like JinaChat, LocalAI and Prem.\\\"\\n                ),\\n            },\\n            \\\"openai_api_key\\\": {\\n                \\\"display_name\\\": \\\"OpenAI API Key\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"password\\\": True,\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"advanced\\\": False,\\n                \\\"required\\\": False,\\n                \\\"value\\\": 0.7,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        max_tokens: Optional[int] = 256,\\n        model_kwargs: NestedDict = {},\\n        model_name: str = \\\"gpt-4-1106-preview\\\",\\n        openai_api_base: Optional[str] = None,\\n        openai_api_key: Optional[str] = None,\\n        temperature: float = 0.7,\\n    ) -> Union[BaseLanguageModel, BaseLLM]:\\n        if not openai_api_base:\\n            openai_api_base = \\\"https://api.openai.com/v1\\\"\\n        return ChatOpenAI(\\n            max_tokens=max_tokens,\\n            model_kwargs=model_kwargs,\\n            model=model_name,\\n            base_url=openai_api_base,\\n            api_key=openai_api_key,\\n            temperature=temperature,\\n        )\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"max_tokens\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":256,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"max_tokens\",\"display_name\":\"Max Tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_kwargs\":{\"type\":\"NestedDict\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":{},\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"model_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"gpt-4-1106-preview\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"gpt-4-turbo-preview\",\"gpt-4-0125-preview\",\"gpt-4-1106-preview\",\"gpt-4-vision-preview\",\"gpt-3.5-turbo-0125\",\"gpt-3.5-turbo-1106\"],\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_base\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"openai_api_key\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.7,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"documentation\":\"\",\"custom_fields\":{\"max_tokens\":null,\"model_kwargs\":null,\"model_name\":null,\"openai_api_base\":null,\"openai_api_key\":null,\"temperature\":null},\"output_types\":[\"BaseLanguageModel\",\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"OllamaLLMSpecs\":{\"template\":{\"base_url\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"base_url\",\"display_name\":\"Base URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain_community.llms.ollama import Ollama\\n\\nfrom langflow import CustomComponent\\n\\n\\nclass OllamaLLM(CustomComponent):\\n    display_name = \\\"Ollama\\\"\\n    description = \\\"Local LLM with Ollama.\\\"\\n\\n    def build_config(self) -> dict:\\n        return {\\n            \\\"base_url\\\": {\\n                \\\"display_name\\\": \\\"Base URL\\\",\\n                \\\"info\\\": \\\"Endpoint of the Ollama API. Defaults to 'http://localhost:11434' if not specified.\\\",\\n            },\\n            \\\"model\\\": {\\n                \\\"display_name\\\": \\\"Model Name\\\",\\n                \\\"value\\\": \\\"llama2\\\",\\n                \\\"info\\\": \\\"Refer to https://ollama.ai/library for more models.\\\",\\n            },\\n            \\\"temperature\\\": {\\n                \\\"display_name\\\": \\\"Temperature\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"value\\\": 0.8,\\n                \\\"info\\\": \\\"Controls the creativity of model responses.\\\",\\n            },\\n            \\\"mirostat\\\": {\\n                \\\"display_name\\\": \\\"Mirostat\\\",\\n                \\\"options\\\": [\\\"Disabled\\\", \\\"Mirostat\\\", \\\"Mirostat 2.0\\\"],\\n                \\\"info\\\": \\\"Enable/disable Mirostat sampling for controlling perplexity.\\\",\\n                \\\"value\\\": \\\"Disabled\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"mirostat_eta\\\": {\\n                \\\"display_name\\\": \\\"Mirostat Eta\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Learning rate influencing the algorithm's response to feedback.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"mirostat_tau\\\": {\\n                \\\"display_name\\\": \\\"Mirostat Tau\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Controls balance between coherence and diversity.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"num_ctx\\\": {\\n                \\\"display_name\\\": \\\"Context Window Size\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Size of the context window for generating the next token.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"num_gpu\\\": {\\n                \\\"display_name\\\": \\\"Number of GPUs\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Number of GPUs to use for computation.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"num_thread\\\": {\\n                \\\"display_name\\\": \\\"Number of Threads\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Number of threads to use during computation.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"repeat_last_n\\\": {\\n                \\\"display_name\\\": \\\"Repeat Last N\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Sets how far back the model looks to prevent repetition.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"repeat_penalty\\\": {\\n                \\\"display_name\\\": \\\"Repeat Penalty\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Penalty for repetitions in generated text.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"stop\\\": {\\n                \\\"display_name\\\": \\\"Stop Tokens\\\",\\n                \\\"info\\\": \\\"List of tokens to signal the model to stop generating text.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"tfs_z\\\": {\\n                \\\"display_name\\\": \\\"TFS Z\\\",\\n                \\\"field_type\\\": \\\"float\\\",\\n                \\\"info\\\": \\\"Tail free sampling to reduce impact of less probable tokens.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_k\\\": {\\n                \\\"display_name\\\": \\\"Top K\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Limits token selection to top K for reducing nonsense generation.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n            \\\"top_p\\\": {\\n                \\\"display_name\\\": \\\"Top P\\\",\\n                \\\"field_type\\\": \\\"int\\\",\\n                \\\"info\\\": \\\"Works with top-k to control diversity of generated text.\\\",\\n                \\\"advanced\\\": True,\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        base_url: Optional[str],\\n        model: str,\\n        temperature: Optional[float],\\n        mirostat: Optional[str],\\n        mirostat_eta: Optional[float] = None,\\n        mirostat_tau: Optional[float] = None,\\n        num_ctx: Optional[int] = None,\\n        num_gpu: Optional[int] = None,\\n        num_thread: Optional[int] = None,\\n        repeat_last_n: Optional[int] = None,\\n        repeat_penalty: Optional[float] = None,\\n        stop: Optional[List[str]] = None,\\n        tfs_z: Optional[float] = None,\\n        top_k: Optional[int] = None,\\n        top_p: Optional[int] = None,\\n    ) -> BaseLLM:\\n        if not base_url:\\n            base_url = \\\"http://localhost:11434\\\"\\n\\n        # Mapping mirostat settings to their corresponding values\\n        mirostat_options = {\\\"Mirostat\\\": 1, \\\"Mirostat 2.0\\\": 2}\\n\\n        # Default to 0 for 'Disabled'\\n        mirostat_value = mirostat_options.get(mirostat, 0)  # type: ignore\\n\\n        # Set mirostat_eta and mirostat_tau to None if mirostat is disabled\\n        if mirostat_value == 0:\\n            mirostat_eta = None\\n            mirostat_tau = None\\n\\n        try:\\n            llm = Ollama(\\n                base_url=base_url,\\n                model=model,\\n                mirostat=mirostat_value,\\n                mirostat_eta=mirostat_eta,\\n                mirostat_tau=mirostat_tau,\\n                num_ctx=num_ctx,\\n                num_gpu=num_gpu,\\n                num_thread=num_thread,\\n                repeat_last_n=repeat_last_n,\\n                repeat_penalty=repeat_penalty,\\n                temperature=temperature,\\n                stop=stop,\\n                tfs_z=tfs_z,\\n                top_k=top_k,\\n                top_p=top_p,\\n            )\\n\\n        except Exception as e:\\n            raise ValueError(\\\"Could not connect to Ollama.\\\") from e\\n\\n        return llm\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"mirostat\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Disabled\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Disabled\",\"Mirostat\",\"Mirostat 2.0\"],\"name\":\"mirostat\",\"display_name\":\"Mirostat\",\"advanced\":true,\"dynamic\":false,\"info\":\"Enable/disable Mirostat sampling for controlling perplexity.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"mirostat_eta\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_eta\",\"display_name\":\"Mirostat Eta\",\"advanced\":true,\"dynamic\":false,\"info\":\"Learning rate influencing the algorithm's response to feedback.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"mirostat_tau\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"mirostat_tau\",\"display_name\":\"Mirostat Tau\",\"advanced\":true,\"dynamic\":false,\"info\":\"Controls balance between coherence and diversity.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"model\":{\"type\":\"str\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"llama2\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"Refer to https://ollama.ai/library for more models.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"num_ctx\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_ctx\",\"display_name\":\"Context Window Size\",\"advanced\":true,\"dynamic\":false,\"info\":\"Size of the context window for generating the next token.\",\"title_case\":false},\"num_gpu\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_gpu\",\"display_name\":\"Number of GPUs\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of GPUs to use for computation.\",\"title_case\":false},\"num_thread\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"num_thread\",\"display_name\":\"Number of Threads\",\"advanced\":true,\"dynamic\":false,\"info\":\"Number of threads to use during computation.\",\"title_case\":false},\"repeat_last_n\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_last_n\",\"display_name\":\"Repeat Last N\",\"advanced\":true,\"dynamic\":false,\"info\":\"Sets how far back the model looks to prevent repetition.\",\"title_case\":false},\"repeat_penalty\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"repeat_penalty\",\"display_name\":\"Repeat Penalty\",\"advanced\":true,\"dynamic\":false,\"info\":\"Penalty for repetitions in generated text.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"stop\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"stop\",\"display_name\":\"Stop Tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"List of tokens to signal the model to stop generating text.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"temperature\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":0.8,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Controls the creativity of model responses.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"tfs_z\":{\"type\":\"float\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"tfs_z\",\"display_name\":\"TFS Z\",\"advanced\":true,\"dynamic\":false,\"info\":\"Tail free sampling to reduce impact of less probable tokens.\",\"rangeSpec\":{\"min\":-1.0,\"max\":1.0,\"step\":0.1},\"title_case\":false},\"top_k\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_k\",\"display_name\":\"Top K\",\"advanced\":true,\"dynamic\":false,\"info\":\"Limits token selection to top K for reducing nonsense generation.\",\"title_case\":false},\"top_p\":{\"type\":\"int\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top P\",\"advanced\":true,\"dynamic\":false,\"info\":\"Works with top-k to control diversity of generated text.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Local LLM with Ollama.\",\"base_classes\":[\"BaseLanguageModel\",\"Runnable\",\"Generic\",\"RunnableSerializable\",\"Serializable\",\"object\",\"BaseLLM\"],\"display_name\":\"Ollama\",\"documentation\":\"\",\"custom_fields\":{\"base_url\":null,\"model\":null,\"temperature\":null,\"mirostat\":null,\"mirostat_eta\":null,\"mirostat_tau\":null,\"num_ctx\":null,\"num_gpu\":null,\"num_thread\":null,\"repeat_last_n\":null,\"repeat_penalty\":null,\"stop\":null,\"tfs_z\":null,\"top_k\":null,\"top_p\":null},\"output_types\":[\"BaseLLM\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"io\":{\"ChatOutput\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional, Union\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\nfrom langflow.schema import Record\\n\\n\\nclass ChatOutput(CustomComponent):\\n    display_name = \\\"Chat Output\\\"\\n    description = \\\"Used to send a message to the chat.\\\"\\n\\n    field_config = {\\n        \\\"code\\\": {\\n            \\\"show\\\": True,\\n        }\\n    }\\n\\n    def build_config(self):\\n        return {\\n            \\\"message\\\": {\\\"input_types\\\": [\\\"Text\\\"], \\\"display_name\\\": \\\"Message\\\"},\\n            \\\"sender\\\": {\\n                \\\"options\\\": [\\\"Machine\\\", \\\"User\\\"],\\n                \\\"display_name\\\": \\\"Sender Type\\\",\\n            },\\n            \\\"sender_name\\\": {\\\"display_name\\\": \\\"Sender Name\\\"},\\n            \\\"session_id\\\": {\\n                \\\"display_name\\\": \\\"Session ID\\\",\\n                \\\"info\\\": \\\"Session ID of the chat history.\\\",\\n                \\\"input_types\\\": [\\\"Text\\\"],\\n            },\\n            \\\"return_record\\\": {\\n                \\\"display_name\\\": \\\"Return Record\\\",\\n                \\\"info\\\": \\\"Return the message as a record containing the sender, sender_name, and session_id.\\\",\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        sender: Optional[str] = \\\"Machine\\\",\\n        sender_name: Optional[str] = \\\"AI\\\",\\n        session_id: Optional[str] = None,\\n        message: Optional[str] = None,\\n        return_record: Optional[bool] = False,\\n    ) -> Union[Text, Record]:\\n        if return_record:\\n            if isinstance(message, Record):\\n                # Update the data of the record\\n                message.data[\\\"sender\\\"] = sender\\n                message.data[\\\"sender_name\\\"] = sender_name\\n                message.data[\\\"session_id\\\"] = session_id\\n            else:\\n                message = Record(\\n                    text=message,\\n                    data={\\n                        \\\"sender\\\": sender,\\n                        \\\"sender_name\\\": sender_name,\\n                        \\\"session_id\\\": session_id,\\n                    },\\n                )\\n        if not message:\\n            message = \\\"\\\"\\n        self.status = message\\n        return message\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"message\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"message\",\"display_name\":\"Message\",\"advanced\":false,\"input_types\":[\"Text\",\"Text\"],\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"return_record\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_record\",\"display_name\":\"Return Record\",\"advanced\":false,\"dynamic\":false,\"info\":\"Return the message as a record containing the sender, sender_name, and session_id.\",\"title_case\":false},\"sender\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"Machine\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Machine\",\"User\"],\"name\":\"sender\",\"display_name\":\"Sender Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"sender_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"AI\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender_name\",\"display_name\":\"Sender Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"display_name\":\"Session ID\",\"advanced\":false,\"input_types\":[\"Text\",\"Text\"],\"dynamic\":false,\"info\":\"Session ID of the chat history.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Used to send a message to the chat.\",\"base_classes\":[\"Text\",\"object\",\"Record\"],\"display_name\":\"Chat Output\",\"documentation\":\"\",\"custom_fields\":{\"sender\":null,\"sender_name\":null,\"session_id\":null,\"message\":null,\"return_record\":null},\"output_types\":[\"Text\",\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"MessageHistory\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.memory import get_messages\\nfrom langflow.schema import Record\\n\\n\\nclass MessageHistoryComponent(CustomComponent):\\n    display_name = \\\"Message History\\\"\\n    description = \\\"Used to retrieve stored messages.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"sender\\\": {\\n                \\\"options\\\": [\\\"Machine\\\", \\\"User\\\"],\\n                \\\"display_name\\\": \\\"Sender Type\\\",\\n            },\\n            \\\"sender_name\\\": {\\\"display_name\\\": \\\"Sender Name\\\"},\\n            \\\"file_path\\\": {\\n                \\\"display_name\\\": \\\"File Path\\\",\\n                \\\"info\\\": \\\"Path of the local JSON file to store the messages. It should be a unique path for each chat history.\\\",\\n            },\\n            \\\"n_messages\\\": {\\n                \\\"display_name\\\": \\\"Number of Messages\\\",\\n                \\\"info\\\": \\\"Number of messages to retrieve.\\\",\\n            },\\n            \\\"session_id\\\": {\\n                \\\"display_name\\\": \\\"Session ID\\\",\\n                \\\"info\\\": \\\"Session ID of the chat history.\\\",\\n                \\\"input_types\\\": [\\\"Text\\\"],\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        sender: Optional[str] = None,\\n        sender_name: Optional[str] = None,\\n        session_id: Optional[str] = None,\\n        n_messages: int = 5,\\n    ) -> List[Record]:\\n        messages = get_messages(\\n            sender=sender,\\n            sender_name=sender_name,\\n            session_id=session_id,\\n            limit=n_messages,\\n        )\\n        self.status = messages\\n        return messages\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"n_messages\":{\"type\":\"int\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":5,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"n_messages\",\"display_name\":\"Number of Messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"Number of messages to retrieve.\",\"title_case\":false},\"sender\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Machine\",\"User\"],\"name\":\"sender\",\"display_name\":\"Sender Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"sender_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender_name\",\"display_name\":\"Sender Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"display_name\":\"Session ID\",\"advanced\":false,\"input_types\":[\"Text\",\"Text\"],\"dynamic\":false,\"info\":\"Session ID of the chat history.\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"Used to retrieve stored messages.\",\"base_classes\":[\"Record\"],\"display_name\":\"Message History\",\"documentation\":\"\",\"custom_fields\":{\"sender\":null,\"sender_name\":null,\"session_id\":null,\"n_messages\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"TextOutput\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass TextOutput(CustomComponent):\\n    display_name = \\\"Text Output\\\"\\n    description = \\\"Used to pass text output to the next component.\\\"\\n\\n    field_config = {\\n        \\\"value\\\": {\\\"display_name\\\": \\\"Value\\\"},\\n    }\\n\\n    def build(self, value: Optional[str] = \\\"\\\") -> Text:\\n        self.status = value\\n        if not value:\\n            value = \\\"\\\"\\n        return value\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"value\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"value\",\"display_name\":\"Value\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Used to pass text output to the next component.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Text Output\",\"documentation\":\"\",\"custom_fields\":{\"value\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"StoreMessages\":{\"template\":{\"records\":{\"type\":\"Record\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"records\",\"display_name\":\"Records\",\"advanced\":false,\"dynamic\":false,\"info\":\"The list of records to store. Each record should contain the keys 'sender', 'sender_name', and 'session_id'.\",\"title_case\":false},\"texts\":{\"type\":\"Text\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"texts\",\"display_name\":\"Texts\",\"advanced\":false,\"dynamic\":false,\"info\":\"The list of texts to store. If records is not provided, texts must be provided.\",\"title_case\":false},\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import List, Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\nfrom langflow.memory import add_messages\\nfrom langflow.schema import Record\\n\\n\\nclass StoreMessages(CustomComponent):\\n    display_name = \\\"Store Messages\\\"\\n    description = \\\"Used to store messages.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"records\\\": {\\n                \\\"display_name\\\": \\\"Records\\\",\\n                \\\"info\\\": \\\"The list of records to store. Each record should contain the keys 'sender', 'sender_name', and 'session_id'.\\\",\\n            },\\n            \\\"texts\\\": {\\n                \\\"display_name\\\": \\\"Texts\\\",\\n                \\\"info\\\": \\\"The list of texts to store. If records is not provided, texts must be provided.\\\",\\n            },\\n            \\\"session_id\\\": {\\n                \\\"display_name\\\": \\\"Session ID\\\",\\n                \\\"info\\\": \\\"The session ID to store.\\\",\\n            },\\n            \\\"sender\\\": {\\n                \\\"display_name\\\": \\\"Sender\\\",\\n                \\\"info\\\": \\\"The sender to store.\\\",\\n            },\\n            \\\"sender_name\\\": {\\n                \\\"display_name\\\": \\\"Sender Name\\\",\\n                \\\"info\\\": \\\"The sender name to store.\\\",\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        records: Optional[List[Record]] = None,\\n        texts: Optional[List[Text]] = None,\\n        session_id: Optional[str] = None,\\n        sender: Optional[str] = None,\\n        sender_name: Optional[str] = None,\\n    ) -> List[Record]:\\n        # Records is the main way to store messages\\n        # If records is not provided, we can use texts\\n        # but we need to create the records from the texts\\n        # and the other parameters\\n        if not texts and not records:\\n            raise ValueError(\\\"Either texts or records must be provided.\\\")\\n\\n        if not records:\\n            records = []\\n            if not session_id or not sender or not sender_name:\\n                raise ValueError(\\\"If passing texts, session_id, sender, and sender_name must be provided.\\\")\\n            for text in texts:\\n                record = Record(\\n                    text=text,\\n                    data={\\n                        \\\"session_id\\\": session_id,\\n                        \\\"sender\\\": sender,\\n                        \\\"sender_name\\\": sender_name,\\n                    },\\n                )\\n                records.append(record)\\n        elif isinstance(records, Record):\\n            records = [records]\\n\\n        self.status = records\\n        records = add_messages(records)\\n        return records\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"sender\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender\",\"display_name\":\"Sender\",\"advanced\":false,\"dynamic\":false,\"info\":\"The sender to store.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"sender_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender_name\",\"display_name\":\"Sender Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"The sender name to store.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"display_name\":\"Session ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"The session ID to store.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Used to store messages.\",\"base_classes\":[\"Record\"],\"display_name\":\"Store Messages\",\"documentation\":\"\",\"custom_fields\":{\"records\":null,\"texts\":null,\"session_id\":null,\"sender\":null,\"sender_name\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"ChatInput\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.schema import Record\\n\\n\\nclass ChatInput(CustomComponent):\\n    display_name = \\\"Chat Input\\\"\\n    description = \\\"Used to get user input from the chat.\\\"\\n\\n    def build_config(self):\\n        return {\\n            \\\"message\\\": {\\n                \\\"input_types\\\": [\\\"Text\\\"],\\n                \\\"display_name\\\": \\\"Message\\\",\\n                \\\"multiline\\\": True,\\n            },\\n            \\\"sender\\\": {\\n                \\\"options\\\": [\\\"Machine\\\", \\\"User\\\"],\\n                \\\"display_name\\\": \\\"Sender Type\\\",\\n            },\\n            \\\"sender_name\\\": {\\\"display_name\\\": \\\"Sender Name\\\"},\\n            \\\"session_id\\\": {\\n                \\\"display_name\\\": \\\"Session ID\\\",\\n                \\\"info\\\": \\\"Session ID of the chat history.\\\",\\n            },\\n            \\\"return_record\\\": {\\n                \\\"display_name\\\": \\\"Return Record\\\",\\n                \\\"info\\\": \\\"Return the message as a record containing the sender, sender_name, and session_id.\\\",\\n            },\\n        }\\n\\n    def build(\\n        self,\\n        sender: Optional[str] = \\\"User\\\",\\n        sender_name: Optional[str] = \\\"User\\\",\\n        message: Optional[str] = None,\\n        session_id: Optional[str] = None,\\n        return_record: Optional[bool] = False,\\n    ) -> Record:\\n        if return_record:\\n            if isinstance(message, Record):\\n                # Update the data of the record\\n                message.data[\\\"sender\\\"] = sender\\n                message.data[\\\"sender_name\\\"] = sender_name\\n                message.data[\\\"session_id\\\"] = session_id\\n            else:\\n                message = Record(\\n                    text=message,\\n                    data={\\n                        \\\"sender\\\": sender,\\n                        \\\"sender_name\\\": sender_name,\\n                        \\\"session_id\\\": session_id,\\n                    },\\n                )\\n        if not message:\\n            message = \\\"\\\"\\n        self.status = message\\n        return message\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"message\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"message\",\"display_name\":\"Message\",\"advanced\":false,\"input_types\":[\"Text\",\"Text\"],\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"return_record\":{\"type\":\"bool\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"return_record\",\"display_name\":\"Return Record\",\"advanced\":false,\"dynamic\":false,\"info\":\"Return the message as a record containing the sender, sender_name, and session_id.\",\"title_case\":false},\"sender\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":true,\"show\":true,\"multiline\":false,\"value\":\"User\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"options\":[\"Machine\",\"User\"],\"name\":\"sender\",\"display_name\":\"Sender Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"sender_name\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"User\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"sender_name\",\"display_name\":\"Sender Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"session_id\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"session_id\",\"display_name\":\"Session ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"Session ID of the chat history.\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Used to get user input from the chat.\",\"base_classes\":[\"Record\"],\"display_name\":\"Chat Input\",\"documentation\":\"\",\"custom_fields\":{\"sender\":null,\"sender_name\":null,\"message\":null,\"session_id\":null,\"return_record\":null},\"output_types\":[\"Record\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true},\"TextInput\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from typing import Optional\\n\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Text\\n\\n\\nclass TextInput(CustomComponent):\\n    display_name = \\\"Text Input\\\"\\n    description = \\\"Used to pass text input to the next component.\\\"\\n\\n    field_config = {\\n        \\\"value\\\": {\\\"display_name\\\": \\\"Value\\\"},\\n    }\\n\\n    def build(self, value: Optional[str] = \\\"\\\") -> Text:\\n        self.status = value\\n        if not value:\\n            value = \\\"\\\"\\n        return value\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"value\":{\"type\":\"str\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"value\",\"display_name\":\"Value\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"title_case\":false,\"input_types\":[\"Text\"]},\"_type\":\"CustomComponent\"},\"description\":\"Used to pass text input to the next component.\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Text Input\",\"documentation\":\"\",\"custom_fields\":{\"value\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}},\"prompts\":{\"Prompt\":{\"template\":{\"code\":{\"type\":\"code\",\"required\":true,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":true,\"value\":\"from langchain_core.prompts import PromptTemplate\\nfrom langflow import CustomComponent\\nfrom langflow.field_typing import Prompt, InputField, Text\\n\\n\\nclass PromptComponent(CustomComponent):\\n    display_name: str = \\\"Prompt\\\"\\n    description: str = \\\"A component for creating prompts using templates\\\"\\n    beta = True\\n\\n    def build_config(self):\\n        return {\\n            \\\"template\\\": InputField(display_name=\\\"Template\\\"),\\n            \\\"code\\\": InputField(advanced=True),\\n        }\\n\\n    def build(\\n        self,\\n        template: Prompt,\\n        **kwargs,\\n    ) -> Text:\\n        prompt_template = PromptTemplate.from_template(template)\\n\\n        attributes_to_check = [\\\"text\\\", \\\"page_content\\\"]\\n        for key, value in kwargs.items():\\n            for attribute in attributes_to_check:\\n                if hasattr(value, attribute):\\n                    kwargs[key] = getattr(value, attribute)\\n\\n        try:\\n            formated_prompt = prompt_template.format(**kwargs)\\n        except Exception as exc:\\n            raise ValueError(f\\\"Error formatting prompt: {exc}\\\") from exc\\n        self.status = f'Prompt: \\\"{formated_prompt}\\\"'\\n        return formated_prompt\\n\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"title_case\":false},\"template\":{\"type\":\"prompt\",\"required\":false,\"placeholder\":\"\",\"list\":false,\"show\":true,\"multiline\":false,\"value\":\"\",\"fileTypes\":[],\"file_path\":\"\",\"password\":false,\"name\":\"template\",\"display_name\":\"Template\",\"advanced\":false,\"input_types\":[\"Text\"],\"dynamic\":false,\"info\":\"\",\"title_case\":false},\"_type\":\"CustomComponent\"},\"description\":\"A component for creating prompts using templates\",\"base_classes\":[\"Text\",\"object\"],\"display_name\":\"Prompt\",\"documentation\":\"\",\"custom_fields\":{\"template\":null},\"output_types\":[\"Text\"],\"field_formatters\":{},\"pinned\":false,\"beta\":true}}}"
           },
           "headersSize": -1,
           "bodySize": -1,
           "redirectURL": ""
         },
         "cache": {},
-        "timings": { "send": -1, "wait": -1, "receive": 2.771 }
+        "timings": {
+          "send": -1,
+          "wait": -1,
+          "receive": 2.771
+        }
       },
       {
         "startedDateTime": "2024-02-28T14:32:30.976Z",
@@ -286,21 +612,66 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Accept", "value": "application/json, text/plain, */*" },
-            { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" },
-            { "name": "Accept-Language", "value": "en-US,en;q=0.9" },
-            { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
-            { "name": "Connection", "value": "keep-alive" },
-            { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
-            { "name": "Host", "value": "localhost:3000" },
-            { "name": "Referer", "value": "http://localhost:3000/" },
-            { "name": "Sec-Fetch-Dest", "value": "empty" },
-            { "name": "Sec-Fetch-Mode", "value": "cors" },
-            { "name": "Sec-Fetch-Site", "value": "same-origin" },
-            { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" },
-            { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" },
-            { "name": "sec-ch-ua-mobile", "value": "?0" },
-            { "name": "sec-ch-ua-platform", "value": "\"Linux\"" }
+            {
+              "name": "Accept",
+              "value": "application/json, text/plain, */*"
+            },
+            {
+              "name": "Accept-Encoding",
+              "value": "gzip, deflate, br, zstd"
+            },
+            {
+              "name": "Accept-Language",
+              "value": "en-US,en;q=0.9"
+            },
+            {
+              "name": "Authorization",
+              "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc"
+            },
+            {
+              "name": "Connection",
+              "value": "keep-alive"
+            },
+            {
+              "name": "Cookie",
+              "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc"
+            },
+            {
+              "name": "Host",
+              "value": "localhost:3000"
+            },
+            {
+              "name": "Referer",
+              "value": "http://localhost:3000/"
+            },
+            {
+              "name": "Sec-Fetch-Dest",
+              "value": "empty"
+            },
+            {
+              "name": "Sec-Fetch-Mode",
+              "value": "cors"
+            },
+            {
+              "name": "Sec-Fetch-Site",
+              "value": "same-origin"
+            },
+            {
+              "name": "User-Agent",
+              "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36"
+            },
+            {
+              "name": "sec-ch-ua",
+              "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\""
+            },
+            {
+              "name": "sec-ch-ua-mobile",
+              "value": "?0"
+            },
+            {
+              "name": "sec-ch-ua-platform",
+              "value": "\"Linux\""
+            }
           ],
           "queryString": [],
           "headersSize": -1,
@@ -312,12 +683,30 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Access-Control-Allow-Origin", "value": "*" },
-            { "name": "connection", "value": "close" },
-            { "name": "content-length", "value": "16" },
-            { "name": "content-type", "value": "application/json" },
-            { "name": "date", "value": "Wed, 28 Feb 2024 14:32:30 GMT" },
-            { "name": "server", "value": "uvicorn" }
+            {
+              "name": "Access-Control-Allow-Origin",
+              "value": "*"
+            },
+            {
+              "name": "connection",
+              "value": "close"
+            },
+            {
+              "name": "content-length",
+              "value": "16"
+            },
+            {
+              "name": "content-type",
+              "value": "application/json"
+            },
+            {
+              "name": "date",
+              "value": "Wed, 28 Feb 2024 14:32:30 GMT"
+            },
+            {
+              "name": "server",
+              "value": "uvicorn"
+            }
           ],
           "content": {
             "size": -1,
@@ -329,7 +718,11 @@
           "redirectURL": ""
         },
         "cache": {},
-        "timings": { "send": -1, "wait": -1, "receive": 0.753 }
+        "timings": {
+          "send": -1,
+          "wait": -1,
+          "receive": 0.753
+        }
       },
       {
         "startedDateTime": "2024-02-28T14:32:30.976Z",
@@ -340,21 +733,66 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Accept", "value": "application/json, text/plain, */*" },
-            { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" },
-            { "name": "Accept-Language", "value": "en-US,en;q=0.9" },
-            { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
-            { "name": "Connection", "value": "keep-alive" },
-            { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
-            { "name": "Host", "value": "localhost:3000" },
-            { "name": "Referer", "value": "http://localhost:3000/" },
-            { "name": "Sec-Fetch-Dest", "value": "empty" },
-            { "name": "Sec-Fetch-Mode", "value": "cors" },
-            { "name": "Sec-Fetch-Site", "value": "same-origin" },
-            { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" },
-            { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" },
-            { "name": "sec-ch-ua-mobile", "value": "?0" },
-            { "name": "sec-ch-ua-platform", "value": "\"Linux\"" }
+            {
+              "name": "Accept",
+              "value": "application/json, text/plain, */*"
+            },
+            {
+              "name": "Accept-Encoding",
+              "value": "gzip, deflate, br, zstd"
+            },
+            {
+              "name": "Accept-Language",
+              "value": "en-US,en;q=0.9"
+            },
+            {
+              "name": "Authorization",
+              "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc"
+            },
+            {
+              "name": "Connection",
+              "value": "keep-alive"
+            },
+            {
+              "name": "Cookie",
+              "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc"
+            },
+            {
+              "name": "Host",
+              "value": "localhost:3000"
+            },
+            {
+              "name": "Referer",
+              "value": "http://localhost:3000/"
+            },
+            {
+              "name": "Sec-Fetch-Dest",
+              "value": "empty"
+            },
+            {
+              "name": "Sec-Fetch-Mode",
+              "value": "cors"
+            },
+            {
+              "name": "Sec-Fetch-Site",
+              "value": "same-origin"
+            },
+            {
+              "name": "User-Agent",
+              "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36"
+            },
+            {
+              "name": "sec-ch-ua",
+              "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\""
+            },
+            {
+              "name": "sec-ch-ua-mobile",
+              "value": "?0"
+            },
+            {
+              "name": "sec-ch-ua-platform",
+              "value": "\"Linux\""
+            }
           ],
           "queryString": [],
           "headersSize": -1,
@@ -366,12 +804,30 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Access-Control-Allow-Origin", "value": "*" },
-            { "name": "connection", "value": "close" },
-            { "name": "content-length", "value": "38" },
-            { "name": "content-type", "value": "application/json" },
-            { "name": "date", "value": "Wed, 28 Feb 2024 14:32:30 GMT" },
-            { "name": "server", "value": "uvicorn" }
+            {
+              "name": "Access-Control-Allow-Origin",
+              "value": "*"
+            },
+            {
+              "name": "connection",
+              "value": "close"
+            },
+            {
+              "name": "content-length",
+              "value": "38"
+            },
+            {
+              "name": "content-type",
+              "value": "application/json"
+            },
+            {
+              "name": "date",
+              "value": "Wed, 28 Feb 2024 14:32:30 GMT"
+            },
+            {
+              "name": "server",
+              "value": "uvicorn"
+            }
           ],
           "content": {
             "size": -1,
@@ -383,7 +839,11 @@
           "redirectURL": ""
         },
         "cache": {},
-        "timings": { "send": -1, "wait": -1, "receive": 0.525 }
+        "timings": {
+          "send": -1,
+          "wait": -1,
+          "receive": 0.525
+        }
       },
       {
         "startedDateTime": "2024-02-28T14:32:30.976Z",
@@ -394,21 +854,66 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Accept", "value": "application/json, text/plain, */*" },
-            { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" },
-            { "name": "Accept-Language", "value": "en-US,en;q=0.9" },
-            { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
-            { "name": "Connection", "value": "keep-alive" },
-            { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
-            { "name": "Host", "value": "localhost:3000" },
-            { "name": "Referer", "value": "http://localhost:3000/" },
-            { "name": "Sec-Fetch-Dest", "value": "empty" },
-            { "name": "Sec-Fetch-Mode", "value": "cors" },
-            { "name": "Sec-Fetch-Site", "value": "same-origin" },
-            { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" },
-            { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" },
-            { "name": "sec-ch-ua-mobile", "value": "?0" },
-            { "name": "sec-ch-ua-platform", "value": "\"Linux\"" }
+            {
+              "name": "Accept",
+              "value": "application/json, text/plain, */*"
+            },
+            {
+              "name": "Accept-Encoding",
+              "value": "gzip, deflate, br, zstd"
+            },
+            {
+              "name": "Accept-Language",
+              "value": "en-US,en;q=0.9"
+            },
+            {
+              "name": "Authorization",
+              "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc"
+            },
+            {
+              "name": "Connection",
+              "value": "keep-alive"
+            },
+            {
+              "name": "Cookie",
+              "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc"
+            },
+            {
+              "name": "Host",
+              "value": "localhost:3000"
+            },
+            {
+              "name": "Referer",
+              "value": "http://localhost:3000/"
+            },
+            {
+              "name": "Sec-Fetch-Dest",
+              "value": "empty"
+            },
+            {
+              "name": "Sec-Fetch-Mode",
+              "value": "cors"
+            },
+            {
+              "name": "Sec-Fetch-Site",
+              "value": "same-origin"
+            },
+            {
+              "name": "User-Agent",
+              "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36"
+            },
+            {
+              "name": "sec-ch-ua",
+              "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\""
+            },
+            {
+              "name": "sec-ch-ua-mobile",
+              "value": "?0"
+            },
+            {
+              "name": "sec-ch-ua-platform",
+              "value": "\"Linux\""
+            }
           ],
           "queryString": [],
           "headersSize": -1,
@@ -420,13 +925,34 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Access-Control-Allow-Origin", "value": "*" },
-            { "name": "connection", "value": "close" },
-            { "name": "content-length", "value": "227" },
-            { "name": "content-type", "value": "application/json" },
-            { "name": "date", "value": "Wed, 28 Feb 2024 14:32:30 GMT" },
-            { "name": "server", "value": "uvicorn" },
-            { "name": "set-cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc; Path=/; SameSite=none; Secure" }
+            {
+              "name": "Access-Control-Allow-Origin",
+              "value": "*"
+            },
+            {
+              "name": "connection",
+              "value": "close"
+            },
+            {
+              "name": "content-length",
+              "value": "227"
+            },
+            {
+              "name": "content-type",
+              "value": "application/json"
+            },
+            {
+              "name": "date",
+              "value": "Wed, 28 Feb 2024 14:32:30 GMT"
+            },
+            {
+              "name": "server",
+              "value": "uvicorn"
+            },
+            {
+              "name": "set-cookie",
+              "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc; Path=/; SameSite=none; Secure"
+            }
           ],
           "content": {
             "size": -1,
@@ -438,7 +964,11 @@
           "redirectURL": ""
         },
         "cache": {},
-        "timings": { "send": -1, "wait": -1, "receive": 0.658 }
+        "timings": {
+          "send": -1,
+          "wait": -1,
+          "receive": 0.658
+        }
       },
       {
         "startedDateTime": "2024-02-28T14:32:31.023Z",
@@ -449,21 +979,66 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Accept", "value": "application/json, text/plain, */*" },
-            { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" },
-            { "name": "Accept-Language", "value": "en-US,en;q=0.9" },
-            { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
-            { "name": "Connection", "value": "keep-alive" },
-            { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
-            { "name": "Host", "value": "localhost:3000" },
-            { "name": "Referer", "value": "http://localhost:3000/" },
-            { "name": "Sec-Fetch-Dest", "value": "empty" },
-            { "name": "Sec-Fetch-Mode", "value": "cors" },
-            { "name": "Sec-Fetch-Site", "value": "same-origin" },
-            { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" },
-            { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" },
-            { "name": "sec-ch-ua-mobile", "value": "?0" },
-            { "name": "sec-ch-ua-platform", "value": "\"Linux\"" }
+            {
+              "name": "Accept",
+              "value": "application/json, text/plain, */*"
+            },
+            {
+              "name": "Accept-Encoding",
+              "value": "gzip, deflate, br, zstd"
+            },
+            {
+              "name": "Accept-Language",
+              "value": "en-US,en;q=0.9"
+            },
+            {
+              "name": "Authorization",
+              "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc"
+            },
+            {
+              "name": "Connection",
+              "value": "keep-alive"
+            },
+            {
+              "name": "Cookie",
+              "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc"
+            },
+            {
+              "name": "Host",
+              "value": "localhost:3000"
+            },
+            {
+              "name": "Referer",
+              "value": "http://localhost:3000/"
+            },
+            {
+              "name": "Sec-Fetch-Dest",
+              "value": "empty"
+            },
+            {
+              "name": "Sec-Fetch-Mode",
+              "value": "cors"
+            },
+            {
+              "name": "Sec-Fetch-Site",
+              "value": "same-origin"
+            },
+            {
+              "name": "User-Agent",
+              "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36"
+            },
+            {
+              "name": "sec-ch-ua",
+              "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\""
+            },
+            {
+              "name": "sec-ch-ua-mobile",
+              "value": "?0"
+            },
+            {
+              "name": "sec-ch-ua-platform",
+              "value": "\"Linux\""
+            }
           ],
           "queryString": [],
           "headersSize": -1,
@@ -475,12 +1050,30 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Access-Control-Allow-Origin", "value": "*" },
-            { "name": "connection", "value": "close" },
-            { "name": "content-length", "value": "253" },
-            { "name": "content-type", "value": "application/json" },
-            { "name": "date", "value": "Wed, 28 Feb 2024 14:32:30 GMT" },
-            { "name": "server", "value": "uvicorn" }
+            {
+              "name": "Access-Control-Allow-Origin",
+              "value": "*"
+            },
+            {
+              "name": "connection",
+              "value": "close"
+            },
+            {
+              "name": "content-length",
+              "value": "253"
+            },
+            {
+              "name": "content-type",
+              "value": "application/json"
+            },
+            {
+              "name": "date",
+              "value": "Wed, 28 Feb 2024 14:32:30 GMT"
+            },
+            {
+              "name": "server",
+              "value": "uvicorn"
+            }
           ],
           "content": {
             "size": -1,
@@ -492,7 +1085,11 @@
           "redirectURL": ""
         },
         "cache": {},
-        "timings": { "send": -1, "wait": -1, "receive": 0.787 }
+        "timings": {
+          "send": -1,
+          "wait": -1,
+          "receive": 0.787
+        }
       },
       {
         "startedDateTime": "2024-02-28T14:32:32.479Z",
@@ -503,21 +1100,66 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Accept", "value": "application/json, text/plain, */*" },
-            { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" },
-            { "name": "Accept-Language", "value": "en-US,en;q=0.9" },
-            { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
-            { "name": "Connection", "value": "keep-alive" },
-            { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
-            { "name": "Host", "value": "localhost:3000" },
-            { "name": "Referer", "value": "http://localhost:3000/flows" },
-            { "name": "Sec-Fetch-Dest", "value": "empty" },
-            { "name": "Sec-Fetch-Mode", "value": "cors" },
-            { "name": "Sec-Fetch-Site", "value": "same-origin" },
-            { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" },
-            { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" },
-            { "name": "sec-ch-ua-mobile", "value": "?0" },
-            { "name": "sec-ch-ua-platform", "value": "\"Linux\"" }
+            {
+              "name": "Accept",
+              "value": "application/json, text/plain, */*"
+            },
+            {
+              "name": "Accept-Encoding",
+              "value": "gzip, deflate, br, zstd"
+            },
+            {
+              "name": "Accept-Language",
+              "value": "en-US,en;q=0.9"
+            },
+            {
+              "name": "Authorization",
+              "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc"
+            },
+            {
+              "name": "Connection",
+              "value": "keep-alive"
+            },
+            {
+              "name": "Cookie",
+              "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc"
+            },
+            {
+              "name": "Host",
+              "value": "localhost:3000"
+            },
+            {
+              "name": "Referer",
+              "value": "http://localhost:3000/flows"
+            },
+            {
+              "name": "Sec-Fetch-Dest",
+              "value": "empty"
+            },
+            {
+              "name": "Sec-Fetch-Mode",
+              "value": "cors"
+            },
+            {
+              "name": "Sec-Fetch-Site",
+              "value": "same-origin"
+            },
+            {
+              "name": "User-Agent",
+              "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36"
+            },
+            {
+              "name": "sec-ch-ua",
+              "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\""
+            },
+            {
+              "name": "sec-ch-ua-mobile",
+              "value": "?0"
+            },
+            {
+              "name": "sec-ch-ua-platform",
+              "value": "\"Linux\""
+            }
           ],
           "queryString": [],
           "headersSize": -1,
@@ -529,12 +1171,30 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Access-Control-Allow-Origin", "value": "*" },
-            { "name": "connection", "value": "close" },
-            { "name": "content-length", "value": "2" },
-            { "name": "content-type", "value": "application/json" },
-            { "name": "date", "value": "Wed, 28 Feb 2024 14:32:31 GMT" },
-            { "name": "server", "value": "uvicorn" }
+            {
+              "name": "Access-Control-Allow-Origin",
+              "value": "*"
+            },
+            {
+              "name": "connection",
+              "value": "close"
+            },
+            {
+              "name": "content-length",
+              "value": "2"
+            },
+            {
+              "name": "content-type",
+              "value": "application/json"
+            },
+            {
+              "name": "date",
+              "value": "Wed, 28 Feb 2024 14:32:31 GMT"
+            },
+            {
+              "name": "server",
+              "value": "uvicorn"
+            }
           ],
           "content": {
             "size": -1,
@@ -546,7 +1206,11 @@
           "redirectURL": ""
         },
         "cache": {},
-        "timings": { "send": -1, "wait": -1, "receive": 0.836 }
+        "timings": {
+          "send": -1,
+          "wait": -1,
+          "receive": 0.836
+        }
       },
       {
         "startedDateTime": "2024-02-28T14:32:36.617Z",
@@ -557,24 +1221,78 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Accept", "value": "application/json, text/plain, */*" },
-            { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" },
-            { "name": "Accept-Language", "value": "en-US,en;q=0.9" },
-            { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
-            { "name": "Connection", "value": "keep-alive" },
-            { "name": "Content-Length", "value": "170" },
-            { "name": "Content-Type", "value": "application/json" },
-            { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
-            { "name": "Host", "value": "localhost:3000" },
-            { "name": "Origin", "value": "http://localhost:3000" },
-            { "name": "Referer", "value": "http://localhost:3000/flows" },
-            { "name": "Sec-Fetch-Dest", "value": "empty" },
-            { "name": "Sec-Fetch-Mode", "value": "cors" },
-            { "name": "Sec-Fetch-Site", "value": "same-origin" },
-            { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" },
-            { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" },
-            { "name": "sec-ch-ua-mobile", "value": "?0" },
-            { "name": "sec-ch-ua-platform", "value": "\"Linux\"" }
+            {
+              "name": "Accept",
+              "value": "application/json, text/plain, */*"
+            },
+            {
+              "name": "Accept-Encoding",
+              "value": "gzip, deflate, br, zstd"
+            },
+            {
+              "name": "Accept-Language",
+              "value": "en-US,en;q=0.9"
+            },
+            {
+              "name": "Authorization",
+              "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc"
+            },
+            {
+              "name": "Connection",
+              "value": "keep-alive"
+            },
+            {
+              "name": "Content-Length",
+              "value": "170"
+            },
+            {
+              "name": "Content-Type",
+              "value": "application/json"
+            },
+            {
+              "name": "Cookie",
+              "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc"
+            },
+            {
+              "name": "Host",
+              "value": "localhost:3000"
+            },
+            {
+              "name": "Origin",
+              "value": "http://localhost:3000"
+            },
+            {
+              "name": "Referer",
+              "value": "http://localhost:3000/flows"
+            },
+            {
+              "name": "Sec-Fetch-Dest",
+              "value": "empty"
+            },
+            {
+              "name": "Sec-Fetch-Mode",
+              "value": "cors"
+            },
+            {
+              "name": "Sec-Fetch-Site",
+              "value": "same-origin"
+            },
+            {
+              "name": "User-Agent",
+              "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36"
+            },
+            {
+              "name": "sec-ch-ua",
+              "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\""
+            },
+            {
+              "name": "sec-ch-ua-mobile",
+              "value": "?0"
+            },
+            {
+              "name": "sec-ch-ua-platform",
+              "value": "\"Linux\""
+            }
           ],
           "queryString": [],
           "headersSize": -1,
@@ -591,14 +1309,38 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "access-control-allow-credentials", "value": "true" },
-            { "name": "access-control-allow-origin", "value": "http://localhost:3000" },
-            { "name": "connection", "value": "close" },
-            { "name": "content-length", "value": "319" },
-            { "name": "content-type", "value": "application/json" },
-            { "name": "date", "value": "Wed, 28 Feb 2024 14:32:35 GMT" },
-            { "name": "server", "value": "uvicorn" },
-            { "name": "vary", "value": "Origin" }
+            {
+              "name": "access-control-allow-credentials",
+              "value": "true"
+            },
+            {
+              "name": "access-control-allow-origin",
+              "value": "http://localhost:3000"
+            },
+            {
+              "name": "connection",
+              "value": "close"
+            },
+            {
+              "name": "content-length",
+              "value": "319"
+            },
+            {
+              "name": "content-type",
+              "value": "application/json"
+            },
+            {
+              "name": "date",
+              "value": "Wed, 28 Feb 2024 14:32:35 GMT"
+            },
+            {
+              "name": "server",
+              "value": "uvicorn"
+            },
+            {
+              "name": "vary",
+              "value": "Origin"
+            }
           ],
           "content": {
             "size": -1,
@@ -610,7 +1352,11 @@
           "redirectURL": ""
         },
         "cache": {},
-        "timings": { "send": -1, "wait": -1, "receive": 1.679 }
+        "timings": {
+          "send": -1,
+          "wait": -1,
+          "receive": 1.679
+        }
       },
       {
         "startedDateTime": "2024-02-28T14:32:36.774Z",
@@ -621,21 +1367,66 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Accept", "value": "application/json, text/plain, */*" },
-            { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" },
-            { "name": "Accept-Language", "value": "en-US,en;q=0.9" },
-            { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
-            { "name": "Connection", "value": "keep-alive" },
-            { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
-            { "name": "Host", "value": "localhost:3000" },
-            { "name": "Referer", "value": "http://localhost:3000/flow/b3aad40d-cf3b-49d2-804f-bfa33b70beef" },
-            { "name": "Sec-Fetch-Dest", "value": "empty" },
-            { "name": "Sec-Fetch-Mode", "value": "cors" },
-            { "name": "Sec-Fetch-Site", "value": "same-origin" },
-            { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" },
-            { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" },
-            { "name": "sec-ch-ua-mobile", "value": "?0" },
-            { "name": "sec-ch-ua-platform", "value": "\"Linux\"" }
+            {
+              "name": "Accept",
+              "value": "application/json, text/plain, */*"
+            },
+            {
+              "name": "Accept-Encoding",
+              "value": "gzip, deflate, br, zstd"
+            },
+            {
+              "name": "Accept-Language",
+              "value": "en-US,en;q=0.9"
+            },
+            {
+              "name": "Authorization",
+              "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc"
+            },
+            {
+              "name": "Connection",
+              "value": "keep-alive"
+            },
+            {
+              "name": "Cookie",
+              "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc"
+            },
+            {
+              "name": "Host",
+              "value": "localhost:3000"
+            },
+            {
+              "name": "Referer",
+              "value": "http://localhost:3000/flow/b3aad40d-cf3b-49d2-804f-bfa33b70beef"
+            },
+            {
+              "name": "Sec-Fetch-Dest",
+              "value": "empty"
+            },
+            {
+              "name": "Sec-Fetch-Mode",
+              "value": "cors"
+            },
+            {
+              "name": "Sec-Fetch-Site",
+              "value": "same-origin"
+            },
+            {
+              "name": "User-Agent",
+              "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36"
+            },
+            {
+              "name": "sec-ch-ua",
+              "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\""
+            },
+            {
+              "name": "sec-ch-ua-mobile",
+              "value": "?0"
+            },
+            {
+              "name": "sec-ch-ua-platform",
+              "value": "\"Linux\""
+            }
           ],
           "queryString": [
             {
@@ -652,12 +1443,30 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Access-Control-Allow-Origin", "value": "*" },
-            { "name": "connection", "value": "close" },
-            { "name": "content-length", "value": "20" },
-            { "name": "content-type", "value": "application/json" },
-            { "name": "date", "value": "Wed, 28 Feb 2024 14:32:35 GMT" },
-            { "name": "server", "value": "uvicorn" }
+            {
+              "name": "Access-Control-Allow-Origin",
+              "value": "*"
+            },
+            {
+              "name": "connection",
+              "value": "close"
+            },
+            {
+              "name": "content-length",
+              "value": "20"
+            },
+            {
+              "name": "content-type",
+              "value": "application/json"
+            },
+            {
+              "name": "date",
+              "value": "Wed, 28 Feb 2024 14:32:35 GMT"
+            },
+            {
+              "name": "server",
+              "value": "uvicorn"
+            }
           ],
           "content": {
             "size": -1,
@@ -669,7 +1478,11 @@
           "redirectURL": ""
         },
         "cache": {},
-        "timings": { "send": -1, "wait": -1, "receive": 1.023 }
+        "timings": {
+          "send": -1,
+          "wait": -1,
+          "receive": 1.023
+        }
       },
       {
         "startedDateTime": "2024-02-28T14:32:49.526Z",
@@ -680,22 +1493,70 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "Accept", "value": "application/json, text/plain, */*" },
-            { "name": "Accept-Encoding", "value": "gzip, deflate, br, zstd" },
-            { "name": "Accept-Language", "value": "en-US,en;q=0.9" },
-            { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
-            { "name": "Connection", "value": "keep-alive" },
-            { "name": "Cookie", "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc" },
-            { "name": "Host", "value": "localhost:3000" },
-            { "name": "Origin", "value": "http://localhost:3000" },
-            { "name": "Referer", "value": "http://localhost:3000/flow/b3aad40d-cf3b-49d2-804f-bfa33b70beef" },
-            { "name": "Sec-Fetch-Dest", "value": "empty" },
-            { "name": "Sec-Fetch-Mode", "value": "cors" },
-            { "name": "Sec-Fetch-Site", "value": "same-origin" },
-            { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36" },
-            { "name": "sec-ch-ua", "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\"" },
-            { "name": "sec-ch-ua-mobile", "value": "?0" },
-            { "name": "sec-ch-ua-platform", "value": "\"Linux\"" }
+            {
+              "name": "Accept",
+              "value": "application/json, text/plain, */*"
+            },
+            {
+              "name": "Accept-Encoding",
+              "value": "gzip, deflate, br, zstd"
+            },
+            {
+              "name": "Accept-Language",
+              "value": "en-US,en;q=0.9"
+            },
+            {
+              "name": "Authorization",
+              "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc"
+            },
+            {
+              "name": "Connection",
+              "value": "keep-alive"
+            },
+            {
+              "name": "Cookie",
+              "value": "access_token_lf=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMzg5ZmIyOS1kYWE2LTQwOGMtYjhjYi1iOGZmOGQxNzM0M2EiLCJleHAiOjE3NDA2NjY3NTB9.ef5W5jwNOeVzU3JZ7ylLYf2MLEJcVxC4-fF7EK9Ecdc"
+            },
+            {
+              "name": "Host",
+              "value": "localhost:3000"
+            },
+            {
+              "name": "Origin",
+              "value": "http://localhost:3000"
+            },
+            {
+              "name": "Referer",
+              "value": "http://localhost:3000/flow/b3aad40d-cf3b-49d2-804f-bfa33b70beef"
+            },
+            {
+              "name": "Sec-Fetch-Dest",
+              "value": "empty"
+            },
+            {
+              "name": "Sec-Fetch-Mode",
+              "value": "cors"
+            },
+            {
+              "name": "Sec-Fetch-Site",
+              "value": "same-origin"
+            },
+            {
+              "name": "User-Agent",
+              "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36"
+            },
+            {
+              "name": "sec-ch-ua",
+              "value": "\"Chromium\";v=\"123\", \"Not:A-Brand\";v=\"8\""
+            },
+            {
+              "name": "sec-ch-ua-mobile",
+              "value": "?0"
+            },
+            {
+              "name": "sec-ch-ua-platform",
+              "value": "\"Linux\""
+            }
           ],
           "queryString": [],
           "headersSize": -1,
@@ -707,14 +1568,38 @@
           "httpVersion": "HTTP/1.1",
           "cookies": [],
           "headers": [
-            { "name": "access-control-allow-credentials", "value": "true" },
-            { "name": "access-control-allow-origin", "value": "http://localhost:3000" },
-            { "name": "connection", "value": "close" },
-            { "name": "content-length", "value": "39" },
-            { "name": "content-type", "value": "application/json" },
-            { "name": "date", "value": "Wed, 28 Feb 2024 14:32:48 GMT" },
-            { "name": "server", "value": "uvicorn" },
-            { "name": "vary", "value": "Origin" }
+            {
+              "name": "access-control-allow-credentials",
+              "value": "true"
+            },
+            {
+              "name": "access-control-allow-origin",
+              "value": "http://localhost:3000"
+            },
+            {
+              "name": "connection",
+              "value": "close"
+            },
+            {
+              "name": "content-length",
+              "value": "39"
+            },
+            {
+              "name": "content-type",
+              "value": "application/json"
+            },
+            {
+              "name": "date",
+              "value": "Wed, 28 Feb 2024 14:32:48 GMT"
+            },
+            {
+              "name": "server",
+              "value": "uvicorn"
+            },
+            {
+              "name": "vary",
+              "value": "Origin"
+            }
           ],
           "content": {
             "size": -1,
@@ -726,7 +1611,11 @@
           "redirectURL": ""
         },
         "cache": {},
-        "timings": { "send": -1, "wait": -1, "receive": 0.977 }
+        "timings": {
+          "send": -1,
+          "wait": -1,
+          "receive": 0.977
+        }
       }
     ]
   }
diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json
index c2d3e3f03..d6b9d3b74 100644
--- a/src/frontend/package-lock.json
+++ b/src/frontend/package-lock.json
@@ -8,18 +8,18 @@
       "name": "langflow",
       "version": "0.1.2",
       "dependencies": {
-        "@headlessui/react": "^1.7.17",
-        "@hookform/resolvers": "^3.3.4",
-        "@million/lint": "^0.0.73",
+        "@headlessui/react": "^2.0.4",
+        "@hookform/resolvers": "^3.6.0",
+        "@million/lint": "^1.0.0-rc.26",
         "@radix-ui/react-accordion": "^1.1.2",
         "@radix-ui/react-checkbox": "^1.0.4",
-        "@radix-ui/react-dialog": "^1.0.4",
-        "@radix-ui/react-dropdown-menu": "^2.0.5",
+        "@radix-ui/react-dialog": "^1.0.5",
+        "@radix-ui/react-dropdown-menu": "^2.0.6",
         "@radix-ui/react-form": "^0.0.3",
         "@radix-ui/react-icons": "^1.3.0",
         "@radix-ui/react-label": "^2.0.2",
-        "@radix-ui/react-menubar": "^1.0.3",
-        "@radix-ui/react-popover": "^1.0.6",
+        "@radix-ui/react-menubar": "^1.0.4",
+        "@radix-ui/react-popover": "^1.0.7",
         "@radix-ui/react-progress": "^1.0.3",
         "@radix-ui/react-select": "^2.0.0",
         "@radix-ui/react-separator": "^1.0.3",
@@ -27,89 +27,88 @@
         "@radix-ui/react-switch": "^1.0.3",
         "@radix-ui/react-tabs": "^1.0.4",
         "@radix-ui/react-toggle": "^1.0.3",
-        "@radix-ui/react-tooltip": "^1.0.6",
-        "@tabler/icons-react": "^2.32.0",
-        "@tailwindcss/forms": "^0.5.6",
+        "@radix-ui/react-tooltip": "^1.0.7",
+        "@tabler/icons-react": "^3.6.0",
+        "@tailwindcss/forms": "^0.5.7",
         "@tailwindcss/line-clamp": "^0.4.4",
         "@types/axios": "^0.14.0",
-        "ace-builds": "^1.24.1",
-        "ag-grid-community": "^31.2.1",
-        "ag-grid-react": "^31.2.1",
+        "ace-builds": "^1.35.0",
+        "ag-grid-community": "^31.3.2",
+        "ag-grid-react": "^31.3.2",
         "ansi-to-html": "^0.7.2",
-        "axios": "^1.5.0",
+        "axios": "^1.7.2",
         "base64-js": "^1.5.1",
-        "class-variance-authority": "^0.6.1",
-        "clsx": "^1.2.1",
+        "class-variance-authority": "^0.7.0",
+        "clsx": "^2.1.1",
         "cmdk": "^1.0.0",
-        "dompurify": "^3.0.5",
+        "dompurify": "^3.1.5",
         "dotenv": "^16.4.5",
         "emoji-regex": "^10.3.0",
-        "esbuild": "^0.17.19",
+        "esbuild": "^0.21.5",
         "file-saver": "^2.0.5",
-        "framer-motion": "^11.0.6",
+        "framer-motion": "^11.2.10",
         "lodash": "^4.17.21",
-        "lucide-react": "^0.394.0",
-        "million": "^3.0.6",
-        "moment": "^2.29.4",
+        "lucide-react": "^0.395.0",
+        "million": "^3.1.11",
+        "moment": "^2.30.1",
         "openseadragon": "^4.1.1",
         "p-debounce": "^4.0.0",
-        "playwright": "^1.42.0",
-        "react": "^18.2.21",
-        "react-ace": "^10.1.0",
-        "react-cookie": "^4.1.1",
-        "react-dom": "^18.2.21",
-        "react-error-boundary": "^4.0.11",
-        "react-hook-form": "^7.51.4",
+        "playwright": "^1.44.1",
+        "react": "^18.3.1",
+        "react-ace": "^11.0.1",
+        "react-cookie": "^7.1.4",
+        "react-dom": "^18.3.1",
+        "react-error-boundary": "^4.0.13",
+        "react-hook-form": "^7.52.0",
         "react-hotkeys-hook": "^4.5.0",
-        "react-icons": "^5.0.1",
+        "react-icons": "^5.2.1",
         "react-laag": "^2.0.5",
         "react-markdown": "^8.0.7",
         "react-pdf": "^9.0.0",
-        "react-router-dom": "^6.15.0",
+        "react-router-dom": "^6.23.1",
         "react-syntax-highlighter": "^15.5.0",
-        "react18-json-view": "^0.2.3",
-        "reactflow": "^11.9.2",
+        "react18-json-view": "^0.2.8",
+        "reactflow": "^11.11.3",
         "rehype-mathjax": "^4.0.3",
-        "remark-gfm": "^3.0.1",
-        "remark-math": "^5.1.1",
-        "shadcn-ui": "^0.2.3",
-        "short-unique-id": "^4.4.4",
-        "tailwind-merge": "^1.14.0",
+        "remark-gfm": "3.0.1",
+        "remark-math": "^6.0.0",
+        "shadcn-ui": "^0.8.0",
+        "short-unique-id": "^5.2.0",
+        "tailwind-merge": "^2.3.0",
         "tailwindcss-animate": "^1.0.7",
-        "uuid": "^9.0.0",
-        "vite-plugin-svgr": "^3.2.0",
-        "web-vitals": "^2.1.4",
-        "zod": "^3.23.7",
-        "zustand": "^4.4.7"
+        "uuid": "^10.0.0",
+        "vite-plugin-svgr": "^4.2.0",
+        "web-vitals": "^4.1.1",
+        "zod": "^3.23.8",
+        "zustand": "^4.5.2"
       },
       "devDependencies": {
-        "@playwright/test": "^1.44.0",
-        "@swc/cli": "^0.1.62",
-        "@swc/core": "^1.3.80",
-        "@tailwindcss/typography": "^0.5.9",
-        "@testing-library/jest-dom": "^5.17.0",
-        "@testing-library/react": "^13.4.0",
-        "@testing-library/user-event": "^13.5.0",
-        "@types/jest": "^27.5.2",
-        "@types/lodash": "^4.14.197",
-        "@types/node": "^16.18.46",
-        "@types/react": "^18.2.21",
-        "@types/react-dom": "^18.2.7",
-        "@types/uuid": "^9.0.2",
-        "@vitejs/plugin-react-swc": "^3.3.2",
-        "autoprefixer": "^10.4.15",
-        "daisyui": "^4.0.4",
+        "@playwright/test": "^1.44.1",
+        "@swc/cli": "^0.3.12",
+        "@swc/core": "^1.6.1",
+        "@tailwindcss/typography": "^0.5.13",
+        "@testing-library/jest-dom": "^6.4.6",
+        "@testing-library/react": "^16.0.0",
+        "@testing-library/user-event": "^14.5.2",
+        "@types/jest": "^29.5.12",
+        "@types/lodash": "4.17.5",
+        "@types/node": "^20.14.2",
+        "@types/react": "^18.3.3",
+        "@types/react-dom": "^18.3.0",
+        "@types/uuid": "^9.0.8",
+        "@vitejs/plugin-react-swc": "^3.7.0",
+        "autoprefixer": "^10.4.19",
         "eslint": "^9.5.0",
-        "postcss": "^8.4.29",
+        "postcss": "^8.4.38",
         "prettier": "^3.3.2",
-        "prettier-plugin-organize-imports": "^3.2.3",
+        "prettier-plugin-organize-imports": "^3.2.4",
         "prettier-plugin-tailwindcss": "^0.6.4",
         "simple-git-hooks": "^2.11.1",
-        "tailwindcss": "^3.3.3",
+        "tailwindcss": "^3.4.4",
         "tailwindcss-dotted-background": "^1.1.0",
-        "typescript": "^5.2.2",
-        "ua-parser-js": "^1.0.37",
-        "vite": "^4.5.2"
+        "typescript": "^5.4.5",
+        "ua-parser-js": "^1.0.38",
+        "vite": "^5.3.1"
       }
     },
     "node_modules/@adobe/css-tools": {
@@ -155,6 +154,26 @@
         "nun": "bin/nun.mjs"
       }
     },
+    "node_modules/@axiomhq/js": {
+      "version": "1.0.0-rc.3",
+      "resolved": "https://registry.npmjs.org/@axiomhq/js/-/js-1.0.0-rc.3.tgz",
+      "integrity": "sha512-Zm10TczcMLounWqC42nMkXQ7XKLqjzLrd5ia022oBKDUZqAFVg2y9d1quQVNV4FlXyg9MKDdfMjpKQRmzEGaog==",
+      "dependencies": {
+        "fetch-retry": "^6.0.0",
+        "uuid": "^8.3.2"
+      },
+      "engines": {
+        "node": ">=16"
+      }
+    },
+    "node_modules/@axiomhq/js/node_modules/uuid": {
+      "version": "8.3.2",
+      "resolved": "https://registry.npmjs.org/uuid/-/uuid-8.3.2.tgz",
+      "integrity": "sha512-+NYs2QeMWy+GWFOEm9xnn6HCDp0l7QBD7ml8zLUmJ+93Q5NF0NocErnwkTkXVFNiX3/fpC6afS8Dhb/gz7R7eg==",
+      "bin": {
+        "uuid": "dist/bin/uuid"
+      }
+    },
     "node_modules/@babel/code-frame": {
       "version": "7.24.7",
       "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.24.7.tgz",
@@ -176,20 +195,20 @@
       }
     },
     "node_modules/@babel/core": {
-      "version": "7.24.7",
-      "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.24.7.tgz",
-      "integrity": "sha512-nykK+LEK86ahTkX/3TgauT0ikKoNCfKHEaZYTUVupJdTLzGNvrblu4u6fa7DhZONAltdf8e662t/abY8idrd/g==",
+      "version": "7.24.6",
+      "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.24.6.tgz",
+      "integrity": "sha512-qAHSfAdVyFmIvl0VHELib8xar7ONuSHrE2hLnsaWkYNTI68dmi1x8GYDhJjMI/e7XWal9QBlZkwbOnkcw7Z8gQ==",
       "dependencies": {
         "@ampproject/remapping": "^2.2.0",
-        "@babel/code-frame": "^7.24.7",
-        "@babel/generator": "^7.24.7",
-        "@babel/helper-compilation-targets": "^7.24.7",
-        "@babel/helper-module-transforms": "^7.24.7",
-        "@babel/helpers": "^7.24.7",
-        "@babel/parser": "^7.24.7",
-        "@babel/template": "^7.24.7",
-        "@babel/traverse": "^7.24.7",
-        "@babel/types": "^7.24.7",
+        "@babel/code-frame": "^7.24.6",
+        "@babel/generator": "^7.24.6",
+        "@babel/helper-compilation-targets": "^7.24.6",
+        "@babel/helper-module-transforms": "^7.24.6",
+        "@babel/helpers": "^7.24.6",
+        "@babel/parser": "^7.24.6",
+        "@babel/template": "^7.24.6",
+        "@babel/traverse": "^7.24.6",
+        "@babel/types": "^7.24.6",
         "convert-source-map": "^2.0.0",
         "debug": "^4.1.0",
         "gensync": "^1.0.0-beta.2",
@@ -218,6 +237,43 @@
         "node": ">=6.9.0"
       }
     },
+    "node_modules/@babel/generator/node_modules/@babel/types": {
+      "version": "7.24.7",
+      "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.24.7.tgz",
+      "integrity": "sha512-XEFXSlxiG5td2EJRe8vOmRbaXVgfcBlszKujvVmWIK/UpywWljQCfzAv3RQCGujWQ1RD4YYWEAqDXfuJiy8f5Q==",
+      "dependencies": {
+        "@babel/helper-string-parser": "^7.24.7",
+        "@babel/helper-validator-identifier": "^7.24.7",
+        "to-fast-properties": "^2.0.0"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-annotate-as-pure": {
+      "version": "7.24.7",
+      "resolved": "https://registry.npmjs.org/@babel/helper-annotate-as-pure/-/helper-annotate-as-pure-7.24.7.tgz",
+      "integrity": "sha512-BaDeOonYvhdKw+JoMVkAixAAJzG2jVPIwWoKBPdYuY9b452e2rPuI9QPYh3KpofZ3pW2akOmwZLOiOsHMiqRAg==",
+      "dependencies": {
+        "@babel/types": "^7.24.7"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-annotate-as-pure/node_modules/@babel/types": {
+      "version": "7.24.7",
+      "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.24.7.tgz",
+      "integrity": "sha512-XEFXSlxiG5td2EJRe8vOmRbaXVgfcBlszKujvVmWIK/UpywWljQCfzAv3RQCGujWQ1RD4YYWEAqDXfuJiy8f5Q==",
+      "dependencies": {
+        "@babel/helper-string-parser": "^7.24.7",
+        "@babel/helper-validator-identifier": "^7.24.7",
+        "to-fast-properties": "^2.0.0"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
     "node_modules/@babel/helper-compilation-targets": {
       "version": "7.24.7",
       "resolved": "https://registry.npmjs.org/@babel/helper-compilation-targets/-/helper-compilation-targets-7.24.7.tgz",
@@ -233,6 +289,28 @@
         "node": ">=6.9.0"
       }
     },
+    "node_modules/@babel/helper-create-class-features-plugin": {
+      "version": "7.24.7",
+      "resolved": "https://registry.npmjs.org/@babel/helper-create-class-features-plugin/-/helper-create-class-features-plugin-7.24.7.tgz",
+      "integrity": "sha512-kTkaDl7c9vO80zeX1rJxnuRpEsD5tA81yh11X1gQo+PhSti3JS+7qeZo9U4RHobKRiFPKaGK3svUAeb8D0Q7eg==",
+      "dependencies": {
+        "@babel/helper-annotate-as-pure": "^7.24.7",
+        "@babel/helper-environment-visitor": "^7.24.7",
+        "@babel/helper-function-name": "^7.24.7",
+        "@babel/helper-member-expression-to-functions": "^7.24.7",
+        "@babel/helper-optimise-call-expression": "^7.24.7",
+        "@babel/helper-replace-supers": "^7.24.7",
+        "@babel/helper-skip-transparent-expression-wrappers": "^7.24.7",
+        "@babel/helper-split-export-declaration": "^7.24.7",
+        "semver": "^6.3.1"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      },
+      "peerDependencies": {
+        "@babel/core": "^7.0.0"
+      }
+    },
     "node_modules/@babel/helper-environment-visitor": {
       "version": "7.24.7",
       "resolved": "https://registry.npmjs.org/@babel/helper-environment-visitor/-/helper-environment-visitor-7.24.7.tgz",
@@ -244,6 +322,19 @@
         "node": ">=6.9.0"
       }
     },
+    "node_modules/@babel/helper-environment-visitor/node_modules/@babel/types": {
+      "version": "7.24.7",
+      "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.24.7.tgz",
+      "integrity": "sha512-XEFXSlxiG5td2EJRe8vOmRbaXVgfcBlszKujvVmWIK/UpywWljQCfzAv3RQCGujWQ1RD4YYWEAqDXfuJiy8f5Q==",
+      "dependencies": {
+        "@babel/helper-string-parser": "^7.24.7",
+        "@babel/helper-validator-identifier": "^7.24.7",
+        "to-fast-properties": "^2.0.0"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
     "node_modules/@babel/helper-function-name": {
       "version": "7.24.7",
       "resolved": "https://registry.npmjs.org/@babel/helper-function-name/-/helper-function-name-7.24.7.tgz",
@@ -256,6 +347,19 @@
         "node": ">=6.9.0"
       }
     },
+    "node_modules/@babel/helper-function-name/node_modules/@babel/types": {
+      "version": "7.24.7",
+      "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.24.7.tgz",
+      "integrity": "sha512-XEFXSlxiG5td2EJRe8vOmRbaXVgfcBlszKujvVmWIK/UpywWljQCfzAv3RQCGujWQ1RD4YYWEAqDXfuJiy8f5Q==",
+      "dependencies": {
+        "@babel/helper-string-parser": "^7.24.7",
+        "@babel/helper-validator-identifier": "^7.24.7",
+        "to-fast-properties": "^2.0.0"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
     "node_modules/@babel/helper-hoist-variables": {
       "version": "7.24.7",
       "resolved": "https://registry.npmjs.org/@babel/helper-hoist-variables/-/helper-hoist-variables-7.24.7.tgz",
@@ -267,6 +371,44 @@
         "node": ">=6.9.0"
       }
     },
+    "node_modules/@babel/helper-hoist-variables/node_modules/@babel/types": {
+      "version": "7.24.7",
+      "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.24.7.tgz",
+      "integrity": "sha512-XEFXSlxiG5td2EJRe8vOmRbaXVgfcBlszKujvVmWIK/UpywWljQCfzAv3RQCGujWQ1RD4YYWEAqDXfuJiy8f5Q==",
+      "dependencies": {
+        "@babel/helper-string-parser": "^7.24.7",
+        "@babel/helper-validator-identifier": "^7.24.7",
+        "to-fast-properties": "^2.0.0"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-member-expression-to-functions": {
+      "version": "7.24.7",
+      "resolved": "https://registry.npmjs.org/@babel/helper-member-expression-to-functions/-/helper-member-expression-to-functions-7.24.7.tgz",
+      "integrity": "sha512-LGeMaf5JN4hAT471eJdBs/GK1DoYIJ5GCtZN/EsL6KUiiDZOvO/eKE11AMZJa2zP4zk4qe9V2O/hxAmkRc8p6w==",
+      "dependencies": {
+        "@babel/traverse": "^7.24.7",
+        "@babel/types": "^7.24.7"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-member-expression-to-functions/node_modules/@babel/types": {
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       }
     },
-    "node_modules/wrap-ansi/node_modules/strip-ansi": {
-      "version": "7.1.0",
-      "resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-7.1.0.tgz",
-      "integrity": "sha512-iq6eVVI64nQQTRYq2KtEg2d2uU7LElhTJwsH4YzIHZshxlgZms/wIc4VoDQTlG/IvVIrBKG06CrZnp0qv7hkcQ==",
-      "dependencies": {
-        "ansi-regex": "^6.0.1"
-      },
-      "engines": {
-        "node": ">=12"
-      },
-      "funding": {
-        "url": "https://github.com/chalk/strip-ansi?sponsor=1"
-      }
+    "node_modules/wrap-ansi/node_modules/color-name": {
+      "version": "1.1.4",
+      "resolved": "https://registry.npmjs.org/color-name/-/color-name-1.1.4.tgz",
+      "integrity": "sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA=="
     },
     "node_modules/wrappy": {
       "version": "1.0.2",
@@ -13384,9 +14711,9 @@
       "devOptional": true
     },
     "node_modules/ws": {
-      "version": "8.17.0",
-      "resolved": "https://registry.npmjs.org/ws/-/ws-8.17.0.tgz",
-      "integrity": "sha512-uJq6108EgZMAl20KagGkzCKfMEjxmKvZHG7Tlq0Z6nOky7YF7aq4mOx6xK8TJ/i1LeK4Qus7INktacctDgY8Ow==",
+      "version": "8.17.1",
+      "resolved": "https://registry.npmjs.org/ws/-/ws-8.17.1.tgz",
+      "integrity": "sha512-6XQFvXTkbfUOZOKKILFG1PDK2NDQs4azKQl26T0YS5CxqWLgXajbPZ+h4gZekJyRqFU8pvnbAbbs/3TgRPy+GQ==",
       "engines": {
         "node": ">=10.0.0"
       },
@@ -13424,6 +14751,14 @@
         "node": ">=0.1"
       }
     },
+    "node_modules/xmlhttprequest-ssl": {
+      "version": "2.0.0",
+      "resolved": "https://registry.npmjs.org/xmlhttprequest-ssl/-/xmlhttprequest-ssl-2.0.0.tgz",
+      "integrity": "sha512-QKxVRxiRACQcVuQEYFsI1hhkrMlrXHPegbbd1yn9UHOmRxY+si12nQYzri3vbzt8VdTTRviqcKxcyllFas5z2A==",
+      "engines": {
+        "node": ">=0.4.0"
+      }
+    },
     "node_modules/xtend": {
       "version": "4.0.2",
       "resolved": "https://registry.npmjs.org/xtend/-/xtend-4.0.2.tgz",
@@ -13432,6 +14767,22 @@
         "node": ">=0.4"
       }
     },
+    "node_modules/xycolors": {
+      "version": "0.1.2",
+      "resolved": "https://registry.npmjs.org/xycolors/-/xycolors-0.1.2.tgz",
+      "integrity": "sha512-iUIDKoRUq/6Nfkiwv/PqxR6ENzgLkaaOeWwY54CtObpEwmvQHCvsgxd5xIGfEF/QU75H2quxIffOoU4tf2kKDg==",
+      "funding": {
+        "url": "https://github.com/sponsors/xinyao27"
+      }
+    },
+    "node_modules/y18n": {
+      "version": "5.0.8",
+      "resolved": "https://registry.npmjs.org/y18n/-/y18n-5.0.8.tgz",
+      "integrity": "sha512-0pfFzegeDWJHJIAmTLRP2DwHjdF5s7jo9tuztdQxAhINCdvS+3nGINqPd00AphqJR/0LhANUS6/+7SCb98YOfA==",
+      "engines": {
+        "node": ">=10"
+      }
+    },
     "node_modules/yallist": {
       "version": "3.1.1",
       "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz",
@@ -13448,6 +14799,31 @@
         "node": ">= 14"
       }
     },
+    "node_modules/yargs": {
+      "version": "17.7.2",
+      "resolved": "https://registry.npmjs.org/yargs/-/yargs-17.7.2.tgz",
+      "integrity": "sha512-7dSzzRQ++CKnNI/krKnYRV7JKKPUXMEh61soaHKg9mrWEhzFWhFnxPxGl+69cD1Ou63C13NUPCnmIcrvqCuM6w==",
+      "dependencies": {
+        "cliui": "^8.0.1",
+        "escalade": "^3.1.1",
+        "get-caller-file": "^2.0.5",
+        "require-directory": "^2.1.1",
+        "string-width": "^4.2.3",
+        "y18n": "^5.0.5",
+        "yargs-parser": "^21.1.1"
+      },
+      "engines": {
+        "node": ">=12"
+      }
+    },
+    "node_modules/yargs-parser": {
+      "version": "21.1.1",
+      "resolved": "https://registry.npmjs.org/yargs-parser/-/yargs-parser-21.1.1.tgz",
+      "integrity": "sha512-tVpsJW7DdjecAiFpbIB1e3qxIQsE6NoPc5/eTdrbbIC4h0LVsWhnoa3g+m2HclBIujHzsxZ4VJVA+GUuc2/LBw==",
+      "engines": {
+        "node": ">=12"
+      }
+    },
     "node_modules/yocto-queue": {
       "version": "0.1.0",
       "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz",
diff --git a/src/frontend/package.json b/src/frontend/package.json
index f3eb36409..a4b10db6c 100644
--- a/src/frontend/package.json
+++ b/src/frontend/package.json
@@ -3,18 +3,18 @@
   "version": "0.1.2",
   "private": true,
   "dependencies": {
-    "@headlessui/react": "^1.7.17",
-    "@hookform/resolvers": "^3.3.4",
-    "@million/lint": "^0.0.73",
+    "@headlessui/react": "^2.0.4",
+    "@hookform/resolvers": "^3.6.0",
+    "@million/lint": "^1.0.0-rc.26",
     "@radix-ui/react-accordion": "^1.1.2",
     "@radix-ui/react-checkbox": "^1.0.4",
-    "@radix-ui/react-dialog": "^1.0.4",
-    "@radix-ui/react-dropdown-menu": "^2.0.5",
+    "@radix-ui/react-dialog": "^1.0.5",
+    "@radix-ui/react-dropdown-menu": "^2.0.6",
     "@radix-ui/react-form": "^0.0.3",
     "@radix-ui/react-icons": "^1.3.0",
     "@radix-ui/react-label": "^2.0.2",
-    "@radix-ui/react-menubar": "^1.0.3",
-    "@radix-ui/react-popover": "^1.0.6",
+    "@radix-ui/react-menubar": "^1.0.4",
+    "@radix-ui/react-popover": "^1.0.7",
     "@radix-ui/react-progress": "^1.0.3",
     "@radix-ui/react-select": "^2.0.0",
     "@radix-ui/react-separator": "^1.0.3",
@@ -22,60 +22,60 @@
     "@radix-ui/react-switch": "^1.0.3",
     "@radix-ui/react-tabs": "^1.0.4",
     "@radix-ui/react-toggle": "^1.0.3",
-    "@radix-ui/react-tooltip": "^1.0.6",
-    "@tabler/icons-react": "^2.32.0",
-    "@tailwindcss/forms": "^0.5.6",
+    "@radix-ui/react-tooltip": "^1.0.7",
+    "@tabler/icons-react": "^3.6.0",
+    "@tailwindcss/forms": "^0.5.7",
     "@tailwindcss/line-clamp": "^0.4.4",
     "@types/axios": "^0.14.0",
-    "ace-builds": "^1.24.1",
-    "ag-grid-community": "^31.2.1",
-    "ag-grid-react": "^31.2.1",
+    "ace-builds": "^1.35.0",
+    "ag-grid-community": "^31.3.2",
+    "ag-grid-react": "^31.3.2",
     "ansi-to-html": "^0.7.2",
-    "axios": "^1.5.0",
+    "axios": "^1.7.2",
     "base64-js": "^1.5.1",
-    "class-variance-authority": "^0.6.1",
-    "clsx": "^1.2.1",
+    "class-variance-authority": "^0.7.0",
+    "clsx": "^2.1.1",
     "cmdk": "^1.0.0",
-    "dompurify": "^3.0.5",
+    "dompurify": "^3.1.5",
     "dotenv": "^16.4.5",
     "emoji-regex": "^10.3.0",
-    "esbuild": "^0.17.19",
+    "esbuild": "^0.21.5",
     "file-saver": "^2.0.5",
-    "framer-motion": "^11.0.6",
+    "framer-motion": "^11.2.10",
     "lodash": "^4.17.21",
-    "lucide-react": "^0.394.0",
-    "million": "^3.0.6",
-    "moment": "^2.29.4",
+    "lucide-react": "^0.395.0",
+    "million": "^3.1.11",
+    "moment": "^2.30.1",
     "openseadragon": "^4.1.1",
     "p-debounce": "^4.0.0",
-    "playwright": "^1.42.0",
-    "react": "^18.2.21",
-    "react-ace": "^10.1.0",
-    "react-cookie": "^4.1.1",
-    "react-dom": "^18.2.21",
-    "react-error-boundary": "^4.0.11",
-    "react-hook-form": "^7.51.4",
+    "playwright": "^1.44.1",
+    "react": "^18.3.1",
+    "react-ace": "^11.0.1",
+    "react-cookie": "^7.1.4",
+    "react-dom": "^18.3.1",
+    "react-error-boundary": "^4.0.13",
+    "react-hook-form": "^7.52.0",
     "react-hotkeys-hook": "^4.5.0",
-    "react-icons": "^5.0.1",
+    "react-icons": "^5.2.1",
     "react-laag": "^2.0.5",
     "react-markdown": "^8.0.7",
     "react-pdf": "^9.0.0",
-    "react-router-dom": "^6.15.0",
+    "react-router-dom": "^6.23.1",
     "react-syntax-highlighter": "^15.5.0",
-    "react18-json-view": "^0.2.3",
-    "reactflow": "^11.9.2",
+    "react18-json-view": "^0.2.8",
+    "reactflow": "^11.11.3",
     "rehype-mathjax": "^4.0.3",
-    "remark-gfm": "^3.0.1",
-    "remark-math": "^5.1.1",
-    "shadcn-ui": "^0.2.3",
-    "short-unique-id": "^4.4.4",
-    "tailwind-merge": "^1.14.0",
+    "remark-gfm": "3.0.1",
+    "remark-math": "^6.0.0",
+    "shadcn-ui": "^0.8.0",
+    "short-unique-id": "^5.2.0",
+    "tailwind-merge": "^2.3.0",
     "tailwindcss-animate": "^1.0.7",
-    "uuid": "^9.0.0",
-    "vite-plugin-svgr": "^3.2.0",
-    "web-vitals": "^2.1.4",
-    "zod": "^3.23.7",
-    "zustand": "^4.4.7"
+    "uuid": "^10.0.0",
+    "vite-plugin-svgr": "^4.2.0",
+    "web-vitals": "^4.1.1",
+    "zod": "^3.23.8",
+    "zustand": "^4.5.2"
   },
   "scripts": {
     "dev:docker": "vite --host 0.0.0.0",
@@ -105,32 +105,31 @@
   },
   "proxy": "http://127.0.0.1:7860",
   "devDependencies": {
-    "@playwright/test": "^1.44.0",
-    "@swc/cli": "^0.1.62",
-    "@swc/core": "^1.3.80",
-    "@tailwindcss/typography": "^0.5.9",
-    "@testing-library/jest-dom": "^5.17.0",
-    "@testing-library/react": "^13.4.0",
-    "@testing-library/user-event": "^13.5.0",
-    "@types/jest": "^27.5.2",
-    "@types/lodash": "^4.14.197",
-    "@types/node": "^16.18.46",
-    "@types/react": "^18.2.21",
-    "@types/react-dom": "^18.2.7",
-    "@types/uuid": "^9.0.2",
-    "@vitejs/plugin-react-swc": "^3.3.2",
-    "autoprefixer": "^10.4.15",
-    "daisyui": "^4.0.4",
+    "@playwright/test": "^1.44.1",
+    "@swc/cli": "^0.3.12",
+    "@swc/core": "^1.6.1",
+    "@tailwindcss/typography": "^0.5.13",
+    "@testing-library/jest-dom": "^6.4.6",
+    "@testing-library/react": "^16.0.0",
+    "@testing-library/user-event": "^14.5.2",
+    "@types/jest": "^29.5.12",
+    "@types/node": "^20.14.2",
+    "@types/react": "^18.3.3",
+    "@types/react-dom": "^18.3.0",
+    "@types/uuid": "^9.0.8",
+    "@types/lodash": "4.17.5",
+    "@vitejs/plugin-react-swc": "^3.7.0",
+    "autoprefixer": "^10.4.19",
     "eslint": "^9.5.0",
-    "postcss": "^8.4.29",
+    "postcss": "^8.4.38",
     "prettier": "^3.3.2",
-    "prettier-plugin-organize-imports": "^3.2.3",
+    "prettier-plugin-organize-imports": "^3.2.4",
     "prettier-plugin-tailwindcss": "^0.6.4",
     "simple-git-hooks": "^2.11.1",
-    "tailwindcss": "^3.3.3",
+    "tailwindcss": "^3.4.4",
     "tailwindcss-dotted-background": "^1.1.0",
-    "typescript": "^5.2.2",
-    "ua-parser-js": "^1.0.37",
-    "vite": "^4.5.2"
+    "typescript": "^5.4.5",
+    "ua-parser-js": "^1.0.38",
+    "vite": "^5.3.1"
   }
-}
\ No newline at end of file
+}
diff --git a/src/frontend/prettier.config.js b/src/frontend/prettier.config.js
deleted file mode 100644
index d57311886..000000000
--- a/src/frontend/prettier.config.js
+++ /dev/null
@@ -1,3 +0,0 @@
-module.exports = {
-  plugins: ["prettier-plugin-tailwindcss"],
-};
diff --git a/src/frontend/src/App.tsx b/src/frontend/src/App.tsx
index b84c0d792..720ffb6b2 100644
--- a/src/frontend/src/App.tsx
+++ b/src/frontend/src/App.tsx
@@ -29,10 +29,10 @@ export default function App() {
   useTrackLastVisitedPath();
 
   const removeFromTempNotificationList = useAlertStore(
-    (state) => state.removeFromTempNotificationList
+    (state) => state.removeFromTempNotificationList,
   );
   const tempNotificationList = useAlertStore(
-    (state) => state.tempNotificationList
+    (state) => state.tempNotificationList,
   );
   const [fetchError, setFetchError] = useState(false);
   const isLoading = useFlowsManagerStore((state) => state.isLoading);
@@ -48,7 +48,7 @@ export default function App() {
   const refreshVersion = useDarkStore((state) => state.refreshVersion);
   const refreshStars = useDarkStore((state) => state.refreshStars);
   const setGlobalVariables = useGlobalVariablesStore(
-    (state) => state.setGlobalVariables
+    (state) => state.setGlobalVariables,
   );
   const checkHasStore = useStoreStore((state) => state.checkHasStore);
   const navigate = useNavigate();
diff --git a/src/frontend/src/CustomNodes/GenericNode/components/HandleTooltipComponent/index.tsx b/src/frontend/src/CustomNodes/GenericNode/components/HandleTooltipComponent/index.tsx
new file mode 100644
index 000000000..58e28ff3a
--- /dev/null
+++ b/src/frontend/src/CustomNodes/GenericNode/components/HandleTooltipComponent/index.tsx
@@ -0,0 +1,30 @@
+import { TOOLTIP_EMPTY } from "../../../../constants/constants";
+import useFlowStore from "../../../../stores/flowStore";
+import { useTypesStore } from "../../../../stores/typesStore";
+import { NodeType } from "../../../../types/flow";
+import { groupByFamily } from "../../../../utils/utils";
+import TooltipRenderComponent from "../tooltipRenderComponent";
+
+export default function HandleTooltips({
+  left,
+  tooltipTitle,
+}: {
+  left: boolean;
+  nodes: NodeType[];
+  tooltipTitle: string;
+}) {
+  const myData = useTypesStore((state) => state.data);
+  const nodes = useFlowStore((state) => state.nodes);
+
+  let groupedObj: any = groupByFamily(myData, tooltipTitle!, left, nodes!);
+
+  if (groupedObj && groupedObj.length > 0) {
+    //@ts-ignore
+    return groupedObj.map((item, index) => {
+      return <TooltipRenderComponent index={index} item={item} left={left} />;
+    });
+  } else {
+    //@ts-ignore
+    return <span data-testid={`empty-tooltip-filter`}>{TOOLTIP_EMPTY}</span>;
+  }
+}
diff --git a/src/frontend/src/CustomNodes/GenericNode/components/OutputComponent/index.tsx b/src/frontend/src/CustomNodes/GenericNode/components/OutputComponent/index.tsx
new file mode 100644
index 000000000..c5a9bb218
--- /dev/null
+++ b/src/frontend/src/CustomNodes/GenericNode/components/OutputComponent/index.tsx
@@ -0,0 +1,91 @@
+import { cloneDeep } from "lodash";
+import { useUpdateNodeInternals } from "reactflow";
+import ForwardedIconComponent from "../../../../components/genericIconComponent";
+import ShadTooltip from "../../../../components/shadTooltipComponent";
+import { Button } from "../../../../components/ui/button";
+import {
+  DropdownMenu,
+  DropdownMenuContent,
+  DropdownMenuItem,
+  DropdownMenuTrigger,
+} from "../../../../components/ui/dropdown-menu";
+import useFlowStore from "../../../../stores/flowStore";
+import { outputComponentType } from "../../../../types/components";
+import { NodeDataType } from "../../../../types/flow";
+import { cn } from "../../../../utils/utils";
+
+export default function OutputComponent({
+  selected,
+  types,
+  frozen = false,
+  nodeId,
+  idx,
+  name,
+  proxy,
+}: outputComponentType) {
+  const setNode = useFlowStore((state) => state.setNode);
+  const updateNodeInternals = useUpdateNodeInternals();
+
+  const displayProxy = (children) => {
+    if (proxy) {
+      return (
+        <ShadTooltip content={<span>{proxy.nodeDisplayName}</span>}>
+          {children}
+        </ShadTooltip>
+      );
+    } else {
+      return children;
+    }
+  };
+
+  return displayProxy(
+    <span className={cn(frozen ? "text-ice" : "")}>{name}</span>,
+  );
+
+  // ! DEACTIVATED UNTIL BETTER IMPLEMENTATION
+  // return (
+  //   <div className="nocopy nopan nodelete nodrag noundo flex items-center gap-2">
+  //     <DropdownMenu>
+  //       <DropdownMenuTrigger asChild>
+  //         <Button
+  //           disabled={frozen}
+  //           variant="primary"
+  //           size="xs"
+  //           className={cn(
+  //             frozen ? "text-ice" : "",
+  //             "items-center gap-1 pl-2 pr-1.5 align-middle text-xs font-normal",
+  //           )}
+  //         >
+  //           <span className="pb-px">{selected}</span>
+  //           <ForwardedIconComponent name="ChevronDown" className="h-3 w-3" />
+  //         </Button>
+  //       </DropdownMenuTrigger>
+  //       <DropdownMenuContent>
+  //         {types.map((type) => (
+  //           <DropdownMenuItem
+  //             onSelect={() => {
+  //               // TODO: UDPDATE SET NODE TO NEW NODE FORM
+  //               setNode(nodeId, (node) => {
+  //                 const newNode = cloneDeep(node);
+  //                 (newNode.data as NodeDataType).node!.outputs![idx].selected =
+  //                   type;
+  //                 return newNode;
+  //               });
+  //               updateNodeInternals(nodeId);
+  //             }}
+  //           >
+  //             {type}
+  //           </DropdownMenuItem>
+  //         ))}
+  //       </DropdownMenuContent>
+  //     </DropdownMenu>
+  //     {proxy ? (
+  //       <ShadTooltip content={<span>{proxy.nodeDisplayName}</span>}>
+  //         <span>{name}</span>
+  //       </ShadTooltip>
+  //     ) : (
+  //       <span>{name}</span>
+  //     )}
+  //   </div>
+  // );
+}
diff --git a/src/frontend/src/CustomNodes/GenericNode/components/handleRenderComponent/index.tsx b/src/frontend/src/CustomNodes/GenericNode/components/handleRenderComponent/index.tsx
new file mode 100644
index 000000000..4e53881d4
--- /dev/null
+++ b/src/frontend/src/CustomNodes/GenericNode/components/handleRenderComponent/index.tsx
@@ -0,0 +1,103 @@
+import { title } from "process";
+import { Handle, Position } from "reactflow";
+import ShadTooltip from "../../../../components/shadTooltipComponent";
+import { Button } from "../../../../components/ui/button";
+import {
+  isValidConnection,
+  scapedJSONStringfy,
+} from "../../../../utils/reactflowUtils";
+import { classNames, cn, groupByFamily } from "../../../../utils/utils";
+import HandleTooltips from "../HandleTooltipComponent";
+
+export default function HandleRenderComponent({
+  left,
+  nodes,
+  tooltipTitle = "",
+  proxy,
+  id,
+  title,
+  edges,
+  myData,
+  colors,
+  setFilterEdge,
+  showNode,
+}: {
+  left: boolean;
+  nodes: any;
+  tooltipTitle?: string;
+  proxy: any;
+  id: any;
+  title: string;
+  edges: any;
+  myData: any;
+  colors: string[];
+  setFilterEdge: any;
+  showNode: any;
+}) {
+  return (
+    <Button
+      unstyled
+      className="h-7 truncate bg-muted p-0 text-sm font-normal text-black hover:bg-muted"
+    >
+      <ShadTooltip
+        styleClasses={"tooltip-fixed-width custom-scroll nowheel"}
+        delayDuration={1000}
+        content={
+          <HandleTooltips
+            left={left}
+            nodes={nodes}
+            tooltipTitle={tooltipTitle!}
+          />
+        }
+        side={left ? "left" : "right"}
+      >
+        <Handle
+          data-test-id={`handle-${title.toLowerCase()}-${
+            !showNode ? (left ? "target" : "source") : left ? "left" : "right"
+          }`}
+          type={left ? "target" : "source"}
+          position={left ? Position.Left : Position.Right}
+          key={scapedJSONStringfy(proxy ? { ...id, proxy } : id)}
+          id={scapedJSONStringfy(proxy ? { ...id, proxy } : id)}
+          isValidConnection={(connection) =>
+            isValidConnection(connection, nodes, edges)
+          }
+          className={classNames(
+            left ? "-ml-0.5" : "-mr-0.5",
+            "z-20 h-3 w-3 rounded-full border-none bg-background",
+          )}
+          style={{
+            background:
+              "conic-gradient(" +
+              colors
+                .concat(colors[0])
+                .map(
+                  (color, index) =>
+                    color +
+                    " " +
+                    ((360 / colors.length) * index -
+                      360 / (colors.length * 4)) +
+                    "deg " +
+                    ((360 / colors.length) * index +
+                      360 / (colors.length * 4)) +
+                    "deg",
+                )
+                .join(" ,") +
+              ")",
+            WebkitMaskImage: "radial-gradient(transparent 40%, black 44%)",
+            maskImage: "radial-gradient(transparent 40%, black 44%)",
+          }}
+          onClick={() => {
+            setFilterEdge(groupByFamily(myData, tooltipTitle!, left, nodes!));
+          }}
+        />
+      </ShadTooltip>
+      <div
+        className={cn(
+          "absolute top-[50%] z-10 h-3 w-3 translate-y-[-50%] rounded-full bg-background",
+          left ? "-left-[4px] -ml-0.5" : "-right-[4px] -mr-0.5",
+        )}
+      />
+    </Button>
+  );
+}
diff --git a/src/frontend/src/CustomNodes/GenericNode/components/outputModal/components/switchOutputView/index.tsx b/src/frontend/src/CustomNodes/GenericNode/components/outputModal/components/switchOutputView/index.tsx
index 437adb511..0a751c8e5 100644
--- a/src/frontend/src/CustomNodes/GenericNode/components/outputModal/components/switchOutputView/index.tsx
+++ b/src/frontend/src/CustomNodes/GenericNode/components/outputModal/components/switchOutputView/index.tsx
@@ -1,5 +1,5 @@
+import DataOutputComponent from "../../../../../../components/dataOutputComponent";
 import ForwardedIconComponent from "../../../../../../components/genericIconComponent";
-import RecordsOutputComponent from "../../../../../../components/recordsOutputComponent";
 import {
   Alert,
   AlertDescription,
@@ -9,41 +9,46 @@ import { Case } from "../../../../../../shared/components/caseComponent";
 import TextOutputView from "../../../../../../shared/components/textOutputView";
 import useFlowStore from "../../../../../../stores/flowStore";
 import ErrorOutput from "./components";
-
-export default function SwitchOutputView(nodeId): JSX.Element {
-  const nodeIdentity = nodeId.nodeId;
-
-  const nodes = useFlowStore((state) => state.nodes);
+// Define the props type
+interface SwitchOutputViewProps {
+  nodeId: string;
+  outputName: string;
+}
+const SwitchOutputView: React.FC<SwitchOutputViewProps> = ({
+  nodeId,
+  outputName,
+}) => {
   const flowPool = useFlowStore((state) => state.flowPool);
-  const node = nodes.find((node) => node?.id === nodeIdentity);
-
-  const flowPoolNode = (flowPool[nodeIdentity] ?? [])[
-    (flowPool[nodeIdentity]?.length ?? 1) - 1
+  const flowPoolNode = (flowPool[nodeId] ?? [])[
+    (flowPool[nodeId]?.length ?? 1) - 1
   ];
-
-  const results = flowPoolNode?.data?.logs[0] ?? "";
+  let results = flowPoolNode?.data?.logs[outputName] ?? "";
+  if (Array.isArray(results)) {
+    return;
+  }
   const resultType = results?.type;
   let resultMessage = results?.message;
-  const RECORD_TYPES = ["record", "object", "array", "message"];
-  if (resultMessage.raw) {
+  const RECORD_TYPES = ["data", "object", "array", "message"];
+  if (resultMessage?.raw) {
     resultMessage = resultMessage.raw;
   }
-
   return (
     <>
       <Case condition={!resultType || resultType === "unknown"}>
         <div>NO OUTPUT</div>
       </Case>
-      <Case condition={resultType === "ValueError"}>
-        <ErrorOutput value={resultMessage}></ErrorOutput>
+      <Case condition={resultType === "error" || resultType === "ValueError"}>
+        <ErrorOutput
+          value={`${resultMessage.errorMessage}\n\n${resultMessage.stackTrace}`}
+        ></ErrorOutput>
       </Case>
 
