.wolf.jina.ai/
-๐ Read more about managing the server: https://github.com/jina-ai/langchain-serve
-```
-
-
- Show complete (example) output
-
-```text
- ๐ Deploying Langflow server on Jina AI Cloud
- โญโโโโโโโโโโโโโโโโโโโโโโโโโ ๐ Flow is available! โโโโโโโโโโโโโโโโโโโโโโโโโโโฎ
- โ โ
- โ ID langflow-e3dd8820ec โ
- โ Gateway (Websocket) wss://langflow-e3dd8820ec.wolf.jina.ai โ
- โ Dashboard https://dashboard.wolf.jina.ai/flow/e3dd8820ec โ
- โ โ
- โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
- โญโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ
- โ App ID โ langflow-e3dd8820ec โ
- โโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค
- โ Phase โ Serving โ
- โโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค
- โ Endpoint โ wss://langflow-e3dd8820ec.wolf.jina.ai โ
- โโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค
- โ App logs โ dashboards.wolf.jina.ai โ
- โโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค
- โ Swagger UI โ https://langflow-e3dd8820ec.wolf.jina.ai/docs โ
- โโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค
- โ OpenAPI JSON โ https://langflow-e3dd8820ec.wolf.jina.ai/openapi.json โ
- โฐโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
-
- ๐ Langflow server successfully deployed on Jina AI Cloud ๐
- ๐ Click on the link to open the server (please allow ~1-2 minutes for the server to startup): https://langflow-e3dd8820ec.wolf.jina.ai/
- ๐ Read more about managing the server: https://github.com/jina-ai/langchain-serve
-```
-
-
-
-#### API Usage
-
-You can use Langflow directly on your browser, or use the API endpoints on Jina AI Cloud to interact with the server.
-
-
- Show API usage (with python)
-
-```python
-import requests
-
-BASE_API_URL = "https://langflow-e3dd8820ec.wolf.jina.ai/api/v1/predict"
-FLOW_ID = "864c4f98-2e59-468b-8e13-79cd8da07468"
-# You can tweak the flow by adding a tweaks dictionary
-# e.g {"OpenAI-XXXXX": {"model_name": "gpt-4"}}
-TWEAKS = {
-"ChatOpenAI-g4jEr": {},
-"ConversationChain-UidfJ": {}
-}
-
-def run_flow(message: str, flow_id: str, tweaks: dict = None) -> dict:
- """
- Run a flow with a given message and optional tweaks.
-
- :param message: The message to send to the flow
- :param flow_id: The ID of the flow to run
- :param tweaks: Optional tweaks to customize the flow
- :return: The JSON response from the flow
- """
- api_url = f"{BASE_API_URL}/{flow_id}"
-
- payload = {"message": message}
-
- if tweaks:
- payload["tweaks"] = tweaks
-
- response = requests.post(api_url, json=payload)
- return response.json()
-
-# Setup any tweaks you want to apply to the flow
-print(run_flow("Your message", flow_id=FLOW_ID, tweaks=TWEAKS))
-```
-
-```json
-{
- "result": "Great choice! Bangalore in the 1920s was a vibrant city with a rich cultural and political scene. Here are some suggestions for things to see and do:\n\n1. Visit the Bangalore Palace - built in 1887, this stunning palace is a perfect example of Tudor-style architecture. It was home to the Maharaja of Mysore and is now open to the public.\n\n2. Attend a performance at the Ravindra Kalakshetra - this cultural center was built in the 1920s and is still a popular venue for music and dance performances.\n\n3. Explore the neighborhoods of Basavanagudi and Malleswaram - both of these areas have retained much of their old-world charm and are great places to walk around and soak up the atmosphere.\n\n4. Check out the Bangalore Club - founded in 1868, this exclusive social club was a favorite haunt of the British expat community in the 1920s.\n\n5. Attend a meeting of the Indian National Congress - founded in 1885, the INC was a major force in the Indian independence movement and held many meetings and rallies in Bangalore in the 1920s.\n\nHope you enjoy your trip to 1920s Bangalore!"
-}
-```
-
-
-
-> Read more about resource customization, cost, and management of Langflow apps on Jina AI Cloud in the **[langchain-serve](https://github.com/jina-ai/langchain-serve)** repository.
