merge dev into feat-more

.devcontainer/demo/devcontainer.json

@@ -1,32 +1,33 @@
 // For format details, see https://aka.ms/devcontainer.json. For config options, see the
 // README at: https://github.com/devcontainers/templates/tree/main/src/universal
 {
-	"name": "LangChain Demo Container",
+  "name": "Langflow Demo Container",
-	// Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
-	"image": "mcr.microsoft.com/devcontainers/python:3.10",
+  "image": "mcr.microsoft.com/devcontainers/python:3.10",
-	"features": {
+  "features": {
-		"ghcr.io/devcontainers/features/aws-cli:1": {},
+    "ghcr.io/devcontainers/features/aws-cli:1": {},
-		"ghcr.io/devcontainers/features/docker-in-docker": {},
+    "ghcr.io/devcontainers/features/docker-in-docker": {},
-		"ghcr.io/devcontainers/features/node": {}
+    "ghcr.io/devcontainers/features/node": {}
-	},
+  },
-	"customizations": {
+  "customizations": {
-		"vscode": {
+    "vscode": {
-			"extensions": [
+      "extensions": [
-				"actboy168.tasks",
+        "actboy168.tasks",
-				"GitHub.copilot",
+        "GitHub.copilot",
-				"ms-python.python",
+        "ms-python.python",
-				"eamodio.gitlens"
+        "eamodio.gitlens",
-			]
+        "GitHub.vscode-pull-request-github"
-		}
+      ]
-	},
+    }
-	// Features to add to the dev container. More info: https://containers.dev/features.
+  },
-	// "features": {},
+  // Features to add to the dev container. More info: https://containers.dev/features.
-	// Use 'forwardPorts' to make a list of ports inside the container available locally.
+  // "features": {},
-	// "forwardPorts": [],
+  // Use 'forwardPorts' to make a list of ports inside the container available locally.
-	// Use 'postCreateCommand' to run commands after the container is created.
+  // "forwardPorts": [],
-	"postCreateCommand": "pipx install 'langflow>=0.0.33' && langflow --host 0.0.0.0"
+  // Use 'postCreateCommand' to run commands after the container is created.
-	// Configure tool-specific properties.
+  "postCreateCommand": "pipx install 'langflow>=0.0.33' && langflow --host 0.0.0.0"
-	// "customizations": {},
+  // Configure tool-specific properties.
-	// Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
+  // "customizations": {},
-	// "remoteUser": "root"
+  // Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
+  // "remoteUser": "root"
 }

.devcontainer/devcontainer.json

@@ -1,35 +1,41 @@
 // For format details, see https://aka.ms/devcontainer.json. For config options, see the
 // README at: https://github.com/devcontainers/templates/tree/main/src/universal
 {
-	"name": "LangChain Dev Container",
+  "name": "Langflow Dev Container",
-	// Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
-	"image": "mcr.microsoft.com/devcontainers/universal:2-linux",
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.10-bullseye",
-	"features": {
-		"ghcr.io/devcontainers/features/aws-cli:1": {},
-		"ghcr.io/devcontainers/features/docker-in-docker": {}
-	},
-	"customizations": {
-		"vscode": {"extensions": [
-				"actboy168.tasks",
-				"GitHub.copilot",
-				"ms-python.python",
-				"sourcery.sourcery",
-				"eamodio.gitlens"
-			]}
-	},
 
-	// Features to add to the dev container. More info: https://containers.dev/features.
+  // Features to add to the dev container. More info: https://containers.dev/features.
-	// "features": {},
+  "features": {
+    "ghcr.io/devcontainers/features/node": {},
+    "ghcr.io/devcontainers-contrib/features/poetry": {}
+  },
 
-	// Use 'forwardPorts' to make a list of ports inside the container available locally.
+  // Use 'forwardPorts' to make a list of ports inside the container available locally.
-	// "forwardPorts": [],
+  // "forwardPorts": [],
 
-	// Use 'postCreateCommand' to run commands after the container is created.
+  // Use 'postCreateCommand' to run commands after the container is created.
-	"postCreateCommand": "poetry install"
+  "postCreateCommand": "make install_frontend && make install_backend",
 
-	// Configure tool-specific properties.
+  "containerEnv": {
-	// "customizations": {},
+    "POETRY_VIRTUALENVS_IN_PROJECT": "true"
+  },
 
-	// Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
+  // Configure tool-specific properties.
-	// "remoteUser": "root"
+  "customizations": {
+    "vscode": {
+      "extensions": [
+        "actboy168.tasks",
+        "GitHub.copilot",
+        "ms-python.python",
+        "sourcery.sourcery",
+        "eamodio.gitlens",
+        "ms-vscode.makefile-tools",
+        "GitHub.vscode-pull-request-github"
+      ]
+    }
+  }
+
+  // Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
+  // "remoteUser": "root"
 }

.dockerignore

@@ -0,0 +1 @@
+.venv/

.gitattributes

@@ -0,0 +1,34 @@
+# Set the default behavior, in case people don't have core.autocrlf set.
+* text eol=lf
+
+# Explicitly declare text files you want to always be normalized and converted
+# to native line endings on checkout.
+*.c text
+*.h text
+*.py text
+*.js text
+*.jsx text
+*.ts text
+*.tsx text
+*.md text
+*.mdx text
+*.yml text
+*.yaml text
+*.xml text
+*.csv text
+*.json text
+*.sh text
+*.Dockerfile text
+Dockerfile text
+
+# Declare files that will always have CRLF line endings on checkout.
+*.sln text eol=crlf
+
+# Denote all files that are truly binary and should not be modified.
+*.png binary
+*.jpg binary
+*.ico binary
+*.gif binary
+*.mp4 binary
+*.svg binary
+*.csv binary

.github/workflows/pre-release.yml

@@ -14,9 +14,7 @@ env:
 
 jobs:
   if_release:
-    if: |
+    if: ${{ (github.event.pull_request.merged == true) && contains(github.event.pull_request.labels.*.name, 'pre-release') }}
-      ${{ github.event.pull_request.merged == true }}
-      && ${{ contains(github.event.pull_request.labels.*.name, 'pre-release') }}
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
@@ -40,6 +38,7 @@ jobs:
           token: ${{ secrets.GITHUB_TOKEN }}
           draft: false
           generateReleaseNotes: true
+          prerelease: true
           tag: v${{ steps.check-version.outputs.version }}
           commit: main
       - name: Publish to PyPI

.gitignore

@@ -253,3 +253,4 @@ langflow.db
 .docusaurus/
 
 /tmp/*
+src/backend/langflow/frontend/

.vscode/launch.json

@@ -1,4 +1,5 @@
 {
+  "version": "0.2.0",
   "configurations": [
     {
       "name": "Debug Backend",
@@ -38,6 +39,15 @@
       "request": "launch",
       "url": "http://localhost:3000/",
       "webRoot": "${workspaceRoot}/src/frontend"
+    },
+    {
+      "name": "Python: Debug Tests",
+      "type": "python",
+      "request": "launch",
+      "program": "${file}",
+      "purpose": ["debug-test"],
+      "console": "integratedTerminal",
+      "justMyCode": false
     }
   ]
 }

.vscode/tasks.json

@@ -0,0 +1,48 @@
+{
+  // See https://go.microsoft.com/fwlink/?LinkId=733558
+  // for the documentation about the tasks.json format
+  "version": "2.0.0",
+  "tasks": [
+    {
+      "label": "Init",
+      "type": "shell",
+      "command": "make init"
+    },
+    // make backend
+    {
+      "label": "Backend",
+      "type": "shell",
+      "command": "make backend"
+    },
+    // make frontend
+    {
+      "label": "Frontend",
+      "type": "shell",
+      "command": "make frontend"
+    },
+    // make test
+    {
+      "label": "Test",
+      "type": "shell",
+      "command": "make tests"
+    },
+    // make lint
+    {
+      "label": "Lint",
+      "type": "shell",
+      "command": "make lint"
+    },
+    // make format
+    {
+      "label": "Format",
+      "type": "shell",
+      "command": "make format"
+    },
+    // make install
+    {
+      "label": "Install",
+      "type": "shell",
+      "command": "make install_backend && make install_frontend"
+    }
+  ]
+}

CONTRIBUTING.md

@@ -7,6 +7,11 @@ to contributions, whether it be in the form of a new feature, improved infra, or
 To contribute to this project, please follow a ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow.
 Please do not try to push directly to this repo unless you are a maintainer.
 
+The branch structure is as follows:
+
+- `main`: The stable version of Langflow
+- `dev`: The development version of Langflow. This branch is used to test new features before they are merged into `main` and, as such, may be unstable.
+
 ## 🗺️Contributing Guidelines
 
 ## 🚩GitHub Issues

Makefile

@@ -19,7 +19,7 @@ coverage:
 		--cov-report term-missing:skip-covered
 
 tests:
-	poetry run pytest tests
+	poetry run pytest tests -n auto
 
 format:
 	poetry run black .
@@ -27,20 +27,38 @@ format:
 	cd src/frontend && npm run format
 
 lint:
-	poetry run mypy .
+	poetry run mypy src/backend/langflow
 	poetry run black . --check
 	poetry run ruff . --fix
 
 install_frontend:
 	cd src/frontend && npm install
 
+install_frontendc:
+	cd src/frontend && rm -rf node_modules package-lock.json && npm install
+
 run_frontend:
 	cd src/frontend && npm start
 
+run_cli:
+	poetry run langflow run --path src/frontend/build
+
+run_cli_debug:
+	poetry run langflow run --path src/frontend/build --log-level debug
+
+setup_devcontainer:
+	make init
+	make build_frontend
+	poetry run langflow --path src/frontend/build
+
 frontend:
 	make install_frontend
 	make run_frontend
 
+frontendc:
+	make install_frontendc
+	make run_frontend
+
 install_backend:
 	poetry install
 
@@ -51,7 +69,7 @@ backend:
 build_and_run:
 	echo 'Removing dist folder'
 	rm -rf dist
-	make build && poetry run pip install dist/*.tar.gz && poetry run langflow
+	make build && poetry run pip install dist/*.tar.gz && poetry run langflow run
 
 build_and_install:
 	echo 'Removing dist folder'

README.md

@@ -245,7 +245,7 @@ print(run_flow("Your message", flow_id=FLOW_ID, tweaks=TWEAKS))
 
 ## Deploy on Railway
 
-[![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/Emy2sU?referralCode=MnPSdg)
+[![Deploy on Railway](https://railway.app/button.svg)](https://railway.app/template/JMXEWp?referralCode=MnPSdg)
 
 ## Deploy on Render
 
@@ -275,6 +275,8 @@ flow("Hey, have you heard of Langflow?")
 
 We welcome contributions from developers of all levels to our open-source project on GitHub. If you'd like to contribute, please check our [contributing guidelines](./CONTRIBUTING.md) and help make Langflow more accessible.
 
+---
+
 Join our [Discord](https://discord.com/invite/EqksyE2EX9) server to ask questions, make suggestions and showcase your projects! 🦾
 
 <p>

dev.Dockerfile

@@ -15,4 +15,4 @@ COPY ./ ./
 # Install dependencies
 RUN poetry config virtualenvs.create false && poetry install --no-interaction --no-ansi
 
-CMD ["uvicorn","--factory", "langflow.main:create_app", "--host", "0.0.0.0", "--port", "5003", "--reload", "log-level", "debug"]
+CMD ["uvicorn", "--factory", "src.backend.langflow.main:create_app", "--host", "0.0.0.0", "--port", "7860", "--reload", "--log-level", "debug"]

docker-compose.debug.yml

@@ -11,7 +11,7 @@ services:
       [
         "sh",
         "-c",
-        "pip install debugpy -t /tmp && python /tmp/debugpy --wait-for-client --listen 0.0.0.0:5678 -m uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --reload",
+        "pip install debugpy -t /tmp && python /tmp/debugpy --wait-for-client --listen 0.0.0.0:5678 -m uvicorn --factory src.backend.langflow.main:create_app --host 0.0.0.0 --port 7860 --reload",
       ]
     ports:
       - 7860:7860

docker-compose.yml

@@ -9,7 +9,7 @@ services:
       - "7860:7860"
     volumes:
       - ./:/app
-    command: bash -c "uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --reload"
+    command: bash -c "uvicorn --factory src.backend.langflow.main:create_app --host 0.0.0.0 --port 7860 --reload"
 
   frontend:
     build:

docker_example/README.md

@@ -0,0 +1,9 @@
+# LangFlow Docker Running
+
+```sh
+git clone git@github.com:logspace-ai/langflow.git
+cd langflow/docker_example
+docker compose up
+```
+
+The web UI will be accessible on port [7860](http://localhost:7860/)

docs/docs/components/custom.mdx

@@ -33,6 +33,7 @@ The CustomComponent class serves as the foundation for creating custom component
   | Supported Types                                           |
   | --------------------------------------------------------- |
   | _`str`_, _`int`_, _`float`_, _`bool`_, _`list`_, _`dict`_ |
+  | _`langflow.field_typing.NestedDict`_                      |
   | _`langchain.chains.base.Chain`_                           |
   | _`langchain.PromptTemplate`_                              |
   | _`langchain.llms.base.BaseLLM`_                           |
@@ -44,6 +45,8 @@ The CustomComponent class serves as the foundation for creating custom component
   | _`langchain.embeddings.base.Embeddings`_                  |
   | _`langchain.schema.BaseRetriever`_                        |
 
+  The difference between _`dict`_ and _`langflow.field_typing.NestedDict`_ is that one adds a simple key-value pair field, while the other opens a more robust dictionary editor.
+
   <Admonition type="info">
     Unlike Langchain types, base Python types do not add a
     [handle](../guidelines/components) to the field by default. To add handles,

docs/docs/components/embeddings.mdx

@@ -73,3 +73,25 @@ Used to load [OpenAI’s](https://openai.com/) embedding models.
 - **request_timeout:** Used to specify the maximum amount of time, in milliseconds, to wait for a response from the OpenAI API when generating embeddings for a given text.
 
 - **tiktoken_model_name:** Used to count the number of tokens in documents to constrain them to be under a certain limit. By default, when set to None, this will be the same as the embedding model name.
+
+---
+
+### VertexAIEmbeddings
+
+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).
+
+:::info
+Vertex AI is a cloud computing platform offered by Google Cloud Platform (GCP). It provides access, management, and development of applications and services through global data centers. To use Vertex AI PaLM, you need to have the [google-cloud-aiplatform](https://pypi.org/project/google-cloud-aiplatform/) Python package installed and credentials configured for your environment.
+:::
+
+- **credentials:** The default custom credentials (google.auth.credentials.Credentials) to use.
+- **location:** The default location to use when making API calls – defaults to `us-central1`.
+- **max_output_tokens:** Token limit determines the maximum amount of text output from one prompt – defaults to `128`.
+- **model_name:** The name of the Vertex AI large language model – defaults to `text-bison`.
+- **project:** The default GCP project to use when making Vertex API calls.
+- **request_parallelism:** The amount of parallelism allowed for requests issued to VertexAI models – defaults to `5`.
+- **temperature:** Tunes the degree of randomness in text generations. Should be a non-negative value – defaults to `0`.
+- **top_k:** How the model selects tokens for output, the next token is selected from – defaults to `40`.
+- **top_p:** Tokens are selected from most probable to least until the sum of their – defaults to `0.95`.
+- **tuned_model_name:** The name of a tuned model. If provided, model_name is ignored.
+- **verbose:** This parameter is used to control the level of detail in the output of the chain. When set to True, it will print out some internal states of the chain while it is being run, which can help debug and understand the chain's behavior. If set to False, it will suppress the verbose output – defaults to `False`.

docs/docs/components/llms.mdx

@@ -196,3 +196,25 @@ Vertex AI is a cloud computing platform offered by Google Cloud Platform (GCP).
 - **top_p:** Tokens are selected from most probable to least until the sum of their – defaults to `0.95`.
 - **tuned_model_name:** The name of a tuned model. If provided, model_name is ignored.
 - **verbose:** This parameter is used to control the level of detail in the output of the chain. When set to True, it will print out some internal states of the chain while it is being run, which can help debug and understand the chain's behavior. If set to False, it will suppress the verbose output – defaults to `False`.
+
+---
+
+### ChatVertexAI
+
+Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) large language models.
+
+:::info
+Vertex AI is a cloud computing platform offered by Google Cloud Platform (GCP). It provides access, management, and development of applications and services through global data centers. To use Vertex AI PaLM, you need to have the [google-cloud-aiplatform](https://pypi.org/project/google-cloud-aiplatform/) Python package installed and credentials configured for your environment.
+:::
+
+- **credentials:** The default custom credentials (google.auth.credentials.Credentials) to use.
+- **location:** The default location to use when making API calls – defaults to `us-central1`.
+- **max_output_tokens:** Token limit determines the maximum amount of text output from one prompt – defaults to `128`.
+- **model_name:** The name of the Vertex AI large language model – defaults to `text-bison`.
+- **project:** The default GCP project to use when making Vertex API calls.
+- **request_parallelism:** The amount of parallelism allowed for requests issued to VertexAI models – defaults to `5`.
+- **temperature:** Tunes the degree of randomness in text generations. Should be a non-negative value – defaults to `0`.
+- **top_k:** How the model selects tokens for output, the next token is selected from – defaults to `40`.
+- **top_p:** Tokens are selected from most probable to least until the sum of their – defaults to `0.95`.
+- **tuned_model_name:** The name of a tuned model. If provided, model_name is ignored.
+- **verbose:** This parameter is used to control the level of detail in the output of the chain. When set to True, it will print out some internal states of the chain while it is being run, which can help debug and understand the chain's behavior. If set to False, it will suppress the verbose output – defaults to `False`.

docs/docs/components/text-splitters.mdx

@@ -1,11 +1,13 @@
-import Admonition from '@theme/Admonition';
+import Admonition from "@theme/Admonition";
 
 # Text Splitters
 
 <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>
 
 A text splitter is a tool that divides a document or text into smaller chunks or segments. It is used to break down large texts into more manageable pieces for analysis or processing.
@@ -22,11 +24,11 @@ The `CharacterTextSplitter` is used to split a long text into smaller chunks bas
 
 - **chunk_overlap:** Determines the number of characters that overlap between consecutive chunks when splitting text. It specifies how much of the previous chunk should be included in the next chunk.
 
