Merge cz/mergeAll to two_edges

.gitattributes

@@ -32,3 +32,4 @@ Dockerfile text
 *.mp4 binary
 *.svg binary
 *.csv binary
+

.vscode/launch.json

@@ -3,7 +3,7 @@
   "configurations": [
     {
       "name": "Debug Backend",
-      "type": "python",
+      "type": "debugpy",
       "request": "launch",
       "module": "uvicorn",
       "args": [
@@ -26,7 +26,7 @@
     },
     {
       "name": "Debug CLI",
-      "type": "python",
+      "type": "debugpy",
       "request": "launch",
       "module": "langflow",
       "args": [
@@ -43,7 +43,7 @@
     },
     {
       "name": "Python: Remote Attach",
-      "type": "python",
+      "type": "debugpy",
       "request": "attach",
       "justMyCode": true,
       "connect": {
@@ -65,7 +65,7 @@
     },
     {
       "name": "Python: Debug Tests",
-      "type": "python",
+      "type": "debugpy",
       "request": "launch",
       "program": "${file}",
       "purpose": ["debug-test"],

Makefile

@@ -44,7 +44,8 @@ coverage:
 	poetry run pytest --cov \
 		--cov-config=.coveragerc \
 		--cov-report xml \
-		--cov-report term-missing:skip-covered
+		--cov-report term-missing:skip-covered \
+		--cov-report lcov:coverage/lcov-pytest.info
 
 # allow passing arguments to pytest
 tests:

docs/docs/components/agents.mdx

@@ -1,11 +1,13 @@
-import Admonition from '@theme/Admonition';
+import Admonition from "@theme/Admonition";
 
 # Agents
 
 <Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
-    <p>
+  <p>
-        We appreciate your understanding as we polish our documentation – it may contain some rough edges. Share your feedback or report issues to help us improve! 🛠️📝
+    We appreciate your understanding as we polish our documentation – it may
-    </p>
+    contain some rough edges. Share your feedback or report issues to help us
+    improve! 🛠️📝
+  </p>
 </Admonition>
 
 Agents are components that use reasoning to make decisions and take actions, designed to autonomously perform tasks or provide services with some degree of agency. LLM chains can only perform hardcoded sequences of actions, while agents use LLMs to reason through which actions to take, and in which order.
@@ -87,4 +89,4 @@ The `ZeroShotAgent` uses the ReAct framework to decide which tool to use based o
 **Parameters**:
 
 - **Allowed Tools:** The tools accessible to the agent.
-- **LLM Chain:** The LLM Chain used by the agent.
+- **LLM Chain:** The LLM Chain used by the agent.

docs/docs/components/chains.mdx

@@ -6,11 +6,11 @@ import Admonition from "@theme/Admonition";
 # Chains
 
 <Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
-<p>
+  <p>
-  Thank you for your patience while we enhance our documentation. It may
+    Thank you for your patience while we enhance our documentation. It may have
-  have some imperfections. Share your feedback or report issues to help us
+    some imperfections. Share your feedback or report issues to help us improve!
-  improve! 🛠️📝
+    🛠️📝
-</p>
+  </p>
 </Admonition>
 
 Chains, in the context of language models, refer to a series of calls made to a language model. This approach allows for using the output of one call as the input for another. Different chain types facilitate varying complexity levels, making them useful for creating pipelines and executing specific scenarios.

docs/docs/components/data.mdx

@@ -1,4 +1,4 @@
-import Admonition from '@theme/Admonition';
+import Admonition from "@theme/Admonition";
 
 # Data
 

docs/docs/components/embeddings.mdx

@@ -4,113 +4,113 @@
 
 Used to load embedding models from [Amazon Bedrock](https://aws.amazon.com/bedrock/).
 
-| **Parameter**               | **Type**          | **Description**                                                                                                                    | **Default** |
+| **Parameter**              | **Type** | **Description**                                                                                                                                     | **Default** |
-|-----------------------------|-------------------|------------------------------------------------------------------------------------------------------------------------------------|-------------|
+| -------------------------- | -------- | --------------------------------------------------------------------------------------------------------------------------------------------------- | ----------- |
-| `credentials_profile_name`  | `str`             | Name of the AWS credentials profile in ~/.aws/credentials or ~/.aws/config, which has access keys or role information.            |             |
+| `credentials_profile_name` | `str`    | Name of the AWS credentials profile in ~/.aws/credentials or ~/.aws/config, which has access keys or role information.                              |             |
-| `model_id`                  | `str`             | ID of the model to call, e.g., `amazon.titan-embed-text-v1`. This is equivalent to the `modelId` property in the `list-foundation-models` API. |             |
+| `model_id`                 | `str`    | ID of the model to call, e.g., `amazon.titan-embed-text-v1`. This is equivalent to the `modelId` property in the `list-foundation-models` API.      |             |
-| `endpoint_url`              | `str`             | URL to set a specific service endpoint other than the default AWS endpoint.                                                       |             |
+| `endpoint_url`             | `str`    | URL to set a specific service endpoint other than the default AWS endpoint.                                                                         |             |
-| `region_name`               | `str`             | AWS region to use, e.g., `us-west-2`. Falls back to `AWS_DEFAULT_REGION` environment variable or region specified in ~/.aws/config if not provided. |             |
+| `region_name`              | `str`    | AWS region to use, e.g., `us-west-2`. Falls back to `AWS_DEFAULT_REGION` environment variable or region specified in ~/.aws/config if not provided. |             |
 
 ## Cohere Embeddings
 
 Used to load embedding models from [Cohere](https://cohere.com/).
 
-| **Parameter**       | **Type**          | **Description**                                                                                                               | **Default**           |
+| **Parameter**    | **Type** | **Description**                                                           | **Default**          |
-|---------------------|-------------------|-------------------------------------------------------------------------------------------------------------------------------|-----------------------|
+| ---------------- | -------- | ------------------------------------------------------------------------- | -------------------- |
-| `cohere_api_key`    | `str`             | API key required to authenticate with the Cohere service.                                                                      |                       |
+| `cohere_api_key` | `str`    | API key required to authenticate with the Cohere service.                 |                      |
-| `model`             | `str`             | Language model used for embedding text documents and performing queries.                                                       | `embed-english-v2.0`  |
+| `model`          | `str`    | Language model used for embedding text documents and performing queries.  | `embed-english-v2.0` |
-| `truncate`          | `bool`            | Whether to truncate the input text to fit within the model's constraints.                                                      | `False`               |
+| `truncate`       | `bool`   | Whether to truncate the input text to fit within the model's constraints. | `False`              |
 
 ## Azure OpenAI Embeddings
 
 Generate embeddings using Azure OpenAI models.
 
-| **Parameter**       | **Type**          | **Description**                                                                                                               | **Default**           |
+| **Parameter**     | **Type** | **Description**                                                                                    | **Default** |
-|---------------------|-------------------|-------------------------------------------------------------------------------------------------------------------------------|-----------------------|
+| ----------------- | -------- | -------------------------------------------------------------------------------------------------- | ----------- |
-| `Azure Endpoint`    | `str`             | Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/`                             |                       |
+| `Azure Endpoint`  | `str`    | Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/` |             |
-| `Deployment Name`   | `str`             | The name of the deployment.                                                                                                    |                       |
+| `Deployment Name` | `str`    | The name of the deployment.                                                                        |             |
-| `API Version`       | `str`             | The API version to use, options include various dates.                                                                         |                       |
+| `API Version`     | `str`    | The API version to use, options include various dates.                                             |             |
-| `API Key`           | `str`             | The API key to access the Azure OpenAI service.                                                                                |                       |
+| `API Key`         | `str`    | The API key to access the Azure OpenAI service.                                                    |             |
 
 ## Hugging Face API Embeddings
 
 Generate embeddings using Hugging Face Inference API models.
 
-| **Parameter**       | **Type**          | **Description**                                                                                                               | **Default**           |
+| **Parameter**   | **Type** | **Description**                                       | **Default**              |
-|---------------------|-------------------|-------------------------------------------------------------------------------------------------------------------------------|-----------------------|
+| --------------- | -------- | ----------------------------------------------------- | ------------------------ |
-| `API Key`           | `str`             | API key for accessing the Hugging Face Inference API.                                                                          |                       |
+| `API Key`       | `str`    | API key for accessing the Hugging Face Inference API. |                          |
-| `API URL`           | `str`             | URL of the Hugging Face Inference API.                                                                                         | `http://localhost:8080` |
+| `API URL`       | `str`    | URL of the Hugging Face Inference API.                | `http://localhost:8080`  |
-| `Model Name`        | `str`             | Name of the model to use for embeddings.                                                                                       | `BAAI/bge-large-en-v1.5` |
+| `Model Name`    | `str`    | Name of the model to use for embeddings.              | `BAAI/bge-large-en-v1.5` |
-| `Cache Folder`      | `str`             | Folder path to cache Hugging Face models.                                                                                      |                       |
+| `Cache Folder`  | `str`    | Folder path to cache Hugging Face models.             |                          |
-| `Encode Kwargs`     | `dict`            | Additional arguments for the encoding process.                                                                                 |                       |
+| `Encode Kwargs` | `dict`   | Additional arguments for the encoding process.        |                          |
-| `Model Kwargs`      | `dict`            | Additional arguments for the model.                                                                                            |                       |
+| `Model Kwargs`  | `dict`   | Additional arguments for the model.                   |                          |
-| `Multi Process`     | `bool`            | Whether to use multiple processes.                                                                                             | `False`               |
+| `Multi Process` | `bool`   | Whether to use multiple processes.                    | `False`                  |
 
 ## Hugging Face Embeddings
 
 Used to load embedding models from [HuggingFace](https://huggingface.co).
 
-| **Parameter**       | **Type**          | **Description**                                                                                                               | **Default**           |
+| **Parameter**   | **Type** | **Description**                                | **Default**                               |
-|---------------------|-------------------|-------------------------------------------------------------------------------------------------------------------------------|-----------------------|
+| --------------- | -------- | ---------------------------------------------- | ----------------------------------------- |
-| `Cache Folder`      | `str`             | Folder path to cache HuggingFace models.                                                                                       |                       |
+| `Cache Folder`  | `str`    | Folder path to cache HuggingFace models.       |                                           |
-| `Encode Kwargs`     | `dict`            | Additional arguments for the encoding process.                                                                                 |                       |
+| `Encode Kwargs` | `dict`   | Additional arguments for the encoding process. |                                           |
-| `Model Kwargs`      | `dict`            | Additional arguments for the model.                                                                                            |                       |
+| `Model Kwargs`  | `dict`   | Additional arguments for the model.            |                                           |
-| `Model Name`        | `str`             | Name of the HuggingFace model to use.                                                                                          | `sentence-transformers/all-mpnet-base-v2` |
+| `Model Name`    | `str`    | Name of the HuggingFace model to use.          | `sentence-transformers/all-mpnet-base-v2` |
-| `Multi Process`     | `bool`            | Whether to use multiple processes.                                                                                             | `False`               |
+| `Multi Process` | `bool`   | Whether to use multiple processes.             | `False`                                   |
 
 ## OpenAI Embeddings
 
 Used to load embedding models from [OpenAI](https://openai.com/).
 