-      <Case condition={node && resultType === "text"}>
+      <Case condition={resultType === "text"}>
         <TextOutputView left={false} value={resultMessage} />
       </Case>
 
       <Case condition={RECORD_TYPES.includes(resultType)}>
-        <RecordsOutputComponent
+        <DataOutputComponent
           rows={
             Array.isArray(resultMessage)
               ? (resultMessage as Array<any>).every((item) => item.data)
@@ -74,4 +79,6 @@ export default function SwitchOutputView(nodeId): JSX.Element {
       </Case>
     </>
   );
-}
+};
+
+export default SwitchOutputView;
diff --git a/src/frontend/src/CustomNodes/GenericNode/components/outputModal/index.tsx b/src/frontend/src/CustomNodes/GenericNode/components/outputModal/index.tsx
index f717099cb..e34ee6ec1 100644
--- a/src/frontend/src/CustomNodes/GenericNode/components/outputModal/index.tsx
+++ b/src/frontend/src/CustomNodes/GenericNode/components/outputModal/index.tsx
@@ -2,7 +2,12 @@ import { Button } from "../../../../components/ui/button";
 import BaseModal from "../../../../modals/baseModal";
 import SwitchOutputView from "./components/switchOutputView";
 
-export default function OutputModal({ open, setOpen, nodeId }): JSX.Element {
+export default function OutputModal({
+  open,
+  setOpen,
+  nodeId,
+  outputName,
+}): JSX.Element {
   return (
     <BaseModal open={open} setOpen={setOpen} size="medium-tall">
       <BaseModal.Header description="Inspect the output of the component below.">
@@ -11,7 +16,7 @@ export default function OutputModal({ open, setOpen, nodeId }): JSX.Element {
         </div>
       </BaseModal.Header>
       <BaseModal.Content>
-        <SwitchOutputView nodeId={nodeId} />
+        <SwitchOutputView nodeId={nodeId} outputName={outputName} />
       </BaseModal.Content>
       <BaseModal.Footer>
         <div className="flex w-full justify-end pt-2">
diff --git a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx
index 1cfde5828..7ebfe5339 100644
--- a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx
+++ b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx
@@ -1,7 +1,7 @@
 import { cloneDeep } from "lodash";
 import { ReactNode, useEffect, useRef, useState } from "react";
 import { useHotkeys } from "react-hotkeys-hook";
-import { Handle, Position, useUpdateNodeInternals } from "reactflow";
+import { useUpdateNodeInternals } from "reactflow";
 import CodeAreaComponent from "../../../../components/codeAreaComponent";
 import DictComponent from "../../../../components/dictComponent";
 import Dropdown from "../../../../components/dropdownComponent";
@@ -18,10 +18,7 @@ import TextAreaComponent from "../../../../components/textAreaComponent";
 import ToggleShadComponent from "../../../../components/toggleShadComponent";
 import { Button } from "../../../../components/ui/button";
 import { RefreshButton } from "../../../../components/ui/refreshButton";
-import {
-  LANGFLOW_SUPPORTED_TYPES,
-  TOOLTIP_EMPTY,
-} from "../../../../constants/constants";
+import { LANGFLOW_SUPPORTED_TYPES } from "../../../../constants/constants";
 import { Case } from "../../../../shared/components/caseComponent";
 import useFlowStore from "../../../../stores/flowStore";
 import useFlowsManagerStore from "../../../../stores/flowsManagerStore";
@@ -36,22 +33,25 @@ import {
 import {
   convertObjToArray,
   convertValuesToNumbers,
+  getGroupOutputNodeId,
   hasDuplicateKeys,
-  isValidConnection,
   scapedJSONStringfy,
 } from "../../../../utils/reactflowUtils";
-import { nodeColors } from "../../../../utils/styleUtils";
 import {
   classNames,
-  groupByFamily,
+  cn,
   isThereModal,
+  logHasMessage,
+  logTypeIsError,
+  logTypeIsUnknown,
 } from "../../../../utils/utils";
 import useFetchDataOnMount from "../../../hooks/use-fetch-data-on-mount";
 import useHandleOnNewValue from "../../../hooks/use-handle-new-value";
 import useHandleNodeClass from "../../../hooks/use-handle-node-class";
 import useHandleRefreshButtonPress from "../../../hooks/use-handle-refresh-buttons";
+import OutputComponent from "../OutputComponent";
+import HandleRenderComponent from "../handleRenderComponent";
 import OutputModal from "../outputModal";
-import TooltipRenderComponent from "../tooltipRenderComponent";
 import { TEXT_FIELD_TYPES } from "./constants";
 
 export default function ParameterComponent({
@@ -60,7 +60,7 @@ export default function ParameterComponent({
   data,
   tooltipTitle,
   title,
-  color,
+  colors,
   type,
   name = "",
   required = false,
@@ -68,13 +68,13 @@ export default function ParameterComponent({
   info = "",
   proxy,
   showNode,
-  index = "",
+  index,
+  outputName,
   selected,
+  outputProxy,
 }: ParameterComponentType): JSX.Element {
   const ref = useRef<HTMLDivElement>(null);
-  const refHtml = useRef<HTMLDivElement & ReactNode>(null);
   const infoHtml = useRef<HTMLDivElement & ReactNode>(null);
-  const currentFlow = useFlowsManagerStore((state) => state.currentFlow);
   const nodes = useFlowStore((state) => state.nodes);
   const edges = useFlowStore((state) => state.edges);
   const setNode = useFlowStore((state) => state.setNode);
@@ -83,22 +83,42 @@ export default function ParameterComponent({
   const [isLoading, setIsLoading] = useState(false);
   const updateNodeInternals = useUpdateNodeInternals();
   const [errorDuplicateKey, setErrorDuplicateKey] = useState(false);
-  const flow = currentFlow?.data?.nodes ?? null;
-  const groupedEdge = useRef(null);
   const setFilterEdge = useFlowStore((state) => state.setFilterEdge);
   const [openOutputModal, setOpenOutputModal] = useState(false);
   const flowPool = useFlowStore((state) => state.flowPool);
 
-  const displayOutputPreview =
-    !!flowPool[data.id] &&
-    flowPool[data.id][flowPool[data.id].length - 1]?.valid &&
-    flowPool[data.id][flowPool[data.id].length - 1]?.data?.logs[0]?.message;
+  let flowPoolId = data.id;
+  let internalOutputName = outputName;
 
-  const unknownOutput = !!(
-    flowPool[data.id] &&
-    flowPool[data.id][flowPool[data.id].length - 1]?.data?.logs[0]?.type ===
-      "unknown"
+  if (data.node?.flow && outputProxy) {
+    const realOutput = getGroupOutputNodeId(
+      data.node.flow,
+      outputProxy.name,
+      outputProxy.id,
+    );
+    if (realOutput) {
+      flowPoolId = realOutput.id;
+      internalOutputName = realOutput.outputName;
+    }
+  }
+
+  const flowPoolNode = (flowPool[flowPoolId] ?? [])[
+    (flowPool[flowPoolId]?.length ?? 1) - 1
+  ];
+
+  const displayOutputPreview =
+    !!flowPool[flowPoolId] &&
+    logHasMessage(flowPoolNode?.data, internalOutputName);
+
+  const unknownOutput = logTypeIsUnknown(
+    flowPoolNode?.data,
+    internalOutputName,
   );
+  const errorOutput = logTypeIsError(flowPoolNode?.data, internalOutputName);
+
+  if (outputProxy) {
+    console.log(logHasMessage(flowPoolNode?.data, internalOutputName));
+  }
 
   const preventDefault = true;
 
@@ -120,8 +140,7 @@ export default function ParameterComponent({
     handleUpdateValues,
     debouncedHandleUpdateValues,
     setNode,
-    renderTooltips,
-    setIsLoading
+    setIsLoading,
   );
 
   const { handleNodeClass: handleNodeClassHook } = useHandleNodeClass(
@@ -130,34 +149,32 @@ export default function ParameterComponent({
     takeSnapshot,
     setNode,
     updateNodeInternals,
-    renderTooltips
   );
 
   const { handleRefreshButtonPress: handleRefreshButtonPressHook } =
-    useHandleRefreshButtonPress(setIsLoading, setNode, renderTooltips);
+    useHandleRefreshButtonPress(setIsLoading, setNode);
 
   let disabled =
     edges.some(
       (edge) =>
-        edge.targetHandle === scapedJSONStringfy(proxy ? { ...id, proxy } : id)
+        edge.targetHandle === scapedJSONStringfy(proxy ? { ...id, proxy } : id),
+    ) ?? false;
+
+  let disabledOutput =
+    edges.some(
+      (edge) =>
+        edge.sourceHandle === scapedJSONStringfy(proxy ? { ...id, proxy } : id),
     ) ?? false;
 
   const handleRefreshButtonPress = async (name, data) => {
     handleRefreshButtonPressHook(name, data);
   };
 
-  useFetchDataOnMount(
-    data,
-    name,
-    handleUpdateValues,
-    setNode,
-    renderTooltips,
-    setIsLoading
-  );
+  useFetchDataOnMount(data, name, handleUpdateValues, setNode, setIsLoading);
 
   const handleOnNewValue = async (
     newValue: string | string[] | boolean | Object[],
-    skipSnapshot: boolean | undefined = false
+    skipSnapshot: boolean | undefined = false,
   ): Promise<void> => {
     handleOnNewValueHook(newValue, skipSnapshot);
   };
@@ -179,21 +196,24 @@ export default function ParameterComponent({
     );
   }, [info]);
 
-  function renderTooltips() {
-    let groupedObj: any = groupByFamily(myData, tooltipTitle!, left, flow!);
-    groupedEdge.current = groupedObj;
-
-    if (groupedObj && groupedObj.length > 0) {
-      //@ts-ignore
-      refHtml.current = groupedObj.map((item, index) => {
-        return <TooltipRenderComponent index={index} item={item} left={left} />;
-      });
-    } else {
-      //@ts-ignore
-      refHtml.current = (
-        <span data-testid={`empty-tooltip-filter`}>{TOOLTIP_EMPTY}</span>
-      );
-    }
+  function renderTitle() {
+    return !left ? (
+      <OutputComponent
+        proxy={outputProxy}
+        idx={index}
+        types={type?.split("|") ?? []}
+        selected={
+          data.node?.outputs![index].selected ??
+          data.node?.outputs![index].types[0] ??
+          title
+        }
+        nodeId={data.id}
+        frozen={data.node?.frozen}
+        name={title ?? type}
+      />
+    ) : (
+      <span>{title}</span>
+    );
   }
 
   useEffect(() => {
@@ -202,56 +222,49 @@ export default function ParameterComponent({
     }
   }, [optionalHandle]);
 
+  const handleUpdateOutputHide = (value?: boolean) => {
+    setNode(data.id, (oldNode) => {
+      let newNode = cloneDeep(oldNode);
+      newNode.data = {
+        ...newNode.data,
+        node: {
+          ...newNode.data.node,
+          outputs: newNode.data.node.outputs?.map((output, i) => {
+            if (i === index) {
+              output.hidden = value ?? !output.hidden;
+            }
+            return output;
+          }),
+        },
+      };
+      return newNode;
+    });
+    updateNodeInternals(data.id);
+  };
+
   useEffect(() => {
-    renderTooltips();
-  }, [tooltipTitle, flow]);
+    if (disabledOutput) {
+      handleUpdateOutputHide(false);
+    }
+  }, [disabledOutput]);
 
   return !showNode ? (
     left && LANGFLOW_SUPPORTED_TYPES.has(type ?? "") && !optionalHandle ? (
       <></>
     ) : (
-      <Button className="h-7 truncate bg-muted p-0 text-sm font-normal text-black hover:bg-muted">
-        <div className="flex">
-          <ShadTooltip
-            styleClasses={"tooltip-fixed-width custom-scroll nowheel"}
-            delayDuration={1000}
-            content={refHtml.current}
-            side={left ? "left" : "right"}
-          >
-            <Handle
-              data-test-id={`handle-${title.toLowerCase()}-${
-                left ? "target" : "source"
-              }`}
-              type={left ? "target" : "source"}
-              position={left ? Position.Left : Position.Right}
-              key={
-                proxy
-                  ? scapedJSONStringfy({ ...id, proxy })
-                  : scapedJSONStringfy(id)
-              }
-              id={
-                proxy
-                  ? scapedJSONStringfy({ ...id, proxy })
-                  : scapedJSONStringfy(id)
-              }
-              isValidConnection={(connection) =>
-                isValidConnection(connection, nodes, edges)
-              }
-              className={classNames(
-                left ? "my-12 -ml-0.5" : "my-12 -mr-0.5",
-                "h-3 w-3 rounded-full border-2 bg-background",
-                !showNode ? "mt-0" : ""
-              )}
-              style={{
-                borderColor: color ?? nodeColors.unknown,
-              }}
-              onClick={() => {
-                setFilterEdge(groupedEdge.current);
-              }}
-            ></Handle>
-          </ShadTooltip>
-        </div>
-      </Button>
+      <HandleRenderComponent
+        left={left}
+        nodes={nodes}
+        tooltipTitle={tooltipTitle}
+        proxy={proxy}
+        id={id}
+        title={title}
+        edges={edges}
+        myData={myData}
+        colors={colors}
+        setFilterEdge={setFilterEdge}
+        showNode={showNode}
+      />
     )
   ) : (
     <div
@@ -271,6 +284,25 @@ export default function ParameterComponent({
             (left ? "" : " justify-end")
           }
         >
+          {!left && (
+            <div className="flex-1">
+              <Button
+                disabled={disabledOutput}
+                unstyled
+                onClick={() => handleUpdateOutputHide()}
+                data-testid={`output-inspection-${title.toLowerCase()}`}
+              >
+                <IconComponent
+                  className={cn(
+                    "h-4 w-4",
+                    disabledOutput ? "text-muted-foreground" : "",
+                  )}
+                  strokeWidth={1.5}
+                  name={data.node?.outputs![index].hidden ? "EyeOff" : "Eye"}
+                />
+              </Button>
+            </div>
+          )}
           <Case condition={!left && data.node?.frozen}>
             <div className="pr-1">
               <IconComponent className="h-5 w-5 text-ice" name={"Snowflake"} />
@@ -279,14 +311,12 @@ export default function ParameterComponent({
 
           {proxy ? (
             <ShadTooltip content={<span>{proxy.id}</span>}>
-              <span className={!left && data.node?.frozen ? "text-ice" : ""}>
-                {title}
-              </span>
+              {renderTitle()}
             </ShadTooltip>
           ) : (
             <div className="flex gap-2">
               <span className={!left && data.node?.frozen ? "text-ice" : ""}>
-                {title}
+                {renderTitle()}
               </span>
               {!left && (
                 <ShadTooltip
@@ -299,21 +329,29 @@ export default function ParameterComponent({
                   }
                 >
                   <Button
-                    variant="none"
-                    size="none"
+                    unstyled
                     disabled={!displayOutputPreview || unknownOutput}
                     onClick={() => setOpenOutputModal(true)}
                     data-testid={`output-inspection-${title.toLowerCase()}`}
                   >
-                    <IconComponent
-                      className={classNames(
-                        "h-5 w-5 rounded-md",
-                        displayOutputPreview && !unknownOutput
-                          ? "hover:bg-secondary-foreground/5 hover:text-medium-indigo"
-                          : "cursor-not-allowed text-muted-foreground"
-                      )}
-                      name={"ScanEye"}
-                    />
+                    {errorOutput ? (
+                      <IconComponent
+                        className={classNames(
+                          "h-5 w-5 rounded-md text-status-red",
+                        )}
+                        name={"X"}
+                      />
+                    ) : (
+                      <IconComponent
+                        className={classNames(
+                          "h-5 w-5 rounded-md",
+                          displayOutputPreview && !unknownOutput
+                            ? "hover:text-medium-indigo"
+                            : "cursor-not-allowed text-muted-foreground",
+                        )}
+                        name={"ScanEye"}
+                      />
+                    )}
                   </Button>
                 </ShadTooltip>
               )}
@@ -336,38 +374,23 @@ export default function ParameterComponent({
             )}
           </div>
         </div>
+
         {left && LANGFLOW_SUPPORTED_TYPES.has(type ?? "") && !optionalHandle ? (
           <></>
         ) : (
-          <Button className="h-7 truncate bg-muted p-0 text-sm font-normal text-black hover:bg-muted">
-            <div className="flex">
-              <ShadTooltip
-                styleClasses={"tooltip-fixed-width custom-scroll nowheel"}
-                delayDuration={1000}
-                content={refHtml.current}
-                side={left ? "left" : "right"}
-              >
-                <Handle
-                  data-test-id={`handle-${title.toLowerCase()}-${
-                    left ? "left" : "right"
-                  }`}
-                  type={left ? "target" : "source"}
-                  position={left ? Position.Left : Position.Right}
-                  key={scapedJSONStringfy(proxy ? { ...id, proxy } : id)}
-                  id={scapedJSONStringfy(proxy ? { ...id, proxy } : id)}
-                  isValidConnection={(connection) =>
-                    isValidConnection(connection, nodes, edges)
-                  }
-                  className={classNames(
-                    left ? "-ml-0.5" : "-mr-0.5",
-                    "h-3 w-3 rounded-full border-2 bg-background"
-                  )}
-                  style={{ borderColor: color ?? nodeColors.unknown }}
-                  onClick={() => setFilterEdge(groupedEdge.current)}
-                />
-              </ShadTooltip>
-            </div>
-          </Button>
+          <HandleRenderComponent
+            left={left}
+            nodes={nodes}
+            tooltipTitle={tooltipTitle}
+            proxy={proxy}
+            id={id}
+            title={title}
+            edges={edges}
+            myData={myData}
+            colors={colors}
+            setFilterEdge={setFilterEdge}
+            showNode={showNode}
+          />
         )}
 
         <Case
@@ -384,7 +407,7 @@ export default function ParameterComponent({
                   // Commenting this out until we have a better
                   // way to display
                   // (data.node?.template[name]?.refresh ? "w-5/6 " : "") +
-                  "flex-grow"
+                  "mt-2 flex-grow"
                 }
               >
                 <InputListComponent
@@ -601,7 +624,9 @@ export default function ParameterComponent({
         </Case>
 
         <Case condition={left === true && type === "NestedDict"}>
-          <div className="mt-2 w-full">
+          <div
+            className={"mt-2 w-full" + (disabled ? " cursor-not-allowed" : "")}
+          >
             <DictComponent
               disabled={disabled}
               editNode={false}
@@ -646,8 +671,9 @@ export default function ParameterComponent({
         {openOutputModal && (
           <OutputModal
             open={openOutputModal}
-            nodeId={data.id}
+            nodeId={flowPoolId}
             setOpen={setOpenOutputModal}
+            outputName={internalOutputName}
           />
         )}
       </>
diff --git a/src/frontend/src/CustomNodes/GenericNode/components/tooltipRenderComponent/index.tsx b/src/frontend/src/CustomNodes/GenericNode/components/tooltipRenderComponent/index.tsx
index c76bc7293..ed2760161 100644
--- a/src/frontend/src/CustomNodes/GenericNode/components/tooltipRenderComponent/index.tsx
+++ b/src/frontend/src/CustomNodes/GenericNode/components/tooltipRenderComponent/index.tsx
@@ -24,7 +24,7 @@ const TooltipRenderComponent = ({ item, index, left }) => {
       <span
         key={index}
         className={classNames(
-          index > 0 ? "mt-2 flex items-center" : "mt-3 flex items-center"
+          index > 0 ? "mt-2 flex items-center" : "mt-3 flex items-center",
         )}
       >
         <div
diff --git a/src/frontend/src/CustomNodes/GenericNode/index.tsx b/src/frontend/src/CustomNodes/GenericNode/index.tsx
index 03122f542..922c33dc6 100644
--- a/src/frontend/src/CustomNodes/GenericNode/index.tsx
+++ b/src/frontend/src/CustomNodes/GenericNode/index.tsx
@@ -3,7 +3,9 @@ import { useEffect, useMemo, useState } from "react";
 import { useHotkeys } from "react-hotkeys-hook";
 import Markdown from "react-markdown";
 import { NodeToolbar, useUpdateNodeInternals } from "reactflow";
-import IconComponent from "../../components/genericIconComponent";
+import IconComponent, {
+  ForwardedIconComponent,
+} from "../../components/genericIconComponent";
 import InputComponent from "../../components/inputComponent";
 import ShadTooltip from "../../components/shadTooltipComponent";
 import { Button } from "../../components/ui/button";
@@ -12,6 +14,7 @@ import {
   RUN_TIMESTAMP_PREFIX,
   STATUS_BUILD,
   STATUS_BUILDING,
+  STATUS_INACTIVE,
   TOOLTIP_OUTDATED_NODE,
 } from "../../constants/constants";
 import { BuildStatus } from "../../constants/enums";
@@ -30,6 +33,8 @@ import { nodeColors, nodeIconsLucide } from "../../utils/styleUtils";
 import { classNames, cn } from "../../utils/utils";
 import { countHandlesFn } from "../helpers/count-handles";
 import { getSpecificClassFromBuildStatus } from "../helpers/get-class-from-build-status";
+import { getNodeInputColors } from "../helpers/get-node-input-colors";
+import { getNodeOutputColors } from "../helpers/get-node-output-colors";
 import useCheckCodeValidity from "../hooks/use-check-code-validity";
 import useIconNodeRender from "../hooks/use-icon-render";
 import useIconStatus from "../hooks/use-icons-status";
@@ -60,21 +65,23 @@ export default function GenericNode({
   const updateNodeInternals = useUpdateNodeInternals();
   const setErrorData = useAlertStore((state) => state.setErrorData);
   const isDark = useDarkStore((state) => state.dark);
-  const buildStatus = useFlowStore(
-    (state) => state.flowBuildStatus[data.id]?.status
-  );
-  const lastRunTime = useFlowStore(
-    (state) => state.flowBuildStatus[data.id]?.timestamp
-  );
+
   const takeSnapshot = useFlowsManagerStore((state) => state.takeSnapshot);
 
   const [inputName, setInputName] = useState(false);
   const [nodeName, setNodeName] = useState(data.node!.display_name);
   const [inputDescription, setInputDescription] = useState(false);
   const [nodeDescription, setNodeDescription] = useState(
-    data.node?.description!
+    data.node?.description!,
   );
   const [isOutdated, setIsOutdated] = useState(false);
+  const [isUserEdited, setIsUserEdited] = useState(false);
+  const buildStatus = useFlowStore(
+    (state) => state.flowBuildStatus[data.id]?.status,
+  );
+  const lastRunTime = useFlowStore(
+    (state) => state.flowBuildStatus[data.id]?.timestamp,
+  );
   const [validationStatus, setValidationStatus] =
     useState<VertexBuildTypeAPI | null>(null);
   const [handles, setHandles] = useState<number>(0);
@@ -90,7 +97,8 @@ export default function GenericNode({
     data.node!,
     setNode,
     setIsOutdated,
-    updateNodeInternals
+    setIsUserEdited,
+    updateNodeInternals,
   );
 
   const name = nodeIconsLucide[data.type] ? data.type : types[data.type];
@@ -117,12 +125,12 @@ export default function GenericNode({
     selected: boolean,
     showNode: boolean,
     buildStatus: BuildStatus | undefined,
-    validationStatus: VertexBuildTypeAPI | null
+    validationStatus: VertexBuildTypeAPI | null,
   ) => {
     const specificClassFromBuildStatus = getSpecificClassFromBuildStatus(
       buildStatus,
       validationStatus,
-      isDark
+      isDark,
     );
 
     const baseBorderClass = getBaseBorderClass(selected);
@@ -131,7 +139,7 @@ export default function GenericNode({
       baseBorderClass,
       nodeSizeClass,
       "generic-node-div group/node",
-      specificClassFromBuildStatus
+      specificClassFromBuildStatus,
     );
     return names;
   };
@@ -164,9 +172,9 @@ export default function GenericNode({
     deleteNode(data.id);
   }
 
-  useCheckCodeValidity(data, templates, setIsOutdated, types);
-  useValidationStatusString(validationStatus, setValidationString);
+  useCheckCodeValidity(data, templates, setIsOutdated, setIsUserEdited, types);
   useUpdateValidationStatus(data?.id, flowPool, setValidationStatus);
+  useValidationStatusString(validationStatus, setValidationString);
 
   const iconNodeRender = useIconNodeRender(
     data,
@@ -176,7 +184,7 @@ export default function GenericNode({
     showNode,
     isEmoji,
     nodeIconFragment,
-    checkNodeIconFragment
+    checkNodeIconFragment,
   );
 
   function countHandles(): void {
@@ -209,6 +217,8 @@ export default function GenericNode({
 
   const [loadingUpdate, setLoadingUpdate] = useState(false);
 
+  const [showHiddenOutputs, setShowHiddenOutputs] = useState(false);
+
   const handleUpdateCode = () => {
     setLoadingUpdate(true);
     takeSnapshot();
@@ -240,6 +250,12 @@ export default function GenericNode({
     }
   }
 
+  const shownOutputs =
+    data.node!.outputs?.filter((output) => !output.hidden) ?? [];
+
+  const hiddenOutputs =
+    data.node!.outputs?.filter((output) => output.hidden) ?? [];
+
   function handlePlayWShortcut() {
     if (buildStatus === BuildStatus.BUILDING || isBuilding || !selected) return;
     setValidationStatus(null);
@@ -255,6 +271,43 @@ export default function GenericNode({
 
   const shortcuts = useShortcutsStore((state) => state.shortcuts);
 
+  const renderOutputParameter = (output, idx) => {
+    return (
+      <ParameterComponent
+        index={idx}
+        key={
+          scapedJSONStringfy({
+            output_types: output.types,
+            name: output.name,
+            id: data.id,
+            dataType: data.type,
+          }) + idx
+        }
+        data={data}
+        colors={getNodeOutputColors(output, data, types)}
+        outputProxy={output.proxy}
+        title={output.display_name ?? output.name}
+        tooltipTitle={output.selected ?? output.types[0]}
+        id={{
+          output_types: [output.selected ?? output.types[0]],
+          id: data.id,
+          dataType: data.type,
+          name: output.name,
+        }}
+        type={output.types.join("|")}
+        left={false}
+        showNode={showNode}
+        outputName={output.name}
+      />
+    );
+  };
+
+  useEffect(() => {
+    if (hiddenOutputs && hiddenOutputs.length == 0) {
+      setShowHiddenOutputs(false);
+    }
+  }, [hiddenOutputs]);
+
   const memoizedNodeToolbarComponent = useMemo(() => {
     return (
       <NodeToolbar>
@@ -274,11 +327,12 @@ export default function GenericNode({
           }}
           setShowState={setShowNode}
           numberOfHandles={handles}
+          numberOfOutputHandles={shownOutputs.length ?? 0}
           showNode={showNode}
           openAdvancedModal={false}
           onCloseAdvancedModal={() => {}}
-          selected={selected}
           updateNode={handleUpdateCode}
+          isOutdated={isOutdated && isUserEdited}
         />
       </NodeToolbar>
     );
@@ -292,6 +346,7 @@ export default function GenericNode({
     showNode,
     updateNodeCode,
     isOutdated,
+    isUserEdited,
     selected,
     shortcuts,
     //    openWDoubleCLick,
@@ -309,7 +364,7 @@ export default function GenericNode({
           selected,
           showNode,
           buildStatus,
-          validationStatus
+          validationStatus,
         )}
       >
         {data.node?.beta && showNode && (
@@ -383,12 +438,11 @@ export default function GenericNode({
                           {data.node?.display_name}
                         </div>
                       </ShadTooltip>
-                      {isOutdated && (
+                      {isOutdated && !isUserEdited && (
                         <ShadTooltip content={TOOLTIP_OUTDATED_NODE}>
                           <Button
                             onClick={handleUpdateCode}
-                            variant="none"
-                            size="none"
+                            unstyled
                             className={"group p-1"}
                             loading={loadingUpdate}
                           >
@@ -415,7 +469,7 @@ export default function GenericNode({
                         !data.node!.template[templateField]?.advanced && (
                           <ParameterComponent
                             selected={selected}
-                            index={idx.toString()}
+                            index={idx}
                             key={scapedJSONStringfy({
                               inputTypes:
                                 data.node!.template[templateField].input_types,
@@ -425,39 +479,14 @@ export default function GenericNode({
                               proxy: data.node!.template[templateField].proxy,
                             })}
                             data={data}
-                            color={
-                              data.node?.template[templateField].input_types &&
-                              data.node?.template[templateField].input_types!
-                                .length > 0
-                                ? nodeColors[
-                                    data.node?.template[templateField]
-                                      .input_types![
-                                      data.node?.template[templateField]
-                                        .input_types!.length - 1
-                                    ]
-                                  ] ??
-                                  nodeColors[
-                                    types[
-                                      data.node?.template[templateField]
-                                        .input_types![
-                                        data.node?.template[templateField]
-                                          .input_types!.length - 1
-                                      ]
-                                    ]
-                                  ]
-                                : nodeColors[
-                                    data.node?.template[templateField].type!
-                                  ] ??
-                                  nodeColors[
-                                    types[
-                                      data.node?.template[templateField].type!
-                                    ]
-                                  ] ??
-                                  nodeColors.unknown
-                            }
+                            colors={getNodeInputColors(
+                              data.node?.template[templateField].input_types,
+                              data.node?.template[templateField].type,
+                              types,
+                            )}
                             title={getFieldTitle(
                               data.node?.template!,
-                              templateField
+                              templateField,
                             )}
                             info={data.node?.template[templateField].info}
                             name={templateField}
@@ -485,33 +514,11 @@ export default function GenericNode({
                             proxy={data.node?.template[templateField].proxy}
                             showNode={showNode}
                           />
-                        )
+                        ),
                     )}
-                  <ParameterComponent
-                    selected={selected}
-                    key={scapedJSONStringfy({
-                      baseClasses: data.node!.base_classes,
-                      id: data.id,
-                      dataType: data.type,
-                    })}
-                    data={data}
-                    color={nodeColors[types[data.type]] ?? nodeColors.unknown}
-                    title={
-                      data.node?.output_types &&
-                      data.node.output_types.length > 0
-                        ? data.node.output_types.join(" | ")
-                        : data.type
-                    }
-                    tooltipTitle={data.node?.base_classes.join("\n")}
-                    id={{
-                      baseClasses: data.node!.base_classes,
-                      id: data.id,
-                      dataType: data.type,
-                    }}
-                    type={data.node?.base_classes.join("|")}
-                    left={false}
-                    showNode={showNode}
-                  />
+                  {shownOutputs &&
+                    shownOutputs.length > 0 &&
+                    renderOutputParameter(shownOutputs[0], 0)}
                 </>
               )}
             </div>
@@ -522,11 +529,18 @@ export default function GenericNode({
                     content={
                       buildStatus === BuildStatus.BUILDING ? (
                         <span> {STATUS_BUILDING} </span>
+                      ) : buildStatus === BuildStatus.INACTIVE ? (
+                        <span> {STATUS_INACTIVE} </span>
                       ) : !validationStatus ? (
                         <span className="flex">{STATUS_BUILD}</span>
                       ) : (
                         <div className="max-h-100 p-2">
-                          <div>
+                          <div className="max-h-80 overflow-auto">
+                            {validationString && (
+                              <div className="ml-1 pb-2 text-status-red">
+                                {validationString}
+                              </div>
+                            )}
                             {lastRunTime && (
                               <div className="justify-left flex font-normal text-muted-foreground">
                                 <div>{RUN_TIMESTAMP_PREFIX}</div>
@@ -556,8 +570,7 @@ export default function GenericNode({
                       setValidationStatus(null);
                       buildFlow({ stopNodeId: data.id });
                     }}
-                    variant="none"
-                    size="none"
+                    unstyled
                     className="group p-1"
                   >
                     <div
@@ -581,13 +594,14 @@ export default function GenericNode({
 
         {showNode && (
           <div
-            className={
+            className={cn(
               showNode
                 ? data.node?.description === "" && !nameEditable
-                  ? "pb-5"
-                  : "py-5"
-                : ""
-            }
+                  ? "pb-8"
+                  : "pb-8 pt-5"
+                : "",
+              "relative",
+            )}
           >
             {/* increase height!! */}
             <div className="generic-node-desc">
@@ -643,7 +657,7 @@ export default function GenericNode({
                       !data.node?.description) &&
                       nameEditable
                       ? "font-light italic"
-                      : ""
+                      : "",
                   )}
                   onDoubleClick={(e) => {
                     setInputDescription(true);
@@ -671,7 +685,7 @@ export default function GenericNode({
                     !data.node!.template[templateField]?.advanced ? (
                       <ParameterComponent
                         selected={selected}
-                        index={idx.toString()}
+                        index={idx}
                         key={scapedJSONStringfy({
                           inputTypes:
                             data.node!.template[templateField].input_types,
@@ -681,42 +695,20 @@ export default function GenericNode({
                           proxy: data.node!.template[templateField].proxy,
                         })}
                         data={data}
-                        color={
-                          data.node?.template[templateField].input_types &&
-                          data.node?.template[templateField].input_types!
-                            .length > 0
-                            ? nodeColors[
-                                data.node?.template[templateField].input_types![
-                                  data.node?.template[templateField]
-                                    .input_types!.length - 1
-                                ]
-                              ] ??
-                              nodeColors[
-                                types[
-                                  data.node?.template[templateField]
-                                    .input_types![
-                                    data.node?.template[templateField]
-                                      .input_types!.length - 1
-                                  ]
-                                ]
-                              ]
-                            : nodeColors[
-                                data.node?.template[templateField].type!
-                              ] ??
-                              nodeColors[
-                                types[data.node?.template[templateField].type!]
-                              ] ??
-                              nodeColors.unknown
-                        }
+                        colors={getNodeInputColors(
+                          data.node?.template[templateField].input_types,
+                          data.node?.template[templateField].type,
+                          types,
+                        )}
                         title={getFieldTitle(
                           data.node?.template!,
-                          templateField
+                          templateField,
                         )}
                         info={data.node?.template[templateField].info}
                         name={templateField}
                         tooltipTitle={
                           data.node?.template[templateField].input_types?.join(
-                            "\n"
+                            "\n",
                           ) ?? data.node?.template[templateField].type
                         }
                         required={data.node!.template[templateField].required}
@@ -743,42 +735,61 @@ export default function GenericNode({
               <div
                 className={classNames(
                   Object.keys(data.node!.template).length < 1 ? "hidden" : "",
-                  "flex-max-width justify-center"
+                  "flex-max-width justify-center",
                 )}
               >
                 {" "}
               </div>
-              {data.node!.base_classes.length > 0 && (
-                <ParameterComponent
-                  selected={selected}
-                  key={scapedJSONStringfy({
-                    baseClasses: data.node!.base_classes,
-                    id: data.id,
-                    dataType: data.type,
-                  })}
-                  data={data}
-                  color={
-                    (data.node?.output_types &&
-                    data.node.output_types.length > 0
-                      ? nodeColors[data.node.output_types[0]] ??
-                        nodeColors[types[data.node.output_types[0]]]
-                      : nodeColors[types[data.type]]) ?? nodeColors.unknown
-                  }
-                  title={
-                    data.node?.output_types && data.node.output_types.length > 0
-                      ? data.node.output_types.join(" | ")
-                      : data.type
-                  }
-                  tooltipTitle={data.node?.base_classes.join("\n")}
-                  id={{
-                    baseClasses: data.node!.base_classes,
-                    id: data.id,
-                    dataType: data.type,
-                  }}
-                  type={data.node?.base_classes.join("|")}
-                  left={false}
-                  showNode={showNode}
-                />
+              {!showHiddenOutputs &&
+                shownOutputs &&
+                shownOutputs.map((output, idx) =>
+                  renderOutputParameter(
+                    output,
+                    data.node!.outputs?.findIndex(
+                      (out) => out.name === output.name,
+                    ) ?? idx,
+                  ),
+                )}
+              <div
+                className={cn(showHiddenOutputs ? "" : "h-0 overflow-hidden")}
+              >
+                <div className="block">
+                  {data.node!.outputs &&
+                    data.node!.outputs.map((output, idx) =>
+                      renderOutputParameter(
+                        output,
+                        data.node!.outputs?.findIndex(
+                          (out) => out.name === output.name,
+                        ) ?? idx,
+                      ),
+                    )}
+                </div>
+              </div>
+              {hiddenOutputs && hiddenOutputs.length > 0 && (
+                <div
+                  className={cn(
+                    "absolute left-0 right-0 flex justify-center",
+                    (shownOutputs && shownOutputs.length > 0) ||
+                      showHiddenOutputs
+                      ? "bottom-5"
+                      : "bottom-1.5",
+                  )}
+                >
+                  <Button
+                    unstyled
+                    className="left-0 right-0 rounded-full border bg-background"
+                    onClick={() => setShowHiddenOutputs(!showHiddenOutputs)}
+                  >
+                    <ForwardedIconComponent
+                      name={"ChevronDown"}
+                      strokeWidth={1.5}
+                      className={cn(
+                        "h-5 w-5 pt-px text-muted-foreground group-hover:text-medium-indigo group-hover/node:opacity-100",
+                        showHiddenOutputs ? "rotate-180 transform" : "",
+                      )}
+                    />
+                  </Button>
+                </div>
               )}
             </>
           </div>
diff --git a/src/frontend/src/CustomNodes/helpers/count-handles.ts b/src/frontend/src/CustomNodes/helpers/count-handles.ts
index a7c8697ce..f529d21dc 100644
--- a/src/frontend/src/CustomNodes/helpers/count-handles.ts
+++ b/src/frontend/src/CustomNodes/helpers/count-handles.ts
@@ -2,7 +2,11 @@ import { NodeDataType } from "../../types/flow";
 
 export function countHandlesFn(data: NodeDataType): number {
   let count = Object.keys(data.node!.template)
-    .filter((templateField) => templateField.charAt(0) !== "_")
+    .filter(
+      (templateField) =>
+        templateField.charAt(0) !== "_" &&
+        !data.node!.template[templateField].advanced,
+    )
     .map((templateCamp) => {
       const { template } = data.node!;
       if (template[templateCamp]?.input_types) return true;
diff --git a/src/frontend/src/CustomNodes/helpers/get-class-from-build-status.ts b/src/frontend/src/CustomNodes/helpers/get-class-from-build-status.ts
index cf251c40c..d21d2eb17 100644
--- a/src/frontend/src/CustomNodes/helpers/get-class-from-build-status.ts
+++ b/src/frontend/src/CustomNodes/helpers/get-class-from-build-status.ts
@@ -4,14 +4,9 @@ import { VertexBuildTypeAPI } from "../../types/api";
 export const getSpecificClassFromBuildStatus = (
   buildStatus: BuildStatus | undefined,
   validationStatus: VertexBuildTypeAPI | null,
-  isDark: boolean
+  isDark: boolean,
 ) => {
   let isInvalid = validationStatus && !validationStatus.valid;
-
-  if (buildStatus === BuildStatus.INACTIVE) {
-    // INACTIVE should have its own class
-    return "inactive-status";
-  }
   if (
     (buildStatus === BuildStatus.BUILT && isInvalid) ||
     buildStatus === BuildStatus.ERROR
diff --git a/src/frontend/src/CustomNodes/helpers/get-node-input-colors.ts b/src/frontend/src/CustomNodes/helpers/get-node-input-colors.ts
new file mode 100644
index 000000000..abff92936
--- /dev/null
+++ b/src/frontend/src/CustomNodes/helpers/get-node-input-colors.ts
@@ -0,0 +1,34 @@
+import { nodeColors } from "../../utils/styleUtils";
+
+export function getNodeInputColors(input_types, type, types) {
+  // Helper function to get the color based on type
+  const getColorByType = (type) => nodeColors[type] ?? nodeColors.unknown;
+
+  // If input_types is not null and has elements, map colors based on input_types
+  if (input_types && input_types.length > 0) {
+    // Map through input_types and get colors from nodeColors
+    const colorsFromInputs = input_types
+      .map((input) => nodeColors[input])
+      .filter((color) => color);
+    if (colorsFromInputs.length > 0) {
+      return colorsFromInputs;
+    }
+
+    // If no valid colors found in the previous step, map colors based on types[nodeColors[input]]
+    const colorsFromInputTypes = input_types
+      .map((input) => getColorByType(types[input]))
+      .filter((color) => color);
+    if (colorsFromInputTypes.length > 0) {
+      return colorsFromInputTypes;
+    }
+  }
+
+  // If input_types is null or empty, use the fallback logic
+  const fallbackColors = [getColorByType(type)];
+  if (fallbackColors.length > 0) {
+    return fallbackColors;
+  }
+
+  // Default to unknown color
+  return [nodeColors.unknown];
+}
diff --git a/src/frontend/src/CustomNodes/helpers/get-node-output-colors.ts b/src/frontend/src/CustomNodes/helpers/get-node-output-colors.ts
new file mode 100644
index 000000000..d846a304e
--- /dev/null
+++ b/src/frontend/src/CustomNodes/helpers/get-node-output-colors.ts
@@ -0,0 +1,33 @@
+import { nodeColors } from "../../utils/styleUtils";
+
+export function getNodeOutputColors(output, data, types): string[] {
+  // Helper function to get the color based on type
+  const getColorByType = (type) => nodeColors[type] ?? nodeColors.unknown;
+
+  // Try to get the color based on the selected node
+  let color: string = nodeColors[output.selected];
+  if (color) return [color];
+
+  // Try to get the colors based on the output types
+  let colors: string[] = output.types
+    .map((type) => nodeColors[type])
+    .filter((color) => color);
+  if (colors.length > 0) return colors;
+
+  // Try to get the color based on the type of the selected node
+  color = nodeColors[types[output.selected]];
+  if (color) return [color];
+
+  // Try to get the colors based on the types of output
+  colors = output.types
+    .map((type) => getColorByType(types[type]))
+    .filter((color) => color);
+  if (colors.length > 0) return colors;
+
+  // Try to get the color based on the type in data
+  color = nodeColors[types[data.type]];
+  if (color) return [color];
+
+  // Default to unknown color
+  return [nodeColors.unknown];
+}
diff --git a/src/frontend/src/CustomNodes/hooks/use-check-code-validity.tsx b/src/frontend/src/CustomNodes/hooks/use-check-code-validity.tsx
index 3a49ef62f..af9e0d14e 100644
--- a/src/frontend/src/CustomNodes/hooks/use-check-code-validity.tsx
+++ b/src/frontend/src/CustomNodes/hooks/use-check-code-validity.tsx
@@ -6,7 +6,8 @@ const useCheckCodeValidity = (
   data: NodeDataType,
   templates: { [key: string]: any },
   setIsOutdated: (value: boolean) => void,
-  types
+  setIsUserEdited: (value: boolean) => void,
+  types,
 ) => {
   useEffect(() => {
     // This one should run only once
@@ -24,17 +25,17 @@ const useCheckCodeValidity = (
     const currentCode = thisNodeTemplate.code?.value;
     const thisNodesCode = data.node!.template?.code?.value;
     const componentsToIgnore = ["CustomComponent", "Prompt"];
-    if (
-      currentCode !== thisNodesCode &&
-      !componentsToIgnore.includes(data.type) &&
-      !(data.node?.edited ?? false)
-    ) {
-      setIsOutdated(true);
-    } else {
-      setIsOutdated(false);
-    }
+    setIsOutdated(
+      currentCode !== thisNodesCode && !componentsToIgnore.includes(data.type),
+    );
+    setIsUserEdited(data.node?.edited ?? false);
     // template.code can be undefined
-  }, [data.node?.template?.code?.value, templates, setIsOutdated]);
+  }, [
+    data.node?.template?.code?.value,
+    templates,
+    setIsOutdated,
+    setIsUserEdited,
+  ]);
 };
 
 export default useCheckCodeValidity;
diff --git a/src/frontend/src/CustomNodes/hooks/use-fetch-data-on-mount.tsx b/src/frontend/src/CustomNodes/hooks/use-fetch-data-on-mount.tsx
index d8545d72a..5802863b0 100644
--- a/src/frontend/src/CustomNodes/hooks/use-fetch-data-on-mount.tsx
+++ b/src/frontend/src/CustomNodes/hooks/use-fetch-data-on-mount.tsx
@@ -12,8 +12,7 @@ const useFetchDataOnMount = (
   name,
   handleUpdateValues,
   setNode,
-  renderTooltips,
-  setIsLoading
+  setIsLoading,
 ) => {
   const setErrorData = useAlertStore((state) => state.setErrorData);
 
@@ -50,7 +49,6 @@ const useFetchDataOnMount = (
           });
         }
         setIsLoading(false);
-        renderTooltips();
       }
     }
     fetchData();
diff --git a/src/frontend/src/CustomNodes/hooks/use-handle-new-value.tsx b/src/frontend/src/CustomNodes/hooks/use-handle-new-value.tsx
index b41491f97..b9690e270 100644
--- a/src/frontend/src/CustomNodes/hooks/use-handle-new-value.tsx
+++ b/src/frontend/src/CustomNodes/hooks/use-handle-new-value.tsx
@@ -13,8 +13,7 @@ const useHandleOnNewValue = (
   handleUpdateValues,
   debouncedHandleUpdateValues,
   setNode,
-  renderTooltips,
-  setIsLoading
+  setIsLoading,
 ) => {
   const setErrorData = useAlertStore((state) => state.setErrorData);
 
@@ -71,8 +70,6 @@ const useHandleOnNewValue = (
 
       return newNode;
     });
-
-    renderTooltips();
   };
 
   return { handleOnNewValue };
diff --git a/src/frontend/src/CustomNodes/hooks/use-handle-node-class.tsx b/src/frontend/src/CustomNodes/hooks/use-handle-node-class.tsx
index bdcf1f8cc..6fb78ce10 100644
--- a/src/frontend/src/CustomNodes/hooks/use-handle-node-class.tsx
+++ b/src/frontend/src/CustomNodes/hooks/use-handle-node-class.tsx
@@ -6,7 +6,6 @@ const useHandleNodeClass = (
   takeSnapshot,
   setNode,
   updateNodeInternals,
-  renderTooltips
 ) => {
   const handleNodeClass = (newNodeClass, code) => {
     if (!data.node) return;
@@ -30,8 +29,6 @@ const useHandleNodeClass = (
     });
 
     updateNodeInternals(data.id);
-
-    renderTooltips();
   };
 
   return { handleNodeClass };
diff --git a/src/frontend/src/CustomNodes/hooks/use-handle-refresh-buttons.tsx b/src/frontend/src/CustomNodes/hooks/use-handle-refresh-buttons.tsx
index 8d9e1a694..e2ecb3f46 100644
--- a/src/frontend/src/CustomNodes/hooks/use-handle-refresh-buttons.tsx
+++ b/src/frontend/src/CustomNodes/hooks/use-handle-refresh-buttons.tsx
@@ -7,7 +7,7 @@ import useAlertStore from "../../stores/alertStore";
 import { ResponseErrorDetailAPI } from "../../types/api";
 import { handleUpdateValues } from "../../utils/parameterUtils";
 
-const useHandleRefreshButtonPress = (setIsLoading, setNode, renderTooltips) => {
+const useHandleRefreshButtonPress = (setIsLoading, setNode) => {
   const setErrorData = useAlertStore((state) => state.setErrorData);
 
   const handleRefreshButtonPress = async (name, data) => {
@@ -36,7 +36,6 @@ const useHandleRefreshButtonPress = (setIsLoading, setNode, renderTooltips) => {
       });
     }
     setIsLoading(false);
-    renderTooltips();
   };
 
   return { handleRefreshButtonPress };
diff --git a/src/frontend/src/CustomNodes/hooks/use-icon-render.tsx b/src/frontend/src/CustomNodes/hooks/use-icon-render.tsx
index cc9e29c0e..181b4f515 100644
--- a/src/frontend/src/CustomNodes/hooks/use-icon-render.tsx
+++ b/src/frontend/src/CustomNodes/hooks/use-icon-render.tsx
@@ -12,8 +12,8 @@ const useIconNodeRender = (
   checkNodeIconFragment: (
     iconColor: string,
     iconName: string,
-    iconClassName: string
-  ) => JSX.Element
+    iconClassName: string,
+  ) => JSX.Element,
 ) => {
   const iconNodeRender = useCallback(() => {
     const iconElement = data?.node?.icon;
diff --git a/src/frontend/src/CustomNodes/hooks/use-icons-status.tsx b/src/frontend/src/CustomNodes/hooks/use-icons-status.tsx
index 19c6112d5..155592b78 100644
--- a/src/frontend/src/CustomNodes/hooks/use-icons-status.tsx
+++ b/src/frontend/src/CustomNodes/hooks/use-icons-status.tsx
@@ -1,3 +1,4 @@
+import ForwardedIconComponent from "../../components/genericIconComponent";
 import Checkmark from "../../components/ui/checkmark";
 import Loading from "../../components/ui/loading";
 import Xmark from "../../components/ui/xmark";
@@ -6,12 +7,11 @@ import { VertexBuildTypeAPI } from "../../types/api";
 
 const useIconStatus = (
   buildStatus: BuildStatus | undefined,
-  validationStatus: VertexBuildTypeAPI | null
+  validationStatus: VertexBuildTypeAPI | null,
 ) => {
   const conditionSuccess = validationStatus && validationStatus.valid;
-  const conditionError =
-    buildStatus === BuildStatus.ERROR ||
-    (validationStatus && !validationStatus.valid);
+  const conditionError = buildStatus === BuildStatus.ERROR;
+  const conditionInactive = buildStatus === BuildStatus.INACTIVE;
 
   const renderIconStatus = () => {
     if (buildStatus === BuildStatus.BUILDING) {
@@ -29,6 +29,11 @@ const useIconStatus = (
               isVisible={true}
               className="h-6 w-6 fill-current stroke-2 text-status-red"
             />
+          ) : conditionInactive ? (
+            <ForwardedIconComponent
+              name="CircleOff"
+              className="h-5 w-5 text-muted-foreground"
+            />
           ) : (
             <></>
           )}
diff --git a/src/frontend/src/CustomNodes/hooks/use-update-node-code.tsx b/src/frontend/src/CustomNodes/hooks/use-update-node-code.tsx
index d919a4fa9..d6c972ddf 100644
--- a/src/frontend/src/CustomNodes/hooks/use-update-node-code.tsx
+++ b/src/frontend/src/CustomNodes/hooks/use-update-node-code.tsx
@@ -7,7 +7,8 @@ const useUpdateNodeCode = (
   dataNode: APIClassType, // Define YourNodeType according to your data structure
   setNode: (id: string, callback: (oldNode) => any) => void,
   setIsOutdated: (value: boolean) => void,
-  updateNodeInternals: (id: string) => void
+  setIsUserEdited: (value: boolean) => void,
+  updateNodeInternals: (id: string) => void,
 ) => {
   const updateNodeCode = useCallback(
     (newNodeClass: APIClassType, code: string, name: string) => {
@@ -24,13 +25,14 @@ const useUpdateNodeCode = (
 
         newNode.data.node.template[name].value = code;
         setIsOutdated(false);
+        setIsUserEdited(false);
 
         return newNode;
       });
 
       updateNodeInternals(dataId);
     },
-    [dataId, dataNode, setNode, setIsOutdated, updateNodeInternals]
+    [dataId, dataNode, setNode, setIsOutdated, updateNodeInternals],
   );
 
   return updateNodeCode;
diff --git a/src/frontend/src/CustomNodes/hooks/use-validation-status-string.tsx b/src/frontend/src/CustomNodes/hooks/use-validation-status-string.tsx
index acc4a1190..a8f964a2c 100644
--- a/src/frontend/src/CustomNodes/hooks/use-validation-status-string.tsx
+++ b/src/frontend/src/CustomNodes/hooks/use-validation-status-string.tsx
@@ -1,22 +1,23 @@
 import { useEffect } from "react";
+import { VertexBuildTypeAPI } from "../../types/api";
+import { isErrorLog } from "../../types/utils/typeCheckingUtils";
 
-const useValidationStatusString = (validationStatus, setValidationString) => {
+const useValidationStatusString = (
+  validationStatus: VertexBuildTypeAPI | null,
+  setValidationString,
+) => {
   useEffect(() => {
-    if (validationStatus?.data.logs) {
+    if (validationStatus && validationStatus.data?.logs) {
       // if it is not a string turn it into a string
       let newValidationString = "";
-      if (Array.isArray(validationStatus.data.logs)) {
-        newValidationString = validationStatus.data.logs
-          .map((log) => (log?.message ? log.message : JSON.stringify(log)))
-          .join("\n");
-      }
-      if (typeof newValidationString !== "string") {
-        newValidationString = JSON.stringify(validationStatus.data.logs);
-      }
-
+      Object.values(validationStatus?.data?.logs).forEach((log: any) => {
+        if (isErrorLog(log)) {
+          newValidationString += `${log.message.errorMessage}\n`;
+        }
+      });
       setValidationString(newValidationString);
     }
-  }, [validationStatus, validationStatus?.data.logs, setValidationString]);
+  }, [validationStatus, validationStatus?.data?.logs, setValidationString]);
 };
 
 export default useValidationStatusString;
diff --git a/src/frontend/src/CustomNodes/utils/get-field-title.tsx b/src/frontend/src/CustomNodes/utils/get-field-title.tsx
index a00829a90..e448c4f01 100644
--- a/src/frontend/src/CustomNodes/utils/get-field-title.tsx
+++ b/src/frontend/src/CustomNodes/utils/get-field-title.tsx
@@ -2,7 +2,7 @@ import { APITemplateType } from "../../types/api";
 
 export default function getFieldTitle(
   template: APITemplateType,
-  templateField: string
+  templateField: string,
 ): string {
   return template[templateField].display_name
     ? template[templateField].display_name!
diff --git a/src/frontend/src/alerts/alertDropDown/index.tsx b/src/frontend/src/alerts/alertDropDown/index.tsx
index 597431884..48c51088d 100644
--- a/src/frontend/src/alerts/alertDropDown/index.tsx
+++ b/src/frontend/src/alerts/alertDropDown/index.tsx
@@ -16,13 +16,13 @@ export default function AlertDropdown({
 }: AlertDropdownType): JSX.Element {
   const notificationList = useAlertStore((state) => state.notificationList);
   const clearNotificationList = useAlertStore(
-    (state) => state.clearNotificationList
+    (state) => state.clearNotificationList,
   );
   const removeFromNotificationList = useAlertStore(
-    (state) => state.removeFromNotificationList
+    (state) => state.removeFromNotificationList,
   );
   const setNotificationCenter = useAlertStore(
-    (state) => state.setNotificationCenter
+    (state) => state.setNotificationCenter,
   );
 
   const [open, setOpen] = useState(false);
diff --git a/src/frontend/src/components/ImageViewer/index.tsx b/src/frontend/src/components/ImageViewer/index.tsx
index e82836441..9e7f091d2 100644
--- a/src/frontend/src/components/ImageViewer/index.tsx
+++ b/src/frontend/src/components/ImageViewer/index.tsx
@@ -31,14 +31,14 @@ export default function ImageViewer({ image }) {
         const fullPageButton = document.getElementById("full-page-button");
 
         zoomInButton!.addEventListener("click", () =>
-          viewer.viewport.zoomBy(1.2)
+          viewer.viewport.zoomBy(1.2),
         );
         zoomOutButton!.addEventListener("click", () =>
-          viewer.viewport.zoomBy(0.8)
+          viewer.viewport.zoomBy(0.8),
         );
         homeButton!.addEventListener("click", () => viewer.viewport.goHome());
         fullPageButton!.addEventListener("click", () =>
-          viewer.setFullScreen(true)
+          viewer.setFullScreen(true),
         );
 
         // Optionally, you can set additional viewer options here
@@ -47,16 +47,16 @@ export default function ImageViewer({ image }) {
         return () => {
           viewer.destroy();
           zoomInButton!.removeEventListener("click", () =>
-            viewer.viewport.zoomBy(1.2)
+            viewer.viewport.zoomBy(1.2),
           );
           zoomOutButton!.removeEventListener("click", () =>
-            viewer.viewport.zoomBy(0.8)
+            viewer.viewport.zoomBy(0.8),
           );
           homeButton!.removeEventListener("click", () =>
-            viewer.viewport.goHome()
+            viewer.viewport.goHome(),
           );
           fullPageButton!.removeEventListener("click", () =>
-            viewer.setFullScreen(true)
+            viewer.setFullScreen(true),
           );
         };
       }
diff --git a/src/frontend/src/components/accordionComponent/composite/folderAccordionComponent/index.tsx b/src/frontend/src/components/accordionComponent/composite/folderAccordionComponent/index.tsx
index 212a03fa2..d4fb95b5c 100644
--- a/src/frontend/src/components/accordionComponent/composite/folderAccordionComponent/index.tsx
+++ b/src/frontend/src/components/accordionComponent/composite/folderAccordionComponent/index.tsx
@@ -15,7 +15,7 @@ export default function FolderAccordionComponent({
   options,
 }: AccordionComponentType): JSX.Element {
   const [value, setValue] = useState(
-    open.length === 0 ? "" : getOpenAccordion()
+    open.length === 0 ? "" : getOpenAccordion(),
   );
 
   function getOpenAccordion(): string {
diff --git a/src/frontend/src/components/accordionComponent/index.tsx b/src/frontend/src/components/accordionComponent/index.tsx
index 43a0aef79..c9c21b8b2 100644
--- a/src/frontend/src/components/accordionComponent/index.tsx
+++ b/src/frontend/src/components/accordionComponent/index.tsx
@@ -17,7 +17,7 @@ export default function AccordionComponent({
   sideBar,
 }: AccordionComponentType): JSX.Element {
   const [value, setValue] = useState(
-    open.length === 0 ? "" : getOpenAccordion()
+    open.length === 0 ? "" : getOpenAccordion(),
   );
 
   function getOpenAccordion(): string {
@@ -52,7 +52,7 @@ export default function AccordionComponent({
             disabled={disabled}
             className={cn(
               sideBar ? "w-full bg-muted px-[0.75rem] py-[0.5rem]" : "ml-3",
-              disabled ? "cursor-not-allowed" : "cursor-pointer"
+              disabled ? "cursor-not-allowed" : "cursor-pointer",
             )}
           >
             {trigger}
diff --git a/src/frontend/src/components/addNewVariableButtonComponent/addNewVariableButton.tsx b/src/frontend/src/components/addNewVariableButtonComponent/addNewVariableButton.tsx
index d56a0e608..415cd92d7 100644
--- a/src/frontend/src/components/addNewVariableButtonComponent/addNewVariableButton.tsx
+++ b/src/frontend/src/components/addNewVariableButtonComponent/addNewVariableButton.tsx
@@ -29,19 +29,19 @@ export default function AddNewVariableButton({
   const setErrorData = useAlertStore((state) => state.setErrorData);
   const componentFields = useTypesStore((state) => state.ComponentFields);
   const unavaliableFields = new Set(
-    Object.keys(useGlobalVariablesStore((state) => state.unavaliableFields))
+    Object.keys(useGlobalVariablesStore((state) => state.unavaliableFields)),
   );
 
   const availableFields = () => {
     const fields = Array.from(componentFields).filter(
-      (field) => !unavaliableFields.has(field)
+      (field) => !unavaliableFields.has(field),
     );
 
     return sortByName(fields);
   };
 
   const addGlobalVariable = useGlobalVariablesStore(
-    (state) => state.addGlobalVariable
+    (state) => state.addGlobalVariable,
   );
 
   function handleSaveVariable() {
diff --git a/src/frontend/src/components/cardComponent/components/dragCardComponent/index.tsx b/src/frontend/src/components/cardComponent/components/dragCardComponent/index.tsx
index 54dbf4846..28674f3bc 100644
--- a/src/frontend/src/components/cardComponent/components/dragCardComponent/index.tsx
+++ b/src/frontend/src/components/cardComponent/components/dragCardComponent/index.tsx
@@ -10,7 +10,7 @@ export default function DragCardComponent({ data }: { data: storeComponent }) {
         draggable
         //TODO check color schema
         className={cn(
-          "group relative flex flex-col justify-between overflow-hidden transition-all hover:bg-muted/50 hover:shadow-md hover:dark:bg-[#ffffff10]"
+          "group relative flex flex-col justify-between overflow-hidden transition-all hover:bg-muted/50 hover:shadow-md hover:dark:bg-[#ffffff10]",
         )}
       >
         <div>
@@ -22,7 +22,7 @@ export default function DragCardComponent({ data }: { data: storeComponent }) {
                     "visible flex-shrink-0",
                     data.is_component
                       ? "mx-0.5 h-6 w-6 text-component-icon"
-                      : "h-7 w-7 flex-shrink-0 text-flow-icon"
+                      : "h-7 w-7 flex-shrink-0 text-flow-icon",
                   )}
                   name={data.is_component ? "ToyBrick" : "Group"}
                 />
diff --git a/src/frontend/src/components/cardComponent/index.tsx b/src/frontend/src/components/cardComponent/index.tsx
index 03c71feee..ba00e6958 100644
--- a/src/frontend/src/components/cardComponent/index.tsx
+++ b/src/frontend/src/components/cardComponent/index.tsx
@@ -60,11 +60,11 @@ export default function CollectionCardComponent({
   const [loading, setLoading] = useState(false);
   const [loadingLike, setLoadingLike] = useState(false);
   const [liked_by_user, setLiked_by_user] = useState(
-    data?.liked_by_user ?? false
+    data?.liked_by_user ?? false,
   );
   const [likes_count, setLikes_count] = useState(data?.liked_by_count ?? 0);
   const [downloads_count, setDownloads_count] = useState(
-    data?.downloads_count ?? 0
+    data?.downloads_count ?? 0,
   );
   const currentFlow = useFlowsManagerStore((state) => state.currentFlow);
   const setCurrentFlow = useFlowsManagerStore((state) => state.setCurrentFlow);
@@ -75,12 +75,12 @@ export default function CollectionCardComponent({
   const [openPlayground, setOpenPlayground] = useState(false);
   const [openDelete, setOpenDelete] = useState(false);
   const setCurrentFlowId = useFlowsManagerStore(
-    (state) => state.setCurrentFlowId
+    (state) => state.setCurrentFlowId,
   );
   const [loadingPlayground, setLoadingPlayground] = useState(false);
 
   const selectedFlowsComponentsCards = useFlowsManagerStore(
-    (state) => state.selectedFlowsComponentsCards
+    (state) => state.selectedFlowsComponentsCards,
   );
 
   const name = data.is_component ? "Component" : "Flow";
@@ -220,7 +220,7 @@ export default function CollectionCardComponent({
           "group relative flex h-[11rem] flex-col justify-between overflow-hidden hover:bg-muted/50 hover:shadow-md hover:dark:bg-[#5f5f5f0e]",
           disabled ? "pointer-events-none opacity-50" : "",
           onClick ? "cursor-pointer" : "",
-          isSelectedCard ? "border border-selected" : ""
+          isSelectedCard ? "border border-selected" : "",
         )}
         onClick={onClick}
       >
@@ -233,7 +233,7 @@ export default function CollectionCardComponent({
                     "visible flex-shrink-0",
                     data.is_component
                       ? "mx-0.5 h-6 w-6 text-component-icon"
-                      : "h-7 w-7 flex-shrink-0 text-flow-icon"
+                      : "h-7 w-7 flex-shrink-0 text-flow-icon",
                   )}
                   name={data.is_component ? "ToyBrick" : "Group"}
                 />
@@ -425,7 +425,7 @@ export default function CollectionCardComponent({
                             name="Trash2"
                             className={cn(
                               "h-5 w-5",
-                              !authorized ? "text-ring" : ""
+                              !authorized ? "text-ring" : "",
                             )}
                           />
                         </Button>
@@ -460,7 +460,7 @@ export default function CollectionCardComponent({
                             liked_by_user
                               ? "fill-destructive stroke-destructive"
                               : "",
-                            !authorized ? "text-ring" : ""
+                            !authorized ? "text-ring" : "",
                           )}
                         />
                       </Button>
@@ -498,7 +498,7 @@ export default function CollectionCardComponent({
                         }
                         className={cn(
                           loading ? "h-5 w-5 animate-spin" : "h-5 w-5",
-                          !authorized ? "text-ring" : ""
+                          !authorized ? "text-ring" : "",
                         )}
                       />
                     </Button>
diff --git a/src/frontend/src/components/cardsWrapComponent/index.tsx b/src/frontend/src/components/cardsWrapComponent/index.tsx
index 0de3f1a2f..c7ca01588 100644
--- a/src/frontend/src/components/cardsWrapComponent/index.tsx
+++ b/src/frontend/src/components/cardsWrapComponent/index.tsx
@@ -65,7 +65,7 @@ export default function CardsWrapComponent({
         "h-full w-full",
         isDragging
           ? "mb-36 flex flex-col items-center justify-center gap-4 text-2xl font-light"
-          : ""
+          : "",
       )}
     >
       {isDragging ? (
diff --git a/src/frontend/src/components/chatComponent/index.tsx b/src/frontend/src/components/chatComponent/index.tsx
index 1dae86775..83c6c80c1 100644
--- a/src/frontend/src/components/chatComponent/index.tsx
+++ b/src/frontend/src/components/chatComponent/index.tsx
@@ -65,7 +65,7 @@ export default function FlowToolbar(): JSX.Element {
             "relative inline-flex h-full w-full items-center justify-center gap-[4px] bg-muted px-5 py-3 text-sm font-semibold text-foreground transition-all duration-150 ease-in-out hover:bg-background hover:bg-hover",
             !hasApiKey || !validApiKey || !hasStore
               ? "button-disable text-muted-foreground"
-              : ""
+              : "",
           )}
         >
           <ForwardedIconComponent
@@ -74,7 +74,7 @@ export default function FlowToolbar(): JSX.Element {
               "-m-0.5 -ml-1 h-6 w-6",
               !hasApiKey || !validApiKey || !hasStore
                 ? "extra-side-bar-save-disable"
-                : ""
+                : "",
             )}
           />
           Share
@@ -88,7 +88,7 @@ export default function FlowToolbar(): JSX.Element {
       hasStore,
       openShareModal,
       setOpenShareModal,
-    ]
+    ],
   );
 
   return (
@@ -144,7 +144,7 @@ export default function FlowToolbar(): JSX.Element {
                 >
                   <div
                     className={classNames(
-                      "relative inline-flex w-full items-center justify-center gap-1 px-5 py-3 text-sm font-semibold text-foreground transition-all duration-150 ease-in-out hover:bg-hover"
+                      "relative inline-flex w-full items-center justify-center gap-1 px-5 py-3 text-sm font-semibold text-foreground transition-all duration-150 ease-in-out hover:bg-hover",
                     )}
                   >
                     <ForwardedIconComponent
diff --git a/src/frontend/src/components/codeAreaComponent/index.tsx b/src/frontend/src/components/codeAreaComponent/index.tsx
index 92fa810d5..00721137f 100644
--- a/src/frontend/src/components/codeAreaComponent/index.tsx
+++ b/src/frontend/src/components/codeAreaComponent/index.tsx
@@ -18,7 +18,7 @@ export default function CodeAreaComponent({
   setOpen,
 }: CodeAreaComponentType) {
   const [myValue, setMyValue] = useState(
-    typeof value == "string" ? value : JSON.stringify(value)
+    typeof value == "string" ? value : JSON.stringify(value),
   );
   useEffect(() => {
     if (disabled && myValue !== "") {
@@ -46,7 +46,7 @@ export default function CodeAreaComponent({
           onChange(value);
         }}
       >
-        <div className="flex w-full items-center">
+        <div className="flex w-full items-center gap-3">
           <span
             id={id}
             data-testid={id}
@@ -63,7 +63,7 @@ export default function CodeAreaComponent({
             <IconComponent
               name="ExternalLink"
               className={
-                "icons-parameters-comp" +
+                "icons-parameters-comp shrink-0" +
                 (disabled ? " text-ring" : " hover:text-accent-foreground")
               }
             />
diff --git a/src/frontend/src/components/codeTabsComponent/index.tsx b/src/frontend/src/components/codeTabsComponent/index.tsx
index 448eca948..8b0106902 100644
--- a/src/frontend/src/components/codeTabsComponent/index.tsx
+++ b/src/frontend/src/components/codeTabsComponent/index.tsx
@@ -138,7 +138,7 @@ export default function CodeTabsComponent({
               <Label
                 className={cn(
                   "relative right-1 top-[4px] text-xs font-medium text-muted-foreground",
-                  activeTweaks ? "text-primary" : ""
+                  activeTweaks ? "text-primary" : "",
                 )}
                 htmlFor="tweaks-switch"
               >
@@ -193,7 +193,7 @@ export default function CodeTabsComponent({
               <div className="api-modal-according-display">
                 <div
                   className={classNames(
-                    "h-[70vh] w-full overflow-y-auto overflow-x-hidden rounded-lg bg-muted custom-scroll"
+                    "h-[70vh] w-full overflow-y-auto overflow-x-hidden rounded-lg bg-muted custom-scroll",
                   )}
                 >
                   {data?.map((node: any, i) => (
@@ -232,8 +232,8 @@ export default function CodeTabsComponent({
                                         .show &&
                                       LANGFLOW_SUPPORTED_TYPES.has(
                                         node.data.node.template[templateField]
-                                          .type
-                                      )
+                                          .type,
+                                      ),
                                   )
                                   .map((templateField, indx) => {
                                     return (
@@ -291,7 +291,7 @@ export default function CodeTabsComponent({
                                                       target,
                                                       node.data.node.template[
                                                         templateField
-                                                      ]
+                                                      ],
                                                     );
                                                   }}
                                                 />
@@ -337,7 +337,7 @@ export default function CodeTabsComponent({
                                                         target,
                                                         node.data.node.template[
                                                           templateField
-                                                        ]
+                                                        ],
                                                       );
                                                     }}
                                                   />
@@ -390,7 +390,7 @@ export default function CodeTabsComponent({
                                                       target,
                                                       node.data.node.template[
                                                         templateField
-                                                      ]
+                                                      ],
                                                     );
                                                   }}
                                                 />
@@ -427,7 +427,7 @@ export default function CodeTabsComponent({
                                                       e,
                                                       node.data.node.template[
                                                         templateField
-                                                      ]
+                                                      ],
                                                     );
                                                   }}
                                                   size="small"
@@ -458,7 +458,7 @@ export default function CodeTabsComponent({
                                                     ].fileTypes
                                                   }
                                                   onFileChange={(
-                                                    value: any
+                                                    value: any,
                                                   ) => {
                                                     node.data.node.template[
                                                       templateField
@@ -511,7 +511,7 @@ export default function CodeTabsComponent({
                                                       target,
                                                       node.data.node.template[
                                                         templateField
-                                                      ]
+                                                      ],
                                                     );
                                                   }}
                                                 />
@@ -551,7 +551,7 @@ export default function CodeTabsComponent({
                                                       target,
                                                       node.data.node.template[
                                                         templateField
-                                                      ]
+                                                      ],
                                                     );
                                                   }}
                                                   value={
@@ -613,7 +613,7 @@ export default function CodeTabsComponent({
                                                       target,
                                                       node.data.node.template[
                                                         templateField
-                                                      ]
+                                                      ],
                                                     );
                                                   }}
                                                 />
@@ -659,7 +659,7 @@ export default function CodeTabsComponent({
                                                       target,
                                                       node.data.node.template[
                                                         templateField
-                                                      ]
+                                                      ],
                                                     );
                                                   }}
                                                 />
@@ -705,7 +705,7 @@ export default function CodeTabsComponent({
                                                       target,
                                                       node.data.node.template[
                                                         templateField
-                                                      ]
+                                                      ],
                                                     );
                                                   }}
                                                 />
@@ -737,8 +737,8 @@ export default function CodeTabsComponent({
                                                           ].value,
                                                           type(
                                                             node,
-                                                            templateField
-                                                          )
+                                                            templateField,
+                                                          ),
                                                         )
                                                   }
                                                   duplicateKey={
@@ -747,15 +747,15 @@ export default function CodeTabsComponent({
                                                   onChange={(target) => {
                                                     const valueToNumbers =
                                                       convertValuesToNumbers(
-                                                        target
+                                                        target,
                                                       );
                                                     node.data.node!.template[
                                                       templateField
                                                     ].value = valueToNumbers;
                                                     setErrorDuplicateKey(
                                                       hasDuplicateKeys(
-                                                        valueToNumbers
-                                                      )
+                                                        valueToNumbers,
+                                                      ),
                                                     );
                                                     setData((old) => {
                                                       let newInputList =
@@ -772,7 +772,7 @@ export default function CodeTabsComponent({
                                                       target,
                                                       node.data.node.template[
                                                         templateField
-                                                      ]
+                                                      ],
                                                     );
                                                   }}
                                                   isList={
@@ -820,7 +820,7 @@ export default function CodeTabsComponent({
                                                       target,
                                                       node.data.node.template[
                                                         templateField
-                                                      ]
+                                                      ],
                                                     );
                                                   }}
                                                 />
diff --git a/src/frontend/src/components/csvOutputComponent/index.tsx b/src/frontend/src/components/csvOutputComponent/index.tsx
index e88bc7af5..78f5e22a9 100644
--- a/src/frontend/src/components/csvOutputComponent/index.tsx
+++ b/src/frontend/src/components/csvOutputComponent/index.tsx
@@ -67,7 +67,7 @@ function CsvOutputComponent({
     if (file) {
       const { rowData: data, colDefs: columns } = convertCSVToData(
         file,
-        separator
+        separator,
       );
       setRowData(data);
       setColDefs(columns);
diff --git a/src/frontend/src/components/recordsOutputComponent/index.tsx b/src/frontend/src/components/dataOutputComponent/index.tsx
similarity index 76%
rename from src/frontend/src/components/recordsOutputComponent/index.tsx
rename to src/frontend/src/components/dataOutputComponent/index.tsx
index 957a43cc7..9a3b2f66d 100644
--- a/src/frontend/src/components/recordsOutputComponent/index.tsx
+++ b/src/frontend/src/components/dataOutputComponent/index.tsx
@@ -4,7 +4,7 @@ import "ag-grid-community/styles/ag-theme-balham.css"; // Optional Theme applied
 import { extractColumnsFromRows } from "../../utils/utils";
 import TableComponent from "../tableComponent";
 