-    For example, if the `chunk_overlap` is set to 20 and the `chunk_size` is set to 100, the splitter will create chunks of 100 characters each, but the last 20 characters of each chunk will overlap with the first 20 characters of the next chunk. This allows for a smoother transition between chunks and ensures that no information is lost – defaults to `200`.
+  For example, if the `chunk_overlap` is set to 20 and the `chunk_size` is set to 100, the splitter will create chunks of 100 characters each, but the last 20 characters of each chunk will overlap with the first 20 characters of the next chunk. This allows for a smoother transition between chunks and ensures that no information is lost – defaults to `200`.
 
 - **chunk_size:** Determines the maximum number of characters in each chunk when splitting a text. It specifies the size or length of each chunk.
 
-    For example, if the chunk_size is set to 100, the splitter will create chunks of 100 characters each. If the text is longer than 100 characters, it will be divided into multiple chunks of equal size, except for the last chunk, which may be smaller if there are remaining characters –defaults to `1000`.
+  For example, if the chunk_size is set to 100, the splitter will create chunks of 100 characters each. If the text is longer than 100 characters, it will be divided into multiple chunks of equal size, except for the last chunk, which may be smaller if there are remaining characters –defaults to `1000`.
 
 - **separator:** Specifies the character that will be used to split the text into chunks – defaults to `.`
 
@@ -44,6 +46,18 @@ The `RecursiveCharacterTextSplitter` splits the text by trying to keep paragra
 
 - **chunk_size:** Determines the maximum number of characters in each chunk when splitting a text. It specifies the size or length of each chunk.
 
-- **separator_type:** The parameter allows the user to split the code with multiple language support. It supports various languages such as Text, Ruby, Python, Solidity, Java, and more. Defaults to `Text`.
+- **separators:** The `separators` in RecursiveCharacterTextSplitter are the characters used to split the text into chunks. The text splitter tries to create chunks based on splitting on the first character in the list of `separators`. If any chunks are too large, it moves on to the next character in the list and continues splitting. Defaults to ["\n\n", "\n", " ", ""].
 
-- **separators:** The `separators` in RecursiveCharacterTextSplitter are the characters used to split the text into chunks. The text splitter tries to create chunks based on splitting on the first character in the list of `separators`. If any chunks are too large, it moves on to the next character in the list and continues splitting. Defaults to `.`
+### LanguageRecursiveTextSplitter
+
+The `LanguageRecursiveTextSplitter` is a text splitter that splits the text into smaller chunks based on the (programming) language of the text.
+
+**Params**
+
+- **Documents:** Input documents to split.
+
+- **chunk_overlap:** Determines the number of characters that overlap between consecutive chunks when splitting text. It specifies how much of the previous chunk should be included in the next chunk.
+
+- **chunk_size:** Determines the maximum number of characters in each chunk when splitting a text. It specifies the size or length of each chunk.
+
+- **separator_type:** The parameter allows the user to split the code with multiple language support. It supports various languages such as Ruby, Python, Solidity, Java, and more. Defaults to `Python`.

docs/docs/components/tools.mdx

@@ -7,3 +7,57 @@ import Admonition from '@theme/Admonition';
         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! 🛠️📝
     </p>
 </Admonition>
+
+
+### BingSearchRun
+
+Bing Search is a web search engine owned and operated by Microsoft. It provides search results for various types of content, including web pages, images, videos, and news articles. It uses a combination of algorithms and human editors to deliver search results to users.
+
+**Params**
+
+- **Api Wrapper:** A BingSearchAPIWrapper component that takes the search URL and a subscription key.
+
+
+### Calculator
+
+The calculator tool provides mathematical calculation capabilities to an agent by leveraging an LLMMathChain. It allows the agent to perform math when needed to answer questions.
+
+**Params**
+
+- **LLM:** Language Model to use in the calculation.
+
+
+### GoogleSearchResults
+
+A wrapper around Google Search. Useful for when the user needs to answer questions about with more control over the JSON data returned from the API. It returns the full JSON response configured based on the parameters passed to the API wrapper.
+
+**Params**
+
+- **Api Wrapper:** A GoogleSearchAPIWrapper with Google API key and CSE ID
+
+
+### GoogleSearchRun
+
+A quick wrapper around Google Search. It executes the search query and returns just the first result snippet from the highest-priority result type.
+
+**Params**
+
+- **Api Wrapper:** A GoogleSearchAPIWrapper with Google API key and CSE ID
+
+
+### GoogleSerperRun
+
+A low-cost Google Search API.
+
+**Params**
+
+- **Api Wrapper:** A GoogleSerperAPIWrapper component with API key and result keys
+
+
+### InfoSQLDatabaseTool
+
+Tool for getting metadata about a SQL database. The input to this tool is a comma-separated list of tables, and the output is the schema and sample rows for those tables. Example Input: `“table1`, `table2`, `table3”`.
+
+**Params**
+
+- **Db:** SQLDatabase to query.

docs/docs/components/utilities.mdx

@@ -1,10 +1,76 @@
-import Admonition from '@theme/Admonition';
+import Admonition from "@theme/Admonition";
 
 # Utilities
 
 <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>
 
+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.
+
+---
+
+### GET Request
+
+Make a GET request to the given URL.
+
+**Params**
+
+- **URL:** The URL to make the request to. There can be more than one URL, in which case the request will be made to each URL in order.
+- **Headers:** A dictionary of headers to send with the request.
+
+**Output**
+
+- **List of Documents:** A list of Documents containing the JSON response from each request.
+
+---
+
+### POST Request
+
+Make a POST request to the given URL.
+
+**Params**
+
+- **URL:** The URL to make the request to.
+- **Headers:** A dictionary of headers to send with the request.
+- **Document:** The Document containing a JSON object to send with the request.
+
+**Output**
+
+- **Document:** The JSON response from the request as a Document.
+
+---
+
+### Update Request
+
+Make a PATCH or PUT request to the given URL.
+
+**Params**
+
+- **URL:** The URL to make the request to.
+- **Headers:** A dictionary of headers to send with the request.
+- **Document:** The Document containing a JSON object to send with the request.
+- **Method:** The HTTP method to use for the request. Can be either `PATCH` or `PUT`.
+
+**Output**
+
+- **Document:** The JSON response from the request as a Document.
+
+---
+
+### JSON Document Builder
+
+Build a Document containing a JSON object using a key and another Document page content.
+
+**Params**
+
+- **Key:** The key to use for the JSON object.
+- **Document:** The Document page to use for the JSON object.
+
+**Output**
+
+- **List of Documents:** A list containing the Document with the JSON object.

docs/docs/guidelines/custom-component.mdx

@@ -65,7 +65,6 @@ class DocumentProcessor(CustomComponent):
         light: "img/document_processor.png",
     }}
     style={{
-        width: "40%",
         margin: "0 auto",
         display: "flex",
         justifyContent: "center",
@@ -388,7 +387,7 @@ Your structure should look something like this:
 The recommended way to load custom components is to set the _`LANGFLOW_COMPONENTS_PATH`_ environment variable to the path of your custom components directory. Then, run the Langflow CLI as usual.
 
 ```bash
-export LANGFLOW_COMPONENTS_PATH=/path/to/components
+export LANGFLOW_COMPONENTS_PATH='["/path/to/components"]'
 langflow
 ```
 

docs/docs/guides/langfuse_integration.mdx

@@ -0,0 +1,49 @@
+# Integrating Langfuse with Langflow
+
+## Introduction
+
+Langfuse is an open-source tracing and analytics tool designed for LLM applications. Integrating Langfuse with Langflow provides detailed production traces and granular insights into quality, cost, and latency. This integration allows you to monitor and debug your Langflow's chat or APIs easily.
+
+## Step-by-Step Instructions
+
+### Step 1: Create a Langfuse account
+
+1. Go to [Langfuse](https://langfuse.com) and click on the "Sign In" button in the top right corner.
+2. Click on the "Sign Up" button and create an account.
+3. Once logged in, click on "Settings" and then on "Create new API keys."
+4. Copy the Public key and the Secret Key and save them somewhere safe.
+   {/* Add these keys to your environment variables in the following step. */}
+
+### Step 2: Set up Langfuse in Langflow
+
+1. **Export the Environment Variables**: You'll need to export the environment variables `LANGFLOW_LANGFUSE_SECRET_KEY` and `LANGFLOW_LANGFUSE_PUBLIC_KEY` with the values obtained in Step 1.
+
+   You can do this by executing the following commands in your terminal:
+
+   ```bash
+   export LANGFLOW_LANGFUSE_SECRET_KEY=<your secret key>
+   export LANGFLOW_LANGFUSE_PUBLIC_KEY=<your public key>
+   ```
+
+   Alternatively, you can run the Langflow CLI command:
+
+   ```bash
+   LANGFLOW_LANGFUSE_SECRET_KEY=<your secret key> LANGFLOW_LANGFUSE_PUBLIC_KEY=<your public key> langflow
+   ```
+
+   If you are self-hosting Langfuse, you can also set the environment variable `LANGFLOW_LANGFUSE_HOST` to point to your Langfuse instance. By default, Langfuse points to the cloud instance at `https://cloud.langfuse.com`.
+
+2. **Verify Integration**: Ensure that the environment variables are set correctly by checking their existence in your environment, for example by running:
+
+   ```bash
+   echo $LANGFLOW_LANGFUSE_SECRET_KEY
+   echo $LANGFLOW_LANGFUSE_PUBLIC_KEY
+   ```
+
+3. **Monitor Langflow**: Now, whenever you use Langflow's chat or API, you will be able to see the tracing of your conversations in Langfuse.
+
+That's it! You have successfully integrated Langfuse with Langflow, enhancing observability and debugging capabilities for your LLM application.
+
+---
+
+Note: For more details or customized configurations, please refer to the official [Langfuse documentation](https://langfuse.com/docs/integrations/langchain).

docs/docs/guides/loading_document.mdx

@@ -43,7 +43,7 @@ This guide takes you through the process of augmenting the "Basic Chat with Prom
 
 8. Connect this loader to the `{context}` variable that we just added.
 
-9. In the "Web Page" field, enter "https://langflow.org/how-upload-examples".
+9. In the "Web Page" field, enter "https://docs.langflow.org/how-upload-examples".
 
 10. Now, click on "ConversationBufferMemory".
 

docs/package-lock.json

@@ -16,7 +16,7 @@
         "@docusaurus/theme-classic": "^2.4.1",
         "@docusaurus/theme-search-algolia": "^2.4.1",
         "@mdx-js/react": "^2.3.0",
-        "@mendable/search": "^0.0.114",
+        "@mendable/search": "^0.0.154",
         "@pbe/react-yandex-maps": "^1.2.4",
         "@prismicio/client": "^7.0.1",
         "@uiball/loaders": "^1.2.6",
@@ -3250,10 +3250,11 @@
       }
     },
     "node_modules/@mendable/search": {
-      "version": "0.0.114",
+      "version": "0.0.154",
-      "resolved": "https://registry.npmjs.org/@mendable/search/-/search-0.0.114.tgz",
+      "resolved": "https://registry.npmjs.org/@mendable/search/-/search-0.0.154.tgz",
-      "integrity": "sha512-0uR+zxONuu/16bpLli49Jocr5fee1WIjs06KzU1AnHsR+fdFBmfrlpgTDWctgGuXPzS5Dorlw4VMlR5dPW5qVQ==",
+      "integrity": "sha512-adNwXlIaMXVMCkPU2uUdghfn05Dmxb0BnE95SRLQJ6evHajsNFQdRl5Ltj3WijG+qo4ozTIJcPOBYrDPKMTPVw==",
       "dependencies": {
+        "html-react-parser": "^4.2.0",
         "posthog-js": "^1.45.1"
       },
       "peerDependencies": {
@@ -9351,6 +9352,33 @@
         "safe-buffer": "~5.1.0"
       }
     },
+    "node_modules/html-dom-parser": {
+      "version": "4.0.0",
+      "resolved": "https://registry.npmjs.org/html-dom-parser/-/html-dom-parser-4.0.0.tgz",
+      "integrity": "sha512-TUa3wIwi80f5NF8CVWzkopBVqVAtlawUzJoLwVLHns0XSJGynss4jiY0mTWpiDOsuyw+afP+ujjMgRh9CoZcXw==",
+      "dependencies": {
+        "domhandler": "5.0.3",
+        "htmlparser2": "9.0.0"
+      }
+    },
+    "node_modules/html-dom-parser/node_modules/htmlparser2": {
+      "version": "9.0.0",
+      "resolved": "https://registry.npmjs.org/htmlparser2/-/htmlparser2-9.0.0.tgz",
+      "integrity": "sha512-uxbSI98wmFT/G4P2zXx4OVx04qWUmyFPrD2/CNepa2Zo3GPNaCaaxElDgwUrwYWkK1nr9fft0Ya8dws8coDLLQ==",
+      "funding": [
+        "https://github.com/fb55/htmlparser2?sponsor=1",
+        {
+          "type": "github",
+          "url": "https://github.com/sponsors/fb55"
+        }
+      ],
+      "dependencies": {
+        "domelementtype": "^2.3.0",
+        "domhandler": "^5.0.3",
+        "domutils": "^3.1.0",
+        "entities": "^4.5.0"
+      }
+    },
     "node_modules/html-entities": {
       "version": "2.4.0",
       "resolved": "https://registry.npmjs.org/html-entities/-/html-entities-2.4.0.tgz",
@@ -9394,6 +9422,20 @@
         "node": ">= 12"
       }
     },
+    "node_modules/html-react-parser": {
+      "version": "4.2.1",
+      "resolved": "https://registry.npmjs.org/html-react-parser/-/html-react-parser-4.2.1.tgz",
+      "integrity": "sha512-Dxzdowj5Zu/+7mr8X8PzCFbPXGuwCwGB2u4cB6oxZGES9inw85qlvnlfPD75VGKUGjcgsXs+9Dpj+THWNQyOBw==",
+      "dependencies": {
+        "domhandler": "5.0.3",
+        "html-dom-parser": "4.0.0",
+        "react-property": "2.0.0",
+        "style-to-js": "1.1.3"
+      },
+      "peerDependencies": {
+        "react": "0.14 || 15 || 16 || 17 || 18"
+      }
+    },
     "node_modules/html-tags": {
       "version": "3.3.1",
       "resolved": "https://registry.npmjs.org/html-tags/-/html-tags-3.3.1.tgz",
@@ -15324,6 +15366,11 @@
         "react": ">=16.6.0"
       }
     },
+    "node_modules/react-property": {
+      "version": "2.0.0",
+      "resolved": "https://registry.npmjs.org/react-property/-/react-property-2.0.0.tgz",
+      "integrity": "sha512-kzmNjIgU32mO4mmH5+iUyrqlpFQhF8K2k7eZ4fdLSOPFrD1XgEuSBv9LDEgxRXTMBqMd8ppT0x6TIzqE5pdGdw=="
+    },
     "node_modules/react-router": {
       "version": "5.3.4",
       "resolved": "https://registry.npmjs.org/react-router/-/react-router-5.3.4.tgz",
@@ -17510,6 +17557,22 @@
         "url": "https://github.com/sponsors/sindresorhus"
       }
     },
+    "node_modules/style-to-js": {
+      "version": "1.1.3",
+      "resolved": "https://registry.npmjs.org/style-to-js/-/style-to-js-1.1.3.tgz",
+      "integrity": "sha512-zKI5gN/zb7LS/Vm0eUwjmjrXWw8IMtyA8aPBJZdYiQTXj4+wQ3IucOLIOnF7zCHxvW8UhIGh/uZh/t9zEHXNTQ==",
+      "dependencies": {
+        "style-to-object": "0.4.1"
+      }
+    },
+    "node_modules/style-to-js/node_modules/style-to-object": {
+      "version": "0.4.1",
+      "resolved": "https://registry.npmjs.org/style-to-object/-/style-to-object-0.4.1.tgz",
+      "integrity": "sha512-HFpbb5gr2ypci7Qw+IOhnP2zOU7e77b+rzM+wTzXzfi1PrtBCX0E7Pk4wL4iTLnhzZ+JgEGAhX81ebTg/aYjQw==",
+      "dependencies": {
+        "inline-style-parser": "0.1.1"
+      }
+    },
     "node_modules/style-to-object": {
       "version": "0.3.0",
       "resolved": "https://registry.npmjs.org/style-to-object/-/style-to-object-0.3.0.tgz",

docs/package.json

@@ -22,7 +22,7 @@
     "@docusaurus/theme-classic": "^2.4.1",
     "@docusaurus/theme-search-algolia": "^2.4.1",
     "@mdx-js/react": "^2.3.0",
-    "@mendable/search": "^0.0.114",
+    "@mendable/search": "^0.0.154",
     "@pbe/react-yandex-maps": "^1.2.4",
     "@prismicio/client": "^7.0.1",
     "@uiball/loaders": "^1.2.6",

docs/sidebars.js

@@ -42,6 +42,7 @@ module.exports = {
         "components/text-splitters",
         "components/toolkits",
         "components/tools",
+        "components/utilities",
         "components/vector-stores",
         "components/wrappers",
       ],
@@ -50,7 +51,11 @@ module.exports = {
       type: "category",
       label: "Step-by-Step Guides",
       collapsed: false,
-      items: ["guides/loading_document", "guides/chatprompttemplate_guide"],
+      items: [
+        "guides/loading_document",
+        "guides/chatprompttemplate_guide",
+        "guides/langfuse_integration",
+      ],
     },
     // {
     //   type: 'category',

docs/src/theme/Footer.js

@@ -37,7 +37,7 @@ export default function FooterWrapper(props) {
 
   const mendableFloatingButton = React.createElement(MendableFloatingButton, {
     floatingButtonStyle: { color: "#000000", backgroundColor: "#f6f6f6" },
-    anon_key: customFields.mendableAnonKey, // Mendable Search Public ANON key, ok to be public
+    anon_key: 'b7f52734-297c-41dc-8737-edbd13196394', // Mendable Search Public ANON key, ok to be public
     showSimpleSearch: true,
     icon: icon,
   });

docs/static/CNAME

@@ -1 +1 @@
-langflow.org
+docs.langflow.org

package.json

@@ -1,5 +0,0 @@
-{
-  "devDependencies": {
-    "@svgr/cli": "^8.0.1"
-  }
-}

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langflow"
-version = "0.4.1"
+version = "0.5.0a1"
 description = "A Python package with a built-in web application"
 authors = ["Logspace <contact@logspace.ai>"]
 maintainers = [
@@ -19,7 +19,7 @@ readme = "README.md"
 keywords = ["nlp", "langchain", "openai", "gpt", "gui"]
 packages = [{ include = "langflow", from = "src/backend" }]
 include = ["src/backend/langflow/*", "src/backend/langflow/**/*"]
-
+documentation = "https://docs.langflow.org"
 