-| **Parameter**               | **Type**          | **Description**                                                                                                                                                       | **Default** |
+| **Parameter**              | **Type**         | **Description**                                  | **Default**              |
-|-----------------------------|-------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|
+| -------------------------- | ---------------- | ------------------------------------------------ | ------------------------ |
-| `OpenAI API Key`            | `str`             | The API key to use for accessing the OpenAI API.                                                                                                                      |             |
+| `OpenAI API Key`           | `str`            | The API key to use for accessing the OpenAI API. |                          |
-| `Default Headers`           | `Dict[str, str]`  | Default headers for the HTTP requests.                                                                                                                                |             |
+| `Default Headers`          | `Dict[str, str]` | Default headers for the HTTP requests.           |                          |
-| `Default Query`             | `NestedDict`      | Default query parameters for the HTTP requests.                                                                                                                       |             |
+| `Default Query`            | `NestedDict`     | Default query parameters for the HTTP requests.  |                          |
-| `Allowed Special`           | `List[str]`       | Special tokens allowed for processing.                                                                                                                                | `[]`        |
+| `Allowed Special`          | `List[str]`      | Special tokens allowed for processing.           | `[]`                     |
-| `Disallowed Special`        | `List[str]`       | Special tokens disallowed for processing.                                                                                                                              | `["all"]`   |
+| `Disallowed Special`       | `List[str]`      | Special tokens disallowed for processing.        | `["all"]`                |
-| `Chunk Size`                | `int`             | Chunk size for processing.                                                                                                                                            | `1000`      |
+| `Chunk Size`               | `int`            | Chunk size for processing.                       | `1000`                   |
-| `Client`                    | `Any`             | HTTP client for making requests.                                                                                                                                      |             |
+| `Client`                   | `Any`            | HTTP client for making requests.                 |                          |
-| `Deployment`                | `str`             | Deployment name for the model.                                                                                                                                        | `text-embedding-3-small` |
+| `Deployment`               | `str`            | Deployment name for the model.                   | `text-embedding-3-small` |
-| `Embedding Context Length`  | `int`             | Length of embedding context.                                                                                                                                          | `8191`      |
+| `Embedding Context Length` | `int`            | Length of embedding context.                     | `8191`                   |
-| `Max Retries`               | `int`             | Maximum number of retries for failed requests.                                                                                                                        | `6`         |
+| `Max Retries`              | `int`            | Maximum number of retries for failed requests.   | `6`                      |
-| `Model`                     | `str`             | Name of the model to use.                                                                                                                                             | `text-embedding-3-small` |
+| `Model`                    | `str`            | Name of the model to use.                        | `text-embedding-3-small` |
-| `Model Kwargs`              | `NestedDict`      | Additional keyword arguments for the model.                                                                                                                           |             |
+| `Model Kwargs`             | `NestedDict`     | Additional keyword arguments for the model.      |                          |
-| `OpenAI API Base`           | `str`             | Base URL of the OpenAI API.                                                                                                                                           |             |
+| `OpenAI API Base`          | `str`            | Base URL of the OpenAI API.                      |                          |
-| `OpenAI API Type`           | `str`             | Type of the OpenAI API.                                                                                                                                               |             |
+| `OpenAI API Type`          | `str`            | Type of the OpenAI API.                          |                          |
-| `OpenAI API Version`        | `str`             | Version of the OpenAI API.                                                                                                                                            |             |
+| `OpenAI API Version`       | `str`            | Version of the OpenAI API.                       |                          |
-| `OpenAI Organization`       | `str`             | Organization associated with the API key.                                                                                                                             |             |
+| `OpenAI Organization`      | `str`            | Organization associated with the API key.        |                          |
-| `OpenAI Proxy`              | `str`             | Proxy server for the requests.                                                                                                                                        |             |
+| `OpenAI Proxy`             | `str`            | Proxy server for the requests.                   |                          |
-| `Request Timeout`           | `float`           | Timeout for the HTTP requests.                                                                                                                                        |             |
+| `Request Timeout`          | `float`          | Timeout for the HTTP requests.                   |                          |
-| `Show Progress Bar`         | `bool`            | Whether to show a progress bar for processing.                                                                                                                        | `False`     |
+| `Show Progress Bar`        | `bool`           | Whether to show a progress bar for processing.   | `False`                  |
-| `Skip Empty`                | `bool`            | Whether to skip empty inputs.                                                                                                                                         | `False`     |
+| `Skip Empty`               | `bool`           | Whether to skip empty inputs.                    | `False`                  |
-| `TikToken Enable`           | `bool`            | Whether to enable TikToken.                                                                                                                                           | `True`      |
+| `TikToken Enable`          | `bool`           | Whether to enable TikToken.                      | `True`                   |
-| `TikToken Model Name`       | `str`             | Name of the TikToken model.                                                                                                                                           |             |
+| `TikToken Model Name`      | `str`            | Name of the TikToken model.                      |                          |
 
 ## Ollama Embeddings
 
 Generate embeddings using Ollama models.
 
-| **Parameter**       | **Type**          | **Description**                                                                                                    | **Default**               |
+| **Parameter**       | **Type** | **Description**                                                                          | **Default**              |
-|---------------------|-------------------|--------------------------------------------------------------------------------------------------------------------|---------------------------|
+| ------------------- | -------- | ---------------------------------------------------------------------------------------- | ------------------------ |
-| `Ollama Model`      | `str`             | Name of the Ollama model to use.                                                                                   | `llama2`                  |
+| `Ollama Model`      | `str`    | Name of the Ollama model to use.                                                         | `llama2`                 |
-| `Ollama Base URL`   | `str`             | Base URL of the Ollama API.                                                                                        | `http://localhost:11434`  |
+| `Ollama Base URL`   | `str`    | Base URL of the Ollama API.                                                              | `http://localhost:11434` |
-| `Model Temperature` | `float`           | Temperature parameter for the model. Adjusts the randomness in the generated embeddings.                           |                           |
+| `Model Temperature` | `float`  | Temperature parameter for the model. Adjusts the randomness in the generated embeddings. |                          |
 
 ## VertexAI Embeddings
 
 Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) [Embeddings API](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings).
 
-| **Parameter**               | **Type**          | **Description**                                                                                                                                                       | **Default** |
+| **Parameter**         | **Type**      | **Description**                                                                                                                      | **Default**   |
-|-----------------------------|-------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------|
+| --------------------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------ | ------------- |
-| `credentials`               | `Credentials`     | The default custom credentials to use.                                                                                                                               |             |
+| `credentials`         | `Credentials` | The default custom credentials to use.                                                                                               |               |
-| `location`                  | `str`             | The default location to use when making API calls.                                                                                                                    | `us-central1`|
+| `location`            | `str`         | The default location to use when making API calls.                                                                                   | `us-central1` |
-| `max_output_tokens`         | `int`             | Token limit determines the maximum amount of text output from one prompt.                                                                                             | `128`       |
+| `max_output_tokens`   | `int`         | Token limit determines the maximum amount of text output from one prompt.                                                            | `128`         |
-| `model_name`                | `str`             | The name of the Vertex AI large language model.                                                                                                                      | `text-bison`|
+| `model_name`          | `str`         | The name of the Vertex AI large language model.                                                                                      | `text-bison`  |
-| `project`                   | `str`             | The default GCP project to use when making Vertex API calls.                                                                                                          |             |
+| `project`             | `str`         | The default GCP project to use when making Vertex API calls.                                                                         |               |
-| `request_parallelism`       | `int`             | The amount of parallelism allowed for requests issued to VertexAI models.                                                                                            | `5`         |
+| `request_parallelism` | `int`         | The amount of parallelism allowed for requests issued to VertexAI models.                                                            | `5`           |
-| `temperature`               | `float`           | Tunes the degree of randomness in text generations. Should be a non-negative value.                                                                                  | `0`         |
+| `temperature`         | `float`       | Tunes the degree of randomness in text generations. Should be a non-negative value.                                                  | `0`           |
-| `top_k`                     | `int`             | How the model selects tokens for output, the next token is selected from the top `k` tokens.                                                                         | `40`        |
+| `top_k`               | `int`         | How the model selects tokens for output, the next token is selected from the top `k` tokens.                                         | `40`          |
-| `top_p`                     | `float`           | Tokens are selected from the most probable to least until the sum of their probabilities exceeds the top `p` value.                                                   | `0.95`      |
+| `top_p`               | `float`       | Tokens are selected from the most probable to least until the sum of their probabilities exceeds the top `p` value.                  | `0.95`        |
-| `tuned_model_name`          | `str`             | The name of a tuned model. If provided, `model_name` is ignored.                                                                                                      |             |
+| `tuned_model_name`    | `str`         | The name of a tuned model. If provided, `model_name` is ignored.                                                                     |               |
-| `verbose`                   | `bool`            | This parameter controls the level of detail in the output. When set to `True`, it prints internal states of the chain to help debug.                                 | `False`     |
+| `verbose`             | `bool`        | This parameter controls the level of detail in the output. When set to `True`, it prints internal states of the chain to help debug. | `False`       |

docs/docs/components/experimental.mdx

@@ -1,4 +1,4 @@
-import Admonition from '@theme/Admonition';
+import Admonition from "@theme/Admonition";
 
 # Experimental
 
@@ -29,10 +29,12 @@ This component extracts specified keys from a record.
 **Parameters**
 
 - **Record:**
+
   - **Display Name:** Record
   - **Info:** The record from which to extract keys.
 
 - **Keys:**
+
   - **Display Name:** Keys
   - **Info:** The keys to be extracted.
 
@@ -54,6 +56,7 @@ This component turns a function running a flow into a Tool.
 **Parameters**
 
 - **Flow Name:**
+
   - **Display Name:** Flow Name
   - **Info:** Select the flow to run.
   - **Options:** List of available flows.
@@ -61,10 +64,12 @@ This component turns a function running a flow into a Tool.
   - **Refresh Button:** True
 
 - **Name:**
+
   - **Display Name:** Name
   - **Description:** The tool's name.
 
 - **Description:**
+
   - **Display Name:** Description
   - **Description:** Describes the tool.
 
@@ -127,10 +132,12 @@ This component generates a notification.
 **Parameters**
 
 - **Name:**
+
   - **Display Name:** Name
   - **Info:** The notification's name.
 
 - **Record:**
+
   - **Display Name:** Record
   - **Info:** Optionally, a record to store in the notification.
 
@@ -151,10 +158,12 @@ This component runs a specified flow.
 **Parameters**
 
 - **Input Value:**
+
   - **Display Name:** Input Value
   - **Multiline:** True
 
 - **Flow Name:**
+
   - **Display Name:** Flow Name
   - **Info:** Select the flow to run.
   - **Options:** List of available flows.
@@ -177,14 +186,17 @@ This component executes a specified runnable.
 **Parameters**
 
 - **Input Key:**
+
   - **Display Name:** Input Key
   - **Info:** The input key.
 
 - **Inputs:**
+
   - **Display Name:** Inputs
   - **Info:** Inputs for the runnable.
 
 - **Runnable:**
+
   - **Display Name:** Runnable
   - **Info:** The runnable to execute.
 
@@ -205,14 +217,17 @@ This component executes an SQL query.
 **Parameters**
 
 - **Database URL:**
+
   - **Display Name:** Database URL
   - **Info:** The database's URL.
 
 - **Include Columns:**
+
   - **Display Name:** Include Columns
   - **Info:** Whether to include columns in the result.
 
 - **Passthrough:**
+
   - **Display Name:** Passthrough
   - **Info:** Returns the query instead of raising an exception if an error occurs.
 
@@ -233,10 +248,12 @@ This component dynamically generates a tool from a flow.
 **Parameters**
 
 - **Input Value:**
+
   - **Display Name:** Input Value
   - **Multiline:** True
 
 - **Flow Name:**
+
   - **Display Name:** Flow Name
   - **Info:** Select the flow to run.
   - **Options:** List of available flows.

docs/docs/components/helpers.mdx

@@ -1,4 +1,4 @@
-import Admonition from '@theme/Admonition';
+import Admonition from "@theme/Admonition";
 
 # Helpers
 
@@ -49,9 +49,10 @@ Use this component as a template to create your custom component.
 - **Parameter:** Describe the purpose of this parameter.
 
 <Admonition type="info" title="Info">
-    <p>
+  <p>
-        Customize the <code>build_config</code> and <code>build</code> methods according to your requirements.
+    Customize the <code>build_config</code> and <code>build</code> methods
-    </p>
+    according to your requirements.
+  </p>
 </Admonition>
 
 Learn more about creating custom components at [Custom Component](http://docs.langflow.org/components/custom).

docs/docs/components/memories.mdx

@@ -1,11 +1,13 @@
-import Admonition from '@theme/Admonition';
+import Admonition from "@theme/Admonition";
 
 # Memories
 
 <Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
-    <p>
+  <p>
-        Thanks for your patience as we improve our documentation—it might have some rough edges. Share your feedback or report issues to help us enhance it! 🛠️📝
+    Thanks for your patience as we improve our documentation—it might have some
-    </p>
+    rough edges. Share your feedback or report issues to help us enhance it!
+    🛠️📝
+  </p>
 </Admonition>
 
 Memory is a concept in chat-based applications that allows the system to remember previous interactions. This capability helps maintain the context of the conversation and enables the system to understand new messages in light of past messages.
@@ -24,9 +26,13 @@ This component retrieves stored messages using various filters such as sender ty
 - **number_of_messages**: Specifies the number of messages to retrieve. Defaults to `5`. Determines the number of recent messages from the chat history to fetch.
 
 <Admonition type="note" title="Note">
-    <p>
+  <p>
-        The component retrieves messages based on the provided criteria, including the specific file path for stored messages. If no specific criteria are provided, it returns the most recent messages up to the specified limit. This component can be used to review past interactions and analyze conversation flows.
+    The component retrieves messages based on the provided criteria, including
-    </p>
+    the specific file path for stored messages. If no specific criteria are
+    provided, it returns the most recent messages up to the specified limit.
+    This component can be used to review past interactions and analyze
+    conversation flows.
+  </p>
 </Admonition>
 
 ### ConversationBufferMemory
@@ -84,7 +90,8 @@ The `ConversationKGMemory` utilizes a knowledge graph to enhance memory capabili
 - **memory_key**: Specifies the prompt variable name where the memory stores and retrieves chat messages. Defaults to `chat_history`.
 - **output_key**: Identifies the key under which the generated response
 
- is stored, enabling retrieval using this key.
+is stored, enabling retrieval using this key.
+
 - **return_messages**: Controls whether the history is returned as a string or as a list of messages. Defaults to `False`.
 