-function RecordsOutputComponent({
+function DataOutputComponent({
   pagination,
   rows,
   columnMode = "union",
@@ -13,19 +13,17 @@ function RecordsOutputComponent({
   rows: any;
   columnMode?: "intersection" | "union";
 }) {
-  console.log("rows", rows);
   const columns = extractColumnsFromRows(rows, columnMode);
-  console.log("columns", columns);
 
   const columnDefs = columns.map((col, idx) => ({
     ...col,
-    resizable: idx !== columns.length - 1,
-    flex: idx !== columns.length - 1 ? 1 : 2,
+    resizable: true,
   })) as (ColDef<any> | ColGroupDef<any>)[];
 
   return (
     <TableComponent
-      key={"recordsOutputComponent"}
+      autoSizeStrategy={{ type: "fitGridWidth", defaultMinWidth: 100 }}
+      key={"dataOutputComponent"}
       overlayNoRowsTemplate="No data available"
       suppressRowClickSelection={true}
       pagination={pagination}
@@ -35,4 +33,4 @@ function RecordsOutputComponent({
   );
 }
 
-export default RecordsOutputComponent;
+export default DataOutputComponent;
diff --git a/src/frontend/src/components/dictComponent/index.tsx b/src/frontend/src/components/dictComponent/index.tsx
index b0340bf1a..e17411833 100644
--- a/src/frontend/src/components/dictComponent/index.tsx
+++ b/src/frontend/src/components/dictComponent/index.tsx
@@ -24,7 +24,8 @@ export default function DictComponent({
     <div
       className={classNames(
         value.length > 1 && editNode ? "my-1" : "",
-        "flex w-full flex-col gap-3"
+        "flex w-full flex-col gap-3",
+        disabled ? "pointer-events-none" : "",
       )}
     >
       {
@@ -34,6 +35,7 @@ export default function DictComponent({
             onChange={(obj) => {
               onChange(obj);
             }}
+            disabled={disabled}
           >
             <Input
               type="text"
@@ -42,7 +44,7 @@ export default function DictComponent({
                   ? "input-edit-node input-disable pointer-events-none cursor-pointer"
                   : "input-disable pointer-events-none cursor-pointer"
               }
-              placeholder="Click to edit your dictionary..."
+              placeholder={disabled ? "" : "Click to edit your dictionary..."}
               data-testid="dict-input"
             />
           </DictAreaModal>
diff --git a/src/frontend/src/components/dropdownComponent/index.tsx b/src/frontend/src/components/dropdownComponent/index.tsx
index 0fd981603..7a3f0fcf2 100644
--- a/src/frontend/src/components/dropdownComponent/index.tsx
+++ b/src/frontend/src/components/dropdownComponent/index.tsx
@@ -35,10 +35,9 @@ export default function Dropdown({
 
   const PopoverContentDropdown =
     children || editNode ? PopoverContent : PopoverContentWithoutPortal;
-
   return (
     <>
-      {Object.keys(options)?.length > 0 ? (
+      {Object.keys(options ?? [])?.length > 0 ? (
         <>
           <Popover open={open} onOpenChange={children ? () => {} : setOpen}>
             {children ? (
@@ -58,7 +57,7 @@ export default function Dropdown({
                       ? "dropdown-component-outline"
                       : "dropdown-component-false-outline",
                     "w-full justify-between font-normal",
-                    editNode ? "input-edit-node" : "py-2"
+                    editNode ? "input-edit-node" : "py-2",
                   )}
                 >
                   <span data-testid={`value-dropdown-` + id}>
@@ -106,7 +105,7 @@ export default function Dropdown({
                           name="Check"
                           className={cn(
                             "ml-auto h-4 w-4 text-primary",
-                            value === option ? "opacity-100" : "opacity-0"
+                            value === option ? "opacity-100" : "opacity-0",
                           )}
                         />
                       </CommandItem>
diff --git a/src/frontend/src/components/editFlowSettingsComponent/index.tsx b/src/frontend/src/components/editFlowSettingsComponent/index.tsx
index 085ebf68f..b15e0c369 100644
--- a/src/frontend/src/components/editFlowSettingsComponent/index.tsx
+++ b/src/frontend/src/components/editFlowSettingsComponent/index.tsx
@@ -2,12 +2,13 @@ import React, { ChangeEvent, useState } from "react";
 import { Input } from "../../components/ui/input";
 import { Label } from "../../components/ui/label";
 import { Textarea } from "../../components/ui/textarea";
+import useFlowsManagerStore from "../../stores/flowsManagerStore";
 import { InputProps } from "../../types/components";
 import { cn } from "../../utils/utils";
 
 export const EditFlowSettings: React.FC<InputProps> = ({
   name,
-  invalidNameList,
+  invalidNameList = [],
   description,
   endpointName,
   maxLength = 50,
@@ -17,6 +18,8 @@ export const EditFlowSettings: React.FC<InputProps> = ({
 }: InputProps): JSX.Element => {
   const [isMaxLength, setIsMaxLength] = useState(false);
   const [isEndpointNameValid, setIsEndpointNameValid] = useState(true);
+  const [isInvalidName, setIsInvalidName] = useState(false);
+  const currentFlow = useFlowsManagerStore((state) => state.currentFlow);
 
   const handleNameChange = (event: ChangeEvent<HTMLInputElement>) => {
     const { value } = event.target;
@@ -25,6 +28,19 @@ export const EditFlowSettings: React.FC<InputProps> = ({
     } else {
       setIsMaxLength(false);
     }
+    let invalid = false;
+    for (let i = 0; i < invalidNameList!.length; i++) {
+      if (value === invalidNameList![i]) {
+        invalid = true;
+        break;
+      }
+      if (value === currentFlow?.name) {
+        invalid = true;
+        break;
+      }
+      invalid = false;
+    }
+    setIsInvalidName(invalid);
     setName!(value);
   };
 
@@ -55,6 +71,9 @@ export const EditFlowSettings: React.FC<InputProps> = ({
           {isMaxLength && (
             <span className="edit-flow-span">Character limit reached</span>
           )}
+          {isInvalidName && (
+            <span className="edit-flow-span">Invalid name</span>
+          )}
         </div>
         {setName ? (
           <Input
@@ -89,8 +108,8 @@ export const EditFlowSettings: React.FC<InputProps> = ({
             onChange={handleDescriptionChange}
             value={description!}
             placeholder="Flow description"
-            className="mt-2 max-h-[100px] resize-none font-normal"
-            rows={3}
+            className="mt-2 max-h-[250px] resize-none font-normal"
+            rows={5}
             onDoubleClickCapture={(event) => {
               handleFocus(event);
             }}
@@ -99,7 +118,7 @@ export const EditFlowSettings: React.FC<InputProps> = ({
           <span
             className={cn(
               "font-normal text-muted-foreground word-break-break-word",
-              description === "" ? "font-light italic" : ""
+              description === "" ? "font-light italic" : "",
             )}
           >
             {description === "" ? "No description" : description}
diff --git a/src/frontend/src/components/exampleComponent/index.tsx b/src/frontend/src/components/exampleComponent/index.tsx
index b96289bdf..02af6b9bc 100644
--- a/src/frontend/src/components/exampleComponent/index.tsx
+++ b/src/frontend/src/components/exampleComponent/index.tsx
@@ -28,7 +28,7 @@ export default function CollectionCardComponent({
   return (
     <Card
       className={cn(
-        "group relative flex h-48 w-2/6 flex-col justify-between overflow-hidden transition-all hover:shadow-md"
+        "group relative flex h-48 w-2/6 flex-col justify-between overflow-hidden transition-all hover:shadow-md",
       )}
     >
       <div>
diff --git a/src/frontend/src/components/genericIconComponent/index.tsx b/src/frontend/src/components/genericIconComponent/index.tsx
index 398ae6e7f..9f7c687a1 100644
--- a/src/frontend/src/components/genericIconComponent/index.tsx
+++ b/src/frontend/src/components/genericIconComponent/index.tsx
@@ -18,7 +18,7 @@ export const ForwardedIconComponent = memo(
         strokeWidth,
         id = "",
       }: IconComponentProps,
-      ref
+      ref,
     ) => {
       const [showFallback, setShowFallback] = useState(false);
 
@@ -65,8 +65,8 @@ export const ForwardedIconComponent = memo(
           />
         </Suspense>
       );
-    }
-  )
+    },
+  ),
 );
 
 export default ForwardedIconComponent;
diff --git a/src/frontend/src/components/headerComponent/components/menuBar/index.tsx b/src/frontend/src/components/headerComponent/components/menuBar/index.tsx
index a27661d47..63a8742dd 100644
--- a/src/frontend/src/components/headerComponent/components/menuBar/index.tsx
+++ b/src/frontend/src/components/headerComponent/components/menuBar/index.tsx
@@ -18,6 +18,7 @@ import useAlertStore from "../../../../stores/alertStore";
 import useFlowStore from "../../../../stores/flowStore";
 import useFlowsManagerStore from "../../../../stores/flowsManagerStore";
 import { useShortcutsStore } from "../../../../stores/shortcuts";
+import { useTypesStore } from "../../../../stores/typesStore";
 import { cn } from "../../../../utils/utils";
 import IconComponent from "../../../genericIconComponent";
 import ShadTooltip from "../../../shadTooltipComponent";
@@ -37,6 +38,7 @@ export const MenuBar = ({}: {}): JSX.Element => {
   const uploadFlow = useFlowsManagerStore((state) => state.uploadFlow);
   const navigate = useNavigate();
   const isBuilding = useFlowStore((state) => state.isBuilding);
+  const getTypes = useTypesStore((state) => state.getTypes);
 
   function handleAddFlow() {
     try {
@@ -48,6 +50,12 @@ export const MenuBar = ({}: {}): JSX.Element => {
     }
   }
 
+  function handleReloadComponents() {
+    getTypes(true).then(() => {
+      setSuccessData({ title: "Components reloaded successfully" });
+    });
+  }
+
   function printByBuildStatus() {
     if (isBuilding) {
       return "Building...";
@@ -113,7 +121,7 @@ export const MenuBar = ({}: {}): JSX.Element => {
                       title: UPLOAD_ERROR_ALERT,
                       list: [error],
                     });
-                  }
+                  },
                 );
               }}
             >
@@ -161,6 +169,18 @@ export const MenuBar = ({}: {}): JSX.Element => {
                 }
               />
             </DropdownMenuItem>
+            <DropdownMenuItem
+              onClick={() => {
+                handleReloadComponents();
+              }}
+              className="cursor-pointer"
+            >
+              <IconComponent
+                name="RefreshCcw"
+                className="header-menu-options"
+              />
+              Refresh All
+            </DropdownMenuItem>
           </DropdownMenuContent>
         </DropdownMenu>
         <FlowSettingsModal
@@ -187,7 +207,7 @@ export const MenuBar = ({}: {}): JSX.Element => {
               name={isBuilding || saveLoading ? "Loader2" : "CheckCircle2"}
               className={cn(
                 "h-4 w-4",
-                isBuilding || saveLoading ? "animate-spin" : "animate-wiggle"
+                isBuilding || saveLoading ? "animate-spin" : "animate-wiggle",
               )}
             />
             {printByBuildStatus()}
diff --git a/src/frontend/src/components/headerComponent/index.tsx b/src/frontend/src/components/headerComponent/index.tsx
index 7e5ddcc8f..ab186d1db 100644
--- a/src/frontend/src/components/headerComponent/index.tsx
+++ b/src/frontend/src/components/headerComponent/index.tsx
@@ -18,7 +18,7 @@ import useFlowStore from "../../stores/flowStore";
 import useFlowsManagerStore from "../../stores/flowsManagerStore";
 import { useLocationStore } from "../../stores/locationStore";
 import { useStoreStore } from "../../stores/storeStore";
-import IconComponent from "../genericIconComponent";
+import IconComponent, { ForwardedIconComponent } from "../genericIconComponent";
 import { Button } from "../ui/button";
 import {
   DropdownMenu,
@@ -49,6 +49,12 @@ export default function Header(): JSX.Element {
 
   const routeHistory = useLocationStore((state) => state.routeHistory);
 
+  const profileImageUrl =
+    `${BACKEND_URL.slice(
+      0,
+      BACKEND_URL.length - 1,
+    )}${BASE_URL_API}files/profile_pictures/${userData?.profile_image}` ??
+    profileCircle;
   async function checkForChanges(): Promise<void> {
     if (nodes.length === 0) {
       await removeFlow(id!);
@@ -59,7 +65,7 @@ export default function Header(): JSX.Element {
     const lastFlowVisitedIndex = routeHistory
       .reverse()
       .findIndex(
-        (path) => path.includes("/flow/") && path !== location.pathname
+        (path) => path.includes("/flow/") && path !== location.pathname,
       );
 
     const lastFlowVisited = routeHistory[lastFlowVisitedIndex];
@@ -85,8 +91,7 @@ export default function Header(): JSX.Element {
         </Link>
         {showArrowReturnIcon && (
           <Button
-            variant="none"
-            size="none"
+            unstyled
             onClick={() => {
               checkForChanges();
               redirectToLastLocation();
@@ -192,8 +197,7 @@ export default function Header(): JSX.Element {
             <DropdownMenu>
               <DropdownMenuTrigger asChild>
                 <Button
-                  variant="none"
-                  size="none"
+                  unstyled
                   data-testid="user-profile-settings"
                   className="shrink-0"
                 >
@@ -201,7 +205,7 @@ export default function Header(): JSX.Element {
                     src={
                       `${BACKEND_URL.slice(
                         0,
-                        BACKEND_URL.length - 1
+                        BACKEND_URL.length - 1,
                       )}${BASE_URL_API}files/profile_pictures/${
                         userData?.profile_image ?? "Space/046-rocket.svg"
                       }` ?? profileCircle
@@ -210,7 +214,7 @@ export default function Header(): JSX.Element {
                   />
                 </Button>
               </DropdownMenuTrigger>
-              <DropdownMenuContent>
+              <DropdownMenuContent className="mr-1 mt-1 min-w-40">
                 {!autoLogin && (
                   <>
                     <DropdownMenuLabel>
@@ -219,7 +223,7 @@ export default function Header(): JSX.Element {
                           src={
                             `${BACKEND_URL.slice(
                               0,
-                              BACKEND_URL.length - 1
+                              BACKEND_URL.length - 1,
                             )}${BASE_URL_API}files/profile_pictures/${
                               userData?.profile_image ?? "Space/046-rocket.svg"
                             }` ?? profileCircle
@@ -235,30 +239,62 @@ export default function Header(): JSX.Element {
                 )}
                 <DropdownMenuLabel>General</DropdownMenuLabel>
                 <DropdownMenuItem
-                  className="cursor-pointer"
+                  className="cursor-pointer gap-2"
                   onClick={() => navigate("/settings")}
                 >
+                  <ForwardedIconComponent name="Settings" className="w-4" />
                   Settings
                 </DropdownMenuItem>
                 {!autoLogin && (
                   <>
-                    <DropdownMenuSeparator />
-                    <DropdownMenuLabel>My Account</DropdownMenuLabel>
                     {isAdmin && (
                       <DropdownMenuItem
-                        className="cursor-pointer"
+                        className="cursor-pointer gap-2"
                         onClick={() => navigate("/admin")}
                       >
+                        <ForwardedIconComponent name="Shield" className="w-4" />
                         Admin Page
                       </DropdownMenuItem>
                     )}
+                  </>
+                )}
+                <DropdownMenuSeparator />
+                <DropdownMenuLabel>Help</DropdownMenuLabel>
+                <DropdownMenuItem
+                  className="cursor-pointer gap-2"
+                  onClick={() =>
+                    window.open("https://pre-release.langflow.org/", "_blank")
+                  }
+                >
+                  <ForwardedIconComponent name="FileText" className="w-4" />
+                  Docs
+                </DropdownMenuItem>
+                <DropdownMenuItem
+                  className="cursor-pointer gap-2"
+                  onClick={() =>
+                    window.open(
+                      "https://github.com/langflow-ai/langflow/discussions",
+                      "_blank",
+                    )
+                  }
+                >
+                  <ForwardedIconComponent
+                    name="MessagesSquare"
+                    className="w-4"
+                  />
+                  Discussions
+                </DropdownMenuItem>
+                {!autoLogin && (
+                  <>
+                    <DropdownMenuSeparator />
                     <DropdownMenuItem
-                      className="cursor-pointer"
+                      className="cursor-pointer gap-2"
                       onClick={() => {
                         logout();
                       }}
                     >
-                      Sign Out
+                      <ForwardedIconComponent name="LogOut" className="w-4" />
+                      Log Out
                     </DropdownMenuItem>
                   </>
                 )}
diff --git a/src/frontend/src/components/horizontalScrollFadeComponent/index.tsx b/src/frontend/src/components/horizontalScrollFadeComponent/index.tsx
index 7e0c788a6..e0bf48917 100644
--- a/src/frontend/src/components/horizontalScrollFadeComponent/index.tsx
+++ b/src/frontend/src/components/horizontalScrollFadeComponent/index.tsx
@@ -32,11 +32,11 @@ export default function HorizontalScrollFadeComponent({
 
       fadeContainerRef.current.classList.toggle(
         "fade-left",
-        isScrollable && !atStart
+        isScrollable && !atStart,
       );
       fadeContainerRef.current.classList.toggle(
         "fade-right",
-        isScrollable && !atEnd
+        isScrollable && !atEnd,
       );
     };
 
diff --git a/src/frontend/src/components/inputComponent/components/popover/index.tsx b/src/frontend/src/components/inputComponent/components/popover/index.tsx
index 9445f334b..d0c51f064 100644
--- a/src/frontend/src/components/inputComponent/components/popover/index.tsx
+++ b/src/frontend/src/components/inputComponent/components/popover/index.tsx
@@ -75,9 +75,9 @@ const CustomInputPopover = ({
             (selectedOption !== "" || !onChange) && setSelectedOption
               ? selectedOption
               : (selectedOptions?.length !== 0 || !onChange) &&
-                setSelectedOptions
-              ? selectedOptions?.join(", ")
-              : value
+                  setSelectedOptions
+                ? selectedOptions?.join(", ")
+                : value
           }
           autoFocus={autoFocus}
           disabled={disabled}
@@ -103,7 +103,7 @@ const CustomInputPopover = ({
               (password && !(setSelectedOption || setSelectedOptions))
               ? "pr-8"
               : "",
-            className!
+            className!,
           )}
           placeholder={password && editNode ? "Key" : placeholder}
           onChange={handleInputChange}
@@ -141,15 +141,15 @@ const CustomInputPopover = ({
                   onSelect={(currentValue) => {
                     setSelectedOption &&
                       setSelectedOption(
-                        currentValue === selectedOption ? "" : currentValue
+                        currentValue === selectedOption ? "" : currentValue,
                       );
                     setSelectedOptions &&
                       setSelectedOptions(
                         selectedOptions?.includes(currentValue)
                           ? selectedOptions.filter(
-                              (item) => item !== currentValue
+                              (item) => item !== currentValue,
                             )
-                          : [...selectedOptions, currentValue]
+                          : [...selectedOptions, currentValue],
                       );
                     !setSelectedOptions && setShowOptions(false);
                   }}
@@ -162,7 +162,7 @@ const CustomInputPopover = ({
                           selectedOption === option ||
                             selectedOptions?.includes(option)
                             ? "opacity-100"
-                            : "opacity-0"
+                            : "opacity-0",
                         )}
                       >
                         <div className="absolute opacity-100 transition-all group-hover:opacity-0">
diff --git a/src/frontend/src/components/inputComponent/components/popoverObject/index.tsx b/src/frontend/src/components/inputComponent/components/popoverObject/index.tsx
index d43022845..e907ec239 100644
--- a/src/frontend/src/components/inputComponent/components/popoverObject/index.tsx
+++ b/src/frontend/src/components/inputComponent/components/popoverObject/index.tsx
@@ -60,14 +60,14 @@ const CustomInputPopoverObject = ({
               ? options.find((option) => option.id === selectedOption)?.name ||
                 ""
               : (selectedOptions?.length !== 0 || !onChange) &&
-                setSelectedOptions
-              ? selectedOptions
-                  .map(
-                    (optionId) =>
-                      options.find((option) => option.id === optionId)?.name
-                  )
-                  .join(", ")
-              : value
+                  setSelectedOptions
+                ? selectedOptions
+                    .map(
+                      (optionId) =>
+                        options.find((option) => option.id === optionId)?.name,
+                    )
+                    .join(", ")
+                : value
           }
           autoFocus={autoFocus}
           disabled={disabled}
@@ -115,15 +115,15 @@ const CustomInputPopoverObject = ({
                   onSelect={(currentValue) => {
                     setSelectedOption &&
                       setSelectedOption(
-                        currentValue === selectedOption ? "" : currentValue
+                        currentValue === selectedOption ? "" : currentValue,
                       );
                     setSelectedOptions &&
                       setSelectedOptions(
                         selectedOptions?.includes(currentValue)
                           ? selectedOptions.filter(
-                              (item) => item !== currentValue
+                              (item) => item !== currentValue,
                             )
-                          : [...selectedOptions, currentValue]
+                          : [...selectedOptions, currentValue],
                       );
                     !setSelectedOptions && setShowOptions(false);
                   }}
@@ -136,7 +136,7 @@ const CustomInputPopoverObject = ({
                           selectedOption === option.id ||
                             selectedOptions?.includes(option.id)
                             ? "opacity-100"
-                            : "opacity-0"
+                            : "opacity-0",
                         )}
                       >
                         <div className="absolute opacity-100 transition-all group-hover:opacity-0">
diff --git a/src/frontend/src/components/inputComponent/index.tsx b/src/frontend/src/components/inputComponent/index.tsx
index 10763de70..b5875b3dc 100644
--- a/src/frontend/src/components/inputComponent/index.tsx
+++ b/src/frontend/src/components/inputComponent/index.tsx
@@ -24,7 +24,6 @@ export default function InputComponent({
   blurOnEnter = false,
   optionsIcon = "ChevronsUpDown",
   selectedOption,
-
   setSelectedOption,
   selectedOptions = [],
   setSelectedOptions,
@@ -72,7 +71,7 @@ export default function InputComponent({
               editNode ? "input-edit-node" : "",
               password && editNode ? "pr-8" : "",
               password && !editNode ? "pr-10" : "",
-              className!
+              className!,
             )}
             placeholder={password && editNode ? "Key" : placeholder}
             onChange={(e) => {
@@ -155,7 +154,7 @@ export default function InputComponent({
         <span
           className={cn(
             password && selectedOption === "" ? "right-8" : "right-0",
-            "absolute inset-y-0 flex items-center pr-2.5"
+            "absolute inset-y-0 flex items-center pr-2.5",
           )}
         >
           <button
@@ -168,7 +167,7 @@ export default function InputComponent({
               onChange && setSelectedOption && selectedOption !== ""
                 ? "text-medium-indigo"
                 : "text-muted-foreground",
-              "hover:text-accent-foreground"
+              "hover:text-accent-foreground",
             )}
           >
             <ForwardedIconComponent
@@ -188,7 +187,7 @@ export default function InputComponent({
             "mb-px",
             editNode
               ? "input-component-true-button"
-              : "input-component-false-button"
+              : "input-component-false-button",
           )}
           onClick={(event) => {
             event.preventDefault();
@@ -205,7 +204,7 @@ export default function InputComponent({
               className={classNames(
                 editNode
                   ? "input-component-true-svg"
-                  : "input-component-false-svg"
+                  : "input-component-false-svg",
               )}
             >
               <path
@@ -224,7 +223,7 @@ export default function InputComponent({
               className={classNames(
                 editNode
                   ? "input-component-true-svg"
-                  : "input-component-false-svg"
+                  : "input-component-false-svg",
               )}
             >
               <path
diff --git a/src/frontend/src/components/inputFileComponent/index.tsx b/src/frontend/src/components/inputFileComponent/index.tsx
index 22002c0cd..9710f7f61 100644
--- a/src/frontend/src/components/inputFileComponent/index.tsx
+++ b/src/frontend/src/components/inputFileComponent/index.tsx
@@ -8,6 +8,7 @@ import useAlertStore from "../../stores/alertStore";
 import useFlowsManagerStore from "../../stores/flowsManagerStore";
 import { FileComponentType } from "../../types/components";
 import IconComponent from "../genericIconComponent";
+import { Button } from "../ui/button";
 
 export default function InputFileComponent({
   value,
@@ -98,33 +99,33 @@ export default function InputFileComponent({
 
   return (
     <div className={disabled ? "input-component-div" : "w-full"}>
-      <div className="input-file-component">
+      <div className="input-file-component gap-3">
         <span
           onClick={handleButtonClick}
           className={
             editNode
               ? "input-edit-node input-dialog text-muted-foreground"
               : disabled
-              ? "input-disable input-dialog primary-input"
-              : "input-dialog primary-input text-muted-foreground"
+                ? "input-disable input-dialog primary-input"
+                : "input-dialog primary-input text-muted-foreground"
           }
         >
           {myValue !== "" ? myValue : "No file"}
         </span>
-        <button onClick={handleButtonClick}>
-          {!editNode && !loading && (
+        {!editNode && (
+          <Button
+            unstyled
+            className="inline-flex items-center justify-center"
+            onClick={handleButtonClick}
+            loading={loading}
+            disabled={disabled}
+          >
             <IconComponent
               name="FileSearch2"
-              className={
-                "icons-parameters-comp" +
-                (disabled ? " text-ring" : " hover:text-accent-foreground")
-              }
+              className="icons-parameters-comp shrink-0"
             />
-          )}
-          {!editNode && loading && (
-            <span className="loading loading-spinner loading-sm pointer-events-none h-8 pl-3"></span>
-          )}
-        </button>
+          </Button>
+        )}
       </div>
     </div>
   );
diff --git a/src/frontend/src/components/inputGlobalComponent/index.tsx b/src/frontend/src/components/inputGlobalComponent/index.tsx
index 21c93e6bf..4d20e8132 100644
--- a/src/frontend/src/components/inputGlobalComponent/index.tsx
+++ b/src/frontend/src/components/inputGlobalComponent/index.tsx
@@ -19,23 +19,24 @@ export default function InputGlobalComponent({
   editNode = false,
 }: InputGlobalComponentType): JSX.Element {
   const globalVariablesEntries = useGlobalVariablesStore(
-    (state) => state.globalVariablesEntries
+    (state) => state.globalVariablesEntries,
   );
 
   const getVariableId = useGlobalVariablesStore((state) => state.getVariableId);
   const unavaliableFields = useGlobalVariablesStore(
-    (state) => state.unavaliableFields
+    (state) => state.unavaliableFields,
   );
   const removeGlobalVariable = useGlobalVariablesStore(
-    (state) => state.removeGlobalVariable
+    (state) => state.removeGlobalVariable,
   );
   const setErrorData = useAlertStore((state) => state.setErrorData);
 
   useEffect(() => {
     if (data)
       if (
-        globalVariablesEntries &&
-        !globalVariablesEntries.includes(data.value) &&
+        ((globalVariablesEntries &&
+          !globalVariablesEntries.includes(data.value)) ||
+          !globalVariablesEntries) &&
         data.load_from_db
       ) {
         setTimeout(() => {
@@ -43,7 +44,7 @@ export default function InputGlobalComponent({
           setDb(false);
         }, 100);
       }
-  }, [globalVariablesEntries]);
+  }, [globalVariablesEntries, data]);
 
   useEffect(() => {
     if (!data.value && data.display_name) {
@@ -121,7 +122,7 @@ export default function InputGlobalComponent({
             <ForwardedIconComponent
               name="Trash2"
               className={cn(
-                "h-4 w-4 text-primary opacity-0 hover:text-status-red group-hover:opacity-100"
+                "h-4 w-4 text-primary opacity-0 hover:text-status-red group-hover:opacity-100",
               )}
               aria-hidden="true"
             />
diff --git a/src/frontend/src/components/inputListComponent/index.tsx b/src/frontend/src/components/inputListComponent/index.tsx
index ce55ff1d4..ff852350d 100644
--- a/src/frontend/src/components/inputListComponent/index.tsx
+++ b/src/frontend/src/components/inputListComponent/index.tsx
@@ -2,8 +2,9 @@ import { useEffect } from "react";
 import { InputListComponentType } from "../../types/components";
 
 import _ from "lodash";
-import { classNames } from "../../utils/utils";
+import { classNames, cn } from "../../utils/utils";
 import IconComponent from "../genericIconComponent";
+import { Button } from "../ui/button";
 import { Input } from "../ui/input";
 
 export default function InputListComponent({
@@ -31,7 +32,7 @@ export default function InputListComponent({
     <div
       className={classNames(
         value.length > 1 && editNode ? "my-1" : "",
-        "flex flex-col gap-3"
+        "flex w-full flex-col gap-3",
       )}
     >
       {value.map((singleValue, idx) => {
@@ -53,8 +54,14 @@ export default function InputListComponent({
                 idx
               }
             />
-            {idx === value.length - 1 ? (
-              <button
+            {idx === 0 ? (
+              <Button
+                unstyled
+                className={cn(
+                  disabled || playgroundDisabled
+                    ? "cursor-not-allowed text-muted-foreground"
+                    : "text-primary hover:text-accent-foreground",
+                )}
                 onClick={(e) => {
                   let newInputList = _.cloneDeep(value);
                   newInputList.push("");
@@ -68,13 +75,16 @@ export default function InputListComponent({
                 }
                 disabled={disabled || playgroundDisabled}
               >
-                <IconComponent
-                  name="Plus"
-                  className={"h-4 w-4 hover:text-accent-foreground"}
-                />
-              </button>
+                <IconComponent name="Plus" className="h-4 w-4" />
+              </Button>
             ) : (
-              <button
+              <Button
+                unstyled
+                className={cn(
+                  disabled || playgroundDisabled
+                    ? "cursor-not-allowed text-muted-foreground"
+                    : "text-primary hover:text-accent-foreground",
+                )}
                 data-testid={
                   `input-list-minus-btn${
                     editNode ? "-edit" : ""
@@ -88,15 +98,8 @@ export default function InputListComponent({
                 }}
                 disabled={disabled || playgroundDisabled}
               >
-                <IconComponent
-                  name="X"
-                  className={`h-4 w-4 ${
-                    disabled || playgroundDisabled
-                      ? ""
-                      : "hover:text-accent-foreground"
-                  }`}
-                />
-              </button>
+                <IconComponent name="X" className="h-4 w-4" />
+              </Button>
             )}
           </div>
         );
diff --git a/src/frontend/src/components/keypairListComponent/index.tsx b/src/frontend/src/components/keypairListComponent/index.tsx
index 2fa12e3b8..89a5252e6 100644
--- a/src/frontend/src/components/keypairListComponent/index.tsx
+++ b/src/frontend/src/components/keypairListComponent/index.tsx
@@ -1,7 +1,7 @@
-import { useEffect, useRef } from "react";
+import { useEffect, useRef, useState } from "react";
 import { KeyPairListComponentType } from "../../types/components";
 
-import _ from "lodash";
+import { cloneDeep } from "lodash";
 import { classNames } from "../../utils/utils";
 import IconComponent from "../genericIconComponent";
 import { Input } from "../ui/input";
@@ -20,42 +20,36 @@ export default function KeypairListComponent({
     }
   }, [disabled]);
 
-  const checkValueType = (value) => {
-    return Array.isArray(value) ? value : [value];
-  };
-
-  const ref = useRef<any>([]);
-  ref.current =
-    !value || value?.length === 0 ? [{ "": "" }] : checkValueType(value);
-
-  useEffect(() => {
-    if (JSON.stringify(value) !== JSON.stringify(ref.current)) {
-      ref.current = value;
-      onChange(value);
-    }
-  }, [value]);
+  const myValue = Array.isArray(value) ? value : [value];
 
   const handleChangeKey = (event, idx) => {
-    const oldKey = Object.keys(ref.current[idx])[0];
-    const updatedObj = { [event.target.value]: ref.current[idx][oldKey] };
-    ref.current[idx] = updatedObj;
-    onChange(ref.current);
+    const oldKey = Object.keys(myValue[idx])[0];
+    const updatedObj = { [event.target.value]: myValue[idx][oldKey] };
+
+    const newValue = cloneDeep(myValue);
+    newValue[idx] = updatedObj;
+
+    onChange(newValue);
   };
 
-  const handleChangeValue = (newValue, idx) => {
-    const key = Object.keys(ref.current[idx])[0];
-    ref.current[idx][key] = newValue;
-    onChange(ref.current);
+  const handleChangeValue = (event, idx) => {
+    const key = Object.keys(myValue[idx])[0];
+    const updatedObj = { [key]: event.target.value };
+
+    const newValue = cloneDeep(myValue);
+    newValue[idx] = updatedObj;
+
+    onChange(newValue);
   };
 
   return (
     <div
       className={classNames(
-        ref.current?.length > 1 && editNode ? "mx-2 my-1" : "",
-        "flex h-full flex-col gap-3"
+        myValue?.length > 1 && editNode ? "mx-2 my-1" : "",
+        "flex h-full flex-col gap-3",
       )}
     >
-      {ref.current?.map((obj, index) => {
+      {myValue?.map((obj, index) => {
         return Object.keys(obj).map((key, idx) => {
           return (
             <div key={idx} className="flex w-full gap-2">
@@ -68,7 +62,7 @@ export default function KeypairListComponent({
                 value={key.trim()}
                 className={classNames(
                   editNode ? "input-edit-node" : "",
-                  duplicateKey ? "input-invalid" : ""
+                  duplicateKey ? "input-invalid" : "",
                 )}
                 placeholder="Type key..."
                 onChange={(event) => handleChangeKey(event, index)}
@@ -90,16 +84,14 @@ export default function KeypairListComponent({
                 value={obj[key]}
                 className={editNode ? "input-edit-node" : ""}
                 placeholder="Type a value..."
-                onChange={(event) =>
-                  handleChangeValue(event.target.value, index)
-                }
+                onChange={(event) => handleChangeValue(event, index)}
               />
 
-              {isList && index === ref.current.length - 1 ? (
+              {isList && index === myValue.length - 1 ? (
                 <button
                   disabled={disabled}
                   onClick={() => {
-                    let newInputList = _.cloneDeep(ref.current);
+                    let newInputList = cloneDeep(myValue);
                     newInputList.push({ "": "" });
                     onChange(newInputList);
                   }}
@@ -122,7 +114,7 @@ export default function KeypairListComponent({
               ) : isList ? (
                 <button
                   onClick={() => {
-                    let newInputList = _.cloneDeep(ref.current);
+                    let newInputList = cloneDeep(myValue);
                     newInputList.splice(index, 1);
                     onChange(newInputList);
                   }}
diff --git a/src/frontend/src/components/paginatorComponent/index.tsx b/src/frontend/src/components/paginatorComponent/index.tsx
index 09c5e0702..da6a9492a 100644
--- a/src/frontend/src/components/paginatorComponent/index.tsx
+++ b/src/frontend/src/components/paginatorComponent/index.tsx
@@ -20,7 +20,7 @@ export default function PaginatorComponent({
 }: PaginatorComponentType) {
   const [size, setPageSize] = useState(pageSize);
   const [maxIndex, setMaxPageIndex] = useState(
-    Math.ceil(totalRowsCount / pageSize)
+    Math.ceil(totalRowsCount / pageSize),
   );
 
   useEffect(() => {
diff --git a/src/frontend/src/components/promptComponent/index.tsx b/src/frontend/src/components/promptComponent/index.tsx
index 99e44ba5d..0dcec3dbf 100644
--- a/src/frontend/src/components/promptComponent/index.tsx
+++ b/src/frontend/src/components/promptComponent/index.tsx
@@ -4,6 +4,7 @@ import { TypeModal } from "../../constants/enums";
 import GenericModal from "../../modals/genericModal";
 import { PromptAreaComponentType } from "../../types/components";
 import IconComponent from "../genericIconComponent";
+import { Button } from "../ui/button";
 
 export default function PromptAreaComponent({
   field_name,
@@ -36,30 +37,32 @@ export default function PromptAreaComponent({
         nodeClass={nodeClass}
         setNodeClass={setNodeClass}
       >
-        <div className="flex w-full items-center">
-          <span
-            id={id}
-            data-testid={id}
-            className={
-              editNode
-                ? "input-edit-node input-dialog"
-                : (disabled ? "input-disable text-ring " : "") +
-                  " primary-input text-muted-foreground"
-            }
-          >
-            {value !== "" ? value : "Type your prompt here..."}
-          </span>
-          {!editNode && (
-            <IconComponent
+        <Button unstyled className="w-full">
+          <div className="flex w-full items-center gap-3">
+            <span
               id={id}
-              name="ExternalLink"
+              data-testid={id}
               className={
-                "icons-parameters-comp" +
-                (disabled ? " text-ring" : " hover:text-accent-foreground")
+                editNode
+                  ? "input-edit-node input-dialog"
+                  : (disabled ? "input-disable text-ring " : "") +
+                    " primary-input text-muted-foreground"
               }
-            />
-          )}
-        </div>
+            >
+              {value !== "" ? value : "Type your prompt here..."}
+            </span>
+            {!editNode && (
+              <IconComponent
+                id={id}
+                name="ExternalLink"
+                className={
+                  "icons-parameters-comp shrink-0" +
+                  (disabled ? " text-ring" : " hover:text-accent-foreground")
+                }
+              />
+            )}
+          </div>
+        </Button>
       </GenericModal>
     </div>
   );
diff --git a/src/frontend/src/components/sidebarComponent/components/sideBarButtons/index.tsx b/src/frontend/src/components/sidebarComponent/components/sideBarButtons/index.tsx
index 8e14f8fc1..5b382bd15 100644
--- a/src/frontend/src/components/sidebarComponent/components/sideBarButtons/index.tsx
+++ b/src/frontend/src/components/sidebarComponent/components/sideBarButtons/index.tsx
@@ -27,7 +27,7 @@ const SideBarButtonsComponent = ({
               pathname === item.href
                 ? "border border-border bg-muted hover:bg-muted"
                 : "border border-transparent hover:border-border hover:bg-transparent",
-              "flex w-full shrink-0 justify-start gap-4"
+              "flex w-full shrink-0 justify-start gap-4",
             )}
           >
             {item.icon}
diff --git a/src/frontend/src/components/sidebarComponent/components/sideBarFolderButtons/index.tsx b/src/frontend/src/components/sidebarComponent/components/sideBarFolderButtons/index.tsx
index 9ed4a15cb..35b167492 100644
--- a/src/frontend/src/components/sidebarComponent/components/sideBarFolderButtons/index.tsx
+++ b/src/frontend/src/components/sidebarComponent/components/sideBarFolderButtons/index.tsx
@@ -31,7 +31,7 @@ const SideBarFoldersButtonsComponent = ({
   const [foldersNames, setFoldersNames] = useState({});
   const takeSnapshot = useFlowsManagerStore((state) => state.takeSnapshot);
   const [editFolders, setEditFolderName] = useState(
-    folders.map((obj) => ({ name: obj.name, edit: false }))
+    folders.map((obj) => ({ name: obj.name, edit: false })),
   );
   const uploadFolder = useFolderStore((state) => state.uploadFolder);
   const currentFolder = pathname.split("/");
@@ -52,13 +52,13 @@ const SideBarFoldersButtonsComponent = ({
   const setErrorData = useAlertStore((state) => state.setErrorData);
   const setSuccessData = useAlertStore((state) => state.setSuccessData);
 
-  const handleFolderChange = (folderId: string) => {
+  const handleFolderChange = () => {
     getFolderById(folderId);
   };
 
   const { dragOver, dragEnter, dragLeave, onDrop } = useFileDrop(
     folderId,
-    handleFolderChange
+    handleFolderChange,
   );
 
   const handleUploadFlowsToFolder = () => {
@@ -86,7 +86,7 @@ const SideBarFoldersButtonsComponent = ({
     addFolder({ name: "New Folder", parent_id: null, description: "" }).then(
       (res) => {
         refreshFolders();
-      }
+      },
     );
   }
 
@@ -104,6 +104,50 @@ const SideBarFoldersButtonsComponent = ({
     folders.map((obj) => ({ name: obj.name, edit: false }));
   }, [folders]);
 
+  const handleEditNameFolder = async (item) => {
+    const newEditFolders = editFolders.map((obj) => {
+      if (obj.name === item.name) {
+        return { name: item.name, edit: false };
+      }
+      return { name: obj.name, edit: false };
+    });
+    setEditFolderName(newEditFolders);
+    if (foldersNames[item.name].trim() !== "") {
+      setFoldersNames((old) => ({
+        ...old,
+        [item.name]: foldersNames[item.name],
+      }));
+      const body = {
+        ...item,
+        name: foldersNames[item.name],
+        flows: item.flows?.length > 0 ? item.flows : [],
+        components: item.components?.length > 0 ? item.components : [],
+      };
+      const updatedFolder = await updateFolder(body, item.id!);
+
+      const updatedFolderIndex = folders.findIndex(
+        (f) => f.id === updatedFolder.id,
+      );
+
+      const updateFolders = [...folders];
+      updateFolders[updatedFolderIndex] = updatedFolder;
+
+      setFolders(updateFolders);
+      setFoldersNames({});
+      setEditFolderName(
+        folders.map((obj) => ({
+          name: obj.name,
+          edit: false,
+        })),
+      );
+    } else {
+      setFoldersNames((old) => ({
+        ...old,
+        [item.name]: item.name,
+      }));
+    }
+  };
+
   return (
     <>
       <div className="flex shrink-0 items-center justify-between gap-2">
@@ -132,7 +176,7 @@ const SideBarFoldersButtonsComponent = ({
         <>
           {folders.map((item, index) => {
             const editFolderName = editFolders?.filter(
-              (folder) => folder.name === item.name
+              (folder) => folder.name === item.name,
             )[0];
             return (
               <div
@@ -148,7 +192,7 @@ const SideBarFoldersButtonsComponent = ({
                     ? "border border-border bg-muted hover:bg-muted"
                     : "border hover:bg-transparent lg:border-transparent lg:hover:border-border",
                   "group flex w-full shrink-0 cursor-pointer gap-2 opacity-100 lg:min-w-full",
-                  folderIdDragging === item.id! ? "bg-border" : ""
+                  folderIdDragging === item.id! ? "bg-border" : "",
                 )}
                 onClick={() => handleChangeFolder!(item.id!)}
               >
@@ -190,11 +234,11 @@ const SideBarFoldersButtonsComponent = ({
                     event.stopPropagation();
                     event.preventDefault();
                   }}
-                  className="flex w-full items-center gap-4"
+                  className="flex w-full items-center gap-2"
                 >
                   <IconComponent
                     name={"folder"}
-                    className="w-4 flex-shrink-0 justify-start stroke-[1.5] opacity-100"
+                    className="mr-2 w-4 flex-shrink-0 justify-start stroke-[1.5] opacity-100"
                   />
                   {editFolderName?.edit ? (
                     <div>
@@ -218,7 +262,7 @@ const SideBarFoldersButtonsComponent = ({
                               folders.map((obj) => ({
                                 name: obj.name,
                                 edit: false,
-                              }))
+                              })),
                             );
                           }
                           if (e.key === "Enter") {
@@ -228,48 +272,7 @@ const SideBarFoldersButtonsComponent = ({
                         }}
                         autoFocus={true}
                         onBlur={async () => {
-                          const newEditFolders = editFolders.map((obj) => {
-                            if (obj.name === item.name) {
-                              return { name: item.name, edit: false };
-                            }
-                            return { name: obj.name, edit: false };
-                          });
-                          setEditFolderName(newEditFolders);
-                          if (foldersNames[item.name].trim() !== "") {
-                            setFoldersNames((old) => ({
-                              ...old,
-                              [item.name]: foldersNames[item.name],
-                            }));
-                            const body = {
-                              ...item,
-                              name: foldersNames[item.name],
-                              flows: item.flows?.length > 0 ? item.flows : [],
-                              components:
-                                item.components?.length > 0
-                                  ? item.components
-                                  : [],
-                            };
-                            const updatedFolder = await updateFolder(
-                              body,
-                              item.id!
-                            );
-                            const updateFolders = folders.filter(
-                              (f) => f.name !== item.name
-                            );
-                            setFolders([...updateFolders, updatedFolder]);
-                            setFoldersNames({});
-                            setEditFolderName(
-                              folders.map((obj) => ({
-                                name: obj.name,
-                                edit: false,
-                              }))
-                            );
-                          } else {
-                            setFoldersNames((old) => ({
-                              ...old,
-                              [item.name]: item.name,
-                            }));
-                          }
+                          handleEditNameFolder(item);
                         }}
                         value={foldersNames[item.name]}
                         id={`input-folder-${item.name}`}
@@ -290,10 +293,11 @@ const SideBarFoldersButtonsComponent = ({
                         e.preventDefault();
                       }}
                       variant={"ghost"}
+                      size={"icon"}
                     >
                       <IconComponent
                         name={"trash"}
-                        className="w-4 stroke-[1.5]"
+                        className="w-4 stroke-[1.5] p-0"
                       />
                     </Button>
                   )}
@@ -304,8 +308,7 @@ const SideBarFoldersButtonsComponent = ({
                       e.stopPropagation();
                       e.preventDefault();
                     }}
-                    size="none"
-                    variant="none"
+                    unstyled
                   >
                     <IconComponent
                       name={"Download"}
diff --git a/src/frontend/src/components/sidebarComponent/hooks/use-on-file-drop.tsx b/src/frontend/src/components/sidebarComponent/hooks/use-on-file-drop.tsx
index ab4b9d27f..c75bf4bec 100644
--- a/src/frontend/src/components/sidebarComponent/hooks/use-on-file-drop.tsx
+++ b/src/frontend/src/components/sidebarComponent/hooks/use-on-file-drop.tsx
@@ -12,7 +12,7 @@ import { addVersionToDuplicates } from "../../../utils/reactflowUtils";
 const useFileDrop = (folderId, folderChangeCallback) => {
   const setFolderDragging = useFolderStore((state) => state.setFolderDragging);
   const setFolderIdDragging = useFolderStore(
-    (state) => state.setFolderIdDragging
+    (state) => state.setFolderIdDragging,
   );
 
   const setErrorData = useAlertStore((state) => state.setErrorData);
@@ -45,7 +45,7 @@ const useFileDrop = (folderId, folderChangeCallback) => {
       | React.DragEvent<HTMLDivElement>
       | React.DragEvent<HTMLButtonElement>
       | React.DragEvent<HTMLAnchorElement>,
-    folderId: string
+    folderId: string,
   ) => {
     e.preventDefault();
 
@@ -60,7 +60,7 @@ const useFileDrop = (folderId, folderChangeCallback) => {
       | React.DragEvent<HTMLDivElement>
       | React.DragEvent<HTMLButtonElement>
       | React.DragEvent<HTMLAnchorElement>,
-    folderId: string
+    folderId: string,
   ) => {
     if (e.dataTransfer.types.some((types) => types === "Files")) {
       setFolderDragging(true);
@@ -73,7 +73,7 @@ const useFileDrop = (folderId, folderChangeCallback) => {
     e:
       | React.DragEvent<HTMLDivElement>
       | React.DragEvent<HTMLButtonElement>
-      | React.DragEvent<HTMLAnchorElement>
+      | React.DragEvent<HTMLAnchorElement>,
   ) => {
     e.preventDefault();
     if (e.target === e.currentTarget) {
@@ -87,7 +87,7 @@ const useFileDrop = (folderId, folderChangeCallback) => {
       | React.DragEvent<HTMLDivElement>
       | React.DragEvent<HTMLButtonElement>
       | React.DragEvent<HTMLAnchorElement>,
-    folderId: string
+    folderId: string,
   ) => {
     if (e?.dataTransfer?.getData("flow")) {
       const data = JSON.parse(e?.dataTransfer?.getData("flow"));
diff --git a/src/frontend/src/components/stringReaderComponent/index.tsx b/src/frontend/src/components/stringReaderComponent/index.tsx
index 8b1b0e41f..4019c610b 100644
--- a/src/frontend/src/components/stringReaderComponent/index.tsx
+++ b/src/frontend/src/components/stringReaderComponent/index.tsx
@@ -1,7 +1,13 @@
+import TextModal from "../../modals/textModal";
+
 export default function StringReader({
   string,
 }: {
   string: string;
 }): JSX.Element {
-  return <span className="truncate">{string}</span>;
+  return (
+    <TextModal value={string}>
+      <span className="truncate">{string}</span>
+    </TextModal>
+  );
 }
diff --git a/src/frontend/src/components/tableComponent/components/TableOptions/index.tsx b/src/frontend/src/components/tableComponent/components/TableOptions/index.tsx
index 4b0ac5373..05d3ea271 100644
--- a/src/frontend/src/components/tableComponent/components/TableOptions/index.tsx
+++ b/src/frontend/src/components/tableComponent/components/TableOptions/index.tsx
@@ -22,8 +22,7 @@ export default function TableOptions({
         <div>
           <ShadTooltip content="Reset Columns">
             <Button
-              variant="none"
-              size="none"
+              unstyled
               onClick={() => {
                 resetGrid();
               }}
@@ -33,7 +32,7 @@ export default function TableOptions({
                 name="RotateCcw"
                 strokeWidth={2}
                 className={cn(
-                  "h-5 w-5 text-primary transition-all hover:text-accent-foreground"
+                  "h-5 w-5 text-primary transition-all hover:text-accent-foreground",
                 )}
               />
             </Button>
@@ -50,12 +49,7 @@ export default function TableOptions({
                 )
               }
             >
-              <Button
-                variant="none"
-                size="none"
-                onClick={duplicateRow}
-                disabled={!hasSelection}
-              >
+              <Button unstyled onClick={duplicateRow} disabled={!hasSelection}>
                 <IconComponent
                   name="Copy"
                   className={cn("h-5 w-5 text-primary transition-all")}
@@ -75,17 +69,12 @@ export default function TableOptions({
                 )
               }
             >
-              <Button
-                variant="none"
-                size="none"
-                onClick={deleteRow}
-                disabled={!hasSelection}
-              >
+              <Button unstyled onClick={deleteRow} disabled={!hasSelection}>
                 <IconComponent
                   name="Trash2"
                   className={cn(
                     "h-5 w-5 text-primary transition-all",
-                    !hasSelection ? "" : "hover:text-status-red"
+                    !hasSelection ? "" : "hover:text-status-red",
                   )}
                 />
               </Button>
diff --git a/src/frontend/src/components/tableComponent/components/tableAutoCellRender/index.tsx b/src/frontend/src/components/tableComponent/components/tableAutoCellRender/index.tsx
index 120860dbd..14e191976 100644
--- a/src/frontend/src/components/tableComponent/components/tableAutoCellRender/index.tsx
+++ b/src/frontend/src/components/tableComponent/components/tableAutoCellRender/index.tsx
@@ -30,10 +30,10 @@ export default function TableAutoCellRender({
               variant="outline"
               size="sq"
               className={cn(
-                "min-w-min bg-success-background text-success-foreground hover:bg-success-background"
+                "h-[18px] w-full justify-center bg-success-background text-success-foreground hover:bg-success-background",
               )}
             >
-              Success
+              {value}
             </Badge>
           );
         } else if (value === "failure") {
@@ -42,10 +42,10 @@ export default function TableAutoCellRender({
               variant="outline"
               size="sq"
               className={cn(
-                "min-w-min bg-error-background text-error-foreground hover:bg-error-background"
+                "h-[18px] w-full justify-center bg-error-background text-error-foreground hover:bg-error-background",
               )}
             >
-              Failure
+              {value}
             </Badge>
           );
         } else {
@@ -59,7 +59,7 @@ export default function TableAutoCellRender({
   }
 
   return (
-    <div className="group flex h-full w-full items-center truncate align-middle">
+    <div className="group flex h-full w-full truncate text-align-last-left">
       {getCellType()}
     </div>
   );
diff --git a/src/frontend/src/components/tableComponent/components/tableNodeCellRender/index.tsx b/src/frontend/src/components/tableComponent/components/tableNodeCellRender/index.tsx
index 925848d82..b55788134 100644
--- a/src/frontend/src/components/tableComponent/components/tableNodeCellRender/index.tsx
+++ b/src/frontend/src/components/tableComponent/components/tableNodeCellRender/index.tsx
@@ -72,8 +72,8 @@ export default function TableNodeCellRender({
                 ...id,
                 proxy: templateData.proxy,
               }
-            : id
-        )
+            : id,
+        ),
     ) ?? false;
   function getCellType() {
     switch (templateData.type) {
@@ -131,7 +131,9 @@ export default function TableNodeCellRender({
           <DictComponent
             disabled={disabled}
             editNode={true}
-            value={templateValue.toString() === "{}" ? {} : templateValue}
+            value={
+              (templateValue || "").toString() === "{}" ? {} : templateValue
+            }
             onChange={(newValue) => {
               handleOnNewValue(newValue, templateData.key);
             }}
@@ -144,7 +146,7 @@ export default function TableNodeCellRender({
           <div
             className={classNames(
               "max-h-48 w-full overflow-auto custom-scroll",
-              templateValue?.length > 1 ? "my-3" : ""
+              templateValue?.length > 1 ? "my-3" : "",
             )}
           >
             <KeypairListComponent
diff --git a/src/frontend/src/components/tableComponent/index.tsx b/src/frontend/src/components/tableComponent/index.tsx
index 5998963d4..48143367e 100644
--- a/src/frontend/src/components/tableComponent/index.tsx
+++ b/src/frontend/src/components/tableComponent/index.tsx
@@ -1,6 +1,7 @@
 import "ag-grid-community/styles/ag-grid.css"; // Mandatory CSS required by the grid
 import "ag-grid-community/styles/ag-theme-quartz.css"; // Optional Theme applied to the grid
 import { AgGridReact, AgGridReactProps } from "ag-grid-react";
+import cloneDeep from "lodash";
 import { ElementRef, forwardRef, useRef, useState } from "react";
 import {
   DEFAULT_TABLE_ALERT_MSG,
@@ -35,19 +36,12 @@ const TableComponent = forwardRef<
       alertDescription = DEFAULT_TABLE_ALERT_MSG,
       ...props
     },
-    ref
+    ref,
   ) => {
     let colDef = props.columnDefs.map((col, index) => {
       let newCol = {
         ...col,
-        headerName: col.headerName,
       };
-      if (index === props.columnDefs.length - 1) {
-        newCol = {
-          ...newCol,
-          resizable: false,
-        };
-      }
       if (props.onSelectionChanged && index === 0) {
         newCol = {
           ...newCol,
@@ -77,17 +71,11 @@ const TableComponent = forwardRef<
     const initialColumnDefs = useRef(colDef);
     const [columnStateChange, setColumnStateChange] = useState(false);
     const storeReference = props.columnDefs.map((e) => e.headerName).join("_");
-    const makeLastColumnNonResizable = (columnDefs) => {
-      columnDefs.forEach((colDef, index) => {
-        colDef.resizable = index !== columnDefs.length - 1;
-      });
-      return columnDefs;
-    };
 
     const onGridReady = (params) => {
       // @ts-ignore
       realRef.current = params;
-      const updatedColumnDefs = makeLastColumnNonResizable([...colDef]);
+      const updatedColumnDefs = [...colDef];
       params.api.setGridOption("columnDefs", updatedColumnDefs);
       const customInit = localStorage.getItem(storeReference);
       initialColumnDefs.current = params.api.getColumnDefs();
@@ -110,8 +98,8 @@ const TableComponent = forwardRef<
       if (props.onGridReady) props.onGridReady(params);
     };
     const onColumnMoved = (params) => {
-      const updatedColumnDefs = makeLastColumnNonResizable(
-        params.columnApi.getAllGridColumns().map((col) => col.getColDef())
+      const updatedColumnDefs = cloneDeep(
+        params.columnApi.getAllGridColumns().map((col) => col.getColDef()),
       );
       params.api.setGridOption("columnDefs", updatedColumnDefs);
       if (props.onColumnMoved) props.onColumnMoved(params);
@@ -135,7 +123,7 @@ const TableComponent = forwardRef<
         className={cn(
           dark ? "ag-theme-quartz-dark" : "ag-theme-quartz",
           "ag-theme-shadcn flex h-full flex-col",
-          "relative"
+          "relative",
         )} // applying the grid theme
       >
         <AgGridReact
@@ -152,7 +140,7 @@ const TableComponent = forwardRef<
             if (e.sources.some((source) => source.includes("column"))) {
               localStorage.setItem(
                 storeReference,
-                JSON.stringify(realRef.current?.api?.getColumnState())
+                JSON.stringify(realRef.current?.api?.getColumnState()),
               );
               setColumnStateChange(true);
             }
@@ -175,7 +163,7 @@ const TableComponent = forwardRef<
         )}
       </div>
     );
-  }
+  },
 );
 
 export default TableComponent;
diff --git a/src/frontend/src/components/tagsSelectorComponent/index.tsx b/src/frontend/src/components/tagsSelectorComponent/index.tsx
index 6bf0c1d3c..95bcc1101 100644
--- a/src/frontend/src/components/tagsSelectorComponent/index.tsx
+++ b/src/frontend/src/components/tagsSelectorComponent/index.tsx
@@ -48,7 +48,7 @@ export function TagsSelector({
               className={cn(
                 selectedTags.some((category) => category === tag.name)
                   ? "min-w-min bg-beta-foreground text-background hover:bg-beta-foreground"
-                  : ""
+                  : "",
               )}
             >
               {tag.name}
diff --git a/src/frontend/src/components/textAreaComponent/index.tsx b/src/frontend/src/components/textAreaComponent/index.tsx
index 7bd10153b..ce42437d7 100644
--- a/src/frontend/src/components/textAreaComponent/index.tsx
+++ b/src/frontend/src/components/textAreaComponent/index.tsx
@@ -4,6 +4,7 @@ import { TypeModal } from "../../constants/enums";
 import GenericModal from "../../modals/genericModal";
 import { TextAreaComponentType } from "../../types/components";
 import IconComponent from "../genericIconComponent";
+import { Button } from "../ui/button";
 import { Input } from "../ui/input";
 
 export default function TextAreaComponent({
@@ -22,7 +23,7 @@ export default function TextAreaComponent({
 
   return (
     <div className={"flex w-full items-center " + (disabled ? "" : "")}>
-      <div className="flex w-full items-center" data-testid={"div-" + id}>
+      <div className="flex w-full items-center gap-3" data-testid={"div-" + id}>
         <Input
           id={id}
           data-testid={id}
@@ -34,7 +35,7 @@ export default function TextAreaComponent({
             onChange(event.target.value);
           }}
         />
-        <div>
+        <div className="flex items-center">
           <GenericModal
             type={TypeModal.TEXT}
             buttonText="Finish Editing"
@@ -43,17 +44,20 @@ export default function TextAreaComponent({
             setValue={(value: string) => {
               onChange(value);
             }}
+            disabled={disabled}
           >
             {!editNode && (
-              <IconComponent
-                strokeWidth={1.5}
-                id={id}
-                name="ExternalLink"
-                className={
-                  "icons-parameters-comp w-[1.35rem]" +
-                  (disabled ? " text-ring" : " hover:text-accent-foreground")
-                }
-              />
+              <Button unstyled>
+                <IconComponent
+                  strokeWidth={1.5}
+                  id={id}
+                  name="ExternalLink"
+                  className={
+                    "icons-parameters-comp shrink-0" +
+                    (disabled ? " text-ring" : " hover:text-accent-foreground")
+                  }
+                />
+              </Button>
             )}
           </GenericModal>
         </div>
diff --git a/src/frontend/src/components/ui/accordion.tsx b/src/frontend/src/components/ui/accordion.tsx
index 7006e8333..d7862e1e5 100644
--- a/src/frontend/src/components/ui/accordion.tsx
+++ b/src/frontend/src/components/ui/accordion.tsx
@@ -34,19 +34,19 @@ const AccordionTrigger = React.forwardRef<
       <div
         className={cn(
           "flex flex-1 cursor-pointer items-center justify-between py-4 text-sm font-medium transition-all [&[data-state=open]>svg]:rotate-180",
-          className
+          className,
         )}
       >
         {children}
         <ShadTooltip
           styleClasses="z-50"
-          content={disabled ? "Empty" : undefined}
+          content={disabled ? "Empty" : "Open"}
           side="top"
         >
           <ChevronDownIcon
             className={cn(
               "h-4 w-4 font-bold transition-transform duration-200",
-              disabled ? "text-muted-foreground" : "text-primary"
+              disabled ? "text-muted-foreground" : "text-primary",
             )}
           />
         </ShadTooltip>
@@ -64,7 +64,7 @@ const AccordionContent = React.forwardRef<
     ref={ref}
     className={cn(
       "data-[state=closed]:animate-accordion-up data-[state=open]:animate-accordion-down overflow-hidden text-sm",
-      className
+      className,
     )}
     {...props}
   >
diff --git a/src/frontend/src/components/ui/alert.tsx b/src/frontend/src/components/ui/alert.tsx
index a584cea26..05c5b7fa7 100644
--- a/src/frontend/src/components/ui/alert.tsx
+++ b/src/frontend/src/components/ui/alert.tsx
@@ -15,7 +15,7 @@ const alertVariants = cva(
     defaultVariants: {
       variant: "default",
     },
-  }
+  },
 );
 
 const Alert = React.forwardRef<
diff --git a/src/frontend/src/components/ui/badge.tsx b/src/frontend/src/components/ui/badge.tsx
index 8f8d1f6aa..6eea7971a 100644
--- a/src/frontend/src/components/ui/badge.tsx
+++ b/src/frontend/src/components/ui/badge.tsx
@@ -27,7 +27,7 @@ const badgeVariants = cva(
     defaultVariants: {
       variant: "default",
     },
-  }
+  },
 );
 
 export interface BadgeProps
diff --git a/src/frontend/src/components/ui/button.tsx b/src/frontend/src/components/ui/button.tsx
index 76f2316a0..f491abdfe 100644
--- a/src/frontend/src/components/ui/button.tsx
+++ b/src/frontend/src/components/ui/button.tsx
@@ -20,7 +20,6 @@ const buttonVariants = cva(
           "border border-muted bg-muted text-secondary-foreground hover:bg-secondary-foreground/5",
         ghost: "hover:bg-accent hover:text-accent-foreground",
         link: "underline-offset-4 hover:underline text-primary",
-        none: "",
       },
       size: {
         default: "h-10 py-2 px-4",
@@ -28,14 +27,13 @@ const buttonVariants = cva(
         xs: "py-0.5 px-3 rounded-md",
         lg: "h-11 px-8 rounded-md",
         icon: "py-1 px-1 rounded-md",
-        none: "",
       },
     },
     defaultVariants: {
       variant: "default",
       size: "default",
     },
-  }
+  },
 );
 
 export interface ButtonProps
@@ -43,6 +41,7 @@ export interface ButtonProps
     VariantProps<typeof buttonVariants> {
   asChild?: boolean;
   loading?: boolean;
+  unstyled?: boolean;
 }
 
 function toTitleCase(text: string) {
@@ -57,6 +56,7 @@ const Button = React.forwardRef<HTMLButtonElement, ButtonProps>(
     {
       className,
       variant,
+      unstyled,
       size,
       loading,
       type,
@@ -65,7 +65,7 @@ const Button = React.forwardRef<HTMLButtonElement, ButtonProps>(
       children,
       ...props
     },
-    ref
+    ref,
   ) => {
     const Comp = asChild ? Slot : "button";
     let newChildren = children;
@@ -75,7 +75,11 @@ const Button = React.forwardRef<HTMLButtonElement, ButtonProps>(
     return (
       <>
         <Comp
-          className={cn(buttonVariants({ variant, size, className }))}
+          className={
+            !unstyled
+              ? buttonVariants({ variant, size, className })
+              : cn(className, "nocopy nowheel nopan nodelete nodrag noundo")
+          }
           disabled={loading || disabled}
           {...(asChild ? {} : { type: type || "button" })}
           ref={ref}
@@ -84,10 +88,10 @@ const Button = React.forwardRef<HTMLButtonElement, ButtonProps>(
           {loading ? (
             <span className="relative">
               <span className="invisible">{newChildren}</span>
-              <span className="absolute inset-0 flex items-center justify-center">
+              <span className="absolute inset-0">
                 <ForwardedIconComponent
                   name={"Loader2"}
-                  className={"animate-spin"}
+                  className={"m-auto h-full animate-spin"}
                 />
               </span>
             </span>
@@ -97,7 +101,7 @@ const Button = React.forwardRef<HTMLButtonElement, ButtonProps>(
         </Comp>
       </>
     );
-  }
+  },
 );
 Button.displayName = "Button";
 
diff --git a/src/frontend/src/components/ui/card.tsx b/src/frontend/src/components/ui/card.tsx
index eb9bd7b56..11a72e215 100644
--- a/src/frontend/src/components/ui/card.tsx
+++ b/src/frontend/src/components/ui/card.tsx
@@ -9,7 +9,7 @@ const Card = React.forwardRef<
     ref={ref}
     className={cn(
       "flex flex-col justify-between rounded-lg border bg-muted text-card-foreground shadow-sm transition-all",
-      className
+      className,
     )}
     {...props}
   />
@@ -36,7 +36,7 @@ const CardTitle = React.forwardRef<
     ref={ref}
     className={cn(
       "text-base font-semibold leading-tight tracking-tight",
-      className
+      className,
     )}
     {...props}
   />
diff --git a/src/frontend/src/components/ui/checkbox.tsx b/src/frontend/src/components/ui/checkbox.tsx
index 2c48fde84..1c3d1e4fe 100644
--- a/src/frontend/src/components/ui/checkbox.tsx
+++ b/src/frontend/src/components/ui/checkbox.tsx
@@ -13,7 +13,7 @@ const Checkbox = React.forwardRef<
     ref={ref}
     className={cn(
       "peer h-4 w-4 shrink-0 rounded-sm border border-primary ring-offset-background focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50 data-[state=checked]:bg-primary data-[state=checked]:text-primary-foreground",
-      className
+      className,
     )}
     {...props}
   >
@@ -37,7 +37,7 @@ const CheckBoxDiv = ({
     className={cn(
       className,
       "peer h-4 w-4 shrink-0 rounded-sm border border-primary ring-offset-background focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50",
-      checked ? "bg-primary text-primary-foreground" : ""
+      checked ? "bg-primary text-primary-foreground" : "",
     )}
   >
     {checked && (
diff --git a/src/frontend/src/components/ui/combobox.tsx b/src/frontend/src/components/ui/combobox.tsx
index faaafda56..3f35ce6a7 100644
--- a/src/frontend/src/components/ui/combobox.tsx
+++ b/src/frontend/src/components/ui/combobox.tsx
@@ -65,7 +65,7 @@ export function Combobox({
                     "mr-2 h-4 w-4",
                     value.includes(framework.value)
                       ? "opacity-100"
-                      : "opacity-0"
+                      : "opacity-0",
                   )}
                 />
                 {framework.label}
diff --git a/src/frontend/src/components/ui/command.tsx b/src/frontend/src/components/ui/command.tsx
index 57088882b..a98bc0cf0 100644
--- a/src/frontend/src/components/ui/command.tsx
+++ b/src/frontend/src/components/ui/command.tsx
@@ -16,7 +16,7 @@ const Command = React.forwardRef<
     ref={ref}
     className={cn(
       "flex h-full w-full flex-col overflow-hidden rounded-md bg-popover text-popover-foreground",
-      className
+      className,
     )}
     {...props}
   />
@@ -47,7 +47,7 @@ const CommandInput = React.forwardRef<
       ref={ref}
       className={cn(
         "flex h-11 w-full rounded-md bg-transparent py-3 text-sm outline-none placeholder:text-muted-foreground disabled:cursor-not-allowed disabled:opacity-50",
-        className
+        className,
       )}
       {...props}
     />
@@ -90,7 +90,7 @@ const CommandGroup = React.forwardRef<
     ref={ref}
     className={cn(
       "overflow-hidden p-1 text-foreground [&_[cmdk-group-heading]]:px-2 [&_[cmdk-group-heading]]:py-1.5 [&_[cmdk-group-heading]]:text-xs [&_[cmdk-group-heading]]:font-medium [&_[cmdk-group-heading]]:text-muted-foreground",
-      className
+      className,
     )}
     {...props}
   />
@@ -118,7 +118,7 @@ const CommandItem = React.forwardRef<
     ref={ref}
     className={cn(
       "relative flex cursor-default select-none items-center rounded-sm px-2 py-1.5 text-sm outline-none aria-selected:bg-accent aria-selected:text-accent-foreground data-[disabled=true]:pointer-events-none data-[disabled=true]:opacity-50",
-      className
+      className,
     )}
     {...props}
   />
@@ -134,7 +134,7 @@ const CommandShortcut = ({
     <span
       className={cn(
         "ml-auto text-xs tracking-widest text-muted-foreground",
-        className
+        className,
       )}
       {...props}
     />
diff --git a/src/frontend/src/components/ui/custom-accordion.tsx b/src/frontend/src/components/ui/custom-accordion.tsx
index b43c0f71c..238e0ddab 100644
--- a/src/frontend/src/components/ui/custom-accordion.tsx
+++ b/src/frontend/src/components/ui/custom-accordion.tsx
@@ -24,7 +24,7 @@ const AccordionTrigger = React.forwardRef<
       <div
         className={cn(
           "flex flex-1 cursor-pointer items-center justify-between border-[1px] py-2 text-sm font-medium data-[state=closed]:rounded-md data-[state=open]:rounded-t-md data-[state=open]:border-b-0 data-[state=open]:bg-muted [&[data-state=open]>svg]:rotate-180",
-          className
+          className,
         )}
       >
         {children}
@@ -43,7 +43,7 @@ const AccordionContent = React.forwardRef<
     ref={ref}
     className={cn(
       "data-[state=closed]:animate-accordion-up data-[state=open]:animate-accordion-down overflow-hidden border-[1px] text-sm data-[state=open]:rounded-b-md data-[state=open]:border-t-0 data-[state=open]:bg-muted",
-      className
+      className,
     )}
     {...props}
   >
diff --git a/src/frontend/src/components/ui/dialog-with-no-close.tsx b/src/frontend/src/components/ui/dialog-with-no-close.tsx
index 929172e00..262dee67f 100644
--- a/src/frontend/src/components/ui/dialog-with-no-close.tsx
+++ b/src/frontend/src/components/ui/dialog-with-no-close.tsx
@@ -26,7 +26,7 @@ const DialogOverlay = React.forwardRef<
     ref={ref}
     className={cn(
       "nopan nodelete nodrag noundo nocopy fixed inset-0 bottom-0 left-0 right-0 top-0 z-50 overflow-auto bg-blur-shared backdrop-blur-sm data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0",
-      className
+      className,
     )}
     {...props}
   />
@@ -43,7 +43,7 @@ const DialogContent = React.forwardRef<
       ref={ref}
       className={cn(
         "fixed z-50 flex w-full max-w-lg flex-col gap-4 border bg-background p-6 shadow-lg duration-200 data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0 data-[state=closed]:zoom-out-95 data-[state=open]:zoom-in-95 data-[state=closed]:slide-out-to-left-1/2 data-[state=closed]:slide-out-to-top-[48%] sm:rounded-lg md:w-full",
-        className
+        className,
       )}
       {...props}
     >
@@ -60,7 +60,7 @@ const DialogHeader = ({
   <div
     className={cn(
       "flex flex-col space-y-1.5 text-center sm:text-left",
-      className
+      className,
     )}
     {...props}
   />
@@ -74,7 +74,7 @@ const DialogFooter = ({
   <div
     className={cn(
       "flex flex-col-reverse sm:flex-row sm:justify-end sm:space-x-2",
-      className
+      className,
     )}
     {...props}
   />
@@ -89,7 +89,7 @@ const DialogTitle = React.forwardRef<
     ref={ref}
     className={cn(
       "text-lg font-semibold leading-none tracking-tight",
-      className
+      className,
     )}
     {...props}
   />
diff --git a/src/frontend/src/components/ui/dialog.tsx b/src/frontend/src/components/ui/dialog.tsx
index 76b86f592..915292b98 100644
--- a/src/frontend/src/components/ui/dialog.tsx
+++ b/src/frontend/src/components/ui/dialog.tsx
@@ -27,7 +27,7 @@ const DialogOverlay = React.forwardRef<
     ref={ref}
     className={cn(
       "nopan nodelete nodrag noundo nocopy fixed inset-0 bottom-0 left-0 right-0 top-0 z-50 overflow-auto bg-blur-shared backdrop-blur-sm data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0",
-      className
+      className,
     )}
     {...props}
   />
@@ -44,7 +44,7 @@ const DialogContent = React.forwardRef<
       ref={ref}
       className={cn(
         "fixed z-50 flex w-full max-w-lg flex-col gap-4 border bg-background p-6 shadow-lg duration-200 data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0 data-[state=closed]:zoom-out-95 data-[state=open]:zoom-in-95 data-[state=closed]:slide-out-to-left-1/2 data-[state=closed]:slide-out-to-top-[48%] sm:rounded-lg md:w-full",
-        className
+        className,
       )}
       {...props}
     >
@@ -65,7 +65,7 @@ const DialogHeader = ({
   <div
     className={cn(
       "flex flex-col space-y-1 text-center sm:text-left",
-      className
+      className,
     )}
     {...props}
   />
@@ -79,7 +79,7 @@ const DialogFooter = ({
   <div
     className={cn(
       "flex flex-col-reverse sm:flex-row sm:justify-end sm:space-x-2",
-      className
+      className,
     )}
     {...props}
   />
@@ -94,7 +94,7 @@ const DialogTitle = React.forwardRef<
     ref={ref}
     className={cn(
       "text-lg font-semibold leading-none tracking-tight",
-      className
+      className,
     )}
     {...props}
   />
diff --git a/src/frontend/src/components/ui/dropdown-menu.tsx b/src/frontend/src/components/ui/dropdown-menu.tsx
index f9a0d87d3..2c3cdcd7f 100644
--- a/src/frontend/src/components/ui/dropdown-menu.tsx
+++ b/src/frontend/src/components/ui/dropdown-menu.tsx
@@ -28,7 +28,7 @@ const DropdownMenuSubTrigger = React.forwardRef<
     className={cn(
       "flex cursor-default select-none items-center rounded-sm px-2 py-1.5 text-sm outline-none focus:bg-accent data-[state=open]:bg-accent",
       inset && "pl-8",
-      className
+      className,
     )}
     {...props}
   >
@@ -47,7 +47,7 @@ const DropdownMenuSubContent = React.forwardRef<
     ref={ref}
     className={cn(
       "z-50 min-w-[8rem] overflow-hidden rounded-md border bg-popover p-1 text-popover-foreground shadow-md animate-in data-[side=bottom]:slide-in-from-top-1 data-[side=left]:slide-in-from-right-1 data-[side=right]:slide-in-from-left-1 data-[side=top]:slide-in-from-bottom-1",
-      className
+      className,
     )}
     {...props}
   />
@@ -65,7 +65,7 @@ const DropdownMenuContent = React.forwardRef<
       sideOffset={sideOffset}
       className={cn(
         "z-50 min-w-[8rem] overflow-hidden rounded-md border bg-popover p-1 text-popover-foreground shadow-md animate-in data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2",
-        className
+        className,
       )}
       {...props}
     />
@@ -84,7 +84,7 @@ const DropdownMenuItem = React.forwardRef<
     className={cn(
       "relative flex cursor-default select-none items-center rounded-sm px-2 py-1.5 text-sm outline-none transition-colors focus:bg-accent focus:text-accent-foreground data-[disabled]:pointer-events-none data-[disabled]:opacity-50",
       inset && "pl-8",
-      className
+      className,
     )}
     {...props}
   />
@@ -99,7 +99,7 @@ const DropdownMenuCheckboxItem = React.forwardRef<
     ref={ref}
     className={cn(
       "relative flex cursor-default select-none items-center rounded-sm py-1.5 pl-8 pr-2 text-sm outline-none transition-colors focus:bg-accent focus:text-accent-foreground data-[disabled]:pointer-events-none data-[disabled]:opacity-50",
-      className
+      className,
     )}
     checked={checked}
     {...props}
@@ -123,7 +123,7 @@ const DropdownMenuRadioItem = React.forwardRef<
     ref={ref}
     className={cn(
       "relative flex cursor-default select-none items-center rounded-sm py-1.5 pl-8 pr-2 text-sm outline-none transition-colors focus:bg-accent focus:text-accent-foreground data-[disabled]:pointer-events-none data-[disabled]:opacity-50",
-      className
+      className,
     )}
     {...props}
   >
@@ -148,7 +148,7 @@ const DropdownMenuLabel = React.forwardRef<
     className={cn(
       "px-2 py-1.5 pl-2 text-sm font-semibold",
       inset && "pl-8",
-      className
+      className,
     )}
     {...props}
   />
diff --git a/src/frontend/src/components/ui/form.tsx b/src/frontend/src/components/ui/form.tsx
index dcfd39449..69789b6f3 100644
--- a/src/frontend/src/components/ui/form.tsx
+++ b/src/frontend/src/components/ui/form.tsx
@@ -16,18 +16,18 @@ const Form = FormProvider;
 
 type FormFieldContextValue<
   TFieldValues extends FieldValues = FieldValues,
-  TName extends FieldPath<TFieldValues> = FieldPath<TFieldValues>
+  TName extends FieldPath<TFieldValues> = FieldPath<TFieldValues>,
 > = {
   name: TName;
 };
 
 const FormFieldContext = React.createContext<FormFieldContextValue>(
-  {} as FormFieldContextValue
+  {} as FormFieldContextValue,
 );
 
 const FormField = <
   TFieldValues extends FieldValues = FieldValues,
-  TName extends FieldPath<TFieldValues> = FieldPath<TFieldValues>
+  TName extends FieldPath<TFieldValues> = FieldPath<TFieldValues>,
 >({
   ...props
 }: ControllerProps<TFieldValues, TName>) => {
@@ -66,7 +66,7 @@ type FormItemContextValue = {
 };
 
 const FormItemContext = React.createContext<FormItemContextValue>(
-  {} as FormItemContextValue
+  {} as FormItemContextValue,
 );
 
 const FormItem = React.forwardRef<
diff --git a/src/frontend/src/components/ui/input.tsx b/src/frontend/src/components/ui/input.tsx
index ed0e22993..eb0587e00 100644
--- a/src/frontend/src/components/ui/input.tsx
+++ b/src/frontend/src/components/ui/input.tsx
@@ -12,13 +12,13 @@ const Input = React.forwardRef<HTMLInputElement, InputProps>(
         type={type}
         className={cn(
           "nopan nodelete nodrag noundo nocopy primary-input",
-          className
+          className,
         )}
         ref={ref}
         {...props}
       />
     );
-  }
+  },
 );
 Input.displayName = "Input";
 
diff --git a/src/frontend/src/components/ui/label.tsx b/src/frontend/src/components/ui/label.tsx
index f8d45c11d..dab1236ba 100644
--- a/src/frontend/src/components/ui/label.tsx
+++ b/src/frontend/src/components/ui/label.tsx
@@ -6,7 +6,7 @@ import * as React from "react";
 import { cn } from "../../utils/utils";
 
 const labelVariants = cva(
-  "text-sm font-medium leading-none peer-disabled:cursor-not-allowed peer-disabled:opacity-70"
+  "text-sm font-medium leading-none peer-disabled:cursor-not-allowed peer-disabled:opacity-70",
 );
 
 const Label = React.forwardRef<
diff --git a/src/frontend/src/components/ui/menubar.tsx b/src/frontend/src/components/ui/menubar.tsx
index 512bb6e63..e71efe5e9 100644
--- a/src/frontend/src/components/ui/menubar.tsx
+++ b/src/frontend/src/components/ui/menubar.tsx
@@ -24,7 +24,7 @@ const Menubar = React.forwardRef<
     ref={ref}
     className={cn(
       "flex h-10 items-center space-x-1 rounded-md border bg-background p-1",
-      className
+      className,
     )}
     {...props}
   />
@@ -39,7 +39,7 @@ const MenubarTrigger = React.forwardRef<
     ref={ref}
     className={cn(
       "flex cursor-default select-none items-center rounded-sm px-3 py-1.5 text-sm font-medium outline-none focus:bg-accent focus:text-accent-foreground data-[state=open]:bg-accent data-[state=open]:text-accent-foreground",
-      className
+      className,
     )}
     {...props}
   />
@@ -57,7 +57,7 @@ const MenubarSubTrigger = React.forwardRef<
     className={cn(
       "flex cursor-default select-none items-center rounded-sm px-2 py-1.5 text-sm outline-none focus:bg-accent focus:text-accent-foreground data-[state=open]:bg-accent data-[state=open]:text-accent-foreground",
       inset && "pl-8",
-      className
+      className,
     )}
     {...props}
   >
@@ -75,7 +75,7 @@ const MenubarSubContent = React.forwardRef<
     ref={ref}
     className={cn(
       "z-50 min-w-[8rem] overflow-hidden rounded-md border bg-popover p-1 text-popover-foreground shadow-md animate-in data-[side=bottom]:slide-in-from-top-1 data-[side=left]:slide-in-from-right-1 data-[side=right]:slide-in-from-left-1 data-[side=top]:slide-in-from-bottom-1",
-      className
+      className,
     )}
     {...props}
   />
@@ -88,7 +88,7 @@ const MenubarContent = React.forwardRef<
 >(
   (
     { className, align = "start", alignOffset = -4, sideOffset = 8, ...props },
-    ref
+    ref,
   ) => (
     <MenubarPrimitive.Portal>
       <MenubarPrimitive.Content
@@ -98,12 +98,12 @@ const MenubarContent = React.forwardRef<
         sideOffset={sideOffset}
         className={cn(
           "z-50 min-w-[12rem] overflow-hidden rounded-md border bg-popover p-1 text-popover-foreground shadow-md animate-in slide-in-from-top-1",
-          className
+          className,
         )}
         {...props}
       />
     </MenubarPrimitive.Portal>
-  )
+  ),
 );
 MenubarContent.displayName = MenubarPrimitive.Content.displayName;
 