-
## Deploy on Railway
[](https://railway.app/template/JMXEWp?referralCode=MnPSdg)
@@ -255,7 +143,7 @@ print(run_flow("Your message", flow_id=FLOW_ID, tweaks=TWEAKS))
# ๐จ Creating Flows
-Creating flows with Langflow is easy. Simply drag sidebar components onto the canvas and connect them together to create your pipeline. Langflow provides a range of [LangChain components](https://langchain.readthedocs.io/en/latest/reference.html) to choose from, including LLMs, prompt serializers, agents, and chains.
+Creating flows with Langflow is easy. Simply drag sidebar components onto the canvas and connect them together to create your pipeline. Langflow provides a range of [LangChain components](https://docs.langchain.com/docs/category/components) to choose from, including LLMs, prompt serializers, agents, and chains.
Explore by editing prompt parameters, link chains and agents, track an agent's thought process, and export your flow.
@@ -275,6 +163,8 @@ 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! ๐ฆพ
diff --git a/base.Dockerfile b/base.Dockerfile
new file mode 100644
index 000000000..c76bbbcda
--- /dev/null
+++ b/base.Dockerfile
@@ -0,0 +1,97 @@
+
+
+# syntax=docker/dockerfile:1
+# Keep this syntax directive! It's used to enable Docker BuildKit
+
+# Based on https://github.com/python-poetry/poetry/discussions/1879?sort=top#discussioncomment-216865
+# but I try to keep it updated (see history)
+
+################################
+# PYTHON-BASE
+# Sets up all our shared environment variables
+################################
+FROM python:3.10-slim as python-base
+
+# python
+ENV PYTHONUNBUFFERED=1 \
+ # prevents python creating .pyc files
+ PYTHONDONTWRITEBYTECODE=1 \
+ \
+ # pip
+ PIP_DISABLE_PIP_VERSION_CHECK=on \
+ PIP_DEFAULT_TIMEOUT=100 \
+ \
+ # poetry
+ # https://python-poetry.org/docs/configuration/#using-environment-variables
+ POETRY_VERSION=1.5.1 \
+ # make poetry install to this location
+ POETRY_HOME="/opt/poetry" \
+ # make poetry create the virtual environment in the project's root
+ # it gets named `.venv`
+ POETRY_VIRTUALENVS_IN_PROJECT=true \
+ # do not ask any interactive question
+ POETRY_NO_INTERACTION=1 \
+ \
+ # paths
+ # this is where our requirements + virtual environment will live
+ PYSETUP_PATH="/opt/pysetup" \
+ VENV_PATH="/opt/pysetup/.venv"
+
+
+# prepend poetry and venv to path
+ENV PATH="$POETRY_HOME/bin:$VENV_PATH/bin:$PATH"
+
+
+################################
+# BUILDER-BASE
+# Used to build deps + create our virtual environment
+################################
+FROM python-base as builder-base
+RUN apt-get update \
+ && apt-get install --no-install-recommends -y \
+ # deps for installing poetry
+ curl \
+ # deps for building python deps
+ build-essential
+
+
+# install poetry - respects $POETRY_VERSION & $POETRY_HOME
+# The --mount will mount the buildx cache directory to where
+# Poetry and Pip store their cache so that they can re-use it
+RUN --mount=type=cache,target=/root/.cache \
+ curl -sSL https://install.python-poetry.org | python3 -
+
+# copy project requirement files here to ensure they will be cached.
+WORKDIR $PYSETUP_PATH
+COPY poetry.lock pyproject.toml ./
+COPY ./src/backend/langflow/main.py ./src/backend/langflow/main.py
+# Copy README.md to the build context
+COPY README.md .