 ---
@@ -124,4 +131,4 @@ The `VectorRetrieverMemory` retrieves vectors based on queries, facilitating vec
 - **Retriever**: The tool used to fetch documents.
 - **input_key**: Identifies where input messages are stored in the memory object, facilitating their retrieval and manipulation.
 - **memory_key**: Specifies the prompt variable name where the memory stores and retrieves chat messages. Defaults to `chat_history`.
-- **return_messages**: Controls whether the history is returned as a string or as a list of messages. Defaults to `False`.
+- **return_messages**: Controls whether the history is returned as a string or as a list of messages. Defaults to `False`.

docs/docs/components/model_specs.mdx

@@ -1,11 +1,13 @@
-import Admonition from '@theme/Admonition';
+import Admonition from "@theme/Admonition";
 
 # Large Language Models (LLMs)
 
 <Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
-    <p>
+  <p>
-        Thank you for your patience as we refine our documentation. You might encounter some inconsistencies. Please help us improve by sharing your feedback or reporting any issues! 🛠️📝
+    Thank you for your patience as we refine our documentation. You might
-    </p>
+    encounter some inconsistencies. Please help us improve by sharing your
+    feedback or reporting any issues! 🛠️📝
+  </p>
 </Admonition>
 
 A Large Language Model (LLM) is a foundational component of Langflow. It provides a uniform interface for interacting with LLMs from various providers, including OpenAI, Cohere, and HuggingFace. Langflow extensively uses LLMs across its chains and agents, employing them to generate text based on specific prompts or inputs.
@@ -37,7 +39,9 @@ This is a wrapper for Anthropic's large language model designed for chat-based i
 `CTransformers` provides access to Transformer models implemented in C/C++ using the [GGML](https://github.com/ggerganov/ggml) library.
 
 <Admonition type="info">
-Ensure the `ctransformers` Python package is installed. Discover more about installation, supported models, and usage [here](https://github.com/marella/ctransformers).
+  Ensure the `ctransformers` Python package is installed. Discover more about
+  installation, supported models, and usage
+  [here](https://github.com/marella/ctransformers).
 </Admonition>
 
 - **config:** This configuration is for the Transformer models. Check the default settings and possible configurations at [config](https://github.com/marella/ctransformers#config).
@@ -128,7 +132,8 @@ This component integrates with [Google Vertex AI](https://cloud.google.com/verte
 
 - **credentials**: Custom
 
- credentials used for API interactions.
+credentials used for API interactions.
+
 - **location**: The default location for API calls, defaulting to `us-central1`.
 - **max_output_tokens**: Limits the output tokens per prompt, defaulting to `128`.
 - **model_name**: The name of the Vertex AI model in use, defaulting to `text-bison`.
@@ -140,4 +145,4 @@ This component integrates with [Google Vertex AI](https://cloud.google.com/verte
 - **tuned_model_name**: Specifies a tuned model name, which overrides the default model name if provided.
 - **verbose**: Controls the output verbosity to assist in debugging and understanding the operational details, defaulting to `False`.
 
----
+---

docs/docs/components/retrievers.mdx

@@ -1,11 +1,13 @@
-import Admonition from '@theme/Admonition';
+import Admonition from "@theme/Admonition";
 
 # Retrievers
 
 <Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
-    <p>
+  <p>
-        We appreciate your patience as we enhance our documentation. It may have some imperfections. Please share your feedback or report issues to help us improve. 🛠️📝
+    We appreciate your patience as we enhance our documentation. It may have
-    </p>
+    some imperfections. Please share your feedback or report issues to help us
+    improve. 🛠️📝
+  </p>
 </Admonition>
 
 A retriever is an interface that returns documents in response to an unstructured query. It's broader than a vector store because it doesn't need to store documents; it only needs to retrieve them.

docs/docs/components/toolkits.mdx

@@ -1,9 +1,11 @@
-import Admonition from '@theme/Admonition';
+import Admonition from "@theme/Admonition";
 
 # Toolkits
 
 <Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
-    <p>
+  <p>
-        We appreciate your understanding as we polish our documentation – it may contain some rough edges. Share your feedback or report issues to help us improve! 🛠️📝
+    We appreciate your understanding as we polish our documentation - it may
-    </p>
+    contain some rough edges. Share your feedback or report issues to help us
-</Admonition>
+    improve! 🛠️📝
+  </p>
+</Admonition>

docs/docs/components/tools.mdx

@@ -1,11 +1,13 @@
-import Admonition from '@theme/Admonition';
+import Admonition from "@theme/Admonition";
 
 # Tools
 
 <Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
-    <p>
+  <p>
-        Thanks for your patience as we refine our documentation. It might have some rough edges currently. Please share your feedback or report issues to help us enhance it! 🛠️📝
+    Thanks for your patience as we refine our documentation. It might have some
-    </p>
+    rough edges currently. Please share your feedback or report issues to help
+    us enhance it! 🛠️📝
+  </p>
 </Admonition>
 
 ### SearchApi

docs/docs/components/utilities.mdx

@@ -3,9 +3,9 @@ import Admonition from "@theme/Admonition";
 # Utilities
 
 <Admonition type="caution" icon="🚧" title="Zone Under Construction">
-  We appreciate your understanding as we polish our documentation—it may
+  We appreciate your understanding as we polish our documentation—it may contain
-  contain some rough edges. Share your feedback or report issues to help us
+  some rough edges. Share your feedback or report issues to help us improve!
-  improve! 🛠️📝
+  🛠️📝
 </Admonition>
 
 Utilities are a set of actions that can be used to perform common tasks in a flow. They are available in the **Utilities** section in the sidebar.
@@ -86,7 +86,11 @@ Generates a unique identifier (UUID) for each instance it is invoked, providing
 - Returns a unique identifier (UUID) as a string. This UUID is generated using Python's `uuid` module, ensuring that each identifier is unique and can be used as a reliable reference in your application.
 
 <Admonition type="note" title="Note">
-    The Unique ID Generator is crucial for scenarios requiring distinct identifiers, such as session management, transaction tracking, or any context where different instances or entities must be uniquely identified. The generated UUID is provided as a hexadecimal string, offering a high level of uniqueness and security for identification purposes.
+  The Unique ID Generator is crucial for scenarios requiring distinct
+  identifiers, such as session management, transaction tracking, or any context
+  where different instances or entities must be uniquely identified. The
+  generated UUID is provided as a hexadecimal string, offering a high level of
+  uniqueness and security for identification purposes.
 </Admonition>
 
 For additional information and examples, please consult the [Langflow Components Custom Documentation](http://docs.langflow.org/components/custom).

docs/docs/deployment/backend-only.md

@@ -4,7 +4,7 @@ You can run Langflow in `--backend-only` mode to expose your Langflow app as an
 
 Start langflow in backend-only mode with `python3 -m langflow run --backend-only`.
 
-The terminal prints `  Welcome to ⛓ Langflow  `, and a blank window opens at `http://127.0.0.1:7864/all`.
+The terminal prints `Welcome to ⛓ Langflow`, and a blank window opens at `http://127.0.0.1:7864/all`.
 Langflow will now serve requests to its API without the frontend running.
 
 ## Prerequisites
@@ -42,7 +42,7 @@ Note the flow ID of `ef7e0554-69e5-4e3e-ab29-ee83bcd8d9ef`. You can find this ID
 
 1. Stop Langflow with Ctrl+C.
 2. Start langflow in backend-only mode with `python3 -m langflow run --backend-only`.
-   The terminal prints `  Welcome to ⛓ Langflow  `, and a blank window opens at `http://127.0.0.1:7864/all`.
+   The terminal prints `Welcome to ⛓ Langflow`, and a blank window opens at `http://127.0.0.1:7864/all`.
    Langflow will now serve requests to its API.
 3. Run the curl code you copied from the UI.
    You should get a result like this:

poetry.lock

@@ -471,17 +471,17 @@ files = [
 
 [[package]]
 name = "boto3"
-version = "1.34.121"
+version = "1.34.122"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "boto3-1.34.121-py3-none-any.whl", hash = "sha256:4e79e400d6d44b4eee5deda6ac0ecd08a3f5a30c45a0d30712795cdc4459fd79"},
+    {file = "boto3-1.34.122-py3-none-any.whl", hash = "sha256:b2d7400ff84fa547e53b3d9acfa3c95d65d45b5886ba1ede1f7df4768d1cc0b1"},
-    {file = "boto3-1.34.121.tar.gz", hash = "sha256:ec89f3e0b0dc959c418df29e14d3748c0b05ab7acf7c0b90c839e9f340a659fa"},
+    {file = "boto3-1.34.122.tar.gz", hash = "sha256:56840d8ce91654d182f1c113f0791fa2113c3aa43230c50b4481f235348a6037"},
 ]
 
 [package.dependencies]
-botocore = ">=1.34.121,<1.35.0"
+botocore = ">=1.34.122,<1.35.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"
 
@@ -490,13 +490,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.34.121"
+version = "1.34.122"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "botocore-1.34.121-py3-none-any.whl", hash = "sha256:25b05c7646a9f240cde1c8f839552a43f27e71e15c42600275dea93e219f7dd9"},
+    {file = "botocore-1.34.122-py3-none-any.whl", hash = "sha256:6d75df3af831b62f0c7baa109728d987e0a8d34bfadf0476eb32e2f29a079a36"},
-    {file = "botocore-1.34.121.tar.gz", hash = "sha256:1a8f94b917c47dfd84a0b531ab607dc53570efb0d073d8686600f2d2be985323"},
+    {file = "botocore-1.34.122.tar.gz", hash = "sha256:9374e16a36f1062c3e27816e8599b53eba99315dfac71cc84fc3aee3f5d3cbe3"},
 ]
 
 [package.dependencies]
@@ -1450,13 +1450,13 @@ tests = ["pytest"]
 
 [[package]]
 name = "dataclasses-json"
-version = "0.6.6"
+version = "0.6.7"
 description = "Easily serialize dataclasses to and from JSON."
 optional = false
 python-versions = "<4.0,>=3.7"
 files = [
-    {file = "dataclasses_json-0.6.6-py3-none-any.whl", hash = "sha256:e54c5c87497741ad454070ba0ed411523d46beb5da102e221efb873801b0ba85"},
+    {file = "dataclasses_json-0.6.7-py3-none-any.whl", hash = "sha256:0dbf33f26c8d5305befd61b39d2b3414e8a407bedc2834dea9b8d642666fb40a"},
-    {file = "dataclasses_json-0.6.6.tar.gz", hash = "sha256:0c09827d26fffda27f1be2fed7a7a01a29c5ddcd2eb6393ad5ebf9d77e9deae8"},
+    {file = "dataclasses_json-0.6.7.tar.gz", hash = "sha256:b6b3e528266ea45b9535223bc53ca645f5208833c29229e847b3f26a1cc55fc0"},
 ]
 
 [package.dependencies]
@@ -1590,6 +1590,23 @@ files = [
 [package.dependencies]
 packaging = "*"
 
+[[package]]
+name = "dictdiffer"
+version = "0.9.0"
+description = "Dictdiffer is a library that helps you to diff and patch dictionaries."
+optional = false
+python-versions = "*"
+files = [
+    {file = "dictdiffer-0.9.0-py2.py3-none-any.whl", hash = "sha256:442bfc693cfcadaf46674575d2eba1c53b42f5e404218ca2c2ff549f2df56595"},
+    {file = "dictdiffer-0.9.0.tar.gz", hash = "sha256:17bacf5fbfe613ccf1b6d512bd766e6b21fb798822a133aa86098b8ac9997578"},
+]
+
+[package.extras]
+all = ["Sphinx (>=3)", "check-manifest (>=0.42)", "mock (>=1.3.0)", "numpy (>=1.13.0)", "numpy (>=1.15.0)", "numpy (>=1.18.0)", "numpy (>=1.20.0)", "pytest (==5.4.3)", "pytest (>=6)", "pytest-cov (>=2.10.1)", "pytest-isort (>=1.2.0)", "pytest-pycodestyle (>=2)", "pytest-pycodestyle (>=2.2.0)", "pytest-pydocstyle (>=2)", "pytest-pydocstyle (>=2.2.0)", "sphinx (>=3)", "sphinx-rtd-theme (>=0.2)", "tox (>=3.7.0)"]
+docs = ["Sphinx (>=3)", "sphinx-rtd-theme (>=0.2)"]
+numpy = ["numpy (>=1.13.0)", "numpy (>=1.15.0)", "numpy (>=1.18.0)", "numpy (>=1.20.0)"]
+tests = ["check-manifest (>=0.42)", "mock (>=1.3.0)", "pytest (==5.4.3)", "pytest (>=6)", "pytest-cov (>=2.10.1)", "pytest-isort (>=1.2.0)", "pytest-pycodestyle (>=2)", "pytest-pycodestyle (>=2.2.0)", "pytest-pydocstyle (>=2)", "pytest-pydocstyle (>=2.2.0)", "sphinx (>=3)", "tox (>=3.7.0)"]
+
 [[package]]
 name = "dill"
 version = "0.3.7"
@@ -2392,8 +2409,8 @@ 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" = "*"
@@ -2552,12 +2569,12 @@ 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"
@@ -2626,13 +2643,13 @@ httplib2 = ">=0.19.0"
 
 [[package]]
 name = "google-cloud-aiplatform"
-version = "1.54.0"
+version = "1.54.1"
 description = "Vertex AI API client library"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "google-cloud-aiplatform-1.54.0.tar.gz", hash = "sha256:6f5187d35a32951028465804fbb42b478362bf41e2b634ddd22b150299f6e1d8"},
+    {file = "google-cloud-aiplatform-1.54.1.tar.gz", hash = "sha256:01c231961cc1a1a3b049ea3ef71fb11e77b2d56d632d020ce09e419b27ff77f2"},
-    {file = "google_cloud_aiplatform-1.54.0-py2.py3-none-any.whl", hash = "sha256:7b3ed849b9fb59a01bd6f44444ccbb7d18495b867a26f913542f6b2d4c3de252"},
+    {file = "google_cloud_aiplatform-1.54.1-py2.py3-none-any.whl", hash = "sha256:43f70fcd572f15317d769e5a0e04cfb7c0e259ead3fe581d2fba4f203ace5617"},
 ]
 