@@ -118,7 +118,7 @@ const MenubarItem = React.forwardRef<
     className={cn(
       "relative flex cursor-default select-none items-center rounded-sm px-2 py-1.5 text-sm outline-none focus:bg-accent focus:text-accent-foreground data-[disabled]:pointer-events-none data-[disabled]:opacity-50",
       inset && "pl-8",
-      className
+      className,
     )}
     {...props}
   />
@@ -133,7 +133,7 @@ const MenubarCheckboxItem = React.forwardRef<
     ref={ref}
     className={cn(
       "relative flex cursor-default select-none items-center rounded-sm py-1.5 pl-8 pr-2 text-sm outline-none focus:bg-accent focus:text-accent-foreground data-[disabled]:pointer-events-none data-[disabled]:opacity-50",
-      className
+      className,
     )}
     checked={checked}
     {...props}
@@ -156,7 +156,7 @@ const MenubarRadioItem = React.forwardRef<
     ref={ref}
     className={cn(
       "relative flex cursor-default select-none items-center rounded-sm py-1.5 pl-8 pr-2 text-sm outline-none focus:bg-accent focus:text-accent-foreground data-[disabled]:pointer-events-none data-[disabled]:opacity-50",
-      className
+      className,
     )}
     {...props}
   >
@@ -181,7 +181,7 @@ const MenubarLabel = React.forwardRef<
     className={cn(
       "px-2 py-1.5 text-sm font-semibold",
       inset && "pl-8",
-      className
+      className,
     )}
     {...props}
   />
@@ -208,7 +208,7 @@ const MenubarShortcut = ({
     <span
       className={cn(
         "ml-auto text-xs tracking-widest text-muted-foreground",
-        className
+        className,
       )}
       {...props}
     />
diff --git a/src/frontend/src/components/ui/popover.tsx b/src/frontend/src/components/ui/popover.tsx
index 34a6f3142..380ab7c5b 100644
--- a/src/frontend/src/components/ui/popover.tsx
+++ b/src/frontend/src/components/ui/popover.tsx
@@ -19,7 +19,7 @@ const PopoverContent = React.forwardRef<
       sideOffset={sideOffset}
       className={cn(
         "z-50 w-72 rounded-md border bg-popover p-4 text-popover-foreground shadow-md outline-none data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0 data-[state=closed]:zoom-out-95 data-[state=open]:zoom-in-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2",
-        className
+        className,
       )}
       {...props}
     />
@@ -37,7 +37,7 @@ const PopoverContentWithoutPortal = React.forwardRef<
     sideOffset={sideOffset}
     className={cn(
       "z-50 w-72 rounded-md border bg-popover p-4 text-popover-foreground shadow-md outline-none data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0 data-[state=closed]:zoom-out-95 data-[state=open]:zoom-in-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2",
-      className
+      className,
     )}
     {...props}
   />
diff --git a/src/frontend/src/components/ui/progress.tsx b/src/frontend/src/components/ui/progress.tsx
index c2afa8a4c..ff55ab76d 100644
--- a/src/frontend/src/components/ui/progress.tsx
+++ b/src/frontend/src/components/ui/progress.tsx
@@ -12,7 +12,7 @@ const Progress = React.forwardRef<
     ref={ref}
     className={cn(
       "relative h-4 w-full overflow-hidden rounded-full bg-secondary",
-      className
+      className,
     )}
     {...props}
   >
diff --git a/src/frontend/src/components/ui/rename-label.tsx b/src/frontend/src/components/ui/rename-label.tsx
index 0273e1ceb..27a21056b 100644
--- a/src/frontend/src/components/ui/rename-label.tsx
+++ b/src/frontend/src/components/ui/rename-label.tsx
@@ -61,7 +61,7 @@ export default function RenameLabel(props) {
           onInput={resizeInput}
           className={cn(
             "nopan nodelete nodrag noundo nocopy rounded-md bg-transparent px-2 outline-ring hover:outline focus:border-none focus:outline active:outline",
-            props.className
+            props.className,
           )}
           onBlur={() => {
             setIsRename(false);
diff --git a/src/frontend/src/components/ui/select-custom.tsx b/src/frontend/src/components/ui/select-custom.tsx
index 86a7bf3ac..82ba540fe 100644
--- a/src/frontend/src/components/ui/select-custom.tsx
+++ b/src/frontend/src/components/ui/select-custom.tsx
@@ -36,7 +36,7 @@ const SelectContent = React.forwardRef<
         "relative z-50 min-w-[14rem] overflow-hidden rounded-md border bg-popover text-popover-foreground shadow-md data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0 data-[state=closed]:zoom-out-95 data-[state=open]:zoom-in-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2",
         position === "popper" &&
           "data-[side=bottom]:translate-y-1 data-[side=left]:-translate-x-1 data-[side=right]:translate-x-1 data-[side=top]:-translate-y-1",
-        className
+        className,
       )}
       position={position}
       {...props}
@@ -45,7 +45,7 @@ const SelectContent = React.forwardRef<
         className={cn(
           "p-1",
           position === "popper" &&
-            "h-[var(--radix-select-trigger-height)] w-full min-w-[var(--radix-select-trigger-width)]"
+            "h-[var(--radix-select-trigger-height)] w-full min-w-[var(--radix-select-trigger-width)]",
         )}
       >
         {children}
@@ -75,7 +75,7 @@ const SelectItem = React.forwardRef<
     ref={ref}
     className={cn(
       "relative flex w-full cursor-pointer select-none items-center rounded-sm py-1.5 pl-3 pr-2 text-sm outline-none focus:bg-accent focus:text-accent-foreground data-[disabled]:pointer-events-none data-[disabled]:opacity-50",
-      className
+      className,
     )}
     {...props}
   >
diff --git a/src/frontend/src/components/ui/select.tsx b/src/frontend/src/components/ui/select.tsx
index 7430a0d46..812fc8754 100644
--- a/src/frontend/src/components/ui/select.tsx
+++ b/src/frontend/src/components/ui/select.tsx
@@ -19,7 +19,7 @@ const SelectTrigger = React.forwardRef<
     ref={ref}
     className={cn(
       "flex h-8 items-center justify-between rounded-md border border-ring/60 px-4 py-2 text-sm text-primary ring-offset-background placeholder:text-muted-foreground hover:bg-muted focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-50",
-      className
+      className,
     )}
     {...props}
   >
@@ -42,7 +42,7 @@ const SelectContent = React.forwardRef<
         "relative z-50 min-w-[8rem] overflow-hidden rounded-md border bg-popover text-popover-foreground shadow-md data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0 data-[state=closed]:zoom-out-95 data-[state=open]:zoom-in-95 data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-right-2 data-[side=right]:slide-in-from-left-2 data-[side=top]:slide-in-from-bottom-2",
         position === "popper" &&
           "data-[side=bottom]:translate-y-1 data-[side=left]:-translate-x-1 data-[side=right]:translate-x-1 data-[side=top]:-translate-y-1",
-        className
+        className,
       )}
       position={position}
       {...props}
@@ -51,7 +51,7 @@ const SelectContent = React.forwardRef<
         className={cn(
           "p-1",
           position === "popper" &&
-            "h-[var(--radix-select-trigger-height)] w-full min-w-[var(--radix-select-trigger-width)]"
+            "h-[var(--radix-select-trigger-height)] w-full min-w-[var(--radix-select-trigger-width)]",
         )}
       >
         {children}
@@ -81,7 +81,7 @@ const SelectItem = React.forwardRef<
     ref={ref}
     className={cn(
       "relative flex w-full cursor-default select-none items-center rounded-sm py-1.5 pl-8 pr-8 text-sm outline-none focus:bg-accent focus:text-accent-foreground data-[disabled]:pointer-events-none data-[disabled]:opacity-50",
-      className
+      className,
     )}
     {...props}
   >
diff --git a/src/frontend/src/components/ui/separator.tsx b/src/frontend/src/components/ui/separator.tsx
index 1fec6d2ca..8532fc89e 100644
--- a/src/frontend/src/components/ui/separator.tsx
+++ b/src/frontend/src/components/ui/separator.tsx
@@ -10,7 +10,7 @@ const Separator = React.forwardRef<
 >(
   (
     { className, orientation = "horizontal", decorative = true, ...props },
-    ref
+    ref,
   ) => (
     <SeparatorPrimitive.Root
       ref={ref}
@@ -19,11 +19,11 @@ const Separator = React.forwardRef<
       className={cn(
         "shrink-0 bg-ring/40",
         orientation === "horizontal" ? "h-[1px] w-full" : "h-full w-[1px]",
-        className
+        className,
       )}
       {...props}
     />
-  )
+  ),
 );
 Separator.displayName = SeparatorPrimitive.Root.displayName;
 
diff --git a/src/frontend/src/components/ui/switch.tsx b/src/frontend/src/components/ui/switch.tsx
index 0d4bc8224..8b89baa50 100644
--- a/src/frontend/src/components/ui/switch.tsx
+++ b/src/frontend/src/components/ui/switch.tsx
@@ -11,7 +11,7 @@ const Switch = React.forwardRef<
   <SwitchPrimitives.Root
     className={cn(
       "peer inline-flex h-[24px] w-[44px] shrink-0 cursor-pointer items-center rounded-full border-2 border-transparent transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 focus-visible:ring-offset-background disabled:cursor-not-allowed disabled:opacity-50 data-[state=checked]:bg-primary data-[state=unchecked]:bg-ring",
-      className
+      className,
     )}
     {...props}
     ref={ref}
@@ -19,7 +19,7 @@ const Switch = React.forwardRef<
     <SwitchPrimitives.Thumb
       aria-disabled={props.disabled}
       className={cn(
-        "pointer-events-none block h-5 w-5 rounded-full bg-background shadow-lg ring-0 transition-transform aria-disabled:bg-muted/70 data-[state=checked]:translate-x-5 data-[state=unchecked]:translate-x-0"
+        "pointer-events-none block h-5 w-5 rounded-full bg-background shadow-lg ring-0 transition-transform aria-disabled:bg-muted/70 data-[state=checked]:translate-x-5 data-[state=unchecked]:translate-x-0",
       )}
     />
   </SwitchPrimitives.Root>
diff --git a/src/frontend/src/components/ui/table.tsx b/src/frontend/src/components/ui/table.tsx
index eb1e3bda6..cac4b5b70 100644
--- a/src/frontend/src/components/ui/table.tsx
+++ b/src/frontend/src/components/ui/table.tsx
@@ -55,7 +55,7 @@ const TableRow = React.forwardRef<
     ref={ref}
     className={cn(
       "border-b transition-colors hover:bg-muted/50 data-[state=selected]:bg-muted",
-      className
+      className,
     )}
     {...props}
   />
@@ -70,7 +70,7 @@ const TableHead = React.forwardRef<
     ref={ref}
     className={cn(
       "h-12 px-4 text-left align-middle font-medium text-muted-foreground [&:has([role=checkbox])]:pr-0",
-      className
+      className,
     )}
     {...props}
   />
diff --git a/src/frontend/src/components/ui/tabs.tsx b/src/frontend/src/components/ui/tabs.tsx
index a1f365ba6..e6eefc5f8 100644
--- a/src/frontend/src/components/ui/tabs.tsx
+++ b/src/frontend/src/components/ui/tabs.tsx
@@ -14,7 +14,7 @@ const TabsList = React.forwardRef<
     ref={ref}
     className={cn(
       "inline-flex h-10 items-center justify-center rounded-md p-1 text-muted-foreground",
-      className
+      className,
     )}
     {...props}
   />
@@ -29,7 +29,7 @@ const TabsTrigger = React.forwardRef<
     ref={ref}
     className={cn(
       "inline-flex items-center justify-center whitespace-nowrap rounded-sm px-3 py-1.5 text-sm font-medium ring-offset-background transition-all focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50 data-[state=active]:border data-[state=inactive]:border data-[state=inactive]:border-muted data-[state=active]:bg-background data-[state=active]:text-foreground data-[state=active]:shadow-sm data-[state=inactive]:hover:bg-secondary/80",
-      className
+      className,
     )}
     {...props}
   />
@@ -44,7 +44,7 @@ const TabsContent = React.forwardRef<
     ref={ref}
     className={cn(
       "mt-2 ring-offset-background focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2",
-      className
+      className,
     )}
     {...props}
   />
diff --git a/src/frontend/src/components/ui/textarea.tsx b/src/frontend/src/components/ui/textarea.tsx
index 1f2970545..c18d57cfc 100644
--- a/src/frontend/src/components/ui/textarea.tsx
+++ b/src/frontend/src/components/ui/textarea.tsx
@@ -10,13 +10,13 @@ const Textarea = React.forwardRef<HTMLTextAreaElement, TextareaProps>(
       <textarea
         className={cn(
           "nopan nodelete nodrag noundo nocopy textarea-primary",
-          className
+          className,
         )}
         ref={ref}
         {...props}
       />
     );
-  }
+  },
 );
 Textarea.displayName = "Textarea";
 
diff --git a/src/frontend/src/components/ui/toggle.tsx b/src/frontend/src/components/ui/toggle.tsx
index 08ebb1380..f41840dce 100644
--- a/src/frontend/src/components/ui/toggle.tsx
+++ b/src/frontend/src/components/ui/toggle.tsx
@@ -25,7 +25,7 @@ const toggleVariants = cva(
       variant: "default",
       size: "default",
     },
-  }
+  },
 );
 
 const Toggle = React.forwardRef<
diff --git a/src/frontend/src/components/ui/tooltip.tsx b/src/frontend/src/components/ui/tooltip.tsx
index 7f81bc3bd..3ee8b9c7a 100644
--- a/src/frontend/src/components/ui/tooltip.tsx
+++ b/src/frontend/src/components/ui/tooltip.tsx
@@ -20,7 +20,7 @@ const TooltipContent = React.forwardRef<
       sideOffset={sideOffset}
       className={cn(
         "z-45 overflow-y-auto rounded-md border bg-popover px-3 py-1.5 text-sm text-popover-foreground shadow-md animate-in fade-in-50 data-[side=bottom]:slide-in-from-top-1 data-[side=left]:slide-in-from-right-1 data-[side=right]:slide-in-from-left-1 data-[side=top]:slide-in-from-bottom-1",
-        className
+        className,
       )}
       {...props}
     />
@@ -37,7 +37,7 @@ const TooltipContentWithoutPortal = React.forwardRef<
     sideOffset={sideOffset}
     className={cn(
       "z-45 overflow-y-auto rounded-md border bg-popover px-3 py-1.5 text-sm text-popover-foreground shadow-md animate-in fade-in-50 data-[side=bottom]:slide-in-from-top-1 data-[side=left]:slide-in-from-right-1 data-[side=right]:slide-in-from-left-1 data-[side=top]:slide-in-from-bottom-1",
-      className
+      className,
     )}
     {...props}
   />
diff --git a/src/frontend/src/constants/constants.ts b/src/frontend/src/constants/constants.ts
index 07dface8e..7d95d7415 100644
--- a/src/frontend/src/constants/constants.ts
+++ b/src/frontend/src/constants/constants.ts
@@ -71,6 +71,16 @@ export const programmingLanguages: languageMap = {
  */
 export const MAX_LENGTH_TO_SCROLL_TOOLTIP = 200;
 
+export const MESSAGES_TABLE_ORDER = [
+  "timestamp",
+  "message",
+  "text",
+  "sender",
+  "sender_name",
+  "session_id",
+  "files",
+];
+
 /**
  * Number maximum of components to scroll on tooltips
  * @constant
@@ -131,6 +141,7 @@ export const CODE_PROMPT_DIALOG_SUBTITLE =
 
 export const CODE_DICT_DIALOG_SUBTITLE =
   "Edit your dictionary. This dialog allows you to create your own customized dictionary. You can add as many key-value pairs as you want. While in edit mode, you can enter ({}) or ([]), and this will result in adding a new object or array.";
+
 /**
  * The base text for subtitle of Prompt Dialog
  * @constant
@@ -654,17 +665,17 @@ export const OUTPUT_TYPES = new Set([
   "JsonOutput",
   "KeyPairOutput",
   "StringListOutput",
-  "RecordsOutput",
+  "DataOutput",
   "TableOutput",
 ]);
 
 export const CHAT_FIRST_INITIAL_TEXT =
-  "Start a conversation and click the agent's thoughts";
+  "Start a conversation and click the agent's memories";
 
 export const TOOLTIP_OUTDATED_NODE =
   "Your component is outdated. Click to update (data may be lost)";
 
-export const CHAT_SECOND_INITIAL_TEXT = "to inspect the chaining process.";
+export const CHAT_SECOND_INITIAL_TEXT = "to inspect previous messages.";
 
 export const ZERO_NOTIFICATIONS = "No new notifications";
 
@@ -689,11 +700,12 @@ export const MY_COLLECTION_DESC =
   "Manage your personal projects. Download and upload entire collections.";
 export const STORE_DESC = "Explore community-shared flows and components.";
 export const STORE_TITLE = "Langflow Store";
-export const NO_API_KEY = "You don't have an API key. ";
+export const NO_API_KEY = "You don't have an API key.";
 export const INSERT_API_KEY = "Insert your Langflow API key.";
 export const INVALID_API_KEY = "Your API key is not valid. ";
 export const CREATE_API_KEY = `Don’t have an API key? Sign up at`;
 export const STATUS_BUILD = "Build to validate status.";
+export const STATUS_INACTIVE = "Execution blocked";
 export const STATUS_BUILDING = "Building...";
 export const SAVED_HOVER = "Last saved at ";
 export const RUN_TIMESTAMP_PREFIX = "Last Run: ";
@@ -838,7 +850,15 @@ export const MAX_BATCH_SIZE = 50;
 export const MODAL_CLASSES =
   "nopan nodelete nodrag noundo nocopy fixed inset-0 bottom-0 left-0 right-0 top-0 z-50 overflow-auto bg-blur-shared backdrop-blur-sm data-[state=open]:animate-in data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=open]:fade-in-0";
 
+export const ALLOWED_IMAGE_INPUT_EXTENSIONS = ["png", "jpg", "jpeg"];
+
+export const FS_ERROR_TEXT =
+  "Please ensure your file has one of the following extensions:";
+export const SN_ERROR_TEXT = ALLOWED_IMAGE_INPUT_EXTENSIONS.join(", ");
+
 export const ERROR_UPDATING_COMPONENT =
   "An unexpected error occurred while updating the Component. Please try again.";
 export const TITLE_ERROR_UPDATING_COMPONENT =
   "Error while updating the Component";
+
+export const EMPTY_INPUT_SEND_MESSAGE = "No input message provided.";
diff --git a/src/frontend/src/contexts/authContext.tsx b/src/frontend/src/contexts/authContext.tsx
index f6b81a235..bd5c2d02c 100644
--- a/src/frontend/src/contexts/authContext.tsx
+++ b/src/frontend/src/contexts/authContext.tsx
@@ -31,17 +31,17 @@ export function AuthProvider({ children }): React.ReactElement {
   const navigate = useNavigate();
   const cookies = new Cookies();
   const [accessToken, setAccessToken] = useState<string | null>(
-    cookies.get("access_token_lf") ?? null
+    cookies.get("access_token_lf") ?? null,
   );
   const [isAuthenticated, setIsAuthenticated] = useState<boolean>(
-    !!cookies.get("access_token_lf")
+    !!cookies.get("access_token_lf"),
   );
   const [isAdmin, setIsAdmin] = useState<boolean>(false);
   const [userData, setUserData] = useState<Users | null>(null);
   const [autoLogin, setAutoLogin] = useState<boolean>(false);
   const setLoading = useAlertStore((state) => state.setLoading);
   const [apiKey, setApiKey] = useState<string | null>(
-    cookies.get("apikey_tkn_lflw")
+    cookies.get("apikey_tkn_lflw"),
   );
 
   const getFoldersApi = useFolderStore((state) => state.getFoldersApi);
diff --git a/src/frontend/src/controllers/API/api.tsx b/src/frontend/src/controllers/API/api.tsx
index 6bd73f5f1..0ac3efa6b 100644
--- a/src/frontend/src/controllers/API/api.tsx
+++ b/src/frontend/src/controllers/API/api.tsx
@@ -48,7 +48,7 @@ function ApiInterceptor() {
         }
         await clearBuildVerticesState(error);
         return Promise.reject(error);
-      }
+      },
     );
 
     const isAuthorizedURL = (url) => {
@@ -65,10 +65,10 @@ function ApiInterceptor() {
         const parsedURL = new URL(url);
 
         const isDomainAllowed = authorizedDomains.some(
-          (domain) => parsedURL.origin === new URL(domain).origin
+          (domain) => parsedURL.origin === new URL(domain).origin,
         );
         const isEndpointAllowed = authorizedEndpoints.some((endpoint) =>
-          parsedURL.pathname.includes(endpoint)
+          parsedURL.pathname.includes(endpoint),
         );
 
         return isDomainAllowed || isEndpointAllowed;
@@ -102,7 +102,7 @@ function ApiInterceptor() {
       },
       (error) => {
         return Promise.reject(error);
-      }
+      },
     );
 
     return () => {
@@ -134,7 +134,7 @@ function ApiInterceptor() {
       if (error?.config?.headers) {
         delete error.config.headers["Authorization"];
         error.config.headers["Authorization"] = `Bearer ${cookies.get(
-          "access_token_lf"
+          "access_token_lf",
         )}`;
         const response = await axios.request(error.config);
         return response;
diff --git a/src/frontend/src/controllers/API/helpers/check-duplicate-requests.ts b/src/frontend/src/controllers/API/helpers/check-duplicate-requests.ts
index ec3c74268..85c6608ac 100644
--- a/src/frontend/src/controllers/API/helpers/check-duplicate-requests.ts
+++ b/src/frontend/src/controllers/API/helpers/check-duplicate-requests.ts
@@ -9,7 +9,7 @@ export function checkDuplicateRequestAndStoreRequest(config) {
   const currentUrl = window.location.pathname;
   const currentTime = Date.now();
   const isContained = AUTHORIZED_DUPLICATE_REQUESTS.some((request) =>
-    config?.url!.includes(request)
+    config?.url!.includes(request),
   );
 
   if (
diff --git a/src/frontend/src/controllers/API/index.ts b/src/frontend/src/controllers/API/index.ts
index 481aa2135..b20a4c23b 100644
--- a/src/frontend/src/controllers/API/index.ts
+++ b/src/frontend/src/controllers/API/index.ts
@@ -34,10 +34,13 @@ import {
 /**
  * Fetches all objects from the API endpoint.
  *
+ * @param {boolean} force_refresh - Whether to force a refresh of the data.
  * @returns {Promise<AxiosResponse<APIObjectType>>} A promise that resolves to an AxiosResponse containing all the objects.
  */
-export async function getAll(): Promise<AxiosResponse<APIObjectType>> {
-  return await api.get(`${BASE_URL_API}all`);
+export async function getAll(
+  force_refresh: boolean = true,
+): Promise<AxiosResponse<APIObjectType>> {
+  return await api.get(`${BASE_URL_API}all?force_refresh=${force_refresh}`);
 }
 
 const GITHUB_API_URL = "https://api.github.com";
@@ -63,7 +66,7 @@ export async function sendAll(data: sendAllProps) {
 }
 
 export async function postValidateCode(
-  code: string
+  code: string,
 ): Promise<AxiosResponse<errorsTypeAPI>> {
   return await api.post(`${BASE_URL_API}validate/code`, { code });
 }
@@ -78,7 +81,7 @@ export async function postValidateCode(
 export async function postValidatePrompt(
   name: string,
   template: string,
-  frontend_node: APIClassType
+  frontend_node: APIClassType,
 ): Promise<AxiosResponse<PromptTypeAPI>> {
   return api.post(`${BASE_URL_API}validate/prompt`, {
     name,
@@ -124,6 +127,7 @@ export async function saveFlowToDatabase(newFlow: {
   style?: FlowStyleType;
   is_component?: boolean;
   folder_id?: string;
+  endpoint_name?: string;
 }): Promise<FlowType> {
   try {
     const response = await api.post(`${BASE_URL_API}flows/`, {
@@ -132,6 +136,7 @@ export async function saveFlowToDatabase(newFlow: {
       description: newFlow.description,
       is_component: newFlow.is_component,
       folder_id: newFlow.folder_id === "" ? null : newFlow.folder_id,
+      endpoint_name: newFlow.endpoint_name,
     });
 
     if (response.status !== 201) {
@@ -151,7 +156,7 @@ export async function saveFlowToDatabase(newFlow: {
  * @throws Will throw an error if the update fails.
  */
 export async function updateFlowInDatabase(
-  updatedFlow: FlowType
+  updatedFlow: FlowType,
 ): Promise<FlowType> {
   try {
     const response = await api.patch(`${BASE_URL_API}flows/${updatedFlow.id}`, {
@@ -329,7 +334,7 @@ export async function getHealth() {
  *
  */
 export async function getBuildStatus(
-  flowId: string
+  flowId: string,
 ): Promise<AxiosResponse<BuildStatusTypeAPI>> {
   return await api.get(`${BASE_URL_API}build/${flowId}/status`);
 }
@@ -342,7 +347,7 @@ export async function getBuildStatus(
  *
  */
 export async function postBuildInit(
-  flow: FlowType
+  flow: FlowType,
 ): Promise<AxiosResponse<InitTypeAPI>> {
   return await api.post(`${BASE_URL_API}build/init/${flow.id}`, flow);
 }
@@ -358,7 +363,7 @@ export async function postBuildInit(
  */
 export async function uploadFile(
   file: File,
-  id: string
+  id: string,
 ): Promise<AxiosResponse<UploadFileTypeAPI>> {
   const formData = new FormData();
   formData.append("file", file);
@@ -380,7 +385,7 @@ export async function getProfilePictures(): Promise<ProfilePicturesTypeAPI | nul
 
 export async function postCustomComponent(
   code: string,
-  apiClass: APIClassType
+  apiClass: APIClassType,
 ): Promise<AxiosResponse<APIClassType>> {
   // let template = apiClass.template;
   return await api.post(`${BASE_URL_API}custom_component`, {
@@ -393,7 +398,7 @@ export async function postCustomComponentUpdate(
   code: string,
   template: APITemplateType,
   field: string,
-  field_value: any
+  field_value: any,
 ): Promise<AxiosResponse<APIClassType>> {
   return await api.post(`${BASE_URL_API}custom_component/update`, {
     code,
@@ -415,7 +420,7 @@ export async function onLogin(user: LoginType) {
         headers: {
           "Content-Type": "application/x-www-form-urlencoded",
         },
-      }
+      },
     );
 
     if (response.status === 200) {
@@ -477,11 +482,11 @@ export async function addUser(user: UserInputType): Promise<Array<Users>> {
 
 export async function getUsersPage(
   skip: number,
-  limit: number
+  limit: number,
 ): Promise<Array<Users>> {
   try {
     const res = await api.get(
-      `${BASE_URL_API}users/?skip=${skip}&limit=${limit}`
+      `${BASE_URL_API}users/?skip=${skip}&limit=${limit}`,
     );
     if (res.status === 200) {
       return res.data;
@@ -518,7 +523,7 @@ export async function resetPassword(user_id: string, user: resetPasswordType) {
   try {
     const res = await api.patch(
       `${BASE_URL_API}users/${user_id}/reset-password`,
-      user
+      user,
     );
     if (res.status === 200) {
       return res.data;
@@ -592,7 +597,7 @@ export async function saveFlowStore(
     last_tested_version?: string;
   },
   tags: string[],
-  publicFlow = false
+  publicFlow = false,
 ): Promise<FlowType> {
   try {
     const response = await api.post(`${BASE_URL_API}store/components/`, {
@@ -721,7 +726,7 @@ export async function postStoreComponents(component: Component) {
 export async function getComponent(component_id: string) {
   try {
     const res = await api.get(
-      `${BASE_URL_API}store/components/${component_id}`
+      `${BASE_URL_API}store/components/${component_id}`,
     );
     if (res.status === 200) {
       return res.data;
@@ -736,7 +741,7 @@ export async function searchComponent(
   page?: number | null,
   limit?: number | null,
   status?: string | null,
-  tags?: string[]
+  tags?: string[],
 ): Promise<StoreComponentResponse | undefined> {
   try {
     let url = `${BASE_URL_API}store/components/`;
@@ -848,7 +853,7 @@ export async function updateFlowStore(
   },
   tags: string[],
   publicFlow = false,
-  id: string
+  id: string,
 ): Promise<FlowType> {
   try {
     const response = await api.patch(`${BASE_URL_API}store/components/${id}`, {
@@ -932,7 +937,7 @@ export async function deleteGlobalVariable(id: string) {
 export async function updateGlobalVariable(
   name: string,
   value: string,
-  id: string
+  id: string,
 ) {
   try {
     const response = api.patch(`${BASE_URL_API}variables/${id}`, {
@@ -951,7 +956,7 @@ export async function getVerticesOrder(
   startNodeId?: string | null,
   stopNodeId?: string | null,
   nodes?: Node[],
-  Edges?: Edge[]
+  Edges?: Edge[],
 ): Promise<AxiosResponse<VerticesOrderTypeAPI>> {
   // nodeId is optional and is a query parameter
   // if nodeId is not provided, the API will return all vertices
@@ -971,7 +976,7 @@ export async function getVerticesOrder(
   return await api.post(
     `${BASE_URL_API}build/${flowId}/vertices`,
     data,
-    config
+    config,
   );
 }
 
@@ -979,7 +984,7 @@ export async function postBuildVertex(
   flowId: string,
   vertexId: string,
   input_value: string,
-  files?: string[]
+  files?: string[],
 ): Promise<AxiosResponse<VertexBuildTypeAPI>> {
   // input_value is optional and is a query parameter
   let data = {};
@@ -991,7 +996,7 @@ export async function postBuildVertex(
   }
   return await api.post(
     `${BASE_URL_API}build/${flowId}/vertices/${vertexId}`,
-    data
+    data,
   );
 }
 
@@ -1015,7 +1020,7 @@ export async function getFlowPool({
 }
 
 export async function deleteFlowPool(
-  flowId: string
+  flowId: string,
 ): Promise<AxiosResponse<any>> {
   const config = {};
   config["params"] = { flow_id: flowId };
@@ -1029,7 +1034,7 @@ export async function deleteFlowPool(
  * @returns A promise that resolves to an array of AxiosResponse objects representing the delete responses.
  */
 export async function multipleDeleteFlowsComponents(
-  flowIds: string[]
+  flowIds: string[],
 ): Promise<AxiosResponse<any>[]> {
   const batches: string[][] = [];
 
@@ -1052,7 +1057,7 @@ export async function multipleDeleteFlowsComponents(
 
   // Execute all delete requests
   const responses: Promise<AxiosResponse<any>>[] = batches.map((batch) =>
-    deleteBatch(batch)
+    deleteBatch(batch),
   );
 
   // Return the responses after all requests are completed
@@ -1062,7 +1067,7 @@ export async function multipleDeleteFlowsComponents(
 export async function getTransactionTable(
   id: string,
   mode: "intersection" | "union",
-  params = {}
+  params = {},
 ): Promise<{ rows: Array<object>; columns: Array<ColDef | ColGroupDef> }> {
   const config = {};
   config["params"] = { flow_id: id };
@@ -1078,7 +1083,7 @@ export async function getMessagesTable(
   mode: "intersection" | "union",
   id?: string,
   excludedFields?: string[],
-  params = {}
+  params = {},
 ): Promise<{ rows: Array<Message>; columns: Array<ColDef | ColGroupDef> }> {
   const config = {};
   if (id) {
diff --git a/src/frontend/src/icons/AWS/index.tsx b/src/frontend/src/icons/AWS/index.tsx
index caedd54aa..7bdb4c4d1 100644
--- a/src/frontend/src/icons/AWS/index.tsx
+++ b/src/frontend/src/icons/AWS/index.tsx
@@ -4,5 +4,5 @@ import SvgAWS from "./AWS";
 export const AWSIcon = forwardRef<SVGSVGElement, React.PropsWithChildren<{}>>(
   (props, ref) => {
     return <SvgAWS ref={ref} {...props} />;
-  }
+  },
 );
diff --git a/src/frontend/src/icons/AzLogo/index.tsx b/src/frontend/src/icons/AzLogo/index.tsx
index 4fa00c204..fa8f5605e 100644
--- a/src/frontend/src/icons/AzLogo/index.tsx
+++ b/src/frontend/src/icons/AzLogo/index.tsx
@@ -4,5 +4,5 @@ import SvgAzLogo from "./AzLogo";
 export const AzIcon = forwardRef<SVGSVGElement, React.PropsWithChildren<{}>>(
   (props, ref) => {
     return <SvgAzLogo ref={ref} {...props} />;
-  }
+  },
 );
diff --git a/src/frontend/src/icons/Azure/index.tsx b/src/frontend/src/icons/Azure/index.tsx
index 346868ab4..cde247d1b 100644
--- a/src/frontend/src/icons/Azure/index.tsx
+++ b/src/frontend/src/icons/Azure/index.tsx
@@ -4,5 +4,5 @@ import SvgAzure from "./Azure";
 export const AzureIcon = forwardRef<SVGSVGElement, React.PropsWithChildren<{}>>(
   (props, ref) => {
     return <SvgAzure ref={ref} {...props} />;
-  }
+  },
 );
diff --git a/src/frontend/src/icons/Bing/index.tsx b/src/frontend/src/icons/Bing/index.tsx
index 8e030ca88..60e70d6a6 100644
--- a/src/frontend/src/icons/Bing/index.tsx
+++ b/src/frontend/src/icons/Bing/index.tsx
@@ -4,5 +4,5 @@ import SvgBing from "./Bing";
 export const BingIcon = forwardRef<SVGSVGElement, React.PropsWithChildren<{}>>(
   (props, ref) => {
     return <SvgBing ref={ref} {...props} />;
-  }
+  },
 );
diff --git a/src/frontend/src/icons/FacebookMessenger/index.tsx b/src/frontend/src/icons/FacebookMessenger/index.tsx
index 621e5b766..5264b376d 100644
--- a/src/frontend/src/icons/FacebookMessenger/index.tsx
+++ b/src/frontend/src/icons/FacebookMessenger/index.tsx
@@ -4,5 +4,5 @@ import SvgFacebookMessengerLogo2020 from "./FacebookMessengerLogo2020";
 export const FBIcon = forwardRef<SVGSVGElement, React.PropsWithChildren<{}>>(
   (props, ref) => {
     return <SvgFacebookMessengerLogo2020 ref={ref} {...props} />;
-  }
+  },
 );
diff --git a/src/frontend/src/icons/Groq/index.tsx b/src/frontend/src/icons/Groq/index.tsx
index 291696154..6c4283d29 100644
--- a/src/frontend/src/icons/Groq/index.tsx
+++ b/src/frontend/src/icons/Groq/index.tsx
@@ -4,5 +4,5 @@ import SvgGroqLogo from "./GroqLogo";
 export const GroqIcon = forwardRef<SVGSVGElement, React.PropsWithChildren<{}>>(
   (props, ref) => {
     return <SvgGroqLogo ref={ref} {...props} />;
-  }
+  },
 );
diff --git a/src/frontend/src/icons/IFixIt/index.tsx b/src/frontend/src/icons/IFixIt/index.tsx
index 7d94a4fcd..318ae72a6 100644
--- a/src/frontend/src/icons/IFixIt/index.tsx
+++ b/src/frontend/src/icons/IFixIt/index.tsx
@@ -4,5 +4,5 @@ import SvgIfixitSeeklogocom from "./IfixitSeeklogoCom";
 export const IFixIcon = forwardRef<SVGSVGElement, React.PropsWithChildren<{}>>(
   (props, ref) => {
     return <SvgIfixitSeeklogocom ref={ref} {...props} />;
-  }
+  },
 );
diff --git a/src/frontend/src/icons/Meta/index.tsx b/src/frontend/src/icons/Meta/index.tsx
index 0882b673d..9fb123e86 100644
--- a/src/frontend/src/icons/Meta/index.tsx
+++ b/src/frontend/src/icons/Meta/index.tsx
@@ -4,5 +4,5 @@ import SvgMetaIcon from "./MetaIcon";
 export const MetaIcon = forwardRef<SVGSVGElement, React.PropsWithChildren<{}>>(
   (props, ref) => {
     return <SvgMetaIcon ref={ref} {...props} />;
-  }
+  },
 );
diff --git a/src/frontend/src/icons/Redis/index.tsx b/src/frontend/src/icons/Redis/index.tsx
index b1624e4a5..80f94bd85 100644
--- a/src/frontend/src/icons/Redis/index.tsx
+++ b/src/frontend/src/icons/Redis/index.tsx
@@ -4,5 +4,5 @@ import { SvgRedis } from "./Redis";
 export const RedisIcon = forwardRef<SVGSVGElement, React.PropsWithChildren<{}>>(
   (props, ref) => {
     return <SvgRedis ref={ref} {...props} />;
-  }
+  },
 );
diff --git a/src/frontend/src/icons/Searx/index.tsx b/src/frontend/src/icons/Searx/index.tsx
index f78252072..d1e07c3b4 100644
--- a/src/frontend/src/icons/Searx/index.tsx
+++ b/src/frontend/src/icons/Searx/index.tsx
@@ -4,5 +4,5 @@ import SvgSearxLogo from "./SearxLogo";
 export const SearxIcon = forwardRef<SVGSVGElement, React.PropsWithChildren<{}>>(
   (props, ref) => {
     return <SvgSearxLogo ref={ref} {...props} />;
-  }
+  },
 );
diff --git a/src/frontend/src/icons/Share/index.tsx b/src/frontend/src/icons/Share/index.tsx
index 00beb1c4b..2a68b6c19 100644
--- a/src/frontend/src/icons/Share/index.tsx
+++ b/src/frontend/src/icons/Share/index.tsx
@@ -4,5 +4,5 @@ import SvgShare from "./Share";
 export const ShareIcon = forwardRef<SVGSVGElement, React.PropsWithChildren<{}>>(
   (props, ref) => {
     return <SvgShare ref={ref} {...props} />;
-  }
+  },
 );
diff --git a/src/frontend/src/icons/Slack/index.tsx b/src/frontend/src/icons/Slack/index.tsx
index eab13486f..d37a8ce1a 100644
--- a/src/frontend/src/icons/Slack/index.tsx
+++ b/src/frontend/src/icons/Slack/index.tsx
@@ -4,5 +4,5 @@ import SvgSlackIcon from "./SlackIcon";
 export const SlackIcon = forwardRef<SVGSVGElement, React.PropsWithChildren<{}>>(
   (props, ref) => {
     return <SvgSlackIcon ref={ref} {...props} />;
-  }
+  },
 );
diff --git a/src/frontend/src/icons/Streamlit/index.tsx b/src/frontend/src/icons/Streamlit/index.tsx
index 6b3391021..1a4c55119 100644
--- a/src/frontend/src/icons/Streamlit/index.tsx
+++ b/src/frontend/src/icons/Streamlit/index.tsx
@@ -4,5 +4,5 @@ import SvgStreamlit from "./SvgStreamlit";
 export const Streamlit = forwardRef<SVGSVGElement, React.PropsWithChildren<{}>>(
   (props, ref) => {
     return <SvgStreamlit className="icon" ref={ref} {...props} />;
-  }
+  },
 );
diff --git a/src/frontend/src/icons/Upstash/UpstashIcon.jsx b/src/frontend/src/icons/Upstash/UpstashIcon.jsx
new file mode 100644
index 000000000..95e7a062c
--- /dev/null
+++ b/src/frontend/src/icons/Upstash/UpstashIcon.jsx
@@ -0,0 +1,45 @@
+import React from "react";
+
+const UpstashIcon = (props) => (
+  <svg
+    width="256px"
+    height="341px"
+    viewBox="0 0 256 341"
+    version="1.1"
+    xmlns="http://www.w3.org/2000/svg"
+    preserveAspectRatio="xMidYMid"
+    {...props}
+  >
+    <title>upstash</title>
+    <g>
+      <path
+        d="M0,298.416784 C56.5542815,354.970323 148.246768,354.970323 204.801032,298.416784 C261.354571,241.86252 261.354571,150.170106 204.801032,93.6158424 L179.200462,119.215688 C221.61634,161.631567 221.61634,230.401059 179.200462,272.816213 C136.785307,315.232092 68.0157428,315.232092 25.5998642,272.816213 L0,298.416784 Z"
+        fill="#00C98D"
+      ></path>
+      <path
+        d="M51.200362,247.216367 C79.4772765,275.493137 125.323122,275.493137 153.600615,247.216367 C181.877385,218.939598 181.877385,173.093028 153.600615,144.816259 L128.000769,170.416105 C142.139154,184.55449 142.139154,207.477412 128.000769,221.616521 C113.86166,235.754906 90.9387378,235.754906 76.800353,221.616521 L51.200362,247.216367 Z"
+        fill="#00C98D"
+      ></path>
+      <path
+        d="M256,42.415426 C199.445737,-14.1384753 107.753322,-14.1384753 51.1994207,42.415426 C-5.35485714,98.9696894 -5.35485714,190.662104 51.1994207,247.216367 L76.7989048,221.616521 C34.3841124,179.200643 34.3841124,110.431151 76.7989048,68.0159962 C119.214783,25.6001177 187.984275,25.6001177 230.39943,68.0159962 L256,42.415426 Z"
+        fill="#00C98D"
+      ></path>
+      <path
+        d="M204.800308,93.6158424 C176.523538,65.3390727 130.676245,65.3390727 102.399475,93.6158424 C74.1219813,121.893336 74.1219813,167.739181 102.399475,196.015951 L127.999321,170.416105 C113.860936,156.27772 113.860936,133.354797 127.999321,119.215688 C142.137706,105.077304 165.060629,105.077304 179.199738,119.215688 L204.800308,93.6158424 Z"
+        fill="#00C98D"
+      ></path>
+      <path
+        d="M256,42.415426 C199.445737,-14.1384753 107.753322,-14.1384753 51.1994207,42.415426 C-5.35485714,98.9696894 -5.35485714,190.662104 51.1994207,247.216367 L76.7989048,221.616521 C34.3841124,179.200643 34.3841124,110.431151 76.7989048,68.0159962 C119.214783,25.6001177 187.984275,25.6001177 230.39943,68.0159962 L256,42.415426 Z"
+        fillOpacity="0.4"
+        fill="#FFFFFF"
+      ></path>
+      <path
+        d="M204.800308,93.6158424 C176.523538,65.3390727 130.676245,65.3390727 102.399475,93.6158424 C74.1219813,121.893336 74.1219813,167.739181 102.399475,196.015951 L127.999321,170.416105 C113.860936,156.27772 113.860936,133.354797 127.999321,119.215688 C142.137706,105.077304 165.060629,105.077304 179.199738,119.215688 L204.800308,93.6158424 Z"
+        fillOpacity="0.4"
+        fill="#FFFFFF"
+      ></path>
+    </g>
+  </svg>
+);
+
+export default UpstashIcon;
diff --git a/src/frontend/src/icons/Upstash/index.tsx b/src/frontend/src/icons/Upstash/index.tsx
new file mode 100644
index 000000000..b76110168
--- /dev/null
+++ b/src/frontend/src/icons/Upstash/index.tsx
@@ -0,0 +1,9 @@
+import React, { forwardRef } from "react";
+import UpstashIcon from "./UpstashIcon";
+
+export const UpstashSvgIcon = forwardRef<
+  SVGSVGElement,
+  React.PropsWithChildren<{}>
+>((props, ref) => {
+  return <UpstashIcon ref={ref} {...props} />;
+});
diff --git a/src/frontend/src/icons/Upstash/upstash-icon-seeklogo.svg b/src/frontend/src/icons/Upstash/upstash-icon-seeklogo.svg
new file mode 100644
index 000000000..a0fb96a79
--- /dev/null
+++ b/src/frontend/src/icons/Upstash/upstash-icon-seeklogo.svg
@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg width="256px" height="341px" viewBox="0 0 256 341" version="1.1" xmlns="http://www.w3.org/2000/svg" preserveAspectRatio="xMidYMid">
+    <title>upstash</title>
+    <g>
+        <path d="M0,298.416784 C56.5542815,354.970323 148.246768,354.970323 204.801032,298.416784 C261.354571,241.86252 261.354571,150.170106 204.801032,93.6158424 L179.200462,119.215688 C221.61634,161.631567 221.61634,230.401059 179.200462,272.816213 C136.785307,315.232092 68.0157428,315.232092 25.5998642,272.816213 L0,298.416784 Z" fill="#00C98D"></path>
+        <path d="M51.200362,247.216367 C79.4772765,275.493137 125.323122,275.493137 153.600615,247.216367 C181.877385,218.939598 181.877385,173.093028 153.600615,144.816259 L128.000769,170.416105 C142.139154,184.55449 142.139154,207.477412 128.000769,221.616521 C113.86166,235.754906 90.9387378,235.754906 76.800353,221.616521 L51.200362,247.216367 Z" fill="#00C98D"></path>
+        <path d="M256,42.415426 C199.445737,-14.1384753 107.753322,-14.1384753 51.1994207,42.415426 C-5.35485714,98.9696894 -5.35485714,190.662104 51.1994207,247.216367 L76.7989048,221.616521 C34.3841124,179.200643 34.3841124,110.431151 76.7989048,68.0159962 C119.214783,25.6001177 187.984275,25.6001177 230.39943,68.0159962 L256,42.415426 Z" fill="#00C98D"></path>
+        <path d="M204.800308,93.6158424 C176.523538,65.3390727 130.676245,65.3390727 102.399475,93.6158424 C74.1219813,121.893336 74.1219813,167.739181 102.399475,196.015951 L127.999321,170.416105 C113.860936,156.27772 113.860936,133.354797 127.999321,119.215688 C142.137706,105.077304 165.060629,105.077304 179.199738,119.215688 L204.800308,93.6158424 Z" fill="#00C98D"></path>
+        <path d="M256,42.415426 C199.445737,-14.1384753 107.753322,-14.1384753 51.1994207,42.415426 C-5.35485714,98.9696894 -5.35485714,190.662104 51.1994207,247.216367 L76.7989048,221.616521 C34.3841124,179.200643 34.3841124,110.431151 76.7989048,68.0159962 C119.214783,25.6001177 187.984275,25.6001177 230.39943,68.0159962 L256,42.415426 Z" fill-opacity="0.4" fill="#FFFFFF"></path>
+        <path d="M204.800308,93.6158424 C176.523538,65.3390727 130.676245,65.3390727 102.399475,93.6158424 C74.1219813,121.893336 74.1219813,167.739181 102.399475,196.015951 L127.999321,170.416105 C113.860936,156.27772 113.860936,133.354797 127.999321,119.215688 C142.137706,105.077304 165.060629,105.077304 179.199738,119.215688 L204.800308,93.6158424 Z" fill-opacity="0.4" fill="#FFFFFF"></path>
+    </g>
+</svg>
diff --git a/src/frontend/src/icons/Word/index.tsx b/src/frontend/src/icons/Word/index.tsx
index ac7842e2f..9df0d60cc 100644
--- a/src/frontend/src/icons/Word/index.tsx
+++ b/src/frontend/src/icons/Word/index.tsx
@@ -4,5 +4,5 @@ import SvgWord from "./Word";
 export const WordIcon = forwardRef<SVGSVGElement, React.PropsWithChildren<{}>>(
   (props, ref) => {
     return <SvgWord ref={ref} {...props} />;
-  }
+  },
 );
diff --git a/src/frontend/src/icons/mistral/index.tsx b/src/frontend/src/icons/mistral/index.tsx
new file mode 100644
index 000000000..6125953aa
--- /dev/null
+++ b/src/frontend/src/icons/mistral/index.tsx
@@ -0,0 +1,9 @@
+import React, { forwardRef } from "react";
+import SvgMistralIcon from "./mistralIcon";
+
+export const MistralIcon = forwardRef<
+  SVGSVGElement,
+  React.PropsWithChildren<{}>
+>((props, ref) => {
+  return <SvgMistralIcon ref={ref} {...props} />;
+});
diff --git a/src/frontend/src/icons/mistral/mistralIcon.jsx b/src/frontend/src/icons/mistral/mistralIcon.jsx
new file mode 100644
index 000000000..9e8e2a903
--- /dev/null
+++ b/src/frontend/src/icons/mistral/mistralIcon.jsx
@@ -0,0 +1,197 @@
+const SvgMistralIcon = (props) => (
+  <svg
+    version="1.1"
+    width="32px"
+    height="32px"
+    viewBox="0 0 256 233"
+    xmlns="http://www.w3.org/2000/svg"
+    preserveAspectRatio="xMidYMid"
+  >
+    <title>Mistral AI</title>
+    <g>
+      <rect
+        fill="#000000"
+        x="186.181818"
+        y="0"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#F7D046"
+        x="209.454545"
+        y="0"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#000000"
+        x="0"
+        y="0"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#000000"
+        x="0"
+        y="46.5454545"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#000000"
+        x="0"
+        y="93.0909091"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#000000"
+        x="0"
+        y="139.636364"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#000000"
+        x="0"
+        y="186.181818"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#F7D046"
+        x="23.2727273"
+        y="0"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#F2A73B"
+        x="209.454545"
+        y="46.5454545"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#F2A73B"
+        x="23.2727273"
+        y="46.5454545"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#000000"
+        x="139.636364"
+        y="46.5454545"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#F2A73B"
+        x="162.909091"
+        y="46.5454545"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#F2A73B"
+        x="69.8181818"
+        y="46.5454545"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#EE792F"
+        x="116.363636"
+        y="93.0909091"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#EE792F"
+        x="162.909091"
+        y="93.0909091"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#EE792F"
+        x="69.8181818"
+        y="93.0909091"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#000000"
+        x="93.0909091"
+        y="139.636364"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#EB5829"
+        x="116.363636"
+        y="139.636364"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#EE792F"
+        x="209.454545"
+        y="93.0909091"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#EE792F"
+        x="23.2727273"
+        y="93.0909091"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#000000"
+        x="186.181818"
+        y="139.636364"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#EB5829"
+        x="209.454545"
+        y="139.636364"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#000000"
+        x="186.181818"
+        y="186.181818"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#EB5829"
+        x="23.2727273"
+        y="139.636364"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#EA3326"
+        x="209.454545"
+        y="186.181818"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+      <rect
+        fill="#EA3326"
+        x="23.2727273"
+        y="186.181818"
+        width="46.5454545"
+        height="46.5454545"
+      ></rect>
+    </g>
+  </svg>
+);
+export default SvgMistralIcon;
diff --git a/src/frontend/src/index.tsx b/src/frontend/src/index.tsx
index 78a57298f..006d93ff2 100644
--- a/src/frontend/src/index.tsx
+++ b/src/frontend/src/index.tsx
@@ -11,11 +11,11 @@ import "./style/applies.css";
 import "./style/classes.css";
 
 const root = ReactDOM.createRoot(
-  document.getElementById("root") as HTMLElement
+  document.getElementById("root") as HTMLElement,
 );
 root.render(
   <ContextWrapper>
     <App />
-  </ContextWrapper>
+  </ContextWrapper>,
 );
 reportWebVitals();
diff --git a/src/frontend/src/modals/BundleModal/hooks/submit-folder.tsx b/src/frontend/src/modals/BundleModal/hooks/submit-folder.tsx
index 1347a323b..cb12ca7cb 100644
--- a/src/frontend/src/modals/BundleModal/hooks/submit-folder.tsx
+++ b/src/frontend/src/modals/BundleModal/hooks/submit-folder.tsx
@@ -27,7 +27,7 @@ const useFolderSubmit = (setOpen, folderToEdit) => {
             getFoldersApi(true);
             setOpen(false);
           }
-        }
+        },
       );
     } else {
       addFolder(data).then(
@@ -42,7 +42,7 @@ const useFolderSubmit = (setOpen, folderToEdit) => {
           setErrorData({
             title: `Error creating folder.`,
           });
-        }
+        },
       );
     }
   };
diff --git a/src/frontend/src/modals/IOModal/components/IOFieldView/components/csvSelect/index.tsx b/src/frontend/src/modals/IOModal/components/IOFieldView/components/csvSelect/index.tsx
index 1438237b7..5adb156ef 100644
--- a/src/frontend/src/modals/IOModal/components/IOFieldView/components/csvSelect/index.tsx
+++ b/src/frontend/src/modals/IOModal/components/IOFieldView/components/csvSelect/index.tsx
@@ -29,7 +29,7 @@ export default function CsvSelect({ node, handleChangeSelect }): JSX.Element {
                   <SelectItem key={separator} value={separator}>
                     {separator}
                   </SelectItem>
-                )
+                ),
               )}
             </SelectGroup>
           </SelectContent>
diff --git a/src/frontend/src/modals/IOModal/components/IOFieldView/index.tsx b/src/frontend/src/modals/IOModal/components/IOFieldView/index.tsx
index f8a86da95..1ae4383d7 100644
--- a/src/frontend/src/modals/IOModal/components/IOFieldView/index.tsx
+++ b/src/frontend/src/modals/IOModal/components/IOFieldView/index.tsx
@@ -2,9 +2,9 @@ import { cloneDeep } from "lodash";
 import { useState } from "react";
 import ImageViewer from "../../../../components/ImageViewer";
 import CsvOutputComponent from "../../../../components/csvOutputComponent";
+import DataOutputComponent from "../../../../components/dataOutputComponent";
 import InputListComponent from "../../../../components/inputListComponent";
 import PdfViewer from "../../../../components/pdfViewer";
-import RecordsOutputComponent from "../../../../components/recordsOutputComponent";
 import { Textarea } from "../../../../components/ui/textarea";
 import { PDFViewConstant } from "../../../../constants/constants";
 import { InputOutput } from "../../../../constants/enums";
@@ -47,11 +47,7 @@ export default function IOFieldView({
 
   const textOutputValue =
     (flowPool[node!.id] ?? [])[(flowPool[node!.id]?.length ?? 1) - 1]?.data
-      .results.result ?? "";
-
-  console.log(
-    (flowPool[node!.id] ?? [])[(flowPool[node!.id]?.length ?? 1) - 1]?.data
-  );
+      .results.text ?? "";
 
   function handleOutputType() {
     if (!node) return <>"No node found!"</>;
@@ -249,15 +245,15 @@ export default function IOFieldView({
                 />
               </>
             );
-          case "RecordsOutput":
+          case "DataOutput":
             return (
               <div className={left ? "h-56" : "h-full"}>
-                <RecordsOutputComponent
+                <DataOutputComponent
                   pagination={!left}
                   rows={
                     Array.isArray(flowPoolNode?.data?.artifacts)
                       ? flowPoolNode?.data?.artifacts?.map(
-                          (artifact) => artifact.data
+                          (artifact) => artifact.data,
                         ) ?? []
                       : [flowPoolNode?.data?.artifacts]
                   }
diff --git a/src/frontend/src/modals/IOModal/components/SessionView/hooks/index.tsx b/src/frontend/src/modals/IOModal/components/SessionView/hooks/index.tsx
index 738ea9977..e8e638def 100644
--- a/src/frontend/src/modals/IOModal/components/SessionView/hooks/index.tsx
+++ b/src/frontend/src/modals/IOModal/components/SessionView/hooks/index.tsx
@@ -10,7 +10,7 @@ const useRemoveSession = (setSuccessData, setErrorData) => {
       await deleteMessagesFn(
         messages
           .filter((msg) => msg.session_id === session_id)
-          .map((msg) => msg.index)
+          .map((msg) => msg.index),
       );
       deleteSession(session_id);
       setSuccessData({
diff --git a/src/frontend/src/modals/IOModal/components/SessionView/index.tsx b/src/frontend/src/modals/IOModal/components/SessionView/index.tsx
index a7fc91b03..ef2c44d44 100644
--- a/src/frontend/src/modals/IOModal/components/SessionView/index.tsx
+++ b/src/frontend/src/modals/IOModal/components/SessionView/index.tsx
@@ -5,6 +5,7 @@ import useRemoveMessages from "../../../../pages/SettingsPage/pages/messagesPage
 import useUpdateMessage from "../../../../pages/SettingsPage/pages/messagesPage/hooks/use-updateMessage";
 import useAlertStore from "../../../../stores/alertStore";
 import { useMessagesStore } from "../../../../stores/messagesStore";
+import { messagesSorter } from "../../../../utils/utils";
 
 export default function SessionView({ rows }: { rows: Array<any> }) {
   const columns = useMessagesStore((state) => state.columns);
@@ -17,7 +18,7 @@ export default function SessionView({ rows }: { rows: Array<any> }) {
     setSelectedRows,
     setSuccessData,
     setErrorData,
-    selectedRows
+    selectedRows,
   );
 
   const { handleUpdate } = useUpdateMessage(setSuccessData, setErrorData);
@@ -41,7 +42,7 @@ export default function SessionView({ rows }: { rows: Array<any> }) {
       onCellEditRequest={(event) => {
         handleUpdateMessage(event);
       }}
-      editable={["Sender Name", "Message"]}
+      editable={["Sender Name", "Message", "Text"]}
       overlayNoRowsTemplate="No data available"
       onSelectionChanged={(event: SelectionChangedEvent) => {
         setSelectedRows(event.api.getSelectedRows().map((row) => row.index));
@@ -49,7 +50,7 @@ export default function SessionView({ rows }: { rows: Array<any> }) {
       rowSelection="multiple"
       suppressRowClickSelection={true}
       pagination={true}
-      columnDefs={columns}
+      columnDefs={columns.sort(messagesSorter)}
       rowData={rows}
     />
   );
diff --git a/src/frontend/src/modals/IOModal/components/chatView/chatInput/components/buttonSendWrapper/index.tsx b/src/frontend/src/modals/IOModal/components/chatView/chatInput/components/buttonSendWrapper/index.tsx
index b72fc71d2..81f42675c 100644
--- a/src/frontend/src/modals/IOModal/components/chatView/chatInput/components/buttonSendWrapper/index.tsx
+++ b/src/frontend/src/modals/IOModal/components/chatView/chatInput/components/buttonSendWrapper/index.tsx
@@ -27,14 +27,13 @@ const ButtonSendWrapper = ({
         "form-modal-send-button",
         noInput
           ? "bg-high-indigo text-background"
-          : chatValue === ""
-          ? "text-primary"
-          : "bg-chat-send text-background"
+          : chatValue
+            ? "text-primary"
+            : "bg-chat-send text-background",
       )}
       disabled={lockChat || saveLoading}
       onClick={(): void => send()}
-      variant="none"
-      size="none"
+      unstyled
     >
       <Case
         condition={
diff --git a/src/frontend/src/modals/IOModal/components/chatView/chatInput/components/textAreaWrapper/index.tsx b/src/frontend/src/modals/IOModal/components/chatView/chatInput/components/textAreaWrapper/index.tsx
index 3d41e51f4..29f88453a 100644
--- a/src/frontend/src/modals/IOModal/components/chatView/chatInput/components/textAreaWrapper/index.tsx
+++ b/src/frontend/src/modals/IOModal/components/chatView/chatInput/components/textAreaWrapper/index.tsx
@@ -18,7 +18,7 @@ const TextAreaWrapper = ({
 }) => {
   const getPlaceholderText = (
     isDragging: boolean,
-    noInput: boolean
+    noInput: boolean,
   ): string => {
     if (isDragging) {
       return "Drop here";
@@ -33,8 +33,8 @@ const TextAreaWrapper = ({
     lockChat || saveLoading
       ? "form-modal-lock-true bg-input"
       : noInput
-      ? "form-modal-no-input bg-input"
-      : "form-modal-lock-false bg-background";
+        ? "form-modal-no-input bg-input"
+        : "form-modal-lock-false bg-background";
 
   const fileClass =
     files.length > 0
diff --git a/src/frontend/src/modals/IOModal/components/chatView/chatInput/components/uploadFileButton/index.tsx b/src/frontend/src/modals/IOModal/components/chatView/chatInput/components/uploadFileButton/index.tsx
index 17ca897ea..8876c4517 100644
--- a/src/frontend/src/modals/IOModal/components/chatView/chatInput/components/uploadFileButton/index.tsx
+++ b/src/frontend/src/modals/IOModal/components/chatView/chatInput/components/uploadFileButton/index.tsx
@@ -22,8 +22,7 @@ const UploadFileButton = ({
           lockChat ? "cursor-not-allowed" : "hover:text-muted-foreground"
         }`}
         onClick={handleButtonClick}
-        variant="none"
-        size="none"
+        unstyled
       >
         <ForwardedIconComponent name="PaperclipIcon" />
       </Button>
diff --git a/src/frontend/src/modals/IOModal/components/chatView/chatInput/hooks/use-drag-and-drop.tsx b/src/frontend/src/modals/IOModal/components/chatView/chatInput/hooks/use-drag-and-drop.tsx
index 141754cbd..b1ff9d143 100644
--- a/src/frontend/src/modals/IOModal/components/chatView/chatInput/hooks/use-drag-and-drop.tsx
+++ b/src/frontend/src/modals/IOModal/components/chatView/chatInput/hooks/use-drag-and-drop.tsx
@@ -1,15 +1,16 @@
 import ShortUniqueId from "short-unique-id";
+import {
+  ALLOWED_IMAGE_INPUT_EXTENSIONS,
+  FS_ERROR_TEXT,
+  SN_ERROR_TEXT,
+} from "../../../../../../constants/constants";
 import useFileUpload from "./use-file-upload";
 
-const fsErrorText =
-  "Please ensure your file has one of the following extensions:";
-const snErrorTxt = "png, jpg, jpeg";
-
 const useDragAndDrop = (
   setIsDragging,
   setFiles,
   currentFlowId,
-  setErrorData
+  setErrorData,
 ) => {
   const dragOver = (e) => {
     e.preventDefault();
@@ -48,19 +49,21 @@ const useDragAndDrop = (
 
 const handleFiles = (files, setFiles, currentFlowId, setErrorData) => {
   if (files) {
-    const allowedExtensions = ["png", "jpg", "jpeg"];
     const file = files?.[0];
     const fileExtension = file.name.split(".").pop()?.toLowerCase();
-    if (!fileExtension || !allowedExtensions.includes(fileExtension)) {
+    if (
+      !fileExtension ||
+      !ALLOWED_IMAGE_INPUT_EXTENSIONS.includes(fileExtension)
+    ) {
       console.log("Error uploading file");
       setErrorData({
         title: "Error uploading file",
-        list: [fsErrorText, snErrorTxt],
+        list: [FS_ERROR_TEXT, SN_ERROR_TEXT],
       });
       return;
     }
-    const uid = new ShortUniqueId({ length: 3 });
-    const id = uid();
+    const uid = new ShortUniqueId();
+    const id = uid.randomUUID(3);
     const type = files[0].type.split("/")[0];
     const blob = files[0];
 
diff --git a/src/frontend/src/modals/IOModal/components/chatView/chatInput/hooks/use-handle-file-change.tsx b/src/frontend/src/modals/IOModal/components/chatView/chatInput/hooks/use-handle-file-change.tsx
index 060481cd5..748187133 100644
--- a/src/frontend/src/modals/IOModal/components/chatView/chatInput/hooks/use-handle-file-change.tsx
+++ b/src/frontend/src/modals/IOModal/components/chatView/chatInput/hooks/use-handle-file-change.tsx
@@ -1,32 +1,36 @@
 import ShortUniqueId from "short-unique-id";
+import {
+  ALLOWED_IMAGE_INPUT_EXTENSIONS,
+  FS_ERROR_TEXT,
+  SN_ERROR_TEXT,
+} from "../../../../../../constants/constants";
 import useAlertStore from "../../../../../../stores/alertStore";
 import useFileUpload from "./use-file-upload";
 
-const fsErrorText =
-  "Please ensure your file has one of the following extensions:";
-const snErrorTxt = "png, jpg, jpeg";
-
 export const useHandleFileChange = (setFiles, currentFlowId) => {
   const setErrorData = useAlertStore((state) => state.setErrorData);
   const handleFileChange = async (
-    event: React.ChangeEvent<HTMLInputElement>
+    event: React.ChangeEvent<HTMLInputElement>,
   ) => {
     const fileInput = event.target;
     const file = fileInput.files?.[0];
     if (file) {
-      const allowedExtensions = ["png", "jpg", "jpeg"];
       const fileExtension = file.name.split(".").pop()?.toLowerCase();
 
-      if (!fileExtension || !allowedExtensions.includes(fileExtension)) {
+      if (
+        !fileExtension ||
+        !ALLOWED_IMAGE_INPUT_EXTENSIONS.includes(fileExtension)
+      ) {
         setErrorData({
           title: "Error uploading file",
-          list: [fsErrorText, snErrorTxt],
+          list: [FS_ERROR_TEXT, SN_ERROR_TEXT],
         });
         return;
       }
 
-      const uid = new ShortUniqueId({ length: 10 }); // Increase the length to ensure uniqueness
-      const id = uid();
+      const uid = new ShortUniqueId();
+      const id = uid.randomUUID(10);
+
       const type = file.type.split("/")[0];
       const blob = file;
 
diff --git a/src/frontend/src/modals/IOModal/components/chatView/chatInput/hooks/use-upload.tsx b/src/frontend/src/modals/IOModal/components/chatView/chatInput/hooks/use-upload.tsx
index 94c3df51c..5a9e85195 100644
--- a/src/frontend/src/modals/IOModal/components/chatView/chatInput/hooks/use-upload.tsx
+++ b/src/frontend/src/modals/IOModal/components/chatView/chatInput/hooks/use-upload.tsx
@@ -1,12 +1,13 @@
 import { useEffect } from "react";
 import ShortUniqueId from "short-unique-id";
+import {
+  ALLOWED_IMAGE_INPUT_EXTENSIONS,
+  FS_ERROR_TEXT,
+  SN_ERROR_TEXT,
+} from "../../../../../../constants/constants";
 import useAlertStore from "../../../../../../stores/alertStore";
 import useFileUpload from "./use-file-upload";
 
-const fsErrorText =
-  "Please ensure your file has one of the following extensions:";
-const snErrorTxt = "png, jpg, jpeg";
-
 const useUpload = (uploadFile, currentFlowId, setFiles, lockChat) => {
   const setErrorData = useAlertStore((state) => state.setErrorData);
   useEffect(() => {
@@ -18,20 +19,22 @@ const useUpload = (uploadFile, currentFlowId, setFiles, lockChat) => {
       if (items) {
         for (let i = 0; i < items.length; i++) {
           const type = items[0].type.split("/")[0];
-          const uid = new ShortUniqueId({ length: 3 });
+          const uid = new ShortUniqueId();
           const blob = items[i].getAsFile();
           if (blob) {
-            const allowedExtensions = ["png", "jpg", "jpeg"];
             const fileExtension = blob.name.split(".").pop()?.toLowerCase();
 
-            if (!fileExtension || !allowedExtensions.includes(fileExtension)) {
+            if (
+              !fileExtension ||
+              !ALLOWED_IMAGE_INPUT_EXTENSIONS.includes(fileExtension)
+            ) {
               setErrorData({
                 title: "Error uploading file",
-                list: [fsErrorText, snErrorTxt],
+                list: [FS_ERROR_TEXT, SN_ERROR_TEXT],
               });
               return;
             }
-            const id = uid();
+            const id = uid.randomUUID(3);
             setFiles((prevFiles) => [
               ...prevFiles,
               { file: blob, loading: true, error: false, id, type },
diff --git a/src/frontend/src/modals/IOModal/components/chatView/chatInput/index.tsx b/src/frontend/src/modals/IOModal/components/chatView/chatInput/index.tsx
index 4311f0ed2..a3c070a5f 100644
--- a/src/frontend/src/modals/IOModal/components/chatView/chatInput/index.tsx
+++ b/src/frontend/src/modals/IOModal/components/chatView/chatInput/index.tsx
@@ -116,7 +116,7 @@ export default function ChatInput({
               key={file.id}
               onDelete={() => {
                 setFiles((prev: FilePreviewType[]) =>
-                  prev.filter((f) => f.id !== file.id)
+                  prev.filter((f) => f.id !== file.id),
                 );
                 // TODO: delete file on backend
               }}
diff --git a/src/frontend/src/modals/IOModal/components/chatView/chatMessage/index.tsx b/src/frontend/src/modals/IOModal/components/chatView/chatMessage/index.tsx
index 020eff65d..2b583dd63 100644
--- a/src/frontend/src/modals/IOModal/components/chatView/chatMessage/index.tsx
+++ b/src/frontend/src/modals/IOModal/components/chatView/chatMessage/index.tsx
@@ -9,6 +9,7 @@ import Robot from "../../../../../assets/robot.png";
 import CodeTabsComponent from "../../../../../components/codeTabsComponent";
 import IconComponent from "../../../../../components/genericIconComponent";
 import SanitizedHTMLWrapper from "../../../../../components/sanitizedHTMLWrapper";
+import { EMPTY_INPUT_SEND_MESSAGE } from "../../../../../constants/constants";
 import useAlertStore from "../../../../../stores/alertStore";
 import useFlowStore from "../../../../../stores/flowStore";
 import { chatMessagePropsType } from "../../../../../types/components";
@@ -115,19 +116,19 @@ export default function ChatMessage({
       <div
         className={classNames(
           "form-modal-chat-position",
-          chat.isSend ? "" : " "
+          chat.isSend ? "" : " ",
         )}
       >
         <div
           className={classNames(
-            "mr-3 mt-1 flex w-24 flex-col items-center gap-1 overflow-hidden px-3 pb-3"
+            "mr-3 mt-1 flex w-24 flex-col items-center gap-1 overflow-hidden px-3 pb-3",
           )}
         >
           <div className="flex flex-col items-center gap-1">
             <div
               className={cn(
                 "relative flex h-8 w-8 items-center justify-center overflow-hidden rounded-md p-5 text-2xl",
-                !chat.isSend ? "bg-chat-bot-icon" : "bg-chat-user-icon"
+                !chat.isSend ? "bg-chat-bot-icon" : "bg-chat-user-icon",
               )}
             >
               <img
@@ -187,7 +188,12 @@ export default function ChatMessage({
                           <Markdown
                             remarkPlugins={[remarkGfm, remarkMath]}
                             rehypePlugins={[rehypeMathjax]}
-                            className="markdown prose flex flex-col text-primary word-break-break-word dark:prose-invert"
+                            className={cn(
+                              "markdown prose flex flex-col word-break-break-word dark:prose-invert",
+                              chatMessage === ""
+                                ? "text-chat-trigger-disabled"
+                                : "text-primary",
+                            )}
                             components={{
                               pre({ node, ...props }) {
                                 return <>{props.children}</>;
@@ -199,8 +205,8 @@ export default function ChatMessage({
                                 children,
                                 ...props
                               }) => {
-                                if (children.length) {
-                                  if (children[0] === "▍") {
+                                if ((children as string)!.length) {
+                                  if (children![0] === "▍") {
                                     return (
                                       <span className="form-modal-markdown-span">
                                         ▍
@@ -208,14 +214,13 @@ export default function ChatMessage({
                                     );
                                   }
 
-                                  children[0] = (children[0] as string).replace(
-                                    "`▍`",
-                                    "▍"
-                                  );
+                                  children![0] = (
+                                    children![0] as string
+                                  ).replace("`▍`", "▍");
                                 }
 
                                 const match = /language-(\w+)/.exec(
-                                  className || ""
+                                  className || "",
                                 );
 
                                 return !inline ? (
@@ -230,7 +235,7 @@ export default function ChatMessage({
                                         language: (match && match[1]) || "",
                                         code: String(children).replace(
                                           /\n$/,
-                                          ""
+                                          "",
                                         ),
                                       },
                                     ]}
@@ -245,10 +250,12 @@ export default function ChatMessage({
                               },
                             }}
                           >
-                            {chatMessage}
+                            {chatMessage === ""
+                              ? EMPTY_INPUT_SEND_MESSAGE
+                              : chatMessage}
                           </Markdown>
                         ),
-                      [chat.message, chatMessage]
+                      [chat.message, chatMessage],
                     )}
                   </div>
                 </div>
@@ -274,7 +281,16 @@ export default function ChatMessage({
                     }
                   />
                 </button>
-                <span className="prose text-primary word-break-break-word dark:prose-invert">
+                <span
+                  className={cn(
+                    "prose word-break-break-word dark:prose-invert",
+                    chatMessage !== ""
+                      ? EMPTY_INPUT_SEND_MESSAGE
+                      : chatMessage
+                        ? "text-primary"
+                        : "text-chat-trigger-disabled",
+                  )}
+                >
                   {promptOpen
                     ? template?.split("\n")?.map((line, index) => {
                         const regex = /{([^}]+)}/g;
@@ -291,7 +307,7 @@ export default function ChatMessage({
                             parts.push(
                               <span className="chat-message-highlight">
                                 {chat.message[match[1]]}
-                              </span>
+                              </span>,
                             );
                           }
 
@@ -304,18 +320,24 @@ export default function ChatMessage({
                         }
                         return <p>{parts}</p>;
                       })
-                    : chatMessage}
+                    : chatMessage === ""
+                      ? EMPTY_INPUT_SEND_MESSAGE
+                      : chatMessage}
                 </span>
               </>
             ) : (
               <div className="flex flex-col">
                 <span
-                  className="prose text-primary word-break-break-word dark:prose-invert"
+                  className={`prose word-break-break-word dark:prose-invert ${
+                    chatMessage === ""
+                      ? "text-chat-trigger-disabled"
+                      : "text-primary"
+                  }`}
                   data-testid={
                     "chat-message-" + chat.sender_name + "-" + chatMessage
                   }
                 >
-                  {chatMessage}
+                  {chatMessage === "" ? EMPTY_INPUT_SEND_MESSAGE : chatMessage}
                 </span>
                 {chat.files && (
                   <div className="my-2 flex flex-col gap-5">
diff --git a/src/frontend/src/modals/IOModal/components/chatView/fileComponent/components/downloadButton/downloadButton.tsx b/src/frontend/src/modals/IOModal/components/chatView/fileComponent/components/downloadButton/downloadButton.tsx
index 7e706895e..1fdca0395 100644
--- a/src/frontend/src/modals/IOModal/components/chatView/fileComponent/components/downloadButton/downloadButton.tsx
+++ b/src/frontend/src/modals/IOModal/components/chatView/fileComponent/components/downloadButton/downloadButton.tsx
@@ -14,7 +14,7 @@ export default function DownloadButton({
         className={`absolute right-1 top-1 rounded-md bg-muted text-sm font-bold text-foreground`}
       >
         <Button
-          variant={"none"}
+          unstyled
           className="bg-transparent px-2 py-1 text-ring"
           onClick={handleDownload}
         >
diff --git a/src/frontend/src/modals/IOModal/components/chatView/fileComponent/index.tsx b/src/frontend/src/modals/IOModal/components/chatView/fileComponent/index.tsx
index 43310850e..fa9c505ea 100644
--- a/src/frontend/src/modals/IOModal/components/chatView/fileComponent/index.tsx
+++ b/src/frontend/src/modals/IOModal/components/chatView/fileComponent/index.tsx
@@ -29,11 +29,9 @@ export default function FileCard({
 
   const imgSrc = `${BACKEND_URL.slice(
     0,
-    BACKEND_URL.length - 1
+    BACKEND_URL.length - 1,
   )}${BASE_URL_API}files/images/${content}`;
 
-  console.log(imgSrc);
-
   if (showFile) {
     if (imgTypes.has(fileType)) {
       return (
diff --git a/src/frontend/src/modals/IOModal/components/chatView/fileComponent/utils/handle-download.tsx b/src/frontend/src/modals/IOModal/components/chatView/fileComponent/utils/handle-download.tsx
index e82bf3167..a4be91d50 100644
--- a/src/frontend/src/modals/IOModal/components/chatView/fileComponent/utils/handle-download.tsx
+++ b/src/frontend/src/modals/IOModal/components/chatView/fileComponent/utils/handle-download.tsx
@@ -20,8 +20,8 @@ export default async function handleDownload({
     const response = await fetch(
       `${BACKEND_URL.slice(
         0,
-        BACKEND_URL.length - 1
-      )}${BASE_URL_API}files/download/${content}`
+        BACKEND_URL.length - 1,
+      )}${BASE_URL_API}files/download/${content}`,
     );
     if (!response.ok) {
       throw new Error("Network response was not ok");
diff --git a/src/frontend/src/modals/IOModal/components/chatView/filePreviewChat/utils/format-file-name.tsx b/src/frontend/src/modals/IOModal/components/chatView/filePreviewChat/utils/format-file-name.tsx
index b474f8294..94258b6af 100644
--- a/src/frontend/src/modals/IOModal/components/chatView/filePreviewChat/utils/format-file-name.tsx
+++ b/src/frontend/src/modals/IOModal/components/chatView/filePreviewChat/utils/format-file-name.tsx
@@ -1,6 +1,6 @@
 export default function formatFileName(
   name: string,
-  numberToTruncate: number = 25
+  numberToTruncate: number = 25,
 ): string {
   if (name[numberToTruncate] === undefined) {
     return name;
diff --git a/src/frontend/src/modals/IOModal/components/chatView/index.tsx b/src/frontend/src/modals/IOModal/components/chatView/index.tsx
index cf8a687ad..1149425dc 100644
--- a/src/frontend/src/modals/IOModal/components/chatView/index.tsx
+++ b/src/frontend/src/modals/IOModal/components/chatView/index.tsx
@@ -4,6 +4,7 @@ import { Button } from "../../../../components/ui/button";
 import {
   CHAT_FIRST_INITIAL_TEXT,
   CHAT_SECOND_INITIAL_TEXT,
+  EMPTY_INPUT_SEND_MESSAGE,
 } from "../../../../constants/constants";
 import { deleteFlowPool } from "../../../../controllers/API";
 import useAlertStore from "../../../../stores/alertStore";
@@ -58,7 +59,7 @@ export default function ChatView({
       //
       .filter(
         (output) =>
-          output.data.message || (!output.data.message && output.artifacts)
+          output.data.message || (!output.data.message && output.artifacts),
       )
       .map((output, index) => {
         try {
@@ -76,9 +77,10 @@ export default function ChatView({
 
           const is_ai =
             sender === "Machine" || sender === null || sender === undefined;
+
           return {
             isSend: !is_ai,
-            message: message,
+            message,
             sender_name,
             componentId: output.id,
             stream_url: stream_url,
@@ -138,7 +140,7 @@ export default function ChatView({
   function updateChat(
     chat: ChatMessageType,
     message: string,
-    stream_url?: string
+    stream_url?: string,
   ) {
     chat.message = message;
     updateFlowPool(chat.componentId, {
@@ -154,7 +156,7 @@ export default function ChatView({
     setIsDragging,
     setFiles,
     currentFlowId,
-    setErrorData
+    setErrorData,
   );
 
   return (
@@ -169,8 +171,7 @@ export default function ChatView({
         <div className="eraser-position">
           <Button
             className="flex gap-1"
-            size="none"
-            variant="none"
+            unstyled
             disabled={lockChat}
             onClick={() => handleSelectChange("builds")}
           >
diff --git a/src/frontend/src/modals/IOModal/index.tsx b/src/frontend/src/modals/IOModal/index.tsx
index f1dc14182..ae3c7aba1 100644
--- a/src/frontend/src/modals/IOModal/index.tsx
+++ b/src/frontend/src/modals/IOModal/index.tsx
@@ -36,25 +36,25 @@ export default function IOModal({
   const allNodes = useFlowStore((state) => state.nodes);
   const setMessages = useMessagesStore((state) => state.setMessages);
   const inputs = useFlowStore((state) => state.inputs).filter(
-    (input) => input.type !== "ChatInput"
+    (input) => input.type !== "ChatInput",
   );
   const chatInput = useFlowStore((state) => state.inputs).find(
-    (input) => input.type === "ChatInput"
+    (input) => input.type === "ChatInput",
   );
   const outputs = useFlowStore((state) => state.outputs).filter(
-    (output) => output.type !== "ChatOutput"
+    (output) => output.type !== "ChatOutput",
   );
   const chatOutput = useFlowStore((state) => state.outputs).find(
-    (output) => output.type === "ChatOutput"
+    (output) => output.type === "ChatOutput",
   );
   const nodes = useFlowStore((state) => state.nodes).filter(
     (node) =>
       inputs.some((input) => input.id === node.id) ||
-      outputs.some((output) => output.id === node.id)
+      outputs.some((output) => output.id === node.id),
   );
   const haveChat = chatInput || chatOutput;
   const [selectedTab, setSelectedTab] = useState(
-    inputs.length > 0 ? 1 : outputs.length > 0 ? 2 : 0
+    inputs.length > 0 ? 1 : outputs.length > 0 ? 2 : 0,
   );
   const setErrorData = useAlertStore((state) => state.setErrorData);
   const setSuccessData = useAlertStore((state) => state.setSuccessData);
@@ -86,9 +86,6 @@ export default function IOModal({
   const messages = useMessagesStore((state) => state.messages);
   const setColumns = useMessagesStore((state) => state.setColumns);
   const flowPool = useFlowStore((state) => state.flowPool);
-  async function updateVertices() {
-    return updateVerticesOrder(currentFlow!.id, null);
-  }
 
   async function sendMessage({
     repeat = 1,
@@ -107,6 +104,7 @@ export default function IOModal({
         startNodeId: chatInput?.id,
         files: files,
         silent: true,
+        setLockChat,
       }).catch((err) => {
         console.error(err);
         setLockChat(false);
@@ -131,7 +129,7 @@ export default function IOModal({
 
   const { handleRemoveSession } = useRemoveSession(
     setSuccessData,
-    setErrorData
+    setErrorData,
   );
 
   useEffect(() => {
@@ -155,7 +153,7 @@ export default function IOModal({
         ({ rows, columns }) => {
           setMessages(rows);
           setColumns(columns);
-        }
+        },
       );
     }
   }, [open]);
@@ -194,7 +192,7 @@ export default function IOModal({
           <div className="flex-max-width h-full">
             <div
               className={cn(
-                "mr-6 flex h-full w-2/6 flex-shrink-0 flex-col justify-start transition-all duration-300"
+                "mr-6 flex h-full w-2/6 flex-shrink-0 flex-col justify-start transition-all duration-300",
               )}
             >
               <Tabs
@@ -223,11 +221,11 @@ export default function IOModal({
                 <TabsContent value={"1"} className="api-modal-tabs-content">
                   {nodes
                     .filter((node) =>
-                      inputs.some((input) => input.id === node.id)
+                      inputs.some((input) => input.id === node.id),
                     )
                     .map((node, index) => {
                       const input = inputs.find(
-                        (input) => input.id === node.id
+                        (input) => input.id === node.id,
                       )!;
                       return (
                         <div
@@ -284,11 +282,11 @@ export default function IOModal({
                 <TabsContent value={"2"} className="api-modal-tabs-content">
                   {nodes
                     .filter((node) =>
-                      outputs.some((output) => output.id === node.id)
+                      outputs.some((output) => output.id === node.id),
                     )
                     .map((node, index) => {
                       const output = outputs.find(
-                        (output) => output.id === node.id
+                        (output) => output.id === node.id,
                       )!;
                       const textOutputValue =
                         (flowPool[node!.id] ?? [])[
@@ -379,7 +377,7 @@ export default function IOModal({
                           </ShadTooltip>
                           <div className="flex shrink-0 items-center justify-center gap-2 align-middle">
                             <Button
-                              variant="none"
+                              unstyled
                               size="icon"
                               onClick={(e) => {
                                 e.preventDefault();
@@ -439,7 +437,7 @@ export default function IOModal({
                 <div
                   className={cn(
                     "flex h-full w-full flex-col items-start gap-4 pt-4",
-                    !selectedViewField ? "hidden" : ""
+                    !selectedViewField ? "hidden" : "",
                   )}
                 >
                   <div className="font-xl flex items-center justify-center gap-3 font-semibold">
@@ -458,7 +456,7 @@ export default function IOModal({
                   </div>
                   <div className="h-full w-full">
                     {inputs.some(
-                      (input) => input.id === selectedViewField.id
+                      (input) => input.id === selectedViewField.id,
                     ) && (
                       <IOFieldView
                         type={InputOutput.INPUT}
@@ -468,7 +466,7 @@ export default function IOModal({
                       />
                     )}
                     {outputs.some(
-                      (output) => output.id === selectedViewField.id
+                      (output) => output.id === selectedViewField.id,
                     ) && (
                       <IOFieldView
                         type={InputOutput.OUTPUT}
@@ -478,12 +476,12 @@ export default function IOModal({
                       />
                     )}
                     {sessions.some(
-                      (session) => session === selectedViewField.id
+                      (session) => session === selectedViewField.id,
                     ) && (
                       <SessionView
                         rows={messages.filter(
                           (message) =>
-                            message.session_id === selectedViewField.id
+                            message.session_id === selectedViewField.id,
                         )}
                       />
                     )}
@@ -493,7 +491,7 @@ export default function IOModal({
               <div
                 className={cn(
                   "flex h-full w-full",
-                  selectedViewField ? "hidden" : ""
+                  selectedViewField ? "hidden" : "",
                 )}
               >
                 {haveChat ? (
@@ -525,7 +523,7 @@ export default function IOModal({
                   "h-4 w-4",
                   isBuilding
                     ? "animate-spin"
-                    : "fill-current text-medium-indigo"
+                    : "fill-current text-medium-indigo",
                 )}
               />
             ),
diff --git a/src/frontend/src/modals/apiModal/index.tsx b/src/frontend/src/modals/apiModal/index.tsx
index 19f506459..ead038ad2 100644
--- a/src/frontend/src/modals/apiModal/index.tsx
+++ b/src/frontend/src/modals/apiModal/index.tsx
@@ -39,7 +39,7 @@ const ApiModal = forwardRef(
       open?: boolean;
       setOpen?: (a: boolean | ((o?: boolean) => boolean)) => void;
     },
-    ref
+    ref,
   ) => {
     const tweak = useTweaksStore((state) => state.tweak);
     const addTweaks = useTweaksStore((state) => state.setTweak);
@@ -57,18 +57,18 @@ const ApiModal = forwardRef(
       flow?.id,
       autoLogin,
       tweak,
-      flow?.endpoint_name
+      flow?.endpoint_name,
     );
     const curl_run_code = getCurlRunCode(
       flow?.id,
       autoLogin,
       tweak,
-      flow?.endpoint_name
+      flow?.endpoint_name,
     );
     const curl_webhook_code = getCurlWebhookCode(
       flow?.id,
       autoLogin,
-      flow?.endpoint_name
+      flow?.endpoint_name,
     );
     const pythonCode = getPythonCode(flow?.name, tweak);
     const widgetCode = getWidgetCode(flow?.id, flow?.name, autoLogin);
@@ -83,7 +83,7 @@ const ApiModal = forwardRef(
       pythonCode,
     ];
     const [tabs, setTabs] = useState(
-      createTabsArray(codesArray, includeWebhook)
+      createTabsArray(codesArray, includeWebhook),
     );
 
     const canShowTweaks =
@@ -132,7 +132,7 @@ const ApiModal = forwardRef(
               buildTweakObject(
                 nodeId,
                 element.data.node.template[templateField].value,
-                element.data.node.template[templateField]
+                element.data.node.template[templateField],
               );
             }
           });
@@ -149,7 +149,7 @@ const ApiModal = forwardRef(
     async function buildTweakObject(
       tw: string,
       changes: string | string[] | boolean | number | Object[] | Object,
-      template: TemplateVariableType
+      template: TemplateVariableType,
     ) {
       changes = getChangesType(changes, template);
 
@@ -191,7 +191,7 @@ const ApiModal = forwardRef(
         flow?.id,
         autoLogin,
         cloneTweak,
-        flow?.endpoint_name
+        flow?.endpoint_name,
       );
       const pythonCode = getPythonCode(flow?.name, cloneTweak);
       const widgetCode = getWidgetCode(flow?.id, flow?.name, autoLogin);
@@ -235,7 +235,7 @@ const ApiModal = forwardRef(
         </BaseModal.Content>
       </BaseModal>
     );
-  }
+  },
 );
 
 export default ApiModal;
diff --git a/src/frontend/src/modals/apiModal/utils/check-can-build-tweak-object.ts b/src/frontend/src/modals/apiModal/utils/check-can-build-tweak-object.ts
index 592a812c4..5a859cae8 100644
--- a/src/frontend/src/modals/apiModal/utils/check-can-build-tweak-object.ts
+++ b/src/frontend/src/modals/apiModal/utils/check-can-build-tweak-object.ts
@@ -6,7 +6,7 @@ export const checkCanBuildTweakObject = (element, templateField) => {
     templateField.charAt(0) !== "_" &&
     element.data.node.template[templateField].show &&
     LANGFLOW_SUPPORTED_TYPES.has(
-      element.data.node.template[templateField].type
+      element.data.node.template[templateField].type,
     ) &&
     templateField !== "code"
   );
diff --git a/src/frontend/src/modals/apiModal/utils/get-changes-types.ts b/src/frontend/src/modals/apiModal/utils/get-changes-types.ts
index 7e295e358..e8e912ff3 100644
--- a/src/frontend/src/modals/apiModal/utils/get-changes-types.ts
+++ b/src/frontend/src/modals/apiModal/utils/get-changes-types.ts
@@ -3,7 +3,7 @@ import { convertArrayToObj } from "../../../utils/reactflowUtils";
 
 export const getChangesType = (
   changes: string | string[] | boolean | number | Object[] | Object,
-  template: TemplateVariableType
+  template: TemplateVariableType,
 ) => {
   if (typeof changes === "string" && template.type === "float") {
     changes = parseFloat(changes);
diff --git a/src/frontend/src/modals/apiModal/utils/get-curl-code.tsx b/src/frontend/src/modals/apiModal/utils/get-curl-code.tsx
index 40257d73d..c17118d1a 100644
--- a/src/frontend/src/modals/apiModal/utils/get-curl-code.tsx
+++ b/src/frontend/src/modals/apiModal/utils/get-curl-code.tsx
@@ -8,14 +8,14 @@ export function getCurlRunCode(
   flowId: string,
   isAuth: boolean,
   tweaksBuildedObject,
-  endpointName?: string
+  endpointName?: string,
 ): string {
   const tweaksObject = tweaksBuildedObject[0];
   // show the endpoint name in the curl command if it exists
   return `curl -X POST \\
     "${window.location.protocol}//${window.location.host}/api/v1/run/${
-    endpointName || flowId
-  }?stream=false" \\
+      endpointName || flowId
+    }?stream=false" \\
     -H 'Content-Type: application/json'\\${
       !isAuth ? `\n  -H 'x-api-key: <your api key>'\\` : ""
     }
diff --git a/src/frontend/src/modals/apiModal/utils/get-nodes-with-default-value.ts b/src/frontend/src/modals/apiModal/utils/get-nodes-with-default-value.ts
index 657526dda..dc4fbaffc 100644
--- a/src/frontend/src/modals/apiModal/utils/get-nodes-with-default-value.ts
+++ b/src/frontend/src/modals/apiModal/utils/get-nodes-with-default-value.ts
@@ -13,8 +13,8 @@ export const getNodesWithDefaultValue = (flow) => {
           templateField.charAt(0) !== "_" &&
           node.data.node.template[templateField]?.show &&
           LANGFLOW_SUPPORTED_TYPES.has(
-            node.data.node.template[templateField]?.type
-          )
+            node.data.node.template[templateField].type,
+          ),
       )
       .map((n, i) => {
         arrNodesWithValues.push(node["id"]);
diff --git a/src/frontend/src/modals/apiModal/utils/get-python-api-code.tsx b/src/frontend/src/modals/apiModal/utils/get-python-api-code.tsx
index a876b18c3..9aa946746 100644
--- a/src/frontend/src/modals/apiModal/utils/get-python-api-code.tsx
+++ b/src/frontend/src/modals/apiModal/utils/get-python-api-code.tsx
@@ -10,7 +10,7 @@ export default function getPythonApiCode(
   flowId: string,
   isAuth: boolean,
   tweaksBuildedObject: any[],
-  endpointName?: string
+  endpointName?: string,
 ): string {
   let tweaksString = "{}";
   if (tweaksBuildedObject && tweaksBuildedObject.length > 0) {
diff --git a/src/frontend/src/modals/apiModal/utils/get-python-code.tsx b/src/frontend/src/modals/apiModal/utils/get-python-code.tsx
index 846a4c6e9..734e16bbe 100644
--- a/src/frontend/src/modals/apiModal/utils/get-python-code.tsx
+++ b/src/frontend/src/modals/apiModal/utils/get-python-code.tsx
@@ -6,7 +6,7 @@
  */
 export default function getPythonCode(
   flowName: string,
-  tweaksBuildedObject: any[]
+  tweaksBuildedObject: any[],
 ): string {
   let tweaksString = "{}";
   if (tweaksBuildedObject && tweaksBuildedObject.length > 0) {
diff --git a/src/frontend/src/modals/apiModal/utils/get-value.ts b/src/frontend/src/modals/apiModal/utils/get-value.ts
index 8db7666bf..df8e5bdde 100644
--- a/src/frontend/src/modals/apiModal/utils/get-value.ts
+++ b/src/frontend/src/modals/apiModal/utils/get-value.ts
@@ -5,7 +5,7 @@ export const getValue = (
   value: string,
   node: NodeType,
   template: TemplateVariableType,
-  tweak: Object[]
+  tweak: Object[],
 ) => {
   let returnValue = value ?? "";
 
diff --git a/src/frontend/src/modals/apiModal/utils/get-widget-code.tsx b/src/frontend/src/modals/apiModal/utils/get-widget-code.tsx
index a05dc192a..0d3d02f91 100644
--- a/src/frontend/src/modals/apiModal/utils/get-widget-code.tsx
+++ b/src/frontend/src/modals/apiModal/utils/get-widget-code.tsx
@@ -6,7 +6,7 @@
 export default function getWidgetCode(
   flowId: string,
   flowName: string,
-  isAuth: boolean
+  isAuth: boolean,
 ): string {
   return `<script src="https://cdn.jsdelivr.net/gh/langflow-ai/langflow-embedded-chat@1.0_alpha/dist/build/static/js/bundle.min.js"></script>
 
diff --git a/src/frontend/src/modals/apiModal/utils/tabs-array.tsx b/src/frontend/src/modals/apiModal/utils/tabs-array.tsx
index adc15f932..deed73332 100644
--- a/src/frontend/src/modals/apiModal/utils/tabs-array.tsx
+++ b/src/frontend/src/modals/apiModal/utils/tabs-array.tsx
@@ -1,7 +1,7 @@
 export function createTabsArray(
   codes,
   includeWebhookCurl = false,
-  includeTweaks = false
+  includeTweaks = false,
 ) {
   const tabs = [
     {
diff --git a/src/frontend/src/modals/baseModal/index.tsx b/src/frontend/src/modals/baseModal/index.tsx
index 728a74895..1ce1a20f3 100644
--- a/src/frontend/src/modals/baseModal/index.tsx
+++ b/src/frontend/src/modals/baseModal/index.tsx
@@ -37,7 +37,7 @@ const Content: React.FC<ContentProps> = ({ children, overflowHidden }) => {
     <div
       className={cn(
         `flex w-full flex-grow flex-col transition-all duration-300`,
-        overflowHidden ? "overflow-hidden" : "overflow-visible"
+        overflowHidden ? "overflow-hidden" : "overflow-visible",
       )}
     >
       {children}
@@ -105,6 +105,7 @@ const Footer: React.FC<{
               type={submit.onClick ? "button" : "submit"}
               onClick={submit.onClick}
               loading={submit.loading}
+              disabled={submit.disabled}
             >
               {submit.icon && submit.icon}
               {submit.label}
@@ -122,7 +123,7 @@ interface BaseModalProps {
     React.ReactElement<ContentProps>,
     React.ReactElement<HeaderProps>,
     React.ReactElement<TriggerProps>?,
-    React.ReactElement<FooterProps>?
+    React.ReactElement<FooterProps>?,
   ];
   open?: boolean;
   setOpen?: (open: boolean) => void;
@@ -158,16 +159,16 @@ function BaseModal({
   onSubmit,
 }: BaseModalProps) {
   const headerChild = React.Children.toArray(children).find(
-    (child) => (child as React.ReactElement).type === Header
+    (child) => (child as React.ReactElement).type === Header,
   );
   const triggerChild = React.Children.toArray(children).find(
-    (child) => (child as React.ReactElement).type === Trigger
+    (child) => (child as React.ReactElement).type === Trigger,
   );
   const ContentChild = React.Children.toArray(children).find(
-    (child) => (child as React.ReactElement).type === Content
+    (child) => (child as React.ReactElement).type === Content,
   );
   const ContentFooter = React.Children.toArray(children).find(
-    (child) => (child as React.ReactElement).type === Footer
+    (child) => (child as React.ReactElement).type === Footer,
   );
 
   let { minWidth, height } = switchCaseModalSize(size);
@@ -189,7 +190,7 @@ function BaseModal({
   const contentClasses = cn(
     minWidth,
     height,
-    "flex flex-col duration-300 overflow-hidden"
+    "flex flex-col duration-300 overflow-hidden",
   );
 