 [tool.poetry.scripts]
 langflow = "langflow.__main__:main"
@@ -32,38 +32,38 @@ beautifulsoup4 = "^4.12.2"
 google-search-results = "^2.4.1"
 google-api-python-client = "^2.79.0"
 typer = "^0.9.0"
-gunicorn = "^21.1.0"
+gunicorn = "^21.2.0"
-langchain = "^0.0.250"
+langchain = "^0.0.274"
 openai = "^0.27.8"
 pandas = "^2.0.0"
-chromadb = "^0.3.21"
+chromadb = "^0.3.0"
 huggingface-hub = { version = "^0.16.0", extras = ["inference"] }
-rich = "^13.4.2"
+rich = "^13.5.0"
 llama-cpp-python = { version = "~0.1.0", optional = true }
 networkx = "^3.1"
-unstructured = "^0.7.0"
+unstructured = "^0.10.0"
-pypdf = "^3.11.0"
+pypdf = "^3.15.0"
 lxml = "^4.9.2"
 pysrt = "^1.1.2"
-fake-useragent = "^1.1.3"
+fake-useragent = "^1.2.1"
 docstring-parser = "^0.15"
 psycopg2-binary = "^2.9.6"
 pyarrow = "^12.0.0"
 tiktoken = "~0.4.0"
 wikipedia = "^1.4.0"
 langchain-serve = { version = ">0.0.51", optional = true }
-qdrant-client = "^1.3.0"
+qdrant-client = "^1.4.0"
 websockets = "^10.3"
-weaviate-client = "^3.21.0"
+weaviate-client = "^3.23.0"
 jina = "3.15.2"
 sentence-transformers = { version = "^2.2.2", optional = true }
 ctransformers = { version = "^0.2.10", optional = true }
-cohere = "^4.11.0"
+cohere = "^4.21.0"
 python-multipart = "^0.0.6"
 sqlmodel = "^0.0.8"
 faiss-cpu = "^1.7.4"
 anthropic = "^0.3.0"
-orjson = "^3.9.1"
+orjson = "3.9.3"
 multiprocess = "^0.70.14"
 cachetools = "^5.3.1"
 types-cachetools = "^5.3.0.5"
@@ -77,6 +77,17 @@ psycopg = "^3.1.9"
 psycopg-binary = "^3.1.9"
 fastavro = "^1.8.0"
 langchain-experimental = "^0.0.8"
+alembic = "^1.11.2"
+passlib = "^1.7.4"
+bcrypt = "^4.0.1"
+python-jose = "^3.3.0"
+metaphor-python = "^0.1.11"
+pywin32 = { version = "^306", markers = "sys_platform == 'win32'" }
+loguru = "^0.7.1"
+langfuse = "^1.0.13"
+pillow = "^10.0.0"
+metal-sdk = "^2.0.2"
+markupsafe = "^2.1.3"
 
 [tool.poetry.group.dev.dependencies]
 black = "^23.1.0"
@@ -92,6 +103,13 @@ pandas-stubs = "^2.0.0.230412"
 types-pillow = "^9.5.0.2"
 types-appdirs = "^1.4.3.5"
 types-pyyaml = "^6.0.12.8"
+types-python-jose = "^3.3.4.8"
+types-passlib = "^1.7.7.13"
+pytest-mock = "^3.11.1"
+pytest-xdist = "^3.3.1"
+types-pywin32 = "^306.0.0.4"
+types-google-cloud-ndb = "^2.2.0.0"
+pytest-sugar = "^0.9.7"
 
 
 [tool.poetry.extras]

render.yaml

@@ -3,9 +3,14 @@ services:
   - type: web
     name: langflow
     runtime: docker
-    plan: free
     dockerfilePath: ./Dockerfile
     repo: https://github.com/logspace-ai/langflow
     branch: main
     healthCheckPath: /health
     autoDeploy: false
+    envVars:
+      - key: LANGFLOW_DATABASE_URL
+        value: sqlite:////home/user/.cache/langflow/langflow.db
+    disk:
+      name: langflow-data
+      mountPath: /home/user/.cache/langflow

src/backend/Dockerfile

@@ -11,4 +11,4 @@ RUN rm *.whl
 
 EXPOSE 80
 
-CMD [ "uvicorn", "--host", "0.0.0.0", "--port", "80", "langflow.backend.app:app" ]
+CMD [ "uvicorn", "--host", "0.0.0.0", "--port", "7860", "--factory", "langflow.main:create_app" ]

src/backend/langflow/__init__.py

@@ -1,5 +1,7 @@
 from importlib import metadata
-from langflow.cache import cache_manager
+
+# Deactivate cache manager for now
+# from langflow.services.cache import cache_manager
 from langflow.processing.process import load_flow_from_json
 from langflow.interface.custom.custom_component import CustomComponent
 

src/backend/langflow/__main__.py

@@ -1,8 +1,11 @@
 import sys
 import time
 import httpx
-from langflow.utils.util import get_number_of_workers
+from langflow.services.database.utils import session_getter
-from multiprocess import Process  # type: ignore
+from langflow.services.manager import initialize_services, initialize_settings_manager
+from langflow.services.utils import get_db_manager, get_settings_manager
+
+from multiprocess import Process, cpu_count  # type: ignore
 import platform
 from pathlib import Path
 from typing import Optional
@@ -10,19 +13,49 @@ import socket
 from rich.panel import Panel
 from rich import box
 from rich import print as rprint
+from rich.table import Table
 import typer
 from langflow.main import setup_app
-from langflow.settings import settings
 from langflow.utils.logger import configure, logger
 import webbrowser
 from dotenv import load_dotenv
 
+from rich.console import Console
+
+console = Console()
+
 app = typer.Typer()
 
 
+def get_number_of_workers(workers=None):
+    if workers == -1 or workers is None:
+        workers = (cpu_count() * 2) + 1
+    logger.debug(f"Number of workers: {workers}")
+    return workers
+
+
+def display_results(results):
+    """
+    Display the results of the migration.
+    """
+    for table_results in results:
+        table = Table(title=f"Migration {table_results.table_name}")
+        table.add_column("Name")
+        table.add_column("Type")
+        table.add_column("Status")
+
+        for result in table_results.results:
+            status = "Success" if result.success else "Failure"
+            color = "green" if result.success else "red"
+            table.add_row(result.name, result.type, f"[{color}]{status}[/{color}]")
+
+        console.print(table)
+        console.print()  # Print a new line
+
+
 def update_settings(
     config: str,
-    cache: str,
+    cache: Optional[str] = None,
     dev: bool = False,
     remove_api_keys: bool = False,
     components_path: Optional[Path] = None,
@@ -30,19 +63,20 @@ def update_settings(
     """Update the settings from a config file."""
 
     # Check for database_url in the environment variables
-
+    initialize_settings_manager()
+    settings_manager = get_settings_manager()
     if config:
         logger.debug(f"Loading settings from {config}")
-        settings.update_from_yaml(config, dev=dev)
+        settings_manager.settings.update_from_yaml(config, dev=dev)
     if remove_api_keys:
         logger.debug(f"Setting remove_api_keys to {remove_api_keys}")
-        settings.update_settings(REMOVE_API_KEYS=remove_api_keys)
+        settings_manager.settings.update_settings(REMOVE_API_KEYS=remove_api_keys)
     if cache:
         logger.debug(f"Setting cache to {cache}")
-        settings.update_settings(CACHE=cache)
+        settings_manager.settings.update_settings(CACHE=cache)
     if components_path:
         logger.debug(f"Adding component path {components_path}")
-        settings.update_settings(COMPONENTS_PATH=components_path)
+        settings_manager.settings.update_settings(COMPONENTS_PATH=components_path)
 
 
 def serve_on_jcloud():
@@ -92,7 +126,7 @@ def serve_on_jcloud():
 
 
 @app.command()
-def serve(
+def run(
     host: str = typer.Option(
         "127.0.0.1", help="Host to bind the server to.", envvar="LANGFLOW_HOST"
     ),
@@ -106,7 +140,9 @@ def serve(
         help="Path to the directory containing custom components.",
         envvar="LANGFLOW_COMPONENTS_PATH",
     ),
-    config: str = typer.Option("config.yaml", help="Path to the configuration file."),
+    config: str = typer.Option(
+        Path(__file__).parent / "config.yaml", help="Path to the configuration file."
+    ),
     # .env file param
     env_file: Path = typer.Option(
         None, help="Path to the .env file containing environment variables."
@@ -117,10 +153,10 @@ def serve(
     log_file: Path = typer.Option(
         "logs/langflow.log", help="Path to the log file.", envvar="LANGFLOW_LOG_FILE"
     ),
-    cache: str = typer.Option(
+    cache: Optional[str] = typer.Option(
         envvar="LANGFLOW_LANGCHAIN_CACHE",
         help="Type of cache to use. (InMemoryCache, SQLiteCache)",
-        default="SQLiteCache",
+        default=None,
     ),
     jcloud: bool = typer.Option(False, help="Deploy on Jina AI Cloud"),
     dev: bool = typer.Option(False, help="Run in development mode (may contain bugs)"),
@@ -146,6 +182,11 @@ def serve(
         help="Remove API keys from the projects saved in the database.",
         envvar="LANGFLOW_REMOVE_API_KEYS",
     ),
+    backend_only: bool = typer.Option(
+        False,
+        help="Run only the backend server without the frontend.",
+        envvar="LANGFLOW_BACKEND_ONLY",
+    ),
 ):
     """
     Run the Langflow server.
@@ -167,7 +208,7 @@ def serve(
     )
     # create path object if path is provided
     static_files_dir: Optional[Path] = Path(path) if path else None
-    app = setup_app(static_files_dir=static_files_dir)
+    app = setup_app(static_files_dir=static_files_dir, backend_only=backend_only)
     # check if port is being used
     if is_port_in_use(port, host):
         port = get_free_port(port)
@@ -179,6 +220,10 @@ def serve(
         "timeout": timeout,
     }
 
+    # Define an env variable to know if we are just testing the server
+    if "pytest" in sys.modules:
+        return
+
     if platform.system() in ["Windows"]:
         # Run using uvicorn on MacOS and Windows
         # Windows doesn't support gunicorn
@@ -299,6 +344,43 @@ def run_langflow(host, port, log_level, options, app):
         sys.exit(1)
 
 
+@app.command()
+def superuser(
+    username: str = typer.Option(..., prompt=True, help="Username for the superuser."),
+    password: str = typer.Option(
+        ..., prompt=True, hide_input=True, help="Password for the superuser."
+    ),
+):
+    initialize_services()
+    db_manager = get_db_manager()
+    with session_getter(db_manager) as session:
+        from langflow.services.auth.utils import create_super_user
+
+        if create_super_user(db=session, username=username, password=password):
+            # Verify that the superuser was created
+            from langflow.services.database.models.user.user import User
+
+            user = session.query(User).filter(User.username == username).first()
+            if user is None:
+                typer.echo("Superuser creation failed.")
+                return
+
+            typer.echo("Superuser created successfully.")
+
+        else:
+            typer.echo("Superuser creation failed.")
+
+
+@app.command()
+def migration(test: bool = typer.Option(False, help="Run migrations in test mode.")):
+    initialize_services()
+    db_manager = get_db_manager()
+    if not test:
+        db_manager.run_migrations()
+    results = db_manager.run_migrations_test()
+    display_results(results)
+
+
 def main():
     app()
 

src/backend/langflow/alembic.ini

@@ -0,0 +1,113 @@
+# A generic, single database configuration.
+
+[alembic]
+# path to migration scripts
+script_location = alembic
+
+# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s
+# Uncomment the line below if you want the files to be prepended with date and time
+# see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file
+# for all available tokens
+# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s
+
+# sys.path path, will be prepended to sys.path if present.
+# defaults to the current working directory.
+prepend_sys_path = .
+
+# timezone to use when rendering the date within the migration file
+# as well as the filename.
+# If specified, requires the python-dateutil library that can be
+# installed by adding `alembic[tz]` to the pip requirements
+# string value is passed to dateutil.tz.gettz()
+# leave blank for localtime
+# timezone =
+
+# max length of characters to apply to the
+# "slug" field
+# truncate_slug_length = 40
+
+# set to 'true' to run the environment during
+# the 'revision' command, regardless of autogenerate
+# revision_environment = false
+
+# set to 'true' to allow .pyc and .pyo files without
+# a source .py file to be detected as revisions in the
+# versions/ directory
+# sourceless = false
+
+# version location specification; This defaults
+# to alembic/versions.  When using multiple version
+# directories, initial revisions must be specified with --version-path.
+# The path separator used here should be the separator specified by "version_path_separator" below.
+# version_locations = %(here)s/bar:%(here)s/bat:alembic/versions
+
+# version path separator; As mentioned above, this is the character used to split
+# version_locations. The default within new alembic.ini files is "os", which uses os.pathsep.
+# If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or commas.
+# Valid values for version_path_separator are:
+#
+# version_path_separator = :
+# version_path_separator = ;
+# version_path_separator = space
+version_path_separator = os  # Use os.pathsep. Default configuration used for new projects.
+
+# set to 'true' to search source files recursively
+# in each "version_locations" directory
+# new in Alembic version 1.10
+# recursive_version_locations = false
+
+# the output encoding used when revision files
+# are written from script.py.mako
+# output_encoding = utf-8
+
+# This is the path to the db in the root of the project.
+# When the user runs the Langflow the database url will
+# be set dinamically.
+sqlalchemy.url = sqlite:///../../../langflow.db
+
+
+[post_write_hooks]
+# post_write_hooks defines scripts or Python functions that are run
+# on newly generated revision scripts.  See the documentation for further
+# detail and examples
+
+# format using "black" - use the console_scripts runner, against the "black" entrypoint
+# hooks = black
+# black.type = console_scripts
+# black.entrypoint = black
+# black.options = -l 79 REVISION_SCRIPT_FILENAME
+
+# Logging configuration
+[loggers]
+keys = root,sqlalchemy,alembic
+
+[handlers]
+keys = console
+
+[formatters]
+keys = generic
+
+[logger_root]
+level = WARN
+handlers = console
+qualname =
+
+[logger_sqlalchemy]
+level = WARN
+handlers =
+qualname = sqlalchemy.engine
+
+[logger_alembic]
+level = INFO
+handlers =
+qualname = alembic
+
+[handler_console]
+class = StreamHandler
+args = (sys.stderr,)
+level = NOTSET
+formatter = generic
+
+[formatter_generic]
+format = %(levelname)-5.5s [%(name)s] %(message)s
+datefmt = %H:%M:%S

src/backend/langflow/alembic/README

@@ -0,0 +1 @@
+Generic single-database configuration.

src/backend/langflow/alembic/env.py

@@ -0,0 +1,81 @@
+from logging.config import fileConfig
+
+from sqlalchemy import engine_from_config
+from sqlalchemy import pool
+
+from alembic import context
+
+from langflow.services.database.manager import SQLModel
+
+# this is the Alembic Config object, which provides
+# access to the values within the .ini file in use.
+config = context.config
+
+# Interpret the config file for Python logging.
+# This line sets up loggers basically.
+if config.config_file_name is not None:
+    fileConfig(config.config_file_name)
+
+# add your model's MetaData object here
+# for 'autogenerate' support
+# from myapp import mymodel
+# target_metadata = mymodel.Base.metadata
+target_metadata = SQLModel.metadata
+
+# other values from the config, defined by the needs of env.py,
+# can be acquired:
+# my_important_option = config.get_main_option("my_important_option")
+# ... etc.
+
+
+def run_migrations_offline() -> None:
+    """Run migrations in 'offline' mode.
+
+    This configures the context with just a URL
+    and not an Engine, though an Engine is acceptable
+    here as well.  By skipping the Engine creation
+    we don't even need a DBAPI to be available.
+
+    Calls to context.execute() here emit the given string to the
+    script output.
+
+    """
+    url = config.get_main_option("sqlalchemy.url")
+    context.configure(
+        url=url,
+        target_metadata=target_metadata,
+        literal_binds=True,
+        dialect_opts={"paramstyle": "named"},
+        render_as_batch=True,
+    )
+
+    with context.begin_transaction():
+        context.run_migrations()
+
+
+def run_migrations_online() -> None:
+    """Run migrations in 'online' mode.
+
+    In this scenario we need to create an Engine
+    and associate a connection with the context.
+
+    """
+    connectable = engine_from_config(
+        config.get_section(config.config_ini_section, {}),
+        prefix="sqlalchemy.",
+        poolclass=pool.NullPool,
+    )
+
+    with connectable.connect() as connection:
+        context.configure(
+            connection=connection, target_metadata=target_metadata, render_as_batch=True
+        )
+
+        with context.begin_transaction():
+            context.run_migrations()
+
+
+if context.is_offline_mode():
+    run_migrations_offline()
+else:
+    run_migrations_online()

src/backend/langflow/alembic/script.py.mako

@@ -0,0 +1,27 @@
+"""${message}
+
+Revision ID: ${up_revision}
+Revises: ${down_revision | comma,n}
+Create Date: ${create_date}
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+import sqlmodel
+${imports if imports else ""}
+
+# revision identifiers, used by Alembic.
+revision: str = ${repr(up_revision)}
+down_revision: Union[str, None] = ${repr(down_revision)}
+branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)}
+depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)}
+
+
+def upgrade() -> None:
+    ${upgrades if upgrades else "pass"}
+
+
+def downgrade() -> None:
+    ${downgrades if downgrades else "pass"}