 [package.dependencies]
@@ -4323,13 +4340,13 @@ extended-testing = ["beautifulsoup4 (>=4.12.3,<5.0.0)", "lxml (>=4.9.3,<6.0)"]
 
 [[package]]
 name = "langchainhub"
-version = "0.1.17"
+version = "0.1.18"
 description = "The LangChain Hub API client"
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langchainhub-0.1.17-py3-none-any.whl", hash = "sha256:4c609b3948252c71670f0d98f73413b515cfd2f6701a7b40ce959203e6133e04"},
+    {file = "langchainhub-0.1.18-py3-none-any.whl", hash = "sha256:11501f15e7f34715ecc8892587daa35c6f2a3005e1f2926c9bcabd31fc2c100c"},
-    {file = "langchainhub-0.1.17.tar.gz", hash = "sha256:af7df0cb1cebc7a6e0864e8632ae48ecad39ed96568f699c78657b9d04e50b46"},
+    {file = "langchainhub-0.1.18.tar.gz", hash = "sha256:f2d0d8bf3abe4ca5e70511d8220bdc9ccea28d5267bcfd0e5ef9c53bd5bd3bad"},
 ]
 
 [package.dependencies]
@@ -4435,13 +4452,13 @@ requests = ">=2,<3"
 
 [[package]]
 name = "litellm"
-version = "1.40.4"
+version = "1.40.7"
 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 = [
-    {file = "litellm-1.40.4-py3-none-any.whl", hash = "sha256:b3b8e4401f717c3a18595446bfdb80fc6bb74974aac4eae537fb7b3be37fbf9e"},
+    {file = "litellm-1.40.7-py3-none-any.whl", hash = "sha256:c98dd8733e632aba16f14bf82e56f7159222097a6d085b242a3140b5d3e7baa4"},
-    {file = "litellm-1.40.4.tar.gz", hash = "sha256:3edaa1189742afd7c7df2b122f77373d47154a8fb6df6187ff5875e188baa3e1"},
+    {file = "litellm-1.40.7.tar.gz", hash = "sha256:557bb19e8e484d0dfe8e4eaa9ccefc888617852988a46d6e7adc41585a2c0600"},
 ]
 
 [package.dependencies]
@@ -4483,13 +4500,13 @@ test = ["httpx (>=0.24.1)", "pytest (>=7.4.0)", "scipy (>=1.10)"]
 
 [[package]]
 name = "locust"
-version = "2.28.0"
+version = "2.29.0"
 description = "Developer-friendly load testing framework"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "locust-2.28.0-py3-none-any.whl", hash = "sha256:766be879db030c0118e7d9fca712f3538c4e628bdebf59468fa1c6c2fab217d3"},
+    {file = "locust-2.29.0-py3-none-any.whl", hash = "sha256:aa9d94d3604ed9f2aab3248460d91e55d3de980a821dffdf8658b439b049d03f"},
-    {file = "locust-2.28.0.tar.gz", hash = "sha256:260557eec866f7e34a767b6c916b5b278167562a280480aadb88f43d962fbdeb"},
+    {file = "locust-2.29.0.tar.gz", hash = "sha256:649c99ce49d00720a3084c0109547035ad9021222835386599a8b545d31ebe51"},
 ]
 
 [package.dependencies]
@@ -4503,7 +4520,10 @@ msgpack = ">=1.0.0"
 psutil = ">=5.9.1"
 pywin32 = {version = "*", markers = "platform_system == \"Windows\""}
 pyzmq = ">=25.0.0"
-requests = ">=2.26.0"
+requests = [
+    {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"
 
@@ -5599,13 +5619,13 @@ sympy = "*"
 
 [[package]]
 name = "openai"
-version = "1.32.0"
+version = "1.33.0"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.7.1"
 files = [
-    {file = "openai-1.32.0-py3-none-any.whl", hash = "sha256:953d57669f309002044fd2f678aba9f07a43256d74b3b00cd04afb5b185568ea"},
+    {file = "openai-1.33.0-py3-none-any.whl", hash = "sha256:621163b56570897ab8389d187f686a53d4771fd6ce95d481c0a9611fe8bc4229"},
-    {file = "openai-1.32.0.tar.gz", hash = "sha256:a6df15a7ab9344b1bc2bc8d83639f68b7a7e2453c0f5e50c1666547eee86f0bd"},
+    {file = "openai-1.33.0.tar.gz", hash = "sha256:1169211a7b326ecbc821cafb427c29bfd0871f9a3e0947dd9e51acb3b0f1df78"},
 ]
 
 [package.dependencies]
@@ -5931,9 +5951,9 @@ 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"
@@ -9068,13 +9088,13 @@ files = [
 
 [[package]]
 name = "typing-extensions"
-version = "4.12.1"
+version = "4.12.2"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.12.1-py3-none-any.whl", hash = "sha256:6024b58b69089e5a89c347397254e35f1bf02a907728ec7fee9bf0fe837d203a"},
+    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
-    {file = "typing_extensions-4.12.1.tar.gz", hash = "sha256:915f5e35ff76f56588223f15fdd5938f9a1cf9195c0de25130c627e4d597f6d1"},
+    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
 ]
 
 [[package]]
@@ -10081,4 +10101,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "d87bda272f67450430630924263690c2ae62416d0b240e029baaa8da07154bec"
+content-hash = "0ee3f3bef82d57be2ab4ae7b70215ebca67b5bd5223e6a9322ee1837516a3cc6"

pyproject.toml

@@ -116,6 +116,7 @@ pytest-asyncio = "^0.23.0"
 pytest-profiling = "^1.7.0"
 pre-commit = "^3.7.0"
 vulture = "^2.11"
+dictdiffer = "^0.9.0"
 
 [tool.poetry.extras]
 deploy = ["celery", "redis", "flower"]

src/backend/base/langflow/api/utils.py

@@ -86,6 +86,10 @@ def update_frontend_node_with_template_values(frontend_node, raw_frontend_node):
 
     update_template_values(frontend_node["template"], raw_frontend_node["template"])
 
+    old_code = raw_frontend_node["template"]["code"]["value"]
+    new_code = frontend_node["template"]["code"]["value"]
+    frontend_node["edited"] = old_code != new_code
+
     return frontend_node
 
 
@@ -204,16 +208,18 @@ def format_elapsed_time(elapsed_time: float) -> str:
         return f"{minutes} {minutes_unit}, {seconds} {seconds_unit}"
 
 
-async def build_and_cache_graph_from_db(
+async def build_and_cache_graph_from_db(flow_id: str, session: Session, chat_service: "ChatService"):
-    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:
         raise ValueError("Invalid flow ID")
     graph = Graph.from_payload(flow.data, flow_id)
+    for vertex_id in graph._has_session_id_vertices:
+        vertex = graph.get_vertex(vertex_id)
+        if vertex is None:
+            raise ValueError(f"Vertex {vertex_id} not found")
+        if not vertex._raw_params.get("session_id"):
+            vertex.update_raw_params({"session_id": flow_id})
     await chat_service.set_cache(flow_id, graph)
     return graph
 
@@ -317,3 +323,4 @@ def parse_exception(exc):
     if hasattr(exc, "body"):
         return exc.body["message"]
     return str(exc)
+    return str(exc)

src/backend/base/langflow/api/v1/chat.py

@@ -22,6 +22,7 @@ from langflow.api.v1.schemas import (
     VertexBuildResponse,
     VerticesOrderResponse,
 )
+from langflow.schema.schema import Log
 from langflow.services.auth.utils import get_current_active_user
 from langflow.services.chat.service import ChatService
 from langflow.services.deps import get_chat_service, get_session, get_session_service
@@ -123,6 +124,7 @@ async def build_vertex(
     vertex_id: str,
     background_tasks: BackgroundTasks,
     inputs: Annotated[Optional[InputValueRequest], Body(embed=True)] = None,
+    files: Optional[list[str]] = None,
     chat_service: "ChatService" = Depends(get_chat_service),
     current_user=Depends(get_current_active_user),
 ):
@@ -159,6 +161,7 @@ async def build_vertex(
         else:
             graph = cache.get("result")
         vertex = graph.get_vertex(vertex_id)
+
         try:
             lock = chat_service._cache_locks[flow_id_str]
             (
@@ -175,19 +178,25 @@ async def build_vertex(
                 vertex_id=vertex_id,
                 user_id=current_user.id,
                 inputs_dict=inputs.model_dump() if inputs else {},
+                files=files,
             )
+            log_obj = Log(message=vertex.artifacts_raw, type=vertex.artifacts_type)
             result_data_response = ResultDataResponse(**result_dict.model_dump())
 
         except Exception as exc:
             logger.exception(f"Error building vertex: {exc}")
             params = format_exception_message(exc)
             valid = False
+            log_obj = Log(message=params, type="error")
             result_data_response = ResultDataResponse(results={})
             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:
             background_tasks.add_task(

src/backend/base/langflow/api/v1/files.py

@@ -2,6 +2,7 @@ import hashlib
 from http import HTTPStatus
 from io import BytesIO
 from uuid import UUID
+from pathlib import Path
 
 from fastapi import APIRouter, Depends, HTTPException, UploadFile
 from fastapi.responses import StreamingResponse
@@ -99,6 +100,46 @@ async def download_image(file_name: str, flow_id: UUID, storage_service: Storage
         raise HTTPException(status_code=500, detail=str(e))
 
 
+@router.get("/profile_pictures/{folder_name}/{file_name}")
+async def download_profile_picture(
+    folder_name: str,
+    file_name: str,
+    storage_service: StorageService = Depends(get_storage_service),
+):
+    try:
+        extension = file_name.split(".")[-1]
+        config_dir = get_storage_service().settings_service.settings.config_dir
+        config_path = Path(config_dir)
+        folder_path = config_path / "profile_pictures" / folder_name
+        content_type = build_content_type_from_extension(extension)
+        file_content = await storage_service.get_file(flow_id=folder_path, file_name=file_name)
+        return StreamingResponse(BytesIO(file_content), media_type=content_type)
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/profile_pictures/list")
+async def list_profile_pictures(storage_service: StorageService = Depends(get_storage_service)):
+    try:
+        config_dir = get_storage_service().settings_service.settings.config_dir
+        config_path = Path(config_dir)
+
+        people_path = config_path / "profile_pictures/People"
+        space_path = config_path / "profile_pictures/Space"
+
+        people = await storage_service.list_files(flow_id=people_path)
+        space = await storage_service.list_files(flow_id=space_path)
+
+        files = [Path("People") / i for i in people]
+        files += [Path("Space") / i for i in space]
+
+        return {"files": files}
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
 @router.get("/list/{flow_id}")
 async def list_files(
     flow_id: UUID = Depends(get_flow_id), storage_service: StorageService = Depends(get_storage_service)

src/backend/base/langflow/api/v1/folders.py

@@ -1,5 +1,7 @@
 from typing import List
 
+from langflow.helpers.flow import generate_unique_flow_name
+from langflow.helpers.folders import generate_unique_folder_name
 import orjson
 from fastapi import APIRouter, Depends, File, HTTPException, Response, UploadFile, status
 from sqlalchemy import or_, update
@@ -203,16 +205,9 @@ async def upload_file(
     if not data:
         raise HTTPException(status_code=400, detail="No flows found in the file")
 
-    folder_results = session.exec(
+    folder_name = generate_unique_folder_name(data["folder_name"], current_user.id, session)
-        select(Folder).where(
-            Folder.name == data["folder_name"],
-            Folder.user_id == current_user.id,
-        )
-    )
-    existing_folder_names = [folder.name for folder in folder_results]
 
-    if existing_folder_names:
+    data["folder_name"] = folder_name
-        data["folder_name"] = f"{data['folder_name']} ({len(existing_folder_names) + 1})"
 
     folder = FolderCreate(name=data["folder_name"], description=data["folder_description"])
 
@@ -232,6 +227,8 @@ async def upload_file(
         raise HTTPException(status_code=400, detail="No flows found in the data")
     # Now we set the user_id for all flows
     for flow in flow_list.flows:
+        flow_name = generate_unique_flow_name(flow.name, current_user.id, session)
+        flow.name = flow_name
         flow.user_id = current_user.id
         flow.folder_id = new_folder.id
 

src/backend/base/langflow/api/v1/monitor.py

@@ -1,4 +1,6 @@
 from typing import List, Optional
+
+
 from fastapi import APIRouter, Depends, HTTPException, Query
 
 from langflow.services.deps import get_monitor_service
@@ -79,7 +81,7 @@ async def delete_messages(
 