   //UPDATE COLORS AND STYLE CLASSSES
diff --git a/src/frontend/src/modals/confirmationModal/index.tsx b/src/frontend/src/modals/confirmationModal/index.tsx
index 9f8efa165..dd7437693 100644
--- a/src/frontend/src/modals/confirmationModal/index.tsx
+++ b/src/frontend/src/modals/confirmationModal/index.tsx
@@ -54,10 +54,10 @@ function ConfirmationModal({
   }, [modalOpen]);
 
   const triggerChild = React.Children.toArray(children).find(
-    (child) => (child as React.ReactElement).type === Trigger
+    (child) => (child as React.ReactElement).type === Trigger,
   );
   const ContentChild = React.Children.toArray(children).find(
-    (child) => (child as React.ReactElement).type === Content
+    (child) => (child as React.ReactElement).type === Content,
   );
 
   return (
diff --git a/src/frontend/src/modals/dictAreaModal/index.tsx b/src/frontend/src/modals/dictAreaModal/index.tsx
index 3947eb069..ff9294d9c 100644
--- a/src/frontend/src/modals/dictAreaModal/index.tsx
+++ b/src/frontend/src/modals/dictAreaModal/index.tsx
@@ -18,10 +18,12 @@ export default function DictAreaModal({
   children,
   onChange,
   value,
+  disabled = false,
 }: {
   children: JSX.Element;
   onChange?: (value: Object) => void;
   value: Object;
+  disabled?: boolean;
 }): JSX.Element {
   const [open, setOpen] = useState(false);
   const isDark = useDarkStore((state) => state.dark);
@@ -35,6 +37,7 @@ export default function DictAreaModal({
     <BaseModal
       size="medium-h-full"
       open={open}
+      disable={disabled}
       setOpen={setOpen}
       onSubmit={
         onChange
diff --git a/src/frontend/src/modals/editNodeModal/hooks/use-column-defs.tsx b/src/frontend/src/modals/editNodeModal/hooks/use-column-defs.tsx
index 83590f409..14263a9c6 100644
--- a/src/frontend/src/modals/editNodeModal/hooks/use-column-defs.tsx
+++ b/src/frontend/src/modals/editNodeModal/hooks/use-column-defs.tsx
@@ -8,7 +8,7 @@ const useColumnDefs = (
   handleOnNewValue: (newValue: any, name: string) => void,
   handleOnChangeDb: (value: boolean, key: string) => void,
   changeAdvanced: (n: string) => void,
-  open: boolean
+  open: boolean,
 ) => {
   const columnDefs: ColDef[] = useMemo(
     () => [
@@ -76,7 +76,7 @@ const useColumnDefs = (
         cellClass: "no-border",
       },
     ],
-    [open, myData]
+    [open, myData],
   );
 
   return columnDefs;
diff --git a/src/frontend/src/modals/editNodeModal/index.tsx b/src/frontend/src/modals/editNodeModal/index.tsx
index c3b84745d..cb6f82f59 100644
--- a/src/frontend/src/modals/editNodeModal/index.tsx
+++ b/src/frontend/src/modals/editNodeModal/index.tsx
@@ -25,7 +25,7 @@ const EditNodeModal = forwardRef(
       //      setOpenWDoubleClick: (open: boolean) => void;
       data: NodeDataType;
     },
-    ref
+    ref,
   ) => {
     const myData = useRef(cloneDeep(data));
 
@@ -53,7 +53,7 @@ const EditNodeModal = forwardRef(
       handleOnNewValue,
       handleOnChangeDb,
       changeAdvanced,
-      open
+      open,
     );
 
     const [gridApi, setGridApi] = useState<GridApi | null>(null);
@@ -119,7 +119,7 @@ const EditNodeModal = forwardRef(
         />
       </BaseModal>
     );
-  }
+  },
 );
 
 export default EditNodeModal;
diff --git a/src/frontend/src/modals/exportModal/index.tsx b/src/frontend/src/modals/exportModal/index.tsx
index 1c038e980..8904f79be 100644
--- a/src/frontend/src/modals/exportModal/index.tsx
+++ b/src/frontend/src/modals/exportModal/index.tsx
@@ -42,10 +42,11 @@ const ExportModal = forwardRef(
                 description,
                 name,
                 last_tested_version: version,
+                endpoint_name: currentFlow!.endpoint_name,
                 is_component: false,
               },
               name!,
-              description
+              description,
             );
             setNoticeData({
               title: API_WARNING_NOTICE_ALERT,
@@ -58,10 +59,11 @@ const ExportModal = forwardRef(
                 description,
                 name,
                 last_tested_version: version,
+                endpoint_name: currentFlow!.endpoint_name,
                 is_component: false,
               }),
               name!,
-              description
+              description,
             );
           setOpen(false);
         }}
@@ -102,6 +104,6 @@ const ExportModal = forwardRef(
         <BaseModal.Footer submit={{ label: "Export" }} />
       </BaseModal>
     );
-  }
+  },
 );
 export default ExportModal;
diff --git a/src/frontend/src/modals/flowLogsModal/index.tsx b/src/frontend/src/modals/flowLogsModal/index.tsx
index 330a39d47..9412389d0 100644
--- a/src/frontend/src/modals/flowLogsModal/index.tsx
+++ b/src/frontend/src/modals/flowLogsModal/index.tsx
@@ -1,102 +1,51 @@
 import { ColDef, ColGroupDef } from "ag-grid-community";
-import { useEffect, useMemo, useRef, useState } from "react";
+import { useEffect, useState } from "react";
 import IconComponent from "../../components/genericIconComponent";
 import TableComponent from "../../components/tableComponent";
-import { Tabs, TabsList, TabsTrigger } from "../../components/ui/tabs";
-import { getMessagesTable, getTransactionTable } from "../../controllers/API";
-import useAlertStore from "../../stores/alertStore";
-import useFlowStore from "../../stores/flowStore";
+import { getTransactionTable } from "../../controllers/API";
 import useFlowsManagerStore from "../../stores/flowsManagerStore";
 import { FlowSettingsPropsType } from "../../types/components";
-import { NodeDataType } from "../../types/flow";
 import BaseModal from "../baseModal";
 
 export default function FlowLogsModal({
   open,
   setOpen,
 }: FlowSettingsPropsType): JSX.Element {
-  const nodes = useFlowStore((state) => state.nodes);
   const currentFlowId = useFlowsManagerStore((state) => state.currentFlowId);
-  const setNoticeData = useAlertStore((state) => state.setNoticeData);
 
   const [columns, setColumns] = useState<Array<ColDef | ColGroupDef>>([]);
   const [rows, setRows] = useState<any>([]);
-  const [activeTab, setActiveTab] = useState("Executions");
-  const noticed = useRef(false);
 
   useEffect(() => {
-    if (activeTab === "Executions") {
-      getTransactionTable(currentFlowId, "union").then((data) => {
-        const { columns, rows } = data;
-        setColumns(columns.map((col) => ({ ...col, editable: true })));
-        setRows(rows);
-      });
-    } else if (activeTab === "Messages") {
-      getMessagesTable("union", currentFlowId, ["index", "flow_id"]).then(
-        (data) => {
-          const { columns, rows } = data;
-          setColumns(columns.map((col) => ({ ...col, editable: true })));
-          setRows(rows);
-        }
-      );
-    }
-
-    if (open && activeTab === "Messages" && !noticed.current) {
-      const haStream = nodes
-        .map((nodes) => (nodes.data as NodeDataType).node!.template)
-        .some((template) => template["stream"] && template["stream"].value);
-      console.log(
-        haStream,
-        nodes.map((nodes) => (nodes.data as NodeDataType).node!.template)
-      );
-      if (haStream) {
-        setNoticeData({
-          title: "Streamed messages will not appear in this table.",
-        });
-        noticed.current = true;
-      }
-    }
-    if (!open) {
-      noticed.current = false;
-    }
-  }, [open, activeTab]);
-
-  const tableComponentRender = useMemo(() => {
-    return (
-      <TableComponent
-        key={activeTab}
-        readOnlyEdit
-        className="h-max-full h-full w-full"
-        pagination={rows.length === 0 ? false : true}
-        columnDefs={columns}
-        rowHeight={48}
-        autoSizeStrategy={{ type: "fitGridWidth" }}
-        rowData={rows}
-        headerHeight={rows.length === 0 ? 0 : undefined}
-      />
-    );
-  }, [activeTab]);
+    getTransactionTable(currentFlowId, "union").then((data) => {
+      const { columns, rows } = data;
+      setColumns(columns.map((col) => ({ ...col, editable: true })));
+      setRows(rows);
+    });
+  }, [open]);
 
   return (
     <BaseModal open={open} setOpen={setOpen} size="large">
-      <BaseModal.Header description="Inspect component executions and monitor sent messages in the playground">
-        <span className="pr-2">Logs</span>
-        <IconComponent name="ScrollText" className="mr-2 h-4 w-4" />
+      <BaseModal.Header description="Inspect component executions.">
+        <div className="flex w-full justify-between">
+          <div className="flex h-fit w-32 items-center">
+            <span className="pr-2">Logs</span>
+            <IconComponent name="ScrollText" className="mr-2 h-4 w-4" />
+          </div>
+          <div className="flex h-fit w-32 items-center"></div>
+        </div>
       </BaseModal.Header>
       <BaseModal.Content>
-        <Tabs
-          value={activeTab}
-          onValueChange={setActiveTab}
-          className={
-            "text-center; inset-0 m-0 mb-2 flex flex-col self-center overflow-hidden rounded-md border bg-muted pb-1"
-          }
-        >
-          <TabsList>
-            <TabsTrigger value={"Executions"}>Executions</TabsTrigger>
-            <TabsTrigger value={"Messages"}>Messages</TabsTrigger>
-          </TabsList>
-        </Tabs>
-        {tableComponentRender}
+        <TableComponent
+          key={"Executions"}
+          readOnlyEdit
+          className="h-max-full h-full w-full"
+          pagination={rows.length === 0 ? false : true}
+          columnDefs={columns}
+          autoSizeStrategy={{ type: "fitGridWidth" }}
+          rowData={rows}
+          headerHeight={rows.length === 0 ? 0 : undefined}
+        ></TableComponent>
       </BaseModal.Content>
     </BaseModal>
   );
diff --git a/src/frontend/src/modals/flowSettingsModal/index.tsx b/src/frontend/src/modals/flowSettingsModal/index.tsx
index d810733f4..e5bc58d0d 100644
--- a/src/frontend/src/modals/flowSettingsModal/index.tsx
+++ b/src/frontend/src/modals/flowSettingsModal/index.tsx
@@ -80,7 +80,7 @@ export default function FlowSettingsModal({
       <BaseModal.Footer
         submit={{
           label: "Save",
-          disabled: nameLists.includes(name) && name !== currentFlow!.name,
+          disabled: nameLists.includes(name) || name === currentFlow!.name,
           dataTestId: "save-flow-settings",
           loading: isSaving,
         }}
diff --git a/src/frontend/src/modals/foldersModal/hooks/submit-folder.tsx b/src/frontend/src/modals/foldersModal/hooks/submit-folder.tsx
index c35d7c450..71a8ac944 100644
--- a/src/frontend/src/modals/foldersModal/hooks/submit-folder.tsx
+++ b/src/frontend/src/modals/foldersModal/hooks/submit-folder.tsx
@@ -33,7 +33,7 @@ const useFolderSubmit = (setOpen, folderToEdit) => {
             getFoldersApi(true);
             setOpen(false);
           }
-        }
+        },
       );
     } else {
       addFolder(data).then(
@@ -49,7 +49,7 @@ const useFolderSubmit = (setOpen, folderToEdit) => {
           setErrorData({
             title: `Error creating folder.`,
           });
-        }
+        },
       );
     }
   };
diff --git a/src/frontend/src/modals/genericModal/index.tsx b/src/frontend/src/modals/genericModal/index.tsx
index 58b921217..78b00e16e 100644
--- a/src/frontend/src/modals/genericModal/index.tsx
+++ b/src/frontend/src/modals/genericModal/index.tsx
@@ -38,6 +38,7 @@ export default function GenericModal({
   nodeClass,
   setNodeClass,
   children,
+  disabled,
   id = "",
   readonly = false,
 }: genericModalPropsType): JSX.Element {
@@ -47,7 +48,7 @@ export default function GenericModal({
   const [myModalType] = useState(type);
   const [inputValue, setInputValue] = useState(value);
   const [isEdit, setIsEdit] = useState(true);
-  const [wordsHighlight, setWordsHighlight] = useState<string[]>([]);
+  const [wordsHighlight, setWordsHighlight] = useState<Set<string>>(new Set());
   const setSuccessData = useAlertStore((state) => state.setSuccessData);
   const setErrorData = useAlertStore((state) => state.setErrorData);
   const setNoticeData = useAlertStore((state) => state.setNoticeData);
@@ -82,23 +83,14 @@ export default function GenericModal({
       }
     }
 
-    const filteredWordsHighlight = matches.filter(
-      (word) => !invalid_chars.includes(word)
+    const filteredWordsHighlight = new Set(
+      matches.filter((word) => !invalid_chars.includes(word)),
     );
 
     setWordsHighlight(filteredWordsHighlight);
   }
 
-  useEffect(() => {
-    if (type === TypeModal.PROMPT && inputValue && inputValue != "") {
-      checkVariables(inputValue);
-    }
-  }, [inputValue, type]);
-
-  useEffect(() => {
-    setInputValue(value);
-  }, [value, modalOpen]);
-  const coloredContent = (inputValue || "")
+  const coloredContent = (typeof inputValue === "string" ? inputValue : "")
     .replace(/</g, "&lt;")
     .replace(/>/g, "&gt;")
     .replace(regexHighlight, (match, p1, p2) => {
@@ -115,6 +107,17 @@ export default function GenericModal({
       return match;
     })
     .replace(/\n/g, "<br />");
+
+  useEffect(() => {
+    if (type === TypeModal.PROMPT && inputValue && inputValue != "") {
+      checkVariables(inputValue);
+    }
+  }, [inputValue, type]);
+
+  useEffect(() => {
+    if (typeof value === "string") setInputValue(value);
+  }, [value, modalOpen]);
+
   function getClassByNumberLength(): string {
     let sumOfCaracteres: number = 0;
     wordsHighlight.forEach((element) => {
@@ -134,7 +137,7 @@ export default function GenericModal({
         // to the first key of the custom_fields object
         if (field_name === "") {
           field_name = Array.isArray(
-            apiReturn.data?.frontend_node?.custom_fields?.[""]
+            apiReturn.data?.frontend_node?.custom_fields?.[""],
           )
             ? apiReturn.data?.frontend_node?.custom_fields?.[""][0] ?? ""
             : apiReturn.data?.frontend_node?.custom_fields?.[""] ?? "";
@@ -180,7 +183,9 @@ export default function GenericModal({
       open={modalOpen}
       setOpen={setModalOpen}
     >
-      <BaseModal.Trigger>{children}</BaseModal.Trigger>
+      <BaseModal.Trigger disable={disabled} asChild>
+        {children}
+      </BaseModal.Trigger>
       <BaseModal.Header
         description={(() => {
           switch (myModalTitle) {
@@ -205,12 +210,7 @@ export default function GenericModal({
         />
       </BaseModal.Header>
       <BaseModal.Content overflowHidden>
-        <div
-          className={classNames(
-            !isEdit ? "rounded-lg border" : "",
-            "flex h-full w-full"
-          )}
-        >
+        <div className={classNames("flex h-full w-full rounded-lg border")}>
           {type === TypeModal.PROMPT && isEdit && !readonly ? (
             <Textarea
               id={"modal-" + id}
@@ -233,7 +233,7 @@ export default function GenericModal({
             />
           ) : type === TypeModal.PROMPT && (!isEdit || readonly) ? (
             <SanitizedHTMLWrapper
-              className={getClassByNumberLength()}
+              className={getClassByNumberLength() + " bg-muted"}
               content={coloredContent}
               onClick={() => {
                 setIsEdit(true);
@@ -279,7 +279,7 @@ export default function GenericModal({
                       Prompt Variables:
                     </span>
 
-                    {wordsHighlight.map((word, index) => (
+                    {Array.from(wordsHighlight).map((word, index) => (
                       <ShadTooltip
                         key={index}
                         content={word.replace(/[{}]/g, "")}
diff --git a/src/frontend/src/modals/newFlowModal/components/undrawCards/index.tsx b/src/frontend/src/modals/newFlowModal/components/undrawCards/index.tsx
index 6f889a407..d872fabe3 100644
--- a/src/frontend/src/modals/newFlowModal/components/undrawCards/index.tsx
+++ b/src/frontend/src/modals/newFlowModal/components/undrawCards/index.tsx
@@ -1,19 +1,11 @@
-import { useLocation, useNavigate } from "react-router-dom";
 /// <reference types="vite-plugin-svgr/client" />
-//@ts-ignore
-import { ReactComponent as TransferFiles } from "../../../../assets/undraw_transfer_files_re_a2a9.svg";
-//@ts-ignore
-//@ts-ignore
-//@ts-ignore
-import { ReactComponent as APIRequest } from "../../../../assets/undraw_real_time_analytics_re_yliv.svg";
-//@ts-ignore
-import { ReactComponent as PromptChaining } from "../../../../assets/undraw_cloud_docs_re_xjht.svg";
-//@ts-ignore
-import { ReactComponent as ChatBot } from "../../../../assets/undraw_chat_bot_re_e2gj.svg";
-//@ts-ignore
-import { ReactComponent as BlogPost } from "../../../../assets/undraw_blog_post_re_fy5x.svg";
-//@ts-ignore
-import { ReactComponent as BasicPrompt } from "../../../../assets/undraw_short_bio_re_fmx0.svg";
+import { useLocation, useNavigate } from "react-router-dom";
+import BlogPost from "../../../../assets/undraw_blog_post_re_fy5x.svg?react";
+import ChatBot from "../../../../assets/undraw_chat_bot_re_e2gj.svg?react";
+import PromptChaining from "../../../../assets/undraw_cloud_docs_re_xjht.svg?react";
+import APIRequest from "../../../../assets/undraw_real_time_analytics_re_yliv.svg?react";
+import BasicPrompt from "../../../../assets/undraw_short_bio_re_fmx0.svg?react";
+import TransferFiles from "../../../../assets/undraw_transfer_files_re_a2a9.svg?react";
 
 import {
   Card,
@@ -43,8 +35,8 @@ export default function UndrawCardComponent({
             style={{
               width: "65%",
               height: "65%",
-              preserveAspectRatio: "xMidYMid meet",
             }}
+            preserveAspectRatio="xMidYMid meet"
           />
         );
       case "Basic Prompting (Hello, World)":
@@ -53,8 +45,8 @@ export default function UndrawCardComponent({
             style={{
               width: "65%",
               height: "65%",
-              preserveAspectRatio: "xMidYMid meet",
             }}
+            preserveAspectRatio="xMidYMid meet"
           />
         );
       case "Memory Chatbot":
@@ -63,8 +55,8 @@ export default function UndrawCardComponent({
             style={{
               width: "70%",
               height: "70%",
-              preserveAspectRatio: "xMidYMid meet",
             }}
+            preserveAspectRatio="xMidYMid meet"
           />
         );
       case "API requests":
@@ -73,8 +65,8 @@ export default function UndrawCardComponent({
             style={{
               width: "70%",
               height: "70%",
-              preserveAspectRatio: "xMidYMid meet",
             }}
+            preserveAspectRatio="xMidYMid meet"
           />
         );
       case "Document QA":
@@ -83,8 +75,8 @@ export default function UndrawCardComponent({
             style={{
               width: "80%",
               height: "80%",
-              preserveAspectRatio: "xMidYMid meet",
             }}
+            preserveAspectRatio="xMidYMid meet"
           />
         );
       case "Vector Store RAG":
@@ -93,8 +85,8 @@ export default function UndrawCardComponent({
             style={{
               width: "80%",
               height: "80%",
-              preserveAspectRatio: "xMidYMid meet",
             }}
+            preserveAspectRatio="xMidYMid meet"
           />
         );
       default:
@@ -103,8 +95,8 @@ export default function UndrawCardComponent({
             style={{
               width: "80%",
               height: "80%",
-              preserveAspectRatio: "xMidYMid meet",
             }}
+            preserveAspectRatio="xMidYMid meet"
           />
         );
     }
diff --git a/src/frontend/src/modals/newFlowModal/index.tsx b/src/frontend/src/modals/newFlowModal/index.tsx
index b5134ddec..37a6afeeb 100644
--- a/src/frontend/src/modals/newFlowModal/index.tsx
+++ b/src/frontend/src/modals/newFlowModal/index.tsx
@@ -32,7 +32,7 @@ export default function NewFlowModal({
               key={0}
               flow={
                 examples.find(
-                  (e) => e.name == "Basic Prompting (Hello, World)"
+                  (e) => e.name == "Basic Prompting (Hello, World)",
                 )!
               }
             />
diff --git a/src/frontend/src/modals/secretKeyModal/index.tsx b/src/frontend/src/modals/secretKeyModal/index.tsx
index bb928ee32..eada8c66a 100644
--- a/src/frontend/src/modals/secretKeyModal/index.tsx
+++ b/src/frontend/src/modals/secretKeyModal/index.tsx
@@ -112,8 +112,7 @@ export default function SecretKeyModal({
                 onClick={() => {
                   handleCopyClick();
                 }}
-                variant="none"
-                size="none"
+                unstyled
               >
                 {textCopied ? (
                   <IconComponent name="Copy" className="h-4 w-4" />
diff --git a/src/frontend/src/modals/shareModal/index.tsx b/src/frontend/src/modals/shareModal/index.tsx
index 2086e877c..13d50f0a9 100644
--- a/src/frontend/src/modals/shareModal/index.tsx
+++ b/src/frontend/src/modals/shareModal/index.tsx
@@ -131,14 +131,14 @@ export default function ShareModal({
             title: "Error sharing " + is_component ? "component" : "flow",
             list: [err["response"]["data"]["detail"]],
           });
-        }
+        },
       );
     else
       updateFlowStore(
         flow!,
         getTagsIds(selectedTags, tags),
         sharePublic,
-        unavaliableNames.find((e) => e.name === name)!.id
+        unavaliableNames.find((e) => e.name === name)!.id,
       ).then(successShare, (err) => {
         setErrorData({
           title: "Error sharing " + is_component ? "component" : "flow",
@@ -205,7 +205,7 @@ export default function ShareModal({
         setOpen={internalSetOpen}
         onSubmit={() => {
           const isNameAvailable = !unavaliableNames.some(
-            (element) => element.name === name
+            (element) => element.name === name,
           );
 
           if (isNameAvailable) {
diff --git a/src/frontend/src/modals/shareModal/utils/get-tags-ids.tsx b/src/frontend/src/modals/shareModal/utils/get-tags-ids.tsx
index 1eb1c650e..364d310a5 100644
--- a/src/frontend/src/modals/shareModal/utils/get-tags-ids.tsx
+++ b/src/frontend/src/modals/shareModal/utils/get-tags-ids.tsx
@@ -1,6 +1,6 @@
 export default function getTagsIds(
   tags: string[],
-  tagListId: { name: string; id: string }[]
+  tagListId: { name: string; id: string }[],
 ) {
   return tags
     .map((tag) => tagListId.find((tagObj) => tagObj.name === tag))!
diff --git a/src/frontend/src/modals/textModal/index.tsx b/src/frontend/src/modals/textModal/index.tsx
new file mode 100644
index 000000000..b79bf1fd1
--- /dev/null
+++ b/src/frontend/src/modals/textModal/index.tsx
@@ -0,0 +1,58 @@
+import "ace-builds/src-noconflict/ace";
+import "ace-builds/src-noconflict/ext-language_tools";
+import "ace-builds/src-noconflict/mode-python";
+import "ace-builds/src-noconflict/theme-github";
+import "ace-builds/src-noconflict/theme-twilight";
+// import "ace-builds/webpack-resolver";
+import { cloneDeep } from "lodash";
+import { useEffect, useState } from "react";
+import JsonView from "react18-json-view";
+import "react18-json-view/src/dark.css";
+import "react18-json-view/src/style.css";
+import IconComponent from "../../components/genericIconComponent";
+import { Button } from "../../components/ui/button";
+import {
+  CODE_DICT_DIALOG_SUBTITLE,
+  TEXT_DIALOG_SUBTITLE,
+} from "../../constants/constants";
+import TextOutputView from "../../shared/components/textOutputView";
+import { useDarkStore } from "../../stores/darkStore";
+import BaseModal from "../baseModal";
+
+export default function TextModal({
+  children,
+  value,
+}: {
+  children: JSX.Element;
+  value: Object;
+}): JSX.Element {
+  const [open, setOpen] = useState(false);
+
+  return (
+    <BaseModal size="medium-h-full" open={open} setOpen={setOpen}>
+      <BaseModal.Trigger className="h-full">{children}</BaseModal.Trigger>
+      <BaseModal.Header description={""}>
+        <span className="pr-2">View Text</span>
+        <IconComponent
+          name="Type"
+          className="h-6 w-6 pl-1 text-primary"
+          aria-hidden="true"
+        />
+      </BaseModal.Header>
+      <BaseModal.Content>
+        <div className="flex h-full w-full flex-col transition-all">
+          <div className="h-[370px]">
+            <TextOutputView value={value} left={false} />
+          </div>
+        </div>
+      </BaseModal.Content>
+      <BaseModal.Footer>
+        <div className="flex w-full justify-end pt-2">
+          <Button className="flex gap-2 px-3" onClick={() => setOpen(false)}>
+            Close
+          </Button>
+        </div>
+      </BaseModal.Footer>
+    </BaseModal>
+  );
+}
diff --git a/src/frontend/src/pages/AdminPage/index.tsx b/src/frontend/src/pages/AdminPage/index.tsx
index fcb732498..80c36adff 100644
--- a/src/frontend/src/pages/AdminPage/index.tsx
+++ b/src/frontend/src/pages/AdminPage/index.tsx
@@ -53,7 +53,7 @@ export default function AdminPage() {
   const { userData } = useContext(AuthContext);
   const [totalRowsCount, setTotalRowsCount] = useState(0);
   const setCurrentFlowId = useFlowsManagerStore(
-    (state) => state.setCurrentFlowId
+    (state) => state.setCurrentFlowId,
   );
 
   // set null id
@@ -114,7 +114,7 @@ export default function AdminPage() {
       setFilterUserList(userList.current);
     } else {
       const filteredList = userList.current.filter((user: Users) =>
-        user.username.toLowerCase().includes(input.toLowerCase())
+        user.username.toLowerCase().includes(input.toLowerCase()),
       );
       setFilterUserList(filteredList);
     }
@@ -331,7 +331,7 @@ export default function AdminPage() {
                                 handleDisableUser(
                                   user.is_active,
                                   user.id,
-                                  user
+                                  user,
                                 );
                               }}
                             >
@@ -363,7 +363,7 @@ export default function AdminPage() {
                                 handleSuperUserEdit(
                                   user.is_superuser,
                                   user.id,
-                                  user
+                                  user,
                                 );
                               }}
                             >
diff --git a/src/frontend/src/pages/FlowPage/components/DisclosureComponent/index.tsx b/src/frontend/src/pages/FlowPage/components/DisclosureComponent/index.tsx
index 267231a7c..145ffbb08 100644
--- a/src/frontend/src/pages/FlowPage/components/DisclosureComponent/index.tsx
+++ b/src/frontend/src/pages/FlowPage/components/DisclosureComponent/index.tsx
@@ -6,10 +6,10 @@ export default function DisclosureComponent({
   button: { title, Icon, buttons = [] },
   isChild = true,
   children,
-  openDisc,
+  defaultOpen,
 }: DisclosureComponentType): JSX.Element {
   return (
-    <Disclosure as="div" defaultOpen={openDisc} key={title}>
+    <Disclosure as="div" defaultOpen={defaultOpen} key={title}>
       {({ open }) => (
         <>
           <div>
@@ -36,7 +36,7 @@ export default function DisclosureComponent({
                   <IconComponent
                     name="ChevronRight"
                     className={`${
-                      open || openDisc ? "rotate-90 transform" : ""
+                      open || defaultOpen ? "rotate-90 transform" : ""
                     } h-4 w-4 text-foreground`}
                   />
                 </div>
diff --git a/src/frontend/src/pages/FlowPage/components/PageComponent/index.tsx b/src/frontend/src/pages/FlowPage/components/PageComponent/index.tsx
index 4516c2e8f..c3632cf6d 100644
--- a/src/frontend/src/pages/FlowPage/components/PageComponent/index.tsx
+++ b/src/frontend/src/pages/FlowPage/components/PageComponent/index.tsx
@@ -38,7 +38,6 @@ import {
   generateNodeFromFlow,
   getNodeId,
   isValidConnection,
-  reconnectEdges,
   scapeJSONParse,
   updateIds,
   validateSelection,
@@ -61,19 +60,19 @@ export default function Page({
 }): JSX.Element {
   const uploadFlow = useFlowsManagerStore((state) => state.uploadFlow);
   const autoSaveCurrentFlow = useFlowsManagerStore(
-    (state) => state.autoSaveCurrentFlow
+    (state) => state.autoSaveCurrentFlow,
   );
   const types = useTypesStore((state) => state.types);
   const templates = useTypesStore((state) => state.templates);
   const setFilterEdge = useFlowStore((state) => state.setFilterEdge);
   const reactFlowWrapper = useRef<HTMLDivElement>(null);
   const [showCanvas, setSHowCanvas] = useState(
-    Object.keys(templates).length > 0 && Object.keys(types).length > 0
+    Object.keys(templates).length > 0 && Object.keys(types).length > 0,
   );
 
   const reactFlowInstance = useFlowStore((state) => state.reactFlowInstance);
   const setReactFlowInstance = useFlowStore(
-    (state) => state.setReactFlowInstance
+    (state) => state.setReactFlowInstance,
   );
   const nodes = useFlowStore((state) => state.nodes);
   const edges = useFlowStore((state) => state.edges);
@@ -90,10 +89,10 @@ export default function Page({
   const paste = useFlowStore((state) => state.paste);
   const resetFlow = useFlowStore((state) => state.resetFlow);
   const lastCopiedSelection = useFlowStore(
-    (state) => state.lastCopiedSelection
+    (state) => state.lastCopiedSelection,
   );
   const setLastCopiedSelection = useFlowStore(
-    (state) => state.setLastCopiedSelection
+    (state) => state.setLastCopiedSelection,
   );
   const onConnect = useFlowStore((state) => state.onConnect);
   const currentFlowId = useFlowsManagerStore((state) => state.currentFlowId);
@@ -116,30 +115,30 @@ export default function Page({
         clonedSelection!,
         clonedNodes,
         clonedEdges,
-        getRandomName()
+        getRandomName(),
       );
       const newGroupNode = generateNodeFromFlow(newFlow, getNodeId);
-      const newEdges = reconnectEdges(newGroupNode, removedEdges);
+      // const newEdges = reconnectEdges(newGroupNode, removedEdges);
       setNodes([
         ...clonedNodes.filter(
           (oldNodes) =>
             !clonedSelection?.nodes.some(
-              (selectionNode) => selectionNode.id === oldNodes.id
-            )
+              (selectionNode) => selectionNode.id === oldNodes.id,
+            ),
         ),
         newGroupNode,
       ]);
-      setEdges([
-        ...clonedEdges.filter(
-          (oldEdge) =>
-            !clonedSelection!.nodes.some(
-              (selectionNode) =>
-                selectionNode.id === oldEdge.target ||
-                selectionNode.id === oldEdge.source
-            )
-        ),
-        ...newEdges,
-      ]);
+      // setEdges([
+      //   ...clonedEdges.filter(
+      //     (oldEdge) =>
+      //       !clonedSelection!.nodes.some(
+      //         (selectionNode) =>
+      //           selectionNode.id === oldEdge.target ||
+      //           selectionNode.id === oldEdge.source,
+      //       ),
+      //   ),
+      //   ...newEdges,
+      // ]);
     } else {
       setErrorData({
         title: INVALID_SELECTION_ERROR_ALERT,
@@ -194,9 +193,9 @@ export default function Page({
   }
 
   function handleGroup(e: KeyboardEvent) {
-    e.preventDefault();
-    (e as unknown as Event).stopImmediatePropagation();
     if (selectionMenuVisible) {
+      e.preventDefault();
+      (e as unknown as Event).stopImmediatePropagation();
       handleGroupNode();
     }
   }
@@ -212,7 +211,7 @@ export default function Page({
         {
           x: position.current.x,
           y: position.current.y,
-        }
+        },
       );
     }
   }
@@ -293,7 +292,7 @@ export default function Page({
 
   useEffect(() => {
     setSHowCanvas(
-      Object.keys(templates).length > 0 && Object.keys(types).length > 0
+      Object.keys(templates).length > 0 && Object.keys(types).length > 0,
     );
   }, [templates, types]);
 
@@ -302,7 +301,7 @@ export default function Page({
       takeSnapshot();
       onConnect(params);
     },
-    [takeSnapshot, onConnect]
+    [takeSnapshot, onConnect],
   );
 
   const onNodeDragStart: NodeDragHandler = useCallback(() => {
@@ -343,7 +342,7 @@ export default function Page({
 
         // Extract the data from the drag event and parse it as a JSON object
         const data: { type: string; node?: APIClassType } = JSON.parse(
-          event.dataTransfer.getData("nodedata")
+          event.dataTransfer.getData("nodedata"),
         );
 
         const newId = getNodeId(data.type);
@@ -359,7 +358,7 @@ export default function Page({
         };
         paste(
           { nodes: [newNode], edges: [] },
-          { x: event.clientX, y: event.clientY }
+          { x: event.clientX, y: event.clientY },
         );
       } else if (event.dataTransfer.types.some((types) => types === "Files")) {
         takeSnapshot();
@@ -388,7 +387,7 @@ export default function Page({
       }
     },
     // Specify dependencies for useCallback
-    [getNodeId, setNodes, takeSnapshot, paste]
+    [getNodeId, setNodes, takeSnapshot, paste],
   );
 
   const onEdgeUpdateStart = useCallback(() => {
@@ -404,7 +403,7 @@ export default function Page({
         setEdges((els) => updateEdge(oldEdge, newConnection, els));
       }
     },
-    [setEdges]
+    [setEdges],
   );
 
   const onEdgeUpdateEnd = useCallback((_, edge: Edge): void => {
@@ -437,7 +436,7 @@ export default function Page({
     (flow: OnSelectionChangeParams): void => {
       setLastSelection(flow);
     },
-    []
+    [],
   );
 
   const onPaneClick = useCallback((flow) => {
diff --git a/src/frontend/src/pages/FlowPage/components/PageComponent/utils/get-random-name.tsx b/src/frontend/src/pages/FlowPage/components/PageComponent/utils/get-random-name.tsx
index a58b83ec3..d3bcf4d55 100644
--- a/src/frontend/src/pages/FlowPage/components/PageComponent/utils/get-random-name.tsx
+++ b/src/frontend/src/pages/FlowPage/components/PageComponent/utils/get-random-name.tsx
@@ -5,7 +5,7 @@ import { toTitleCase } from "../../../../../utils/utils";
 export default function getRandomName(
   retry: number = 0,
   noSpace: boolean = false,
-  maxRetries: number = 3
+  maxRetries: number = 3,
 ): string {
   const left: string[] = ADJECTIVES;
   const right: string[] = NOUNS;
diff --git a/src/frontend/src/pages/FlowPage/components/ParentDisclosureComponent/index.tsx b/src/frontend/src/pages/FlowPage/components/ParentDisclosureComponent/index.tsx
index bbdfc0feb..c7293912b 100644
--- a/src/frontend/src/pages/FlowPage/components/ParentDisclosureComponent/index.tsx
+++ b/src/frontend/src/pages/FlowPage/components/ParentDisclosureComponent/index.tsx
@@ -5,11 +5,11 @@ import { DisclosureComponentType } from "../../../../types/components";
 export default function ParentDisclosureComponent({
   button: { title, Icon, buttons = [] },
   children,
-  openDisc,
+  defaultOpen,
   testId,
 }: DisclosureComponentType): JSX.Element {
   return (
-    <Disclosure as="div" defaultOpen={openDisc} key={title}>
+    <Disclosure as="div" defaultOpen={defaultOpen} key={title}>
       {({ open }) => (
         <>
           <div>
@@ -30,7 +30,7 @@ export default function ParentDisclosureComponent({
                   <IconComponent
                     name="ChevronsUpDownIcon"
                     className={`${
-                      open || openDisc ? "" : ""
+                      open || defaultOpen ? "" : ""
                     } h-4 w-4 text-foreground`}
                   />
                 </div>
diff --git a/src/frontend/src/pages/FlowPage/components/SelectionMenuComponent/index.tsx b/src/frontend/src/pages/FlowPage/components/SelectionMenuComponent/index.tsx
index 1f0db8047..912a03e18 100644
--- a/src/frontend/src/pages/FlowPage/components/SelectionMenuComponent/index.tsx
+++ b/src/frontend/src/pages/FlowPage/components/SelectionMenuComponent/index.tsx
@@ -1,5 +1,7 @@
 import { useEffect, useState } from "react";
+import { useHotkeys } from "react-hotkeys-hook";
 import { NodeToolbar } from "reactflow";
+import { Button } from "../../../../components/ui/button";
 import { GradientGroup } from "../../../../icons/GradientSparkles";
 import useFlowStore from "../../../../stores/flowStore";
 import { validateSelection } from "../../../../utils/reactflowUtils";
@@ -10,15 +12,18 @@ export default function SelectionMenu({
   lastSelection,
 }) {
   const edges = useFlowStore((state) => state.edges);
+  const unselectAll = useFlowStore((state) => state.unselectAll);
   const [disable, setDisable] = useState<boolean>(
     lastSelection && edges.length > 0
       ? validateSelection(lastSelection!, edges).length > 0
-      : false
+      : false,
   );
   const [isOpen, setIsOpen] = useState(false);
   const [isTransitioning, setIsTransitioning] = useState(false);
   const [lastNodes, setLastNodes] = useState(nodes);
 
+  useHotkeys("esc", unselectAll, { preventDefault: true });
+
   useEffect(() => {
     if (isOpen) {
       return setDisable(validateSelection(lastSelection!, edges).length > 0);
@@ -61,7 +66,8 @@ export default function SelectionMenu({
             (isTransitioning ? " opacity-100" : " opacity-0")
           }
         >
-          <button
+          <Button
+            unstyled
             className={`${
               disable
                 ? "flex h-full w-full cursor-not-allowed items-center justify-between text-sm text-muted-foreground"
@@ -77,7 +83,7 @@ export default function SelectionMenu({
               disabled={disable}
             />
             Group
-          </button>
+          </Button>
         </div>
       </div>
     </NodeToolbar>
diff --git a/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx b/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx
index 92a4b0bea..32380b842 100644
--- a/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx
+++ b/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/index.tsx
@@ -41,7 +41,7 @@ export default function ExtraSidebar(): JSX.Element {
   const [search, setSearch] = useState("");
   function onDragStart(
     event: React.DragEvent<any>,
-    data: { type: string; node?: APIClassType }
+    data: { type: string; node?: APIClassType },
   ): void {
     //start drag event
     var crt = event.currentTarget.cloneNode(true);
@@ -67,7 +67,7 @@ export default function ExtraSidebar(): JSX.Element {
         let keys = Object.keys(data[d]).filter(
           (nd) =>
             nd.toLowerCase().includes(e.toLowerCase()) ||
-            data[d][nd].display_name?.toLowerCase().includes(e.toLowerCase())
+            data[d][nd].display_name?.toLowerCase().includes(e.toLowerCase()),
         );
         keys.forEach((element) => {
           ret[d][element] = data[d][element];
@@ -134,7 +134,7 @@ export default function ExtraSidebar(): JSX.Element {
 
             if (filtered.some((x) => x !== "")) {
               let keys = Object.keys(dataClone[d]).filter((nd) =>
-                filtered.includes(nd)
+                filtered.includes(nd),
               );
               Object.keys(dataClone[d]).forEach((element) => {
                 if (!keys.includes(element)) {
@@ -171,7 +171,7 @@ export default function ExtraSidebar(): JSX.Element {
 
             if (filtered.some((x) => x !== "")) {
               let keys = Object.keys(dataClone[d]).filter((nd) =>
-                filtered.includes(nd)
+                filtered.includes(nd),
               );
               Object.keys(dataClone[d]).forEach((element) => {
                 if (!keys.includes(element)) {
@@ -200,7 +200,7 @@ export default function ExtraSidebar(): JSX.Element {
             "extra-side-bar-buttons gap-[4px] text-sm font-semibold",
             !hasApiKey || !validApiKey || !hasStore
               ? "button-disable cursor-default text-muted-foreground"
-              : ""
+              : "",
           )}
         >
           <IconComponent
@@ -209,14 +209,14 @@ export default function ExtraSidebar(): JSX.Element {
               "-m-0.5 -ml-1 h-6 w-6",
               !hasApiKey || !validApiKey || !hasStore
                 ? "extra-side-bar-save-disable"
-                : ""
+                : "",
             )}
           />
           Share
         </button>
       </ShareModal>
     ),
-    [hasApiKey, validApiKey, currentFlow, hasStore]
+    [hasApiKey, validApiKey, currentFlow, hasStore],
   );
 
   const ExportMemo = useMemo(
@@ -227,7 +227,7 @@ export default function ExtraSidebar(): JSX.Element {
         </button>
       </ExportModal>
     ),
-    []
+    [],
   );
 
   const getIcon = useMemo(() => {
@@ -293,7 +293,7 @@ export default function ExtraSidebar(): JSX.Element {
             Object.keys(dataFilter[SBSectionName]).length > 0 ? (
               <>
                 <DisclosureComponent
-                  openDisc={
+                  defaultOpen={
                     getFilterEdge.length !== 0 || search.length !== 0
                       ? true
                       : false
@@ -311,8 +311,8 @@ export default function ExtraSidebar(): JSX.Element {
                       .sort((a, b) =>
                         sensitiveSort(
                           dataFilter[SBSectionName][a].display_name,
-                          dataFilter[SBSectionName][b].display_name
-                        )
+                          dataFilter[SBSectionName][b].display_name,
+                        ),
                       )
                       .map((SBItemName: string, index) => (
                         <ShadTooltip
@@ -356,11 +356,11 @@ export default function ExtraSidebar(): JSX.Element {
               </>
             ) : (
               <div key={index}></div>
-            )
+            ),
           )}{" "}
         <ParentDisclosureComponent
-          openDisc={false}
-          key={"Advanced"}
+          defaultOpen={search.length !== 0 || getFilterEdge.length !== 0}
+          key={`${search.length !== 0}-${getFilterEdge.length !== 0}-Advanced`}
           button={{
             title: "Advanced",
             Icon: nodeIconsLucide.unknown,
@@ -375,7 +375,7 @@ export default function ExtraSidebar(): JSX.Element {
                 <>
                   <DisclosureComponent
                     isChild={false}
-                    openDisc={
+                    defaultOpen={
                       getFilterEdge.length !== 0 || search.length !== 0
                         ? true
                         : false
@@ -393,8 +393,8 @@ export default function ExtraSidebar(): JSX.Element {
                         .sort((a, b) =>
                           sensitiveSort(
                             dataFilter[SBSectionName][a].display_name,
-                            dataFilter[SBSectionName][b].display_name
-                          )
+                            dataFilter[SBSectionName][b].display_name,
+                          ),
                         )
                         .map((SBItemName: string, index) => (
                           <ShadTooltip
@@ -468,7 +468,7 @@ export default function ExtraSidebar(): JSX.Element {
                 </>
               ) : (
                 <div key={index}></div>
-              )
+              ),
             )}
         </ParentDisclosureComponent>
       </div>
diff --git a/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/sideBarDraggableComponent/index.tsx b/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/sideBarDraggableComponent/index.tsx
index a709929db..ed92defb9 100644
--- a/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/sideBarDraggableComponent/index.tsx
+++ b/src/frontend/src/pages/FlowPage/components/extraSidebarComponent/sideBarDraggableComponent/index.tsx
@@ -37,11 +37,11 @@ export const SidebarDraggableComponent = forwardRef(
       onDragStart: DragEventHandler<HTMLDivElement>;
       official: boolean;
     },
-    ref
+    ref,
   ) => {
     const [open, setOpen] = useState(false);
     const deleteComponent = useFlowsManagerStore(
-      (state) => state.deleteComponent
+      (state) => state.deleteComponent,
     );
 
     const version = useDarkStore((state) => state.version);
@@ -67,8 +67,8 @@ export const SidebarDraggableComponent = forwardRef(
           downloadNode(
             createFlowComponent(
               { id: getNodeId(type), type, node: apiClass },
-              version
-            )
+              version,
+            ),
           );
           break;
         case "delete":
@@ -104,7 +104,7 @@ export const SidebarDraggableComponent = forwardRef(
             onDragStart={onDragStart}
             onDragEnd={() => {
               document.body.removeChild(
-                document.getElementsByClassName("cursor-grabbing")[0]
+                document.getElementsByClassName("cursor-grabbing")[0],
               );
             }}
           >
@@ -157,7 +157,7 @@ export const SidebarDraggableComponent = forwardRef(
         </div>
       </Select>
     );
-  }
+  },
 );
 
 export default SidebarDraggableComponent;
diff --git a/src/frontend/src/pages/FlowPage/components/nodeToolbarComponent/index.tsx b/src/frontend/src/pages/FlowPage/components/nodeToolbarComponent/index.tsx
index 2bb4f39fe..c10e17c29 100644
--- a/src/frontend/src/pages/FlowPage/components/nodeToolbarComponent/index.tsx
+++ b/src/frontend/src/pages/FlowPage/components/nodeToolbarComponent/index.tsx
@@ -38,19 +38,20 @@ export default function NodeToolbarComponent({
   deleteNode,
   setShowNode,
   numberOfHandles,
+  numberOfOutputHandles,
   showNode,
   name = "code",
-  selected,
   setShowState,
   onCloseAdvancedModal,
   updateNode,
+  isOutdated,
 }: nodeToolbarPropsType): JSX.Element {
   const version = useDarkStore((state) => state.version);
   const [showModalAdvanced, setShowModalAdvanced] = useState(false);
   const [showconfirmShare, setShowconfirmShare] = useState(false);
   const [showOverrideModal, setShowOverrideModal] = useState(false);
   const [flowComponent, setFlowComponent] = useState<FlowType>(
-    createFlowComponent(cloneDeep(data), version)
+    createFlowComponent(cloneDeep(data), version),
   );
   const preventDefault = true;
   const isMac = navigator.platform.toUpperCase().includes("MAC");
@@ -67,7 +68,7 @@ export default function NodeToolbarComponent({
         data.node.template[templateField]?.type === "Any" ||
         data.node.template[templateField]?.type === "int" ||
         data.node.template[templateField]?.type === "dict" ||
-        data.node.template[templateField]?.type === "NestedDict")
+        data.node.template[templateField]?.type === "NestedDict"),
   ).length;
 
   const hasStore = useStoreStore((state) => state.hasStore);
@@ -89,13 +90,6 @@ export default function NodeToolbarComponent({
     return;
   }
 
-  function handleUpdateWShortcut(e: KeyboardEvent) {
-    e.preventDefault();
-    if (hasApiKey || hasStore) {
-      handleSelectChange("update");
-    }
-  }
-
   function handleGroupWShortcut(e: KeyboardEvent) {
     e.preventDefault();
     if (isGroup) {
@@ -121,9 +115,7 @@ export default function NodeToolbarComponent({
   function handleAdvancedWShortcut(e: KeyboardEvent) {
     e.preventDefault();
     if (isThereModal() && !showModalAdvanced) return;
-    if (!isGroup) {
-      setShowModalAdvanced((state) => !state);
-    }
+    setShowModalAdvanced((state) => !state);
   }
 
   function handleSaveWShortcut(e: KeyboardEvent) {
@@ -157,6 +149,7 @@ export default function NodeToolbarComponent({
 
   function handleFreeze(e: KeyboardEvent) {
     e.preventDefault();
+    if (data.node?.flow) return;
     setNode(data.id, (old) => ({
       ...old,
       data: {
@@ -189,7 +182,7 @@ export default function NodeToolbarComponent({
   useHotkeys(download, handleDownloadWShortcut, { preventDefault });
   useHotkeys(freeze, handleFreeze, { preventDefault });
 
-  const isMinimal = numberOfHandles <= 1;
+  const isMinimal = numberOfHandles <= 1 && numberOfOutputHandles <= 1;
   const isGroup = data.node?.flow ? true : false;
 
   const frozen = data.node?.frozen ?? false;
@@ -220,7 +213,7 @@ export default function NodeToolbarComponent({
   const updateNodeInternals = useUpdateNodeInternals();
 
   const setLastCopiedSelection = useFlowStore(
-    (state) => state.setLastCopiedSelection
+    (state) => state.setLastCopiedSelection,
   );
 
   const setSuccessData = useAlertStore((state) => state.setSuccessData);
@@ -247,6 +240,7 @@ export default function NodeToolbarComponent({
         saveComponent(cloneDeep(data), false);
         break;
       case "freeze":
+        if (data.node?.flow) return;
         setNode(data.id, (old) => ({
           ...old,
           data: {
@@ -294,7 +288,8 @@ export default function NodeToolbarComponent({
           nodes,
           edges,
           setNodes,
-          setEdges
+          setEdges,
+          data.node?.outputs,
         );
         break;
       case "override":
@@ -321,14 +316,14 @@ export default function NodeToolbarComponent({
             y: 10,
             paneX: nodes.find((node) => node.id === data.id)?.position.x,
             paneY: nodes.find((node) => node.id === data.id)?.position.y,
-          }
+          },
         );
         break;
     }
   };
 
   const isSaved = flows.some((flow) =>
-    Object.values(flow).includes(data.node?.display_name!)
+    Object.values(flow).includes(data.node?.display_name!),
   );
 
   function displayShortcut({
@@ -346,7 +341,7 @@ export default function NodeToolbarComponent({
       }
     });
     const filteredShortcut = fixedShortcut.filter(
-      (key) => !key.toLowerCase().includes("shift")
+      (key) => !key.toLowerCase().includes("shift"),
     );
     let shortcutWPlus: string[] = [];
     if (!hasShift) shortcutWPlus = filteredShortcut.join("+").split(" ");
@@ -370,7 +365,7 @@ export default function NodeToolbarComponent({
   const setNode = useFlowStore((state) => state.setNode);
 
   const handleOnNewValue = (
-    newValue: string | string[] | boolean | Object[]
+    newValue: string | string[] | boolean | Object[],
   ): void => {
     if (data.node!.template[name].value !== newValue) {
       takeSnapshot();
@@ -426,8 +421,8 @@ export default function NodeToolbarComponent({
             <ShadTooltip
               content={displayShortcut(
                 shortcuts.find(
-                  ({ name }) => name.split(" ")[0].toLowerCase() === "code"
-                )!
+                  ({ name }) => name.split(" ")[0].toLowerCase() === "code",
+                )!,
               )}
               side="top"
             >
@@ -446,8 +441,8 @@ export default function NodeToolbarComponent({
             <ShadTooltip
               content={displayShortcut(
                 shortcuts.find(
-                  ({ name }) => name.split(" ")[0].toLowerCase() === "advanced"
-                )!
+                  ({ name }) => name.split(" ")[0].toLowerCase() === "advanced",
+                )!,
               )}
               side="top"
             >
@@ -483,42 +478,44 @@ export default function NodeToolbarComponent({
               <IconComponent name="SaveAll" className="h-4 w-4" />
             </button>
           </ShadTooltip>*/}
-          <ShadTooltip
-            content={displayShortcut(
-              shortcuts.find(
-                ({ name }) => name.split(" ")[0].toLowerCase() === "freeze"
-              )!
-            )}
-            side="top"
-          >
-            <button
-              className={classNames(
-                "relative -ml-px inline-flex items-center bg-background px-2 py-2 text-foreground shadow-md ring-1 ring-inset ring-ring transition-all duration-500 ease-in-out hover:bg-muted focus:z-10"
+          {!data.node?.flow && (
+            <ShadTooltip
+              content={displayShortcut(
+                shortcuts.find(
+                  ({ name }) => name.split(" ")[0].toLowerCase() === "freeze",
+                )!,
               )}
-              onClick={(event) => {
-                event.preventDefault();
-                setNode(data.id, (old) => ({
-                  ...old,
-                  data: {
-                    ...old.data,
-                    node: {
-                      ...old.data.node,
-                      frozen: old.data?.node?.frozen ? false : true,
-                    },
-                  },
-                }));
-              }}
+              side="top"
             >
-              <IconComponent
-                name="Snowflake"
-                className={cn(
-                  "h-4 w-4 transition-all",
-                  // TODO UPDATE THIS COLOR TO BE A VARIABLE
-                  frozen ? "animate-wiggle text-ice" : ""
+              <button
+                className={classNames(
+                  "relative -ml-px inline-flex items-center bg-background px-2 py-2 text-foreground shadow-md ring-1 ring-inset ring-ring transition-all duration-500 ease-in-out hover:bg-muted focus:z-10",
                 )}
-              />
-            </button>
-          </ShadTooltip>
+                onClick={(event) => {
+                  event.preventDefault();
+                  setNode(data.id, (old) => ({
+                    ...old,
+                    data: {
+                      ...old.data,
+                      node: {
+                        ...old.data.node,
+                        frozen: old.data?.node?.frozen ? false : true,
+                      },
+                    },
+                  }));
+                }}
+              >
+                <IconComponent
+                  name="Snowflake"
+                  className={cn(
+                    "h-4 w-4 transition-all",
+                    // TODO UPDATE THIS COLOR TO BE A VARIABLE
+                    frozen ? "animate-wiggle text-ice" : "",
+                  )}
+                />
+              </button>
+            </ShadTooltip>
+          )}
 
           {/*<ShadTooltip content={"Duplicate"} side="top">
             <button
@@ -542,7 +539,7 @@ export default function NodeToolbarComponent({
                   <div
                     data-testid="more-options-modal"
                     className={classNames(
-                      "relative -ml-px inline-flex h-8 w-[31px] items-center rounded-r-md bg-background text-foreground shadow-md ring-1 ring-inset ring-ring transition-all duration-500 ease-in-out hover:bg-muted focus:z-10"
+                      "relative -ml-px inline-flex h-8 w-[31px] items-center rounded-r-md bg-background text-foreground shadow-md ring-1 ring-inset ring-ring transition-all duration-500 ease-in-out hover:bg-muted focus:z-10",
                     )}
                   >
                     <IconComponent
@@ -609,6 +606,18 @@ export default function NodeToolbarComponent({
                   dataTestId="copy-button-modal"
                 />
               </SelectItem>
+              {isOutdated && (
+                <SelectItem value={"update"}>
+                  <ToolbarSelectItem
+                    shortcut={
+                      shortcuts.find((obj) => obj.name === "Update")?.shortcut!
+                    }
+                    value={"Restore"}
+                    icon={"RefreshCcwDot"}
+                    dataTestId="update-button-modal"
+                  />
+                </SelectItem>
+              )}
               {hasStore && (
                 <SelectItem
                   value={"Share"}
@@ -676,30 +685,29 @@ export default function NodeToolbarComponent({
                   />
                 </SelectItem>
               )}
-              <SelectItem value="freeze">
-                <ToolbarSelectItem
-                  shortcut={
-                    shortcuts.find((obj) => obj.name === "Freeze")?.shortcut!
-                  }
-                  value={"Freeze"}
-                  icon={"Snowflake"}
-                  dataTestId="group-button-modal"
-                  style={`${frozen ? " text-ice" : ""} transition-all`}
-                />
-              </SelectItem>
-              {(!hasStore || !hasApiKey || !validApiKey) && (
-                <SelectItem value="Download">
+              {!data.node?.flow && (
+                <SelectItem value="freeze">
                   <ToolbarSelectItem
                     shortcut={
-                      shortcuts.find((obj) => obj.name === "Download")
-                        ?.shortcut!
+                      shortcuts.find((obj) => obj.name === "Freeze")?.shortcut!
                     }
-                    value={"Download"}
-                    icon={"Download"}
-                    dataTestId="download-button-modal"
+                    value={"Freeze"}
+                    icon={"Snowflake"}
+                    dataTestId="group-button-modal"
+                    style={`${frozen ? " text-ice" : ""} transition-all`}
                   />
                 </SelectItem>
               )}
+              <SelectItem value="Download">
+                <ToolbarSelectItem
+                  shortcut={
+                    shortcuts.find((obj) => obj.name === "Download")?.shortcut!
+                  }
+                  value={"Download"}
+                  icon={"Download"}
+                  dataTestId="download-button-modal"
+                />
+              </SelectItem>
               <SelectItem
                 value={"delete"}
                 className="focus:bg-red-400/[.20]"
diff --git a/src/frontend/src/pages/FlowPage/components/nodeToolbarComponent/toolbarSelectItem/index.tsx b/src/frontend/src/pages/FlowPage/components/nodeToolbarComponent/toolbarSelectItem/index.tsx
index 82f88007f..377bc4cc4 100644
--- a/src/frontend/src/pages/FlowPage/components/nodeToolbarComponent/toolbarSelectItem/index.tsx
+++ b/src/frontend/src/pages/FlowPage/components/nodeToolbarComponent/toolbarSelectItem/index.tsx
@@ -19,7 +19,7 @@ export default function ToolbarSelectItem({
     }
   });
   const filteredShortcut = fixedShortcut.filter(
-    (key) => !key.toLowerCase().includes("shift")
+    (key) => !key.toLowerCase().includes("shift"),
   );
   let shortcutWPlus: string[] = [];
   if (!hasShift) shortcutWPlus = filteredShortcut.join("+").split(" ");
diff --git a/src/frontend/src/pages/FlowPage/index.tsx b/src/frontend/src/pages/FlowPage/index.tsx
index e5a67bd35..49fdb1eca 100644
--- a/src/frontend/src/pages/FlowPage/index.tsx
+++ b/src/frontend/src/pages/FlowPage/index.tsx
@@ -10,7 +10,7 @@ import ExtraSidebar from "./components/extraSidebarComponent";
 
 export default function FlowPage({ view }: { view?: boolean }): JSX.Element {
   const setCurrentFlowId = useFlowsManagerStore(
-    (state) => state.setCurrentFlowId
+    (state) => state.setCurrentFlowId,
   );
   const version = useDarkStore((state) => state.version);
   const setOnFlowPage = useFlowStore((state) => state.setOnFlowPage);
diff --git a/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-delete-multiple.tsx b/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-delete-multiple.tsx
index af78db50d..395088193 100644
--- a/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-delete-multiple.tsx
+++ b/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-delete-multiple.tsx
@@ -9,7 +9,7 @@ const useDeleteMultipleFlows = (
   myCollectionId,
   getFolderById,
   setSuccessData,
-  setErrorData
+  setErrorData,
 ) => {
   const handleDeleteMultiple = useCallback(() => {
     removeFlow(selectedFlowsComponentsCards)
diff --git a/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-filtered-flows.tsx b/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-filtered-flows.tsx
index b391d9c8e..96b1757ff 100644
--- a/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-filtered-flows.tsx
+++ b/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-filtered-flows.tsx
@@ -4,7 +4,7 @@ import { useEffect } from "react";
 const useFilteredFlows = (
   flowsFromFolder,
   searchFlowsComponents,
-  setAllFlows
+  setAllFlows,
 ) => {
   useEffect(() => {
     const newFlows = cloneDeep(flowsFromFolder || []);
@@ -13,7 +13,7 @@ const useFilteredFlows = (
         f.name.toLowerCase().includes(searchFlowsComponents.toLowerCase()) ||
         f.description
           .toLowerCase()
-          .includes(searchFlowsComponents.toLowerCase())
+          .includes(searchFlowsComponents.toLowerCase()),
     );
 
     if (searchFlowsComponents === "") {
diff --git a/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-handle-duplicate.tsx b/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-handle-duplicate.tsx
index 97e966b91..fc49fc0d1 100644
--- a/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-handle-duplicate.tsx
+++ b/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-handle-duplicate.tsx
@@ -12,16 +12,16 @@ const useDuplicateFlows = (
   setSuccessData,
   setSelectedFlowsComponentsCards,
   handleSelectAll,
-  cardTypes
+  cardTypes,
 ) => {
   const handleDuplicate = useCallback(() => {
     Promise.all(
       selectedFlowsComponentsCards.map((selectedFlow) =>
         addFlow(
           true,
-          allFlows.find((flow) => flow.id === selectedFlow)
-        )
-      )
+          allFlows.find((flow) => flow.id === selectedFlow),
+        ),
+      ),
     ).then(() => {
       resetFilter();
       getFoldersApi(true);
diff --git a/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-handle-export.tsx b/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-handle-export.tsx
index 446525f13..8edabc146 100644
--- a/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-handle-export.tsx
+++ b/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-handle-export.tsx
@@ -9,7 +9,7 @@ const useExportFlows = (
   setSuccessData,
   setSelectedFlowsComponentsCards,
   handleSelectAll,
-  cardTypes
+  cardTypes,
 ) => {
   const handleExport = useCallback(() => {
     selectedFlowsComponentsCards.forEach((selectedFlowId) => {
@@ -22,10 +22,11 @@ const useExportFlows = (
             description: selectedFlow.description,
             name: selectedFlow.name,
             last_tested_version: version,
+            endpoint_name: selectedFlow.endpoint_name,
             is_component: false,
           }),
           selectedFlow.name,
-          selectedFlow.description
+          selectedFlow.description,
         );
       }
     });
diff --git a/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-handle-select-all.tsx b/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-handle-select-all.tsx
index 5b20a4ddc..a81515e0a 100644
--- a/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-handle-select-all.tsx
+++ b/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-handle-select-all.tsx
@@ -16,7 +16,7 @@ const useSelectAll = (flowsFromFolder, getValues, setValue) => {
         setValue(key, false);
       });
     },
-    [flowsFromFolder, getValues, setValue]
+    [flowsFromFolder, getValues, setValue],
   );
 
   return { handleSelectAll };
diff --git a/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-select-options-change.tsx b/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-select-options-change.tsx
index b19ec4f4f..56dc204c7 100644
--- a/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-select-options-change.tsx
+++ b/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-select-options-change.tsx
@@ -5,7 +5,7 @@ const useSelectOptionsChange = (
   setErrorData,
   setOpenDelete,
   handleDuplicate,
-  handleExport
+  handleExport,
 ) => {
   const handleSelectOptionsChange = useCallback(
     (action) => {
@@ -31,7 +31,7 @@ const useSelectOptionsChange = (
       setOpenDelete,
       handleDuplicate,
       handleExport,
-    ]
+    ],
   );
 
   return { handleSelectOptionsChange };
diff --git a/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-selected-flows.tsx b/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-selected-flows.tsx
index aa70b025f..b6f00934e 100644
--- a/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-selected-flows.tsx
+++ b/src/frontend/src/pages/MainPage/components/componentsComponent/hooks/use-selected-flows.tsx
@@ -2,7 +2,7 @@ import { useEffect } from "react";
 
 const useSelectedFlows = (
   entireFormValues,
-  setSelectedFlowsComponentsCards
+  setSelectedFlowsComponentsCards,
 ) => {
   useEffect(() => {
     if (!entireFormValues || Object.keys(entireFormValues).length === 0) return;
diff --git a/src/frontend/src/pages/MainPage/components/componentsComponent/index.tsx b/src/frontend/src/pages/MainPage/components/componentsComponent/index.tsx
index 81d2aa18d..af13e2bb4 100644
--- a/src/frontend/src/pages/MainPage/components/componentsComponent/index.tsx
+++ b/src/frontend/src/pages/MainPage/components/componentsComponent/index.tsx
@@ -40,22 +40,22 @@ export default function ComponentsComponent({
   const allFlows = useFlowsManagerStore((state) => state.allFlows);
 
   const flowsFromFolder = useFolderStore(
-    (state) => state.selectedFolder?.flows
+    (state) => state.selectedFolder?.flows,
   );
 
   const setSuccessData = useAlertStore((state) => state.setSuccessData);
   const setErrorData = useAlertStore((state) => state.setErrorData);
   const [openDelete, setOpenDelete] = useState(false);
   const searchFlowsComponents = useFlowsManagerStore(
-    (state) => state.searchFlowsComponents
+    (state) => state.searchFlowsComponents,
   );
 
   const setSelectedFlowsComponentsCards = useFlowsManagerStore(
-    (state) => state.setSelectedFlowsComponentsCards
+    (state) => state.setSelectedFlowsComponentsCards,
   );
 
   const selectedFlowsComponentsCards = useFlowsManagerStore(
-    (state) => state.selectedFlowsComponentsCards
+    (state) => state.selectedFlowsComponentsCards,
   );
 
   const [handleFileDrop] = useFileDrop(uploadFlow, type)!;
@@ -109,7 +109,7 @@ export default function ComponentsComponent({
   const { handleSelectAll } = useSelectAll(
     flowsFromFolder,
     getValues,
-    setValue
+    setValue,
   );
 
   const { handleDuplicate } = useDuplicateFlows(
@@ -124,7 +124,7 @@ export default function ComponentsComponent({
     setSuccessData,
     setSelectedFlowsComponentsCards,
     handleSelectAll,
-    cardTypes
+    cardTypes,
   );
 
   const version = useDarkStore((state) => state.version);
@@ -138,7 +138,7 @@ export default function ComponentsComponent({
     setSuccessData,
     setSelectedFlowsComponentsCards,
     handleSelectAll,
-    cardTypes
+    cardTypes,
   );
 
   const { handleSelectOptionsChange } = useSelectOptionsChange(
@@ -146,7 +146,7 @@ export default function ComponentsComponent({
     setErrorData,
     setOpenDelete,
     handleDuplicate,
-    handleExport
+    handleExport,
   );
 
   const { handleDeleteMultiple } = useDeleteMultipleFlows(
@@ -158,21 +158,21 @@ export default function ComponentsComponent({
     myCollectionId,
     getFolderById,
     setSuccessData,
-    setErrorData
+    setErrorData,
   );
 
   useSelectedFlows(entireFormValues, setSelectedFlowsComponentsCards);
 
   const descriptionModal = useDescriptionModal(
     selectedFlowsComponentsCards,
-    type
+    type,
   );
 
   const getTotalRowsCount = () => {
     if (type === "all") return allFlows?.length;
 
     return allFlows?.filter(
-      (f) => (f.is_component ?? false) === (type === "component")
+      (f) => (f.is_component ?? false) === (type === "component"),
     )?.length;
   };
 
diff --git a/src/frontend/src/pages/MainPage/components/headerComponent/index.tsx b/src/frontend/src/pages/MainPage/components/headerComponent/index.tsx
index 26999fbd5..cdd152229 100644
--- a/src/frontend/src/pages/MainPage/components/headerComponent/index.tsx
+++ b/src/frontend/src/pages/MainPage/components/headerComponent/index.tsx
@@ -52,8 +52,7 @@ const HeaderComponent = ({
               }
             >
               <Button
-                variant="none"
-                size="none"
+                unstyled
                 onClick={handleExport}
                 disabled={disableFunctions}
               >
@@ -75,8 +74,7 @@ const HeaderComponent = ({
               }
             >
               <Button
-                variant="none"
-                size="none"
+                unstyled
                 onClick={handleDuplicate}
                 disabled={disableFunctions}
               >
@@ -98,8 +96,7 @@ const HeaderComponent = ({
               }
             >
               <Button
-                variant="none"
-                size="none"
+                unstyled
                 onClick={handleDelete}
                 disabled={disableFunctions}
               >
@@ -107,7 +104,7 @@ const HeaderComponent = ({
                   name="Trash2"
                   className={cn(
                     "h-5 w-5 text-primary transition-all",
-                    disableFunctions ? "" : "hover:text-status-red"
+                    disableFunctions ? "" : "hover:text-status-red",
                   )}
                 />
               </Button>
diff --git a/src/frontend/src/pages/MainPage/components/myCollectionComponent/components/headerTabsSearchComponent/index.tsx b/src/frontend/src/pages/MainPage/components/myCollectionComponent/components/headerTabsSearchComponent/index.tsx
index b023216ab..c6ceb44c7 100644
--- a/src/frontend/src/pages/MainPage/components/myCollectionComponent/components/headerTabsSearchComponent/index.tsx
+++ b/src/frontend/src/pages/MainPage/components/myCollectionComponent/components/headerTabsSearchComponent/index.tsx
@@ -11,7 +11,7 @@ const HeaderTabsSearchComponent = ({}: HeaderTabsSearchComponentProps) => {
   const [inputValue, setInputValue] = useState("");
 
   const setSearchFlowsComponents = useFlowsManagerStore(
-    (state) => state.setSearchFlowsComponents
+    (state) => state.setSearchFlowsComponents,
   );
 
   return (
diff --git a/src/frontend/src/pages/MainPage/components/myCollectionComponent/components/inputSearchComponent/index.tsx b/src/frontend/src/pages/MainPage/components/myCollectionComponent/components/inputSearchComponent/index.tsx
index 1d85ed84b..898ad84a8 100644
--- a/src/frontend/src/pages/MainPage/components/myCollectionComponent/components/inputSearchComponent/index.tsx
+++ b/src/frontend/src/pages/MainPage/components/myCollectionComponent/components/inputSearchComponent/index.tsx
@@ -23,7 +23,7 @@ const InputSearchComponent = ({
   const pagePath = window.location.pathname;
   const allFlows = useFlowsManagerStore((state) => state.allFlows);
   const searchFlowsComponents = useFlowsManagerStore(
-    (state) => state.searchFlowsComponents
+    (state) => state.searchFlowsComponents,
   );
 
   const disableInputSearch =
diff --git a/src/frontend/src/pages/MainPage/pages/mainPage/index.tsx b/src/frontend/src/pages/MainPage/pages/mainPage/index.tsx
index 39add5211..6b4892d60 100644
--- a/src/frontend/src/pages/MainPage/pages/mainPage/index.tsx
+++ b/src/frontend/src/pages/MainPage/pages/mainPage/index.tsx
@@ -16,7 +16,7 @@ import useDropdownOptions from "../../hooks/use-dropdown-options";
 export default function HomePage(): JSX.Element {
   const uploadFlow = useFlowsManagerStore((state) => state.uploadFlow);
   const setCurrentFlowId = useFlowsManagerStore(
-    (state) => state.setCurrentFlowId
+    (state) => state.setCurrentFlowId,
   );
 
   const location = useLocation();
diff --git a/src/frontend/src/pages/MainPage/services/index.ts b/src/frontend/src/pages/MainPage/services/index.ts
index fbfc34389..3e1286d5e 100644
--- a/src/frontend/src/pages/MainPage/services/index.ts
+++ b/src/frontend/src/pages/MainPage/services/index.ts
@@ -30,12 +30,12 @@ export async function addFolder(data: AddFolderType): Promise<FolderType> {
 
 export async function updateFolder(
   body: FolderType,
-  folderId: string
+  folderId: string,
 ): Promise<FolderType> {
   try {
     const response = await api.patch(
       `${BASE_URL_API}folders/${folderId}`,
-      body
+      body,
     );
     return response?.data;
   } catch (error) {
@@ -68,7 +68,7 @@ export async function downloadFlowsFromFolders(folderId: string): Promise<{
 }> {
   try {
     const response = await api.get(
-      `${BASE_URL_API}folders/download/${folderId}`
+      `${BASE_URL_API}folders/download/${folderId}`,
     );
     if (response?.status !== 200) {
       throw new Error(`HTTP error! status: ${response?.status}`);
@@ -82,7 +82,7 @@ export async function downloadFlowsFromFolders(folderId: string): Promise<{
 }
 
 export async function uploadFlowsFromFolders(
-  flows: FormData
+  flows: FormData,
 ): Promise<FlowType[]> {
   try {
     const response = await api.post(`${BASE_URL_API}folders/upload/`, flows);
@@ -99,11 +99,11 @@ export async function uploadFlowsFromFolders(
 
 export async function moveFlowToFolder(
   flowId: string,
-  folderId: string
+  folderId: string,
 ): Promise<FlowType> {
   try {
     const response = await api.patch(
-      `${BASE_URL_API}folders/move_to_folder/${flowId}/${folderId}`
+      `${BASE_URL_API}folders/move_to_folder/${flowId}/${folderId}`,
     );
     return response?.data;
   } catch (error) {
diff --git a/src/frontend/src/pages/MainPage/utils/handle-download-folder.ts b/src/frontend/src/pages/MainPage/utils/handle-download-folder.ts
index d6a8e957c..6817622d0 100644
--- a/src/frontend/src/pages/MainPage/utils/handle-download-folder.ts
+++ b/src/frontend/src/pages/MainPage/utils/handle-download-folder.ts
@@ -11,7 +11,7 @@ export function handleDownloadFolderFn(folderId: string) {
     data.folder_description = folder?.description || "";
 
     const jsonString = `data:text/json;chatset=utf-8,${encodeURIComponent(
-      JSON.stringify(data)
+      JSON.stringify(data),
     )}`;
 
     const link = document.createElement("a");
diff --git a/src/frontend/src/pages/Playground/index.tsx b/src/frontend/src/pages/Playground/index.tsx
index d26147df6..4ea3cc506 100644
--- a/src/frontend/src/pages/Playground/index.tsx
+++ b/src/frontend/src/pages/Playground/index.tsx
@@ -11,7 +11,7 @@ export default function PlaygroundPage() {
   const currentFlow = useFlowsManagerStore((state) => state.currentFlow);
   const getFlowById = useFlowsManagerStore((state) => state.getFlowById);
   const setCurrentFlowId = useFlowsManagerStore(
-    (state) => state.setCurrentFlowId
+    (state) => state.setCurrentFlowId,
   );
   const currentFlowId = useFlowsManagerStore((state) => state.currentFlowId);
   const setCurrentFlow = useFlowsManagerStore((state) => state.setCurrentFlow);
diff --git a/src/frontend/src/pages/ProfileSettingsPage/index.tsx b/src/frontend/src/pages/ProfileSettingsPage/index.tsx
index c1635dc9e..5cedc5841 100644
--- a/src/frontend/src/pages/ProfileSettingsPage/index.tsx
+++ b/src/frontend/src/pages/ProfileSettingsPage/index.tsx
@@ -24,11 +24,11 @@ import { gradients } from "../../utils/styleUtils";
 import GradientChooserComponent from "../SettingsPage/pages/GeneralPage/components/ProfilePictureForm/components/profilePictureChooserComponent";
 export default function ProfileSettingsPage(): JSX.Element {
   const setCurrentFlowId = useFlowsManagerStore(
-    (state) => state.setCurrentFlowId
+    (state) => state.setCurrentFlowId,
   );
 
   const [inputState, setInputState] = useState<patchUserInputStateType>(
-    CONTROL_PATCH_USER_STATE
+    CONTROL_PATCH_USER_STATE,
   );
 
   // set null id
diff --git a/src/frontend/src/pages/SettingsPage/index.tsx b/src/frontend/src/pages/SettingsPage/index.tsx
index e6eb41b2f..2e8f49cfc 100644
--- a/src/frontend/src/pages/SettingsPage/index.tsx
+++ b/src/frontend/src/pages/SettingsPage/index.tsx
@@ -8,7 +8,7 @@ import useFlowsManagerStore from "../../stores/flowsManagerStore";
 export default function SettingsPage(): JSX.Element {
   const pathname = location.pathname;
   const setCurrentFlowId = useFlowsManagerStore(
-    (state) => state.setCurrentFlowId
+    (state) => state.setCurrentFlowId,
   );
   useEffect(() => {
     setCurrentFlowId("");
diff --git a/src/frontend/src/pages/SettingsPage/pages/ApiKeysPage/hooks/use-handle-delete-key.tsx b/src/frontend/src/pages/SettingsPage/pages/ApiKeysPage/hooks/use-handle-delete-key.tsx
index 01b4b7415..74d5dae99 100644
--- a/src/frontend/src/pages/SettingsPage/pages/ApiKeysPage/hooks/use-handle-delete-key.tsx
+++ b/src/frontend/src/pages/SettingsPage/pages/ApiKeysPage/hooks/use-handle-delete-key.tsx
@@ -10,7 +10,7 @@ const useDeleteApiKeys = (
   selectedRows,
   resetFilter,
   setSuccessData,
-  setErrorData
+  setErrorData,
 ) => {
   const handleDeleteKey = () => {
     Promise.all(selectedRows.map((selectedRow) => deleteApiKey(selectedRow)))
diff --git a/src/frontend/src/pages/SettingsPage/pages/ApiKeysPage/index.tsx b/src/frontend/src/pages/SettingsPage/pages/ApiKeysPage/index.tsx
index e5b224a58..af5280f72 100644
--- a/src/frontend/src/pages/SettingsPage/pages/ApiKeysPage/index.tsx
+++ b/src/frontend/src/pages/SettingsPage/pages/ApiKeysPage/index.tsx
@@ -25,7 +25,7 @@ export default function ApiKeysPage() {
     userData,
     setLoadingKeys,
     keysList,
-    setUserId
+    setUserId,
   );
 
   function resetFilter() {
@@ -36,7 +36,7 @@ export default function ApiKeysPage() {
     selectedRows,
     resetFilter,
     setSuccessData,
-    setErrorData
+    setErrorData,
   );
 
   const columnDefs = getColumnDefs();
diff --git a/src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/PasswordForm/index.tsx b/src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/PasswordForm/index.tsx
index d99752e7b..28edb2f09 100644
--- a/src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/PasswordForm/index.tsx
+++ b/src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/PasswordForm/index.tsx
@@ -17,7 +17,7 @@ type PasswordFormComponentProps = {
   handlePatchPassword: (
     password: string,
     cnfPassword: string,
-    handleInput: any
+    handleInput: any,
   ) => void;
 };
 const PasswordFormComponent = ({
diff --git a/src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/ProfileGradientForm/index.tsx b/src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/ProfileGradientForm/index.tsx
new file mode 100644
index 000000000..a92a00103
--- /dev/null
+++ b/src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/ProfileGradientForm/index.tsx
@@ -0,0 +1,68 @@
+import * as Form from "@radix-ui/react-form";
+import GradientChooserComponent from "../../../../../../components/gradientChooserComponent";
+import { Button } from "../../../../../../components/ui/button";
+import {
+  Card,
+  CardContent,
+  CardDescription,
+  CardFooter,
+  CardHeader,
+  CardTitle,
+} from "../../../../../../components/ui/card";
+import { gradients } from "../../../../../../utils/styleUtils";
+
+type ProfileGradientFormComponentProps = {
+  gradient: string;
+  handleInput: (event: any) => void;
+  handlePatchGradient: (gradient: string) => void;
+  userData: any;
+};
+const ProfileGradientFormComponent = ({
+  gradient,
+  handleInput,
+  handlePatchGradient,
+  userData,
+}: ProfileGradientFormComponentProps) => {
+  return (
+    <>
+      <Form.Root
+        onSubmit={(event) => {
+          handlePatchGradient(gradient);
+          event.preventDefault();
+        }}
+      >
+        <Card x-chunk="dashboard-04-chunk-1">
+          <CardHeader>
+            <CardTitle>Profile Gradient</CardTitle>
+            <CardDescription>
+              Choose the gradient that appears as your profile picture.
+            </CardDescription>
+          </CardHeader>
+          <CardContent>
+            <div className="py-2">
+              <GradientChooserComponent
+                value={
+                  gradient == ""
+                    ? userData?.profile_image ??
+                      gradients[
+                        parseInt(userData?.id ?? "", 30) % gradients.length
+                      ]
+                    : gradient
+                }
+                onChange={(value) => {
+                  handleInput({ target: { name: "gradient", value } });
+                }}
+              />
+            </div>
+          </CardContent>
+          <CardFooter className="border-t px-6 py-4">
+            <Form.Submit asChild>
+              <Button type="submit">Save</Button>
+            </Form.Submit>
+          </CardFooter>
+        </Card>
+      </Form.Root>
+    </>
+  );
+};
+export default ProfileGradientFormComponent;
diff --git a/src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/ProfilePictureForm/components/profilePictureChooserComponent/hooks/use-preload-images.tsx b/src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/ProfilePictureForm/components/profilePictureChooserComponent/hooks/use-preload-images.tsx
index 1948c9482..988af6ea9 100644
--- a/src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/ProfilePictureForm/components/profilePictureChooserComponent/hooks/use-preload-images.tsx
+++ b/src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/ProfilePictureForm/components/profilePictureChooserComponent/hooks/use-preload-images.tsx
@@ -14,8 +14,8 @@ const usePreloadImages = (profilePictures, setImagesLoaded) => {
             img.src = src;
             img.onload = resolve;
             img.onerror = resolve;
-          })
-      )
+          }),
+      ),
     );
   };
 