src/backend/langflow/alembic/versions/260dbcc8b680_adds_tables.py

@@ -0,0 +1,177 @@
+"""Adds tables
+
+Revision ID: 260dbcc8b680
+Revises:
+Create Date: 2023-08-27 19:49:02.681355
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+import sqlmodel
+from sqlalchemy.engine.reflection import Inspector
+
+# revision identifiers, used by Alembic.
+revision: str = "260dbcc8b680"
+down_revision: Union[str, None] = None
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+
+    conn = op.get_bind()
+    inspector = Inspector.from_engine(conn)
+    # List existing tables
+    existing_tables = inspector.get_table_names()
+    # Drop 'flowstyle' table if it exists
+    # and other related indices
+    if "flowstyle" in existing_tables:
+        op.drop_table("flowstyle")
+        if "ix_flowstyle_flow_id" in [
+            index["name"] for index in inspector.get_indexes("flowstyle")
+        ]:
+            op.drop_index("ix_flowstyle_flow_id", table_name="flowstyle")
+
+    existing_indices_flow = []
+    existing_fks_flow = []
+    if "flow" in existing_tables:
+        existing_indices_flow = [
+            index["name"] for index in inspector.get_indexes("flow")
+        ]
+        # Existing foreign keys for the 'flow' table, if it exists
+        existing_fks_flow = [
+            fk["referred_table"] + "." + fk["referred_columns"][0]
+            for fk in inspector.get_foreign_keys("flow")
+        ]
+        # Now check if the columns user_id exists in the 'flow' table
+        # If it does not exist, we need to create the foreign key
+
+    if "user" not in existing_tables:
+        op.create_table(
+            "user",
+            sa.Column("id", sqlmodel.sql.sqltypes.GUID(), nullable=False),
+            sa.Column("username", sqlmodel.sql.sqltypes.AutoString(), nullable=False),
+            sa.Column("password", sqlmodel.sql.sqltypes.AutoString(), nullable=False),
+            sa.Column("is_active", sa.Boolean(), nullable=False),
+            sa.Column("is_superuser", sa.Boolean(), nullable=False),
+            sa.Column("create_at", sa.DateTime(), nullable=False),
+            sa.Column("updated_at", sa.DateTime(), nullable=False),
+            sa.Column("last_login_at", sa.DateTime(), nullable=True),
+            sa.PrimaryKeyConstraint("id"),
+            sa.UniqueConstraint("id"),
+        )
+        with op.batch_alter_table("user", schema=None) as batch_op:
+            batch_op.create_index(
+                batch_op.f("ix_user_username"), ["username"], unique=True
+            )
+
+    if "apikey" not in existing_tables:
+        op.create_table(
+            "apikey",
+            sa.Column("name", sqlmodel.sql.sqltypes.AutoString(), nullable=True),
+            sa.Column("created_at", sa.DateTime(), nullable=False),
+            sa.Column("last_used_at", sa.DateTime(), nullable=True),
+            sa.Column("total_uses", sa.Integer(), nullable=False, default=0),
+            sa.Column("is_active", sa.Boolean(), nullable=False, default=True),
+            sa.Column("id", sqlmodel.sql.sqltypes.GUID(), nullable=False),
+            sa.Column("api_key", sqlmodel.sql.sqltypes.AutoString(), nullable=False),
+            sa.Column("user_id", sqlmodel.sql.sqltypes.GUID(), nullable=False),
+            sa.ForeignKeyConstraint(
+                ["user_id"],
+                ["user.id"],
+            ),
+            sa.PrimaryKeyConstraint("id"),
+            sa.UniqueConstraint("id"),
+        )
+        with op.batch_alter_table("apikey", schema=None) as batch_op:
+            batch_op.create_index(
+                batch_op.f("ix_apikey_api_key"), ["api_key"], unique=True
+            )
+            batch_op.create_index(batch_op.f("ix_apikey_name"), ["name"], unique=False)
+            batch_op.create_index(
+                batch_op.f("ix_apikey_user_id"), ["user_id"], unique=False
+            )
+    if "flow" not in existing_tables:
+        op.create_table(
+            "flow",
+            sa.Column("data", sa.JSON(), nullable=True),
+            sa.Column("name", sqlmodel.sql.sqltypes.AutoString(), nullable=False),
+            sa.Column("description", sqlmodel.sql.sqltypes.AutoString(), nullable=True),
+            sa.Column("id", sqlmodel.sql.sqltypes.GUID(), nullable=False),
+            sa.Column("user_id", sqlmodel.sql.sqltypes.GUID(), nullable=False),
+            sa.ForeignKeyConstraint(
+                ["user_id"],
+                ["user.id"],
+            ),
+            sa.PrimaryKeyConstraint("id"),
+            sa.UniqueConstraint("id"),
+        )
+    # Conditionally create indices for 'flow' table
+    # if _alembic_tmp_flow exists, then we need to drop it first
+    # This is to deal with SQLite not being able to ROLLBACK
+    # for some unknown reason
+    if "_alembic_tmp_flow" in existing_tables:
+        op.drop_table("_alembic_tmp_flow")
+    with op.batch_alter_table("flow", schema=None) as batch_op:
+        flow_columns = [col["name"] for col in inspector.get_columns("flow")]
+        if "user_id" not in flow_columns:
+            batch_op.add_column(
+                sa.Column(
+                    "user_id",
+                    sqlmodel.sql.sqltypes.GUID(),
+                    nullable=True,  # This should be False, but we need to allow NULL values for now
+                )
+            )
+        if "user.id" not in existing_fks_flow:
+            batch_op.create_foreign_key("fk_flow_user_id", "user", ["user_id"], ["id"])
+        if "ix_flow_description" not in existing_indices_flow:
+            batch_op.create_index(
+                batch_op.f("ix_flow_description"), ["description"], unique=False
+            )
+        if "ix_flow_name" not in existing_indices_flow:
+            batch_op.create_index(batch_op.f("ix_flow_name"), ["name"], unique=False)
+    with op.batch_alter_table("flow", schema=None) as batch_op:
+        if "ix_flow_user_id" not in existing_indices_flow:
+            batch_op.create_index(
+                batch_op.f("ix_flow_user_id"), ["user_id"], unique=False
+            )
+
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+
+    conn = op.get_bind()
+    inspector = Inspector.from_engine(conn)
+    # List existing tables
+    existing_tables = inspector.get_table_names()
+    if "flow" in existing_tables:
+        with op.batch_alter_table("flow", schema=None) as batch_op:
+            batch_op.drop_index(batch_op.f("ix_flow_user_id"))
+            batch_op.drop_index(batch_op.f("ix_flow_name"))
+            batch_op.drop_index(batch_op.f("ix_flow_description"))
+
+        op.drop_table("flow")
+    if "apikey" in existing_tables:
+        with op.batch_alter_table("apikey", schema=None) as batch_op:
+            batch_op.drop_index(batch_op.f("ix_apikey_user_id"))
+            batch_op.drop_index(batch_op.f("ix_apikey_name"))
+            batch_op.drop_index(batch_op.f("ix_apikey_api_key"))
+
+        op.drop_table("apikey")
+    if "user" in existing_tables:
+        with op.batch_alter_table("user", schema=None) as batch_op:
+            batch_op.drop_index(batch_op.f("ix_user_username"))
+
+        op.drop_table("user")
+
+    if "flowstyle" in existing_tables:
+        op.drop_table("flowstyle")
+
+    if "component" in existing_tables:
+        op.drop_table("component")
+    # ### end Alembic commands ###

src/backend/langflow/alembic/versions/67cc006d50bf_add_profile_image_column.py

@@ -0,0 +1,49 @@
+"""Add profile-image column
+
+Revision ID: 67cc006d50bf
+Revises: 260dbcc8b680
+Create Date: 2023-09-08 07:36:13.387318
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+import sqlmodel
+from sqlalchemy.engine.reflection import Inspector
+
+# revision identifiers, used by Alembic.
+revision: str = "67cc006d50bf"
+down_revision: Union[str, None] = "260dbcc8b680"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    conn = op.get_bind()
+    inspector = Inspector.from_engine(conn)
+    if "user" in inspector.get_table_names() and "profile_image" not in [
+        column["name"] for column in inspector.get_columns("user")
+    ]:
+        with op.batch_alter_table("user", schema=None) as batch_op:
+            batch_op.add_column(
+                sa.Column(
+                    "profile_image", sqlmodel.sql.sqltypes.AutoString(), nullable=True
+                )
+            )
+
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    conn = op.get_bind()
+    inspector = Inspector.from_engine(conn)
+    if "user" in inspector.get_table_names() and "profile_image" in [
+        column["name"] for column in inspector.get_columns("user")
+    ]:
+        with op.batch_alter_table("user", schema=None) as batch_op:
+            batch_op.drop_column("profile_image")
+
+    # ### end Alembic commands ###

src/backend/langflow/api/router.py

@@ -5,8 +5,10 @@ from langflow.api.v1 import (
     endpoints_router,
     validate_router,
     flows_router,
-    flow_styles_router,
     component_router,
+    users_router,
+    api_key_router,
+    login_router,
 )
 
 router = APIRouter(
@@ -17,4 +19,6 @@ router.include_router(endpoints_router)
 router.include_router(validate_router)
 router.include_router(component_router)
 router.include_router(flows_router)
-router.include_router(flow_styles_router)
+router.include_router(users_router)
+router.include_router(api_key_router)
+router.include_router(login_router)

src/backend/langflow/api/utils.py

@@ -66,3 +66,30 @@ def merge_nested_dicts(dict1, dict2):
         else:
             dict1[key] = value
     return dict1
+
+
+def merge_nested_dicts_with_renaming(dict1, dict2):
+    for key, value in dict2.items():
+        if (
+            key in dict1
+            and isinstance(value, dict)
+            and isinstance(dict1.get(key), dict)
+        ):
+            for sub_key, sub_value in value.items():
+                if sub_key in dict1[key]:
+                    new_key = get_new_key(dict1[key], sub_key)
+                    dict1[key][new_key] = sub_value
+                else:
+                    dict1[key][sub_key] = sub_value
+        else:
+            dict1[key] = value
+    return dict1
+
+
+def get_new_key(dictionary, original_key):
+    counter = 1
+    new_key = original_key + " (" + str(counter) + ")"
+    while new_key in dictionary:
+        counter += 1
+        new_key = original_key + " (" + str(counter) + ")"
+    return new_key

src/backend/langflow/api/v1/__init__.py

@@ -2,8 +2,10 @@ from langflow.api.v1.endpoints import router as endpoints_router
 from langflow.api.v1.validate import router as validate_router
 from langflow.api.v1.chat import router as chat_router
 from langflow.api.v1.flows import router as flows_router
-from langflow.api.v1.flow_styles import router as flow_styles_router
 from langflow.api.v1.components import router as component_router
+from langflow.api.v1.users import router as users_router
+from langflow.api.v1.api_key import router as api_key_router
+from langflow.api.v1.login import router as login_router
 
 __all__ = [
     "chat_router",
@@ -11,5 +13,7 @@ __all__ = [
     "component_router",
     "validate_router",
     "flows_router",
-    "flow_styles_router",
+    "users_router",
+    "api_key_router",
+    "login_router",
 ]

src/backend/langflow/api/v1/api_key.py

@@ -0,0 +1,61 @@
+from uuid import UUID
+from fastapi import APIRouter, HTTPException, Depends
+from langflow.api.v1.schemas import ApiKeysResponse
+from langflow.services.auth.utils import get_current_active_user
+from langflow.services.database.models.api_key.api_key import (
+    ApiKeyCreate,
+    UnmaskedApiKeyRead,
+)
+
+# Assuming you have these methods in your service layer
+from langflow.services.database.models.api_key.crud import (
+    get_api_keys,
+    create_api_key,
+    delete_api_key,
+)
+from langflow.services.database.models.user.user import User
+from langflow.services.utils import get_session
+from sqlmodel import Session
+
+
+router = APIRouter(tags=["APIKey"], prefix="/api_key")
+
+
+@router.get("/", response_model=ApiKeysResponse)
+def get_api_keys_route(
+    db: Session = Depends(get_session),
+    current_user: User = Depends(get_current_active_user),
+):
+    try:
+        user_id = current_user.id
+        keys = get_api_keys(db, user_id)
+
+        return ApiKeysResponse(total_count=len(keys), user_id=user_id, api_keys=keys)
+    except Exception as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+
+@router.post("/", response_model=UnmaskedApiKeyRead)
+def create_api_key_route(
+    req: ApiKeyCreate,
+    current_user: User = Depends(get_current_active_user),
+    db: Session = Depends(get_session),
+):
+    try:
+        user_id = current_user.id
+        return create_api_key(db, req, user_id=user_id)
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=str(e)) from e
+
+
+@router.delete("/{api_key_id}")
+def delete_api_key_route(
+    api_key_id: UUID,
+    current_user=Depends(get_current_active_user),
+    db: Session = Depends(get_session),
+):
+    try:
+        delete_api_key(db, api_key_id)
+        return {"detail": "API Key deleted"}
+    except Exception as e:
+        raise HTTPException(status_code=400, detail=str(e)) from e

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

@@ -1,3 +1,4 @@
+from typing import Optional
 from langflow.template.frontend_node.base import FrontendNode
 from pydantic import BaseModel, validator
 
@@ -20,7 +21,8 @@ class FrontendNodeRequest(FrontendNode):
 class ValidatePromptRequest(BaseModel):
     name: str
     template: str
-    frontend_node: FrontendNodeRequest
+    # optional for tweak call
+    frontend_node: Optional[FrontendNodeRequest] = None
 
 
 # Build ValidationResponse class for {"imports": {"errors": []}, "function": {"errors": []}}
@@ -39,7 +41,8 @@ class CodeValidationResponse(BaseModel):
 
 class PromptValidationResponse(BaseModel):
     input_variables: list
-    frontend_node: FrontendNodeRequest
+    # object return for tweak call
+    frontend_node: Optional[FrontendNodeRequest] = None
 
 
 INVALID_CHARACTERS = {

src/backend/langflow/api/v1/callback.py

@@ -10,7 +10,7 @@ from fastapi import WebSocket
 
 
 from langchain.schema import AgentAction, LLMResult, AgentFinish
-from langflow.utils.logger import logger
+from loguru import logger
 
 
 # https://github.com/hwchase17/chat-langchain/blob/master/callback.py

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

@@ -1,37 +1,79 @@
-from fastapi import APIRouter, HTTPException, WebSocket, WebSocketException, status
+from fastapi import (
+    APIRouter,
+    Depends,
+    HTTPException,
+    Query,
+    WebSocket,
+    WebSocketException,
+    status,
+)
 from fastapi.responses import StreamingResponse
 from langflow.api.utils import build_input_keys_response
 from langflow.api.v1.schemas import BuildStatus, BuiltResponse, InitResponse, StreamData
 
-from langflow.chat.manager import ChatManager
 from langflow.graph.graph.base import Graph
-from langflow.utils.logger import logger
+from langflow.services.auth.utils import get_current_active_user, get_current_user
+from loguru import logger
+from langflow.services.utils import get_chat_manager, get_session
 from cachetools import LRUCache
+from sqlmodel import Session
+from langflow.services.chat.manager import ChatManager
+
 
 router = APIRouter(tags=["Chat"])
-chat_manager = ChatManager()
+
 flow_data_store: LRUCache = LRUCache(maxsize=10)
 
 
 @router.websocket("/chat/{client_id}")
-async def chat(client_id: str, websocket: WebSocket):
+async def chat(
+    client_id: str,
+    websocket: WebSocket,
+    token: str = Query(...),
+    db: Session = Depends(get_session),
+    chat_manager: "ChatManager" = Depends(get_chat_manager),
+):
     """Websocket endpoint for chat."""
     try:
+        await websocket.accept()
+        user = await get_current_user(token, db)
+        if not user:
+            await websocket.close(
+                code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized"
+            )
+        if not user.is_active:
+            await websocket.close(
+                code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized"
+            )
+
         if client_id in chat_manager.in_memory_cache:
             await chat_manager.handle_websocket(client_id, websocket)
         else:
             # We accept the connection but close it immediately
             # if the flow is not built yet
-            await websocket.accept()
             message = "Please, build the flow before sending messages"
             await websocket.close(code=status.WS_1011_INTERNAL_ERROR, reason=message)
     except WebSocketException as exc:
         logger.error(f"Websocket error: {exc}")
         await websocket.close(code=status.WS_1011_INTERNAL_ERROR, reason=str(exc))
+    except Exception as exc:
+        logger.error(f"Error in chat websocket: {exc}")
+        messsage = exc.detail if isinstance(exc, HTTPException) else str(exc)
+        if "Could not validate credentials" in str(exc):
+            await websocket.close(
+                code=status.WS_1008_POLICY_VIOLATION, reason="Unauthorized"
+            )
+        else:
+            await websocket.close(code=status.WS_1011_INTERNAL_ERROR, reason=messsage)
 
 
 @router.post("/build/init/{flow_id}", response_model=InitResponse, status_code=201)
-async def init_build(graph_data: dict, flow_id: str):
+async def init_build(
+    graph_data: dict,
+    flow_id: str,
+    current_user=Depends(get_current_active_user),
+    chat_manager: "ChatManager" = Depends(get_chat_manager),
+):
     """Initialize the build by storing graph data and returning a unique session ID."""
 
     try:
@@ -52,6 +94,7 @@ async def init_build(graph_data: dict, flow_id: str):
         flow_data_store[flow_id] = {
             "graph_data": graph_data,
             "status": BuildStatus.STARTED,
+            "user_id": current_user.id,
         }
 
         return InitResponse(flowId=flow_id)
@@ -79,7 +122,9 @@ async def build_status(flow_id: str):
 
 
 @router.get("/build/stream/{flow_id}", response_class=StreamingResponse)
-async def stream_build(flow_id: str):
+async def stream_build(
+    flow_id: str, chat_manager: "ChatManager" = Depends(get_chat_manager)
+):
     """Stream the build process based on stored flow data."""
 