 @router.post("/messages/{message_id}", response_model=MessageModelResponse)
 async def update_message(
-    message_id: str,
+    message_id: int,
     message: MessageModelRequest,
     monitor_service: MonitorService = Depends(get_monitor_service),
 ):
@@ -117,6 +119,22 @@ async def get_transactions(
         dicts = monitor_service.get_transactions(
             source=source, target=target, status=status, order_by=order_by, flow_id=flow_id
         )
-        return [TransactionModelResponse(**d) for d in dicts]
+        result = []
+        for d in dicts:
+            d = TransactionModelResponse(
+                index=d["index"],
+                timestamp=d["timestamp"],
+                vertex_id=d["vertex_id"],
+                inputs=d["inputs"],
+                outputs=d["outputs"],
+                status=d["status"],
+                error=d["error"],
+                flow_id=d["flow_id"],
+                source=d["vertex_id"],
+                target=d["target_id"],
+            )
+            result.append(d)
+        return result
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
+        raise HTTPException(status_code=500, detail=str(e))

src/backend/base/langflow/api/v1/schemas.py

@@ -9,7 +9,7 @@ from pydantic import BaseModel, ConfigDict, Field, field_validator, model_serial
 from langflow.graph.schema import RunOutputs
 from langflow.schema import dotdict
 from langflow.schema.graph import Tweaks
-from langflow.schema.schema import InputType, OutputType
+from langflow.schema.schema import InputType, Log, OutputType
 from langflow.services.database.models.api_key.model import ApiKeyRead
 from langflow.services.database.models.base import orjson_dumps
 from langflow.services.database.models.flow import FlowCreate, FlowRead
@@ -245,6 +245,8 @@ class VerticesOrderResponse(BaseModel):
 
 class ResultDataResponse(BaseModel):
     results: Optional[Any] = Field(default_factory=dict)
+    logs: List[Log | None] = Field(default_factory=list)
+    message: Optional[Any] = Field(default_factory=dict)
     artifacts: Optional[Any] = Field(default_factory=dict)
     timedelta: Optional[float] = None
     duration: Optional[str] = None

src/backend/base/langflow/base/agents/agent.py

@@ -7,7 +7,7 @@ from langchain_core.runnables import Runnable
 from langflow.base.agents.utils import get_agents_list, records_to_messages
 from langflow.custom import CustomComponent
 from langflow.field_typing import Text, Tool
-from langflow.schema.schema import Record
+from langflow.schema import Record
 
 
 class LCAgentComponent(CustomComponent):

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.schema import Record
+from langflow.schema import Record
 
 from .default_prompts import XML_AGENT_PROMPT
 

src/backend/base/langflow/base/constants.py

@@ -7,9 +7,11 @@ Constants:
 - FIELD_FORMAT_ATTRIBUTES: A list of attributes used for formatting fields.
 """
 
+import orjson
+
 STREAM_INFO_TEXT = "Stream the response from the model. Streaming works only in Chat."
 
-NODE_FORMAT_ATTRIBUTES = ["beta", "icon", "display_name", "description"]
+NODE_FORMAT_ATTRIBUTES = ["beta", "icon", "display_name", "description", "output_types"]
 
 
 FIELD_FORMAT_ATTRIBUTES = [
@@ -28,3 +30,5 @@ FIELD_FORMAT_ATTRIBUTES = [
     "options",
     "advanced",
 ]
+
+ORJSON_OPTIONS = orjson.OPT_INDENT_2 | orjson.OPT_SORT_KEYS | orjson.OPT_OMIT_MICROSECONDS

src/backend/base/langflow/base/curl/parse.py

@@ -16,7 +16,7 @@ from collections import OrderedDict, namedtuple
 from http.cookies import SimpleCookie
 
 ParsedArgs = namedtuple(
-    "ParsedContext",
+    "ParsedArgs",
     [
         "command",
         "url",
@@ -64,21 +64,20 @@ def parse_curl_command(curl_command):
         "cookies": {},
     }
     args = args_template.copy()
-
+    method_on_curl = None
     i = 0
     while i < len(tokens):
         token = tokens[i]
         if token == "-X":
             i += 1
             args["method"] = tokens[i].lower()
+            method_on_curl = tokens[i].lower()
         elif token in ("-d", "--data"):
             i += 1
             args["data"] = tokens[i]
-            args["method"] = "post"
         elif token in ("-b", "--data-binary", "--data-raw"):
             i += 1
             args["data_binary"] = tokens[i]
-            args["method"] = "post"
         elif token in ("-H", "--header"):
             i += 1
             args["headers"].append(tokens[i])
@@ -106,6 +105,8 @@ def parse_curl_command(curl_command):
                 args["url"] = token
         i += 1
 
+    args["method"] = method_on_curl or args["method"]
+
     return ParsedArgs(**args)
 
 

src/backend/base/langflow/base/data/utils.py

@@ -1,12 +1,14 @@
-import json
+import unicodedata
 import xml.etree.ElementTree as ET
 from concurrent import futures
 from pathlib import Path
 from typing import Callable, List, Optional, Text
+
 import chardet
+import orjson
 import yaml
 
-from langflow.schema.schema import Record
+from langflow.schema import Record
 
 # Types of files that can be read simply by file.read()
 # and have 100% to be completely readable
@@ -31,6 +33,17 @@ TEXT_FILE_TYPES = [
     "tsx",
 ]
 
+IMG_FILE_TYPES = [
+    "jpg",
+    "jpeg",
+    "png",
+    "bmp",
+]
+
+
+def normalize_text(text):
+    return unicodedata.normalize("NFKD", text)
+
 
 def is_hidden(path: Path) -> bool:
     return path.name.startswith(".")
@@ -94,6 +107,9 @@ def read_text_file(file_path: str) -> str:
         result = chardet.detect(raw_data)
         encoding = result["encoding"]
 
+        if encoding in ["Windows-1252", "Windows-1254"]:
+            encoding = "utf-8"
+
     with open(file_path, "r", encoding=encoding) as f:
         return f.read()
 
@@ -121,9 +137,15 @@ def parse_text_file_to_record(file_path: str, silent_errors: bool) -> Optional[R
             text = read_docx_file(file_path)
         else:
             text = read_text_file(file_path)
+
         # if file is json, yaml, or xml, we can parse it
         if file_path.endswith(".json"):
-            text = json.loads(text)
+            text = orjson.loads(text)
+            if isinstance(text, dict):
+                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]
+
         elif file_path.endswith(".yaml") or file_path.endswith(".yml"):
             text = yaml.safe_load(text)
         elif file_path.endswith(".xml"):

src/backend/base/langflow/base/flow_processing/utils.py

@@ -1,7 +1,7 @@
 from typing import List
 
 from langflow.graph.schema import ResultData, RunOutputs
-from langflow.schema.schema import Record
+from langflow.schema import Record
 
 
 def build_records_from_run_outputs(run_outputs: RunOutputs) -> List[Record]:

src/backend/base/langflow/base/io/chat.py

@@ -1,10 +1,10 @@
 from typing import Optional, Union
 
+from langflow.base.data.utils import IMG_FILE_TYPES, TEXT_FILE_TYPES
 from langflow.custom import Component
-from langflow.field_typing import Text
-from langflow.helpers.record import records_to_text
 from langflow.memory import store_message
 from langflow.schema import Record
+from langflow.schema.message import Message
 
 
 class ChatComponent(Component):
@@ -15,7 +15,7 @@ class ChatComponent(Component):
         return {
             "input_value": {
                 "input_types": ["Text"],
-                "display_name": "Message",
+                "display_name": "Text",
                 "multiline": True,
             },
             "sender": {
@@ -40,98 +40,45 @@ class ChatComponent(Component):
                 "info": "In case of Message being a Record, this template will be used to convert it to text.",
                 "advanced": True,
             },
+            "files": {
+                "field_type": "file",
+                "display_name": "Files",
+                "file_types": TEXT_FILE_TYPES + IMG_FILE_TYPES,
+                "info": "Files to be sent with the message.",
+                "advanced": True,
+            },
         }
 
     def store_message(
         self,
-        message: Union[str, Text, Record],
+        message: Message,
-        session_id: Optional[str] = None,
+    ) -> list[Message]:
-        sender: Optional[str] = None,
+        messages = store_message(
-        sender_name: Optional[str] = None,
-    ) -> list[Record]:
-        records = store_message(
             message,
-            session_id=session_id,
-            sender=sender,
-            sender_name=sender_name,
             flow_id=self.graph.flow_id,
         )
 
-        self.status = records
+        self.status = messages
-        return records
+        return messages
 
     def build_with_record(
         self,
         sender: Optional[str] = "User",
         sender_name: Optional[str] = "User",
-        input_value: Optional[Union[str, Record]] = None,
+        input_value: Optional[Union[str, Record, Message]] = None,
+        files: Optional[list[str]] = None,
         session_id: Optional[str] = None,
-        return_record: Optional[bool] = False,
+    ) -> Message:
-        record_template: str = "Text: {text}\nData: {data}",
+        message: Message | None = None
-    ) -> Union[Text, Record]:
-        input_value_record: Optional[Record] = None
-        if return_record:
-            if isinstance(input_value, Record):
-                # Update the data of the record
-                input_value.data["sender"] = sender
-                input_value.data["sender_name"] = sender_name
-                input_value.data["session_id"] = session_id
-            else:
-                input_value_record = Record(
-                    text=input_value,
-                    data={
-                        "sender": sender,
-                        "sender_name": sender_name,
-                        "session_id": session_id,
-                    },
-                )
-        elif isinstance(input_value, Record):
-            input_value = records_to_text(template=record_template, records=input_value)
-        if not input_value:
-            input_value = ""
-        if return_record and input_value_record:
-            result: Union[Text, Record] = input_value_record
-        else:
-            result = input_value
-        self.status = result
-        if session_id and isinstance(result, (Record, str)):
-            self.store_message(result, session_id, sender, sender_name)
-        return result
 
-    def build_no_record(
+        if isinstance(input_value, Record):
-        self,
+            # Update the data of the record
-        sender: Optional[str] = "User",
+            message = Message.from_record(input_value)
-        sender_name: Optional[str] = "User",
-        input_value: Optional[str] = None,
-        session_id: Optional[str] = None,
-        return_record: Optional[bool] = False,
-        record_template: str = "Text: {text}\nData: {data}",
-    ) -> Union[Text, Record]:
-        input_value_record: Optional[Record] = None
-        if return_record:
-            if isinstance(input_value, Record):
-                # Update the data of the record
-                input_value.data["sender"] = sender
-                input_value.data["sender_name"] = sender_name
-                input_value.data["session_id"] = session_id
-            else:
-                input_value_record = Record(
-                    text=input_value,
-                    data={
-                        "sender": sender,
-                        "sender_name": sender_name,
-                        "session_id": session_id,
-                    },
-                )
-        elif isinstance(input_value, Record):
-            input_value = records_to_text(template=record_template, records=input_value)
-        if not input_value:
-            input_value = ""
-        if return_record and input_value_record:
-            result: Union[Text, Record] = input_value_record
         else:
-            result = input_value
+            message = Message(
-        self.status = result
+                text=input_value, sender=sender, sender_name=sender_name, files=files, session_id=session_id
-        if session_id and isinstance(result, (Record, str)):
+            )
-            self.store_message(result, session_id, sender, sender_name)
+        self.status = message
-        return result
+        if session_id and isinstance(message, Message):
+            self.store_message(message)
+        return message

src/backend/base/langflow/base/io/text.py

@@ -3,7 +3,7 @@ from typing import Optional
 from langflow.custom import Component
 from langflow.field_typing import Text
 from langflow.helpers.record import records_to_text
-from langflow.schema.schema import Record
+from langflow.schema import Record
 
 
 class TextComponent(Component):

src/backend/base/langflow/base/memory/memory.py

@@ -1,7 +1,7 @@
 from typing import Optional
 
 from langflow.custom import CustomComponent
-from langflow.schema.schema import Record
+from langflow.schema import Record
 
 
 class BaseMemoryComponent(CustomComponent):

src/backend/base/langflow/base/models/model.py

@@ -1,3 +1,4 @@
+import warnings
 from typing import Optional, Union
 
 from langchain_core.language_models.chat_models import BaseChatModel
@@ -5,6 +6,7 @@ from langchain_core.language_models.llms import LLM
 from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
 
 from langflow.custom import CustomComponent
+from langflow.field_typing.prompt import Prompt
 
 
 class LCModelComponent(CustomComponent):
@@ -53,19 +55,28 @@ class LCModelComponent(CustomComponent):
                 key in response_metadata["token_usage"] for key in inner_openai_keys
             ):
                 token_usage = response_metadata["token_usage"]
-                completion_tokens = token_usage["completion_tokens"]
+                status_message = {
-                prompt_tokens = token_usage["prompt_tokens"]
+                    "tokens": {
-                total_tokens = token_usage["total_tokens"]
+                        "input": token_usage["prompt_tokens"],
-                finish_reason = response_metadata["finish_reason"]
+                        "output": token_usage["completion_tokens"],
-                status_message = f"Tokens:\nInput: {prompt_tokens}\nOutput: {completion_tokens}\nTotal Tokens: {total_tokens}\nStop Reason: {finish_reason}\nResponse: {content}"
+                        "total": token_usage["total_tokens"],
+                        "stop_reason": response_metadata["finish_reason"],
+                        "response": content,
+                    }
+                }
+
             elif all(key in response_metadata for key in anthropic_keys) and all(
                 key in response_metadata["usage"] for key in inner_anthropic_keys
             ):
                 usage = response_metadata["usage"]
-                input_tokens = usage["input_tokens"]
+                status_message = {
-                output_tokens = usage["output_tokens"]
+                    "tokens": {
-                stop_reason = response_metadata["stop_reason"]
+                        "input": usage["input_tokens"],
-                status_message = f"Tokens:\nInput: {input_tokens}\nOutput: {output_tokens}\nStop Reason: {stop_reason}\nResponse: {content}"
+                        "output": usage["output_tokens"],
+                        "stop_reason": response_metadata["stop_reason"],
+                        "response": content,
+                    }
+                }
             else:
                 status_message = f"Response: {content}"
         else:
@@ -73,7 +84,7 @@ class LCModelComponent(CustomComponent):
         return status_message
 