@@ -26,9 +26,9 @@ const usePreloadImages = (profilePictures, setImagesLoaded) => {
     Object.keys(profilePictures).flatMap((folder) =>
       profilePictures[folder].map((path) =>
         imageArray.push(
-          `${firstUrl}${BASE_URL_API}files/profile_pictures/${folder}/${path}`
-        )
-      )
+          `${firstUrl}${BASE_URL_API}files/profile_pictures/${folder}/${path}`,
+        ),
+      ),
     );
 
     preloadImages(imageArray).then(() => {
diff --git a/src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/ProfilePictureForm/components/profilePictureChooserComponent/index.tsx b/src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/ProfilePictureForm/components/profilePictureChooserComponent/index.tsx
index edfe58a16..ac6a215be 100644
--- a/src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/ProfilePictureForm/components/profilePictureChooserComponent/index.tsx
+++ b/src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/ProfilePictureForm/components/profilePictureChooserComponent/index.tsx
@@ -49,8 +49,7 @@ export default function ProfilePictureChooserComponent({
                 {profilePictures[folder].map((path, idx) => (
                   <Button
                     ref={value === folder + "/" + path ? ref : undefined}
-                    size="none"
-                    variant="none"
+                    unstyled
                     onClick={() => onChange(folder + "/" + path)}
                     className="shrink-0 px-0.5 py-2"
                   >
@@ -58,7 +57,7 @@ export default function ProfilePictureChooserComponent({
                       key={idx}
                       src={`${BACKEND_URL.slice(
                         0,
-                        BACKEND_URL.length - 1
+                        BACKEND_URL.length - 1,
                       )}${BASE_URL_API}files/profile_pictures/${
                         folder + "/" + path
                       }`}
diff --git a/src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/StoreApiKeyForm/index.tsx b/src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/StoreApiKeyForm/index.tsx
index 1fbf34024..5bc5ea38b 100644
--- a/src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/StoreApiKeyForm/index.tsx
+++ b/src/frontend/src/pages/SettingsPage/pages/GeneralPage/components/StoreApiKeyForm/index.tsx
@@ -47,8 +47,8 @@ const StoreApiKeyFormComponent = ({
               {(hasApiKey && !validApiKey
                 ? INVALID_API_KEY
                 : !hasApiKey
-                ? NO_API_KEY
-                : "") + INSERT_API_KEY}
+                  ? NO_API_KEY
+                  : "") + INSERT_API_KEY}
             </CardDescription>
           </CardHeader>
           <CardContent>
diff --git a/src/frontend/src/pages/SettingsPage/pages/GeneralPage/index.tsx b/src/frontend/src/pages/SettingsPage/pages/GeneralPage/index.tsx
index 917d61714..239103777 100644
--- a/src/frontend/src/pages/SettingsPage/pages/GeneralPage/index.tsx
+++ b/src/frontend/src/pages/SettingsPage/pages/GeneralPage/index.tsx
@@ -21,13 +21,13 @@ import StoreApiKeyFormComponent from "./components/StoreApiKeyForm";
 
 export default function GeneralPage() {
   const setCurrentFlowId = useFlowsManagerStore(
-    (state) => state.setCurrentFlowId
+    (state) => state.setCurrentFlowId,
   );
 
   const { scrollId } = useParams();
 
   const [inputState, setInputState] = useState<patchUserInputStateType>(
-    CONTROL_PATCH_USER_STATE
+    CONTROL_PATCH_USER_STATE,
   );
 
   const { autoLogin } = useContext(AuthContext);
@@ -48,7 +48,7 @@ export default function GeneralPage() {
   const { handlePatchPassword } = usePatchPassword(
     userData,
     setSuccessData,
-    setErrorData
+    setErrorData,
   );
 
   const { handleGetProfilePictures } = useGetProfilePictures(setErrorData);
@@ -57,7 +57,7 @@ export default function GeneralPage() {
     setSuccessData,
     setErrorData,
     userData,
-    setUserData
+    setUserData,
   );
 
   useScrollToElement(scrollId, setCurrentFlowId);
@@ -67,7 +67,7 @@ export default function GeneralPage() {
     setErrorData,
     setHasApiKey,
     setValidApiKey,
-    setLoadingApiKey
+    setLoadingApiKey,
   );
 
   function handleInput({
diff --git a/src/frontend/src/pages/SettingsPage/pages/GlobalVariablesPage/index.tsx b/src/frontend/src/pages/SettingsPage/pages/GlobalVariablesPage/index.tsx
index ba9c465d7..6f0dac836 100644
--- a/src/frontend/src/pages/SettingsPage/pages/GlobalVariablesPage/index.tsx
+++ b/src/frontend/src/pages/SettingsPage/pages/GlobalVariablesPage/index.tsx
@@ -14,13 +14,13 @@ import { useGlobalVariablesStore } from "../../../../stores/globalVariablesStore
 
 export default function GlobalVariablesPage() {
   const globalVariablesEntries = useGlobalVariablesStore(
-    (state) => state.globalVariablesEntries
+    (state) => state.globalVariablesEntries,
   );
   const removeGlobalVariable = useGlobalVariablesStore(
-    (state) => state.removeGlobalVariable
+    (state) => state.removeGlobalVariable,
   );
   const globalVariables = useGlobalVariablesStore(
-    (state) => state.globalVariables
+    (state) => state.globalVariables,
   );
   const setErrorData = useAlertStore((state) => state.setErrorData);
   const getVariableId = useGlobalVariablesStore((state) => state.getVariableId);
diff --git a/src/frontend/src/pages/SettingsPage/pages/ShortcutsPage/EditShortcutButton/index.tsx b/src/frontend/src/pages/SettingsPage/pages/ShortcutsPage/EditShortcutButton/index.tsx
index 05ea5b39d..883c99b34 100644
--- a/src/frontend/src/pages/SettingsPage/pages/ShortcutsPage/EditShortcutButton/index.tsx
+++ b/src/frontend/src/pages/SettingsPage/pages/ShortcutsPage/EditShortcutButton/index.tsx
@@ -31,7 +31,7 @@ export default function EditShortcutButton({
       ? defaultShortcuts.find(
           (s) =>
             s.name.split(" ")[0].toLowerCase().toLowerCase() ===
-            shortcut[0]?.split(" ")[0].toLowerCase()
+            shortcut[0]?.split(" ")[0].toLowerCase(),
         )?.shortcut
       : "";
   const [key, setKey] = useState<string | null>(null);
@@ -50,7 +50,7 @@ export default function EditShortcutButton({
   }
 
   const setUniqueShortcut = useShortcutsStore(
-    (state) => state.updateUniqueShortcut
+    (state) => state.updateUniqueShortcut,
   );
 
   function editCombination(): void {
@@ -74,7 +74,7 @@ export default function EditShortcutButton({
         setShortcuts(newCombination);
         localStorage.setItem(
           "langflow-shortcuts",
-          JSON.stringify(newCombination)
+          JSON.stringify(newCombination),
         );
         setKey(null);
         setOpen(false);
@@ -116,7 +116,7 @@ export default function EditShortcutButton({
     const keysArr = keys.split(" ");
     let hasNewKey = false;
     return keysArr.some(
-      (k) => k.toLowerCase().trim() === keyToCompare.toLowerCase().trim()
+      (k) => k.toLowerCase().trim() === keyToCompare.toLowerCase().trim(),
     );
   }
 
@@ -137,7 +137,7 @@ export default function EditShortcutButton({
         if (checkForKeys(key, fixedKey)) return;
       }
       setKey((oldKey) =>
-        getFixedCombination({ oldKey: oldKey!, key: fixedKey })
+        getFixedCombination({ oldKey: oldKey!, key: fixedKey }),
       );
     }
 
diff --git a/src/frontend/src/pages/SettingsPage/pages/ShortcutsPage/index.tsx b/src/frontend/src/pages/SettingsPage/pages/ShortcutsPage/index.tsx
index 5e29a68c1..1a0fdf8b5 100644
--- a/src/frontend/src/pages/SettingsPage/pages/ShortcutsPage/index.tsx
+++ b/src/frontend/src/pages/SettingsPage/pages/ShortcutsPage/index.tsx
@@ -40,7 +40,7 @@ export default function ShortcutsPage() {
   const combinationToEdit = shortcuts.filter((s) => s.name === selectedRows[0]);
   const [open, setOpen] = useState(false);
   const updateUniqueShortcut = useShortcutsStore(
-    (state) => state.updateUniqueShortcut
+    (state) => state.updateUniqueShortcut,
   );
 
   function handleRestore() {
diff --git a/src/frontend/src/pages/SettingsPage/pages/hooks/use-patch-gradient.tsx b/src/frontend/src/pages/SettingsPage/pages/hooks/use-patch-gradient.tsx
new file mode 100644
index 000000000..ea61d4722
--- /dev/null
+++ b/src/frontend/src/pages/SettingsPage/pages/hooks/use-patch-gradient.tsx
@@ -0,0 +1,37 @@
+import cloneDeep from "lodash/cloneDeep";
+import {
+  SAVE_ERROR_ALERT,
+  SAVE_SUCCESS_ALERT,
+} from "../../../../constants/alerts_constants";
+import { updateUser } from "../../../../controllers/API";
+
+const usePatchGradient = (
+  setSuccessData,
+  setErrorData,
+  currentUserData,
+  setUserData,
+) => {
+  const handlePatchGradient = async (gradient) => {
+    try {
+      if (gradient !== "") {
+        await updateUser(currentUserData.id, { profile_image: gradient });
+        let newUserData = cloneDeep(currentUserData);
+        newUserData.profile_image = gradient;
+        setUserData(newUserData);
+      }
+      setSuccessData({ title: SAVE_SUCCESS_ALERT });
+    } catch (error) {
+      setErrorData({
+        title: SAVE_ERROR_ALERT,
+        list: [(error as any)?.response?.data?.detail],
+      });
+    }
+  };
+
+  return {
+    currentUserData,
+    handlePatchGradient,
+  };
+};
+
+export default usePatchGradient;
diff --git a/src/frontend/src/pages/SettingsPage/pages/hooks/use-patch-profile-picture.tsx b/src/frontend/src/pages/SettingsPage/pages/hooks/use-patch-profile-picture.tsx
index 537c579af..584300fdf 100644
--- a/src/frontend/src/pages/SettingsPage/pages/hooks/use-patch-profile-picture.tsx
+++ b/src/frontend/src/pages/SettingsPage/pages/hooks/use-patch-profile-picture.tsx
@@ -9,7 +9,7 @@ const usePatchProfilePicture = (
   setSuccessData,
   setErrorData,
   currentUserData,
-  setUserData
+  setUserData,
 ) => {
   const handlePatchProfilePicture = async (profile_picture) => {
     try {
diff --git a/src/frontend/src/pages/SettingsPage/pages/hooks/use-save-key.tsx b/src/frontend/src/pages/SettingsPage/pages/hooks/use-save-key.tsx
index bdd105fef..cf5871a26 100644
--- a/src/frontend/src/pages/SettingsPage/pages/hooks/use-save-key.tsx
+++ b/src/frontend/src/pages/SettingsPage/pages/hooks/use-save-key.tsx
@@ -11,7 +11,7 @@ const useSaveKey = (
   setErrorData,
   setHasApiKey,
   setValidApiKey,
-  setLoadingApiKey
+  setLoadingApiKey,
 ) => {
   const { storeApiKey } = useContext(AuthContext);
 
@@ -35,7 +35,7 @@ const useSaveKey = (
           setHasApiKey(false);
           setValidApiKey(false);
           setLoadingApiKey(false);
-        }
+        },
       );
     }
   };
diff --git a/src/frontend/src/pages/SettingsPage/pages/messagesPage/components/headerMessages/index.tsx b/src/frontend/src/pages/SettingsPage/pages/messagesPage/components/headerMessages/index.tsx
index 505adc872..f7802fe41 100644
--- a/src/frontend/src/pages/SettingsPage/pages/messagesPage/components/headerMessages/index.tsx
+++ b/src/frontend/src/pages/SettingsPage/pages/messagesPage/components/headerMessages/index.tsx
@@ -1,12 +1,6 @@
 import ForwardedIconComponent from "../../../../../../components/genericIconComponent";
 
-type HeaderMessagesComponentProps = {
-  selectedRows: number[];
-  handleRemoveMessages: () => void;
-};
-const HeaderMessagesComponent = ({
-  selectedRows,
-}: HeaderMessagesComponentProps) => {
+const HeaderMessagesComponent = () => {
   return (
     <>
       <div className="flex w-full items-center justify-between gap-4 space-y-0.5">
diff --git a/src/frontend/src/pages/SettingsPage/pages/messagesPage/hooks/use-remove-messages.tsx b/src/frontend/src/pages/SettingsPage/pages/messagesPage/hooks/use-remove-messages.tsx
index f4de52cbb..d7f4d5202 100644
--- a/src/frontend/src/pages/SettingsPage/pages/messagesPage/hooks/use-remove-messages.tsx
+++ b/src/frontend/src/pages/SettingsPage/pages/messagesPage/hooks/use-remove-messages.tsx
@@ -5,7 +5,7 @@ const useRemoveMessages = (
   setSelectedRows,
   setSuccessData,
   setErrorData,
-  selectedRows
+  selectedRows,
 ) => {
   const deleteMessages = useMessagesStore((state) => state.removeMessages);
 
diff --git a/src/frontend/src/pages/SettingsPage/pages/messagesPage/index.tsx b/src/frontend/src/pages/SettingsPage/pages/messagesPage/index.tsx
index 5b1766d8c..bb28ca1be 100644
--- a/src/frontend/src/pages/SettingsPage/pages/messagesPage/index.tsx
+++ b/src/frontend/src/pages/SettingsPage/pages/messagesPage/index.tsx
@@ -8,6 +8,7 @@ import { useState } from "react";
 import TableComponent from "../../../../components/tableComponent";
 import useAlertStore from "../../../../stores/alertStore";
 import { useMessagesStore } from "../../../../stores/messagesStore";
+import { messagesSorter } from "../../../../utils/utils";
 import HeaderMessagesComponent from "./components/headerMessages";
 import useMessagesTable from "./hooks/use-messages-table";
 import useRemoveMessages from "./hooks/use-remove-messages";
@@ -26,7 +27,7 @@ export default function MessagesPage() {
     setSelectedRows,
     setSuccessData,
     setErrorData,
-    selectedRows
+    selectedRows,
   );
 
   const { handleUpdate } = useUpdateMessage(setSuccessData, setErrorData);
@@ -46,10 +47,7 @@ export default function MessagesPage() {
 
   return (
     <div className="flex h-full w-full flex-col justify-between gap-6">
-      <HeaderMessagesComponent
-        selectedRows={selectedRows}
-        handleRemoveMessages={handleRemoveMessages}
-      />
+      <HeaderMessagesComponent />
 
       <div className="flex h-full w-full flex-col justify-between">
         <TableComponent
@@ -59,17 +57,17 @@ export default function MessagesPage() {
           onCellEditRequest={(event) => {
             handleUpdateMessage(event);
           }}
-          editable={["Sender Name", "Message"]}
+          editable={["Sender Name", "Message", "Text"]}
           overlayNoRowsTemplate="No data available"
           onSelectionChanged={(event: SelectionChangedEvent) => {
             setSelectedRows(
-              event.api.getSelectedRows().map((row) => row.index)
+              event.api.getSelectedRows().map((row) => row.index),
             );
           }}
           rowSelection="multiple"
           suppressRowClickSelection={true}
           pagination={true}
-          columnDefs={columns}
+          columnDefs={columns.sort(messagesSorter)}
           rowData={messages}
         />
       </div>
diff --git a/src/frontend/src/pages/StorePage/index.tsx b/src/frontend/src/pages/StorePage/index.tsx
index e3f5b56ce..926e6886d 100644
--- a/src/frontend/src/pages/StorePage/index.tsx
+++ b/src/frontend/src/pages/StorePage/index.tsx
@@ -46,7 +46,7 @@ export default function StorePage(): JSX.Element {
 
   const setErrorData = useAlertStore((state) => state.setErrorData);
   const setCurrentFlowId = useFlowsManagerStore(
-    (state) => state.setCurrentFlowId
+    (state) => state.setCurrentFlowId,
   );
   const currentFlowId = useFlowsManagerStore((state) => state.currentFlowId);
   const [loading, setLoading] = useState(true);
@@ -143,7 +143,7 @@ export default function StorePage(): JSX.Element {
           setTotalRowsCount(
             filteredCategories?.length === 0
               ? Number(res?.count ?? 0)
-              : res?.results?.length ?? 0
+              : res?.results?.length ?? 0,
           );
         }
       })
@@ -183,7 +183,7 @@ export default function StorePage(): JSX.Element {
           disabled={loading}
           className={cn(
             `${!validApiKey ? "animate-pulse border-error" : ""}`,
-            loading ? "cursor-not-allowed" : ""
+            loading ? "cursor-not-allowed" : "",
           )}
           variant="primary"
           onClick={() => {
diff --git a/src/frontend/src/pages/ViewPage/index.tsx b/src/frontend/src/pages/ViewPage/index.tsx
index 2e6ac9bac..5498b77bd 100644
--- a/src/frontend/src/pages/ViewPage/index.tsx
+++ b/src/frontend/src/pages/ViewPage/index.tsx
@@ -6,7 +6,7 @@ import Page from "../FlowPage/components/PageComponent";
 export default function ViewPage() {
   const currentFlow = useFlowsManagerStore((state) => state.currentFlow);
   const setCurrentFlowId = useFlowsManagerStore(
-    (state) => state.setCurrentFlowId
+    (state) => state.setCurrentFlowId,
   );
   const { id } = useParams();
 
diff --git a/src/frontend/src/prettierrc.js b/src/frontend/src/prettierrc.js
deleted file mode 100644
index 0be5884c6..000000000
--- a/src/frontend/src/prettierrc.js
+++ /dev/null
@@ -1,4 +0,0 @@
-module.exports = {
-  // ...
-  organizeImportsSkipDestructiveCodeActions: true,
-};
diff --git a/src/frontend/src/routes.tsx b/src/frontend/src/routes.tsx
index 5623f0871..a1997e510 100644
--- a/src/frontend/src/routes.tsx
+++ b/src/frontend/src/routes.tsx
@@ -11,25 +11,25 @@ import MessagesPage from "./pages/SettingsPage/pages/messagesPage";
 const AdminPage = lazy(() => import("./pages/AdminPage"));
 const LoginAdminPage = lazy(() => import("./pages/AdminPage/LoginPage"));
 const ApiKeysPage = lazy(
-  () => import("./pages/SettingsPage/pages/ApiKeysPage")
+  () => import("./pages/SettingsPage/pages/ApiKeysPage"),
 );
 const DeleteAccountPage = lazy(() => import("./pages/DeleteAccountPage"));
 const FlowPage = lazy(() => import("./pages/FlowPage"));
 const LoginPage = lazy(() => import("./pages/LoginPage"));
 const MyCollectionComponent = lazy(
-  () => import("./pages/MainPage/components/myCollectionComponent")
+  () => import("./pages/MainPage/components/myCollectionComponent"),
 );
 const HomePage = lazy(() => import("./pages/MainPage/pages/mainPage"));
 const PlaygroundPage = lazy(() => import("./pages/Playground"));
 const SettingsPage = lazy(() => import("./pages/SettingsPage"));
 const GeneralPage = lazy(
-  () => import("./pages/SettingsPage/pages/GeneralPage")
+  () => import("./pages/SettingsPage/pages/GeneralPage"),
 );
 const GlobalVariablesPage = lazy(
-  () => import("./pages/SettingsPage/pages/GlobalVariablesPage")
+  () => import("./pages/SettingsPage/pages/GlobalVariablesPage"),
 );
 const ShortcutsPage = lazy(
-  () => import("./pages/SettingsPage/pages/ShortcutsPage")
+  () => import("./pages/SettingsPage/pages/ShortcutsPage"),
 );
 const SignUp = lazy(() => import("./pages/SignUpPage"));
 const StorePage = lazy(() => import("./pages/StorePage"));
diff --git a/src/frontend/src/shared/components/textOutputView/index.tsx b/src/frontend/src/shared/components/textOutputView/index.tsx
index 1b80607d4..0071a654b 100644
--- a/src/frontend/src/shared/components/textOutputView/index.tsx
+++ b/src/frontend/src/shared/components/textOutputView/index.tsx
@@ -1,16 +1,17 @@
 import { Textarea } from "../../../components/ui/textarea";
 
 const TextOutputView = ({ left, value }) => {
+  if (typeof value === "object" && Object.keys(value).includes("text")) {
+    value = value.text;
+  }
   return (
-    <>
-      <Textarea
-        className={`w-full custom-scroll ${left ? "min-h-32" : "h-full"}`}
-        placeholder={"Empty"}
-        // update to real value on flowPool
-        value={value}
-        readOnly
-      />
-    </>
+    <Textarea
+      className={`w-full custom-scroll ${left ? "min-h-32" : "h-full"}`}
+      placeholder={"Empty"}
+      // update to real value on flowPool
+      value={value}
+      readOnly
+    />
   );
 };
 
diff --git a/src/frontend/src/stores/alertStore.ts b/src/frontend/src/stores/alertStore.ts
index e23a8d4e3..03a12050c 100644
--- a/src/frontend/src/stores/alertStore.ts
+++ b/src/frontend/src/stores/alertStore.ts
@@ -6,7 +6,7 @@ import { customStringify } from "../utils/reactflowUtils";
 
 const pushNotificationList = (
   list: AlertItemType[],
-  notification: AlertItemType
+  notification: AlertItemType,
 ) => {
   list.unshift(notification);
   return list;
@@ -147,7 +147,7 @@ const useAlertStore = create<AlertStoreType>((set, get) => ({
   removeFromNotificationList: (index: string) => {
     set({
       notificationList: get().notificationList.filter(
-        (item) => item.id !== index
+        (item) => item.id !== index,
       ),
     });
   },
@@ -160,7 +160,7 @@ const useAlertStore = create<AlertStoreType>((set, get) => ({
   removeFromTempNotificationList: (index: string) => {
     set({
       tempNotificationList: get().tempNotificationList.filter(
-        (item) => item.id !== index
+        (item) => item.id !== index,
       ),
     });
   },
diff --git a/src/frontend/src/stores/darkStore.ts b/src/frontend/src/stores/darkStore.ts
index 79f5518da..8c9c10951 100644
--- a/src/frontend/src/stores/darkStore.ts
+++ b/src/frontend/src/stores/darkStore.ts
@@ -36,7 +36,7 @@ export const useDarkStore = create<DarkStoreType>((set, get) => ({
         window.localStorage.setItem("githubStars", res?.toString() ?? "0");
         window.localStorage.setItem(
           "githubStarsLastUpdated",
-          new Date().toString()
+          new Date().toString(),
         );
         set(() => ({ stars: res, lastUpdated: new Date() }));
       });
diff --git a/src/frontend/src/stores/flowStore.ts b/src/frontend/src/stores/flowStore.ts
index 6d1c2ad5c..597308e25 100644
--- a/src/frontend/src/stores/flowStore.ts
+++ b/src/frontend/src/stores/flowStore.ts
@@ -74,7 +74,7 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
   updateFlowPool: (
     nodeId: string,
     data: VertexBuildTypeAPI | ChatOutputType | ChatInputType,
-    buildId?: string
+    buildId?: string,
   ) => {
     let newFlowPool = cloneDeep({ ...get().flowPool });
     if (!newFlowPool[nodeId]) {
@@ -167,7 +167,7 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
       flowsManager.autoSaveCurrentFlow(
         newChange,
         newEdges,
-        get().reactFlowInstance?.getViewport() ?? { x: 0, y: 0, zoom: 1 }
+        get().reactFlowInstance?.getViewport() ?? { x: 0, y: 0, zoom: 1 },
       );
     }
   },
@@ -183,7 +183,7 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
       flowsManager.autoSaveCurrentFlow(
         get().nodes,
         newChange,
-        get().reactFlowInstance?.getViewport() ?? { x: 0, y: 0, zoom: 1 }
+        get().reactFlowInstance?.getViewport() ?? { x: 0, y: 0, zoom: 1 },
       );
     }
   },
@@ -201,7 +201,7 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
           return newChange;
         }
         return node;
-      })
+      }),
     );
   },
   getNode: (id: string) => {
@@ -212,8 +212,8 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
       get().nodes.filter((node) =>
         typeof nodeId === "string"
           ? node.id !== nodeId
-          : !nodeId.includes(node.id)
-      )
+          : !nodeId.includes(node.id),
+      ),
     );
   },
   deleteEdge: (edgeId) => {
@@ -221,8 +221,8 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
       get().edges.filter((edge) =>
         typeof edgeId === "string"
           ? edge.id !== edgeId
-          : !edgeId.includes(edge.id)
-      )
+          : !edgeId.includes(edge.id),
+      ),
     );
   },
   paste: (selection, position) => {
@@ -288,7 +288,7 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
       let source = idsMap[edge.source];
       let target = idsMap[edge.target];
       const sourceHandleObject: sourceHandleType = scapeJSONParse(
-        edge.sourceHandle!
+        edge.sourceHandle!,
       );
       let sourceHandle = scapedJSONStringfy({
         ...sourceHandleObject,
@@ -298,7 +298,7 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
 
       edge.data.sourceHandle = sourceHandleObject;
       const targetHandleObject: targetHandleType = scapeJSONParse(
-        edge.targetHandle!
+        edge.targetHandle!,
       );
       let targetHandle = scapedJSONStringfy({
         ...targetHandleObject,
@@ -317,7 +317,7 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
           data: cloneDeep(edge.data),
           selected: false,
         },
-        newEdges.map((edge) => ({ ...edge, selected: false }))
+        newEdges.map((edge) => ({ ...edge, selected: false })),
       );
     });
     get().setEdges(newEdges);
@@ -336,10 +336,10 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
       });
 
       const newNodes = get().nodes.filter(
-        (node) => !nodesIdsSelected.includes(node.id)
+        (node) => !nodesIdsSelected.includes(node.id),
       );
       const newEdges = get().edges.filter(
-        (edge) => !edgesIdsSelected.includes(edge.id)
+        (edge) => !edgesIdsSelected.includes(edge.id),
       );
 
       set({ nodes: newNodes, edges: newEdges });
@@ -397,7 +397,7 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
           // style: { stroke: "#555" },
           // className: "stroke-foreground stroke-connection",
         },
-        oldEdges
+        oldEdges,
       );
 
       return newEdges;
@@ -407,7 +407,7 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
       .autoSaveCurrentFlow(
         get().nodes,
         newEdges,
-        get().reactFlowInstance?.getViewport() ?? { x: 0, y: 0, zoom: 1 }
+        get().reactFlowInstance?.getViewport() ?? { x: 0, y: 0, zoom: 1 },
       );
   },
   unselectAll: () => {
@@ -427,12 +427,14 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
     input_value,
     files,
     silent,
+    setLockChat,
   }: {
     startNodeId?: string;
     stopNodeId?: string;
     input_value?: string;
     files?: string[];
     silent?: boolean;
+    setLockChat?: (lock: boolean) => void;
   }) => {
     get().setIsBuilding(true);
     const currentFlow = useFlowsManagerStore.getState().currentFlow;
@@ -442,7 +444,7 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
     function validateSubgraph(nodes: string[]) {
       const errorsObjs = validateNodes(
         get().nodes.filter((node) => nodes.includes(node.id)),
-        get().edges
+        get().edges,
       );
 
       const errors = errorsObjs.map((obj) => obj.errors).flat();
@@ -461,13 +463,13 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
     function handleBuildUpdate(
       vertexBuildData: VertexBuildTypeAPI,
       status: BuildStatus,
-      runId: string
+      runId: string,
     ) {
       if (vertexBuildData && vertexBuildData.inactivated_vertices) {
         get().removeFromVerticesBuild(vertexBuildData.inactivated_vertices);
         get().updateBuildStatus(
           vertexBuildData.inactivated_vertices,
-          BuildStatus.INACTIVE
+          BuildStatus.INACTIVE,
         );
       }
 
@@ -483,14 +485,14 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
 
         // next_vertices_ids should be next_vertices_ids without the inactivated vertices
         const next_vertices_ids = vertexBuildData.next_vertices_ids.filter(
-          (id) => !vertexBuildData.inactivated_vertices?.includes(id)
+          (id) => !vertexBuildData.inactivated_vertices?.includes(id),
         );
         const top_level_vertices = vertexBuildData.top_level_vertices.filter(
-          (vertex) => !vertexBuildData.inactivated_vertices?.includes(vertex)
+          (vertex) => !vertexBuildData.inactivated_vertices?.includes(vertex),
         );
         const nextVertices: VertexLayerElementType[] = zip(
           next_vertices_ids,
-          top_level_vertices
+          top_level_vertices,
         ).map(([id, reference]) => ({ id: id!, reference }));
 
         const newLayers = [
@@ -507,12 +509,13 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
           runId: runId,
           verticesToRun: get().verticesBuild!.verticesToRun,
         });
+
         get().updateBuildStatus(top_level_vertices, BuildStatus.TO_BUILD);
       }
 
       get().addDataToFlowPool(
         { ...vertexBuildData, run_id: runId },
-        vertexBuildData.id
+        vertexBuildData.id,
       );
 
       useFlowStore.getState().updateBuildStatus([vertexBuildData.id], status);
@@ -520,8 +523,9 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
       const verticesIds = get().verticesBuild?.verticesIds;
       const newFlowBuildStatus = { ...get().flowBuildStatus };
       // filter out the vertices that are not status
+
       const verticesToUpdate = verticesIds?.filter(
-        (id) => newFlowBuildStatus[id]?.status !== BuildStatus.BUILT
+        (id) => newFlowBuildStatus[id]?.status !== BuildStatus.BUILT,
       );
 
       if (verticesToUpdate) {
@@ -534,6 +538,7 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
       flowId: currentFlow!.id,
       startNodeId,
       stopNodeId,
+      setLockChat,
       onGetOrderSuccess: () => {
         if (!silent) {
           setNoticeData({ title: "Running components" });
@@ -542,15 +547,15 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
       onBuildComplete: (allNodesValid) => {
         const nodeId = startNodeId || stopNodeId;
         if (!silent) {
-          if (nodeId && allNodesValid) {
+          if (allNodesValid) {
             setSuccessData({
-              title: `${
-                get().nodes.find((node) => node.id === nodeId)?.data.node
-                  ?.display_name
-              } built successfully`,
+              title: nodeId
+                ? `${
+                    get().nodes.find((node) => node.id === nodeId)?.data.node
+                      ?.display_name
+                  } built successfully`
+                : FLOW_BUILD_SUCCESS_ALERT,
             });
-          } else {
-            setSuccessData({ title: FLOW_BUILD_SUCCESS_ALERT });
           }
         }
         get().setIsBuilding(false);
@@ -591,7 +596,7 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
       verticesLayers: VertexLayerElementType[][];
       runId: string;
       verticesToRun: string[];
-    } | null
+    } | null,
   ) => {
     set({ verticesBuild: vertices });
   },
@@ -616,7 +621,7 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
         // that are going to be built
         verticesIds: get().verticesBuild!.verticesIds.filter(
           // keep the vertices that are not in the list of vertices to remove
-          (vertex) => !vertices.includes(vertex)
+          (vertex) => !vertices.includes(vertex),
         ),
       },
     });
@@ -641,6 +646,7 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
         newFlowBuildStatus[id].status = BuildStatus.BUILT;
       }
     });
+    set({ flowBuildStatus: newFlowBuildStatus });
   },
 }));
 
diff --git a/src/frontend/src/stores/flowsManagerStore.ts b/src/frontend/src/stores/flowsManagerStore.ts
index d6bc8338e..a804710ac 100644
--- a/src/frontend/src/stores/flowsManagerStore.ts
+++ b/src/frontend/src/stores/flowsManagerStore.ts
@@ -87,12 +87,12 @@ const useFlowsManagerStore = create<FlowsManagerStoreType>((set, get) => ({
           if (dbData) {
             const { data, flows } = processFlows(dbData);
             const examples = flows.filter(
-              (flow) => flow.folder_id === starterFolderId
+              (flow) => flow.folder_id === starterFolderId,
             );
             get().setExamples(examples);
 
             const flowsWithoutStarterFolder = flows.filter(
-              (flow) => flow.folder_id !== starterFolderId
+              (flow) => flow.folder_id !== starterFolderId,
             );
 
             get().setFlows(flowsWithoutStarterFolder);
@@ -120,7 +120,7 @@ const useFlowsManagerStore = create<FlowsManagerStoreType>((set, get) => ({
     if (get().currentFlow) {
       get().saveFlow(
         { ...get().currentFlow!, data: { nodes, edges, viewport } },
-        true
+        true,
       );
     }
   },
@@ -146,7 +146,7 @@ const useFlowsManagerStore = create<FlowsManagerStoreType>((set, get) => ({
                   return updatedFlow;
                 }
                 return flow;
-              })
+              }),
             );
             //update tabs state
 
@@ -155,9 +155,11 @@ const useFlowsManagerStore = create<FlowsManagerStoreType>((set, get) => ({
           }
         })
         .catch((err) => {
+          useAlertStore.getState().setErrorData({
+            title: "Error while saving changes",
+            list: [(err as AxiosError).message],
+          });
           reject(err);
-          set({ saveLoading: false });
-          throw err;
         });
     });
   }, SAVE_DEBOUNCE_TIME),
@@ -195,13 +197,12 @@ const useFlowsManagerStore = create<FlowsManagerStoreType>((set, get) => ({
     flow?: FlowType,
     override?: boolean,
     position?: XYPosition,
-    fromDragAndDrop?: boolean
+    fromDragAndDrop?: boolean,
   ): Promise<string | undefined> => {
+    let flowData = flow
+      ? processDataFromFlow(flow)
+      : { nodes: [], edges: [], viewport: { zoom: 1, x: 0, y: 0 } };
     if (newProject) {
-      let flowData = flow
-        ? processDataFromFlow(flow)
-        : { nodes: [], edges: [], viewport: { zoom: 1, x: 0, y: 0 } };
-
       // Create a new flow with a default name if no flow is provided.
       const folder_id = useFolderStore.getState().folderUrl;
       const my_collection_id = useFolderStore.getState().myCollectionId;
@@ -211,7 +212,7 @@ const useFlowsManagerStore = create<FlowsManagerStoreType>((set, get) => ({
         const newFlow = createNewFlow(
           flowData!,
           flow!,
-          folder_id || my_collection_id!
+          folder_id || my_collection_id!,
         );
         const { id } = await saveFlowToDatabase(newFlow);
         newFlow.id = id;
@@ -234,7 +235,7 @@ const useFlowsManagerStore = create<FlowsManagerStoreType>((set, get) => ({
       const newFlow = createNewFlow(
         flowData!,
         flow!,
-        folder_id || my_collection_id!
+        folder_id || my_collection_id!,
       );
 
       const newName = addVersionToDuplicates(newFlow, get().flows);
@@ -261,8 +262,20 @@ const useFlowsManagerStore = create<FlowsManagerStoreType>((set, get) => ({
 
         // Return the id
         return id;
-      } catch (error) {
-        // Handle the error if needed
+      } catch (error: any) {
+        if (error.response?.data?.detail) {
+          useAlertStore.getState().setErrorData({
+            title: "Could not load flows from database",
+            list: [error.response?.data?.detail],
+          });
+        } else {
+          useAlertStore.getState().setErrorData({
+            title: "Could not load flows from database",
+            list: [
+              error.message ?? "An unexpected error occurred, please try again",
+            ],
+          });
+        }
         throw error; // Re-throw the error so the caller can handle it if needed
       }
     } else {
@@ -270,7 +283,7 @@ const useFlowsManagerStore = create<FlowsManagerStoreType>((set, get) => ({
         .getState()
         .paste(
           { nodes: flow!.data!.nodes, edges: flow!.data!.edges },
-          position ?? { x: 10, y: 10 }
+          position ?? { x: 10, y: 10 },
         );
     }
   },
@@ -280,7 +293,7 @@ const useFlowsManagerStore = create<FlowsManagerStoreType>((set, get) => ({
         multipleDeleteFlowsComponents(id)
           .then(() => {
             const { data, flows } = processFlows(
-              get().flows.filter((flow) => !id.includes(flow.id))
+              get().flows.filter((flow) => !id.includes(flow.id)),
             );
             get().setFlows(flows);
             set({ isLoading: false });
@@ -300,7 +313,7 @@ const useFlowsManagerStore = create<FlowsManagerStoreType>((set, get) => ({
           deleteFlowFromDatabase(id)
             .then(() => {
               const { data, flows } = processFlows(
-                get().flows.filter((flow) => flow.id !== id)
+                get().flows.filter((flow) => flow.id !== id),
               );
               get().setFlows(flows);
               set({ isLoading: false });
@@ -322,7 +335,7 @@ const useFlowsManagerStore = create<FlowsManagerStoreType>((set, get) => ({
     return new Promise<void>((resolve) => {
       let componentFlow = get().flows.find(
         (componentFlow) =>
-          componentFlow.is_component && componentFlow.name === key
+          componentFlow.is_component && componentFlow.name === key,
       );
 
       if (componentFlow) {
@@ -370,7 +383,7 @@ const useFlowsManagerStore = create<FlowsManagerStoreType>((set, get) => ({
               fileData,
               undefined,
               position,
-              true
+              true,
             );
             resolve(id);
           }
@@ -411,7 +424,7 @@ const useFlowsManagerStore = create<FlowsManagerStoreType>((set, get) => ({
     return get().addFlow(
       true,
       createFlowComponent(component, useDarkStore.getState().version),
-      override
+      override,
     );
   },
   takeSnapshot: () => {
@@ -432,7 +445,7 @@ const useFlowsManagerStore = create<FlowsManagerStoreType>((set, get) => ({
     if (pastLength > 0) {
       past[currentFlowId] = past[currentFlowId].slice(
         pastLength - defaultOptions.maxHistorySize + 1,
-        pastLength
+        pastLength,
       );
 
       past[currentFlowId].push(newState);
diff --git a/src/frontend/src/stores/foldersStore.tsx b/src/frontend/src/stores/foldersStore.tsx
index 02e4c8074..f601ccef2 100644
--- a/src/frontend/src/stores/foldersStore.tsx
+++ b/src/frontend/src/stores/foldersStore.tsx
@@ -18,18 +18,18 @@ export const useFolderStore = create<FoldersStoreType>((set, get) => ({
         getFolders().then(
           async (res) => {
             const foldersWithoutStarterProjects = res?.filter(
-              (folder) => folder.name !== STARTER_FOLDER_NAME
+              (folder) => folder.name !== STARTER_FOLDER_NAME,
             );
 
             const starterProjects = res?.find(
-              (folder) => folder.name === STARTER_FOLDER_NAME
+              (folder) => folder.name === STARTER_FOLDER_NAME,
             );
 
             set({ starterProjectId: starterProjects!.id ?? "" });
             set({ folders: foldersWithoutStarterProjects });
 
             const myCollectionId = res?.find(
-              (f) => f.name === DEFAULT_FOLDER
+              (f) => f.name === DEFAULT_FOLDER,
             )?.id;
 
             set({ myCollectionId });
@@ -55,7 +55,7 @@ export const useFolderStore = create<FoldersStoreType>((set, get) => ({
             set({ folders: [] });
             get().setIsLoadingFolders(false);
             reject(error);
-          }
+          },
         );
       }
     });
@@ -65,18 +65,18 @@ export const useFolderStore = create<FoldersStoreType>((set, get) => ({
       getFolders().then(
         async (res) => {
           const foldersWithoutStarterProjects = res?.filter(
-            (folder) => folder.name !== STARTER_FOLDER_NAME
+            (folder) => folder.name !== STARTER_FOLDER_NAME,
           );
 
           const starterProjects = res?.find(
-            (folder) => folder.name === STARTER_FOLDER_NAME
+            (folder) => folder.name === STARTER_FOLDER_NAME,
           );
 
           set({ starterProjectId: starterProjects!.id ?? "" });
           set({ folders: foldersWithoutStarterProjects });
 
           const myCollectionId = res?.find(
-            (f) => f.name === DEFAULT_FOLDER
+            (f) => f.name === DEFAULT_FOLDER,
           )?.id;
 
           set({ myCollectionId });
@@ -87,7 +87,7 @@ export const useFolderStore = create<FoldersStoreType>((set, get) => ({
           set({ folders: [] });
           get().setIsLoadingFolders(false);
           reject(error);
-        }
+        },
       );
     });
   },
diff --git a/src/frontend/src/stores/globalVariablesStore/globalVariables.ts b/src/frontend/src/stores/globalVariablesStore/globalVariables.ts
index c07813858..708f7ec09 100644
--- a/src/frontend/src/stores/globalVariablesStore/globalVariables.ts
+++ b/src/frontend/src/stores/globalVariablesStore/globalVariables.ts
@@ -45,5 +45,5 @@ export const useGlobalVariablesStore = create<GlobalVariablesStore>(
     getVariableId: (name) => {
       return get().globalVariables[name]?.id;
     },
-  })
+  }),
 );
diff --git a/src/frontend/src/stores/messagesStore.ts b/src/frontend/src/stores/messagesStore.ts
index c6bf2042b..349a1c447 100644
--- a/src/frontend/src/stores/messagesStore.ts
+++ b/src/frontend/src/stores/messagesStore.ts
@@ -5,7 +5,7 @@ export const useMessagesStore = create<MessagesStoreType>((set, get) => ({
   deleteSession: (id) => {
     set((state) => {
       const updatedMessages = state.messages.filter(
-        (msg) => msg.session_id !== id
+        (msg) => msg.session_id !== id,
       );
       return { messages: updatedMessages };
     });
@@ -29,7 +29,7 @@ export const useMessagesStore = create<MessagesStoreType>((set, get) => ({
   updateMessage: (message) => {
     set(() => ({
       messages: get().messages.map((msg) =>
-        msg.index === message.index ? message : msg
+        msg.index === message.index ? message : msg,
       ),
     }));
   },
@@ -41,7 +41,7 @@ export const useMessagesStore = create<MessagesStoreType>((set, get) => ({
       try {
         set((state) => {
           const updatedMessages = state.messages.filter(
-            (msg) => !ids.includes(msg.index)
+            (msg) => !ids.includes(msg.index),
           );
           get().setMessages(updatedMessages);
           resolve(updatedMessages);
diff --git a/src/frontend/src/stores/typesStore.ts b/src/frontend/src/stores/typesStore.ts
index c92236e03..cc73b15d6 100644
--- a/src/frontend/src/stores/typesStore.ts
+++ b/src/frontend/src/stores/typesStore.ts
@@ -21,11 +21,11 @@ export const useTypesStore = create<TypesStoreType>((set, get) => ({
   types: {},
   templates: {},
   data: {},
-  getTypes: () => {
+  getTypes: (force_refresh: boolean = false) => {
     return new Promise<void>(async (resolve, reject) => {
       const setLoading = useFlowsManagerStore.getState().setIsLoading;
       setLoading(true);
-      getAll()
+      getAll(force_refresh)
         .then((response) => {
           const data = response?.data;
           useAlertStore.setState({ loading: false });
diff --git a/src/frontend/src/style/ag-theme-shadcn.css b/src/frontend/src/style/ag-theme-shadcn.css
index 93e996493..1b1bd96a8 100644
--- a/src/frontend/src/style/ag-theme-shadcn.css
+++ b/src/frontend/src/style/ag-theme-shadcn.css
@@ -23,12 +23,27 @@
 .ag-row .ag-cell {
   align-content: center !important;
 }
+
 .ag-cell {
   line-height: 1.25rem;
   padding-top: 0.675rem;
   padding-bottom: 0.675rem;
 }
 
+.ag-cell-wrapper > *:not(.ag-cell-value):not(.ag-group-value) {
+  align-items: self-start;
+  position: relative;
+  top: 2px;
+}
+
+.ag-cell-wrapper > *:not(.ag-cell-value):not(.ag-group-value) {
+  height: 0px;
+}
+
+.ag-cell-wrapper {
+  align-items: normal !important;
+}
+
 .ag-body-horizontal-scroll-viewport,
 .ag-body-vertical-scroll-viewport {
   cursor: auto;
diff --git a/src/frontend/src/style/applies.css b/src/frontend/src/style/applies.css
index 6c325b47b..23ebfe52b 100644
--- a/src/frontend/src/style/applies.css
+++ b/src/frontend/src/style/applies.css
@@ -196,7 +196,7 @@
 
   /* The same as primary-input but no-truncate */
   .textarea-primary {
-    @apply form-input block w-full rounded-md border-border bg-background px-3 text-left shadow-sm placeholder:text-muted-foreground focus:border-ring focus:placeholder-transparent focus:ring-ring disabled:cursor-not-allowed disabled:opacity-50 sm:text-sm;
+    @apply form-input block w-full rounded-md border-border bg-muted px-3 text-left shadow-sm placeholder:text-muted-foreground focus:border-ring focus:placeholder-transparent focus:ring-ring disabled:cursor-not-allowed disabled:opacity-50 sm:text-sm;
   }
 
   .input-edit-node {
@@ -931,7 +931,7 @@
   }
 
   .icons-parameters-comp {
-    @apply ml-3 h-6 w-6;
+    @apply h-6 w-6;
   }
 
   .form-modal-lock-true {
diff --git a/src/frontend/src/types/api/index.ts b/src/frontend/src/types/api/index.ts
index a8a3c6af4..e6dbeb315 100644
--- a/src/frontend/src/types/api/index.ts
+++ b/src/frontend/src/types/api/index.ts
@@ -6,7 +6,7 @@ export type APIDataType = { [key: string]: APIKindType };
 export type APIObjectType = { [key: string]: APIKindType };
 export type APIKindType = { [key: string]: APIClassType };
 export type APITemplateType = {
-  [key: string]: TemplateVariableType;
+  [key: string]: InputFieldType;
 };
 
 export type CustomFieldsType = {
@@ -14,13 +14,15 @@ export type CustomFieldsType = {
 };
 
 export type APIClassType = {
-  base_classes: Array<string>;
+  base_classes?: Array<string>;
   description: string;
   template: APITemplateType;
   display_name: string;
   icon?: string;
+  edited?: boolean;
   is_input?: boolean;
   is_output?: boolean;
+  conditional_paths?: Array<string>;
   input_types?: Array<string>;
   output_types?: Array<string>;
   custom_fields?: CustomFieldsType;
@@ -28,6 +30,7 @@ export type APIClassType = {
   documentation: string;
   error?: string;
   official?: boolean;
+  outputs?: Array<OutputFieldType>;
   frozen?: boolean;
   flow?: FlowType;
   field_order?: string[];
@@ -39,10 +42,11 @@ export type APIClassType = {
     | FlowType
     | CustomFieldsType
     | boolean
-    | undefined;
+    | undefined
+    | Array<{ types: Array<string>; selected?: string }>;
 };
 
-export type TemplateVariableType = {
+export type InputFieldType = {
   type: string;
   required: boolean;
   placeholder?: string;
@@ -61,6 +65,21 @@ export type TemplateVariableType = {
   refresh_button_text?: string;
   [key: string]: any;
 };
+
+export type OutputFieldProxyType = {
+  id: string;
+  name: string;
+  nodeDisplayName: string;
+};
+
+export type OutputFieldType = {
+  types: Array<string>;
+  selected?: string;
+  name: string;
+  display_name: string;
+  hidden?: boolean;
+  proxy?: OutputFieldProxyType;
+};
 export type sendAllProps = {
   nodes: Node[];
   edges: Edge[];
@@ -162,17 +181,27 @@ export type VertexBuildTypeAPI = {
   artifacts: any | ChatOutputType | ChatInputType;
 };
 
+export type ErrorLogType = {
+  errorMessage: string;
+  stackTrace: string;
+};
+
+export type LogType = {
+  message: any | ErrorLogType;
+  type: string;
+};
+
 // data is the object received by the API
 // it has results, artifacts, timedelta, duration
 export type VertexDataTypeAPI = {
   results: { [key: string]: string };
-  logs: { message: any; type: string }[];
+  logs: { [key: string]: LogType };
   messages: ChatOutputType[] | ChatInputType[];
   inactive?: boolean;
   timedelta?: number;
   duration?: string;
   artifacts?: any | ChatOutputType | ChatInputType;
-  message: ChatOutputType | ChatInputType;
+  message?: ChatOutputType | ChatInputType;
 };
 
 export type CodeErrorDataTypeAPI = {
diff --git a/src/frontend/src/types/components/index.ts b/src/frontend/src/types/components/index.ts
index bb4b6d74e..15090b4a0 100644
--- a/src/frontend/src/types/components/index.ts
+++ b/src/frontend/src/types/components/index.ts
@@ -1,7 +1,12 @@
 import { ReactElement, ReactNode, SetStateAction } from "react";
 import { ReactFlowJsonObject } from "reactflow";
 import { InputOutput } from "../../constants/enums";
-import { APIClassType, APITemplateType, TemplateVariableType } from "../api";
+import {
+  APIClassType,
+  APITemplateType,
+  InputFieldType,
+  OutputFieldProxyType,
+} from "../api";
 import { ChatMessageType } from "../chat";
 import { FlowStyleType, FlowType, NodeDataType, NodeType } from "../flow/index";
 import { sourceHandleType, targetHandleType } from "./../flow/index";
@@ -54,11 +59,13 @@ export type DropDownComponentType = {
   children?: ReactNode;
 };
 export type ParameterComponentType = {
-  selected: boolean;
+  selected?: boolean;
   data: NodeDataType;
   title: string;
+  conditionPath?: string | null;
+  key: string;
   id: sourceHandleType | targetHandleType;
-  color: string;
+  colors: string[];
   left: boolean;
   type: string | undefined;
   required?: boolean;
@@ -68,8 +75,10 @@ export type ParameterComponentType = {
   info?: string;
   proxy?: { field: string; id: string };
   showNode?: boolean;
-  index?: string;
+  index: number;
   onCloseModal?: (close: boolean) => void;
+  outputName?: string;
+  outputProxy?: OutputFieldProxyType;
 };
 export type InputListComponentType = {
   value: string[];
@@ -85,7 +94,7 @@ export type InputGlobalComponentType = {
   onChange: (value: string, snapshot?: boolean) => void;
   setDb: (value: boolean) => void;
   name: string;
-  data: TemplateVariableType;
+  data: InputFieldType;
   editNode?: boolean;
   playgroundDisabled?: boolean;
 };
@@ -122,6 +131,16 @@ export type TextAreaComponentType = {
   readonly?: boolean;
 };
 
+export type outputComponentType = {
+  types: string[];
+  selected: string;
+  nodeId: string;
+  frozen?: boolean;
+  idx: number;
+  name: string;
+  proxy?: OutputFieldProxyType;
+};
+
 export type PromptAreaComponentType = {
   field_name?: string;
   nodeClass?: APIClassType;
@@ -161,7 +180,7 @@ export type FileComponentType = {
 
 export type DisclosureComponentType = {
   children: ReactNode;
-  openDisc: boolean;
+  defaultOpen: boolean;
   isChild?: boolean;
   button: {
     title: string;
@@ -231,11 +250,6 @@ export type ProgressBarType = {
   max?: number;
 };
 
-export type RadialProgressType = {
-  value?: number;
-  color?: string;
-};
-
 export type AccordionComponentType = {
   children?: ReactElement;
   open?: string[];
@@ -465,6 +479,7 @@ export type nodeToolbarType = {
       description: string;
       display_name: string;
       documentation: string;
+      edited: boolean;
       template: APITemplateType;
     };
     value: void;
@@ -492,7 +507,7 @@ export type ChatInputType = {
   isDragging: boolean;
   files: FilePreviewType[];
   setFiles: (
-    files: FilePreviewType[] | ((prev: FilePreviewType[]) => FilePreviewType[])
+    files: FilePreviewType[] | ((prev: FilePreviewType[]) => FilePreviewType[]),
   ) => void;
   chatValue: string;
   inputRef: {
@@ -542,11 +557,12 @@ export type nodeToolbarPropsType = {
   deleteNode: (idx: string) => void;
   setShowNode: (boolean: any) => void;
   numberOfHandles: number;
+  numberOfOutputHandles: number;
   showNode: boolean;
   name?: string;
   openAdvancedModal?: boolean;
   onCloseAdvancedModal?: (close: boolean) => void;
-  selected: boolean;
+  isOutdated: boolean;
   setShowState: (show: boolean | SetStateAction<boolean>) => void;
   updateNode: () => void;
 };
@@ -595,7 +611,7 @@ export type chatMessagePropsType = {
   updateChat: (
     chat: ChatMessageType,
     message: string,
-    stream_url?: string
+    stream_url?: string,
   ) => void;
 };
 
@@ -606,6 +622,7 @@ export type genericModalPropsType = {
   buttonText: string;
   modalTitle: string;
   type: number;
+  disabled?: boolean;
   nodeClass?: APIClassType;
   setNodeClass?: (Class: APIClassType, code?: string) => void;
   children: ReactNode;
@@ -686,13 +703,13 @@ export type codeTabsPropsType = {
     getValue?: (
       value: string,
       node: NodeType,
-      template: TemplateVariableType,
-      tweak: tweakType
+      template: InputFieldType,
+      tweak: tweakType,
     ) => string;
     buildTweakObject?: (
       tw: string,
       changes: string | string[] | boolean | number | Object[] | Object,
-      template: TemplateVariableType
+      template: InputFieldType,
     ) => Promise<string | void>;
   };
   activeTweaks?: boolean;
@@ -769,7 +786,7 @@ export type chatViewProps = {
 };
 
 export type IOFileInputProps = {
-  field: TemplateVariableType;
+  field: InputFieldType;
   updateValue: (e: any, type: string) => void;
 };
 
diff --git a/src/frontend/src/types/flow/index.ts b/src/frontend/src/types/flow/index.ts
index b468e77eb..95e04f5b2 100644
--- a/src/frontend/src/types/flow/index.ts
+++ b/src/frontend/src/types/flow/index.ts
@@ -36,6 +36,7 @@ export type NodeDataType = {
   node?: APIClassType;
   id: string;
   output_types?: string[];
+  selected_output_type?: string;
   buildStatus?: BuildStatus;
 };
 // FlowStyleType is the type of the style object that is used to style the
@@ -58,7 +59,9 @@ export type TweaksType = Array<
 export type sourceHandleType = {
   dataType: string;
   id: string;
-  baseClasses: string[];
+  output_types: string[];
+  conditionalPath?: string | null;
+  name: string;
 };
 //left side
 export type targetHandleType = {
diff --git a/src/frontend/src/types/store/index.ts b/src/frontend/src/types/store/index.ts
index a3b26cb64..54b7cb478 100644
--- a/src/frontend/src/types/store/index.ts
+++ b/src/frontend/src/types/store/index.ts
@@ -48,7 +48,7 @@ export type shortcutsStoreType = {
     shortcut: string;
   }>;
   setShortcuts: (
-    newShortcuts: Array<{ name: string; shortcut: string }>
+    newShortcuts: Array<{ name: string; shortcut: string }>,
   ) => void;
   getShortcutsFromStorage: () => void;
 };
diff --git a/src/frontend/src/types/tabs/index.ts b/src/frontend/src/types/tabs/index.ts
index f07bc7752..97693053b 100644
--- a/src/frontend/src/types/tabs/index.ts
+++ b/src/frontend/src/types/tabs/index.ts
@@ -18,12 +18,12 @@ export type FlowsContextType = {
     newProject: boolean,
     flow?: FlowType,
     override?: boolean,
-    position?: XYPosition
+    position?: XYPosition,
   ) => Promise<String | undefined>;
   downloadFlow: (
     flow: FlowType,
     flowName: string,
-    flowDescription?: string
+    flowDescription?: string,
   ) => void;
   //keep
   downloadFlows: () => void;
@@ -43,11 +43,11 @@ export type FlowsContextType = {
   }) => Promise<String | never>;
   tabsState: FlowsState;
   setTabsState: (
-    update: FlowsState | ((oldState: FlowsState) => FlowsState)
+    update: FlowsState | ((oldState: FlowsState) => FlowsState),
   ) => void;
   saveComponent: (
     component: NodeDataType,
-    override: boolean
+    override: boolean,
   ) => Promise<String | undefined>;
   deleteComponent: (key: string) => void;
   version: string;
diff --git a/src/frontend/src/types/utils/typeCheckingUtils.ts b/src/frontend/src/types/utils/typeCheckingUtils.ts
new file mode 100644
index 000000000..4173788a8
--- /dev/null
+++ b/src/frontend/src/types/utils/typeCheckingUtils.ts
@@ -0,0 +1,23 @@
+import { ErrorLogType } from "../api";
+
+export function isErrorLogType(value: any): value is ErrorLogType {
+  return (
+    typeof value === "object" &&
+    value !== null &&
+    "errorMessage" in value &&
+    typeof value.errorMessage === "string" &&
+    "stackTrace" in value &&
+    typeof value.stackTrace === "string"
+  );
+}
+
+export function isErrorLog(
+  log: any,
+): log is { type: "error"; message: ErrorLogType } {
+  return (
+    typeof log === "object" &&
+    log !== null &&
+    (log.type === "error" || log.type === "ValueError") &&
+    isErrorLogType(log.message)
+  );
+}
diff --git a/src/frontend/src/types/zustand/flow/index.ts b/src/frontend/src/types/zustand/flow/index.ts
index e8b88e8d3..77eef030d 100644
--- a/src/frontend/src/types/zustand/flow/index.ts
+++ b/src/frontend/src/types/zustand/flow/index.ts
@@ -86,7 +86,7 @@ export type FlowStoreType = {
     state:
       | FlowState
       | undefined
-      | ((oldState: FlowState | undefined) => FlowState)
+      | ((oldState: FlowState | undefined) => FlowState),
   ) => void;
   nodes: Node[];
   edges: Edge[];
@@ -94,11 +94,11 @@ export type FlowStoreType = {
   onEdgesChange: OnEdgesChange;
   setNodes: (
     update: Node[] | ((oldState: Node[]) => Node[]),
-    skipSave?: boolean
+    skipSave?: boolean,
   ) => void;
   setEdges: (
     update: Edge[] | ((oldState: Edge[]) => Edge[]),
-    skipSave?: boolean
+    skipSave?: boolean,
   ) => void;
   setNode: (id: string, update: Node | ((oldState: Node) => Node)) => void;
   getNode: (id: string) => Node | undefined;
@@ -106,12 +106,12 @@ export type FlowStoreType = {
   deleteEdge: (edgeId: string | Array<string>) => void;
   paste: (
     selection: { nodes: any; edges: any },
-    position: { x: number; y: number; paneX?: number; paneY?: number }
+    position: { x: number; y: number; paneX?: number; paneY?: number },
   ) => void;
   lastCopiedSelection: { nodes: any; edges: any } | null;
   setLastCopiedSelection: (
     newSelection: { nodes: any; edges: any } | null,
-    isCrop?: boolean
+    isCrop?: boolean,
   ) => void;
   cleanFlow: () => void;
   setFilterEdge: (newState) => void;
@@ -124,7 +124,9 @@ export type FlowStoreType = {
     input_value,
     files,
     silent,
+    setLockChat,
   }: {
+    setLockChat?: (lock: boolean) => void;
     startNodeId?: string;
     stopNodeId?: string;
     input_value?: string;
@@ -138,7 +140,7 @@ export type FlowStoreType = {
       verticesLayers: VertexLayerElementType[][];
       runId: string;
       verticesToRun: string[];
-    } | null
+    } | null,
   ) => void;
   addToVerticesBuild: (vertices: string[]) => void;
   removeFromVerticesBuild: (vertices: string[]) => void;
@@ -156,7 +158,7 @@ export type FlowStoreType = {
   updateFlowPool: (
     nodeId: string,
     data: VertexBuildTypeAPI | ChatOutputType | ChatInputType,
-    buildId?: string
+    buildId?: string,
   ) => void;
   getNodePosition: (nodeId: string) => { x: number; y: number };
 };
diff --git a/src/frontend/src/types/zustand/flowsManager/index.ts b/src/frontend/src/types/zustand/flowsManager/index.ts
index e0770626e..aaafebb7b 100644
--- a/src/frontend/src/types/zustand/flowsManager/index.ts
+++ b/src/frontend/src/types/zustand/flowsManager/index.ts
@@ -17,12 +17,12 @@ export type FlowsManagerStoreType = {
   saveFlow: (flow: FlowType, silent?: boolean) => Promise<void> | undefined;
   saveFlowDebounce: (
     flow: FlowType,
-    silent?: boolean
+    silent?: boolean,
   ) => Promise<void> | undefined;
   autoSaveCurrentFlow: (
     nodes: Node[],
     edges: Edge[],
-    viewport: Viewport
+    viewport: Viewport,
   ) => void;
   uploadFlows: () => Promise<void>;
   uploadFlow: ({
@@ -41,13 +41,13 @@ export type FlowsManagerStoreType = {
     flow?: FlowType,
     override?: boolean,
     position?: XYPosition,
-    fromDragAndDrop?: boolean
+    fromDragAndDrop?: boolean,
   ) => Promise<string | undefined>;
   deleteComponent: (key: string) => Promise<void>;
   removeFlow: (id: string | string[]) => Promise<void>;
   saveComponent: (
     component: any,
-    override: boolean
+    override: boolean,
   ) => Promise<string | undefined>;
   undo: () => void;
   redo: () => void;
diff --git a/src/frontend/src/types/zustand/folders/index.ts b/src/frontend/src/types/zustand/folders/index.ts
index 45c1e0bd9..495d65b87 100644
--- a/src/frontend/src/types/zustand/folders/index.ts
+++ b/src/frontend/src/types/zustand/folders/index.ts
@@ -4,7 +4,7 @@ export type FoldersStoreType = {
   folders: FolderType[];
   getFoldersApi: (
     refetch?: boolean,
-    startupApplication?: boolean
+    startupApplication?: boolean,
   ) => Promise<void>;
   setFolders: (folders: FolderType[]) => void;
   isLoadingFolders: boolean;
diff --git a/src/frontend/src/types/zustand/globalVariables/index.ts b/src/frontend/src/types/zustand/globalVariables/index.ts
index 90817780f..4b178088c 100644
--- a/src/frontend/src/types/zustand/globalVariables/index.ts
+++ b/src/frontend/src/types/zustand/globalVariables/index.ts
@@ -21,7 +21,7 @@ export type GlobalVariablesStore = {
     id: string,
     type?: string,
     default_fields?: string[],
-    value?: string
+    value?: string,
   ) => void;
   removeGlobalVariable: (name: string) => Promise<void>;
   getVariableId: (name: string) => string | undefined;
diff --git a/src/frontend/src/types/zustand/types/index.ts b/src/frontend/src/types/zustand/types/index.ts
index 4f817de47..1f2fa1ed2 100644
--- a/src/frontend/src/types/zustand/types/index.ts
+++ b/src/frontend/src/types/zustand/types/index.ts
@@ -7,7 +7,7 @@ export type TypesStoreType = {
   setTemplates: (newState: {}) => void;
   data: APIDataType;
   setData: (newState: {}) => void;
-  getTypes: () => Promise<void>;
+  getTypes: (force_refresh?: boolean) => Promise<void>;
   ComponentFields: Set<string>;
   setComponentFields: (fields: Set<string>) => void;
   addComponentField: (field: string) => void;
diff --git a/src/frontend/src/utils/buildUtils.ts b/src/frontend/src/utils/buildUtils.ts
index 4d0a3e147..3c42f2018 100644
--- a/src/frontend/src/utils/buildUtils.ts
+++ b/src/frontend/src/utils/buildUtils.ts
@@ -5,9 +5,11 @@ import { getVerticesOrder, postBuildVertex } from "../controllers/API";
 import useAlertStore from "../stores/alertStore";
 import useFlowStore from "../stores/flowStore";
 import { VertexBuildTypeAPI } from "../types/api";
+import { isErrorLogType } from "../types/utils/typeCheckingUtils";
 import { VertexLayerElementType } from "../types/zustand/flow";
 
 type BuildVerticesParams = {
+  setLockChat?: (lock: boolean) => void;
   flowId: string; // Assuming FlowType is the type for your flow
   input_value?: any; // Replace any with the actual type if it's not any
   files?: string[];
@@ -17,7 +19,7 @@ type BuildVerticesParams = {
   onBuildUpdate?: (
     data: VertexBuildTypeAPI,
     status: BuildStatus,
-    buildId: string
+    buildId: string,
   ) => void; // Replace any with the actual type if it's not any
   onBuildComplete?: (allNodesValid: boolean) => void;
   onBuildError?: (title, list, idList: VertexLayerElementType[]) => void;
@@ -31,7 +33,7 @@ function getInactiveVertexData(vertexId: string): VertexBuildTypeAPI {
   // Build VertexBuildTypeAPI
   let inactiveData = {
     results: {},
-    logs: [],
+    logs: {},
     messages: [],
     inactive: true,
   };
@@ -44,6 +46,9 @@ function getInactiveVertexData(vertexId: string): VertexBuildTypeAPI {
     top_level_vertices: [],
     inactive_vertices: null,
     valid: false,
+    params: null,
+    messages: [],
+    artifacts: null,
     timestamp: new Date().toISOString(),
   };
 
@@ -52,10 +57,11 @@ function getInactiveVertexData(vertexId: string): VertexBuildTypeAPI {
 
 export async function updateVerticesOrder(
   flowId: string,
+  setLockChat?: (lock: boolean) => void,
   startNodeId?: string | null,
   stopNodeId?: string | null,
   nodes?: Node[],
-  edges?: Edge[]
+  edges?: Edge[],
 ): Promise<{
   verticesLayers: VertexLayerElementType[][];
   verticesIds: string[];
@@ -71,7 +77,7 @@ export async function updateVerticesOrder(
         startNodeId,
         stopNodeId,
         nodes,
-        edges
+        edges,
       );
     } catch (error: any) {
       setErrorData({
@@ -79,6 +85,7 @@ export async function updateVerticesOrder(
         list: [error.response?.data?.detail ?? "Unknown Error"],
       });
       useFlowStore.getState().setIsBuilding(false);
+      setLockChat && setLockChat(false);
       throw new Error("Invalid nodes");
     }
     // orderResponse.data.ids,
@@ -117,6 +124,7 @@ export async function buildVertices({
   onValidateNodes,
   nodes,
   edges,
+  setLockChat,
 }: BuildVerticesParams) {
   // if startNodeId and stopNodeId are provided
   // something is wrong
@@ -125,17 +133,18 @@ export async function buildVertices({
   }
   let verticesOrderResponse = await updateVerticesOrder(
     flowId,
+    setLockChat,
     startNodeId,
     stopNodeId,
     nodes,
-    edges
+    edges,
   );
   if (onValidateNodes) {
     try {
       onValidateNodes(verticesOrderResponse.verticesToRun);
     } catch (e) {
       useFlowStore.getState().setIsBuilding(false);
-
+      setLockChat && setLockChat(false);
       return;
     }
   }
@@ -190,14 +199,14 @@ export async function buildVertices({
             onBuildUpdate(
               getInactiveVertexData(element.id),
               BuildStatus.INACTIVE,
-              runId
+              runId,
             );
           }
           if (element.reference) {
             onBuildUpdate(
               getInactiveVertexData(element.reference),
               BuildStatus.INACTIVE,
-              runId
+              runId,
             );
           }
           buildResults.push(false);
@@ -223,7 +232,7 @@ export async function buildVertices({
         if (stop) {
           return;
         }
-      })
+      }),
     );
     // Once the current layer is built, move to the next layer
     currentLayerIndex += 1;
@@ -265,14 +274,30 @@ async function buildVertex({
     const buildData: VertexBuildTypeAPI = buildRes.data;
     if (onBuildUpdate) {
       if (!buildData.valid) {
+        // lots is a dictionary with the key the output field name and the value the log object
+        // logs: { [key: string]: { message: any; type: string }[] };
+        const errorMessages = Object.keys(buildData.data.logs).map((key) => {
+          const logs = buildData.data.logs[key];
+          if (Array.isArray(logs)) {
+            return logs
+              .filter((log) => isErrorLogType(log.message))
+              .map((log) => log.message.errorMessage);
+          }
+          if (!isErrorLogType(logs.message)) {
+            return [];
+          }
+          return [logs.message.errorMessage];
+        });
         onBuildError!(
           "Error Building Component",
-          buildData.data.logs.map((log) => log.message),
-          verticesIds.map((id) => ({ id }))
+          errorMessages,
+          verticesIds.map((id) => ({ id })),
         );
         stopBuild();
+        onBuildUpdate(buildData, BuildStatus.ERROR);
+      } else {
+        onBuildUpdate(buildData, BuildStatus.BUILT);
       }
-      onBuildUpdate(buildData, BuildStatus.BUILT);
     }
     buildResults.push(buildData.valid);
   } catch (error) {
@@ -283,8 +308,9 @@ async function buildVertex({
         (error as AxiosError<any>).response?.data?.detail ??
           "An unexpected error occurred while building the Component. Please try again.",
       ],
-      verticesIds.map((id) => ({ id }))
+      verticesIds.map((id) => ({ id })),
     );
+    buildResults.push(false);
     stopBuild();
   }
 }
diff --git a/src/frontend/src/utils/parameterUtils.ts b/src/frontend/src/utils/parameterUtils.ts
index a1d2f2154..d1d53dde7 100644
--- a/src/frontend/src/utils/parameterUtils.ts
+++ b/src/frontend/src/utils/parameterUtils.ts
@@ -21,7 +21,7 @@ export const handleUpdateValues = async (name: string, data: NodeDataType) => {
       code,
       template,
       name,
-      data.node?.template[name]?.value
+      data.node?.template[name]?.value,
     );
     if (res.status === 200 && data.node?.template) {
       return res.data.template;
@@ -34,5 +34,5 @@ export const handleUpdateValues = async (name: string, data: NodeDataType) => {
 
 export const debouncedHandleUpdateValues = debounce(
   handleUpdateValues,
-  SAVE_DEBOUNCE_TIME
+  SAVE_DEBOUNCE_TIME,
 );
diff --git a/src/frontend/src/utils/reactflowUtils.ts b/src/frontend/src/utils/reactflowUtils.ts
index 4c267dc32..ad9666201 100644
--- a/src/frontend/src/utils/reactflowUtils.ts
+++ b/src/frontend/src/utils/reactflowUtils.ts
@@ -1,4 +1,4 @@
-import { cloneDeep } from "lodash";
+import { cloneDeep, get } from "lodash";
 import {
   Connection,
   Edge,
@@ -24,7 +24,8 @@ import {
   APIKindType,
   APIObjectType,
   APITemplateType,
-  TemplateVariableType,
+  InputFieldType,
+  OutputFieldType,
 } from "../types/api";
 import {
   FlowType,
@@ -40,13 +41,14 @@ import {
   updateEdgesHandleIdsType,
 } from "../types/utils/reactflowUtils";
 import { createRandomKey, toTitleCase } from "./utils";
-const uid = new ShortUniqueId({ length: 5 });
+const uid = new ShortUniqueId();
 
 export function checkChatInput(nodes: Node[]) {
   return nodes.some((node) => node.data.type === "ChatInput");
 }
 
-export function cleanEdges(nodes: Node[], edges: Edge[]) {
+export function cleanEdges(nodes: NodeType[], edges: Edge[]) {
+  console.log("cleanEdges", nodes, edges);
   let newEdges = cloneDeep(edges);
   edges.forEach((edge) => {
     // check if the source and target node still exists
@@ -76,12 +78,27 @@ export function cleanEdges(nodes: Node[], edges: Edge[]) {
       }
     }
     if (sourceHandle) {
-      const id: sourceHandleType = {
-        id: sourceNode.data.id,
-        baseClasses: sourceNode.data.node!.base_classes,
-        dataType: sourceNode.data.type,
-      };
-      if (scapedJSONStringfy(id) !== sourceHandle) {
+      const parsedSourceHandle = scapeJSONParse(sourceHandle);
+      const name = parsedSourceHandle.name;
+      const output = sourceNode.data.node!.outputs?.find(
+        (output) => output.name === name,
+      );
+      if (output) {
+        const outputTypes =
+          output!.types.length === 1 ? output!.types : [output!.selected!];
+
+        const id: sourceHandleType = {
+          id: sourceNode.data.id,
+          name: name,
+          output_types: outputTypes,
+          dataType: sourceNode.data.type,
+        };
+        console.log("id", id);
+        console.log("parsedSourceHandle", parsedSourceHandle);
+        if (scapedJSONStringfy(id) !== sourceHandle) {
+          newEdges = newEdges.filter((e) => e.id !== edge.id);
+        }
+      } else {
         newEdges = newEdges.filter((e) => e.id !== edge.id);
       }
     }
@@ -100,18 +117,18 @@ export function unselectAllNodes({ updateNodes, data }: unselectAllNodesType) {
 export function isValidConnection(
   { source, target, sourceHandle, targetHandle }: Connection,
   nodes: Node[],
-  edges: Edge[]
+  edges: Edge[],
 ) {
   const targetHandleObject: targetHandleType = scapeJSONParse(targetHandle!);
   const sourceHandleObject: sourceHandleType = scapeJSONParse(sourceHandle!);
   if (
     targetHandleObject.inputTypes?.some(
-      (n) => n === sourceHandleObject.dataType
+      (n) => n === sourceHandleObject.dataType,
     ) ||
-    sourceHandleObject.baseClasses.some(
+    sourceHandleObject.output_types.some(
       (t) =>
         targetHandleObject.inputTypes?.some((n) => n === t) ||
-        t === targetHandleObject.type
+        t === targetHandleObject.type,
     )
   ) {
     let targetNode = nodes.find((node) => node.id === target!)?.data?.node;
@@ -144,7 +161,7 @@ export function removeApiKeys(flow: FlowType): FlowType {
 
 export function updateTemplate(
   reference: APITemplateType,
-  objectToUpdate: APITemplateType
+  objectToUpdate: APITemplateType,
 ): APITemplateType {
   let clonedObject: APITemplateType = cloneDeep(reference);
 
@@ -176,7 +193,10 @@ export const processFlows = (DbData: FlowType[], skipUpdate = true) => {
         (flow.data.nodes[0].data as NodeDataType).node!.display_name =
           flow.name;
         savedComponents[
-          createRandomKey((flow.data.nodes[0].data as NodeDataType).type, uid())
+          createRandomKey(
+            (flow.data.nodes[0].data as NodeDataType).type,
+            uid.randomUUID(5),
+          )
         ] = cloneDeep((flow.data.nodes[0].data as NodeDataType).node!);
         return;
       }
@@ -192,8 +212,8 @@ export const processDataFromFlow = (flow: FlowType, refreshIds = true) => {
   let data = flow?.data ? flow.data : null;
   if (data) {
     processFlowEdges(flow);
-    //prevent node update for now
-    // processFlowNodes(flow);
+    //add dropdown option to nodeOutputs
+    processFlowNodes(flow);
     //add animation to text type edges
     updateEdges(data.edges);
     // updateNodes(data.nodes, data.edges);
@@ -204,7 +224,7 @@ export const processDataFromFlow = (flow: FlowType, refreshIds = true) => {
 
 export function updateIds(
   { edges, nodes }: { edges: Edge[]; nodes: Node[] },
-  selection?: { edges: Edge[]; nodes: Node[] }
+  selection?: { edges: Edge[]; nodes: Node[] },
 ) {
   let idsMap = {};
   const selectionIds = selection?.nodes.map((n) => n.id);
@@ -232,7 +252,7 @@ export function updateIds(
       edge.source = idsMap[edge.source];
       edge.target = idsMap[edge.target];
       const sourceHandleObject: sourceHandleType = scapeJSONParse(
-        edge.sourceHandle!
+        edge.sourceHandle!,
       );
       edge.sourceHandle = scapedJSONStringfy({
         ...sourceHandleObject,
@@ -242,7 +262,7 @@ export function updateIds(
         edge.data.sourceHandle.id = edge.source;
       }
       const targetHandleObject: targetHandleType = scapeJSONParse(
-        edge.targetHandle!
+        edge.targetHandle!,
       );
       edge.targetHandle = scapedJSONStringfy({
         ...targetHandleObject,
@@ -288,11 +308,11 @@ export function validateNode(node: NodeType, edges: Edge[]): Array<string> {
           (scapeJSONParse(edge.targetHandle!) as targetHandleType).fieldName ===
             t &&
           (scapeJSONParse(edge.targetHandle!) as targetHandleType).id ===
-            node.id
+            node.id,
       )
     ) {
       errors.push(
-        `${displayName || type} is missing ${getFieldTitle(template, t)}.`
+        `${displayName || type} is missing ${getFieldTitle(template, t)}.`,
       );
     } else if (
       template[t].type === "dict" &&
@@ -306,15 +326,15 @@ export function validateNode(node: NodeType, edges: Edge[]): Array<string> {
         errors.push(
           `${displayName || type} (${getFieldTitle(
             template,
-            t
-          )}) contains duplicate keys with the same values.`
+            t,
+          )}) contains duplicate keys with the same values.`,
         );
       if (hasEmptyKey(template[t].value))
         errors.push(
           `${displayName || type} (${getFieldTitle(
             template,
-            t
-          )}) field must not be empty.`
+            t,
+          )}) field must not be empty.`,
         );
     }
     return errors;
@@ -323,7 +343,7 @@ export function validateNode(node: NodeType, edges: Edge[]): Array<string> {
 
 export function validateNodes(
   nodes: Node[],
-  edges: Edge[]
+  edges: Edge[],
 ): // this returns an array of tuples with the node id and the errors
 Array<{ id: string; errors: Array<string> }> {
   if (nodes.length === 0) {
@@ -344,7 +364,7 @@ export function updateEdges(edges: Edge[]) {
   if (edges)
     edges.forEach((edge) => {
       const targetHandleObject: targetHandleType = scapeJSONParse(
-        edge.targetHandle!
+        edge.targetHandle!,
       );
       edge.className = "";
     });
@@ -367,6 +387,7 @@ export function updateEdgesHandleIds({
   edges,
   nodes,
 }: updateEdgesHandleIdsType): Edge[] {
+  console.log("updateEdgesHandleIds");
   let newEdges = cloneDeep(edges);
   newEdges.forEach((edge) => {
     const sourceNodeId = edge.source;
@@ -389,10 +410,14 @@ export function updateEdgesHandleIds({
       };
     }
     if (source && sourceNode) {
+      const output_types =
+        sourceNode.data.node!.output_types ??
+        sourceNode.data.node!.base_classes!;
       newSource = {
         id: sourceNode.data.id,
-        baseClasses: sourceNode.data.node!.base_classes,
+        output_types,
         dataType: sourceNode.data.type,
+        name: output_types.join(" | "),
       };
     }
     edge.sourceHandle = scapedJSONStringfy(newSource!);
@@ -406,12 +431,81 @@ export function updateEdgesHandleIds({
   return newEdges;
 }
 
+export function updateNewOutput({ nodes, edges }: updateEdgesHandleIdsType) {
+  let newEdges = cloneDeep(edges);
+  let newNodes = cloneDeep(nodes);
+  newEdges.forEach((edge) => {
+    if (edge.sourceHandle && edge.targetHandle) {
+      let newSourceHandle: sourceHandleType = scapeJSONParse(edge.sourceHandle);
+      let newTargetHandle: targetHandleType = scapeJSONParse(edge.targetHandle);
+      const id = newSourceHandle.id;
+      const sourceNodeIndex = newNodes.findIndex((node) => node.id === id);
+      let sourceNode: NodeType | undefined = undefined;
+      if (sourceNodeIndex !== -1) {
+        sourceNode = newNodes[sourceNodeIndex];
+      }
+
+      let intersection;
+      //@ts-ignore
+      if (newSourceHandle.baseClasses) {
+        if (!newSourceHandle.output_types) {
+          if (sourceNode?.data.node!.output_types) {
+            newSourceHandle.output_types = sourceNode?.data.node!.output_types;
+          } else {
+            //@ts-ignore
+            newSourceHandle.output_types = newSourceHandle.baseClasses;
+          }
+        }
+        //@ts-ignore
+        delete newSourceHandle.baseClasses;
+      }
+      if (newTargetHandle.inputTypes && newTargetHandle.inputTypes.length > 0) {
+        //conjuction subtraction
+        intersection = newSourceHandle.output_types.filter((type) =>
+          newTargetHandle.inputTypes!.includes(type),
+        );
+      } else {
+        intersection = newSourceHandle.output_types.filter(
+          (type) => type === newTargetHandle.type,
+        );
+      }
+      const selected = intersection[0];
+      newSourceHandle.name = newSourceHandle.output_types.join(" | ");
+      newSourceHandle.output_types = [selected];
+      if (sourceNode) {
+        if (!sourceNode.data.node?.outputs) {
+          sourceNode.data.node!.outputs = [];
+        }
+        const types =
+          sourceNode.data.node!.output_types ??
+          sourceNode.data.node!.base_classes!;
+        if (
+          !sourceNode.data.node!.outputs.some(
+            (output) => output.selected === selected,
+          )
+        ) {
+          sourceNode.data.node!.outputs.push({
+            types,
+            selected: selected,
+            name: types.join(" | "),
+            display_name: types.join(" | "),
+          });
+        }
+      }
+
+      edge.sourceHandle = scapedJSONStringfy(newSourceHandle);
+      edge.data.sourceHandle = newSourceHandle;
+    }
+  });
+  return { nodes: newNodes, edges: newEdges };
+}
+
 export function handleKeyDown(
   e:
     | React.KeyboardEvent<HTMLInputElement>
     | React.KeyboardEvent<HTMLTextAreaElement>,
   inputValue: string | string[] | null,
-  block: string
+  block: string,
 ) {
   //condition to fix bug control+backspace on Windows/Linux
   if (
@@ -434,7 +528,7 @@ export function handleKeyDown(
 }
 
 export function handleOnlyIntegerInput(
-  event: React.KeyboardEvent<HTMLInputElement>
+  event: React.KeyboardEvent<HTMLInputElement>,
 ) {
   if (
     event.key === "." ||
@@ -450,7 +544,7 @@ export function handleOnlyIntegerInput(
 
 export function getConnectedNodes(
   edge: Edge,
-  nodes: Array<NodeType>
+  nodes: Array<NodeType>,
 ): Array<NodeType> {
   const sourceId = edge.source;
   const targetId = edge.target;
@@ -551,10 +645,14 @@ export function checkOldEdgesHandles(edges: Edge[]): boolean {
       !edge.sourceHandle ||
       !edge.targetHandle ||
       !edge.sourceHandle.includes("{") ||
-      !edge.targetHandle.includes("{")
+      !edge.targetHandle.includes("{"),
   );
 }
 
+export function checkOldNodesOutput(nodes: NodeType[]): boolean {
+  return nodes.some((node) => !node.data.node?.outputs);
+}
+
 export function customStringify(obj: any): string {
   if (typeof obj === "undefined") {
     return "null";
@@ -574,7 +672,7 @@ export function customStringify(obj: any): string {
 
   const keys = Object.keys(obj).sort();
   const keyValuePairs = keys.map(
-    (key) => `"${key}":${customStringify(obj[key])}`
+    (key) => `"${key}":${customStringify(obj[key])}`,
   );
   return `{${keyValuePairs.join(",")}}`;
 }
@@ -596,14 +694,14 @@ export function getMiddlePoint(nodes: Node[]) {
 }
 
 export function getNodeId(nodeType: string) {
-  return nodeType + "-" + uid();
+  return nodeType + "-" + uid.randomUUID(5);
 }
 
 export function getHandleId(
   source: string,
   sourceHandle: string,
   target: string,
-  targetHandle: string
+  targetHandle: string,
 ) {
   return (
     "reactflow__edge-" + source + sourceHandle + "-" + target + targetHandle
@@ -614,16 +712,15 @@ export function generateFlow(
   selection: OnSelectionChangeParams,
   nodes: Node[],
   edges: Edge[],
-  name: string
+  name: string,
 ): generateFlowType {
   const newFlowData = { nodes, edges, viewport: { zoom: 1, x: 0, y: 0 } };
-  const uid = new ShortUniqueId({ length: 5 });
   /*	remove edges that are not connected to selected nodes on both ends
    */
-  newFlowData.edges = selection.edges.filter(
+  newFlowData.edges = edges.filter(
     (edge) =>
       selection.nodes.some((node) => node.id === edge.target) &&
-      selection.nodes.some((node) => node.id === edge.source)
+      selection.nodes.some((node) => node.id === edge.source),
   );
   newFlowData.nodes = selection.nodes;
 
@@ -633,7 +730,7 @@ export function generateFlow(
     name: name,
     description: "",
     //generating local id instead of using the id from the server, can change in the future
-    id: uid(),
+    id: uid.randomUUID(5),
   };
   // filter edges that are not connected to selected nodes on both ends
   // using O(n²) aproach because the number of edges is small
@@ -644,7 +741,7 @@ export function generateFlow(
       (edge) =>
         (selection.nodes.some((node) => node.id === edge.target) ||
           selection.nodes.some((node) => node.id === edge.source)) &&
-        newFlowData.edges.every((e) => e.id !== edge.id)
+        newFlowData.edges.every((e) => e.id !== edge.id),
     ),
   };
 }
@@ -655,13 +752,13 @@ export function reconnectEdges(groupNode: NodeType, excludedEdges: Edge[]) {
   const { nodes, edges } = groupNode.data.node!.flow!.data!;
   const lastNode = findLastNode(groupNode.data.node!.flow!.data!);
   newEdges = newEdges.filter(
-    (e) => !(nodes.some((n) => n.id === e.source) && e.source !== lastNode?.id)
+    (e) => !(nodes.some((n) => n.id === e.source) && e.source !== lastNode?.id),
   );
   newEdges.forEach((edge) => {
     if (lastNode && edge.source === lastNode.id) {
       edge.source = groupNode.id;
       let newSourceHandle: sourceHandleType = scapeJSONParse(
-        edge.sourceHandle!
+        edge.sourceHandle!,
       );
       newSourceHandle.id = groupNode.id;
       edge.sourceHandle = scapedJSONStringfy(newSourceHandle);
@@ -688,7 +785,7 @@ export function reconnectEdges(groupNode: NodeType, excludedEdges: Edge[]) {
 export function filterFlow(
   selection: OnSelectionChangeParams,
   setNodes: (update: Node[] | ((oldState: Node[]) => Node[])) => void,
-  setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void
+  setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void,
 ) {
   setNodes((nodes) => nodes.filter((node) => !selection.nodes.includes(node)));
   setEdges((edges) => edges.filter((edge) => !selection.edges.includes(edge)));
@@ -726,7 +823,7 @@ export function updateFlowPosition(NewPosition: XYPosition, flow: FlowType) {
 export function concatFlows(
   flow: FlowType,
   setNodes: (update: Node[] | ((oldState: Node[]) => Node[])) => void,
-  setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void
+  setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void,
 ) {
   const { nodes, edges } = flow.data!;
   setNodes((old) => [...old, ...nodes]);
@@ -735,7 +832,7 @@ export function concatFlows(
 
 export function validateSelection(
   selection: OnSelectionChangeParams,
-  edges: Edge[]
+  edges: Edge[],
 ): Array<string> {
   const clonedSelection = cloneDeep(selection);
   const clonedEdges = cloneDeep(edges);
@@ -749,7 +846,7 @@ export function validateSelection(
   let nodesSet = new Set(clonedSelection.nodes.map((n) => n.id));
   // then filter the edges that are connected to the nodes in the set
   let connectedEdges = clonedSelection.edges.filter(
-    (e) => nodesSet.has(e.source) && nodesSet.has(e.target)
+    (e) => nodesSet.has(e.source) && nodesSet.has(e.target),
   );
   // add the edges to the selection
   clonedSelection.edges = connectedEdges;
@@ -763,17 +860,17 @@ export function validateSelection(
     clonedSelection.nodes.some(
       (node) =>
         isInputNode(node.data as NodeDataType) ||
-        isOutputNode(node.data as NodeDataType)
+        isOutputNode(node.data as NodeDataType),
     )
   ) {
     errorsArray.push(
-      "Please select only nodes that are not input or output nodes"
+      "Please select only nodes that are not input or output nodes",
     );
   }
   //check if there are two or more nodes with free outputs
   if (
     clonedSelection.nodes.filter(
-      (n) => !clonedSelection.edges.some((e) => e.source === n.id)
+      (n) => !clonedSelection.edges.some((e) => e.source === n.id),
     ).length > 1
   ) {
     errorsArray.push("Please select only one node with free outputs");
@@ -784,7 +881,7 @@ export function validateSelection(
     clonedSelection.nodes.some(
       (node) =>
         !clonedSelection.edges.some((edge) => edge.target === node.id) &&
-        !clonedSelection.edges.some((edge) => edge.source === node.id)
+        !clonedSelection.edges.some((edge) => edge.source === node.id),
     )
   ) {
     errorsArray.push("Please select only nodes that are connected");
@@ -829,7 +926,7 @@ export function mergeNodeTemplates({
     Object.keys(nodeTemplate)
       .filter((field_name) => field_name.charAt(0) !== "_")
       .forEach((key) => {
-        if (!isHandleConnected(edges, key, nodeTemplate[key], node.id)) {
+        if (!isTargetHandleConnected(edges, key, nodeTemplate[key], node.id)) {
           template[key + "_" + node.id] = nodeTemplate[key];
           template[key + "_" + node.id].proxy = { id: node.id, field: key };
           if (node.type === "groupNode") {
@@ -841,19 +938,19 @@ export function mergeNodeTemplates({
               nodeTemplate[key].display_name
                 ? nodeTemplate[key].display_name
                 : nodeTemplate[key].name
-                ? toTitleCase(nodeTemplate[key].name)
-                : toTitleCase(key);
+                  ? toTitleCase(nodeTemplate[key].name)
+                  : toTitleCase(key);
           }
         }
       });
   });
   return template;
 }
-function isHandleConnected(
+function isTargetHandleConnected(
   edges: Edge[],
   key: string,
-  field: TemplateVariableType,
-  nodeId: string
+  field: InputFieldType,
+  nodeId: string,
 ) {
   /*
 		this function receives a flow and a handleId and check if there is a connection with this handle
@@ -869,7 +966,7 @@ function isHandleConnected(
             id: nodeId,
             proxy: { id: field.proxy!.id, field: field.proxy!.field },
             inputTypes: field.input_types,
-          } as targetHandleType)
+          } as targetHandleType),
       )
     ) {
       return true;
@@ -884,7 +981,7 @@ function isHandleConnected(
             fieldName: key,
             id: nodeId,
             inputTypes: field.input_types,
-          } as targetHandleType)
+          } as targetHandleType),
       )
     ) {
       return true;
@@ -907,25 +1004,24 @@ export function generateNodeTemplate(Flow: FlowType) {
 
 export function generateNodeFromFlow(
   flow: FlowType,
-  getNodeId: (type: string) => string
+  getNodeId: (type: string) => string,
 ): NodeType {
   const { nodes } = flow.data!;
   const outputNode = cloneDeep(findLastNode(flow.data!));
   const position = getMiddlePoint(nodes);
   let data = cloneDeep(flow);
-  const id = getNodeId(outputNode?.data.type!);
+  const id = getNodeId("groupComponent");
   const newGroupNode: NodeType = {
     data: {
       id,
-      type: outputNode?.data.type!,
+      type: "GroupNode",
       node: {
-        output_types: outputNode!.data.node!.output_types,
         display_name: "Group",
         documentation: "",
-        base_classes: outputNode!.data.node!.base_classes,
         description: "",
         template: generateNodeTemplate(data),
         flow: data,
+        outputs: generateNodeOutputs(data),
       },
     },
     id,
@@ -935,10 +1031,47 @@ export function generateNodeFromFlow(
   return newGroupNode;
 }
 