     async def event_stream(flow_id):
@@ -97,6 +142,7 @@ async def stream_build(flow_id: str):
                 return
 
             graph_data = flow_data_store[flow_id].get("graph_data")
+            user_id = flow_data_store[flow_id]["user_id"]
 
             if not graph_data:
                 error_message = "No data provided"
@@ -104,14 +150,9 @@ async def stream_build(flow_id: str):
                 return
 
             logger.debug("Building langchain object")
-            try:
+
-                # Some error could happen when building the graph
+            # Some error could happen when building the graph
-                graph = Graph.from_payload(graph_data)
+            graph = Graph.from_payload(graph_data)
-            except Exception as exc:
-                logger.exception(exc)
-                error_message = str(exc)
-                yield str(StreamData(event="error", data={"error": error_message}))
-                return
 
             number_of_nodes = len(graph.nodes)
             flow_data_store[flow_id]["status"] = BuildStatus.IN_PROGRESS
@@ -122,11 +163,13 @@ async def stream_build(flow_id: str):
                         "log": f"Building node {vertex.vertex_type}",
                     }
                     yield str(StreamData(event="log", data=log_dict))
-                    vertex.build()
+                    vertex.build(user_id)
                     params = vertex._built_object_repr()
                     valid = True
                     logger.debug(f"Building node {str(vertex.vertex_type)}")
-                    logger.debug(f"Output: {params}")
+                    logger.debug(
+                        f"Output: {params[:100]}{'...' if len(params) > 100 else ''}"
+                    )
                     if vertex.artifacts:
                         # The artifacts will be prompt variables
                         # passed to build_input_keys_response
@@ -155,12 +198,11 @@ async def stream_build(flow_id: str):
                 )
             else:
                 input_keys_response = {
-                    "input_keys": {},
+                    "input_keys": None,
                     "memory_keys": [],
                     "handle_keys": [],
                 }
             yield str(StreamData(event="message", data=input_keys_response))
-
             chat_manager.set_cache(flow_id, langchain_object)
             # We need to reset the chat history
             chat_manager.chat_history.empty_history(flow_id)

src/backend/langflow/api/v1/components.py

@@ -1,8 +1,8 @@
 from datetime import timezone
 from typing import List
 from uuid import UUID
-from langflow.database.models.component import Component, ComponentModel
+from langflow.services.database.models.component import Component, ComponentModel
-from langflow.database.base import get_session
+from langflow.services.utils import get_session
 from sqlmodel import Session, select
 from fastapi import APIRouter, Depends, HTTPException
 from sqlalchemy.exc import IntegrityError

src/backend/langflow/api/v1/endpoints.py

@@ -1,14 +1,15 @@
 from http import HTTPStatus
-from typing import Annotated, Optional
+from typing import Annotated, Any, Optional, Union
+from langflow.services.auth.utils import api_key_security, get_current_active_user
 
-from langflow.cache.utils import save_uploaded_file
+from langflow.services.cache.utils import save_uploaded_file
-from langflow.database.models.flow import Flow
+from langflow.services.database.models.flow import Flow
 from langflow.processing.process import process_graph_cached, process_tweaks
-from langflow.utils.logger import logger
+from langflow.services.database.models.user.user import User
-from langflow.settings import settings
+from langflow.services.utils import get_settings_manager
-
+from loguru import logger
-from fastapi import APIRouter, Depends, HTTPException, UploadFile, Body
+from fastapi import APIRouter, Depends, HTTPException, UploadFile, Body, status
-
+import sqlalchemy as sa
 from langflow.interface.custom.custom_component import CustomComponent
 
 
@@ -18,7 +19,7 @@ from langflow.api.v1.schemas import (
     CustomComponentCode,
 )
 
-from langflow.api.utils import merge_nested_dicts
+from langflow.api.utils import merge_nested_dicts_with_renaming
 
 from langflow.interface.types import (
     build_langchain_types_dict,
@@ -26,52 +27,93 @@ from langflow.interface.types import (
     build_langchain_custom_component_list_from_path,
 )
 
-from langflow.database.base import get_session
+from langflow.services.utils import get_session
 from sqlmodel import Session
 
 # build router
 router = APIRouter(tags=["Base"])
 
 
-@router.get("/all")
+@router.get("/all", dependencies=[Depends(get_current_active_user)])
-def get_all():
+def get_all(
+    settings_manager=Depends(get_settings_manager),
+):
     logger.debug("Building langchain types dict")
     native_components = build_langchain_types_dict()
     # custom_components is a list of dicts
     # need to merge all the keys into one dict
-    custom_components_from_file = {}
+    custom_components_from_file: dict[str, Any] = {}
-    if settings.COMPONENTS_PATH:
+    if settings_manager.settings.COMPONENTS_PATH:
-        logger.info(f"Building custom components from {settings.COMPONENTS_PATH}")
+        logger.info(
-        custom_component_dicts = [
+            f"Building custom components from {settings_manager.settings.COMPONENTS_PATH}"
-            build_langchain_custom_component_list_from_path(str(path))
+        )
-            for path in settings.COMPONENTS_PATH
-        ]
-        logger.info(f"Loading {len(custom_component_dicts)} custom components")
 
+        custom_component_dicts = []
+        processed_paths = []
+        for path in settings_manager.settings.COMPONENTS_PATH:
+            if str(path) in processed_paths:
+                continue
+            custom_component_dict = build_langchain_custom_component_list_from_path(
+                str(path)
+            )
+            custom_component_dicts.append(custom_component_dict)
+            processed_paths.append(str(path))
+
+        logger.info(f"Loading {len(custom_component_dicts)} category(ies)")
         for custom_component_dict in custom_component_dicts:
-            custom_components_from_file = merge_nested_dicts(
+            # custom_component_dict is a dict of dicts
+            if not custom_component_dict:
+                continue
+            category = list(custom_component_dict.keys())[0]
+            logger.info(
+                f"Loading {len(custom_component_dict[category])} component(s) from category {category}"
+            )
+            custom_components_from_file = merge_nested_dicts_with_renaming(
                 custom_components_from_file, custom_component_dict
             )
-            logger.info(f"Loaded {custom_component_dict}")
+
-    return merge_nested_dicts(native_components, custom_components_from_file)
+    return merge_nested_dicts_with_renaming(
+        native_components, custom_components_from_file
+    )
 
 
 # For backwards compatibility we will keep the old endpoint
-@router.post("/predict/{flow_id}", response_model=ProcessResponse)
+@router.post(
-@router.post("/process/{flow_id}", response_model=ProcessResponse)
+    "/predict/{flow_id}",
+    response_model=ProcessResponse,
+    dependencies=[Depends(api_key_security)],
+)
+@router.post(
+    "/process/{flow_id}",
+    response_model=ProcessResponse,
+)
 async def process_flow(
+    session: Annotated[Session, Depends(get_session)],
     flow_id: str,
     inputs: Optional[dict] = None,
     tweaks: Optional[dict] = None,
     clear_cache: Annotated[bool, Body(embed=True)] = False,  # noqa: F821
-    session: Session = Depends(get_session),
+    session_id: Annotated[Union[None, str], Body(embed=True)] = None,  # noqa: F821
+    api_key_user: User = Depends(api_key_security),
 ):
     """
     Endpoint to process an input with a given flow_id.
     """
 
     try:
-        flow = session.get(Flow, flow_id)
+        if api_key_user is None:
+            raise HTTPException(
+                status_code=status.HTTP_401_UNAUTHORIZED,
+                detail="Invalid API Key",
+            )
+
+        # Get the flow that matches the flow_id and belongs to the user
+        flow = (
+            session.query(Flow)
+            .filter(Flow.id == flow_id)
+            .filter(Flow.user_id == api_key_user.id)
+            .first()
+        )
         if flow is None:
             raise ValueError(f"Flow {flow_id} not found")
 
@@ -83,10 +125,26 @@ async def process_flow(
                 graph_data = process_tweaks(graph_data, tweaks)
             except Exception as exc:
                 logger.error(f"Error processing tweaks: {exc}")
-        response = process_graph_cached(graph_data, inputs, clear_cache)
+        response, session_id = process_graph_cached(
-        return ProcessResponse(
+            graph_data, inputs, clear_cache, session_id
-            result=response,
         )
+        return ProcessResponse(result=response, session_id=session_id)
+    except sa.exc.StatementError as exc:
+        # StatementError('(builtins.ValueError) badly formed hexadecimal UUID string')
+        if "badly formed hexadecimal UUID string" in str(exc):
+            # This means the Flow ID is not a valid UUID which means it can't find the flow
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND, detail=str(exc)
+            ) from exc
+    except ValueError as exc:
+        if f"Flow {flow_id} not found" in str(exc):
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND, detail=str(exc)
+            ) from exc
+        else:
+            raise HTTPException(
+                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(exc)
+            ) from exc
     except Exception as e:
         # Log stack trace
         logger.exception(e)

src/backend/langflow/api/v1/flow_styles.py

@@ -1,83 +0,0 @@
-from uuid import UUID
-from langflow.database.models.flow_style import (
-    FlowStyle,
-    FlowStyleCreate,
-    FlowStyleRead,
-    FlowStyleUpdate,
-)
-from langflow.database.base import get_session
-from sqlmodel import Session, select
-from fastapi import APIRouter, Depends, HTTPException
-
-
-# build router
-router = APIRouter(prefix="/flow_styles", tags=["FlowStyles"])
-
-# FlowStyleCreate:
-# class FlowStyleBase(SQLModel):
-#     color: str = Field(index=True)
-#     emoji: str = Field(index=False)
-#     flow_id: UUID = Field(default=None, foreign_key="flow.id")
-
-
-@router.post("/", response_model=FlowStyleRead)
-def create_flow_style(
-    *, session: Session = Depends(get_session), flow_style: FlowStyleCreate
-):
-    """Create a new flow_style."""
-    db_flow_style = FlowStyle.from_orm(flow_style)
-    session.add(db_flow_style)
-    session.commit()
-    session.refresh(db_flow_style)
-    return db_flow_style
-
-
-@router.get("/", response_model=list[FlowStyleRead])
-def read_flow_styles(*, session: Session = Depends(get_session)):
-    """Read all flows."""
-    try:
-        flows = session.exec(select(FlowStyle)).all()
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e)) from e
-    return flows
-
-
-@router.get("/{flow_styles_id}", response_model=FlowStyleRead)
-def read_flow_style(*, session: Session = Depends(get_session), flow_styles_id: UUID):
-    """Read a flow_style."""
-    if flow_style := session.get(FlowStyle, flow_styles_id):
-        return flow_style
-    else:
-        raise HTTPException(status_code=404, detail="FlowStyle not found")
-
-
-@router.patch("/{flow_style_id}", response_model=FlowStyleRead)
-def update_flow_style(
-    *,
-    session: Session = Depends(get_session),
-    flow_style_id: UUID,
-    flow_style: FlowStyleUpdate,
-):
-    """Update a flow_style."""
-    db_flow_style = session.get(FlowStyle, flow_style_id)
-    if not db_flow_style:
-        raise HTTPException(status_code=404, detail="FlowStyle not found")
-    flow_data = flow_style.dict(exclude_unset=True)
-    for key, value in flow_data.items():
-        if hasattr(db_flow_style, key) and value is not None:
-            setattr(db_flow_style, key, value)
-    session.add(db_flow_style)
-    session.commit()
-    session.refresh(db_flow_style)
-    return db_flow_style
-
-
-@router.delete("/{flow_id}")
-def delete_flow_style(*, session: Session = Depends(get_session), flow_id: UUID):
-    """Delete a flow_style."""
-    flow_style = session.get(FlowStyle, flow_id)
-    if not flow_style:
-        raise HTTPException(status_code=404, detail="FlowStyle not found")
-    session.delete(flow_style)
-    session.commit()
-    return {"message": "FlowStyle deleted successfully"}

src/backend/langflow/api/v1/flows.py

@@ -1,70 +1,101 @@
 from typing import List
 from uuid import UUID
-from langflow.settings import settings
+from fastapi.encoders import jsonable_encoder
+
 from langflow.api.utils import remove_api_keys
 from langflow.api.v1.schemas import FlowListCreate, FlowListRead
-from langflow.database.models.flow import (
+from langflow.services.auth.utils import get_current_active_user
+from langflow.services.database.models.flow import (
     Flow,
     FlowCreate,
     FlowRead,
-    FlowReadWithStyle,
     FlowUpdate,
 )
-from langflow.database.base import get_session
+from langflow.services.database.models.user.user import User
-from sqlmodel import Session, select
+from langflow.services.utils import get_session
+from langflow.services.utils import get_settings_manager
+import orjson
+from sqlmodel import Session
 from fastapi import APIRouter, Depends, HTTPException
-from fastapi.encoders import jsonable_encoder
 
 from fastapi import File, UploadFile
-import json
 
 # build router
 router = APIRouter(prefix="/flows", tags=["Flows"])
 
 
 @router.post("/", response_model=FlowRead, status_code=201)
-def create_flow(*, session: Session = Depends(get_session), flow: FlowCreate):
+def create_flow(
+    *,
+    session: Session = Depends(get_session),
+    flow: FlowCreate,
+    current_user: User = Depends(get_current_active_user),
+):
     """Create a new flow."""
+    if flow.user_id is None:
+        flow.user_id = current_user.id
+
     db_flow = Flow.from_orm(flow)
+
     session.add(db_flow)
     session.commit()
     session.refresh(db_flow)
     return db_flow
 
 
-@router.get("/", response_model=list[FlowReadWithStyle], status_code=200)
+@router.get("/", response_model=list[FlowRead], status_code=200)
-def read_flows(*, session: Session = Depends(get_session)):
+def read_flows(
+    *,
+    session: Session = Depends(get_session),
+    current_user: User = Depends(get_current_active_user),
+):
     """Read all flows."""
     try:
-        flows = session.exec(select(Flow)).all()
+        flows = current_user.flows
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e)) from e
     return [jsonable_encoder(flow) for flow in flows]
 
 
-@router.get("/{flow_id}", response_model=FlowReadWithStyle, status_code=200)
+@router.get("/{flow_id}", response_model=FlowRead, status_code=200)
-def read_flow(*, session: Session = Depends(get_session), flow_id: UUID):
+def read_flow(
+    *,
+    session: Session = Depends(get_session),
+    flow_id: UUID,
+    current_user: User = Depends(get_current_active_user),
+):
     """Read a flow."""
-    if flow := session.get(Flow, flow_id):
+    if user_flow := (
-        return flow
+        session.query(Flow)
+        .filter(Flow.id == flow_id)
+        .filter(Flow.user_id == current_user.id)
+        .first()
+    ):
+        return user_flow
     else:
         raise HTTPException(status_code=404, detail="Flow not found")
 
 
 @router.patch("/{flow_id}", response_model=FlowRead, status_code=200)
 def update_flow(
-    *, session: Session = Depends(get_session), flow_id: UUID, flow: FlowUpdate
+    *,
+    session: Session = Depends(get_session),
+    flow_id: UUID,
+    flow: FlowUpdate,
+    current_user: User = Depends(get_current_active_user),
+    settings_manager=Depends(get_settings_manager),
 ):
     """Update a flow."""
 
-    db_flow = session.get(Flow, flow_id)
+    db_flow = read_flow(session=session, flow_id=flow_id, current_user=current_user)
     if not db_flow:
         raise HTTPException(status_code=404, detail="Flow not found")
     flow_data = flow.dict(exclude_unset=True)
-    if settings.REMOVE_API_KEYS:
+    if settings_manager.settings.REMOVE_API_KEYS:
         flow_data = remove_api_keys(flow_data)
     for key, value in flow_data.items():
-        setattr(db_flow, key, value)
+        if value is not None:
+            setattr(db_flow, key, value)
     session.add(db_flow)
     session.commit()
     session.refresh(db_flow)
@@ -72,9 +103,14 @@ def update_flow(
 
 
 @router.delete("/{flow_id}", status_code=200)
-def delete_flow(*, session: Session = Depends(get_session), flow_id: UUID):
+def delete_flow(
+    *,
+    session: Session = Depends(get_session),
+    flow_id: UUID,
+    current_user: User = Depends(get_current_active_user),
+):
     """Delete a flow."""
-    flow = session.get(Flow, flow_id)
+    flow = read_flow(session=session, flow_id=flow_id, current_user=current_user)
     if not flow:
         raise HTTPException(status_code=404, detail="Flow not found")
     session.delete(flow)
@@ -86,10 +122,16 @@ def delete_flow(*, session: Session = Depends(get_session), flow_id: UUID):
 
 
 @router.post("/batch/", response_model=List[FlowRead], status_code=201)
-def create_flows(*, session: Session = Depends(get_session), flow_list: FlowListCreate):
+def create_flows(
+    *,
+    session: Session = Depends(get_session),
+    flow_list: FlowListCreate,
+    current_user: User = Depends(get_current_active_user),
+):
     """Create multiple new flows."""
     db_flows = []
     for flow in flow_list.flows:
+        flow.user_id = current_user.id
         db_flow = Flow.from_orm(flow)
         session.add(db_flow)
         db_flows.append(db_flow)
@@ -101,20 +143,31 @@ def create_flows(*, session: Session = Depends(get_session), flow_list: FlowList
 
 @router.post("/upload/", response_model=List[FlowRead], status_code=201)
 async def upload_file(
-    *, session: Session = Depends(get_session), file: UploadFile = File(...)
+    *,
+    session: Session = Depends(get_session),
+    file: UploadFile = File(...),
+    current_user: User = Depends(get_current_active_user),
 ):
     """Upload flows from a file."""
     contents = await file.read()
-    data = json.loads(contents)
+    data = orjson.loads(contents)
     if "flows" in data:
         flow_list = FlowListCreate(**data)
     else:
         flow_list = FlowListCreate(flows=[FlowCreate(**flow) for flow in data])
-    return create_flows(session=session, flow_list=flow_list)
+    # Now we set the user_id for all flows
+    for flow in flow_list.flows:
+        flow.user_id = current_user.id
+
+    return create_flows(session=session, flow_list=flow_list, current_user=current_user)
 