     def get_chat_result(
-        self, runnable: BaseChatModel, stream: bool, input_value: str, system_message: Optional[str] = None
+        self, runnable: BaseChatModel, stream: bool, input_value: str | Prompt, system_message: Optional[str] = None
     ):
         messages: list[Union[HumanMessage, SystemMessage]] = []
         if not input_value and not system_message:
@@ -81,11 +92,21 @@ class LCModelComponent(CustomComponent):
         if system_message:
             messages.append(SystemMessage(content=system_message))
         if input_value:
-            messages.append(HumanMessage(content=input_value))
+            if isinstance(input_value, Prompt):
+                with warnings.catch_warnings():
+                    warnings.simplefilter("ignore")
+                    if "prompt" in input_value:
+                        prompt = input_value.load_lc_prompt()
+                        runnable = prompt | runnable
+                    else:
+                        messages.append(input_value.to_lc_message())
+            else:
+                messages.append(HumanMessage(content=input_value))
+        inputs = messages or {}
         if stream:
-            return runnable.stream(messages)
+            return runnable.stream(inputs)
         else:
-            message = runnable.invoke(messages)
+            message = runnable.invoke(inputs)
             result = message.content
             if isinstance(message, AIMessage):
                 status_message = self.build_status_message(message)

src/backend/base/langflow/base/prompts/utils.py

@@ -1,9 +1,9 @@
 from copy import deepcopy
 
-
 from langchain_core.documents import Document
 
 from langflow.schema import Record
+from langflow.schema.message import Message
 
 
 def record_to_string(record: Record) -> str:
@@ -35,10 +35,14 @@ def dict_values_to_string(d: dict) -> dict:
         # it could be a list of records or documents or strings
         if isinstance(value, list):
             for i, item in enumerate(value):
-                if isinstance(item, Record):
+                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, 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, Document):

src/backend/base/langflow/components/agents/ToolCallingAgent.py

@@ -5,7 +5,7 @@ from langchain_core.prompts import ChatPromptTemplate
 
 from langflow.base.agents.agent import LCAgentComponent
 from langflow.field_typing import BaseLanguageModel, Text, Tool
-from langflow.schema.schema import Record
+from langflow.schema import Record
 
 
 class ToolCallingAgentComponent(LCAgentComponent):

src/backend/base/langflow/components/agents/XMLAgent.py

@@ -3,10 +3,9 @@ from typing import List, Optional
 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.schema import Record
+from langflow.schema import Record
 
 
 class XMLAgentComponent(LCAgentComponent):

src/backend/base/langflow/components/chains/RetrievalQA.py

@@ -5,7 +5,7 @@ from langchain_core.documents import Document
 
 from langflow.custom import CustomComponent
 from langflow.field_typing import BaseLanguageModel, BaseMemory, BaseRetriever, Text
-from langflow.schema.schema import Record
+from langflow.schema import Record
 
 
 class RetrievalQAComponent(CustomComponent):

src/backend/base/langflow/components/data/Webhook.py

@@ -3,8 +3,8 @@ 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.schema.schema import Record
 
 
 class WebhookComponent(CustomComponent):

src/backend/base/langflow/components/experimental/AgentComponent.py

@@ -6,8 +6,8 @@ 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.schema.dotdict import dotdict
-from langflow.schema.schema import Record
 
 
 class AgentComponent(LCAgentComponent):

src/backend/base/langflow/components/experimental/FlowTool.py

@@ -7,8 +7,8 @@ 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.dotdict import dotdict
-from langflow.schema.schema import Record
 
 
 class FlowToolComponent(CustomComponent):

src/backend/base/langflow/components/experimental/StoreMessage.py

@@ -2,7 +2,7 @@ from typing import List, Optional
 
 from langflow.custom import CustomComponent
 from langflow.memory import get_messages, store_message
-from langflow.schema import Record
+from langflow.schema.message import Message
 
 
 class StoreMessageComponent(CustomComponent):
@@ -31,12 +31,11 @@ class StoreMessageComponent(CustomComponent):
         sender_name: Optional[str] = None,
         session_id: Optional[str] = None,
         message: str = "",
-    ) -> List[Record]:
+    ) -> List[Message]:
         store_message(
-            sender=sender,
+            message=Message(
-            sender_name=sender_name,
+                text=message, sender=sender, sender_name=sender_name, flow_id=self.graph.flow_id, session_id=session_id
-            session_id=session_id,
+            )
-            message=message,
         )
 
         self.status = get_messages(session_id=session_id)

src/backend/base/langflow/components/helpers/MemoryComponent.py

@@ -2,9 +2,9 @@ from typing import Optional
 
 from langflow.base.memory.memory import BaseMemoryComponent
 from langflow.field_typing import Text
-from langflow.helpers.record import records_to_text
+from langflow.helpers.record import messages_to_text
 from langflow.memory import get_messages
-from langflow.schema.schema import Record
+from langflow.schema.message import Message
 
 
 class MemoryComponent(BaseMemoryComponent):
@@ -43,7 +43,7 @@ class MemoryComponent(BaseMemoryComponent):
             },
         }
 
-    def get_messages(self, **kwargs) -> list[Record]:
+    def get_messages(self, **kwargs) -> list[Message]:
         # Validate kwargs by checking if it contains the correct keys
         if "sender" not in kwargs:
             kwargs["sender"] = None
@@ -77,6 +77,6 @@ class MemoryComponent(BaseMemoryComponent):
             limit=n_messages,
             order=order,
         )
-        messages_str = records_to_text(template=record_template or "", records=messages)
+        messages_str = messages_to_text(template=record_template or "", messages=messages)
         self.status = messages_str
         return messages_str

src/backend/base/langflow/components/inputs/Prompt.py

@@ -1,7 +1,6 @@
-from langchain_core.prompts import PromptTemplate
-
 from langflow.custom import CustomComponent
-from langflow.field_typing import Input, Prompt, Text
+from langflow.field_typing import Input
+from langflow.field_typing.prompt import Prompt
 
 
 class PromptComponent(CustomComponent):
@@ -15,19 +14,11 @@ class PromptComponent(CustomComponent):
             "code": Input(advanced=True),
         }
 
-    def build(
+    async def build(
         self,
         template: Prompt,
         **kwargs,
-    ) -> Text:
+    ) -> Prompt:
-        from langflow.base.prompts.utils import dict_values_to_string
+        prompt = await Prompt.from_template_and_variables(template, kwargs)
-
+        self.status = prompt.format_text()
-        prompt_template = PromptTemplate.from_template(Text(template))
+        return prompt
-        kwargs = dict_values_to_string(kwargs)
-        kwargs = {k: "\n".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}
-        try:
-            formated_prompt = prompt_template.format(**kwargs)
-        except Exception as exc:
-            raise ValueError(f"Error formatting prompt: {exc}") from exc
-        self.status = f'Prompt:\n"{formated_prompt}"'
-        return formated_prompt

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.schema import Record
+from langflow.schema import Record
 from langflow.services.database.models.base import orjson_dumps
 
 

src/backend/base/langflow/components/memories/AstraDBMessageReader.py

@@ -4,7 +4,7 @@ from langchain_astradb.chat_message_histories import AstraDBChatMessageHistory
 
 from langflow.base.memory.memory import BaseMemoryComponent
 from langflow.field_typing import Text
-from langflow.schema.schema import Record
+from langflow.schema import Record
 
 
 class AstraDBMessageReaderComponent(BaseMemoryComponent):

src/backend/base/langflow/components/memories/AstraDBMessageWriter.py

@@ -1,11 +1,11 @@
 from typing import Optional
 
+from langchain_astradb import AstraDBChatMessageHistory
+from langchain_core.messages import BaseMessage
+
 from langflow.base.memory.memory import BaseMemoryComponent
 from langflow.field_typing import Text
-from langflow.schema.schema import Record
+from langflow.schema import Record
-
-from langchain_core.messages import BaseMessage
-from langchain_astradb import AstraDBChatMessageHistory
 
 
 class AstraDBMessageWriterComponent(BaseMemoryComponent):

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.schema import Record
+from langflow.schema import Record
 
 
 class ZepMessageReaderComponent(BaseMemoryComponent):

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.schema import Record
+from langflow.schema import Record
 
 if TYPE_CHECKING:
     from zep_python.langchain import ZepChatMessageHistory

src/backend/base/langflow/components/models/AmazonBedrockModel.py

@@ -58,7 +58,7 @@ class AmazonBedrockComponent(LCModelComponent):
                 "advanced": True,
             },
             "cache": {"display_name": "Cache"},
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
             "system_message": {
                 "display_name": "System Message",
                 "info": "System message to pass to the model.",

src/backend/base/langflow/components/models/AnthropicModel.py

@@ -63,7 +63,7 @@ class AnthropicLLM(LCModelComponent):
                 "info": "Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.",
             },
             "code": {"show": False},
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
             "stream": {
                 "display_name": "Stream",
                 "advanced": True,

src/backend/base/langflow/components/models/AzureOpenAIModel.py

@@ -78,7 +78,7 @@ class AzureChatOpenAIComponent(LCModelComponent):
                 "info": "The maximum number of tokens to generate. Set to 0 for unlimited tokens.",
             },
             "code": {"show": False},
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
             "stream": {
                 "display_name": "Stream",
                 "info": STREAM_INFO_TEXT,

src/backend/base/langflow/components/models/BaiduQianfanChatModel.py

@@ -81,7 +81,7 @@ class QianfanChatEndpointComponent(LCModelComponent):
                 "info": "Endpoint of the Qianfan LLM, required if custom model used.",
             },
             "code": {"show": False},
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
             "stream": {
                 "display_name": "Stream",
                 "info": STREAM_INFO_TEXT,

src/backend/base/langflow/components/models/ChatLiteLLMModel.py

@@ -111,7 +111,7 @@ class ChatLiteLLMModelComponent(LCModelComponent):
                 "required": False,
                 "default": False,
             },
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
             "stream": {
                 "display_name": "Stream",
                 "info": STREAM_INFO_TEXT,

src/backend/base/langflow/components/models/CohereModel.py

@@ -1,10 +1,11 @@
 from typing import Optional
 
+from langchain_cohere import ChatCohere
 from pydantic.v1 import SecretStr
-from langflow.field_typing import Text
+
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
-from langchain_cohere import ChatCohere
+from langflow.field_typing import Text
 
 
 class CohereComponent(LCModelComponent):
@@ -42,7 +43,7 @@ class CohereComponent(LCModelComponent):
                 "type": "float",
                 "show": True,
             },
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
             "stream": {
                 "display_name": "Stream",
                 "info": STREAM_INFO_TEXT,
@@ -69,3 +70,4 @@ class CohereComponent(LCModelComponent):
             temperature=temperature,
         )
         return self.get_chat_result(output, stream, input_value, system_message)
+        return self.get_chat_result(output, stream, input_value, system_message)

src/backend/base/langflow/components/models/HuggingFaceModel.py

@@ -2,9 +2,10 @@ from typing import Optional
 
 from langchain_community.chat_models.huggingface import ChatHuggingFace
 from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
-from langflow.field_typing import Text
+
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
+from langflow.field_typing import Text
 
 
 class HuggingFaceEndpointsComponent(LCModelComponent):
@@ -36,7 +37,7 @@ class HuggingFaceEndpointsComponent(LCModelComponent):
                 "advanced": True,
             },
             "code": {"show": False},
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
             "stream": {
                 "display_name": "Stream",
                 "info": STREAM_INFO_TEXT,
@@ -72,3 +73,4 @@ class HuggingFaceEndpointsComponent(LCModelComponent):
             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)

src/backend/base/langflow/components/models/MistralModel.py

@@ -27,7 +27,7 @@ class MistralAIModelComponent(LCModelComponent):
 
     def build_config(self):
         return {
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
             "max_tokens": {
                 "display_name": "Max Tokens",
                 "advanced": True,

src/backend/base/langflow/components/models/OllamaModel.py

@@ -194,7 +194,7 @@ class ChatOllamaComponent(LCModelComponent):
                 "info": "Template to use for generating text.",
                 "advanced": True,
             },
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
             "stream": {
                 "display_name": "Stream",
                 "info": STREAM_INFO_TEXT,

src/backend/base/langflow/components/models/OpenAIModel.py

@@ -28,7 +28,7 @@ class OpenAIModelComponent(LCModelComponent):
 
     def build_config(self):
         return {
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
             "max_tokens": {
                 "display_name": "Max Tokens",
                 "advanced": True,
@@ -79,7 +79,7 @@ class OpenAIModelComponent(LCModelComponent):
         input_value: Text,
         openai_api_key: str,
         temperature: float = 0.1,
-        model_name: str = "gpt-4o",
+        model_name: str = "gpt-3.5-turbo",
         max_tokens: Optional[int] = 256,
         model_kwargs: NestedDict = {},
         openai_api_base: Optional[str] = None,

src/backend/base/langflow/components/models/VertexAiModel.py

@@ -1,6 +1,5 @@
 from typing import Optional
 
-
 from langflow.base.constants import STREAM_INFO_TEXT
 from langflow.base.models.model import LCModelComponent
 from langflow.field_typing import Text
@@ -74,7 +73,7 @@ class ChatVertexAIComponent(LCModelComponent):
                 "value": False,
                 "advanced": True,
             },
-            "input_value": {"display_name": "Input"},
+            "input_value": {"display_name": "Input", "input_types": ["Text", "Record", "Prompt"]},
             "stream": {
                 "display_name": "Stream",
                 "info": STREAM_INFO_TEXT,