+function generateNodeOutputs(flow: FlowType) {
+  const { nodes, edges } = flow.data!;
+  const outputs: Array<OutputFieldType> = [];
+  nodes.forEach((node: NodeType) => {
+    if (node.data.node?.outputs) {
+      const nodeOutputs = node.data.node.outputs;
+      nodeOutputs.forEach((output) => {
+        //filter outputs that are not connected
+        console.log(output);
+        console.log(edges);
+        if (
+          !edges.some(
+            (edge) =>
+              edge.source === node.id &&
+              (edge.data.sourceHandle as sourceHandleType).name === output.name,
+          )
+        ) {
+          outputs.push(
+            cloneDeep({
+              ...output,
+              proxy: {
+                id: node.id,
+                name: output.name,
+                nodeDisplayName:
+                  node.data.node!.display_name ?? node.data.node!.name,
+              },
+              name: node.id + "_" + output.name,
+              display_name: output.display_name,
+            }),
+          );
+        }
+      });
+    }
+  });
+  return outputs;
+}
+
 export function connectedInputNodesOnHandle(
   nodeId: string,
   handleId: string,
-  { nodes, edges }: { nodes: NodeType[]; edges: Edge[] }
+  { nodes, edges }: { nodes: NodeType[]; edges: Edge[] },
 ) {
   const connectedNodes: Array<{ name: string; id: string; isGroup: boolean }> =
     [];
@@ -975,7 +1108,7 @@ export function connectedInputNodesOnHandle(
 
 export function updateProxyIdsOnTemplate(
   template: APITemplateType,
-  idsMap: { [key: string]: string }
+  idsMap: { [key: string]: string },
 ) {
   Object.keys(template).forEach((key) => {
     if (template[key].proxy && idsMap[template[key].proxy!.id]) {
@@ -986,7 +1119,7 @@ export function updateProxyIdsOnTemplate(
 
 export function updateEdgesIds(
   edges: Edge[],
-  idsMap: { [key: string]: string }
+  idsMap: { [key: string]: string },
 ) {
   edges.forEach((edge) => {
     let targetHandle: targetHandleType = edge.data.targetHandle;
@@ -1006,6 +1139,15 @@ export function processFlowEdges(flow: FlowType) {
   }
 }
 
+export function processFlowNodes(flow: FlowType) {
+  if (!flow.data || !flow.data.nodes) return;
+  if (checkOldNodesOutput(flow.data.nodes)) {
+    const { nodes, edges } = updateNewOutput(flow.data);
+    flow.data.nodes = nodes;
+    flow.data.edges = edges;
+  }
+}
+
 export function expandGroupNode(
   id: string,
   flow: FlowType,
@@ -1013,7 +1155,8 @@ export function expandGroupNode(
   nodes: Node[],
   edges: Edge[],
   setNodes: (update: Node[] | ((oldState: Node[]) => Node[])) => void,
-  setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void
+  setEdges: (update: Edge[] | ((oldState: Edge[]) => Edge[])) => void,
+  outputs?: OutputFieldType[],
 ) {
   const idsMap = updateIds(flow!.data!);
   updateProxyIdsOnTemplate(template, idsMap);
@@ -1021,51 +1164,51 @@ export function expandGroupNode(
   updateEdgesIds(flowEdges, idsMap);
   const gNodes: NodeType[] = cloneDeep(flow?.data?.nodes!);
   const gEdges = cloneDeep(flow!.data!.edges);
-  //redirect edges to correct proxy node
-  let updatedEdges: Edge[] = [];
-  flowEdges.forEach((edge) => {
-    let newEdge = cloneDeep(edge);
-    if (newEdge.target === id) {
-      const targetHandle: targetHandleType = newEdge.data.targetHandle;
-      if (targetHandle.proxy) {
-        let type = targetHandle.type;
-        let field = targetHandle.proxy.field;
-        let proxyId = targetHandle.proxy.id;
-        let inputTypes = targetHandle.inputTypes;
-        let node: NodeType = gNodes.find((n) => n.id === proxyId)!;
-        if (node) {
-          newEdge.target = proxyId;
-          let newTargetHandle: targetHandleType = {
-            fieldName: field,
-            type,
-            id: proxyId,
-            inputTypes: inputTypes,
-          };
-          if (node.data.node?.flow) {
-            newTargetHandle.proxy = {
-              field: node.data.node.template[field].proxy?.field!,
-              id: node.data.node.template[field].proxy?.id!,
-            };
-          }
-          newEdge.data.targetHandle = newTargetHandle;
-          newEdge.targetHandle = scapedJSONStringfy(newTargetHandle);
-        }
-      }
-    }
-    if (newEdge.source === id) {
-      const lastNode = cloneDeep(findLastNode(flow!.data!));
-      newEdge.source = lastNode!.id;
-      let newSourceHandle: sourceHandleType = scapeJSONParse(
-        newEdge.sourceHandle!
-      );
-      newSourceHandle.id = lastNode!.id;
-      newEdge.data.sourceHandle = newSourceHandle;
-      newEdge.sourceHandle = scapedJSONStringfy(newSourceHandle);
-    }
-    if (edge.target === id || edge.source === id) {
-      updatedEdges.push(newEdge);
-    }
-  });
+  // //redirect edges to correct proxy node
+  // let updatedEdges: Edge[] = [];
+  // flowEdges.forEach((edge) => {
+  //   let newEdge = cloneDeep(edge);
+  //   if (newEdge.target === id) {
+  //     const targetHandle: targetHandleType = newEdge.data.targetHandle;
+  //     if (targetHandle.proxy) {
+  //       let type = targetHandle.type;
+  //       let field = targetHandle.proxy.field;
+  //       let proxyId = targetHandle.proxy.id;
+  //       let inputTypes = targetHandle.inputTypes;
+  //       let node: NodeType = gNodes.find((n) => n.id === proxyId)!;
+  //       if (node) {
+  //         newEdge.target = proxyId;
+  //         let newTargetHandle: targetHandleType = {
+  //           fieldName: field,
+  //           type,
+  //           id: proxyId,
+  //           inputTypes: inputTypes,
+  //         };
+  //         if (node.data.node?.flow) {
+  //           newTargetHandle.proxy = {
+  //             field: node.data.node.template[field].proxy?.field!,
+  //             id: node.data.node.template[field].proxy?.id!,
+  //           };
+  //         }
+  //         newEdge.data.targetHandle = newTargetHandle;
+  //         newEdge.targetHandle = scapedJSONStringfy(newTargetHandle);
+  //       }
+  //     }
+  //   }
+  //   if (newEdge.source === id) {
+  //     const lastNode = cloneDeep(findLastNode(flow!.data!));
+  //     newEdge.source = lastNode!.id;
+  //     let newSourceHandle: sourceHandleType = scapeJSONParse(
+  //       newEdge.sourceHandle!,
+  //     );
+  //     newSourceHandle.id = lastNode!.id;
+  //     newEdge.data.sourceHandle = newSourceHandle;
+  //     newEdge.sourceHandle = scapedJSONStringfy(newSourceHandle);
+  //   }
+  //   if (edge.target === id || edge.source === id) {
+  //     updatedEdges.push(newEdge);
+  //   }
+  // });
   //update template values
   Object.keys(template).forEach((key) => {
     if (template[key].proxy) {
@@ -1100,12 +1243,24 @@ export function expandGroupNode(
       }
     }
   });
-
+  outputs?.forEach((output) => {
+    let nodeIndex = gNodes.findIndex((n) => n.id === output.proxy!.id);
+    if (nodeIndex !== -1) {
+      if (gNodes[nodeIndex].data.node?.outputs) {
+        const nodeOutputIndex = gNodes[nodeIndex].data.node!.outputs!.findIndex(
+          (o) => o.name === output.proxy?.name,
+        );
+        if (nodeOutputIndex !== -1 && output.selected) {
+          gNodes[nodeIndex].data.node!.outputs![nodeOutputIndex].selected =
+            output.selected;
+        }
+      }
+    }
+  });
   const filteredNodes = [...nodes.filter((n) => n.id !== id), ...gNodes];
   const filteredEdges = [
     ...edges.filter((e) => e.target !== id && e.source !== id),
     ...gEdges,
-    ...updatedEdges,
   ];
   setNodes(filteredNodes);
   setEdges(filteredEdges);
@@ -1113,7 +1268,7 @@ export function expandGroupNode(
 
 export function getGroupStatus(
   flow: FlowType,
-  ssData: { [key: string]: { valid: boolean; params: string } }
+  ssData: { [key: string]: { valid: boolean; params: string } },
 ) {
   let status = { valid: true, params: SUCCESS_BUILD };
   const { nodes } = flow.data!;
@@ -1132,7 +1287,7 @@ export function getGroupStatus(
 
 export function createFlowComponent(
   nodeData: NodeDataType,
-  version: string
+  version: string,
 ): FlowType {
   const flowNode: FlowType = {
     data: {
@@ -1168,7 +1323,7 @@ export function downloadNode(NodeFLow: FlowType) {
 
 export function updateComponentNameAndType(
   data: any,
-  component: NodeDataType
+  component: NodeDataType,
 ) {}
 
 export function removeFileNameFromComponents(flow: FlowType) {
@@ -1242,7 +1397,7 @@ export function extractFieldsFromComponenents(data: APIObjectType) {
 export function downloadFlow(
   flow: FlowType,
   flowName: string,
-  flowDescription?: string
+  flowDescription?: string,
 ) {
   let clonedFlow = cloneDeep(flow);
   removeFileNameFromComponents(clonedFlow);
@@ -1252,7 +1407,7 @@ export function downloadFlow(
       ...clonedFlow,
       name: flowName,
       description: flowDescription,
-    })
+    }),
   )}`;
 
   // create a link element and set its properties
@@ -1267,7 +1422,7 @@ export function downloadFlow(
 export function downloadFlows() {
   downloadFlowsFromDatabase().then((flows) => {
     const jsonString = `data:text/json;chatset=utf-8,${encodeURIComponent(
-      JSON.stringify(flows)
+      JSON.stringify(flows),
     )}`;
 
     // create a link element and set its properties
@@ -1291,7 +1446,7 @@ export function getRandomDescription(): string {
 export const createNewFlow = (
   flowData: ReactFlowJsonObject,
   flow: FlowType,
-  folderId: string
+  folderId: string,
 ) => {
   return {
     description: flow?.description ?? getRandomDescription(),
@@ -1300,6 +1455,7 @@ export const createNewFlow = (
     id: "",
     is_component: flow?.is_component ?? false,
     folder_id: folderId,
+    endpoint_name: flow?.endpoint_name ?? undefined,
   };
 };
 
@@ -1333,3 +1489,25 @@ export function updateGroupRecursion(groupNode: NodeType, edges: Edge[]) {
     updateEdgesIds(flowEdges, idsMap);
   }
 }
+
+export function getGroupOutputNodeId(
+  flow: FlowType,
+  p_name: string,
+  p_node_id: string,
+) {
+  let node: NodeType | undefined = flow.data?.nodes.find(
+    (n) => n.id === p_node_id,
+  );
+  if (!node) return;
+  if (node.data.node?.flow) {
+    let output = node.data.node.outputs?.find((o) => o.name === p_name);
+    if (output && output.proxy) {
+      return getGroupOutputNodeId(
+        node.data.node.flow,
+        output.proxy.name,
+        output.proxy.id,
+      );
+    }
+  }
+  return { id: node.id, outputName: p_name };
+}
diff --git a/src/frontend/src/utils/storeUtils.ts b/src/frontend/src/utils/storeUtils.ts
index 637e4331a..c8391b211 100644
--- a/src/frontend/src/utils/storeUtils.ts
+++ b/src/frontend/src/utils/storeUtils.ts
@@ -7,7 +7,7 @@ export default function cloneFLowWithParent(
   flow: FlowType,
   parent: string,
   is_component: boolean,
-  keepId = false
+  keepId = false,
 ) {
   let childFLow = cloneDeep(flow);
   childFLow.parent = parent;
diff --git a/src/frontend/src/utils/styleUtils.ts b/src/frontend/src/utils/styleUtils.ts
index 43e92bfe2..1bc4818b5 100644
--- a/src/frontend/src/utils/styleUtils.ts
+++ b/src/frontend/src/utils/styleUtils.ts
@@ -26,6 +26,7 @@ import {
   ChevronsUpDownIcon,
   Circle,
   CircleDot,
+  CircleOff,
   Clipboard,
   Code,
   Code2,
@@ -34,6 +35,7 @@ import {
   Compass,
   Copy,
   Cpu,
+  CpuIcon,
   Database,
   Delete,
   Dot,
@@ -80,6 +82,7 @@ import {
   Loader2,
   Lock,
   LogIn,
+  LogOut,
   LucideSend,
   Maximize2,
   Menu,
@@ -106,6 +109,7 @@ import {
   PocketKnife,
   Redo,
   RefreshCcw,
+  RefreshCcwDot,
   Repeat,
   RotateCcw,
   Save,
@@ -196,12 +200,14 @@ import { ShareIcon } from "../icons/Share";
 import { Share2Icon } from "../icons/Share2";
 import SvgSlackIcon from "../icons/Slack/SlackIcon";
 import { Streamlit } from "../icons/Streamlit";
+import { UpstashSvgIcon } from "../icons/Upstash";
 import { VectaraIcon } from "../icons/VectaraIcon";
 import { VertexAIIcon } from "../icons/VertexAI";
 import { WeaviateIcon } from "../icons/Weaviate";
 import SvgWikipedia from "../icons/Wikipedia/Wikipedia";
 import SvgWolfram from "../icons/Wolfram/Wolfram";
 import { HackerNewsIcon } from "../icons/hackerNews";
+import { MistralIcon } from "../icons/mistral";
 import { SupabaseIcon } from "../icons/supabase";
 import { iconsType } from "../types/components";
 
@@ -242,12 +248,11 @@ export const gradients = [
 export const nodeColors: { [char: string]: string } = {
   inputs: "#10B981",
   outputs: "#AA2411",
-  data: "#4367BF",
+  data: "#198BF6",
   prompts: "#4367BF",
-  models: "#6344BE",
+  models: "#ab11ab",
   model_specs: "#6344BE",
   chains: "#FE7500",
-  Document: "#7AAE42",
   list: "#9AAE42",
   agents: "#903BBE",
   tools: "#FF3434",
@@ -267,13 +272,19 @@ export const nodeColors: { [char: string]: string } = {
   experimental: "#E6277A",
   langchain_utilities: "#31A3CC",
   output_parsers: "#E6A627",
-  str: "#31a3cc",
-  Text: "#31a3cc",
+  // custom_components: "#ab11ab",
   retrievers: "#e6b25a",
+  //
+  str: "#4F46E5",
+  Text: "#4F46E5",
   unknown: "#9CA3AF",
-  custom_components: "#ab11ab",
-  Records: "#31a3cc",
-  Record: "#31a3cc",
+  Document: "#65a30d",
+  Data: "#dc2626",
+  Message: "#4f46e5",
+  Prompt: "#7c3aed",
+  Embeddings: "#10b981",
+  BaseLanguageModel: "#c026d3",
+  LanguageModel: "#c026d3",
 };
 
 export const nodeNames: { [char: string]: string } = {
@@ -325,7 +336,7 @@ export const nodeIconsLucide: iconsType = {
   Azure: AzureIcon,
   OllamaEmbeddings: OllamaIcon,
   ChatOllamaModel: OllamaIcon,
-  Faiss: MetaIcon,
+  FAISS: MetaIcon,
   FaissSearch: MetaIcon,
   LangChain: LangChainIcon,
   AzureOpenAiModel: AzureIcon,
@@ -423,6 +434,7 @@ export const nodeIconsLucide: iconsType = {
   group_components: GradientUngroup,
   custom: Edit,
   Trash2,
+  CircleOff,
   Boxes,
   Network,
   XCircle,
@@ -443,6 +455,7 @@ export const nodeIconsLucide: iconsType = {
   ChevronLeft,
   SlidersHorizontal,
   Palette,
+  RefreshCcwDot,
   FolderUp,
   Blocks,
   ChevronDown,
@@ -504,6 +517,7 @@ export const nodeIconsLucide: iconsType = {
   Ungroup,
   BookMarked,
   Minus,
+  LogOut,
   Square,
   Minimize2,
   Maximize2,
@@ -548,4 +562,7 @@ export const nodeIconsLucide: iconsType = {
   PaperclipIcon,
   Settings,
   Streamlit,
+  MistralAI: MistralIcon,
+  Upstash: UpstashSvgIcon,
+  PGVector: CpuIcon,
 };
diff --git a/src/frontend/src/utils/utils.ts b/src/frontend/src/utils/utils.ts
index edc7799b6..7ec4ae6f9 100644
--- a/src/frontend/src/utils/utils.ts
+++ b/src/frontend/src/utils/utils.ts
@@ -2,8 +2,8 @@ import { ColDef, ColGroupDef } from "ag-grid-community";
 import clsx, { ClassValue } from "clsx";
 import { twMerge } from "tailwind-merge";
 import TableAutoCellRender from "../components/tableComponent/components/tableAutoCellRender";
-import { MODAL_CLASSES } from "../constants/constants";
-import { APIDataType, TemplateVariableType } from "../types/api";
+import { MESSAGES_TABLE_ORDER, MODAL_CLASSES } from "../constants/constants";
+import { APIDataType, InputFieldType, VertexDataTypeAPI } from "../types/api";
 import {
   groupedObjType,
   nodeGroupedObjType,
@@ -11,6 +11,7 @@ import {
 } from "../types/components";
 import { NodeType } from "../types/flow";
 import { FlowState } from "../types/tabs";
+import { isErrorLog } from "../types/utils/typeCheckingUtils";
 
 export function classNames(...classes: Array<string>): string {
   return classes.filter(Boolean).join(" ");
@@ -56,7 +57,7 @@ export function normalCaseToSnakeCase(str: string): string {
 
 export function toTitleCase(
   str: string | undefined,
-  isNodeField?: boolean
+  isNodeField?: boolean,
 ): string {
   if (!str) return "";
   let result = str
@@ -65,7 +66,7 @@ export function toTitleCase(
       if (isNodeField) return word;
       if (index === 0) {
         return checkUpperWords(
-          word[0].toUpperCase() + word.slice(1).toLowerCase()
+          word[0].toUpperCase() + word.slice(1).toLowerCase(),
         );
       }
       return checkUpperWords(word.toLowerCase());
@@ -78,7 +79,7 @@ export function toTitleCase(
       if (isNodeField) return word;
       if (index === 0) {
         return checkUpperWords(
-          word[0].toUpperCase() + word.slice(1).toLowerCase()
+          word[0].toUpperCase() + word.slice(1).toLowerCase(),
         );
       }
       return checkUpperWords(word.toLowerCase());
@@ -182,7 +183,7 @@ export function checkLocalStorageKey(key: string): boolean {
 
 export function IncrementObjectKey(
   object: object,
-  key: string
+  key: string,
 ): { newKey: string; increment: number } {
   let count = 1;
   const type = removeCountFromString(key);
@@ -217,7 +218,7 @@ export function groupByFamily(
   data: APIDataType,
   baseClasses: string,
   left: boolean,
-  flow?: NodeType[]
+  flow?: NodeType[],
 ): groupedObjType[] {
   const baseClassesSet = new Set(baseClasses.split("\n"));
   let arrOfPossibleInputs: Array<{
@@ -235,7 +236,7 @@ export function groupByFamily(
   let checkedNodes = new Map();
   const excludeTypes = new Set(["bool", "float", "code", "file", "int"]);
 
-  const checkBaseClass = (template: TemplateVariableType) => {
+  const checkBaseClass = (template: InputFieldType) => {
     return (
       template?.type &&
       template?.show &&
@@ -243,7 +244,7 @@ export function groupByFamily(
         baseClassesSet.has(template.type)) ||
         (template?.input_types &&
           template?.input_types.some((inputType) =>
-            baseClassesSet.has(inputType)
+            baseClassesSet.has(inputType),
           )))
     );
   };
@@ -262,8 +263,8 @@ export function groupByFamily(
           Object.values(nodeData.node!.template).some(checkBaseClass),
         hasBaseClassInBaseClasses:
           foundNode?.hasBaseClassInBaseClasses ||
-          nodeData.node!.base_classes.some((baseClass) =>
-            baseClassesSet.has(baseClass)
+          nodeData.node!.base_classes?.some((baseClass) =>
+            baseClassesSet.has(baseClass),
           ), //seta como anterior ou verifica se o node tem base class
         displayName: nodeData.node?.display_name,
       });
@@ -280,10 +281,10 @@ export function groupByFamily(
       if (!foundNode) {
         foundNode = {
           hasBaseClassInTemplate: Object.values(node!.template).some(
-            checkBaseClass
+            checkBaseClass,
           ),
           hasBaseClassInBaseClasses: node!.base_classes?.some((baseClass) =>
-            baseClassesSet.has(baseClass)
+            baseClassesSet.has(baseClass),
           ),
           displayName: node?.display_name,
         };
@@ -355,7 +356,7 @@ export function isTimeStampString(str: string): boolean {
 export function extractColumnsFromRows(
   rows: object[],
   mode: "intersection" | "union",
-  excludeColumns?: Array<string>
+  excludeColumns?: Array<string>,
 ): (ColDef<any> | ColGroupDef<any>)[] {
   let columnsKeys: { [key: string]: ColDef<any> | ColGroupDef<any> } = {};
   if (rows.length === 0) {
@@ -412,3 +413,53 @@ export function isThereModal(): boolean {
   const modal = document.body.getElementsByClassName(MODAL_CLASSES);
   return modal.length > 0;
 }
+
+export function messagesSorter(a: any, b: any) {
+  const indexA = MESSAGES_TABLE_ORDER.indexOf(a.field);
+  const indexB = MESSAGES_TABLE_ORDER.indexOf(b.field);
+
+  // If the field is not in the MESSAGES_TABLE_ORDER, we can place it at the end.
+  const orderA = indexA === -1 ? MESSAGES_TABLE_ORDER.length : indexA;
+  const orderB = indexB === -1 ? MESSAGES_TABLE_ORDER.length : indexB;
+
+  return orderA - orderB;
+}
+
+export const logHasMessage = (
+  data: VertexDataTypeAPI,
+  outputName: string | undefined,
+) => {
+  if (!outputName) return;
+  const logs = data?.logs[outputName];
+  if (Array.isArray(logs) && logs.length > 1) {
+    return logs.some((log) => log.message);
+  } else {
+    return logs?.message;
+  }
+};
+
+export const logTypeIsUnknown = (
+  data: VertexDataTypeAPI,
+  outputName: string | undefined,
+) => {
+  if (!outputName) return;
+  const logs = data?.logs[outputName];
+  if (Array.isArray(logs) && logs.length > 1) {
+    return logs.some((log) => log.type === "unknown");
+  } else {
+    return logs?.type === "unknown";
+  }
+};
+
+export const logTypeIsError = (
+  data: VertexDataTypeAPI,
+  outputName: string | undefined,
+) => {
+  if (!outputName) return;
+  const logs = data?.logs[outputName];
+  if (Array.isArray(logs) && logs.length > 1) {
+    return logs.some((log) => isErrorLog(log));
+  } else {
+    return isErrorLog(logs);
+  }
+};
diff --git a/src/frontend/tailwind.config.js b/src/frontend/tailwind.config.mjs
similarity index 94%
rename from src/frontend/tailwind.config.js
rename to src/frontend/tailwind.config.mjs
index 02ba4460b..4b1df3063 100644
--- a/src/frontend/tailwind.config.js
+++ b/src/frontend/tailwind.config.mjs
@@ -1,10 +1,13 @@
 /** @type {import('tailwindcss').Config} */
-const { fontFamily } = require("tailwindcss/defaultTheme");
+import { fontFamily } from "tailwindcss/defaultTheme";
+import tailwindcssForms from "@tailwindcss/forms";
+import tailwindcssAnimate from "tailwindcss-animate";
+import tailwindcssTypography from "@tailwindcss/typography";
+import tailwindcssDottedBackground from "tailwindcss-dotted-background";
 
 import plugin from "tailwindcss/plugin";
 
-// ! Check if removing the other module.exports made sense
-module.exports = {
+const config = {
   variants: {
     extend: {
       display: ["group-hover"],
@@ -155,8 +158,8 @@ module.exports = {
   },
 
   plugins: [
-    require("tailwindcss-animate"),
-    require("@tailwindcss/forms")({
+    tailwindcssAnimate,
+    tailwindcssForms({
       strategy: "class", // only generate classes
     }),
     plugin(function ({ addUtilities }) {
@@ -245,8 +248,9 @@ module.exports = {
         },
       });
     }),
-    require("@tailwindcss/typography"),
-    require("daisyui"),
-    require("tailwindcss-dotted-background"),
+    tailwindcssTypography,
+    tailwindcssDottedBackground,
   ],
 };
+
+export default config;
\ No newline at end of file
diff --git a/src/frontend/tests/end-to-end/basicExamples.spec.ts b/src/frontend/tests/end-to-end/basicExamples.spec.ts
new file mode 100644
index 000000000..e103fff30
--- /dev/null
+++ b/src/frontend/tests/end-to-end/basicExamples.spec.ts
@@ -0,0 +1,452 @@
+import { expect, test } from "@playwright/test";
+import path from "path";
+
+test("Basic Prompting (Hello, World)", async ({ page }) => {
+  await page.goto("/");
+  await page.waitForTimeout(2000);
+
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.getByText("New Project", { exact: true }).click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
+
+  await page.getByRole("heading", { name: "Basic Prompting" }).click();
+  await page.waitForTimeout(1000);
+
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+
+  if (!process.env.OPENAI_API_KEY) {
+    //You must set the OPENAI_API_KEY on .env file to run this test
+    expect(false).toBe(true);
+  }
+
+  await page
+    .getByTestId("popover-anchor-input-openai_api_key")
+    .fill(process.env.OPENAI_API_KEY ?? "");
+
+  await page.getByTestId("button_run_chat output").click();
+  await page.waitForTimeout(1000);
+  await page.getByText("built successfully").last().click({
+    timeout: 15000,
+  });
+
+  await page.getByText("Playground", { exact: true }).click();
+  await page
+    .getByText("No input message provided.", { exact: true })
+    .last()
+    .isVisible();
+  await page
+    .getByPlaceholder("Send a message...")
+    .last()
+    .fill("Say hello as a pirate");
+  await page.getByTestId("icon-LucideSend").last().click();
+  await page.waitForTimeout(3000);
+
+  await page.getByText("Ahoy").last().isVisible();
+  await page.getByText("Default Session").last().click();
+
+  await page.getByText("timestamp", { exact: true }).last().isVisible();
+  await page.getByText("text", { exact: true }).last().isVisible();
+  await page.getByText("sender", { exact: true }).last().isVisible();
+  await page.getByText("sender_name", { exact: true }).last().isVisible();
+  await page.getByText("session_id", { exact: true }).last().isVisible();
+  await page.getByText("files", { exact: true }).last().isVisible();
+
+  await page.getByRole("gridcell").last().isVisible();
+  await page.getByTestId("icon-Trash2").first().click();
+  await page.getByPlaceholder("Send a message...").last().isVisible();
+});
+
+test("Memory Chatbot", async ({ page }) => {
+  await page.goto("/");
+  await page.waitForTimeout(2000);
+
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.getByText("New Project", { exact: true }).click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
+
+  await page.getByRole("heading", { name: "Memory Chatbot" }).click();
+  await page.waitForTimeout(1000);
+
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+
+  if (!process.env.OPENAI_API_KEY) {
+    //You must set the OPENAI_API_KEY on .env file to run this test
+    expect(false).toBe(true);
+  }
+
+  await page
+    .getByTestId("popover-anchor-input-openai_api_key")
+    .fill(process.env.OPENAI_API_KEY ?? "");
+
+  await page.getByTestId("button_run_chat output").click();
+  await page.waitForTimeout(1000);
+  await page.getByText("built successfully").last().click({
+    timeout: 15000,
+  });
+
+  await page.getByText("Playground", { exact: true }).click();
+  await page
+    .getByText("No input message provided.", { exact: true })
+    .last()
+    .isVisible();
+  await page
+    .getByPlaceholder("Send a message...")
+    .last()
+    .fill("Remember that I'm a lion");
+  await page.getByTestId("icon-LucideSend").last().click();
+  await page.waitForTimeout(3000);
+  await page
+    .getByPlaceholder("Send a message...")
+    .last()
+    .fill("try reproduce the sound I made in words");
+  await page.getByTestId("icon-LucideSend").last().click();
+  await page.waitForTimeout(3000);
+  await page.getByText("roar").last().isVisible();
+  await page.getByText("Default Session").last().click();
+
+  await page.getByText("timestamp", { exact: true }).last().isVisible();
+  await page.getByText("text", { exact: true }).last().isVisible();
+  await page.getByText("sender", { exact: true }).last().isVisible();
+  await page.getByText("sender_name", { exact: true }).last().isVisible();
+  await page.getByText("session_id", { exact: true }).last().isVisible();
+  await page.getByText("files", { exact: true }).last().isVisible();
+
+  await page.getByRole("gridcell").last().isVisible();
+  await page.getByTestId("icon-Trash2").first().click();
+  await page.getByPlaceholder("Send a message...").last().isVisible();
+});
+
+test("Document QA", async ({ page }) => {
+  await page.goto("/");
+  await page.waitForTimeout(2000);
+
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.getByText("New Project", { exact: true }).click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
+
+  await page.getByRole("heading", { name: "Document QA" }).click();
+  await page.waitForTimeout(1000);
+
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+
+  if (!process.env.OPENAI_API_KEY) {
+    //You must set the OPENAI_API_KEY on .env file to run this test
+    expect(false).toBe(true);
+  }
+
+  await page
+    .getByTestId("popover-anchor-input-openai_api_key")
+    .fill(process.env.OPENAI_API_KEY ?? "");
+
+  const fileChooserPromise = page.waitForEvent("filechooser");
+  await page.getByTestId("icon-FileSearch2").click();
+  const fileChooser = await fileChooserPromise;
+  await fileChooser.setFiles(path.join(__dirname, "/assets/test_file.txt"));
+  await page.getByText("test_file.txt").isVisible();
+
+  await page.waitForTimeout(2000);
+
+  await page.getByTestId("button_run_chat output").click();
+  await page.getByText("built successfully").last().click({
+    timeout: 15000,
+  });
+
+  await page.getByText("Playground", { exact: true }).click();
+  await page
+    .getByText("No input message provided.", { exact: true })
+    .last()
+    .isVisible();
+
+  await page
+    .getByPlaceholder("Send a message...")
+    .last()
+    .fill("whats the text in the file?");
+  await page.getByTestId("icon-LucideSend").last().click();
+
+  await page.waitForTimeout(3000);
+
+  await page.getByText("this is a test file").last().isVisible();
+
+  await page.getByText("Default Session").last().click();
+
+  await page.getByText("timestamp", { exact: true }).last().isVisible();
+  await page.getByText("text", { exact: true }).last().isVisible();
+  await page.getByText("sender", { exact: true }).last().isVisible();
+  await page.getByText("sender_name", { exact: true }).last().isVisible();
+  await page.getByText("session_id", { exact: true }).last().isVisible();
+  await page.getByText("files", { exact: true }).last().isVisible();
+
+  await page.getByRole("gridcell").last().isVisible();
+  await page.getByTestId("icon-Trash2").first().click();
+  await page.getByPlaceholder("Send a message...").last().isVisible();
+});
+
+test("Blog Writer", async ({ page }) => {
+  await page.goto("/");
+  await page.waitForTimeout(2000);
+
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.getByText("New Project", { exact: true }).click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
+
+  await page.getByRole("heading", { name: "Blog Writer" }).click();
+  await page.waitForTimeout(1000);
+
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+
+  if (!process.env.OPENAI_API_KEY) {
+    //You must set the OPENAI_API_KEY on .env file to run this test
+    expect(false).toBe(true);
+  }
+
+  await page
+    .getByTestId("popover-anchor-input-openai_api_key")
+    .fill(process.env.OPENAI_API_KEY ?? "");
+
+  await page
+    .getByTestId("input-list-input_urls-0")
+    .nth(0)
+    .fill(
+      "https://www.natgeokids.com/uk/discover/animals/sea-life/turtle-facts/",
+    );
+  await page
+    .getByTestId("input-list-input_urls-0")
+    .nth(1)
+    .fill("https://www.originaldiving.com/blog/top-ten-turtle-facts");
+
+  await page
+    .getByTestId("textarea-input_value")
+    .nth(0)
+    .fill(
+      "Use the references above for style to write a new blog/tutorial about turtles. Suggest non-covered topics.",
+    );
+
+  await page.getByTestId("button_run_chat output").click();
+  await page.waitForTimeout(5000);
+  await page.getByText("built successfully").last().click({
+    timeout: 15000,
+  });
+
+  await page.getByText("Playground", { exact: true }).click();
+  await page
+    .getByPlaceholder(
+      "No chat input variables found. Click to run your flow.",
+      { exact: true },
+    )
+    .last()
+    .isVisible();
+
+  await page.waitForTimeout(3000);
+
+  await page.getByText("turtles").last().isVisible();
+  await page.getByText("sea").last().isVisible();
+  await page.getByText("survival").last().isVisible();
+
+  await page.getByText("Instructions").last().click();
+
+  const value = await page
+    .getByPlaceholder("Enter text...")
+    .last()
+    .inputValue();
+
+  expect(value).toBe(
+    "Use the references above for style to write a new blog/tutorial about turtles. Suggest non-covered topics.",
+  );
+
+  await page.getByTestId("icon-ExternalLink").last().click();
+
+  const count = await page
+    .getByText(
+      "Use the references above for style to write a new blog/tutorial about turtles. Suggest non-covered topics.",
+    )
+    .count();
+
+  if (count <= 1) {
+    expect(false).toBe(true);
+  }
+});
+
+test("Vector Store RAG", async ({ page }) => {
+  await page.goto("/");
+  await page.waitForTimeout(2000);
+
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.getByText("New Project", { exact: true }).click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
+
+  await page.getByRole("heading", { name: "Vector Store RAG" }).click();
+  await page.waitForTimeout(1000);
+
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+
+  if (
+    !process.env.OPENAI_API_KEY ||
+    !process.env.ASTRA_DB_API_ENDPOINT ||
+    !process.env.ASTRA_DB_APPLICATION_TOKEN
+  ) {
+    //You must set the OPENAI_API_KEY, ASTRA_DB_API_ENDPOINT and ASTRA_DB_APPLICATION_TOKEN on .env file to run this test
+    expect(false).toBe(true);
+  }
+
+  await page
+    .getByTestId("popover-anchor-input-openai_api_key")
+    .nth(0)
+    .fill(process.env.OPENAI_API_KEY ?? "");
+
+  await page
+    .getByTestId("popover-anchor-input-openai_api_key")
+    .nth(1)
+    .fill(process.env.OPENAI_API_KEY ?? "");
+
+  await page
+    .getByTestId("popover-anchor-input-openai_api_key")
+    .nth(2)
+    .fill(process.env.OPENAI_API_KEY ?? "");
+
+  await page
+    .getByTestId("popover-anchor-input-token")
+    .nth(0)
+    .fill(process.env.ASTRA_DB_APPLICATION_TOKEN ?? "");
+  await page
+    .getByTestId("popover-anchor-input-token")
+    .nth(1)
+    .fill(process.env.ASTRA_DB_APPLICATION_TOKEN ?? "");
+
+  await page
+    .getByTestId("popover-anchor-input-api_endpoint")
+    .nth(0)
+    .fill(process.env.ASTRA_DB_API_ENDPOINT ?? "");
+  await page
+    .getByTestId("popover-anchor-input-api_endpoint")
+    .nth(1)
+    .fill(process.env.ASTRA_DB_API_ENDPOINT ?? "");
+
+  const fileChooserPromise = page.waitForEvent("filechooser");
+  await page.getByTestId("icon-FileSearch2").last().click();
+  const fileChooser = await fileChooserPromise;
+  await fileChooser.setFiles(path.join(__dirname, "/assets/test_file.txt"));
+  await page.getByText("test_file.txt").isVisible();
+  await page.waitForTimeout(2000);
+
+  await page.getByTestId("button_run_astra db vector store").first().click();
+  await page
+    .getByText("Astra DB Vector Store built successfully")
+    .last()
+    .click({
+      timeout: 15000,
+    });
+
+  await page.getByTestId("button_run_chat output").click();
+  await page.getByText("built successfully").last().click({
+    timeout: 15000,
+  });
+
+  await page.getByText("Playground", { exact: true }).click();
+
+  await page.getByPlaceholder("Send a message...").last().fill("hello");
+
+  await page.getByTestId("icon-LucideSend").last().click();
+
+  await page
+    .getByText("This is a test file.", { exact: true })
+    .last()
+    .isVisible();
+
+  await page.getByText("Extracted Chunks", { exact: true }).last().click();
+
+  const inputChunksValues = await page.getByPlaceholder("Empty").inputValue();
+  expect(inputChunksValues).toBe(
+    "this is a test file---this is a test file---this is a test file---this is a test file",
+  );
+
+  await page.getByTestId("icon-ExternalLink").last().click();
+  await page.getByPlaceholder("Send a message...").last().isHidden();
+  await page.getByText("Memories", { exact: true }).last().click();
+  await page.getByText("Default Session").last().click();
+
+  await page.getByText("timestamp", { exact: true }).last().isVisible();
+  await page.getByText("text", { exact: true }).last().isVisible();
+  await page.getByText("sender", { exact: true }).last().isVisible();
+  await page.getByText("sender_name", { exact: true }).last().isVisible();
+  await page.getByText("session_id", { exact: true }).last().isVisible();
+  await page.getByText("files", { exact: true }).last().isVisible();
+
+  await page.getByRole("gridcell").last().isVisible();
+  await page.getByTestId("icon-Trash2").first().click();
+  await page.getByPlaceholder("Send a message...").last().isVisible();
+});
diff --git a/src/frontend/tests/end-to-end/chatInputOutput.spec.ts b/src/frontend/tests/end-to-end/chatInputOutput.spec.ts
index aa43bfa8a..e71178040 100644
--- a/src/frontend/tests/end-to-end/chatInputOutput.spec.ts
+++ b/src/frontend/tests/end-to-end/chatInputOutput.spec.ts
@@ -24,7 +24,7 @@ test("chat_io_teste", async ({ page }) => {
 
   const jsonContent = readFileSync(
     "src/frontend/tests/end-to-end/assets/ChatTest.json",
-    "utf-8"
+    "utf-8",
   );
 
   await page.getByTestId("blank-flow").click();
@@ -62,7 +62,7 @@ test("chat_io_teste", async ({ page }) => {
   // Click and hold on the first element
   await page
     .locator(
-      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div[2]/div/div[2]/div[8]/button/div/div'
+      '//*[@id="react-flow-id"]/div/div[1]/div/div/div[2]/div[2]/div/div[2]/div[9]/button/div[1]',
     )
     .hover();
   await page.mouse.down();
@@ -70,7 +70,7 @@ test("chat_io_teste", async ({ page }) => {
   // Move to the second element
   await page
     .locator(
-      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div[1]/div/div[2]/div[3]/div/button/div/div'
+      '//*[@id="react-flow-id"]/div/div[1]/div/div/div[2]/div[1]/div/div[2]/div[3]/div/button/div[1]',
     )
     .hover();
 
diff --git a/src/frontend/tests/end-to-end/chatInputOutputUser.spec.ts b/src/frontend/tests/end-to-end/chatInputOutputUser.spec.ts
index 9393a2cb7..012259747 100644
--- a/src/frontend/tests/end-to-end/chatInputOutputUser.spec.ts
+++ b/src/frontend/tests/end-to-end/chatInputOutputUser.spec.ts
@@ -58,7 +58,7 @@ test("user must interact with chat with Input/Output", async ({ page }) => {
     .getByTestId("textarea-input_value")
     .nth(1)
     .fill(
-      "testtesttesttesttesttestte;.;.,;,.;,.;.,;,..,;;;;;;;;;;;;;;;;;;;;;,;.;,.;,.,;.,;.;.,~~çççççççççççççççççççççççççççççççççççççççisdajfdasiopjfaodisjhvoicxjiovjcxizopjviopasjioasfhjaiohf23432432432423423sttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttestççççççççççççççççççççççççççççççççç,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,!"
+      "testtesttesttesttesttestte;.;.,;,.;,.;.,;,..,;;;;;;;;;;;;;;;;;;;;;,;.;,.;,.,;.,;.;.,~~çççççççççççççççççççççççççççççççççççççççisdajfdasiopjfaodisjhvoicxjiovjcxizopjviopasjioasfhjaiohf23432432432423423sttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttestççççççççççççççççççççççççççççççççç,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,!",
     );
   await page.getByText("Playground", { exact: true }).last().click();
   await page.getByTestId("icon-LucideSend").click();
@@ -68,12 +68,17 @@ test("user must interact with chat with Input/Output", async ({ page }) => {
   await page.getByTestId("showsender_name").click();
   await page.getByText("Save Changes", { exact: true }).click();
 
+  await page.getByText("Chat Output", { exact: true }).click();
+  await page.getByTestId("advanced-button-modal").click();
+  await page.getByTestId("showsender_name").click();
+  await page.getByText("Save Changes", { exact: true }).click();
+
   await page
-    .getByTestId("popover-anchor-input-sender_name")
+    .getByTestId("textarea-sender_name")
     .nth(1)
     .fill("TestSenderNameUser");
   await page
-    .getByTestId("popover-anchor-input-sender_name")
+    .getByTestId("textarea-sender_name")
     .nth(0)
     .fill("TestSenderNameAI");
 
@@ -94,9 +99,9 @@ test("user must interact with chat with Input/Output", async ({ page }) => {
     await page
       .getByText(
         "testtesttesttesttesttestte;.;.,;,.;,.;.,;,..,;;;;;;;;;;;;;;;;;;;;;,;.;,.;,.,;.,;.;.,~~çççççççççççççççççççççççççççççççççççççççisdajfdasiopjfaodisjhvoicxjiovjcxizopjviopasjioasfhjaiohf23432432432423423sttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttesttestççççççççççççççççççççççççççççççççç,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,.,!",
-        { exact: true }
+        { exact: true },
       )
-      .isVisible()
+      .isVisible(),
   );
 });
 
@@ -145,9 +150,9 @@ test("user must be able to see output inspection", async ({ page }) => {
 
   await page.getByTestId("button_run_chat output").last().click();
 
-  await page.waitForTimeout(2000);
+  await page.waitForTimeout(5000);
 
-  await page.getByTestId("icon-ScanEye").last().click();
+  await page.getByTestId("icon-ScanEye").nth(4).click();
 
   await page.getByText("Sender", { exact: true }).isVisible();
   await page.getByText("Type", { exact: true }).isVisible();
@@ -206,7 +211,7 @@ test("user must be able to send an image on chat", async ({ page }) => {
 
   const jsonContent = readFileSync(
     "src/frontend/tests/end-to-end/assets/chain.png",
-    "utf-8"
+    "utf-8",
   );
 
   // Create the DataTransfer and File
@@ -230,6 +235,6 @@ test("user must be able to send an image on chat", async ({ page }) => {
   await page.getByText("chain.png").isVisible();
 
   await page.getByText("Close", { exact: true }).click();
-  await page.getByTestId("icon-ScanEye").last().click();
+  await page.getByTestId("icon-ScanEye").nth(4).click();
   await page.getByText("Restart").isHidden();
 });
diff --git a/src/frontend/tests/end-to-end/curl_api_generation.spec.ts b/src/frontend/tests/end-to-end/curl_api_generation.spec.ts
index 4e8f3b8d9..396962afe 100644
--- a/src/frontend/tests/end-to-end/curl_api_generation.spec.ts
+++ b/src/frontend/tests/end-to-end/curl_api_generation.spec.ts
@@ -23,7 +23,7 @@ test("curl_api_generation", async ({ page, context }) => {
   await page.getByRole("tab", { name: "cURL" }).click();
   await page.getByTestId("icon-Copy").last().click();
   const handle = await page.evaluateHandle(() =>
-    navigator.clipboard.readText()
+    navigator.clipboard.readText(),
   );
   const clipboardContent = await handle.jsonValue();
   expect(clipboardContent.length).toBeGreaterThan(0);
diff --git a/src/frontend/tests/end-to-end/deleteComponentFlows.spec.ts b/src/frontend/tests/end-to-end/deleteComponentFlows.spec.ts
index 836e8fe81..7992dcb6e 100644
--- a/src/frontend/tests/end-to-end/deleteComponentFlows.spec.ts
+++ b/src/frontend/tests/end-to-end/deleteComponentFlows.spec.ts
@@ -1,6 +1,6 @@
 import { test } from "@playwright/test";
 
-test("shoud delete a flow", async ({ page }) => {
+test("should delete a flow", async ({ page }) => {
   await page.goto("/");
   await page.waitForTimeout(2000);
   await page.getByText("Store").nth(0).click();
@@ -27,7 +27,7 @@ test("shoud delete a flow", async ({ page }) => {
   await page.getByText("Successfully").first().isVisible();
 });
 
-test("shoud delete a component", async ({ page }) => {
+test("should delete a component", async ({ page }) => {
   await page.goto("/");
   await page.waitForTimeout(2000);
   await page.getByText("Store").nth(0).click();
diff --git a/src/frontend/tests/end-to-end/dragAndDrop.spec.ts b/src/frontend/tests/end-to-end/dragAndDrop.spec.ts
index 3e760901d..03d11aaf7 100644
--- a/src/frontend/tests/end-to-end/dragAndDrop.spec.ts
+++ b/src/frontend/tests/end-to-end/dragAndDrop.spec.ts
@@ -27,7 +27,7 @@ test.describe("drag and drop test", () => {
     // Read your file into a buffer.
     const jsonContent = readFileSync(
       "src/frontend/tests/end-to-end/assets/collection.json",
-      "utf-8"
+      "utf-8",
     );
 
     // Create the DataTransfer and File
@@ -47,7 +47,7 @@ test.describe("drag and drop test", () => {
       "drop",
       {
         dataTransfer,
-      }
+      },
     );
 
     const genericNoda = page.getByTestId("div-generic-node");
diff --git a/src/frontend/tests/end-to-end/dropdownComponent.spec.ts b/src/frontend/tests/end-to-end/dropdownComponent.spec.ts
index 0152ca7ed..9e3fafa36 100644
--- a/src/frontend/tests/end-to-end/dropdownComponent.spec.ts
+++ b/src/frontend/tests/end-to-end/dropdownComponent.spec.ts
@@ -44,18 +44,18 @@ test("dropDownComponent", async ({ page }) => {
 
   await page.getByTestId("dropdown-model_id").click();
 
-  await page.getByTestId("ai21.j2-mid-v1-10-option").click();
+  await page.getByTestId("value-dropdown-dropdown-model_id").click();
 
   let value = await page
     .getByTestId("value-dropdown-dropdown-model_id")
     .first()
     .innerText();
-  if (value !== "ai21.j2-mid-v1") {
+  if (value !== "anthropic.claude-3-haiku-20240307-v1:0") {
     expect(false).toBeTruthy();
   }
 
   await page.getByTestId("dropdown-model_id").click();
-  await page.getByTestId("anthropic.claude-v2:1-6-option").click();
+  await page.getByText("anthropic.claude-v2").last().click();
 
   value = await page.getByTestId("dropdown-model_id").innerText();
   if (value !== "anthropic.claude-v2:1") {
@@ -80,32 +80,32 @@ test("dropDownComponent", async ({ page }) => {
 
   await page.locator('//*[@id="showcredentials_profile_name"]').click();
   expect(
-    await page.locator('//*[@id="showcredentials_profile_name"]').isChecked()
+    await page.locator('//*[@id="showcredentials_profile_name"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showcredentials_profile_name"]').click();
   expect(
-    await page.locator('//*[@id="showcredentials_profile_name"]').isChecked()
+    await page.locator('//*[@id="showcredentials_profile_name"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showendpoint_url"]').click();
   expect(
-    await page.locator('//*[@id="showendpoint_url"]').isChecked()
+    await page.locator('//*[@id="showendpoint_url"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showendpoint_url"]').click();
   expect(
-    await page.locator('//*[@id="showendpoint_url"]').isChecked()
+    await page.locator('//*[@id="showendpoint_url"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showregion_name"]').click();
   expect(
-    await page.locator('//*[@id="showregion_name"]').isChecked()
+    await page.locator('//*[@id="showregion_name"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showregion_name"]').click();
   expect(
-    await page.locator('//*[@id="showregion_name"]').isChecked()
+    await page.locator('//*[@id="showregion_name"]').isChecked(),
   ).toBeTruthy();
 
   // showmodel_id
@@ -115,7 +115,7 @@ test("dropDownComponent", async ({ page }) => {
   // showmodel_id
   await page.locator('//*[@id="showmodel_id"]').click();
   expect(
-    await page.locator('//*[@id="showmodel_id"]').isChecked()
+    await page.locator('//*[@id="showmodel_id"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showcache"]').click();
@@ -126,32 +126,32 @@ test("dropDownComponent", async ({ page }) => {
 
   await page.locator('//*[@id="showcredentials_profile_name"]').click();
   expect(
-    await page.locator('//*[@id="showcredentials_profile_name"]').isChecked()
+    await page.locator('//*[@id="showcredentials_profile_name"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showcredentials_profile_name"]').click();
   expect(
-    await page.locator('//*[@id="showcredentials_profile_name"]').isChecked()
+    await page.locator('//*[@id="showcredentials_profile_name"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showendpoint_url"]').click();
   expect(
-    await page.locator('//*[@id="showendpoint_url"]').isChecked()
+    await page.locator('//*[@id="showendpoint_url"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showendpoint_url"]').click();
   expect(
-    await page.locator('//*[@id="showendpoint_url"]').isChecked()
+    await page.locator('//*[@id="showendpoint_url"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showregion_name"]').click();
   expect(
-    await page.locator('//*[@id="showregion_name"]').isChecked()
+    await page.locator('//*[@id="showregion_name"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showregion_name"]').click();
   expect(
-    await page.locator('//*[@id="showregion_name"]').isChecked()
+    await page.locator('//*[@id="showregion_name"]').isChecked(),
   ).toBeTruthy();
 
   // showmodel_id
@@ -161,81 +161,133 @@ test("dropDownComponent", async ({ page }) => {
   // showmodel_id
   await page.locator('//*[@id="showmodel_id"]').click();
   expect(
-    await page.locator('//*[@id="showmodel_id"]').isChecked()
+    await page.locator('//*[@id="showmodel_id"]').isChecked(),
   ).toBeTruthy();
 
   await page.getByTestId("dropdown-edit-model_id").click();
-  await page.getByTestId("ai21.j2-mid-v1-10-option").click();
+  await page.getByText("cohere").last().click();
 
   value = await page.getByTestId("dropdown-edit-model_id").innerText();
-  if (value !== "ai21.j2-mid-v1") {
+  if (value !== "cohere.embed-multilingual-v3") {
     expect(false).toBeTruthy();
   }
 
   await page.getByText("Save Changes", { exact: true }).click();
 
   value = await page.getByTestId("dropdown-model_id").innerText();
-  if (value !== "ai21.j2-mid-v1") {
+  if (value !== "cohere.embed-multilingual-v3") {
     expect(false).toBeTruthy();
   }
   await page.getByTestId("code-button-modal").click();
   await page
     .locator("#CodeEditor div")
-    .filter({ hasText: "from typing import" })
+    .filter({ hasText: "import ChatBedrock" })
     .nth(1)
     .click();
   await page.locator("textarea").press("Control+a");
-  const emptyOptionsCode = `from typing import Optional
-from langflow.field_typing import BaseLanguageModel
-from langchain_community.llms.bedrock import Bedrock
+  const emptyOptionsCode = `from langchain_community.chat_models.bedrock import BedrockChat
 
-from langflow.interface.custom.custom_component import CustomComponent
+from langflow.base.constants import STREAM_INFO_TEXT
+from langflow.base.models.model import LCModelComponent
+from langflow.field_typing import BaseLanguageModel, Text
+from langflow.io import BoolInput, DictInput, DropdownInput, StrInput
+from langflow.io import MessageInput
+from langflow.io import Output
 
 
-class AmazonBedrockComponent(CustomComponent):
+class AmazonBedrockComponent(LCModelComponent):
     display_name: str = "Amazon Bedrock"
-    description: str = "LLM model from Amazon Bedrock."
+    description: str = "Generate text using Amazon Bedrock LLMs."
     icon = "Amazon"
 
-    def build_config(self):
-        return {
-            "model_id": {
-                "display_name": "Model Id",
-                "options": [],
-            },
-            "credentials_profile_name": {"display_name": "Credentials Profile Name"},
-            "streaming": {"display_name": "Streaming", "field_type": "bool"},
-            "endpoint_url": {"display_name": "Endpoint URL"},
-            "region_name": {"display_name": "Region Name"},
-            "model_kwargs": {"display_name": "Model Kwargs"},
-            "cache": {"display_name": "Cache"},
-            "code": {"advanced": True},
-        }
+    inputs = [
+        MessageInput(name="input_value", display_name="Input"),
+        DropdownInput(
+            name="model_id",
+            display_name="Model Id",
+            options=[
+                "amazon.titan-text-express-v1",
+                "amazon.titan-text-lite-v1",
+                "amazon.titan-text-premier-v1:0",
+                "amazon.titan-embed-text-v1",
+                "amazon.titan-embed-text-v2:0",
+                "amazon.titan-embed-image-v1",
+                "amazon.titan-image-generator-v1",
+                "anthropic.claude-v2",
+                "anthropic.claude-v2:1",
+                "anthropic.claude-3-sonnet-20240229-v1:0",
+                "anthropic.claude-3-haiku-20240307-v1:0",
+                "anthropic.claude-3-opus-20240229-v1:0",
+                "anthropic.claude-instant-v1",
+                "ai21.j2-mid-v1",
+                "ai21.j2-ultra-v1",
+                "cohere.command-text-v14",
+                "cohere.command-light-text-v14",
+                "cohere.command-r-v1:0",
+                "cohere.command-r-plus-v1:0",
+                "cohere.embed-english-v3",
+                "cohere.embed-multilingual-v3",
+                "meta.llama2-13b-chat-v1",
+                "meta.llama2-70b-chat-v1",
+                "meta.llama3-8b-instruct-v1:0",
+                "meta.llama3-70b-instruct-v1:0",
+                "mistral.mistral-7b-instruct-v0:2",
+                "mistral.mixtral-8x7b-instruct-v0:1",
+                "mistral.mistral-large-2402-v1:0",
+                "mistral.mistral-small-2402-v1:0",
+                "stability.stable-diffusion-xl-v0",
+                "stability.stable-diffusion-xl-v1",
+            ],
+            value="anthropic.claude-3-haiku-20240307-v1:0",
+        ),
+        StrInput(name="credentials_profile_name", display_name="Credentials Profile Name"),
+        StrInput(name="region_name", display_name="Region Name"),
+        DictInput(name="model_kwargs", display_name="Model Kwargs", advanced=True),
+        StrInput(name="endpoint_url", display_name="Endpoint URL"),
+        BoolInput(name="cache", display_name="Cache"),
+        StrInput(
+            name="system_message",
+            display_name="System Message",
+            info="System message to pass to the model.",
+            advanced=True,
+        ),
+        BoolInput(name="stream", display_name="Stream", info=STREAM_INFO_TEXT, advanced=True),
+    ]
+    outputs = [
+        Output(display_name="Text", name="text_output", method="text_response"),
+        Output(display_name="Language Model", name="model_output", method="build_model"),
+    ]
 
-    def build(
-        self,
-        model_id: str = "anthropic.claude-instant-v1",
-        credentials_profile_name: Optional[str] = None,
-        region_name: Optional[str] = None,
-        model_kwargs: Optional[dict] = None,
-        endpoint_url: Optional[str] = None,
-        streaming: bool = False,
-        cache: Optional[bool] = None,
-    ) -> BaseLanguageModel:
+    def text_response(self) -> Text:
+        input_value = self.input_value
+        stream = self.stream
+        system_message = self.system_message
+        output = self.build_model()
+        result = self.get_chat_result(output, stream, input_value, system_message)
+        self.status = result
+        return result
+
+    def build_model(self) -> BaseLanguageModel:
+        model_id = self.model_id
+        credentials_profile_name = self.credentials_profile_name
+        region_name = self.region_name
+        model_kwargs = self.model_kwargs
+        endpoint_url = self.endpoint_url
+        cache = self.cache
+        stream = self.stream
         try:
-            output = Bedrock(
+            output = BedrockChat(
                 credentials_profile_name=credentials_profile_name,
                 model_id=model_id,
                 region_name=region_name,
                 model_kwargs=model_kwargs,
                 endpoint_url=endpoint_url,
-                streaming=streaming,
+                streaming=stream,
                 cache=cache,
-            )  # type: ignore
+            )
         except Exception as e:
             raise ValueError("Could not connect to AmazonBedrock API.") from e
         return output
-
   `;
   await page.locator("textarea").fill(emptyOptionsCode);
   await page.getByRole("button", { name: "Check & Save" }).click();
diff --git a/src/frontend/tests/end-to-end/fileUploadComponent.spec.ts b/src/frontend/tests/end-to-end/fileUploadComponent.spec.ts
index 93b471c43..1b6f5b6e7 100644
--- a/src/frontend/tests/end-to-end/fileUploadComponent.spec.ts
+++ b/src/frontend/tests/end-to-end/fileUploadComponent.spec.ts
@@ -1,7 +1,7 @@
 import { expect, test } from "@playwright/test";
 import path from "path";
 
-test("dropDownComponent", async ({ page }) => {
+test("should be able to upload a file", async ({ page }) => {
   await page.goto("/");
   await page.waitForTimeout(2000);
 
@@ -61,10 +61,24 @@ test("dropDownComponent", async ({ page }) => {
   await page.getByTitle("zoom out").click();
   await page.getByTitle("zoom out").click();
 
+  await page.getByPlaceholder("Search").click();
+  await page.getByPlaceholder("Search").fill("parse data");
+  await page
+    .getByTestId("helpersParse Data")
+    .first()
+    .dragTo(page.locator('//*[@id="react-flow-id"]'));
+
+  await page.mouse.up();
+  await page.mouse.down();
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+
   // Click and hold on the first element
   await page
     .locator(
-      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div[1]/div/div[2]/div[6]/button/div/div'
+      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div[1]/div/div[2]/div[6]/button/div[1]',
     )
     .hover();
   await page.mouse.down();
@@ -72,7 +86,25 @@ test("dropDownComponent", async ({ page }) => {
   // Move to the second element
   await page
     .locator(
-      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div[2]/div/div[2]/div[3]/div/button/div/div'
+      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div[3]/div/div[2]/div[4]/div/button/div[1]',
+    )
+    .hover();
+
+  // Release the mouse
+  await page.mouse.up();
+
+  // Click and hold on the first element
+  await page
+    .locator(
+      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div[3]/div/div[2]/div[7]/button/div[1]',
+    )
+    .hover();
+  await page.mouse.down();
+
+  // Move to the second element
+  await page
+    .locator(
+      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div[2]/div/div[2]/div[3]/div/button/div[1]',
     )
     .hover();
 
diff --git a/src/frontend/tests/end-to-end/filterEdge.spec.ts b/src/frontend/tests/end-to-end/filterEdge.spec.ts
index 9cb5a567e..a85f2c3e5 100644
--- a/src/frontend/tests/end-to-end/filterEdge.spec.ts
+++ b/src/frontend/tests/end-to-end/filterEdge.spec.ts
@@ -40,16 +40,28 @@ test("LLMChain - Tooltip", async ({ page }) => {
 
   await page
     .locator(
-      '//*[@id="react-flow-id"]/div[1]/div[1]/div/div/div[2]/div/div/div[2]/div[3]/div/button/div/div'
+      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div/div/div[2]/div[7]/button/div[1]',
     )
     .hover()
     .then(async () => {
       await expect(page.getByTestId("tooltip-Chains").first()).toBeVisible();
-
-      await expect(page.getByTestId("tooltip-Inputs").first()).toBeVisible();
-
-      await expect(page.getByTestId("tooltip-Outputs").first()).toBeVisible();
-
+      await expect(
+        page.getByTestId("tooltip-Model Specs").first(),
+      ).toBeVisible();
+      await expect(
+        page.getByTestId("tooltip-Vector Search").first(),
+      ).toBeVisible();
+      await expect(
+        page.getByTestId("tooltip-Text Splitters").first(),
+      ).toBeVisible();
+      await expect(
+        page.getByTestId("tooltip-Retrievers").first(),
+      ).toBeVisible();
+      await expect(page.getByTestId("tooltip-Tools").first()).toBeVisible();
+      await expect(page.getByTestId("tooltip-Memories").first()).toBeVisible();
+      await expect(page.getByTestId("tooltip-Agents").first()).toBeVisible();
+      await expect(page.getByTestId("tooltip-Helpers").first()).toBeVisible();
+      await expect(page.getByTestId("tooltip-Utilities").first()).toBeVisible();
       await page.getByTestId("icon-X").click();
       await page.waitForTimeout(500);
     });
@@ -60,33 +72,57 @@ test("LLMChain - Tooltip", async ({ page }) => {
   await page.getByTitle("zoom out").click();
   await page
     .locator(
-      '//*[@id="react-flow-id"]/div[1]/div[1]/div/div/div[2]/div/div/div[2]/div[4]/div/button/div/div'
+      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div/div/div[2]/div[4]/div/button/div[1]',
     )
     .hover()
     .then(async () => {
       await expect(
-        page.getByTestId("tooltip-Model Specs").first()
+        page.getByTestId("tooltip-Model Specs").first(),
       ).toBeVisible();
       await page.waitForTimeout(2000);
 
-      await expect(
-        page.getByTestId("tooltip-Model Specs").first()
-      ).toBeVisible();
+      await expect(page.getByTestId("tooltip-Models").first()).toBeVisible();
 
       await page.getByTestId("icon-Search").click();
 
       await page.waitForTimeout(500);
     });
   await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
 
   await page
     .locator(
-      '//*[@id="react-flow-id"]/div[1]/div[1]/div/div/div[2]/div/div/div[2]/div[5]/div/button/div/div'
+      '//*[@id="react-flow-id"]/div/div[1]/div/div/div[2]/div/div/div[2]/div[3]/div/button/div[1]',
+    )
+    .hover()
+    .then(async () => {
+      await page.waitForTimeout(2000);
+
+      await expect(page.getByTestId("tooltip-Chains").first()).toBeVisible();
+      await expect(
+        page.getByTestId("tooltip-Experimental").first(),
+      ).toBeVisible();
+      await expect(page.getByTestId("tooltip-Agents").first()).toBeVisible();
+      await expect(page.getByTestId("tooltip-Helpers").first()).toBeVisible();
+
+      await page.waitForTimeout(500);
+    });
+
+  await page.getByTitle("fit view").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+  await page.getByTitle("zoom out").click();
+
+  await page
+    .locator(
+      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div/div/div[2]/div[5]/div/button/div[1]',
     )
     .hover()
     .then(async () => {
       await expect(
-        page.getByTestId("empty-tooltip-filter").first()
+        page.getByTestId("empty-tooltip-filter").first(),
       ).toBeVisible();
     });
 });
@@ -113,7 +149,7 @@ test("LLMChain - Filter", async ({ page }) => {
   await page.waitForTimeout(1000);
 
   await page.getByTestId(
-    "input-list-plus-btn-edit_metadata_indexing_include-2"
+    "input-list-plus-btn-edit_metadata_indexing_include-2",
   );
 
   await page.getByTestId("blank-flow").click();
@@ -136,7 +172,7 @@ test("LLMChain - Filter", async ({ page }) => {
 
   await page
     .locator(
-      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div/div/div[2]/div[4]/div/button/div/div'
+      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div/div/div[2]/div[7]/button/div[1]',
     )
     .click();
 
@@ -149,14 +185,14 @@ test("LLMChain - Filter", async ({ page }) => {
   await expect(page.getByTestId("model_specsChatOpenAI")).toBeVisible();
   await expect(page.getByTestId("model_specsChatVertexAI")).toBeVisible();
   await expect(
-    page.getByTestId("model_specsGoogle Generative AI")
+    page.getByTestId("model_specsGoogle Generative AI"),
   ).toBeVisible();
   await expect(
-    page.getByTestId("model_specsHugging Face Inference API")
+    page.getByTestId("model_specsHugging Face Inference API"),
   ).toBeVisible();
   await expect(page.getByTestId("model_specsOllama")).toBeVisible();
   await expect(
-    page.getByTestId("model_specsQianfanChatEndpoint")
+    page.getByTestId("model_specsQianfanChatEndpoint"),
   ).toBeVisible();
   await expect(page.getByTestId("model_specsQianfanLLMEndpoint")).toBeVisible();
   await expect(page.getByTestId("model_specsVertexAI")).toBeVisible();
@@ -168,7 +204,7 @@ test("LLMChain - Filter", async ({ page }) => {
   await expect(page.getByTestId("model_specsAmazon Bedrock")).not.toBeVisible();
   await expect(page.getByTestId("modelsAzure OpenAI")).not.toBeVisible();
   await expect(
-    page.getByTestId("model_specsAzureChatOpenAI")
+    page.getByTestId("model_specsAzureChatOpenAI"),
   ).not.toBeVisible();
   await expect(page.getByTestId("model_specsChatAnthropic")).not.toBeVisible();
   await expect(page.getByTestId("model_specsChatLiteLLM")).not.toBeVisible();
@@ -178,19 +214,23 @@ test("LLMChain - Filter", async ({ page }) => {
 
   await page
     .locator(
-      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div/div/div[2]/div[7]/button/div/div'
+      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div/div/div[2]/div[4]/div/button/div[1]',
     )
     .click();
 
+  await expect(page.getByTestId("disclosure-models")).toBeVisible();
+  await expect(page.getByTestId("disclosure-model specs")).toBeVisible();
+
   await page
     .locator(
-      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div/div/div[2]/div[7]/button/div/div'
+      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div/div/div[2]/div[3]/div/button/div[1]',
     )
     .click();
 
-  await expect(page.getByTestId("inputsChat Input")).toBeVisible();
-  await expect(page.getByTestId("outputsChat Output")).toBeVisible();
-  await expect(page.getByTestId("helpersID Generator")).toBeVisible();
-  await expect(page.getByTestId("vectorstoresChroma")).toBeVisible();
-  await expect(page.getByTestId("disclosure-vector stores")).toBeVisible();
+  await expect(page.getByTestId("disclosure-saved")).toBeVisible();
+
+  await expect(page.getByTestId("disclosure-helpers")).toBeVisible();
+  await expect(page.getByTestId("disclosure-agents")).toBeVisible();
+  await expect(page.getByTestId("disclosure-chains")).toBeVisible();
+  await expect(page.getByTestId("disclosure-experimental")).toBeVisible();
 });
diff --git a/src/frontend/tests/end-to-end/floatComponent.spec.ts b/src/frontend/tests/end-to-end/floatComponent.spec.ts
index 0577a62b4..126863a6b 100644
--- a/src/frontend/tests/end-to-end/floatComponent.spec.ts
+++ b/src/frontend/tests/end-to-end/floatComponent.spec.ts
@@ -39,97 +39,61 @@ test("FloatComponent", async ({ page }) => {
   await page.getByTitle("zoom out").click();
   await page.getByTitle("zoom out").click();
 
+  await page.waitForTimeout(2000);
   await page.locator('//*[@id="float-input"]').click();
+  await page.locator('//*[@id="float-input"]').fill("");
+  await page.waitForTimeout(2000);
   await page.locator('//*[@id="float-input"]').fill("3");
 
   let value = await page.locator('//*[@id="float-input"]').inputValue();
 
-  if (value != "1") {
+  if (value != "2") {
     expect(false).toBeTruthy();
   }
 
+  await page.waitForTimeout(2000);
   await page.locator('//*[@id="float-input"]').click();
+  await page.locator('//*[@id="float-input"]').fill("");
+  await page.waitForTimeout(2000);
   await page.locator('//*[@id="float-input"]').fill("-3");
 
   value = await page.locator('//*[@id="float-input"]').inputValue();
 
-  if (value != "-1") {
+  if (value != "-2") {
     expect(false).toBeTruthy();
   }
 
   await page.getByTestId("more-options-modal").click();
   await page.getByTestId("edit-button-modal").click();
 
-  await page.getByTestId("showformat").click();
-  expect(await page.locator('//*[@id="showformat"]').isChecked()).toBeTruthy();
-
-  await page.getByTestId("showformat").click();
-  expect(await page.locator('//*[@id="showformat"]').isChecked()).toBeFalsy();
-
-  await page.getByTestId("showmirostat").click();
-  expect(await page.locator('//*[@id="showmirostat"]').isChecked()).toBeFalsy();
-
-  await page.getByTestId("showmirostat").click();
+  await page.getByTestId("showmirostat_eta").click();
   expect(
-    await page.locator('//*[@id="showmirostat"]').isChecked()
+    await page.locator('//*[@id="showmirostat_eta"]').isChecked(),
   ).toBeTruthy();
 
   await page.getByTestId("showmirostat_eta").click();
   expect(
-    await page.locator('//*[@id="showmirostat_eta"]').isChecked()
+    await page.locator('//*[@id="showmirostat_eta"]').isChecked(),
+  ).toBeFalsy();
+
+  await page.getByTestId("showmirostat_eta").click();
+  expect(
+    await page.locator('//*[@id="showmirostat_eta"]').isChecked(),
   ).toBeTruthy();
 
   await page.getByTestId("showmirostat_eta").click();
   expect(
-    await page.locator('//*[@id="showmirostat_eta"]').isChecked()
+    await page.locator('//*[@id="showmirostat_eta"]').isChecked(),
   ).toBeFalsy();
 
   await page.getByTestId("showmirostat_tau").click();
   expect(
-    await page.locator('//*[@id="showmirostat_tau"]').isChecked()
+    await page.locator('//*[@id="showmirostat_tau"]').isChecked(),
   ).toBeTruthy();
 
   await page.getByTestId("showmirostat_tau").click();
   expect(
-    await page.locator('//*[@id="showmirostat_tau"]').isChecked()
-  ).toBeFalsy();
-
-  await page.getByTestId("showmodel").click();
-  expect(await page.locator('//*[@id="showmodel"]').isChecked()).toBeFalsy();
-
-  await page.getByTestId("showmodel").click();
-  expect(await page.locator('//*[@id="showmodel"]').isChecked()).toBeTruthy();
-
-  await page.getByTestId("shownum_ctx").click();
-  expect(await page.locator('//*[@id="shownum_ctx"]').isChecked()).toBeTruthy();
-
-  await page.getByTestId("shownum_ctx").click();
-  expect(await page.locator('//*[@id="shownum_ctx"]').isChecked()).toBeFalsy();
-
-  await page.getByTestId("shownum_gpu").click();
-  expect(await page.locator('//*[@id="shownum_gpu"]').isChecked()).toBeTruthy();
-
-  await page.getByTestId("shownum_gpu").click();
-  expect(await page.locator('//*[@id="shownum_gpu"]').isChecked()).toBeFalsy();
-
-  await page.getByTestId("shownum_thread").click();
-  expect(
-    await page.locator('//*[@id="shownum_thread"]').isChecked()
-  ).toBeTruthy();
-
-  await page.getByTestId("shownum_thread").click();
-  expect(
-    await page.locator('//*[@id="shownum_thread"]').isChecked()
-  ).toBeFalsy();
-
-  await page.getByTestId("showrepeat_last_n").click();
-  expect(
-    await page.locator('//*[@id="showrepeat_last_n"]').isChecked()
-  ).toBeTruthy();
-
-  await page.getByTestId("showrepeat_last_n").click();
-  expect(
-    await page.locator('//*[@id="showrepeat_last_n"]').isChecked()
+    await page.locator('//*[@id="showmirostat_tau"]').isChecked(),
   ).toBeFalsy();
 
   await page.getByText("Save Changes", { exact: true }).click();
@@ -145,7 +109,7 @@ test("FloatComponent", async ({ page }) => {
     // showtemperature
     await page.locator('//*[@id="showtemperature"]').click();
     expect(
-      await page.locator('//*[@id="showtemperature"]').isChecked()
+      await page.locator('//*[@id="showtemperature"]').isChecked(),
     ).toBeTruthy();
 
     await page.getByText("Save Changes", { exact: true }).click();
diff --git a/src/frontend/tests/end-to-end/flowSettings.spec.ts b/src/frontend/tests/end-to-end/flowSettings.spec.ts
index a2e9f25c0..25807ed88 100644
--- a/src/frontend/tests/end-to-end/flowSettings.spec.ts
+++ b/src/frontend/tests/end-to-end/flowSettings.spec.ts
@@ -29,7 +29,7 @@ test("flowSettings", async ({ page }) => {
   await page
     .getByPlaceholder("Flow name")
     .fill(
-      "Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test"
+      "Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test",
     );
 
   await page.getByText("Character limit reached").isVisible();
@@ -41,7 +41,7 @@ test("flowSettings", async ({ page }) => {
   await page
     .getByPlaceholder("Flow description")
     .fill(
-      "Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test"
+      "Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test Flow Name Test",
     );
 
   await page.getByTestId("save-flow-settings").click();
diff --git a/src/frontend/tests/end-to-end/folders.spec.ts b/src/frontend/tests/end-to-end/folders.spec.ts
index 384816b3f..99ed07669 100644
--- a/src/frontend/tests/end-to-end/folders.spec.ts
+++ b/src/frontend/tests/end-to-end/folders.spec.ts
@@ -49,7 +49,7 @@ test("CRUD folders", async ({ page }) => {
 
   await page.getByText("Delete").last().click();
   await page.waitForTimeout(1000);
-  await page.getByText("Folder deleted succefully").isVisible();
+  await page.getByText("Folder deleted successfully").isVisible();
 });
 
 test("add folder by drag and drop", async ({ page }) => {
@@ -58,7 +58,7 @@ test("add folder by drag and drop", async ({ page }) => {
 
   const jsonContent = readFileSync(
     "src/frontend/tests/end-to-end/assets/collection.json",
-    "utf-8"
+    "utf-8",
   );
 
   // Create the DataTransfer and File
@@ -78,7 +78,7 @@ test("add folder by drag and drop", async ({ page }) => {
     "drop",
     {
       dataTransfer,
-    }
+    },
   );
 
   await page.getByText("Getting Started").first().isVisible();
diff --git a/src/frontend/tests/end-to-end/inputComponent.spec.ts b/src/frontend/tests/end-to-end/inputComponent.spec.ts
index d944cffe5..99bf207cf 100644
--- a/src/frontend/tests/end-to-end/inputComponent.spec.ts
+++ b/src/frontend/tests/end-to-end/inputComponent.spec.ts
@@ -30,7 +30,7 @@ test("InputComponent", async ({ page }) => {
   await page.waitForTimeout(1000);
 
   await page
-    .getByTestId("vectorstoresChroma")
+    .getByTestId("vectorstoresChroma DB")
     .dragTo(page.locator('//*[@id="react-flow-id"]'));
   await page.mouse.up();
   await page.mouse.down();
@@ -60,71 +60,61 @@ test("InputComponent", async ({ page }) => {
   expect(
     await page
       .locator('//*[@id="showchroma_server_cors_allow_origins"]')
-      .isChecked()
+      .isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showchroma_server_grpc_port"]').click();
   expect(
-    await page.locator('//*[@id="showchroma_server_grpc_port"]').isChecked()
+    await page.locator('//*[@id="showchroma_server_grpc_port"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showchroma_server_host"]').click();
   expect(
-    await page.locator('//*[@id="showchroma_server_host"]').isChecked()
+    await page.locator('//*[@id="showchroma_server_host"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showchroma_server_http_port"]').click();
   expect(
-    await page.locator('//*[@id="showchroma_server_http_port"]').isChecked()
+    await page.locator('//*[@id="showchroma_server_http_port"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showchroma_server_ssl_enabled"]').click();
   expect(
-    await page.locator('//*[@id="showchroma_server_ssl_enabled"]').isChecked()
+    await page.locator('//*[@id="showchroma_server_ssl_enabled"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showcollection_name"]').click();
   expect(
-    await page.locator('//*[@id="showcollection_name"]').isChecked()
-  ).toBeFalsy();
-
-  await page.locator('//*[@id="showindex_directory"]').click();
-  expect(
-    await page.locator('//*[@id="showindex_directory"]').isChecked()
+    await page.locator('//*[@id="showcollection_name"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showchroma_server_cors_allow_origins"]').click();
   expect(
     await page
       .locator('//*[@id="showchroma_server_cors_allow_origins"]')
-      .isChecked()
+      .isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showchroma_server_grpc_port"]').click();
   expect(
-    await page.locator('//*[@id="showchroma_server_grpc_port"]').isChecked()
+    await page.locator('//*[@id="showchroma_server_grpc_port"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showchroma_server_host"]').click();
   expect(
-    await page.locator('//*[@id="showchroma_server_host"]').isChecked()
+    await page.locator('//*[@id="showchroma_server_host"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showchroma_server_http_port"]').click();
   expect(
-    await page.locator('//*[@id="showchroma_server_http_port"]').isChecked()
+    await page.locator('//*[@id="showchroma_server_http_port"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showchroma_server_ssl_enabled"]').click();
   expect(
-    await page.locator('//*[@id="showchroma_server_ssl_enabled"]').isChecked()
+    await page.locator('//*[@id="showchroma_server_ssl_enabled"]').isChecked(),
   ).toBeFalsy();
 
-  await page.locator('//*[@id="showindex_directory"]').click();
-  expect(
-    await page.locator('//*[@id="showindex_directory"]').isChecked()
-  ).toBeTruthy();
-
   let valueEditNode = await page
     .getByTestId("popover-anchor-input-collection_name-edit")
     .inputValue();
@@ -152,7 +142,7 @@ test("InputComponent", async ({ page }) => {
 
     await page.locator('//*[@id="showcollection_name"]').click();
     expect(
-      await page.locator('//*[@id="showcollection_name"]').isChecked()
+      await page.locator('//*[@id="showcollection_name"]').isChecked(),
     ).toBeTruthy();
 
     await page.getByText("Save Changes", { exact: true }).click();
diff --git a/src/frontend/tests/end-to-end/inputListComponent.spec.ts b/src/frontend/tests/end-to-end/inputListComponent.spec.ts
index 6414c92db..e28ba67b2 100644
--- a/src/frontend/tests/end-to-end/inputListComponent.spec.ts
+++ b/src/frontend/tests/end-to-end/inputListComponent.spec.ts
@@ -28,7 +28,7 @@ test("InputListComponent", async ({ page }) => {
 
   await page.waitForTimeout(1000);
   await page
-    .getByTestId("vectorstoresAstra DB")
+    .getByTestId("vectorsearchAstra DB Search")
     .dragTo(page.locator('//*[@id="react-flow-id"]'));
   await page.mouse.up();
   await page.mouse.down();
@@ -41,19 +41,19 @@ test("InputListComponent", async ({ page }) => {
   await page.getByTestId("edit-button-modal").click();
 
   expect(
-    await page.getByTestId("showmetadata_indexing_exclude").isChecked()
+    await page.getByTestId("showmetadata_indexing_exclude").isChecked(),
   ).toBeFalsy();
   await page.getByTestId("showmetadata_indexing_exclude").click();
   expect(
-    await page.getByTestId("showmetadata_indexing_exclude").isChecked()
+    await page.getByTestId("showmetadata_indexing_exclude").isChecked(),
   ).toBeTruthy();
 
   expect(
-    await page.getByTestId("showmetadata_indexing_include").isChecked()
+    await page.getByTestId("showmetadata_indexing_include").isChecked(),
   ).toBeFalsy();
   await page.getByTestId("showmetadata_indexing_include").click();
   expect(
-    await page.getByTestId("showmetadata_indexing_include").isChecked()
+    await page.getByTestId("showmetadata_indexing_include").isChecked(),
   ).toBeTruthy();
 
   await page
@@ -89,11 +89,11 @@ test("InputListComponent", async ({ page }) => {
   }
 
   await page
-    .getByTestId("input-list-minus-btn_metadata_indexing_include-0")
+    .getByTestId("input-list-minus-btn_metadata_indexing_include-1")
     .click();
 
   const plusButtonLocator = page.getByTestId(
-    "input-list-plus-btn_metadata_indexing_include-1"
+    "input-list-plus-btn_metadata_indexing_include-1",
   );
   const elementCount = await plusButtonLocator?.count();
 
@@ -105,13 +105,13 @@ test("InputListComponent", async ({ page }) => {
     .getByTestId("input-list-plus-btn_metadata_indexing_include-0")
     .click();
   await page
-    .getByTestId("input-list-plus-btn_metadata_indexing_include-1")
+    .getByTestId("input-list-plus-btn_metadata_indexing_include-0")
     .click();
   await page
-    .getByTestId("input-list-plus-btn_metadata_indexing_include-2")
+    .getByTestId("input-list-plus-btn_metadata_indexing_include-0")
     .click();
   await page
-    .getByTestId("input-list-plus-btn_metadata_indexing_include-3")
+    .getByTestId("input-list-plus-btn_metadata_indexing_include-0")
     .click();
 
   await page
@@ -154,7 +154,7 @@ test("InputListComponent", async ({ page }) => {
   }
 
   await page
-    .getByTestId("input-list-minus-btn-edit_metadata_indexing_include-0")
+    .getByTestId("input-list-minus-btn-edit_metadata_indexing_include-1")
     .click();
   await page
     .getByTestId("input-list-minus-btn-edit_metadata_indexing_include-1")
@@ -164,32 +164,32 @@ test("InputListComponent", async ({ page }) => {
     .click();
 
   const plusButtonLocatorEdit0 = await page.getByTestId(
-    "input-list-plus-btn-edit_metadata_indexing_include-0"
+    "input-list-plus-btn-edit_metadata_indexing_include-0",
   );
   const elementCountEdit0 = await plusButtonLocatorEdit0?.count();
 
   const plusButtonLocatorEdit2 = await page.getByTestId(
-    "input-list-plus-btn-edit_metadata_indexing_include-2"
+    "input-list-plus-btn-edit_metadata_indexing_include-2",
   );
   const elementCountEdit2 = await plusButtonLocatorEdit2?.count();
 
-  if (elementCountEdit0 > 0 || elementCountEdit2 > 0) {
+  if (elementCountEdit0 > 1 || elementCountEdit2 > 0) {
     expect(false).toBeTruthy();
   }
 
   const minusButtonLocatorEdit1 = await page.getByTestId(
-    "input-list-minus-btn-edit_metadata_indexing_include-1"
+    "input-list-minus-btn-edit_metadata_indexing_include-1",
   );
 
   const elementCountMinusEdit1 = await minusButtonLocatorEdit1?.count();
 
   const minusButtonLocatorEdit2 = await page.getByTestId(
-    "input-list-minus-btn-edit_metadata_indexing_include-2"
+    "input-list-minus-btn-edit_metadata_indexing_include-2",
   );
 
   const elementCountMinusEdit2 = await minusButtonLocatorEdit2?.count();
 