 
 @router.get("/download/", response_model=FlowListRead, status_code=200)
-async def download_file(*, session: Session = Depends(get_session)):
+async def download_file(
+    *,
+    session: Session = Depends(get_session),
+    current_user: User = Depends(get_current_active_user),
+):
     """Download all flows as a file."""
-    flows = read_flows(session=session)
+    flows = read_flows(session=session, current_user=current_user)
     return FlowListRead(flows=flows)

src/backend/langflow/api/v1/login.py

@@ -0,0 +1,63 @@
+from sqlmodel import Session
+from fastapi import APIRouter, Depends, HTTPException, status
+from fastapi.security import OAuth2PasswordRequestForm
+
+from langflow.services.utils import get_session
+from langflow.api.v1.schemas import Token
+from langflow.services.auth.utils import (
+    authenticate_user,
+    create_user_tokens,
+    create_refresh_token,
+    create_user_longterm_token,
+    get_current_active_user,
+)
+
+from langflow.services.utils import get_settings_manager
+
+router = APIRouter(tags=["Login"])
+
+
+@router.post("/login", response_model=Token)
+async def login_to_get_access_token(
+    form_data: OAuth2PasswordRequestForm = Depends(),
+    db: Session = Depends(get_session),
+    # _: Session = Depends(get_current_active_user)
+):
+    if user := authenticate_user(form_data.username, form_data.password, db):
+        return create_user_tokens(user_id=user.id, db=db, update_last_login=True)
+    else:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Incorrect username or password",
+            headers={"WWW-Authenticate": "Bearer"},
+        )
+
+
+@router.get("/auto_login")
+async def auto_login(
+    db: Session = Depends(get_session), settings_manager=Depends(get_settings_manager)
+):
+    if settings_manager.auth_settings.AUTO_LOGIN:
+        return create_user_longterm_token(db)
+
+    raise HTTPException(
+        status_code=status.HTTP_400_BAD_REQUEST,
+        detail={
+            "message": "Auto login is disabled. Please enable it in the settings",
+            "auto_login": False,
+        },
+    )
+
+
+@router.post("/refresh")
+async def refresh_token(
+    token: str, current_user: Session = Depends(get_current_active_user)
+):
+    if token:
+        return create_refresh_token(token)
+    else:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Invalid refresh token",
+            headers={"WWW-Authenticate": "Bearer"},
+        )

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

@@ -1,9 +1,13 @@
 from enum import Enum
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
-from langflow.database.models.flow import FlowCreate, FlowRead
+from uuid import UUID
+from langflow.services.database.models.api_key.api_key import ApiKeyRead
+from langflow.services.database.models.flow import FlowCreate, FlowRead
+from langflow.services.database.models.user import UserRead
+from langflow.services.database.models.base import orjson_dumps
+
 from pydantic import BaseModel, Field, validator
-import json
 
 
 class BuildStatus(Enum):
@@ -47,6 +51,7 @@ class ProcessResponse(BaseModel):
     """Process response schema."""
 
     result: dict
+    session_id: Optional[str] = None
 
 
 class ChatMessage(BaseModel):
@@ -115,7 +120,9 @@ class StreamData(BaseModel):
     data: dict
 
     def __str__(self) -> str:
-        return f"event: {self.event}\ndata: {json.dumps(self.data)}\n\n"
+        return (
+            f"event: {self.event}\ndata: {orjson_dumps(self.data, indent_2=False)}\n\n"
+        )
 
 
 class CustomComponentCode(BaseModel):
@@ -133,3 +140,32 @@ class ComponentListCreate(BaseModel):
 
 class ComponentListRead(BaseModel):
     flows: List[FlowRead]
+
+
+class UsersResponse(BaseModel):
+    total_count: int
+    users: List[UserRead]
+
+
+class ApiKeyResponse(BaseModel):
+    id: str
+    api_key: str
+    name: str
+    created_at: str
+    last_used_at: str
+
+
+class ApiKeysResponse(BaseModel):
+    total_count: int
+    user_id: UUID
+    api_keys: List[ApiKeyRead]
+
+
+class CreateApiKeyRequest(BaseModel):
+    name: str
+
+
+class Token(BaseModel):
+    access_token: str
+    refresh_token: str
+    token_type: str

src/backend/langflow/api/v1/users.py

@@ -0,0 +1,194 @@
+from uuid import UUID
+from langflow.api.v1.schemas import UsersResponse
+from langflow.services.database.models.user import (
+    User,
+    UserCreate,
+    UserRead,
+    UserUpdate,
+)
+
+from sqlalchemy import func
+from sqlalchemy.exc import IntegrityError
+
+from sqlmodel import Session, select
+from fastapi import APIRouter, Depends, HTTPException
+
+from langflow.services.utils import get_session
+from langflow.services.auth.utils import (
+    get_current_active_superuser,
+    get_current_active_user,
+    get_password_hash,
+    verify_password,
+)
+from langflow.services.database.models.user.crud import (
+    get_user_by_id,
+    update_user,
+)
+
+router = APIRouter(tags=["Users"], prefix="/users")
+
+
+@router.post("/", response_model=UserRead, status_code=201)
+def add_user(
+    user: UserCreate,
+    session: Session = Depends(get_session),
+) -> User:
+    """
+    Add a new user to the database.
+    """
+    new_user = User.from_orm(user)
+    try:
+        new_user.password = get_password_hash(user.password)
+
+        session.add(new_user)
+        session.commit()
+        session.refresh(new_user)
+    except IntegrityError as e:
+        session.rollback()
+        raise HTTPException(
+            status_code=400, detail="This username is unavailable."
+        ) from e
+
+    return new_user
+
+
+@router.get("/whoami", response_model=UserRead)
+def read_current_user(
+    current_user: User = Depends(get_current_active_user),
+) -> User:
+    """
+    Retrieve the current user's data.
+    """
+    return current_user
+
+
+@router.get("/", response_model=UsersResponse)
+def read_all_users(
+    skip: int = 0,
+    limit: int = 10,
+    current_user: Session = Depends(get_current_active_superuser),
+    session: Session = Depends(get_session),
+) -> UsersResponse:
+    """
+    Retrieve a list of users from the database with pagination.
+    """
+    query = select(User).offset(skip).limit(limit)
+    users = session.execute(query).fetchall()
+
+    count_query = select(func.count()).select_from(User)  # type: ignore
+    total_count = session.execute(count_query).scalar()
+
+    return UsersResponse(
+        total_count=total_count,  # type: ignore
+        users=[UserRead(**dict(user.User)) for user in users],
+    )
+
+
+@router.patch("/{user_id}", response_model=UserRead)
+def patch_user(
+    user_id: UUID,
+    user_update: UserUpdate,
+    user: User = Depends(get_current_active_user),
+    session: Session = Depends(get_session),
+) -> User:
+    """
+    Update an existing user's data.
+    """
+    if not user.is_superuser and user.id != user_id:
+        raise HTTPException(
+            status_code=403, detail="You don't have the permission to update this user"
+        )
+    if user_update.password:
+        raise HTTPException(
+            status_code=400, detail="You can't change your password here"
+        )
+
+    if user_db := get_user_by_id(session, user_id):
+        return update_user(user_db, user_update, session)
+    else:
+        raise HTTPException(status_code=404, detail="User not found")
+
+
+@router.patch("/{user_id}/reset-password", response_model=UserRead)
+def reset_password(
+    user_id: UUID,
+    user_update: UserUpdate,
+    user: User = Depends(get_current_active_user),
+    session: Session = Depends(get_session),
+) -> User:
+    """
+    Reset a user's password.
+    """
+    if user_id != user.id:
+        raise HTTPException(
+            status_code=400, detail="You can't change another user's password"
+        )
+
+    if not user:
+        raise HTTPException(status_code=404, detail="User not found")
+    if verify_password(user_update.password, user.password):
+        raise HTTPException(
+            status_code=400, detail="You can't use your current password"
+        )
+    new_password = get_password_hash(user_update.password)
+    user.password = new_password
+    session.commit()
+    session.refresh(user)
+
+    return user
+
+
+@router.delete("/{user_id}", response_model=dict)
+def delete_user(
+    user_id: UUID,
+    current_user: User = Depends(get_current_active_superuser),
+    session: Session = Depends(get_session),
+) -> dict:
+    """
+    Delete a user from the database.
+    """
+    if current_user.id == user_id:
+        raise HTTPException(
+            status_code=400, detail="You can't delete your own user account"
+        )
+    elif not current_user.is_superuser:
+        raise HTTPException(
+            status_code=403, detail="You don't have the permission to delete this user"
+        )
+
+    user_db = session.query(User).filter(User.id == user_id).first()
+    if not user_db:
+        raise HTTPException(status_code=404, detail="User not found")
+
+    session.delete(user_db)
+    session.commit()
+
+    return {"detail": "User deleted"}
+
+
+# TODO: REMOVE - Just for testing purposes
+@router.post("/super_user", response_model=User)
+def add_super_user_for_testing_purposes_delete_me_before_merge_into_dev(
+    session: Session = Depends(get_session),
+) -> User:
+    """
+    Add a superuser for testing purposes.
+    (This should be removed in production)
+    """
+    new_user = User(
+        username="superuser",
+        password=get_password_hash("12345"),
+        is_active=True,
+        is_superuser=True,
+        last_login_at=None,
+    )
+
+    try:
+        session.add(new_user)
+        session.commit()
+        session.refresh(new_user)
+    except IntegrityError as e:
+        session.rollback()
+        raise HTTPException(status_code=400, detail="User exists") from e
+
+    return new_user

src/backend/langflow/api/v1/validate.py

@@ -8,7 +8,7 @@ from langflow.api.v1.base import (
     validate_prompt,
 )
 from langflow.template.field.base import TemplateField
-from langflow.utils.logger import logger
+from loguru import logger
 from langflow.utils.validate import validate_code
 
 # build router
@@ -31,7 +31,12 @@ def post_validate_code(code: Code):
 def post_validate_prompt(prompt_request: ValidatePromptRequest):
     try:
         input_variables = validate_prompt(prompt_request.template)
-
+        # Check if frontend_node is None before proceeding to avoid attempting to update a non-existent node.
+        if prompt_request.frontend_node is None:
+            return PromptValidationResponse(
+                input_variables=input_variables,
+                frontend_node=None,
+            )
         old_custom_fields = get_old_custom_fields(prompt_request)
 
         add_new_variables_to_template(input_variables, prompt_request)
@@ -53,6 +58,16 @@ def post_validate_prompt(prompt_request: ValidatePromptRequest):
 
 def get_old_custom_fields(prompt_request):
     try:
+        if (
+            len(prompt_request.frontend_node.custom_fields) == 1
+            and prompt_request.name == ""
+        ):
+            # If there is only one custom field and the name is empty string
+            # then we are dealing with the first prompt request after the node was created
+            prompt_request.name = list(
+                prompt_request.frontend_node.custom_fields.keys()
+            )[0]
+
         old_custom_fields = prompt_request.frontend_node.custom_fields[
             prompt_request.name
         ].copy()

src/backend/langflow/cache/__init__.py

@@ -1,7 +0,0 @@
-from langflow.cache.manager import cache_manager
-from langflow.cache.flow import InMemoryCache
-
-__all__ = [
-    "cache_manager",
-    "InMemoryCache",
-]

src/backend/langflow/components/agents/OpenAIConversationalAgent.py

@@ -0,0 +1,82 @@
+from langflow import CustomComponent
+from typing import Optional
+from langchain.prompts import SystemMessagePromptTemplate
+from langchain.tools import Tool
+from langchain.schema.memory import BaseMemory
+from langchain.chat_models import ChatOpenAI
+
+from langchain.agents.agent import AgentExecutor
+from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
+from langchain.memory.token_buffer import ConversationTokenBufferMemory
+from langchain.prompts.chat import MessagesPlaceholder
+from langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import (
+    _get_default_system_message,
+)
+
+
+class ConversationalAgent(CustomComponent):
+    display_name: str = "OpenAI Conversational Agent"
+    description: str = "Conversational Agent that can use OpenAI's function calling API"
+
+    def build_config(self):
+        openai_function_models = [
+            "gpt-3.5-turbo-0613",
+            "gpt-3.5-turbo-16k-0613",
+            "gpt-4-0613",
+            "gpt-4-32k-0613",
+        ]
+        return {
+            "tools": {"is_list": True, "display_name": "Tools"},
+            "memory": {"display_name": "Memory"},
+            "system_message": {"display_name": "System Message"},
+            "max_token_limit": {"display_name": "Max Token Limit"},
+            "model_name": {
+                "display_name": "Model Name",
+                "options": openai_function_models,
+                "value": openai_function_models[0],
+            },
+            "code": {"show": False},
+        }
+
+    def build(
+        self,
+        model_name: str,
+        openai_api_key: str,
+        tools: Tool,
+        openai_api_base: Optional[str] = None,
+        memory: Optional[BaseMemory] = None,
+        system_message: Optional[SystemMessagePromptTemplate] = None,
+        max_token_limit: int = 2000,
+    ) -> AgentExecutor:
+        llm = ChatOpenAI(
+            model=model_name,
+            openai_api_key=openai_api_key,
+            openai_api_base=openai_api_base,
+        )
+        if not memory:
+            memory_key = "chat_history"
+            memory = ConversationTokenBufferMemory(
+                memory_key=memory_key,
+                return_messages=True,
+                output_key="output",
+                llm=llm,
+                max_token_limit=max_token_limit,
+            )
+        else:
+            memory_key = memory.memory_key  # type: ignore
+
+        _system_message = system_message or _get_default_system_message()
+        prompt = OpenAIFunctionsAgent.create_prompt(
+            system_message=_system_message,  # type: ignore
+            extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)],
+        )
+        agent = OpenAIFunctionsAgent(
+            llm=llm, tools=tools, prompt=prompt  # type: ignore
+        )
+        return AgentExecutor(
+            agent=agent,
+            tools=tools,  # type: ignore
+            memory=memory,
+            verbose=True,
+            return_intermediate_steps=True,
+        )

src/backend/langflow/components/chains/PromptRunner.py

@@ -16,17 +16,14 @@ class PromptRunner(CustomComponent):
             "info": "Make sure the prompt has all variables filled.",
         },
         "code": {"show": False},
-        "inputs": {"field_type": "code"},
     }
 
     def build(
-        self,
+        self, llm: BaseLLM, prompt: PromptTemplate, inputs: dict = {}
-        llm: BaseLLM,
-        prompt: PromptTemplate,
     ) -> Document:
         chain = prompt | llm
         # The input is an empty dict because the prompt is already filled
-        result = chain.invoke({})
+        result = chain.invoke(input=inputs)
         if hasattr(result, "content"):
             result = result.content
         self.repr_value = result

src/backend/langflow/components/llms/HuggingFaceEndpoints.py

@@ -0,0 +1,42 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.llms import HuggingFaceEndpoint
+from langchain.llms.base import BaseLLM
+
+
+class HuggingFaceEndpointsComponent(CustomComponent):
+    display_name: str = "Hugging Face Inference API"
+    description: str = "LLM model from Hugging Face Inference API."
+
+    def build_config(self):
+        return {
+            "endpoint_url": {"display_name": "Endpoint URL", "password": True},
+            "task": {
+                "display_name": "Task",
+                "type": "select",
+                "options": ["text2text-generation", "text-generation", "summarization"],
+            },
+            "huggingfacehub_api_token": {"display_name": "API token", "password": True},
+            "model_kwargs": {
+                "display_name": "Model Keyword Arguments",
+                "field_type": "code",
+            },
+            "code": {"show": False},
+        }
+
+    def build(
+        self,
+        endpoint_url: str,
+        task="text2text-generation",
+        huggingfacehub_api_token: Optional[str] = None,
+        model_kwargs: Optional[dict] = None,
+    ) -> BaseLLM:
+        try:
+            output = HuggingFaceEndpoint(
+                endpoint_url=endpoint_url,
+                task=task,
+                huggingfacehub_api_token=huggingfacehub_api_token,
+            )
+        except Exception as e:
+            raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
+        return output

src/backend/langflow/components/retrievers/MetalRetriever.py

@@ -0,0 +1,28 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.retrievers import MetalRetriever
+from langchain.schema import BaseRetriever
+from metal_sdk.metal import Metal  # type: ignore
+
+
+class MetalRetrieverComponent(CustomComponent):
+    display_name: str = "Metal Retriever"
+    description: str = "Retriever that uses the Metal API."
+
+    def build_config(self):
+        return {
+            "api_key": {"display_name": "API Key", "password": True},
+            "client_id": {"display_name": "Client ID", "password": True},
+            "index_id": {"display_name": "Index ID"},
+            "params": {"display_name": "Parameters"},
+            "code": {"show": False},
+        }
+
+    def build(
+        self, api_key: str, client_id: str, index_id: str, params: Optional[dict] = None
+    ) -> BaseRetriever:
+        try:
+            metal = Metal(api_key=api_key, client_id=client_id, index_id=index_id)
+        except Exception as e:
+            raise ValueError("Could not connect to Metal API.") from e
+        return MetalRetriever(client=metal, params=params or {})

src/backend/langflow/components/textsplitters/LanguageRecursiveTextSplitter.py

@@ -0,0 +1,80 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.text_splitter import Language
+from langchain.schema import Document
+
+
+class LanguageRecursiveTextSplitterComponent(CustomComponent):
+    display_name: str = "Language Recursive Text Splitter"
+    description: str = "Split text into chunks of a specified length based on language."
+    documentation: str = "https://docs.langflow.org/components/text-splitters#languagerecursivetextsplitter"
+
+    def build_config(self):
+        options = [x.value for x in Language]
+        return {
+            "documents": {
+                "display_name": "Documents",
+                "info": "The documents to split.",
+            },
+            "separator_type": {
+                "display_name": "Separator Type",
+                "info": "The type of separator to use.",
+                "field_type": "str",
+                "options": options,
+                "value": "Python",
+            },
+            "separators": {
+                "display_name": "Separators",
+                "info": "The characters to split on.",
+                "is_list": True,
+            },
+            "chunk_size": {
+                "display_name": "Chunk Size",
+                "info": "The maximum length of each chunk.",
+                "field_type": "int",
+                "value": 1000,
+            },
+            "chunk_overlap": {
+                "display_name": "Chunk Overlap",
+                "info": "The amount of overlap between chunks.",
+                "field_type": "int",
+                "value": 200,
+            },
+            "code": {"show": False},
+        }
+
+    def build(
+        self,
+        documents: list[Document],
+        chunk_size: Optional[int] = 1000,
+        chunk_overlap: Optional[int] = 200,
+        separator_type: Optional[str] = "Python",
+    ) -> list[Document]:
+        """
+        Split text into chunks of a specified length.
+
+        Args:
+            separators (list[str]): The characters to split on.
+            chunk_size (int): The maximum length of each chunk.
+            chunk_overlap (int): The amount of overlap between chunks.
+            length_function (function): The function to use to calculate the length of the text.
+
+        Returns:
+            list[str]: The chunks of text.
+        """
+        from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+        # Make sure chunk_size and chunk_overlap are ints
+        if isinstance(chunk_size, str):
+            chunk_size = int(chunk_size)
+        if isinstance(chunk_overlap, str):
+            chunk_overlap = int(chunk_overlap)
+
+        splitter = RecursiveCharacterTextSplitter.from_language(
+            language=Language(separator_type),
+            chunk_size=chunk_size,
+            chunk_overlap=chunk_overlap,
+        )
+
+        docs = splitter.split_documents(documents)
+        return docs