+# install runtime deps - uses $POETRY_VIRTUALENVS_IN_PROJECT internally
+RUN --mount=type=cache,target=/root/.cache \
+ poetry install --without dev --extras deploy
+
+
+################################
+# DEVELOPMENT
+# Image used during development / testing
+################################
+FROM python-base as development
+WORKDIR $PYSETUP_PATH
+
+# copy in our built poetry + venv
+COPY --from=builder-base $POETRY_HOME $POETRY_HOME
+COPY --from=builder-base $PYSETUP_PATH $PYSETUP_PATH
+
+# Copy just one file to avoid rebuilding the whole image
+COPY ./src/backend/langflow/__init__.py ./src/backend/langflow/__init__.py
+# quicker install as runtime deps are already installed
+RUN --mount=type=cache,target=/root/.cache \
+ poetry install --with=dev --extras deploy
+
+# copy in our app code
+COPY ./src/backend ./src/backend
+RUN --mount=type=cache,target=/root/.cache \
+ poetry install --with=dev --extras deploy
+COPY ./tests ./tests=
+
diff --git a/deploy/.env.example b/deploy/.env.example
new file mode 100644
index 000000000..e67307406
--- /dev/null
+++ b/deploy/.env.example
@@ -0,0 +1,57 @@
+DOMAIN=localhost
+STACK_NAME=langflow-stack
+ENVIRONMENT=development
+
+TRAEFIK_PUBLIC_NETWORK=traefik-public
+TRAEFIK_TAG=langflow-traefik
+TRAEFIK_PUBLIC_TAG=traefik-public
+
+# RabbitMQ configuration
+RABBITMQ_DEFAULT_USER=langflow
+RABBITMQ_DEFAULT_PASS=langflow
+
+# Database configuration
+DB_USER=langflow
+DB_PASSWORD=langflow
+DB_HOST=db
+DB_PORT=5432
+DB_NAME=langflow
+
+# Logging configuration
+LOG_LEVEL=debug
+
+# DB configuration
+POSTGRES_USER=langflow
+POSTGRES_PASSWORD=langflow
+POSTGRES_DB=langflow
+POSTGRES_PORT=5432
+
+# Flower configuration
+LANGFLOW_CACHE_TYPE=redis
+LANGFLOW_REDIS_HOST=result_backend
+LANGFLOW_REDIS_PORT=6379
+LANGFLOW_REDIS_DB=0
+LANGFLOW_REDIS_EXPIRE=3600
+LANGFLOW_REDIS_PASSWORD=
+FLOWER_UNAUTHENTICATED_API=True
+BROKER_URL=amqp://langflow:langflow@broker:5672
+RESULT_BACKEND=redis://result_backend:6379/0
+C_FORCE_ROOT="true"
+
+# Frontend configuration
+VITE_PROXY_TARGET=http://backend:7860/api/
+BACKEND_URL=http://backend:7860
+
+# PGAdmin configuration
+PGADMIN_DEFAULT_EMAIL=admin@admin.com
+PGADMIN_DEFAULT_PASSWORD=admin
+
+# OpenAI configuration (for testing purposes)
+OPENAI_API_KEY=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
+
+# Superuser configuration
+LANGFLOW_SUPERUSER=superuser
+LANGFLOW_SUPERUSER_PASSWORD=superuser
+
+# New user configuration
+LANGFLOW_NEW_USER_IS_ACTIVE=False
\ No newline at end of file
diff --git a/deploy/.gitignore b/deploy/.gitignore
new file mode 100644
index 000000000..fdbcefec6
--- /dev/null
+++ b/deploy/.gitignore
@@ -0,0 +1 @@
+pgadmin
\ No newline at end of file
diff --git a/deploy/base.Dockerfile b/deploy/base.Dockerfile
new file mode 100644
index 000000000..323663283
--- /dev/null
+++ b/deploy/base.Dockerfile
@@ -0,0 +1,92 @@
+
+
+# syntax=docker/dockerfile:1
+# Keep this syntax directive! It's used to enable Docker BuildKit
+
+# Based on https://github.com/python-poetry/poetry/discussions/1879?sort=top#discussioncomment-216865
+# but I try to keep it updated (see history)
+
+################################
+# PYTHON-BASE
+# Sets up all our shared environment variables
+################################
+FROM python:3.10-slim as python-base
+
+# python
+ENV PYTHONUNBUFFERED=1 \
+ # prevents python creating .pyc files
+ PYTHONDONTWRITEBYTECODE=1 \
+ \
+ # pip
+ PIP_DISABLE_PIP_VERSION_CHECK=on \
+ PIP_DEFAULT_TIMEOUT=100 \
+ \
+ # poetry
+ # https://python-poetry.org/docs/configuration/#using-environment-variables
+ POETRY_VERSION=1.5.1 \
+ # make poetry install to this location
+ POETRY_HOME="/opt/poetry" \
+ # make poetry create the virtual environment in the project's root
+ # it gets named `.venv`
+ POETRY_VIRTUALENVS_IN_PROJECT=true \
+ # do not ask any interactive question