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.schema import Record
+from langflow.schema import Record
 from langflow.utils.util import unescape_string
 
 

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.schema import Record
+from langflow.schema import Record
 
 
 class LanguageRecursiveTextSplitterComponent(CustomComponent):

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.schema import Record
+from langflow.schema import Record
 from langflow.services.database.models.base import orjson_dumps
 
 

src/backend/base/langflow/components/vectorsearch/RedisSearch.py

@@ -1,10 +1,11 @@
 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.field_typing import Text
 from langflow.schema import Record
-from langchain_core.embeddings import Embeddings
 
 
 class RedisSearchComponent(RedisComponent, LCVectorStoreComponent):

src/backend/base/langflow/components/vectorsearch/WeaviateSearch.py

@@ -1,10 +1,11 @@
 from typing import List, Optional
 
+from langchain_core.embeddings import Embeddings
+
 from langflow.components.vectorstores.base.model import LCVectorStoreComponent
 from langflow.components.vectorstores.Weaviate import WeaviateVectorStoreComponent
 from langflow.field_typing import Text
 from langflow.schema import Record
-from langchain_core.embeddings import Embeddings
 
 
 class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreComponent):

src/backend/base/langflow/components/vectorsearch/pgvectorSearch.py

@@ -1,10 +1,11 @@
 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.field_typing import Text
 from langflow.schema import Record
-from langchain_core.embeddings import Embeddings
 
 
 class PGVectorSearchComponent(PGVectorComponent, LCVectorStoreComponent):

src/backend/base/langflow/components/vectorstores/AstraDB.py

@@ -1,11 +1,12 @@
 from typing import List, Optional, Union
+
 from langchain_astradb import AstraDBVectorStore
 from langchain_astradb.utils.astradb 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 langchain_core.retrievers import BaseRetriever
 
 
 class AstraDBVectorStoreComponent(CustomComponent):
@@ -156,3 +157,4 @@ class AstraDBVectorStoreComponent(CustomComponent):
             )
 
         return vector_store
+        return vector_store

src/backend/base/langflow/components/vectorstores/Chroma.py

@@ -8,7 +8,7 @@ from langchain_core.retrievers import BaseRetriever
 from langchain_core.vectorstores import VectorStore
 
 from langflow.custom import CustomComponent
-from langflow.schema.schema import Record
+from langflow.schema import Record
 
 
 class ChromaComponent(CustomComponent):

src/backend/base/langflow/components/vectorstores/Couchbase.py

@@ -1,18 +1,16 @@
-from typing import List, Optional, Union
-
-from langchain_community.vectorstores import CouchbaseVectorStore
-
-from langflow.custom import CustomComponent
-from langflow.field_typing import Embeddings, VectorStore
-from langflow.schema import Record
-
 from datetime import timedelta
+from typing import List, Optional, Union
 
 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
 from langchain_core.retrievers import BaseRetriever
 
+from langflow.custom import CustomComponent
+from langflow.field_typing import Embeddings, VectorStore
+from langflow.schema import Record
+
 
 class CouchbaseComponent(CustomComponent):
     display_name = "Couchbase"

src/backend/base/langflow/components/vectorstores/FAISS.py

@@ -6,7 +6,7 @@ from langchain_core.vectorstores import VectorStore
 
 from langflow.custom import CustomComponent
 from langflow.field_typing import Embeddings
-from langflow.schema.schema import Record
+from langflow.schema import Record
 
 
 class FAISSComponent(CustomComponent):

src/backend/base/langflow/components/vectorstores/MongoDBAtlasVector.py

@@ -4,7 +4,7 @@ from langchain_community.vectorstores.mongodb_atlas import MongoDBAtlasVectorSea
 
 from langflow.custom import CustomComponent
 from langflow.field_typing import Embeddings
-from langflow.schema.schema import Record
+from langflow.schema import Record
 
 
 class MongoDBAtlasComponent(CustomComponent):

src/backend/base/langflow/components/vectorstores/Pinecone.py

@@ -8,7 +8,7 @@ from langchain_pinecone.vectorstores import PineconeVectorStore
 
 from langflow.custom import CustomComponent
 from langflow.field_typing import Embeddings
-from langflow.schema.schema import Record
+from langflow.schema import Record
 
 
 class PineconeComponent(CustomComponent):

src/backend/base/langflow/components/vectorstores/Qdrant.py

@@ -6,7 +6,7 @@ from langchain_core.vectorstores import VectorStore
 
 from langflow.custom import CustomComponent
 from langflow.field_typing import Embeddings
-from langflow.schema.schema import Record
+from langflow.schema import Record
 
 
 class QdrantComponent(CustomComponent):

src/backend/base/langflow/components/vectorstores/Redis.py

@@ -6,7 +6,7 @@ from langchain_core.retrievers import BaseRetriever
 from langchain_core.vectorstores import VectorStore
 
 from langflow.custom import CustomComponent
-from langflow.schema.schema import Record
+from langflow.schema import Record
 
 
 class RedisComponent(CustomComponent):

src/backend/base/langflow/components/vectorstores/SupabaseVectorStore.py

@@ -7,7 +7,7 @@ from supabase.client import Client, create_client
 
 from langflow.custom import CustomComponent
 from langflow.field_typing import Embeddings
-from langflow.schema.schema import Record
+from langflow.schema import Record
 
 
 class SupabaseComponent(CustomComponent):

src/backend/base/langflow/components/vectorstores/Upstash.py

@@ -6,7 +6,7 @@ from langchain_core.retrievers import BaseRetriever
 from langchain_core.vectorstores import VectorStore
 
 from langflow.custom import CustomComponent
-from langflow.schema.schema import Record
+from langflow.schema import Record
 
 
 class UpstashVectorStoreComponent(CustomComponent):

src/backend/base/langflow/components/vectorstores/Vectara.py

@@ -9,7 +9,7 @@ from langchain_core.vectorstores import VectorStore
 
 from langflow.custom import CustomComponent
 from langflow.field_typing import BaseRetriever
-from langflow.schema.schema import Record
+from langflow.schema import Record
 
 
 class VectaraComponent(CustomComponent):

src/backend/base/langflow/components/vectorstores/Weaviate.py

@@ -8,7 +8,7 @@ from langchain_core.retrievers import BaseRetriever
 from langchain_core.vectorstores import VectorStore
 
 from langflow.custom import CustomComponent
-from langflow.schema.schema import Record
+from langflow.schema import Record
 
 
 class WeaviateVectorStoreComponent(CustomComponent):

src/backend/base/langflow/components/vectorstores/pgvector.py

@@ -6,7 +6,7 @@ from langchain_core.retrievers import BaseRetriever
 from langchain_core.vectorstores import VectorStore
 
 from langflow.custom import CustomComponent
-from langflow.schema.schema import Record
+from langflow.schema import Record
 
 
 class PGVectorComponent(CustomComponent):

src/backend/base/langflow/custom/custom_component/custom_component.py

@@ -378,13 +378,14 @@ class CustomComponent(BaseComponent):
             The variable for the current user with the specified name.
         """
 
-        def get_variable(name: str):
+        def get_variable(name: str, field: str):
             if hasattr(self, "_user_id") and not self._user_id:
                 raise ValueError(f"User id is not set for {self.__class__.__name__}")
             variable_service = get_variable_service()  # Get service instance
             # Retrieve and decrypt the variable by name for the current user
             with session_scope() as session:
-                return variable_service.get_variable(user_id=self._user_id or "", name=name, session=session)
+                user_id = self._user_id or ""
+                return variable_service.get_variable(user_id=user_id, name=name, field=field, session=session)
 
         return get_variable
 

src/backend/base/langflow/field_typing/__init__.py

@@ -19,13 +19,13 @@ from .constants import (
     Embeddings,
     NestedDict,
     Object,
-    Prompt,
     PromptTemplate,
     Text,
     TextSplitter,
     Tool,
     VectorStore,
 )
+from .prompt import Prompt
 from .range_spec import RangeSpec
 
 

src/backend/base/langflow/field_typing/constants.py

@@ -15,6 +15,8 @@ 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]]
 
@@ -27,10 +29,6 @@ class Data:
     pass
 
 
-class Prompt:
-    pass
-
-
 class Code:
     pass
 

src/backend/base/langflow/field_typing/prompt.py

@@ -0,0 +1,41 @@
+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)
+        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)])
+        instance.prompt = prompt_template.to_json()
+        return instance

src/backend/base/langflow/graph/graph/base.py

@@ -4,7 +4,6 @@ from collections import defaultdict, deque
 from functools import partial
 from itertools import chain
 from typing import TYPE_CHECKING, Callable, Coroutine, Dict, Generator, List, Optional, Tuple, Type, Union
-
 from loguru import logger
 
 from langflow.graph.edge.base import ContractEdge
@@ -20,6 +19,7 @@ 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
 
 if TYPE_CHECKING:
     from langflow.graph.schema import ResultData
@@ -725,6 +725,7 @@ class Graph:
         chat_service: ChatService,
         vertex_id: str,
         inputs_dict: Optional[Dict[str, str]] = None,
+        files: Optional[list[str]] = None,
         user_id: Optional[str] = None,
         fallback_to_env_vars: bool = False,
     ):
@@ -752,7 +753,9 @@ class Graph:
                 # Check the cache for the vertex
                 cached_result = await chat_service.get_cache(key=vertex.id)
                 if isinstance(cached_result, CacheMiss):
-                    await vertex.build(user_id=user_id, inputs=inputs_dict, fallback_to_env_vars=fallback_to_env_vars)
+                    await vertex.build(
+                        user_id=user_id, inputs=inputs_dict, fallback_to_env_vars=fallback_to_env_vars, files=files
+                    )
                     await chat_service.set_cache(key=vertex.id, data=vertex)
                 else:
                     cached_vertex = cached_result["result"]
@@ -766,7 +769,10 @@ class Graph:
                         vertex.result.used_frozen_result = True
 
             else:
-                await vertex.build(user_id=user_id, inputs=inputs_dict, fallback_to_env_vars=fallback_to_env_vars)
+                await vertex.build(
+                    user_id=user_id, inputs=inputs_dict, fallback_to_env_vars=fallback_to_env_vars, files=files
+                )
+                await chat_service.set_cache(key=vertex.id, data=vertex)
 
             if vertex.result is not None:
                 params = f"{vertex._built_object_repr()}{params}"
@@ -779,9 +785,13 @@ class Graph:
             next_runnable_vertices, top_level_vertices = await self.get_next_and_top_level_vertices(
                 lock, set_cache_coro, vertex
             )
+            flow_id = self.flow_id
+            log_transaction(flow_id, vertex, status="success")
             return next_runnable_vertices, top_level_vertices, result_dict, params, valid, artifacts, vertex
         except Exception as exc:
             logger.exception(f"Error building vertex: {exc}")
+            flow_id = self.flow_id
+            log_transaction(flow_id, vertex, status="failure", error=str(exc))
             raise exc
 
     async def get_next_and_top_level_vertices(

src/backend/base/langflow/graph/schema.py

@@ -1,15 +1,17 @@
 from enum import Enum
 from typing import Any, List, Optional
 
-from pydantic import BaseModel, Field, field_serializer
+from pydantic import BaseModel, Field, field_serializer, model_validator
 
 from langflow.graph.utils import serialize_field
+from langflow.schema.schema import Log, StreamURL
 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)
     messages: Optional[list[ChatOutputResponse]] = Field(default_factory=list)
     timedelta: Optional[float] = None
     duration: Optional[str] = None
@@ -23,6 +25,24 @@ class ResultData(BaseModel):
             return {key: serialize_field(val) for key, val in value.items()}
         return serialize_field(value)
 
+    @model_validator(mode="before")
+    @classmethod
+    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}
+
+            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"])]
+        return values
+
 
 class InterfaceComponentTypes(str, Enum, metaclass=ContainsEnumMeta):
     # ChatInput and ChatOutput are the only ones that are

src/backend/base/langflow/graph/utils.py

@@ -1,9 +1,12 @@
-from typing import Any, Union
+from enum import Enum
+from typing import Any, Generator, Union
 