-  if (elementCountMinusEdit1 > 0 || elementCountMinusEdit2 > 0) {
+  if (elementCountMinusEdit1 > 1 || elementCountMinusEdit2 > 0) {
     expect(false).toBeTruthy();
   }
 });
diff --git a/src/frontend/tests/end-to-end/intComponent.spec.ts b/src/frontend/tests/end-to-end/intComponent.spec.ts
index 50fb0d648..aa54ac5b4 100644
--- a/src/frontend/tests/end-to-end/intComponent.spec.ts
+++ b/src/frontend/tests/end-to-end/intComponent.spec.ts
@@ -87,77 +87,77 @@ test("IntComponent", async ({ page }) => {
 
   await page.locator('//*[@id="showmodel_kwargs"]').click();
   expect(
-    await page.locator('//*[@id="showmodel_kwargs"]').isChecked()
+    await page.locator('//*[@id="showmodel_kwargs"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showmodel_name"]').click();
   expect(
-    await page.locator('//*[@id="showmodel_name"]').isChecked()
+    await page.locator('//*[@id="showmodel_name"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showopenai_api_base"]').click();
   expect(
-    await page.locator('//*[@id="showopenai_api_base"]').isChecked()
+    await page.locator('//*[@id="showopenai_api_base"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showopenai_api_key"]').click();
   expect(
-    await page.locator('//*[@id="showopenai_api_key"]').isChecked()
+    await page.locator('//*[@id="showopenai_api_key"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showtemperature"]').click();
   expect(
-    await page.locator('//*[@id="showtemperature"]').isChecked()
+    await page.locator('//*[@id="showtemperature"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showmodel_kwargs"]').click();
   expect(
-    await page.locator('//*[@id="showmodel_kwargs"]').isChecked()
+    await page.locator('//*[@id="showmodel_kwargs"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showmodel_name"]').click();
   expect(
-    await page.locator('//*[@id="showmodel_name"]').isChecked()
+    await page.locator('//*[@id="showmodel_name"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showopenai_api_base"]').click();
   expect(
-    await page.locator('//*[@id="showopenai_api_base"]').isChecked()
+    await page.locator('//*[@id="showopenai_api_base"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showopenai_api_key"]').click();
   expect(
-    await page.locator('//*[@id="showopenai_api_key"]').isChecked()
+    await page.locator('//*[@id="showopenai_api_key"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showtemperature"]').click();
   expect(
-    await page.locator('//*[@id="showtemperature"]').isChecked()
+    await page.locator('//*[@id="showtemperature"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showmodel_kwargs"]').click();
   expect(
-    await page.locator('//*[@id="showmodel_kwargs"]').isChecked()
+    await page.locator('//*[@id="showmodel_kwargs"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showmodel_name"]').click();
   expect(
-    await page.locator('//*[@id="showmodel_name"]').isChecked()
+    await page.locator('//*[@id="showmodel_name"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showopenai_api_base"]').click();
   expect(
-    await page.locator('//*[@id="showopenai_api_base"]').isChecked()
+    await page.locator('//*[@id="showopenai_api_base"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showopenai_api_key"]').click();
   expect(
-    await page.locator('//*[@id="showopenai_api_key"]').isChecked()
+    await page.locator('//*[@id="showopenai_api_key"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showtemperature"]').click();
   expect(
-    await page.locator('//*[@id="showtemperature"]').isChecked()
+    await page.locator('//*[@id="showtemperature"]').isChecked(),
   ).toBeFalsy();
 
   await page.getByText("Save Changes", { exact: true }).click();
@@ -172,7 +172,7 @@ test("IntComponent", async ({ page }) => {
 
     await page.locator('//*[@id="showtimeout"]').click();
     expect(
-      await page.locator('//*[@id="showtimeout"]').isChecked()
+      await page.locator('//*[@id="showtimeout"]').isChecked(),
     ).toBeTruthy();
 
     const valueEditNode = await page
diff --git a/src/frontend/tests/end-to-end/keyPairListComponent.spec.ts b/src/frontend/tests/end-to-end/keyPairListComponent.spec.ts
index 165dd685b..31a4521ae 100644
--- a/src/frontend/tests/end-to-end/keyPairListComponent.spec.ts
+++ b/src/frontend/tests/end-to-end/keyPairListComponent.spec.ts
@@ -81,7 +81,7 @@ test("KeypairListComponent", async ({ page }) => {
   expect(await page.locator('//*[@id="showcache"]').isChecked()).toBeFalsy();
   await page.locator('//*[@id="showcredentials_profile_name"]').click();
   expect(
-    await page.locator('//*[@id="showcredentials_profile_name"]').isChecked()
+    await page.locator('//*[@id="showcredentials_profile_name"]').isChecked(),
   ).toBeFalsy();
   await page.getByText("Save Changes", { exact: true }).click();
 
@@ -96,7 +96,7 @@ test("KeypairListComponent", async ({ page }) => {
 
     await page.locator('//*[@id="showcredentials_profile_name"]').click();
     expect(
-      await page.locator('//*[@id="showcredentials_profile_name"]').isChecked()
+      await page.locator('//*[@id="showcredentials_profile_name"]').isChecked(),
     ).toBeTruthy();
     await page.locator('//*[@id="showcache"]').click();
     expect(await page.locator('//*[@id="showcache"]').isChecked()).toBeTruthy();
diff --git a/src/frontend/tests/end-to-end/langflowShortcuts.spec.ts b/src/frontend/tests/end-to-end/langflowShortcuts.spec.ts
index 58615dd55..42b8744d1 100644
--- a/src/frontend/tests/end-to-end/langflowShortcuts.spec.ts
+++ b/src/frontend/tests/end-to-end/langflowShortcuts.spec.ts
@@ -61,7 +61,7 @@ test("LangflowShortcuts", async ({ page }) => {
 
   await page
     .locator(
-      '//*[@id="react-flow-id"]/div[1]/div[1]/div[1]/div/div[2]/div[2]/div/div[1]/div/div[1]/div/div/div[1]'
+      '//*[@id="react-flow-id"]/div[1]/div[1]/div[1]/div/div[2]/div[2]/div/div[1]/div/div[1]/div/div/div[1]',
     )
     .click();
   await page.keyboard.press("Backspace");
@@ -84,7 +84,7 @@ test("LangflowShortcuts", async ({ page }) => {
 
   await page
     .locator(
-      '//*[@id="react-flow-id"]/div[1]/div[1]/div[1]/div/div[2]/div[2]/div/div[1]/div/div[1]/div/div/div[1]'
+      '//*[@id="react-flow-id"]/div[1]/div[1]/div[1]/div/div[2]/div[2]/div/div[1]/div/div[1]/div/div/div[1]',
     )
     .click();
   await page.keyboard.press("Backspace");
diff --git a/src/frontend/tests/end-to-end/logs.spec.ts b/src/frontend/tests/end-to-end/logs.spec.ts
index 9d3c0120b..8f5ce4a2d 100644
--- a/src/frontend/tests/end-to-end/logs.spec.ts
+++ b/src/frontend/tests/end-to-end/logs.spec.ts
@@ -42,13 +42,18 @@ test("should able to see and interact with logs", async ({ page }) => {
   await page.getByTestId("icon-ChevronDown").click();
   await page.getByText("Logs").click();
 
-  await page.getByText("timestamp").isVisible();
-  await page.getByText("flow_id").isVisible();
-  await page.getByText("source").isVisible();
-  await page.getByText("target", { exact: true }).isVisible();
-  await page.getByText("target_args", { exact: true }).isVisible();
+  await page.getByText("timestamp").first().isVisible();
+  await page.getByText("flow_id").first().isVisible();
+  await page.getByText("source").first().isVisible();
+  await page.getByText("target", { exact: true }).first().isVisible();
+  await page.getByText("target_args", { exact: true }).first().isVisible();
   await page.getByRole("gridcell").first().isVisible();
 
+  await page.keyboard.press("Escape");
+
+  await page.getByTestId("user-profile-settings").first().click();
+  await page.getByText("Settings", { exact: true }).click();
+
   await page.getByText("Messages", { exact: true }).click();
   await page.getByText("index", { exact: true }).last().isVisible();
   await page.getByText("timestamp", { exact: true }).isVisible();
diff --git a/src/frontend/tests/end-to-end/nestedComponent.spec.ts b/src/frontend/tests/end-to-end/nestedComponent.spec.ts
index 3cd319576..a00e2f9b3 100644
--- a/src/frontend/tests/end-to-end/nestedComponent.spec.ts
+++ b/src/frontend/tests/end-to-end/nestedComponent.spec.ts
@@ -37,156 +37,137 @@ test("NestedComponent", async ({ page }) => {
   await page.getByTestId("more-options-modal").click();
   await page.getByTestId("edit-button-modal").click();
 
-  //showpool_threads
-  await page.locator('//*[@id="showpool_threads"]').click();
+  // showindex_name
+  await page.locator('//*[@id="showindex_name"]').click();
 
   expect(
-    await page.locator('//*[@id="showpool_threads"]').isChecked()
+    await page.locator('//*[@id="showindex_name"]').isChecked(),
+  ).toBeFalsy();
+
+  // shownamespace
+  await page.locator('//*[@id="shownamespace"]').click();
+
+  expect(
+    await page.locator('//*[@id="shownamespace"]').isChecked(),
+  ).toBeFalsy();
+
+  // showpinecone_api_key
+  await page.locator('//*[@id="showpinecone_api_key"]').click();
+
+  expect(
+    await page.locator('//*[@id="showpinecone_api_key"]').isChecked(),
+  ).toBeFalsy();
+
+  // showindex_name
+  await page.locator('//*[@id="showindex_name"]').click();
+
+  expect(
+    await page.locator('//*[@id="showindex_name"]').isChecked(),
+  ).toBeTruthy();
+
+  // shownamespace
+  await page.locator('//*[@id="shownamespace"]').click();
+
+  expect(
+    await page.locator('//*[@id="shownamespace"]').isChecked(),
+  ).toBeTruthy();
+
+  // showpinecone_api_key
+  await page.locator('//*[@id="showpinecone_api_key"]').click();
+
+  expect(
+    await page.locator('//*[@id="showpinecone_api_key"]').isChecked(),
+  ).toBeTruthy();
+
+  // showindex_name
+  await page.locator('//*[@id="showindex_name"]').click();
+
+  expect(
+    await page.locator('//*[@id="showindex_name"]').isChecked(),
+  ).toBeFalsy();
+
+  // shownamespace
+  await page.locator('//*[@id="shownamespace"]').click();
+
+  expect(
+    await page.locator('//*[@id="shownamespace"]').isChecked(),
+  ).toBeFalsy();
+
+  // showpinecone_api_key
+  await page.locator('//*[@id="showpinecone_api_key"]').click();
+
+  expect(
+    await page.locator('//*[@id="showpinecone_api_key"]').isChecked(),
+  ).toBeFalsy();
+
+  // showindex_name
+  await page.locator('//*[@id="showindex_name"]').click();
+
+  expect(
+    await page.locator('//*[@id="showindex_name"]').isChecked(),
+  ).toBeTruthy();
+
+  // shownamespace
+  await page.locator('//*[@id="shownamespace"]').click();
+
+  expect(
+    await page.locator('//*[@id="shownamespace"]').isChecked(),
+  ).toBeTruthy();
+
+  // showpinecone_api_key
+  await page.locator('//*[@id="showpinecone_api_key"]').click();
+
+  expect(
+    await page.locator('//*[@id="showpinecone_api_key"]').isChecked(),
+  ).toBeTruthy();
+
+  // showindex_name
+  await page.locator('//*[@id="showindex_name"]').click();
+
+  expect(
+    await page.locator('//*[@id="showindex_name"]').isChecked(),
+  ).toBeFalsy();
+
+  // shownamespace
+  await page.locator('//*[@id="shownamespace"]').click();
+
+  expect(
+    await page.locator('//*[@id="shownamespace"]').isChecked(),
+  ).toBeFalsy();
+
+  // showpinecone_api_key
+  await page.locator('//*[@id="showpinecone_api_key"]').click();
+
+  expect(
+    await page.locator('//*[@id="showpinecone_api_key"]').isChecked(),
+  ).toBeFalsy();
+
+  // showindex_name
+  await page.locator('//*[@id="showindex_name"]').click();
+
+  expect(
+    await page.locator('//*[@id="showindex_name"]').isChecked(),
+  ).toBeTruthy();
+
+  // shownamespace
+  await page.locator('//*[@id="shownamespace"]').click();
+
+  expect(
+    await page.locator('//*[@id="shownamespace"]').isChecked(),
+  ).toBeTruthy();
+
+  // showpinecone_api_key
+  await page.locator('//*[@id="showpinecone_api_key"]').click();
+
+  expect(
+    await page.locator('//*[@id="showpinecone_api_key"]').isChecked(),
   ).toBeTruthy();
 
   //showtext_key
   await page.locator('//*[@id="showtext_key"]').click();
 
-  expect(await page.locator('//*[@id="showtext_key"]').isChecked()).toBeFalsy();
-
-  // showindex_name
-  await page.locator('//*[@id="showindex_name"]').click();
-
   expect(
-    await page.locator('//*[@id="showindex_name"]').isChecked()
-  ).toBeFalsy();
-
-  // shownamespace
-  await page.locator('//*[@id="shownamespace"]').click();
-
-  expect(
-    await page.locator('//*[@id="shownamespace"]').isChecked()
-  ).toBeFalsy();
-
-  // showpinecone_api_key
-  await page.locator('//*[@id="showpinecone_api_key"]').click();
-
-  expect(
-    await page.locator('//*[@id="showpinecone_api_key"]').isChecked()
-  ).toBeFalsy();
-
-  // showindex_name
-  await page.locator('//*[@id="showindex_name"]').click();
-
-  expect(
-    await page.locator('//*[@id="showindex_name"]').isChecked()
-  ).toBeTruthy();
-
-  // shownamespace
-  await page.locator('//*[@id="shownamespace"]').click();
-
-  expect(
-    await page.locator('//*[@id="shownamespace"]').isChecked()
-  ).toBeTruthy();
-
-  // showpinecone_api_key
-  await page.locator('//*[@id="showpinecone_api_key"]').click();
-
-  expect(
-    await page.locator('//*[@id="showpinecone_api_key"]').isChecked()
-  ).toBeTruthy();
-
-  // showindex_name
-  await page.locator('//*[@id="showindex_name"]').click();
-
-  expect(
-    await page.locator('//*[@id="showindex_name"]').isChecked()
-  ).toBeFalsy();
-
-  // shownamespace
-  await page.locator('//*[@id="shownamespace"]').click();
-
-  expect(
-    await page.locator('//*[@id="shownamespace"]').isChecked()
-  ).toBeFalsy();
-
-  // showpinecone_api_key
-  await page.locator('//*[@id="showpinecone_api_key"]').click();
-
-  expect(
-    await page.locator('//*[@id="showpinecone_api_key"]').isChecked()
-  ).toBeFalsy();
-
-  // showindex_name
-  await page.locator('//*[@id="showindex_name"]').click();
-
-  expect(
-    await page.locator('//*[@id="showindex_name"]').isChecked()
-  ).toBeTruthy();
-
-  // shownamespace
-  await page.locator('//*[@id="shownamespace"]').click();
-
-  expect(
-    await page.locator('//*[@id="shownamespace"]').isChecked()
-  ).toBeTruthy();
-
-  // showpinecone_api_key
-  await page.locator('//*[@id="showpinecone_api_key"]').click();
-
-  expect(
-    await page.locator('//*[@id="showpinecone_api_key"]').isChecked()
-  ).toBeTruthy();
-
-  // showindex_name
-  await page.locator('//*[@id="showindex_name"]').click();
-
-  expect(
-    await page.locator('//*[@id="showindex_name"]').isChecked()
-  ).toBeFalsy();
-
-  // shownamespace
-  await page.locator('//*[@id="shownamespace"]').click();
-
-  expect(
-    await page.locator('//*[@id="shownamespace"]').isChecked()
-  ).toBeFalsy();
-
-  // showpinecone_api_key
-  await page.locator('//*[@id="showpinecone_api_key"]').click();
-
-  expect(
-    await page.locator('//*[@id="showpinecone_api_key"]').isChecked()
-  ).toBeFalsy();
-
-  // showindex_name
-  await page.locator('//*[@id="showindex_name"]').click();
-
-  expect(
-    await page.locator('//*[@id="showindex_name"]').isChecked()
-  ).toBeTruthy();
-
-  // shownamespace
-  await page.locator('//*[@id="shownamespace"]').click();
-
-  expect(
-    await page.locator('//*[@id="shownamespace"]').isChecked()
-  ).toBeTruthy();
-
-  // showpinecone_api_key
-  await page.locator('//*[@id="showpinecone_api_key"]').click();
-
-  expect(
-    await page.locator('//*[@id="showpinecone_api_key"]').isChecked()
-  ).toBeTruthy();
-
-  //showpool_threads
-  await page.locator('//*[@id="showpool_threads"]').click();
-
-  expect(
-    await page.locator('//*[@id="showpool_threads"]').isChecked()
-  ).toBeFalsy();
-
-  //showtext_key
-  await page.locator('//*[@id="showtext_key"]').click();
-
-  expect(
-    await page.locator('//*[@id="showtext_key"]').isChecked()
+    await page.locator('//*[@id="showtext_key"]').isChecked(),
   ).toBeTruthy();
 
   await page.getByText("Save Changes", { exact: true }).click();
diff --git a/src/frontend/tests/end-to-end/promptModalComponent.spec.ts b/src/frontend/tests/end-to-end/promptModalComponent.spec.ts
index 75b8513a6..b91cb6ed5 100644
--- a/src/frontend/tests/end-to-end/promptModalComponent.spec.ts
+++ b/src/frontend/tests/end-to-end/promptModalComponent.spec.ts
@@ -29,7 +29,7 @@ test("PromptTemplateComponent", async ({ page }) => {
   await page.waitForTimeout(1000);
 
   await page
-    .locator('//*[@id="inputsPrompt"]')
+    .locator('//*[@id="promptsPrompt"]')
     .dragTo(page.locator('//*[@id="react-flow-id"]'));
   await page.mouse.up();
   await page.mouse.down();
@@ -140,7 +140,7 @@ test("PromptTemplateComponent", async ({ page }) => {
 
   await page.locator('//*[@id="showtemplate"]').click();
   expect(
-    await page.locator('//*[@id="showtemplate"]').isChecked()
+    await page.locator('//*[@id="showtemplate"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showprompt"]').click();
@@ -160,7 +160,7 @@ test("PromptTemplateComponent", async ({ page }) => {
 
   await page.locator('//*[@id="showtemplate"]').click();
   expect(
-    await page.locator('//*[@id="showtemplate"]').isChecked()
+    await page.locator('//*[@id="showtemplate"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showprompt"]').click();
diff --git a/src/frontend/tests/end-to-end/python_api_generation.spec.ts b/src/frontend/tests/end-to-end/python_api_generation.spec.ts
index e9ad74578..849c4e9a9 100644
--- a/src/frontend/tests/end-to-end/python_api_generation.spec.ts
+++ b/src/frontend/tests/end-to-end/python_api_generation.spec.ts
@@ -23,7 +23,7 @@ test("python_api_generation", async ({ page, context }) => {
   await page.getByRole("tab", { name: "Python API" }).click();
   await page.getByTestId("icon-Copy").click();
   const handle = await page.evaluateHandle(() =>
-    navigator.clipboard.readText()
+    navigator.clipboard.readText(),
   );
   const clipboardContent = await handle.jsonValue();
   expect(clipboardContent.length).toBeGreaterThan(0);
diff --git a/src/frontend/tests/end-to-end/saveComponents.spec.ts b/src/frontend/tests/end-to-end/saveComponents.spec.ts
index f8484c722..fa9dbbc00 100644
--- a/src/frontend/tests/end-to-end/saveComponents.spec.ts
+++ b/src/frontend/tests/end-to-end/saveComponents.spec.ts
@@ -27,7 +27,7 @@ test.describe("save component tests", () => {
     // Read your file into a buffer.
     const jsonContent = readFileSync(
       "src/frontend/tests/end-to-end/assets/flow_group_test.json",
-      "utf-8"
+      "utf-8",
     );
 
     // Create the DataTransfer and File
@@ -49,7 +49,7 @@ test.describe("save component tests", () => {
       "drop",
       {
         dataTransfer,
-      }
+      },
     );
 
     const genericNoda = page.getByTestId("div-generic-node");
diff --git a/src/frontend/tests/end-to-end/store.spec.ts b/src/frontend/tests/end-to-end/store.spec.ts
index 13963d698..bcd5ec15b 100644
--- a/src/frontend/tests/end-to-end/store.spec.ts
+++ b/src/frontend/tests/end-to-end/store.spec.ts
@@ -245,6 +245,8 @@ test("should share component with share button", async ({ page }) => {
   }
   await page.waitForTimeout(1000);
 
+  const randomName = Math.random().toString(36).substring(2);
+
   await page.getByRole("heading", { name: "Basic Prompting" }).click();
   await page.waitForTimeout(1000);
   const flowName = await page.getByTestId("flow_name").innerText();
@@ -253,6 +255,7 @@ test("should share component with share button", async ({ page }) => {
   const flowDescription = await page
     .getByPlaceholder("Flow description")
     .inputValue();
+  await page.getByPlaceholder("Flow name").fill(randomName);
   await page.getByText("Save").last().click();
   await page.getByText("Close").last().click();
 
@@ -262,7 +265,7 @@ test("should share component with share button", async ({ page }) => {
   await page.getByText("Set workflow status to public").isVisible();
   await page
     .getByText(
-      "Attention: API keys in specified fields are automatically removed upon sharing."
+      "Attention: API keys in specified fields are automatically removed upon sharing.",
     )
     .isVisible();
   await page.getByText("Export").first().isVisible();
diff --git a/src/frontend/tests/end-to-end/textAreaModalComponent.spec.ts b/src/frontend/tests/end-to-end/textAreaModalComponent.spec.ts
index fa4248a63..590d43933 100644
--- a/src/frontend/tests/end-to-end/textAreaModalComponent.spec.ts
+++ b/src/frontend/tests/end-to-end/textAreaModalComponent.spec.ts
@@ -29,7 +29,7 @@ test("TextAreaModalComponent", async ({ page }) => {
   await page.waitForTimeout(1000);
 
   await page
-    .locator('//*[@id="inputsPrompt"]')
+    .locator('//*[@id="promptsPrompt"]')
     .dragTo(page.locator('//*[@id="react-flow-id"]'));
   await page.mouse.up();
   await page.mouse.down();
@@ -51,7 +51,7 @@ test("TextAreaModalComponent", async ({ page }) => {
   await page
     .getByTestId("textarea-text")
     .fill(
-      "test test test test test test test test test test test !@#%*)( 123456789101010101010101111111111 !!!!!!!!!!"
+      "test test test test test test test test test test test !@#%*)( 123456789101010101010101111111111 !!!!!!!!!!",
     );
 
   await page.getByTestId("textarea-text-ExternalLink").click();
diff --git a/src/frontend/tests/end-to-end/textInputOutput.spec.ts b/src/frontend/tests/end-to-end/textInputOutput.spec.ts
index 7fe74aacb..163ee528e 100644
--- a/src/frontend/tests/end-to-end/textInputOutput.spec.ts
+++ b/src/frontend/tests/end-to-end/textInputOutput.spec.ts
@@ -60,7 +60,7 @@ test("TextInputOutputComponent", async ({ page }) => {
   // Click and hold on the first element
   await page
     .locator(
-      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div[1]/div/div[2]/div[6]/button/div/div'
+      '//*[@id="react-flow-id"]/div/div[1]/div/div/div[2]/div[1]/div/div[2]/div[5]/button/div[1]',
     )
     .hover();
   await page.mouse.down();
@@ -68,7 +68,7 @@ test("TextInputOutputComponent", async ({ page }) => {
   // Move to the second element
   await page
     .locator(
-      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div[2]/div/div[2]/div[9]/div/button/div/div'
+      '//*[@id="react-flow-id"]/div/div[1]/div/div/div[2]/div[2]/div/div[2]/div[3]/div/button/div[1]',
     )
     .hover();
 
@@ -92,7 +92,7 @@ test("TextInputOutputComponent", async ({ page }) => {
   // Click and hold on the first element
   await page
     .locator(
-      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div[2]/div/div[2]/div[13]/button/div/div'
+      '//*[@id="react-flow-id"]/div/div[1]/div/div/div[2]/div[2]/div/div[2]/div[15]/button/div[1]',
     )
     .hover();
   await page.mouse.down();
@@ -100,7 +100,7 @@ test("TextInputOutputComponent", async ({ page }) => {
   // Move to the second element
   await page
     .locator(
-      '//*[@id="react-flow-id"]/div/div[1]/div[1]/div/div[2]/div[3]/div/div[2]/div[3]/div/button/div/div'
+      '//*[@id="react-flow-id"]/div/div[1]/div/div/div[2]/div[3]/div/div[2]/div[3]/div/button/div[1]',
     )
     .hover();
 
diff --git a/src/frontend/tests/end-to-end/toggleComponent.spec.ts b/src/frontend/tests/end-to-end/toggleComponent.spec.ts
index 5a8d9a36a..58d909af4 100644
--- a/src/frontend/tests/end-to-end/toggleComponent.spec.ts
+++ b/src/frontend/tests/end-to-end/toggleComponent.spec.ts
@@ -45,7 +45,7 @@ test("ToggleComponent", async ({ page }) => {
 
   await page.locator('//*[@id="showload_hidden"]').click();
   expect(
-    await page.locator('//*[@id="showload_hidden"]').isChecked()
+    await page.locator('//*[@id="showload_hidden"]').isChecked(),
   ).toBeTruthy();
 
   await page.getByText("Save Changes", { exact: true }).click();
@@ -81,12 +81,12 @@ test("ToggleComponent", async ({ page }) => {
 
   await page.locator('//*[@id="showload_hidden"]').click();
   expect(
-    await page.locator('//*[@id="showload_hidden"]').isChecked()
+    await page.locator('//*[@id="showload_hidden"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showmax_concurrency"]').click();
   expect(
-    await page.locator('//*[@id="showmax_concurrency"]').isChecked()
+    await page.locator('//*[@id="showmax_concurrency"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showpath"]').click();
@@ -94,22 +94,22 @@ test("ToggleComponent", async ({ page }) => {
 
   await page.locator('//*[@id="showrecursive"]').click();
   expect(
-    await page.locator('//*[@id="showrecursive"]').isChecked()
+    await page.locator('//*[@id="showrecursive"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showsilent_errors"]').click();
   expect(
-    await page.locator('//*[@id="showsilent_errors"]').isChecked()
+    await page.locator('//*[@id="showsilent_errors"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showuse_multithreading"]').click();
   expect(
-    await page.locator('//*[@id="showuse_multithreading"]').isChecked()
+    await page.locator('//*[@id="showuse_multithreading"]').isChecked(),
   ).toBeTruthy();
 
   await page.locator('//*[@id="showmax_concurrency"]').click();
   expect(
-    await page.locator('//*[@id="showmax_concurrency"]').isChecked()
+    await page.locator('//*[@id="showmax_concurrency"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showpath"]').click();
@@ -117,17 +117,17 @@ test("ToggleComponent", async ({ page }) => {
 
   await page.locator('//*[@id="showrecursive"]').click();
   expect(
-    await page.locator('//*[@id="showrecursive"]').isChecked()
+    await page.locator('//*[@id="showrecursive"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showsilent_errors"]').click();
   expect(
-    await page.locator('//*[@id="showsilent_errors"]').isChecked()
+    await page.locator('//*[@id="showsilent_errors"]').isChecked(),
   ).toBeFalsy();
 
   await page.locator('//*[@id="showuse_multithreading"]').click();
   expect(
-    await page.locator('//*[@id="showuse_multithreading"]').isChecked()
+    await page.locator('//*[@id="showuse_multithreading"]').isChecked(),
   ).toBeFalsy();
 
   await page.getByText("Save Changes", { exact: true }).click();
@@ -144,38 +144,38 @@ test("ToggleComponent", async ({ page }) => {
 
     await page.locator('//*[@id="showload_hidden"]').click();
     expect(
-      await page.locator('//*[@id="showload_hidden"]').isChecked()
+      await page.locator('//*[@id="showload_hidden"]').isChecked(),
     ).toBeTruthy();
 
     expect(
-      await page.getByTestId("toggle-edit-load_hidden").isChecked()
+      await page.getByTestId("toggle-edit-load_hidden").isChecked(),
     ).toBeTruthy();
 
     await page.getByText("Save Changes", { exact: true }).click();
 
     await page.getByTestId("toggle-load_hidden").click();
     expect(
-      await page.getByTestId("toggle-load_hidden").isChecked()
+      await page.getByTestId("toggle-load_hidden").isChecked(),
     ).toBeFalsy();
 
     await page.getByTestId("toggle-load_hidden").click();
     expect(
-      await page.getByTestId("toggle-load_hidden").isChecked()
+      await page.getByTestId("toggle-load_hidden").isChecked(),
     ).toBeTruthy();
 
     await page.getByTestId("toggle-load_hidden").click();
     expect(
-      await page.getByTestId("toggle-load_hidden").isChecked()
+      await page.getByTestId("toggle-load_hidden").isChecked(),
     ).toBeFalsy();
 
     await page.getByTestId("toggle-load_hidden").click();
     expect(
-      await page.getByTestId("toggle-load_hidden").isChecked()
+      await page.getByTestId("toggle-load_hidden").isChecked(),
     ).toBeTruthy();
 
     await page.getByTestId("toggle-load_hidden").click();
     expect(
-      await page.getByTestId("toggle-load_hidden").isChecked()
+      await page.getByTestId("toggle-load_hidden").isChecked(),
     ).toBeFalsy();
   }
 });
diff --git a/src/frontend/tests/end-to-end/tweaks_test.spec.ts b/src/frontend/tests/end-to-end/tweaks_test.spec.ts
index b3d396f55..871e8a2ba 100644
--- a/src/frontend/tests/end-to-end/tweaks_test.spec.ts
+++ b/src/frontend/tests/end-to-end/tweaks_test.spec.ts
@@ -24,7 +24,7 @@ test("curl_api_generation", async ({ page, context }) => {
   await page.getByRole("tab", { name: "cURL" }).click();
   await page.getByTestId("icon-Copy").click();
   const handle = await page.evaluateHandle(() =>
-    navigator.clipboard.readText()
+    navigator.clipboard.readText(),
   );
   const clipboardContent = await handle.jsonValue();
   const oldValue = clipboardContent;
@@ -50,7 +50,7 @@ test("curl_api_generation", async ({ page, context }) => {
   await page.getByRole("tab", { name: "cURL" }).click();
   await page.getByTestId("icon-Copy").click();
   const handle2 = await page.evaluateHandle(() =>
-    navigator.clipboard.readText()
+    navigator.clipboard.readText(),
   );
   const clipboardContent2 = await handle2.jsonValue();
   const newValue = clipboardContent2;
@@ -90,7 +90,7 @@ test("check if tweaks are updating when someothing on the flow changes", async (
   await page.waitForTimeout(1000);
 
   await page
-    .getByTestId("vectorstoresChroma")
+    .getByTestId("vectorstoresChroma DB")
     .dragTo(page.locator('//*[@id="react-flow-id"]'));
   await page.mouse.up();
   await page.mouse.down();
@@ -103,25 +103,25 @@ test("check if tweaks are updating when someothing on the flow changes", async (
     .getByTestId("popover-anchor-input-collection_name")
     .fill("collection_name_test_123123123!@#$&*(&%$@");
 
-  await page.getByTestId("popover-anchor-input-index_directory").click();
+  await page.getByTestId("popover-anchor-input-persist_directory").click();
   await page
-    .getByTestId("popover-anchor-input-index_directory")
-    .fill("index_directory_123123123!@#$&*(&%$@");
+    .getByTestId("popover-anchor-input-persist_directory")
+    .fill("persist_directory_123123123!@#$&*(&%$@");
 
   await page.getByText("API", { exact: true }).first().click();
 
   await page.getByText("Tweaks").nth(1).click();
 
   await page.getByText("collection_name_test_123123123!@#$&*(&%$@").isVisible();
-  await page.getByText("index_directory_123123123!@#$&*(&%$@").isVisible();
+  await page.getByText("persist_directory_123123123!@#$&*(&%$@").isVisible();
 
   await page.getByText("Python API", { exact: true }).click();
 
   await page.getByText("collection_name_test_123123123!@#$&*(&%$@").isVisible();
-  await page.getByText("index_directory_123123123!@#$&*(&%$@").isVisible();
+  await page.getByText("persist_directory_123123123!@#$&*(&%$@").isVisible();
 
   await page.getByText("Python Code", { exact: true }).click();
 
   await page.getByText("collection_name_test_123123123!@#$&*(&%$@").isVisible();
-  await page.getByText("index_directory_123123123!@#$&*(&%$@").isVisible();
+  await page.getByText("persist_directory_123123123!@#$&*(&%$@").isVisible();
 });
diff --git a/src/frontend/tests/end-to-end/twoEdges.spec.ts b/src/frontend/tests/end-to-end/twoEdges.spec.ts
new file mode 100644
index 000000000..abae18ba3
--- /dev/null
+++ b/src/frontend/tests/end-to-end/twoEdges.spec.ts
@@ -0,0 +1,38 @@
+import { expect, test } from "@playwright/test";
+
+test("user should be able to see multiple edges and interact with them", async ({
+  page,
+}) => {
+  await page.goto("/");
+  await page.waitForTimeout(2000);
+
+  let modalCount = 0;
+  try {
+    const modalTitleElement = await page?.getByTestId("modal-title");
+    if (modalTitleElement) {
+      modalCount = await modalTitleElement.count();
+    }
+  } catch (error) {
+    modalCount = 0;
+  }
+
+  while (modalCount === 0) {
+    await page.getByText("New Project", { exact: true }).click();
+    await page.waitForTimeout(5000);
+    modalCount = await page.getByTestId("modal-title")?.count();
+  }
+  await page.waitForTimeout(1000);
+
+  await page.getByText("Vector Store RAG", { exact: true }).last().click();
+  await page.waitForTimeout(3000);
+  await page.getByText("Retriever", { exact: true }).first().isVisible();
+  await page.getByText("Search Results", { exact: true }).first().isVisible();
+  await page.getByTestId("icon-Eye").nth(24).click();
+  await page.getByText("Retriever", { exact: true }).first().isHidden();
+  await page.getByTestId("icon-ChevronDown").last().isVisible();
+  await page.getByTestId("icon-ChevronDown").last().click();
+  await page.getByText("Retriever", { exact: true }).first().isVisible();
+  await page.getByText("Search Results", { exact: true }).first().isVisible();
+
+  await page.getByTestId("icon-EyeOff").nth(0).isVisible();
+});
diff --git a/src/frontend/vite.config.ts b/src/frontend/vite.config.mts
similarity index 68%
rename from src/frontend/vite.config.ts
rename to src/frontend/vite.config.mts
index b513e36bb..8a4a515ed 100644
--- a/src/frontend/vite.config.ts
+++ b/src/frontend/vite.config.mts
@@ -7,7 +7,7 @@ const apiRoutes = ["^/api/v1/", "/health"];
 const target = process.env.VITE_PROXY_TARGET || "http://127.0.0.1:7860";
 
 // Use environment variable to determine the UI server port
-const port = process.env.VITE_PORT || 3000;
+const port = Number(process.env.VITE_PORT) || 3000;
 
 const proxyTargets = apiRoutes.reduce((proxyObj, route) => {
   proxyObj[route] = {
@@ -18,17 +18,15 @@ const proxyTargets = apiRoutes.reduce((proxyObj, route) => {
   };
   return proxyObj;
 }, {});
-export default defineConfig(() => {
-  return {
-    build: {
-      outDir: "build",
+export default defineConfig({
+  build: {
+    outDir: "build",
+  },
+  plugins: [react(), svgr()],
+  server: {
+    port: port,
+    proxy: {
+      ...proxyTargets,
     },
-    plugins: [react(), svgr()],
-    server: {
-      port: port,
-      proxy: {
-        ...proxyTargets,
-      },
-    },
-  };
+  },
 });
diff --git a/tests/unit/conftest.py b/tests/conftest.py
similarity index 91%
rename from tests/unit/conftest.py
rename to tests/conftest.py
index 355cd5d2d..0033b0652 100644
--- a/tests/unit/conftest.py
+++ b/tests/conftest.py
@@ -13,6 +13,10 @@ import pytest
 from dotenv import load_dotenv
 from fastapi.testclient import TestClient
 from httpx import AsyncClient
+from sqlmodel import Session, SQLModel, create_engine, select
+from sqlmodel.pool import StaticPool
+from typer.testing import CliRunner
+
 from langflow.graph.graph.base import Graph
 from langflow.initial_setup.setup import STARTER_FOLDER_NAME
 from langflow.services.auth.utils import get_password_hash
@@ -22,9 +26,6 @@ from langflow.services.database.models.folder.model import Folder
 from langflow.services.database.models.user.model import User, UserCreate
 from langflow.services.database.utils import session_getter
 from langflow.services.deps import get_db_service
-from sqlmodel import Session, SQLModel, create_engine, select
-from sqlmodel.pool import StaticPool
-from typer.testing import CliRunner
 
 if TYPE_CHECKING:
     from langflow.services.database.service import DatabaseService
@@ -37,7 +38,7 @@ def pytest_configure(config):
     config.addinivalue_line("markers", "noclient: don't create a client for this test")
     config.addinivalue_line("markers", "load_flows: load the flows for this test")
     config.addinivalue_line("markers", "api_key_required: run only if the api key is set in the environment variables")
-    data_path = Path(__file__).parent.parent.absolute() / "data"
+    data_path = Path(__file__).parent.absolute() / "data"
 
     pytest.BASIC_EXAMPLE_PATH = data_path / "basic_example.json"
     pytest.COMPLEX_EXAMPLE_PATH = data_path / "complex_example.json"
@@ -45,6 +46,7 @@ def pytest_configure(config):
     pytest.GROUPED_CHAT_EXAMPLE_PATH = data_path / "grouped_chat.json"
     pytest.ONE_GROUPED_CHAT_EXAMPLE_PATH = data_path / "one_group_chat.json"
     pytest.VECTOR_STORE_GROUPED_EXAMPLE_PATH = data_path / "vector_store_grouped.json"
+    pytest.WEBHOOK_TEST = data_path / "WebhookTest.json"
 
     pytest.BASIC_CHAT_WITH_PROMPT_AND_HISTORY = data_path / "BasicChatwithPromptandHistory.json"
     pytest.CHAT_INPUT = data_path / "ChatInputTest.json"
@@ -222,6 +224,12 @@ def json_vector_store():
         return f.read()
 
 
+@pytest.fixture
+def json_webhook_test():
+    with open(pytest.WEBHOOK_TEST, "r") as f:
+        return f.read()
+
+
 @pytest.fixture(name="client", autouse=True)
 def client_fixture(session: Session, monkeypatch, request, load_flows_dir):
     # Set the database url to a test database
@@ -275,7 +283,7 @@ def test_user(client):
         username="testuser",
         password="testpassword",
     )
-    response = client.post("/api/v1/users", json=user_data.dict())
+    response = client.post("/api/v1/users", json=user_data.model_dump())
     assert response.status_code == 201
     return response.json()
 
@@ -342,7 +350,7 @@ def added_flow_with_prompt_and_history(client, json_flow_with_prompt_and_history
     flow = orjson.loads(json_flow_with_prompt_and_history)
     data = flow["data"]
     flow = FlowCreate(name="Basic Chat", description="description", data=data)
-    response = client.post("api/v1/flows/", json=flow.dict(), headers=logged_in_headers)
+    response = client.post("api/v1/flows/", json=flow.model_dump(), headers=logged_in_headers)
     assert response.status_code == 201
     assert response.json()["name"] == flow.name
     assert response.json()["data"] == flow.data
@@ -354,7 +362,7 @@ def added_flow_chat_input(client, json_chat_input, logged_in_headers):
     flow = orjson.loads(json_chat_input)
     data = flow["data"]
     flow = FlowCreate(name="Chat Input", description="description", data=data)
-    response = client.post("api/v1/flows/", json=flow.dict(), headers=logged_in_headers)
+    response = client.post("api/v1/flows/", json=flow.model_dump(), headers=logged_in_headers)
     assert response.status_code == 201
     assert response.json()["name"] == flow.name
     assert response.json()["data"] == flow.data
@@ -366,7 +374,7 @@ def added_flow_two_outputs(client, json_two_outputs, logged_in_headers):
     flow = orjson.loads(json_two_outputs)
     data = flow["data"]
     flow = FlowCreate(name="Two Outputs", description="description", data=data)
-    response = client.post("api/v1/flows/", json=flow.dict(), headers=logged_in_headers)
+    response = client.post("api/v1/flows/", json=flow.model_dump(), headers=logged_in_headers)
     assert response.status_code == 201
     assert response.json()["name"] == flow.name
     assert response.json()["data"] == flow.data
@@ -378,13 +386,27 @@ def added_vector_store(client, json_vector_store, logged_in_headers):
     vector_store = orjson.loads(json_vector_store)
     data = vector_store["data"]
     vector_store = FlowCreate(name="Vector Store", description="description", data=data)
-    response = client.post("api/v1/flows/", json=vector_store.dict(), headers=logged_in_headers)
+    response = client.post("api/v1/flows/", json=vector_store.model_dump(), headers=logged_in_headers)
     assert response.status_code == 201
     assert response.json()["name"] == vector_store.name
     assert response.json()["data"] == vector_store.data
     return response.json()
 
 
+@pytest.fixture
+def added_webhook_test(client, json_webhook_test, logged_in_headers):
+    webhook_test = orjson.loads(json_webhook_test)
+    data = webhook_test["data"]
+    webhook_test = FlowCreate(
+        name="Webhook Test", description="description", data=data, endpoint_name=webhook_test["endpoint_name"]
+    )
+    response = client.post("api/v1/flows/", json=webhook_test.model_dump(), headers=logged_in_headers)
+    assert response.status_code == 201
+    assert response.json()["name"] == webhook_test.name
+    assert response.json()["data"] == webhook_test.data
+    return response.json()
+
+
 @pytest.fixture
 def created_api_key(active_user):
     hashed = get_password_hash("random_key")
diff --git a/tests/data/WebhookTest.json b/tests/data/WebhookTest.json
new file mode 100644
index 000000000..fd63c5346
--- /dev/null
+++ b/tests/data/WebhookTest.json
@@ -0,0 +1,228 @@
+{
+    "id": "40b2ae66-384b-4978-85ab-f79706287a1a",
+    "data": {
+        "nodes": [
+            {
+                "id": "CustomComponent-xbSW2",
+                "type": "genericNode",
+                "position": {
+                    "x": 888.0012384532345,
+                    "y": 274.9520639008431
+                },
+                "data": {
+                    "type": "CustomComponent",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "# from langflow.field_typing import Data\nfrom langflow.custom import Component\nfrom langflow.io import StrInput\nfrom langflow.schema import Data\nfrom langflow.io import Output\n\n\nclass CustomComponent(Component):\n    display_name = \"Custom Component\"\n    description = \"Use as a template to create your own component.\"\n    documentation: str = \"http://docs.langflow.org/components/custom\"\n    icon = \"custom_components\"\n\n    inputs = [\n        StrInput(name=\"input_value\", display_name=\"Input Value\", value=\"Hello, World!\", input_types=[\"Data\"]),\n    ]\n\n    outputs = [\n        Output(display_name=\"Output\", name=\"output\", method=\"build_output\"),\n    ]\n\n    def build_output(self) -> Data:\n        if isinstance(self.input_value, Data):\n            data = self.input_value\n        else:\n            data = Data(value=self.input_value)\n            \n        if \"path\" in data:\n            path = self.resolve_path(data.path)\n            path_obj = Path(path)\n            with open(path, \"w\") as f:\n                f.write(data.model_dump())\n        self.status = data\n        return data\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "input_value": {
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "value": "",
+                                "name": "input_value",
+                                "display_name": "Input Value",
+                                "advanced": false,
+                                "input_types": [
+                                    "Data"
+                                ],
+                                "dynamic": false,
+                                "info": "",
+                                "title_case": false,
+                                "type": "str"
+                            }
+                        },
+                        "description": "Use as a template to create your own component.",
+                        "icon": "custom_components",
+                        "base_classes": [
+                            "Data"
+                        ],
+                        "display_name": "Custom Component",
+                        "documentation": "http://docs.langflow.org/components/custom",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "output",
+                                "display_name": "Output",
+                                "method": "build_output",
+                                "value": "__UNDEFINED__",
+                                "cache": true
+                            }
+                        ],
+                        "field_order": [
+                            "input_value"
+                        ],
+                        "beta": false,
+                        "edited": true
+                    },
+                    "id": "CustomComponent-xbSW2",
+                    "description": "Use as a template to create your own component.",
+                    "display_name": "Custom Component"
+                },
+                "selected": false,
+                "width": 384,
+                "height": 337,
+                "positionAbsolute": {
+                    "x": 888.0012384532345,
+                    "y": 274.9520639008431
+                },
+                "dragging": false
+            },
+            {
+                "id": "Webhook-eeCWO",
+                "type": "genericNode",
+                "position": {
+                    "x": 418,
+                    "y": 270.2890625
+                },
+                "data": {
+                    "type": "Webhook",
+                    "node": {
+                        "template": {
+                            "_type": "Component",
+                            "code": {
+                                "type": "code",
+                                "required": true,
+                                "placeholder": "",
+                                "list": false,
+                                "show": true,
+                                "multiline": true,
+                                "value": "import json\n\nfrom langflow.custom import Component\nfrom langflow.io import MultilineInput\nfrom langflow.schema import Data\nfrom langflow.io import Output\n\n\nclass WebhookComponent(Component):\n    display_name = \"Webhook Input\"\n    description = \"Defines a webhook input for the flow.\"\n\n    inputs = [\n        MultilineInput(\n            name=\"data\",\n            display_name=\"Data\",\n            info=\"Use this field to quickly test the webhook component by providing a JSON payload.\",\n            multiline=True,\n        )\n    ]\n    outputs = [\n        Output(display_name=\"Data\", name=\"output_data\", method=\"build_data\"),\n    ]\n\n    def build_data(self) -> Data:\n        message = \"\"\n        if not self.data:\n            self.status = \"No data provided.\"\n            return Data(data={})\n        try:\n            body = json.loads(self.data or \"{}\")\n        except json.JSONDecodeError:\n            body = {\"payload\": self.data}\n            message = f\"Invalid JSON payload. Please check the format.\\n\\n{self.data}\"\n        data = Data(data=body)\n        if not message:\n            message = data\n        self.status = message\n        return data\n",
+                                "fileTypes": [],
+                                "file_path": "",
+                                "password": false,
+                                "name": "code",
+                                "advanced": true,
+                                "dynamic": true,
+                                "info": "",
+                                "load_from_db": false,
+                                "title_case": false
+                            },
+                            "data": {
+                                "multiline": true,
+                                "load_from_db": false,
+                                "list": false,
+                                "required": false,
+                                "placeholder": "",
+                                "show": true,
+                                "value": "{\"test\":1}",
+                                "name": "data",
+                                "display_name": "Data",
+                                "advanced": false,
+                                "input_types": [
+                                    "Message"
+                                ],
+                                "dynamic": false,
+                                "info": "Use this field to quickly test the webhook component by providing a JSON payload.",
+                                "title_case": false,
+                                "type": "str"
+                            }
+                        },
+                        "description": "Defines a webhook input for the flow.",
+                        "base_classes": [
+                            "Data"
+                        ],
+                        "display_name": "Webhook Input",
+                        "documentation": "",
+                        "custom_fields": {},
+                        "output_types": [],
+                        "pinned": false,
+                        "conditional_paths": [],
+                        "frozen": false,
+                        "outputs": [
+                            {
+                                "types": [
+                                    "Data"
+                                ],
+                                "selected": "Data",
+                                "name": "output_data",
+                                "display_name": "Data",
+                                "method": "build_data",
+                                "value": "__UNDEFINED__",
+                                "cache": true,
+                                "hidden": false
+                            }
+                        ],
+                        "field_order": [
+                            "data"
+                        ],
+                        "beta": false
+                    },
+                    "id": "Webhook-eeCWO"
+                },
+                "selected": true,
+                "width": 384,
+                "height": 309,
+                "dragging": true,
+                "positionAbsolute": {
+                    "x": 418,
+                    "y": 270.2890625
+                }
+            }
+        ],
+        "edges": [
+            {
+                "source": "Webhook-eeCWO",
+                "sourceHandle": "{œdataTypeœ:œWebhookœ,œidœ:œWebhook-eeCWOœ,œnameœ:œoutput_dataœ,œoutput_typesœ:[œDataœ]}",
+                "target": "CustomComponent-xbSW2",
+                "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œCustomComponent-xbSW2œ,œinputTypesœ:[œDataœ],œtypeœ:œstrœ}",
+                "data": {
+                    "targetHandle": {
+                        "fieldName": "input_value",
+                        "id": "CustomComponent-xbSW2",
+                        "inputTypes": [
+                            "Data"
+                        ],
+                        "type": "str"
+                    },
+                    "sourceHandle": {
+                        "dataType": "Webhook",
+                        "id": "Webhook-eeCWO",
+                        "name": "output_data",
+                        "output_types": [
+                            "Data"
+                        ]
+                    }
+                },
+                "id": "reactflow__edge-Webhook-eeCWO{œdataTypeœ:œWebhookœ,œidœ:œWebhook-eeCWOœ,œnameœ:œoutput_dataœ,œoutput_typesœ:[œDataœ]}-CustomComponent-xbSW2{œfieldNameœ:œinput_valueœ,œidœ:œCustomComponent-xbSW2œ,œinputTypesœ:[œDataœ],œtypeœ:œstrœ}"
+            }
+        ],
+        "viewport": {
+            "x": -243,
+            "y": -16,
+            "zoom": 1
+        }
+    },
+    "description": "The Power of Language at Your Fingertips.",
+    "name": "Webhook Test",
+    "last_tested_version": "1.0.0a59",
+    "endpoint_name": "webhook-test",
+    "is_component": false
+}
\ No newline at end of file
diff --git a/tests/data/component_multiple_outputs.py b/tests/data/component_multiple_outputs.py
new file mode 100644
index 000000000..e0a5f6846
--- /dev/null
+++ b/tests/data/component_multiple_outputs.py
@@ -0,0 +1,20 @@
+from langflow.custom import Component
+from langflow.inputs.inputs import IntInput, TextInput
+from langflow.template.field.base import Output
+
+
+class MultipleOutputsComponent(Component):
+    inputs = [
+        TextInput(display_name="Input", name="input"),
+        IntInput(display_name="Number", name="number"),
+    ]
+    outputs = [
+        Output(display_name="Certain Output", name="certain_output", method="certain_output"),
+        Output(display_name="Other Output", name="other_output", method="other_output"),
+    ]
+
+    def certain_output(self) -> str:
+        return f"This is my string input: {self.input}"
+
+    def other_output(self) -> int:
+        return f"This is my int input multiplied by 2: {self.number * 2}"
diff --git a/tests/data/component_nested_call.py b/tests/data/component_nested_call.py
new file mode 100644
index 000000000..8dbc33cc4
--- /dev/null
+++ b/tests/data/component_nested_call.py
@@ -0,0 +1,22 @@
+from random import randint
+
+from langflow.custom import Component
+from langflow.inputs.inputs import IntInput, TextInput
+from langflow.template.field.base import Output
+
+
+class MultipleOutputsComponent(Component):
+    inputs = [
+        TextInput(display_name="Input", name="input"),
+        IntInput(display_name="Number", name="number"),
+    ]
+    outputs = [
+        Output(display_name="Certain Output", name="certain_output", method="certain_output"),
+        Output(display_name="Other Output", name="other_output", method="other_output"),
+    ]
+
+    def certain_output(self) -> int:
+        return randint(0, self.number)
+
+    def other_output(self) -> int:
+        return self.certain_output()
diff --git a/tests/data/component_with_templatefield.py b/tests/data/component_with_templatefield.py
index 26d7e834b..bc79c80d2 100644
--- a/tests/data/component_with_templatefield.py
+++ b/tests/data/component_with_templatefield.py
@@ -1,7 +1,7 @@
 import random
 
 from langflow.custom import CustomComponent
-from langflow.field_typing import TemplateField
+from langflow.field_typing import Input
 
 
 class TestComponent(CustomComponent):
@@ -11,7 +11,7 @@ class TestComponent(CustomComponent):
         return [f"Random {random.randint(1, 100)}" for _ in range(5)]
 
     def build_config(self):
-        return {"param": TemplateField(display_name="Param", options=self.refresh_values)}
+        return {"param": Input(display_name="Param", options=self.refresh_values)}
 
     def build(self, param: int):
         return param
diff --git a/tests/integration/astra/test_astra_component.py b/tests/integration/astra/test_astra_component.py
index 161e6436c..2a67924c4 100644
--- a/tests/integration/astra/test_astra_component.py
+++ b/tests/integration/astra/test_astra_component.py
@@ -1,20 +1,15 @@
 import os
+
 import pytest
+from langchain_core.documents import Document
+from langflow.components.memories.AstraDBMessageReader import AstraDBMessageReaderComponent
+from langflow.components.memories.AstraDBMessageWriter import AstraDBMessageWriterComponent
+from langflow.components.vectorsearch.AstraDBSearch import AstraDBSearchComponent
+from langflow.components.vectorstores.AstraDB import AstraVectorStoreComponent
+from langflow.schema.data import Data
 
 from integration.utils import MockEmbeddings, check_env_vars
 
-from langflow.components.memories.AstraDBMessageReader import (
-    AstraDBMessageReaderComponent,
-)
-from langflow.components.memories.AstraDBMessageWriter import (
-    AstraDBMessageWriterComponent,
-)
-from langflow.components.vectorsearch.AstraDBSearch import AstraDBSearchComponent
-from langflow.components.vectorstores.AstraDB import AstraDBVectorStoreComponent
-from langflow.schema.record import Record
-
-from langchain_core.documents import Document
-
 COLLECTION = "test_basic"
 SEARCH_COLLECTION = "test_search"
 MEMORY_COLLECTION = "test_memory"
@@ -54,7 +49,7 @@ def test_astra_setup(astra_fixture):
     api_endpoint = os.getenv("ASTRA_DB_API_ENDPOINT")
     embedding = MockEmbeddings()
 
-    component = AstraDBVectorStoreComponent()
+    component = AstraVectorStoreComponent()
     component.build(
         token=application_token,
         api_endpoint=api_endpoint,
@@ -74,9 +69,9 @@ def test_astra_embeds_and_search(astra_fixture):
     embedding = MockEmbeddings()
 
     documents = [Document(page_content="test1"), Document(page_content="test2")]
-    records = [Record.from_document(d) for d in documents]
+    records = [Data.from_document(d) for d in documents]
 
-    component = AstraDBVectorStoreComponent()
+    component = AstraVectorStoreComponent()
     component.build(
         token=application_token,
         api_endpoint=api_endpoint,
@@ -109,7 +104,7 @@ def test_astra_memory():
     writer = AstraDBMessageWriterComponent()
     reader = AstraDBMessageReaderComponent()
 
-    input_value = Record.from_document(
+    input_value = Data.from_document(
         Document(
             page_content="memory1",
             metadata={"session_id": 1, "sender": "human", "sender_name": "Bob"},
@@ -131,7 +126,7 @@ def test_astra_memory():
         collection_name=MEMORY_COLLECTION,
     )
     assert len(records) == 1
-    assert isinstance(records[0], Record)
+    assert isinstance(records[0], Data)
     content = records[0].get_text()
     assert content == "memory1"
 
diff --git a/tests/test_endpoints.py b/tests/test_endpoints.py
index 86e7ce539..510d4a428 100644
--- a/tests/test_endpoints.py
+++ b/tests/test_endpoints.py
@@ -4,6 +4,7 @@ from uuid import UUID, uuid4
 import pytest
 from fastapi import status
 from fastapi.testclient import TestClient
+
 from langflow.custom.directory_reader.directory_reader import DirectoryReader
 from langflow.services.deps import get_settings_service
 
@@ -273,7 +274,7 @@ def test_get_all(client: TestClient, logged_in_headers):
         files
     )  # Less or equal because we might have some files that don't have the dependencies installed
     assert "ChatInput" in json_response["inputs"]
-    assert "Prompt" in json_response["inputs"]
+    assert "Prompt" in json_response["prompts"]
     assert "ChatOutput" in json_response["outputs"]
 
 
@@ -447,9 +448,10 @@ def test_successful_run_no_payload(client, starter_project, created_api_key):
     assert all(["ChatOutput" in _id for _id in ids])
     display_names = [output.get("component_display_name") for output in outputs_dict.get("outputs")]
     assert all([name in display_names for name in ["Chat Output"]])
-    inner_results = [output.get("results").get("result") for output in outputs_dict.get("outputs")]
+    output_results_has_results = all("results" in output.get("results") for output in outputs_dict.get("outputs"))
+    inner_results = [output.get("results") for output in outputs_dict.get("outputs")]
 
-    assert all([result is not None for result in inner_results]), outputs_dict.get("outputs")
+    assert all([result is not None for result in inner_results]), (outputs_dict, output_results_has_results)
 
 
 def test_successful_run_with_output_type_text(client, starter_project, created_api_key):
@@ -477,9 +479,9 @@ def test_successful_run_with_output_type_text(client, starter_project, created_a
     assert all(["ChatOutput" in _id for _id in ids]), ids
     display_names = [output.get("component_display_name") for output in outputs_dict.get("outputs")]
     assert all([name in display_names for name in ["Chat Output"]]), display_names
-    inner_results = [output.get("results").get("result") for output in outputs_dict.get("outputs")]
-    expected_result = ""
-    assert all([expected_result in result for result in inner_results]), inner_results
+    inner_results = [output.get("results") for output in outputs_dict.get("outputs")]
+    expected_keys = ["message"]
+    assert all([key in result for result in inner_results for key in expected_keys]), outputs_dict
 
 
 def test_successful_run_with_output_type_any(client, starter_project, created_api_key):
@@ -508,9 +510,9 @@ def test_successful_run_with_output_type_any(client, starter_project, created_ap
     assert all(["ChatOutput" in _id or "TextOutput" in _id for _id in ids]), ids
     display_names = [output.get("component_display_name") for output in outputs_dict.get("outputs")]
     assert all([name in display_names for name in ["Chat Output"]]), display_names
-    inner_results = [output.get("results").get("result") for output in outputs_dict.get("outputs")]
-    expected_result = ""
-    assert all([expected_result in result for result in inner_results]), inner_results
+    inner_results = [output.get("results") for output in outputs_dict.get("outputs")]
+    expected_keys = ["message"]
+    assert all([key in result for result in inner_results for key in expected_keys]), outputs_dict
 
 
 def test_successful_run_with_output_type_debug(client, starter_project, created_api_key):
@@ -566,7 +568,7 @@ def test_successful_run_with_input_type_text(client, starter_project, created_ap
     text_input_outputs = [output for output in outputs_dict.get("outputs") if "TextInput" in output.get("component_id")]
     assert len(text_input_outputs) == 0
     # Now we check if the input_value is correct
-    assert all([output.get("results").get("result") == "value1" for output in text_input_outputs]), text_input_outputs
+    assert all([output.get("results") == "value1" for output in text_input_outputs]), text_input_outputs
 
 
 # Now do the same for "chat" input type
@@ -597,7 +599,9 @@ def test_successful_run_with_input_type_chat(client, starter_project, created_ap
     chat_input_outputs = [output for output in outputs_dict.get("outputs") if "ChatInput" in output.get("component_id")]
     assert len(chat_input_outputs) == 1
     # Now we check if the input_value is correct
-    assert all([output.get("results").get("text") == "value1" for output in chat_input_outputs]), chat_input_outputs
+    assert all(
+        [output.get("results").get("message").get("text") == "value1" for output in chat_input_outputs]
+    ), chat_input_outputs
 
 
 def test_successful_run_with_input_type_any(client, starter_project, created_api_key):
@@ -631,7 +635,9 @@ def test_successful_run_with_input_type_any(client, starter_project, created_api
     ]
     assert len(any_input_outputs) == 1
     # Now we check if the input_value is correct
-    assert all([output.get("results").get("text") == "value1" for output in any_input_outputs]), any_input_outputs
+    assert all(
+        [output.get("results").get("message").get("text") == "value1" for output in any_input_outputs]
+    ), any_input_outputs
 
 
 @pytest.mark.api_key_required
diff --git a/tests/test_schema.py b/tests/test_schema.py
new file mode 100644
index 000000000..1a2b69c34
--- /dev/null
+++ b/tests/test_schema.py
@@ -0,0 +1,129 @@
+from typing import Union
+
+import pytest
+from pydantic import ValidationError
+
+from langflow.template import Input, Output
+from langflow.template.field.base import UNDEFINED
+from langflow.type_extraction.type_extraction import post_process_type
+
+
+@pytest.fixture(name="client", autouse=True)
+def client_fixture():
+    pass
+
+
+class TestInput:
+    def test_field_type_str(self):
+        input_obj = Input(field_type="str")
+        assert input_obj.field_type == "str"
+
+    def test_field_type_type(self):
+        input_obj = Input(field_type=int)
+        assert input_obj.field_type == "int"
+
+    def test_invalid_field_type(self):
+        with pytest.raises(ValidationError):
+            Input(field_type=123)
+
+    def test_serialize_field_type(self):
+        input_obj = Input(field_type="str")
+        assert input_obj.serialize_field_type("str", None) == "str"
+
+    def test_validate_type_string(self):
+        input_obj = Input(field_type="str")
+        assert input_obj.field_type == "str"
+
+    def test_validate_type_class(self):
+        input_obj = Input(field_type=int)
+        assert input_obj.field_type == "int"
+
+    def test_post_process_type_function(self):
+        assert post_process_type(int) == [int]
+        assert post_process_type(list[int]) == [int]
+        assert post_process_type(Union[int, str]) == [int, str]
+
+    def test_input_to_dict(self):
+        input_obj = Input(field_type="str")
+        assert input_obj.to_dict() == {
+            "type": "str",
+            "required": False,
+            "placeholder": "",
+            "list": False,
+            "show": True,
+            "multiline": False,
+            "fileTypes": [],
+            "file_path": "",
+            "password": False,
+            "advanced": False,
+            "title_case": False,
+            "dynamic": False,
+            "info": "",
+            "input_types": ["Text"],
+            "load_from_db": False,
+        }
+
+
+class TestOutput:
+    def test_output_default(self):
+        output_obj = Output(name="test_output")
+        assert output_obj.name == "test_output"
+        assert output_obj.value == UNDEFINED
+        assert output_obj.cache is True
+
+    def test_output_add_types(self):
+        output_obj = Output(name="test_output")
+        output_obj.add_types(["str", "int"])
+        assert output_obj.types == ["str", "int"]
+
+    def test_output_set_selected(self):
+        output_obj = Output(name="test_output", types=["str", "int"])
+        output_obj.set_selected()
+        assert output_obj.selected == "str"
+
+    def test_output_to_dict(self):
+        output_obj = Output(name="test_output")
+        assert output_obj.to_dict() == {
+            "types": [],
+            "name": "test_output",
+            "display_name": "test_output",
+            "cache": True,
+            "value": "__UNDEFINED__",
+        }
+
+    def test_output_validate_display_name(self):
+        output_obj = Output(name="test_output")
+        assert output_obj.display_name == "test_output"
+
+    def test_output_validate_model(self):
+        output_obj = Output(name="test_output", value="__UNDEFINED__")
+        assert output_obj.validate_model() == output_obj
+
+
+class TestPostProcessType:
+    def test_int_type(self):
+        assert post_process_type(int) == [int]
+
+    def test_list_int_type(self):
+        assert post_process_type(list[int]) == [int]
+
+    def test_union_type(self):
+        assert post_process_type(Union[int, str]) == [int, str]
+
+    def test_custom_type(self):
+        class CustomType:
+            pass
+
+        assert post_process_type(CustomType) == [CustomType]
+
+    def test_list_custom_type(self):
+        class CustomType:
+            pass
+
+        assert post_process_type(list[CustomType]) == [CustomType]
+
+    def test_union_custom_type(self):
+        class CustomType:
+            pass
+
+        assert post_process_type(Union[CustomType, int]) == [CustomType, int]
diff --git a/tests/test_webhook.py b/tests/test_webhook.py
new file mode 100644
index 000000000..c8d75c9cc
--- /dev/null
+++ b/tests/test_webhook.py
@@ -0,0 +1,28 @@
+import tempfile
+from pathlib import Path
+
+
+def test_webhook_endpoint(client, added_webhook_test):
+    # The test is as follows:
+    # 1. The flow when run will get a "path" from the payload and save a file with the path as the name.
+    # We will create a temporary file path and send it to the webhook endpoint, then check if the file exists.
+    # 2. we will delete the file, then send an invalid payload to the webhook endpoint and check if the file exists.
+    endpoint_name = added_webhook_test["endpoint_name"]
+    endpoint = f"api/v1/webhook/{endpoint_name}"
+    # Create a temporary file
+    with tempfile.TemporaryDirectory() as tmp:
+        file_path = Path(tmp) / "test_file.txt"
+
+        payload = {"path": str(file_path)}
+
+        response = client.post(endpoint, json=payload)
+        assert response.status_code == 202
+        assert file_path.exists()
+
+    assert not file_path.exists()
+
+    # Send an invalid payload
+    payload = {"invalid_key": "invalid_value"}
+    response = client.post(endpoint, json=payload)
+    assert response.status_code == 202
+    assert not file_path.exists()
diff --git a/tests/unit/test_custom_component.py b/tests/unit/test_custom_component.py
index 0b64f2c1a..39636d28f 100644
--- a/tests/unit/test_custom_component.py
+++ b/tests/unit/test_custom_component.py
@@ -5,13 +5,21 @@ from uuid import uuid4
 import pytest
 from langchain_core.documents import Document
 
-from langflow.custom import CustomComponent
+from langflow.custom import Component, CustomComponent
 from langflow.custom.code_parser.code_parser import CodeParser, CodeSyntaxError
-from langflow.custom.custom_component.component import Component, ComponentCodeNullError
+from langflow.custom.custom_component.base_component import BaseComponent, ComponentCodeNullError
+from langflow.custom.utils import build_custom_component_template
 from langflow.services.database.models.flow import Flow, FlowCreate
 
+
+@pytest.fixture
+def code_component_with_multiple_outputs():
+    with open("tests/data/component_multiple_outputs.py", "r") as f:
+        code = f.read()
+        return Component(code=code)
+
+
 code_default = """
-from langflow.field_typing import Prompt
 from langflow.custom import CustomComponent
 
 from langflow.field_typing import BaseLanguageModel
@@ -26,12 +34,8 @@ class YourComponent(CustomComponent):
     description: str = "Your description"
     field_config = { "url": { "multiline": True, "required": True } }
 
-    def build(self, url: str, llm: BaseLanguageModel, template: Prompt) -> Document:
-        response = requests.get(url)
-        prompt = PromptTemplate.from_template(template)
-        chain = LLMChain(llm=llm, prompt=prompt)
-        result = chain.run(response.text[:300])
-        return Document(page_content=str(result))
+    def build(self, url: str, llm: BaseLanguageModel) -> Document:
+        return Document(page_content="Hello World")
 """
 
 
@@ -68,7 +72,7 @@ def test_component_init():
     """
     Test the initialization of the Component class.
     """
-    component = Component(code=code_default, function_entrypoint_name="build")
+    component = BaseComponent(code=code_default, function_entrypoint_name="build")
     assert component.code == code_default
     assert component.function_entrypoint_name == "build"
 
@@ -77,7 +81,7 @@ def test_component_get_code_tree():
     """
     Test the get_code_tree method of the Component class.
     """
-    component = Component(code=code_default, function_entrypoint_name="build")
+    component = BaseComponent(code=code_default, function_entrypoint_name="build")
     tree = component.get_code_tree(component.code)
     assert "imports" in tree
 
@@ -87,7 +91,7 @@ def test_component_code_null_error():
     Test the get_function method raises the
     ComponentCodeNullError when the code is empty.
     """
-    component = Component(code="", function_entrypoint_name="")
+    component = BaseComponent(code="", function_entrypoint_name="")
     with pytest.raises(ComponentCodeNullError):
         component.get_function()
 
@@ -191,7 +195,7 @@ def test_component_get_function_valid():
     Test the get_function method of the Component
     class with valid code and function_entrypoint_name.
     """
-    component = Component(code="def build(): pass", function_entrypoint_name="build")
+    component = BaseComponent(code="def build(): pass", function_entrypoint_name="build")
     my_function = component.get_function()
     assert callable(my_function)
 
@@ -203,7 +207,7 @@ def test_custom_component_get_function_entrypoint_args():
     """
     custom_component = CustomComponent(code=code_default, function_entrypoint_name="build")
     args = custom_component.get_function_entrypoint_args
-    assert len(args) == 4
+    assert len(args) == 3
     assert args[0]["name"] == "self"
     assert args[1]["name"] == "url"
     assert args[2]["name"] == "llm"
@@ -348,7 +352,7 @@ def test_component_get_code_tree_syntax_error():
     Test the get_code_tree method of the Component class
     raises the CodeSyntaxError when given incorrect syntax.
     """
-    component = Component(code="import os as", function_entrypoint_name="build")
+    component = BaseComponent(code="import os as", function_entrypoint_name="build")
     with pytest.raises(CodeSyntaxError):
         component.get_code_tree(component.code)
 
@@ -517,3 +521,8 @@ def test_build_config_field_value_keys(component):
     config = component.build_config()
     field_values = config["fields"].values()
     assert all("type" in value for value in field_values)
+
+
+def test_custom_component_multiple_outputs(code_component_with_multiple_outputs, active_user):
+    frontnd_node_dict, _ = build_custom_component_template(code_component_with_multiple_outputs, active_user.id)
+    assert frontnd_node_dict["outputs"][0]["types"] == ["Text"]
diff --git a/tests/unit/test_record.py b/tests/unit/test_data_class.py
similarity index 70%
rename from tests/unit/test_record.py
rename to tests/unit/test_data_class.py
index 45afaa5af..83ee12bd0 100644
--- a/tests/unit/test_record.py
+++ b/tests/unit/test_data_class.py
@@ -1,23 +1,23 @@
+import pytest
 from langchain_core.documents import Document
-
-from langflow.schema import Record
+from langflow.schema import Data
 
 
-def test_record_initialization():
-    record = Record(text_key="msg", data={"msg": "Hello, World!", "extra": "value"})
+def test_data_initialization():
+    record = Data(text_key="msg", data={"msg": "Hello, World!", "extra": "value"})
     assert record.msg == "Hello, World!"
     assert record.extra == "value"
 
 
 def test_validate_data_with_extra_keys():
-    record = Record(dummy_key="dummy", data={"key": "value"})
+    record = Data(dummy_key="dummy", data={"key": "value"})
     assert record.data["dummy_key"] == "dummy"
     assert "dummy_key" in record.data
     assert record.key == "value"
 
 
 def test_conversion_to_document():
-    record = Record(data={"text": "Sample text", "meta": "data"})
+    record = Data(data={"text": "Sample text", "meta": "data"})
     document = record.to_lc_document()
     assert document.page_content == "Sample text"
     assert document.metadata == {"meta": "data"}
@@ -25,45 +25,46 @@ def test_conversion_to_document():
 
 def test_conversion_from_document():
     document = Document(page_content="Doc content", metadata={"meta": "info"})
-    record = Record.from_document(document)
+    record = Data.from_document(document)
     assert record.text == "Doc content"
     assert record.meta == "info"
 
 
 def test_add_method_for_strings():
-    record1 = Record(data={"text": "Hello"})
-    record2 = Record(data={"text": " World"})
+    record1 = Data(data={"text": "Hello"})
+    record2 = Data(data={"text": " World"})
     combined = record1 + record2
     assert combined.text == "Hello World"
 
 
 def test_add_method_for_integers():
-    record1 = Record(data={"number": 5})
-    record2 = Record(data={"number": 10})
+    record1 = Data(data={"number": 5})
+    record2 = Data(data={"number": 10})
     combined = record1 + record2
     assert combined.number == 15
 
 
 def test_add_method_with_non_overlapping_keys():
-    record1 = Record(data={"text": "Hello"})
-    record2 = Record(data={"number": 10})
+    record1 = Data(data={"text": "Hello"})
+    record2 = Data(data={"number": 10})
     combined = record1 + record2
     assert combined.text == "Hello"
     assert combined.number == 10
 
 
 def test_custom_attribute_get_set_del():
-    record = Record()
+    record = Data()
     record.custom_attr = "custom_value"
     assert record.custom_attr == "custom_value"
     del record.custom_attr
-    assert record.custom_attr == record.default_value
+    with pytest.raises(AttributeError):
+        _ = record.custom_attr
 
 
 def test_deep_copy():
     import copy
 
-    record1 = Record(data={"text": "Hello", "number": 10})
+    record1 = Data(data={"text": "Hello", "number": 10})
     record2 = copy.deepcopy(record1)
     assert record2.text == "Hello"
     assert record2.number == 10
@@ -72,20 +73,20 @@ def test_deep_copy():
 
 
 def test_custom_attribute_setting_and_getting():
-    record = Record()
+    record = Data()
     record.dynamic_attribute = "Dynamic Value"
     assert record.dynamic_attribute == "Dynamic Value"
 
 
 def test_str_and_dir_methods():
-    record = Record(text_key="text", data={"text": "Test Text", "key": "value"})
+    record = Data(text_key="text", data={"text": "Test Text", "key": "value"})
     assert "Test Text" in str(record)
     assert "key" in dir(record)
     assert "data" in dir(record)
 
 
 def test_dir_includes_data_keys():
-    record = Record(data={"text": "Hello", "new_attr": "value"})
+    record = Data(data={"text": "Hello", "new_attr": "value"})
     dir_output = dir(record)
 
     # Check for standard attributes
@@ -103,7 +104,7 @@ def test_dir_includes_data_keys():
 
 
 def test_dir_reflects_attribute_deletion():
-    record = Record(data={"removable": "I can be removed"})
+    record = Data(data={"removable": "I can be removed"})
     assert "removable" in dir(record)
 
     # Delete the attribute and check again
@@ -113,27 +114,27 @@ def test_dir_reflects_attribute_deletion():
 
 def test_get_text_with_text_key():
     data = {"text": "Hello, World!"}
-    schema = Record(data=data, text_key="text", default_value="default")
+    schema = Data(data=data, text_key="text", default_value="default")
     result = schema.get_text()
     assert result == "Hello, World!"
 
 
 def test_get_text_without_text_key():
     data = {"other_key": "Hello, World!"}
-    schema = Record(data=data, text_key="text", default_value="default")
+    schema = Data(data=data, text_key="text", default_value="default")
     result = schema.get_text()
     assert result == "default"
 
 
 def test_get_text_with_empty_data():
     data = {}
-    schema = Record(data=data, text_key="text", default_value="default")
+    schema = Data(data=data, text_key="text", default_value="default")
     result = schema.get_text()
     assert result == "default"
 
 
 def test_get_text_with_none_data():
     data = None
-    schema = Record(data=data, text_key="text", default_value="default")
+    schema = Data(data=data, text_key="text", default_value="default")
     result = schema.get_text()
     assert result == "default"
diff --git a/tests/unit/test_data_components.py b/tests/unit/test_data_components.py
index 361ca61cd..79b6c300e 100644
--- a/tests/unit/test_data_components.py
+++ b/tests/unit/test_data_components.py
@@ -1,3 +1,4 @@
+import json
 import os
 from pathlib import Path
 from unittest.mock import Mock, patch
@@ -14,7 +15,7 @@ from langflow.components import data
 @pytest.fixture
 def api_request():
     # This fixture provides an instance of APIRequest for each test case
-    return data.APIRequest()
+    return data.APIRequestComponent()
 
 
 @pytest.mark.asyncio
@@ -103,17 +104,23 @@ async def test_build_with_multiple_urls(api_request):
     #
 
     # Execute the build method
-    results = await api_request.build(method=method, urls=urls)
+    api_request.set_attributes(
+        {
+            "method": method,
+            "urls": urls,
+        }
+    )
+    results = await api_request.make_requests()
 
     # Assertions
     assert len(results) == len(urls)
 
 
-@patch("langflow.components.data.Directory.parallel_load_records")
+@patch("langflow.components.data.Directory.parallel_load_data")
 @patch("langflow.components.data.Directory.retrieve_file_paths")
 @patch("langflow.components.data.DirectoryComponent.resolve_path")
 def test_directory_component_build_with_multithreading(
-    mock_resolve_path, mock_retrieve_file_paths, mock_parallel_load_records
+    mock_resolve_path, mock_retrieve_file_paths, mock_parallel_load_data
 ):
     # Arrange
     directory_component = data.DirectoryComponent()
@@ -129,23 +136,26 @@ def test_directory_component_build_with_multithreading(
     mock_retrieve_file_paths.return_value = [
         os.path.join(path, file) for file in os.listdir(path) if file.endswith(".py")
     ]
-    mock_parallel_load_records.return_value = [Mock()]
+    mock_parallel_load_data.return_value = [Mock()]
 
     # Act
-    directory_component.build(
-        path,
-        depth,
-        max_concurrency,
-        load_hidden,
-        recursive,
-        silent_errors,
-        use_multithreading,
+    directory_component.set_attributes(
+        {
+            "path": path,
+            "depth": depth,
+            "max_concurrency": max_concurrency,
+            "load_hidden": load_hidden,
+            "recursive": recursive,
+            "silent_errors": silent_errors,
+            "use_multithreading": use_multithreading,
+        }
     )
+    directory_component.load_directory()
 
     # Assert
     mock_resolve_path.assert_called_once_with(path)
     mock_retrieve_file_paths.assert_called_once_with(path, load_hidden, recursive, depth)
-    mock_parallel_load_records.assert_called_once_with(
+    mock_parallel_load_data.assert_called_once_with(
         mock_retrieve_file_paths.return_value, silent_errors, max_concurrency
     )
 
@@ -161,26 +171,31 @@ def test_directory_without_mocks():
     # the directory component can be used to load the projects
     # and we can validate if the contents are the same as the projects variable
     setup_path = Path(setup.__file__).parent / "starter_projects"
-    results = directory_component.build(str(setup_path), use_multithreading=False)
+    directory_component.set_attributes({"path": str(setup_path), "use_multithreading": False})
+    results = directory_component.load_directory()
     assert len(results) == len(projects)
-    # each result is a Record that contains the content attribute
+    # each result is a Data that contains the content attribute
     # each are dict that are exactly the same as one of the projects
     for i, result in enumerate(results):
-        assert result.text in projects, list(diff(result.text, projects[i]))
+        file_dict = json.loads(result.text)
+        assert file_dict in projects, list(diff(file_dict, projects[i]))
 
     # in ../docs/docs/components there are many mdx files
     # check if the directory component can load them
     # just check if the number of results is the same as the number of files
+    directory_component = data.DirectoryComponent()
     docs_path = Path(__file__).parent.parent.parent / "docs" / "docs" / "components"
-    results = directory_component.build(str(docs_path), use_multithreading=False)
+    directory_component.set_attributes({"path": str(docs_path), "use_multithreading": False})
+    results = directory_component.load_directory()
     docs_files = list(docs_path.glob("*.mdx"))
     assert len(results) == len(docs_files)
 
 
 def test_url_component():
     url_component = data.URLComponent()
+    url_component.set_attributes({"urls": ["https://langflow.org"]})
     # the url component can be used to load the contents of a website
-    records = url_component.build(["https://langflow.org"])
-    assert all(record.data for record in records)
-    assert all(record.text for record in records)
-    assert all(record.source for record in records)
+    _data = url_component.fetch_content()
+    assert all(value.data for value in _data)
+    assert all(value.text for value in _data)
+    assert all(value.source for value in _data)
diff --git a/tests/unit/test_frontend_nodes.py b/tests/unit/test_frontend_nodes.py
index e92ad1fe4..ad3c44832 100644
--- a/tests/unit/test_frontend_nodes.py
+++ b/tests/unit/test_frontend_nodes.py
@@ -1,16 +1,17 @@
 import pytest
-from langflow.template.field.base import TemplateField
+
+from langflow.template.field.base import Input
 from langflow.template.frontend_node.base import FrontendNode
 from langflow.template.template.base import Template
 
 
 @pytest.fixture
-def sample_template_field() -> TemplateField:
-    return TemplateField(name="test_field", field_type="str")
+def sample_template_field() -> Input:
+    return Input(name="test_field", field_type="str")
 
 
 @pytest.fixture
-def sample_template(sample_template_field: TemplateField) -> Template:
+def sample_template(sample_template_field: Input) -> Template:
     return Template(type_name="test_template", fields=[sample_template_field])
 
 
@@ -24,21 +25,21 @@ def sample_frontend_node(sample_template: Template) -> FrontendNode:
     )
 
 
-def test_template_field_defaults(sample_template_field: TemplateField):
+def test_template_field_defaults(sample_template_field: Input):
     assert sample_template_field.field_type == "str"
     assert sample_template_field.required is False
     assert sample_template_field.placeholder == ""
     assert sample_template_field.is_list is False
     assert sample_template_field.show is True
     assert sample_template_field.multiline is False
-    assert sample_template_field.value == ""
+    assert sample_template_field.value is None
     assert sample_template_field.file_types == []
     assert sample_template_field.file_path == ""
     assert sample_template_field.password is False
     assert sample_template_field.name == "test_field"
 
 
-def test_template_to_dict(sample_template: Template, sample_template_field: TemplateField):
+def test_template_to_dict(sample_template: Template, sample_template_field: Input):
     template_dict = sample_template.to_dict()
     assert template_dict["_type"] == "test_template"
     assert len(template_dict) == 2  # _type and test_field
diff --git a/tests/unit/test_helper_components.py b/tests/unit/test_helper_components.py
index 01a69de74..3baaf1be6 100644
--- a/tests/unit/test_helper_components.py
+++ b/tests/unit/test_helper_components.py
@@ -1,35 +1,34 @@
 from langchain_core.documents import Document
-
 from langflow.components import helpers
 from langflow.custom.utils import build_custom_component_template
-from langflow.schema import Record
+from langflow.schema import Data
 
 
-def test_update_record_component():
+def test_update_data_component():
     # Arrange
-    update_record_component = helpers.UpdateRecordComponent()
+    update_data_component = helpers.UpdateDataComponent()
 
     # Act
     new_data = {"new_key": "new_value"}
-    existing_record = Record(data={"existing_key": "existing_value"})
-    result = update_record_component.build(existing_record, new_data)
+    existing_data = Data(data={"existing_key": "existing_value"})
+    result = update_data_component.build(existing_data, new_data)
     assert result.data == {"existing_key": "existing_value", "new_key": "new_value"}
     assert result.existing_key == "existing_value"
     assert result.new_key == "new_value"
 
 
-def test_document_to_record_component():
+def test_document_to_data_component():
     # Arrange
-    document_to_record_component = helpers.DocumentToRecordComponent()
+    document_to_data_component = helpers.DocumentsToDataComponent()
 
     # Act
     # Replace with your actual test data
     document = Document(page_content="key: value", metadata={"url": "https://example.com"})
-    result = document_to_record_component.build(document)
+    result = document_to_data_component.build(document)
 
     # Assert
     # Replace with your actual expected result
-    assert result == [Record(data={"text": "key: value", "url": "https://example.com"})]
+    assert result == [Data(data={"text": "key: value", "url": "https://example.com"})]
 
 
 def test_uuid_generator_component():
@@ -52,30 +51,31 @@ def test_uuid_generator_component():
     assert len(result) == 36
 
 
-def test_records_as_text_component():
+def test_data_as_text_component():
     # Arrange
-    records_as_text_component = helpers.RecordsToTextComponent()
+    data_as_text_component = helpers.ParseDataComponent()
 
     # Act
     # Replace with your actual test data
-    records = [Record(data={"key": "value", "bacon": "eggs"})]
+    data = [Data(data={"key": "value", "bacon": "eggs"})]
     template = "Data:{data} -- Bacon:{bacon}"
-    result = records_as_text_component.build(records, template=template)
+    data_as_text_component.set_attributes({"data": data, "template": template})
+    result = data_as_text_component.parse_data()
 
     # Assert
     # Replace with your actual expected result
-    assert result == "Data:{'key': 'value', 'bacon': 'eggs'} -- Bacon:eggs"
+    assert result.text == "Data:{'key': 'value', 'bacon': 'eggs'} -- Bacon:eggs"
 
 
-def test_text_to_record_component():
+def test_text_to_data_component():
     # Arrange
-    text_to_record_component = helpers.CreateRecordComponent()
+    text_to_data_component = helpers.CreateDataComponent()
 
     # Act
     # Replace with your actual test data
     dict_with_text = {"field_1": {"key": "value"}}
-    result = text_to_record_component.build(number_of_fields=1, **dict_with_text)
+    result = text_to_data_component.build(number_of_fields=1, **dict_with_text)
 
     # Assert
     # Replace with your actual expected result
-    assert result == Record(data={"key": "value"})
+    assert result == Data(data={"key": "value"})
diff --git a/tests/unit/test_initial_setup.py b/tests/unit/test_initial_setup.py
index d4f86a73f..8bfb3d5df 100644
--- a/tests/unit/test_initial_setup.py
+++ b/tests/unit/test_initial_setup.py
@@ -1,14 +1,10 @@
 from datetime import datetime
 from pathlib import Path
 
+import pytest
 from sqlmodel import select
 
-from langflow.initial_setup.setup import (
-    STARTER_FOLDER_NAME,
-    create_or_update_starter_projects,
-    get_project_data,
-    load_starter_projects,
-)
+from langflow.initial_setup.setup import STARTER_FOLDER_NAME, get_project_data, load_starter_projects
 from langflow.services.database.models.folder.model import Folder
 from langflow.services.deps import session_scope
 
@@ -41,16 +37,15 @@ def test_get_project_data():
         assert isinstance(project_icon_bg_color, str) or project_icon_bg_color is None
 
 
-def test_create_or_update_starter_projects(client):
+@pytest.mark.asyncio
+async def test_create_or_update_starter_projects():
     with session_scope() as session:
-        # Run the function to create or update projects
-        create_or_update_starter_projects()
-
         # Get the number of projects returned by load_starter_projects
         num_projects = len(load_starter_projects())
 
         # Get the number of projects in the database
         folder = session.exec(select(Folder).where(Folder.name == STARTER_FOLDER_NAME)).first()
+        assert folder is not None
         num_db_projects = len(folder.flows)
 
         # Check that the number of projects in the database is the same as the number of projects returned by load_starter_projects
diff --git a/tests/unit/test_template.py b/tests/unit/test_template.py
index 95ea4f528..49bc7bd4d 100644
--- a/tests/unit/test_template.py
+++ b/tests/unit/test_template.py
@@ -2,7 +2,6 @@ import importlib
 from typing import Dict, List, Optional
 
 import pytest
-from langflow.interface.utils import build_template_from_class
 from langflow.utils.util import build_template_from_function, get_base_classes, get_default_factory
 from pydantic import BaseModel
 
@@ -68,25 +67,6 @@ def test_build_template_from_function():
         build_template_from_function("NonExistent", type_to_loader_dict)
 
 
-# Test build_template_from_class
-def test_build_template_from_class():
-    type_to_cls_dict: Dict[str, type] = {"parent": Parent, "child": Child}
-
-    # Test valid input
-    result = build_template_from_class("Child", type_to_cls_dict)
-    assert result is not None
-    assert "template" in result
-    assert "description" in result
-    assert "base_classes" in result
-    assert "Child" in result["base_classes"]
-    assert "Parent" in result["base_classes"]
-    assert result["description"] == "Child Class"
-
-    # Test invalid input
-    with pytest.raises(ValueError, match="InvalidClass not found."):
-        build_template_from_class("InvalidClass", type_to_cls_dict)
-
-
 # Test get_base_classes
 def test_get_base_classes():
     base_classes_parent = get_base_classes(Parent)