+ POETRY_NO_INTERACTION=1 \
+ \
+ # paths
+ # this is where our requirements + virtual environment will live
+ PYSETUP_PATH="/opt/pysetup" \
+ VENV_PATH="/opt/pysetup/.venv"
+
+
+# prepend poetry and venv to path
+ENV PATH="$POETRY_HOME/bin:$VENV_PATH/bin:$PATH"
+
+
+################################
+# BUILDER-BASE
+# Used to build deps + create our virtual environment
+################################
+FROM python-base as builder-base
+RUN apt-get update \
+ && apt-get install --no-install-recommends -y \
+ # deps for installing poetry
+ curl \
+ # deps for building python deps
+ build-essential
+
+# install poetry - respects $POETRY_VERSION & $POETRY_HOME
+# The --mount will mount the buildx cache directory to where
+# Poetry and Pip store their cache so that they can re-use it
+RUN --mount=type=cache,target=/root/.cache \
+ curl -sSL https://install.python-poetry.org | python3 -
+
+# copy project requirement files here to ensure they will be cached.
+WORKDIR $PYSETUP_PATH
+COPY ./poetry.lock ./pyproject.toml ./
+# Copy README.md to the build context
+COPY ./README.md ./
+# install runtime deps - uses $POETRY_VIRTUALENVS_IN_PROJECT internally
+RUN --mount=type=cache,target=/root/.cache \
+ poetry install --without dev --extras deploy
+
+
+################################
+# DEVELOPMENT
+# Image used during development / testing
+################################
+FROM python-base as development
+WORKDIR $PYSETUP_PATH
+
+# copy in our built poetry + venv
+COPY --from=builder-base $POETRY_HOME $POETRY_HOME
+COPY --from=builder-base $PYSETUP_PATH $PYSETUP_PATH
+
+# Copy just one file to avoid rebuilding the whole image
+COPY ./src/backend/langflow/__init__.py ./src/backend/langflow/__init__.py
+# quicker install as runtime deps are already installed
+RUN --mount=type=cache,target=/root/.cache \
+ poetry install --with=dev --extras deploy
+
+# copy in our app code
+COPY ./src/backend ./src/backend
+COPY ./tests ./tests
diff --git a/deploy/docker-compose.override.yml b/deploy/docker-compose.override.yml
new file mode 100644
index 000000000..1992916fb
--- /dev/null
+++ b/deploy/docker-compose.override.yml
@@ -0,0 +1,67 @@
+version: "3.8"
+
+services:
+ proxy:
+ ports:
+ - "80:80"
+ - "8090:8080"
+ command:
+ # Enable Docker in Traefik, so that it reads labels from Docker services
+ - --providers.docker
+ # Add a constraint to only use services with the label for this stack
+ # from the env var TRAEFIK_TAG
+ - --providers.docker.constraints=Label(`traefik.constraint-label-stack`, `${TRAEFIK_TAG?Variable not set}`)
+ # Do not expose all Docker services, only the ones explicitly exposed
+ - --providers.docker.exposedbydefault=false
+ # Disable Docker Swarm mode for local development
+ # - --providers.docker.swarmmode
+ # Enable the access log, with HTTP requests
+ - --accesslog
+ # Enable the Traefik log, for configurations and errors
+ - --log
+ # Enable the Dashboard and API
+ - --api
+ # Enable the Dashboard and API in insecure mode for local development
+ - --api.insecure=true
+ labels:
+ - traefik.enable=true
+ - traefik.http.routers.${STACK_NAME?Variable not set}-traefik-public-http.rule=Host(`${DOMAIN?Variable not set}`)
+ - traefik.http.services.${STACK_NAME?Variable not set}-traefik-public.loadbalancer.server.port=80
+
+ result_backend:
+ ports:
+ - "6379:6379"
+
+ pgadmin:
+ ports:
+ - "5050:5050"
+
+ flower:
+ ports:
+ - "5555:5555"
+
+ backend:
+ labels:
+ - traefik.enable=true
+ - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+ - traefik.http.routers.${STACK_NAME?Variable not set}-backend-http.rule=PathPrefix(`/api/v1`) || PathPrefix(`/docs`) || PathPrefix(`/health`)
+ - traefik.http.services.${STACK_NAME?Variable not set}-backend.loadbalancer.server.port=7860
+
+ frontend:
+ labels:
+ - traefik.enable=true
+ - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+ - traefik.http.routers.${STACK_NAME?Variable not set}-frontend-http.rule=PathPrefix(`/`)
+ - traefik.http.services.${STACK_NAME?Variable not set}-frontend.loadbalancer.server.port=80
+
+ celeryworker:
+ labels:
+ - traefik.enable=true
+ - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+ - traefik.http.routers.${STACK_NAME?Variable not set}-celeryworker-http.rule=PathPrefix(`/api/v1`) || PathPrefix(`/docs`) || PathPrefix(`/health`)
+ - traefik.http.services.${STACK_NAME?Variable not set}-celeryworker.loadbalancer.server.port=7860
+
+networks:
+ traefik-public:
+ # For local dev, don't expect an external Traefik network
+ external: false
diff --git a/deploy/docker-compose.with_tests.yml b/deploy/docker-compose.with_tests.yml
new file mode 100644
index 000000000..c16c14197
--- /dev/null
+++ b/deploy/docker-compose.with_tests.yml
@@ -0,0 +1,277 @@
+version: "3.8"
+
+services:
+ proxy:
+ image: traefik:v3.0
+ env_file:
+ - .env
+ networks:
+ - ${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+ - default
+ volumes:
+ - /var/run/docker.sock:/var/run/docker.sock
+ command:
+ # Enable Docker in Traefik, so that it reads labels from Docker services
+ - --providers.docker
+ # Add a constraint to only use services with the label for this stack
+ # from the env var TRAEFIK_TAG
+ - --providers.docker.constraints=Label(`traefik.constraint-label-stack`, `${TRAEFIK_TAG?Variable not set}`)
+ # Do not expose all Docker services, only the ones explicitly exposed
+ - --providers.docker.exposedbydefault=false
+ # Enable the access log, with HTTP requests
+ - --accesslog
+ # Enable the Traefik log, for configurations and errors
+ - --log
+ # Enable the Dashboard and API
+ - --api
+ deploy:
+ placement:
+ constraints:
+ - node.role == manager
+ labels:
+ # Enable Traefik for this service, to make it available in the public network
+ - traefik.enable=true
+ # Use the traefik-public network (declared below)
+ - traefik.docker.network=${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+ # Use the custom label "traefik.constraint-label=traefik-public"
+ # This public Traefik will only use services with this label
+ - traefik.constraint-label=${TRAEFIK_PUBLIC_TAG?Variable not set}
+ # traefik-http set up only to use the middleware to redirect to https
+ - traefik.http.middlewares.${STACK_NAME?Variable not set}-https-redirect.redirectscheme.scheme=https
+ - traefik.http.middlewares.${STACK_NAME?Variable not set}-https-redirect.redirectscheme.permanent=true
+ # Handle host with and without "www" to redirect to only one of them
+ # Uses environment variable DOMAIN
+ # To disable www redirection remove the Host() you want to discard, here and
+ # below for HTTPS
+ - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-http.rule=Host(`${DOMAIN?Variable not set}`) || Host(`www.${DOMAIN?Variable not set}`)
+ - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-http.entrypoints=http
+ # traefik-https the actual router using HTTPS
+ - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.rule=Host(`${DOMAIN?Variable not set}`) || Host(`www.${DOMAIN?Variable not set}`)
+ - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.entrypoints=https
+ - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.tls=true
+ # Use the "le" (Let's Encrypt) resolver created below
+ - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.tls.certresolver=le
+ # Define the port inside of the Docker service to use
+ - traefik.http.services.${STACK_NAME?Variable not set}-proxy.loadbalancer.server.port=80
+ # Handle domain with and without "www" to redirect to only one
+ # To disable www redirection remove the next line
+ - traefik.http.middlewares.${STACK_NAME?Variable not set}-www-redirect.redirectregex.regex=^https?://(www.)?(${DOMAIN?Variable not set})/(.*)