src/backend/langflow/components/textsplitters/RecursiveCharacterTextSplitter.py

@@ -0,0 +1,79 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.schema import Document
+from langflow.utils.util import build_loader_repr_from_documents
+
+
+class RecursiveCharacterTextSplitterComponent(CustomComponent):
+    display_name: str = "Recursive Character Text Splitter"
+    description: str = "Split text into chunks of a specified length."
+    documentation: str = "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter"
+
+    def build_config(self):
+        return {
+            "documents": {
+                "display_name": "Documents",
+                "info": "The documents to split.",
+            },
+            "separators": {
+                "display_name": "Separators",
+                "info": 'The characters to split on.\nIf left empty defaults to ["\\n\\n", "\\n", " ", ""].',
+                "is_list": True,
+            },
+            "chunk_size": {
+                "display_name": "Chunk Size",
+                "info": "The maximum length of each chunk.",
+                "field_type": "int",
+                "value": 1000,
+            },
+            "chunk_overlap": {
+                "display_name": "Chunk Overlap",
+                "info": "The amount of overlap between chunks.",
+                "field_type": "int",
+                "value": 200,
+            },
+            "code": {"show": False},
+        }
+
+    def build(
+        self,
+        documents: list[Document],
+        separators: Optional[list[str]] = None,
+        chunk_size: Optional[int] = 1000,
+        chunk_overlap: Optional[int] = 200,
+    ) -> list[Document]:
+        """
+        Split text into chunks of a specified length.
+
+        Args:
+            separators (list[str]): The characters to split on.
+            chunk_size (int): The maximum length of each chunk.
+            chunk_overlap (int): The amount of overlap between chunks.
+            length_function (function): The function to use to calculate the length of the text.
+
+        Returns:
+            list[str]: The chunks of text.
+        """
+        from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+        if separators == "":
+            separators = None
+        elif separators:
+            # check if the separators list has escaped characters
+            # if there are escaped characters, unescape them
+            separators = [x.encode().decode("unicode-escape") for x in separators]
+
+        # Make sure chunk_size and chunk_overlap are ints
+        if isinstance(chunk_size, str):
+            chunk_size = int(chunk_size)
+        if isinstance(chunk_overlap, str):
+            chunk_overlap = int(chunk_overlap)
+        splitter = RecursiveCharacterTextSplitter(
+            separators=separators,
+            chunk_size=chunk_size,
+            chunk_overlap=chunk_overlap,
+        )
+
+        docs = splitter.split_documents(documents)
+        self.repr_value = build_loader_repr_from_documents(docs)
+        return docs

src/backend/langflow/components/toolkits/Metaphor.py

@@ -0,0 +1,56 @@
+from typing import List, Union
+from langflow import CustomComponent
+
+from metaphor_python import Metaphor  # type: ignore
+from langchain.tools import Tool
+from langchain.agents import tool
+from langchain.agents.agent_toolkits.base import BaseToolkit
+
+
+class MetaphorToolkit(CustomComponent):
+    display_name: str = "Metaphor"
+    description: str = "Metaphor Toolkit"
+    documentation = (
+        "https://python.langchain.com/docs/integrations/tools/metaphor_search"
+    )
+    beta = True
+    # api key should be password = True
+    field_config = {
+        "metaphor_api_key": {"display_name": "Metaphor API Key", "password": True},
+        "code": {"advanced": True},
+    }
+
+    def build(
+        self,
+        metaphor_api_key: str,
+        use_autoprompt: bool = True,
+        search_num_results: int = 5,
+        similar_num_results: int = 5,
+    ) -> Union[Tool, BaseToolkit]:
+        # If documents, then we need to create a Vectara instance using .from_documents
+        client = Metaphor(api_key=metaphor_api_key)
+
+        @tool
+        def search(query: str):
+            """Call search engine with a query."""
+            return client.search(
+                query, use_autoprompt=use_autoprompt, num_results=search_num_results
+            )
+
+        @tool
+        def get_contents(ids: List[str]):
+            """Get contents of a webpage.
+
+            The ids passed in should be a list of ids as fetched from `search`.
+            """
+            return client.get_contents(ids)
+
+        @tool
+        def find_similar(url: str):
+            """Get search results similar to a given URL.
+
+            The url passed in should be a URL returned from `search`
+            """
+            return client.find_similar(url, num_results=similar_num_results)
+
+        return [search, get_contents, find_similar]  # type: ignore

src/backend/langflow/components/utilities/GetRequest.py

@@ -0,0 +1,75 @@
+from langflow import CustomComponent
+from langchain.schema import Document
+from langflow.services.database.models.base import orjson_dumps
+import requests
+from typing import Optional
+
+
+class GetRequest(CustomComponent):
+    display_name: str = "GET Request"
+    description: str = "Make a GET request to the given URL."
+    output_types: list[str] = ["Document"]
+    documentation: str = "https://docs.langflow.org/components/utilities#get-request"
+    beta = True
+    field_config = {
+        "url": {
+            "display_name": "URL",
+            "info": "The URL to make the request to",
+            "is_list": True,
+        },
+        "headers": {
+            "display_name": "Headers",
+            "info": "The headers to send with the request.",
+        },
+        "code": {"show": False},
+        "timeout": {
+            "display_name": "Timeout",
+            "field_type": "int",
+            "info": "The timeout to use for the request.",
+            "value": 5,
+        },
+    }
+
+    def get_document(
+        self, session: requests.Session, url: str, headers: Optional[dict], timeout: int
+    ) -> Document:
+        try:
+            response = session.get(url, headers=headers, timeout=int(timeout))
+            try:
+                response_json = response.json()
+                result = orjson_dumps(response_json, indent_2=False)
+            except Exception:
+                result = response.text
+            self.repr_value = result
+            return Document(
+                page_content=result,
+                metadata={
+                    "source": url,
+                    "headers": headers,
+                    "status_code": response.status_code,
+                },
+            )
+        except requests.Timeout:
+            return Document(
+                page_content="Request Timed Out",
+                metadata={"source": url, "headers": headers, "status_code": 408},
+            )
+        except Exception as exc:
+            return Document(
+                page_content=str(exc),
+                metadata={"source": url, "headers": headers, "status_code": 500},
+            )
+
+    def build(
+        self,
+        url: str,
+        headers: Optional[dict] = None,
+        timeout: int = 5,
+    ) -> list[Document]:
+        if headers is None:
+            headers = {}
+        urls = url if isinstance(url, list) else [url]
+        with requests.Session() as session:
+            documents = [self.get_document(session, u, headers, timeout) for u in urls]
+            self.repr_value = documents
+        return documents

src/backend/langflow/components/utilities/JSONDocumentBuilder.py

@@ -0,0 +1,55 @@
+### JSON Document Builder
+
+# Build a Document containing a JSON object using a key and another Document page content.
+
+# **Params**
+
+# - **Key:** The key to use for the JSON object.
+# - **Document:** The Document page to use for the JSON object.
+
+# **Output**
+
+# - **Document:** The Document containing the JSON object.
+
+from langflow import CustomComponent
+from langchain.schema import Document
+from langflow.services.database.models.base import orjson_dumps
+
+
+class JSONDocumentBuilder(CustomComponent):
+    display_name: str = "JSON Document Builder"
+    description: str = "Build a Document containing a JSON object using a key and another Document page content."
+    output_types: list[str] = ["Document"]
+    beta = True
+    documentation: str = (
+        "https://docs.langflow.org/components/utilities#json-document-builder"
+    )
+
+    field_config = {
+        "key": {"display_name": "Key"},
+        "document": {"display_name": "Document"},
+    }
+
+    def build(
+        self,
+        key: str,
+        document: Document,
+    ) -> Document:
+        documents = None
+        if isinstance(document, list):
+            documents = [
+                Document(
+                    page_content=orjson_dumps({key: doc.page_content}, indent_2=False)
+                )
+                for doc in document
+            ]
+        elif isinstance(document, Document):
+            documents = Document(
+                page_content=orjson_dumps({key: document.page_content}, indent_2=False)
+            )
+        else:
+            raise TypeError(
+                f"Expected Document or list of Documents, got {type(document)}"
+            )
+        self.repr_value = documents
+        return documents

src/backend/langflow/components/utilities/PostRequest.py

@@ -0,0 +1,80 @@
+from langflow import CustomComponent
+from langchain.schema import Document
+from langflow.services.database.models.base import orjson_dumps
+import requests
+from typing import Optional
+
+
+class PostRequest(CustomComponent):
+    display_name: str = "POST Request"
+    description: str = "Make a POST request to the given URL."
+    output_types: list[str] = ["Document"]
+    documentation: str = "https://docs.langflow.org/components/utilities#post-request"
+    beta = True
+    field_config = {
+        "url": {"display_name": "URL", "info": "The URL to make the request to."},
+        "headers": {
+            "display_name": "Headers",
+            "info": "The headers to send with the request.",
+        },
+        "code": {"show": False},
+        "document": {"display_name": "Document"},
+    }
+
+    def post_document(
+        self,
+        session: requests.Session,
+        document: Document,
+        url: str,
+        headers: Optional[dict] = None,
+    ) -> Document:
+        try:
+            response = session.post(url, headers=headers, data=document.page_content)
+            try:
+                response_json = response.json()
+                result = orjson_dumps(response_json, indent_2=False)
+            except Exception:
+                result = response.text
+            self.repr_value = result
+            return Document(
+                page_content=result,
+                metadata={
+                    "source": url,
+                    "headers": headers,
+                    "status_code": response,
+                },
+            )
+        except Exception as exc:
+            return Document(
+                page_content=str(exc),
+                metadata={
+                    "source": url,
+                    "headers": headers,
+                    "status_code": 500,
+                },
+            )
+
+    def build(
+        self,
+        document: Document,
+        url: str,
+        headers: Optional[dict] = None,
+    ) -> list[Document]:
+        if headers is None:
+            headers = {}
+
+        if not isinstance(document, list) and isinstance(document, Document):
+            documents: list[Document] = [document]
+        elif isinstance(document, list) and all(
+            isinstance(doc, Document) for doc in document
+        ):
+            documents = document
+        else:
+            raise ValueError("document must be a Document or a list of Documents")
+
+        with requests.Session() as session:
+            documents = [
+                self.post_document(session, doc, url, headers) for doc in documents
+            ]
+            self.repr_value = documents
+        return documents

src/backend/langflow/components/utilities/UpdateRequest.py

@@ -0,0 +1,94 @@
+from typing import List, Optional
+import requests
+from langflow import CustomComponent
+from langchain.schema import Document
+from langflow.services.database.models.base import orjson_dumps
+
+
+class UpdateRequest(CustomComponent):
+    display_name: str = "Update Request"
+    description: str = "Make a PATCH request to the given URL."
+    output_types: list[str] = ["Document"]
+    documentation: str = "https://docs.langflow.org/components/utilities#update-request"
+    beta = True
+    field_config = {
+        "url": {"display_name": "URL", "info": "The URL to make the request to."},
+        "headers": {
+            "display_name": "Headers",
+            "field_type": "NestedDict",
+            "info": "The headers to send with the request.",
+        },
+        "code": {"show": False},
+        "document": {"display_name": "Document"},
+        "method": {
+            "display_name": "Method",
+            "field_type": "str",
+            "info": "The HTTP method to use.",
+            "options": ["PATCH", "PUT"],
+            "value": "PATCH",
+        },
+    }
+
+    def update_document(
+        self,
+        session: requests.Session,
+        document: Document,
+        url: str,
+        headers: Optional[dict] = None,
+        method: str = "PATCH",
+    ) -> Document:
+        try:
+            if method == "PATCH":
+                response = session.patch(
+                    url, headers=headers, data=document.page_content
+                )
+            elif method == "PUT":
+                response = session.put(url, headers=headers, data=document.page_content)
+            else:
+                raise ValueError(f"Unsupported method: {method}")
+            try:
+                response_json = response.json()
+                result = orjson_dumps(response_json, indent_2=False)
+            except Exception:
+                result = response.text
+            self.repr_value = result
+            return Document(
+                page_content=result,
+                metadata={
+                    "source": url,
+                    "headers": headers,
+                    "status_code": response.status_code,
+                },
+            )
+        except Exception as exc:
+            return Document(
+                page_content=str(exc),
+                metadata={"source": url, "headers": headers, "status_code": 500},
+            )
+
+    def build(
+        self,
+        method: str,
+        document: Document,
+        url: str,
+        headers: Optional[dict] = None,
+    ) -> List[Document]:
+        if headers is None:
+            headers = {}
+
+        if not isinstance(document, list) and isinstance(document, Document):
+            documents: list[Document] = [document]
+        elif isinstance(document, list) and all(
+            isinstance(doc, Document) for doc in document
+        ):
+            documents = document
+        else:
+            raise ValueError("document must be a Document or a list of Documents")
+
+        with requests.Session() as session:
+            documents = [
+                self.update_document(session, doc, url, headers, method)
+                for doc in documents
+            ]
+            self.repr_value = documents
+        return documents

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

@@ -0,0 +1,109 @@
+from typing import Optional, Union
+from langflow import CustomComponent
+
+from langchain.vectorstores import Chroma
+from langchain.schema import Document
+from langchain.vectorstores.base import VectorStore
+from langchain.schema import BaseRetriever
+from langchain.embeddings.base import Embeddings
+import chromadb  # type: ignore
+
+
+class ChromaComponent(CustomComponent):
+    """
+    A custom component for implementing a Vector Store using Chroma.
+    """
+
+    display_name: str = "Chroma (Custom Component)"
+    description: str = "Implementation of Vector Store using Chroma"
+    documentation = "https://python.langchain.com/docs/integrations/vectorstores/chroma"
+    beta = True
+
+    def build_config(self):
+        """
+        Builds the configuration for the component.
+
+        Returns:
+        - dict: A dictionary containing the configuration options for the component.
+        """
+        return {
+            "collection_name": {"display_name": "Collection Name", "value": "langflow"},
+            "persist": {"display_name": "Persist"},
+            "persist_directory": {"display_name": "Persist Directory"},
+            "code": {"show": False, "display_name": "Code"},
+            "documents": {"display_name": "Documents", "is_list": True},
+            "embedding": {"display_name": "Embedding"},
+            "chroma_server_cors_allow_origins": {
+                "display_name": "Server CORS Allow Origins",
+                "advanced": True,
+            },
+            "chroma_server_host": {"display_name": "Server Host", "advanced": True},
+            "chroma_server_port": {"display_name": "Server Port", "advanced": True},
+            "chroma_server_grpc_port": {
+                "display_name": "Server gRPC Port",
+                "advanced": True,
+            },
+            "chroma_server_ssl_enabled": {
+                "display_name": "Server SSL Enabled",
+                "advanced": True,
+            },
+        }
+
+    def build(
+        self,
+        collection_name: str,
+        persist: bool,
+        chroma_server_ssl_enabled: bool,
+        persist_directory: Optional[str] = None,
+        embedding: Optional[Embeddings] = None,
+        documents: Optional[Document] = None,
+        chroma_server_cors_allow_origins: Optional[str] = None,
+        chroma_server_host: Optional[str] = None,
+        chroma_server_port: Optional[int] = None,
+        chroma_server_grpc_port: Optional[int] = None,
+    ) -> Union[VectorStore, BaseRetriever]:
+        """
+        Builds the Vector Store or BaseRetriever object.
+
+        Args:
+        - collection_name (str): The name of the collection.
+        - persist_directory (Optional[str]): The directory to persist the Vector Store to.
+        - chroma_server_ssl_enabled (bool): Whether to enable SSL for the Chroma server.
+        - persist (bool): Whether to persist the Vector Store or not.
+        - embedding (Optional[Embeddings]): The embeddings to use for the Vector Store.
+        - documents (Optional[Document]): The documents to use for the Vector Store.
+        - chroma_server_cors_allow_origins (Optional[str]): The CORS allow origins for the Chroma server.
+        - chroma_server_host (Optional[str]): The host for the Chroma server.
+        - chroma_server_port (Optional[int]): The port for the Chroma server.
+        - chroma_server_grpc_port (Optional[int]): The gRPC port for the Chroma server.
+
+        Returns:
+        - Union[VectorStore, BaseRetriever]: The Vector Store or BaseRetriever object.
+        """
+
+        # Chroma settings
+        chroma_settings = None
+
+        if chroma_server_host is not None:
+            chroma_settings = chromadb.config.Settings(
+                chroma_server_cors_allow_origins=chroma_server_cors_allow_origins
+                or None,
+                chroma_server_host=chroma_server_host,
+                chroma_server_port=chroma_server_port or None,
+                chroma_server_grpc_port=chroma_server_grpc_port or None,
+                chroma_server_ssl_enabled=chroma_server_ssl_enabled,
+            )
+
+        # If documents, then we need to create a Chroma instance using .from_documents
+        if documents is not None and embedding is not None:
+            return Chroma.from_documents(
+                documents=documents,  # type: ignore
+                persist_directory=persist_directory if persist else None,
+                collection_name=collection_name,
+                embedding=embedding,
+                client_settings=chroma_settings,
+            )
+
+        return Chroma(
+            persist_directory=persist_directory, client_settings=chroma_settings
+        )