 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.message import Message
 
 
 class UnbuiltObject:
@@ -14,6 +17,16 @@ class UnbuiltResult:
     pass
 
 
+class ArtifactType(str, Enum):
+    TEXT = "text"
+    RECORD = "record"
+    OBJECT = "object"
+    ARRAY = "array"
+    STREAM = "stream"
+    UNKNOWN = "unknown"
+    MESSAGE = "message"
+
+
 def validate_prompt(prompt: str):
     """Validate prompt."""
     if extract_input_variables_from_prompt(prompt):
@@ -50,3 +63,38 @@ def serialize_field(value):
     elif isinstance(value, str):
         return {"result": value}
     return value
+
+
+def get_artifact_type(custom_component, build_result) -> str:
+    result = ArtifactType.UNKNOWN
+    value = custom_component.repr_value
+    match value:
+        case Record():
+            result = ArtifactType.RECORD
+
+        case str():
+            result = ArtifactType.TEXT
+
+        case dict():
+            result = ArtifactType.OBJECT
+
+        case list():
+            result = ArtifactType.ARRAY
+
+        case Message():
+            result = ArtifactType.MESSAGE
+
+    if result == ArtifactType.UNKNOWN:
+        if 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 = ""
+
+    return raw

src/backend/base/langflow/graph/vertex/base.py

@@ -4,17 +4,17 @@ import inspect
 import os
 import types
 from enum import Enum
-from typing import TYPE_CHECKING, Any, AsyncIterator, Callable, Dict, Iterator, List, Optional
+from typing import TYPE_CHECKING, Any, AsyncIterator, Callable, Dict, Iterator, List, Mapping, Optional
 
 from loguru import logger
 
 from langflow.graph.schema import INPUT_COMPONENTS, OUTPUT_COMPONENTS, InterfaceComponentTypes, ResultData
-from langflow.graph.utils import UnbuiltObject, UnbuiltResult
+from langflow.graph.utils import ArtifactType, UnbuiltObject, UnbuiltResult
-from langflow.graph.vertex.utils import log_transaction
 from langflow.interface.initialize import loading
 from langflow.interface.listing import lazy_load_dict
 from langflow.schema.schema import INPUT_FIELD_NAME
 from langflow.services.deps import get_storage_service
+from langflow.services.monitor.utils import log_transaction
 from langflow.utils.constants import DIRECT_TYPES
 from langflow.utils.schemas import ChatOutputResponse
 from langflow.utils.util import sync_to_async, unescape_string
@@ -63,6 +63,8 @@ class Vertex:
         self._built_result = None
         self._built = False
         self.artifacts: Dict[str, Any] = {}
+        self.artifacts_raw: Any = None
+        self.artifacts_type: Optional[str] = None
         self.steps: List[Callable] = [self._build]
         self.steps_ran: List[Callable] = []
         self.task_id: Optional[str] = None
@@ -394,7 +396,7 @@ class Vertex:
         self.load_from_db_fields = load_from_db_fields
         self._raw_params = params.copy()
 
-    def update_raw_params(self, new_params: Dict[str, str], overwrite: bool = False):
+    def update_raw_params(self, new_params: Mapping[str, str | list[str]], overwrite: bool = False):
         """
         Update the raw parameters of the vertex with the given new parameters.
 
@@ -445,11 +447,14 @@ class Vertex:
         try:
             messages = [
                 ChatOutputResponse(
-                    message=artifacts["message"],
+                    message=artifacts["text"],
                     sender=artifacts.get("sender"),
                     sender_name=artifacts.get("sender_name"),
                     session_id=artifacts.get("session_id"),
+                    stream_url=artifacts.get("stream_url"),
+                    files=[{"path": file} if isinstance(file, str) else file for file in artifacts.get("files", [])],
                     component_id=self.id,
+                    type=self.artifacts_type,
                 ).model_dump(exclude_none=True)
             ]
         except KeyError:
@@ -462,12 +467,11 @@ class Vertex:
         # We need to set the artifacts to pass information
         # to the frontend
         self.set_artifacts()
-        artifacts = self.artifacts
+        artifacts = self.artifacts_raw
         if isinstance(artifacts, dict):
             messages = self.extract_messages_from_artifacts(artifacts)
         else:
             messages = []
-
         result_dict = ResultData(
             results=result_dict,
             artifacts=artifacts,
@@ -548,12 +552,13 @@ class Vertex:
         Returns:
             The built result if use_result is True, else the built object.
         """
+        flow_id = self.graph.flow_id
         if not self._built:
-            log_transaction(source=self, target=requester, flow_id=self.graph.flow_id, status="error")
+            log_transaction(flow_id, vertex=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(source=self, target=requester, flow_id=self.graph.flow_id, status="success")
+        log_transaction(flow_id, vertex=self, target=requester, status="success")
         return result
 
     async def _build_vertex_and_update_params(self, key, vertex: "Vertex"):
@@ -647,6 +652,8 @@ class Vertex:
                 self._built_object, self.artifacts = result
             elif len(result) == 3:
                 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
         else:
             self._built_object = result
 
@@ -687,6 +694,7 @@ class Vertex:
         self,
         user_id=None,
         inputs: Optional[Dict[str, Any]] = None,
+        files: Optional[list[str]] = None,
         requester: Optional["Vertex"] = None,
         **kwargs,
     ) -> Any:
@@ -704,9 +712,14 @@ class Vertex:
                 return await self.get_requester_result(requester)
             self._reset()
 
-            if self._is_chat_input() and inputs:
+            if self._is_chat_input() and (inputs or files):
-                inputs = {"input_value": inputs.get(INPUT_FIELD_NAME, "")}
+                chat_input = {}
-                self.update_raw_params(inputs, overwrite=True)
+                if inputs:
+                    chat_input.update({"input_value": inputs.get(INPUT_FIELD_NAME, "")})
+                if files:
+                    chat_input.update({"files": files})
+
+                self.update_raw_params(chat_input, overwrite=True)
 
             # Run steps
             for step in self.steps:

src/backend/base/langflow/graph/vertex/types.py

@@ -1,13 +1,12 @@
 import json
-from typing import Any, AsyncIterator, Dict, Iterator, List
+from typing import TYPE_CHECKING, Any, AsyncIterator, Dict, Iterator, List
 
 import yaml
-from git import TYPE_CHECKING
 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 UnbuiltObject, serialize_field
+from langflow.graph.utils import ArtifactType, UnbuiltObject, serialize_field
 from langflow.graph.vertex.base import Vertex
 from langflow.graph.vertex.utils import log_transaction
 from langflow.schema import Record
@@ -153,6 +152,7 @@ class InterfaceVertex(ComponentVertex):
         sender = self.params.get("sender", None)
         sender_name = self.params.get("sender_name", None)
         message = self.params.get(INPUT_FIELD_NAME, None)
+        files = [{"path": file} if isinstance(file, str) else file for file in self.params.get("files", [])]
         if isinstance(message, str):
             message = unescape_string(message)
         stream_url = None
@@ -182,12 +182,14 @@ class InterfaceVertex(ComponentVertex):
             # it means that it is a stream of messages
             else:
                 message = text_output
-
+            artifact_type = ArtifactType.STREAM if stream_url is not None else ArtifactType.OBJECT
             artifacts = ChatOutputResponse(
                 message=message,
                 sender=sender,
                 sender_name=sender_name,
                 stream_url=stream_url,
+                files=files,
+                type=artifact_type,
             )
 
             self.will_stream = stream_url is not None
@@ -269,6 +271,8 @@ class InterfaceVertex(ComponentVertex):
             message=complete_message,
             sender=self.params.get("sender", ""),
             sender_name=self.params.get("sender_name", ""),
+            files=[{"path": file} if isinstance(file, str) else file for file in self.params.get("files", [])],
+            type=ArtifactType.OBJECT.value,
         ).model_dump()
         self.params[INPUT_FIELD_NAME] = complete_message
         self._built_object = Record(text=complete_message, data=self.artifacts)

src/backend/base/langflow/graph/vertex/utils.py

@@ -1,9 +1,5 @@
 from typing import TYPE_CHECKING
 
-from loguru import logger
-
-from langflow.services.deps import get_monitor_service
-
 if TYPE_CHECKING:
     from langflow.graph.vertex.base import Vertex
 
@@ -21,34 +17,3 @@ def build_clean_params(target: "Vertex") -> dict:
         if isinstance(value, list):
             params[key] = [item for item in value if isinstance(item, (str, int, bool, float, list, dict))]
     return params
-
-
-def log_transaction(source: "Vertex", target: "Vertex", flow_id, status, error=None):
-    """
-    Logs a transaction between two vertices.
-
-    Args:
-        source (Vertex): The source vertex of the transaction.
-        target (Vertex): The target vertex of the transaction.
-        status: The status of the transaction.
-        error (Optional): Any error associated with the transaction.
-
-    Raises:
-        Exception: If there is an error while logging the transaction.
-
-    """
-    try:
-        monitor_service = get_monitor_service()
-        clean_params = build_clean_params(target)
-        data = {
-            "source": source.vertex_type,
-            "target": target.vertex_type,
-            "target_args": clean_params,
-            "timestamp": monitor_service.get_timestamp(),
-            "status": status,
-            "error": error,
-            "flow_id": flow_id,
-        }
-        monitor_service.add_row(table_name="transactions", data=data)
-    except Exception as e:
-        logger.error(f"Error logging transaction: {e}")

src/backend/base/langflow/helpers/__init__.py

@@ -1,3 +1,3 @@
-from .record import docs_to_records, records_to_text
+from .record import docs_to_records, records_to_text, messages_to_text
 
-__all__ = ["docs_to_records", "records_to_text"]
+__all__ = ["docs_to_records", "records_to_text", "messages_to_text"]

src/backend/base/langflow/helpers/flow.py

@@ -6,7 +6,8 @@ from pydantic.v1 import BaseModel, Field, create_model
 from sqlmodel import Session, select
 
 from langflow.graph.schema import RunOutputs
-from langflow.schema.schema import INPUT_FIELD_NAME, Record
+from langflow.schema import Record
+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
 
@@ -259,3 +260,24 @@ def get_flow_by_id_or_endpoint_name(
         raise HTTPException(status_code=404, detail=f"Flow identifier {flow_id_or_name} not found")
 
     return flow
+
+
+def generate_unique_flow_name(flow_name, user_id, session):
+    original_name = flow_name
+    n = 1
+    while True:
+        # Check if a flow with the given name exists
+        existing_flow = session.exec(
+            select(Flow).where(
+                Flow.name == flow_name,
+                Flow.user_id == user_id,
+            )
+        ).first()
+
+        # If no flow with the given name exists, return the name
+        if not existing_flow:
+            return flow_name
+
+        # If a flow with the name already exists, append (n) to the name and increment n
+        flow_name = f"{original_name} ({n})"
+        n += 1

src/backend/base/langflow/helpers/folders.py

@@ -0,0 +1,23 @@
+from langflow.services.database.models.folder.model import Folder
+from sqlalchemy import select
+
+
+def generate_unique_folder_name(folder_name, user_id, session):
+    original_name = folder_name
+    n = 1
+    while True:
+        # Check if a folder with the given name exists
+        existing_folder = session.exec(
+            select(Folder).where(
+                Folder.name == folder_name,
+                Folder.user_id == user_id,
+            )
+        ).first()
+
+        # If no folder with the given name exists, return the name
+        if not existing_folder:
+            return folder_name
+
+        # If a folder with the name already exists, append (n) to the name and increment n
+        folder_name = f"{original_name} ({n})"
+        n += 1

src/backend/base/langflow/helpers/record.py

@@ -1,7 +1,9 @@
 from typing import Union
+
 from langchain_core.documents import Document
 
 from langflow.schema import Record
+from langflow.schema.message import Message
 
 
 def docs_to_records(documents: list[Document]) -> list[Record]:
@@ -27,7 +29,7 @@ def records_to_text(template: str, records: Union[Record, list[Record]]) -> str:
     Returns:
         list[str]: The converted list of texts.
     """
-    if isinstance(records, Record):
+    if isinstance(records, (Record)):
         records = [records]
     # Check if there are any format strings in the template
     _records = []
@@ -39,3 +41,27 @@ def records_to_text(template: str, records: Union[Record, list[Record]]) -> str:
 
     formated_records = [template.format(data=record.data, **record.data) for record in _records]
     return "\n".join(formated_records)
+
+
+def messages_to_text(template: str, messages: Union[Message, list[Message]]) -> str:
+    """
+    Converts a list of Messages to a list of texts.
+
+    Args:
+        messages (list[Message]): The list of Messages to convert.
+
+    Returns:
+        list[str]: The converted list of texts.
+    """
+    if isinstance(messages, (Message)):
+        messages = [messages]
+    # Check if there are any format strings in the template
+    _messages = []
+    for message in messages:
+        # If it is not a message, create one with the key "text"
+        if not isinstance(message, Message):
+            raise ValueError("All elements in the list must be of type Message.")
+        _messages.append(message)
+
+    formated_messages = [template.format(data=message.model_dump(), **message.model_dump()) for message in _messages]
+    return "\n".join(formated_messages)