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

@@ -0,0 +1,50 @@
+from typing import Optional, Union
+from langflow import CustomComponent
+
+from langchain.vectorstores import Vectara
+from langchain.schema import Document
+from langchain.vectorstores.base import VectorStore
+from langchain.schema import BaseRetriever
+from langchain.embeddings.base import Embeddings
+
+
+class VectaraComponent(CustomComponent):
+    display_name: str = "Vectara"
+    description: str = "Implementation of Vector Store using Vectara"
+    documentation = (
+        "https://python.langchain.com/docs/integrations/vectorstores/vectara"
+    )
+    beta = True
+    # api key should be password = True
+    field_config = {
+        "vectara_customer_id": {"display_name": "Vectara Customer ID"},
+        "vectara_corpus_id": {"display_name": "Vectara Corpus ID"},
+        "vectara_api_key": {"display_name": "Vectara API Key", "password": True},
+        "code": {"show": False},
+        "documents": {"display_name": "Documents"},
+        "embedding": {"display_name": "Embedding"},
+    }
+
+    def build(
+        self,
+        vectara_customer_id: str,
+        vectara_corpus_id: str,
+        vectara_api_key: str,
+        embedding: Optional[Embeddings] = None,
+        documents: Optional[Document] = None,
+    ) -> Union[VectorStore, BaseRetriever]:
+        # If documents, then we need to create a Vectara instance using .from_documents
+        if documents is not None and embedding is not None:
+            return Vectara.from_documents(
+                documents=documents,  # type: ignore
+                vectara_customer_id=vectara_customer_id,
+                vectara_corpus_id=vectara_corpus_id,
+                vectara_api_key=vectara_api_key,
+                embedding=embedding,
+            )
+
+        return Vectara(
+            vectara_customer_id=vectara_customer_id,
+            vectara_corpus_id=vectara_corpus_id,
+            vectara_api_key=vectara_api_key,
+        )

src/backend/langflow/config.yaml

@@ -104,6 +104,8 @@ embeddings:
     documentation: "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/sentence_transformers"
   CohereEmbeddings:
     documentation: "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/cohere"
+  VertexAIEmbeddings:
+    documentation: "https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/google_vertex_ai_palm"
 llms:
   OpenAI:
     documentation: "https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai"
@@ -127,8 +129,8 @@ llms:
   # There's a bug in this component deactivating until we get it sorted: _language_models.py", line 804, in send_message
   #     is_blocked=safety_attributes.get("blocked", False),
   # AttributeError: 'list' object has no attribute 'get'
-  # ChatVertexAI:
+  ChatVertexAI:
-  #   documentation: "https://python.langchain.com/docs/modules/model_io/models/chat/integrations/google_vertex_ai_palm"
+    documentation: "https://python.langchain.com/docs/modules/model_io/models/chat/integrations/google_vertex_ai_palm"
   ###
 memories:
   # https://github.com/supabase-community/supabase-py/issues/482
@@ -169,8 +171,6 @@ prompts:
 textsplitters:
   CharacterTextSplitter:
     documentation: "https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter"
-  RecursiveCharacterTextSplitter:
-    documentation: "https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter"
 toolkits:
   OpenAPIToolkit:
     documentation: ""

src/backend/langflow/database/base.py

@@ -1,78 +0,0 @@
-from contextlib import contextmanager
-import os
-
-from sqlmodel import SQLModel, Session, create_engine
-from langflow.utils.logger import logger
-
-
-class Engine:
-    _instance = None
-
-    @classmethod
-    def get(cls):
-        logger.debug("Getting database engine")
-        if cls._instance is None:
-            cls.create()
-        return cls._instance
-
-    @classmethod
-    def create(cls):
-        logger.debug("Creating database engine")
-        from langflow.settings import settings
-
-        if langflow_database_url := os.getenv("LANGFLOW_DATABASE_URL"):
-            settings.DATABASE_URL = langflow_database_url
-            logger.debug("Using LANGFLOW_DATABASE_URL")
-
-        if settings.DATABASE_URL and settings.DATABASE_URL.startswith("sqlite"):
-            connect_args = {"check_same_thread": False}
-        else:
-            connect_args = {}
-        if not settings.DATABASE_URL:
-            raise RuntimeError("No database_url provided")
-        cls._instance = create_engine(settings.DATABASE_URL, connect_args=connect_args)
-
-    @classmethod
-    def update(cls):
-        logger.debug("Updating database engine")
-        cls._instance = None
-        cls.create()
-
-
-def create_db_and_tables():
-    logger.debug("Creating database and tables")
-    try:
-        SQLModel.metadata.create_all(Engine.get())
-    except Exception as exc:
-        logger.error(f"Error creating database and tables: {exc}")
-        raise RuntimeError("Error creating database and tables") from exc
-    # Now check if the table Flow exists, if not, something went wrong
-    # and we need to create the tables again.
-    from sqlalchemy import inspect
-
-    inspector = inspect(Engine.get())
-    if "flow" not in inspector.get_table_names():
-        logger.error("Something went wrong creating the database and tables.")
-        logger.error("Please check your database settings.")
-
-        raise RuntimeError("Something went wrong creating the database and tables.")
-    else:
-        logger.debug("Database and tables created successfully")
-
-
-@contextmanager
-def session_getter():
-    try:
-        session = Session(Engine.get())
-        yield session
-    except Exception as e:
-        print("Session rollback because of exception:", e)
-        session.rollback()
-        raise
-    finally:
-        session.close()
-
-
-def get_session():
-    with session_getter() as session:
-        yield session

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

@@ -1,14 +0,0 @@
-from sqlmodel import SQLModel
-import orjson
-
-
-def orjson_dumps(v, *, default):
-    # orjson.dumps returns bytes, to match standard json.dumps we need to decode
-    return orjson.dumps(v, default=default).decode()
-
-
-class SQLModelSerializable(SQLModel):
-    class Config:
-        orm_mode = True
-        json_loads = orjson.loads
-        json_dumps = orjson_dumps

src/backend/langflow/database/models/flow_style.py

@@ -1,33 +0,0 @@
-# Path: src/backend/langflow/database/models/flowstyle.py
-
-from langflow.database.models.base import SQLModelSerializable
-from sqlmodel import Field, Relationship
-from uuid import UUID, uuid4
-from typing import TYPE_CHECKING, Optional
-
-if TYPE_CHECKING:
-    from langflow.database.models.flow import Flow
-
-
-class FlowStyleBase(SQLModelSerializable):
-    color: str
-    emoji: str
-    flow_id: UUID = Field(default=None, foreign_key="flow.id")
-
-
-class FlowStyle(FlowStyleBase, table=True):
-    id: UUID = Field(default_factory=uuid4, primary_key=True, unique=True)
-    flow: "Flow" = Relationship(back_populates="style")
-
-
-class FlowStyleUpdate(SQLModelSerializable):
-    color: Optional[str] = None
-    emoji: Optional[str] = None
-
-
-class FlowStyleCreate(FlowStyleBase):
-    pass
-
-
-class FlowStyleRead(FlowStyleBase):
-    id: UUID

src/backend/langflow/field_typing/__init__.py

@@ -0,0 +1,3 @@
+from .base import NestedDict
+
+__all__ = ["NestedDict"]

src/backend/langflow/field_typing/base.py

@@ -0,0 +1,4 @@
+from typing import Union, Dict
+
+# Type alias for more complex dicts
+NestedDict = Dict[str, Union[str, Dict]]

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

@@ -1,4 +1,4 @@
-from langflow.utils.logger import logger
+from loguru import logger
 from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
@@ -40,7 +40,6 @@ class Edge:
         if no_matched_type:
             logger.debug(self.source_types)
             logger.debug(self.target_reqs)
-        if no_matched_type:
             raise ValueError(
                 f"Edge between {self.source.vertex_type} and {self.target.vertex_type} "
                 f"has no matched type"

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

@@ -1,7 +1,7 @@
 from typing import Dict, Generator, List, Type, Union
 
 from langflow.graph.edge.base import Edge
-from langflow.graph.graph.constants import VERTEX_TYPE_MAP
+from langflow.graph.graph.constants import lazy_load_vertex_dict
 from langflow.graph.vertex.base import Vertex
 from langflow.graph.vertex.types import (
     FileToolVertex,
@@ -10,7 +10,7 @@ from langflow.graph.vertex.types import (
 )
 from langflow.interface.tools.constants import FILE_TOOLS
 from langflow.utils import payload
-from langflow.utils.logger import logger
+from loguru import logger
 from langchain.chains.base import Chain
 
 
@@ -144,7 +144,7 @@ class Graph:
 
         return list(reversed(sorted_vertices))
 
-    def generator_build(self) -> Generator:
+    def generator_build(self) -> Generator[Vertex, None, None]:
         """Builds each vertex in the graph and yields it."""
         sorted_vertices = self.topological_sort()
         logger.debug("Sorted vertices: %s", sorted_vertices)
@@ -187,10 +187,12 @@ class Graph:
         """Returns the node class based on the node type."""
         if node_type in FILE_TOOLS:
             return FileToolVertex
-        if node_type in VERTEX_TYPE_MAP:
+        if node_type in lazy_load_vertex_dict.VERTEX_TYPE_MAP:
-            return VERTEX_TYPE_MAP[node_type]
+            return lazy_load_vertex_dict.VERTEX_TYPE_MAP[node_type]
         return (
-            VERTEX_TYPE_MAP[node_lc_type] if node_lc_type in VERTEX_TYPE_MAP else Vertex
+            lazy_load_vertex_dict.VERTEX_TYPE_MAP[node_lc_type]
+            if node_lc_type in lazy_load_vertex_dict.VERTEX_TYPE_MAP
+            else Vertex
         )
 
     def _build_vertices(self) -> List[Vertex]:

src/backend/langflow/graph/graph/constants.py

@@ -1,4 +1,3 @@
-from langflow.graph.vertex.base import Vertex
 from langflow.graph.vertex import types
 from langflow.interface.agents.base import agent_creator
 from langflow.interface.chains.base import chain_creator
@@ -15,23 +14,45 @@ from langflow.interface.wrappers.base import wrapper_creator
 from langflow.interface.output_parsers.base import output_parser_creator
 from langflow.interface.retrievers.base import retriever_creator
 from langflow.interface.custom.base import custom_component_creator
-from typing import Dict, Type
+from langflow.utils.lazy_load import LazyLoadDictBase
 
 
-VERTEX_TYPE_MAP: Dict[str, Type[Vertex]] = {
+class VertexTypesDict(LazyLoadDictBase):
-    **{t: types.PromptVertex for t in prompt_creator.to_list()},
+    def __init__(self):
-    **{t: types.AgentVertex for t in agent_creator.to_list()},
+        self._all_types_dict = None
-    **{t: types.ChainVertex for t in chain_creator.to_list()},
+
-    **{t: types.ToolVertex for t in tool_creator.to_list()},
+    @property
-    **{t: types.ToolkitVertex for t in toolkits_creator.to_list()},
+    def VERTEX_TYPE_MAP(self):
-    **{t: types.WrapperVertex for t in wrapper_creator.to_list()},
+        return self.all_types_dict
-    **{t: types.LLMVertex for t in llm_creator.to_list()},
+
-    **{t: types.MemoryVertex for t in memory_creator.to_list()},
+    def _build_dict(self):
-    **{t: types.EmbeddingVertex for t in embedding_creator.to_list()},
+        langchain_types_dict = self.get_type_dict()
-    **{t: types.VectorStoreVertex for t in vectorstore_creator.to_list()},
+        return {
-    **{t: types.DocumentLoaderVertex for t in documentloader_creator.to_list()},
+            **langchain_types_dict,
-    **{t: types.TextSplitterVertex for t in textsplitter_creator.to_list()},
+            "Custom": ["Custom Tool", "Python Function"],
-    **{t: types.OutputParserVertex for t in output_parser_creator.to_list()},
+        }
-    **{t: types.CustomComponentVertex for t in custom_component_creator.to_list()},
+
-    **{t: types.RetrieverVertex for t in retriever_creator.to_list()},
+    def get_type_dict(self):
-}
+        return {
+            **{t: types.PromptVertex for t in prompt_creator.to_list()},
+            **{t: types.AgentVertex for t in agent_creator.to_list()},
+            **{t: types.ChainVertex for t in chain_creator.to_list()},
+            **{t: types.ToolVertex for t in tool_creator.to_list()},
+            **{t: types.ToolkitVertex for t in toolkits_creator.to_list()},
+            **{t: types.WrapperVertex for t in wrapper_creator.to_list()},
+            **{t: types.LLMVertex for t in llm_creator.to_list()},
+            **{t: types.MemoryVertex for t in memory_creator.to_list()},
+            **{t: types.EmbeddingVertex for t in embedding_creator.to_list()},
+            **{t: types.VectorStoreVertex for t in vectorstore_creator.to_list()},
+            **{t: types.DocumentLoaderVertex for t in documentloader_creator.to_list()},
+            **{t: types.TextSplitterVertex for t in textsplitter_creator.to_list()},
+            **{t: types.OutputParserVertex for t in output_parser_creator.to_list()},
+            **{
+                t: types.CustomComponentVertex
+                for t in custom_component_creator.to_list()
+            },
+            **{t: types.RetrieverVertex for t in retriever_creator.to_list()},
+        }
+
+
+lazy_load_vertex_dict = VertexTypesDict()

src/backend/langflow/graph/utils.py

@@ -3,6 +3,10 @@ from typing import Any, Union
 from langflow.interface.utils import extract_input_variables_from_prompt
 
 
+class UnbuiltObject:
+    pass
+
+
 def validate_prompt(prompt: str):
     """Validate prompt."""
     if extract_input_variables_from_prompt(prompt):

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

@@ -1,8 +1,9 @@
 import ast
+from langflow.graph.utils import UnbuiltObject
 from langflow.interface.initialize import loading
-from langflow.interface.listing import ALL_TYPES_DICT
+from langflow.interface.listing import lazy_load_dict
 from langflow.utils.constants import DIRECT_TYPES
-from langflow.utils.logger import logger
+from loguru import logger
 from langflow.utils.util import sync_to_async
 
 
@@ -22,7 +23,7 @@ class Vertex:
         self.edges: List["Edge"] = []
         self.base_type: Optional[str] = base_type
         self._parse_data()
-        self._built_object = None
+        self._built_object = UnbuiltObject()
         self._built = False
         self.artifacts: Dict[str, Any] = {}
 
@@ -62,7 +63,7 @@ class Vertex:
         )
 
         if self.base_type is None:
-            for base_type, value in ALL_TYPES_DICT.items():
+            for base_type, value in lazy_load_dict.ALL_TYPES_DICT.items():
                 if self.vertex_type in value:
                     self.base_type = base_type
                     break
@@ -121,6 +122,19 @@ class Vertex:
                     except Exception as exc:
                         logger.debug(f"Error parsing code: {exc}")
                         params[key] = value.get("value")
+                elif value.get("type") in ["dict", "NestedDict"]:
+                    # When dict comes from the frontend it comes as a
+                    # list of dicts, so we need to convert it to a dict
+                    # before passing it to the build method
+                    _value = value.get("value")
+                    if isinstance(_value, list):
+                        params[key] = {
+                            k: v
+                            for item in value.get("value", [])
+                            for k, v in item.items()
+                        }
+                    elif isinstance(_value, dict):
+                        params[key] = _value
                 else:
                     params[key] = value.get("value")
 
@@ -132,13 +146,13 @@ class Vertex:
         # Add _type to params
         self.params = params
 
-    def _build(self):
+    def _build(self, user_id=None):
         """
         Initiate the build process.
         """
         logger.debug(f"Building {self.vertex_type}")
         self._build_each_node_in_params_dict()
-        self._get_and_instantiate_class()
+        self._get_and_instantiate_class(user_id)
         self._validate_built_object()
 
         self._built = True
@@ -168,23 +182,25 @@ class Vertex:
         """
         return all(self._is_node(node) for node in value)
 
-    def _build_node_and_update_params(self, key, node):
+    def _build_node_and_update_params(self, key, node, user_id=None):
         """
         Builds a given node and updates the params dictionary accordingly.
         """
-        result = node.build()
+        result = node.build(user_id)
         self._handle_func(key, result)
         if isinstance(result, list):
             self._extend_params_list_with_result(key, result)
         self.params[key] = result
 
-    def _build_list_of_nodes_and_update_params(self, key, nodes):
+    def _build_list_of_nodes_and_update_params(
+        self, key, nodes: List["Vertex"], user_id=None
+    ):
         """
         Iterates over a list of nodes, builds each and updates the params dictionary.
         """
         self.params[key] = []
         for node in nodes:
-            built = node.build()
+            built = node.build(user_id)
             if isinstance(built, list):
                 if key not in self.params:
                     self.params[key] = []
@@ -214,7 +230,7 @@ class Vertex:
         if isinstance(self.params[key], list):
             self.params[key].extend(result)
 
-    def _get_and_instantiate_class(self):
+    def _get_and_instantiate_class(self, user_id=None):
         """
         Gets the class from a dictionary and instantiates it with the params.
         """
@@ -225,6 +241,7 @@ class Vertex:
                 node_type=self.vertex_type,
                 base_type=self.base_type,
                 params=self.params,
+                user_id=user_id,
             )
             self._update_built_object_and_artifacts(result)
         except Exception as exc:
@@ -245,12 +262,18 @@ class Vertex:
         """
         Checks if the built object is None and raises a ValueError if so.
         """
-        if self._built_object is None:
+        if isinstance(self._built_object, UnbuiltObject):
-            raise ValueError(f"Node type {self.vertex_type} not found")
+            raise ValueError(f"{self.vertex_type}: {self._built_object_repr()}")
+        elif self._built_object is None:
+            message = f"{self.vertex_type} returned None."
+            if self.base_type == "custom_components":
+                message += " Make sure your build method returns a component."
 
-    def build(self, force: bool = False) -> Any:
+            raise ValueError(message)
+
+    def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any:
         if not self._built or force:
-            self._build()
+            self._build(user_id, *args, **kwargs)
 
         return self._built_object