+ # Redirect a domain with www to non-www
+ # To disable it remove the next line
+ - traefik.http.middlewares.${STACK_NAME?Variable not set}-www-redirect.redirectregex.replacement=https://${DOMAIN?Variable not set}/$${3}
+ # Redirect a domain without www to www
+ # To enable it remove the previous line and uncomment the next
+ # - traefik.http.middlewares.${STACK_NAME}-www-redirect.redirectregex.replacement=https://www.${DOMAIN}/$${3}
+ # Middleware to redirect www, to disable it remove the next line
+ - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.middlewares=${STACK_NAME?Variable not set}-www-redirect
+ # Middleware to redirect www, and redirect HTTP to HTTPS
+ # to disable www redirection remove the section: ${STACK_NAME?Variable not set}-www-redirect,
+ - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-http.middlewares=${STACK_NAME?Variable not set}-www-redirect,${STACK_NAME?Variable not set}-https-redirect
+
+ backend: &backend
+ image: "ogabrielluiz/langflow:latest"
+ build:
+ context: ../
+ dockerfile: base.Dockerfile
+ depends_on:
+ - db
+ - broker
+ - result_backend
+ env_file:
+ - .env
+ volumes:
+ - ../:/app
+ - ./startup-backend.sh:/startup-backend.sh # Ensure the paths match
+ command: /startup-backend.sh # Fixed the path
+ healthcheck:
+ test: "exit 0"
+ deploy:
+ labels:
+ - traefik.enable=true
+ - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+ - traefik.http.routers.${STACK_NAME?Variable not set}-backend-http.rule=PathPrefix(`/api/v1`) || PathPrefix(`/docs`) || PathPrefix(`/health`)
+ - traefik.http.services.${STACK_NAME?Variable not set}-backend.loadbalancer.server.port=7860
+
+ db:
+ image: postgres:15.4
+ volumes:
+ - app-db-data:/var/lib/postgresql/data/pgdata
+ environment:
+ - PGDATA=/var/lib/postgresql/data/pgdata
+ deploy:
+ placement:
+ constraints:
+ - node.labels.app-db-data == true
+ healthcheck:
+ test: "exit 0"
+ env_file:
+ - .env
+
+ pgadmin:
+ image: dpage/pgadmin4
+ networks:
+ - ${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+ - default
+ volumes:
+ - pgadmin-data:/var/lib/pgadmin
+ env_file:
+ - .env
+ deploy:
+ labels:
+ - traefik.enable=true
+ - traefik.docker.network=${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+ - traefik.constraint-label=${TRAEFIK_PUBLIC_TAG?Variable not set}
+ - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-http.rule=Host(`pgadmin.${DOMAIN?Variable not set}`)
+ - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-http.entrypoints=http
+ - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-http.middlewares=${STACK_NAME?Variable not set}-https-redirect
+ - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-https.rule=Host(`pgadmin.${DOMAIN?Variable not set}`)
+ - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-https.entrypoints=https
+ - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-https.tls=true
+ - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-https.tls.certresolver=le
+ - traefik.http.services.${STACK_NAME?Variable not set}-pgadmin.loadbalancer.server.port=5050
+
+ result_backend:
+ image: redis:6.2.5
+ env_file:
+ - .env
+ # ports:
+ # - 6379:6379
+ healthcheck:
+ test: "exit 0"
+
+ celeryworker:
+ <<: *backend
+ env_file:
+ - .env
+ build:
+ context: ../
+ dockerfile: base.Dockerfile
+ command: celery -A langflow.worker.celery_app worker --loglevel=INFO --concurrency=1 -n lf-worker@%h
+ healthcheck:
+ test: "exit 0"
+ deploy:
+ replicas: 1
+
+ flower:
+ <<: *backend
+ env_file:
+ - .env
+ networks:
+ - default
+ build:
+ context: ../
+ dockerfile: base.Dockerfile
+ environment:
+ - FLOWER_PORT=5555
+
+ command: /bin/sh -c "celery -A langflow.worker.celery_app --broker=${BROKER_URL?Variable not set} flower --port=5555"
+ deploy:
+ labels:
+ - traefik.enable=true
+ - traefik.docker.network=${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+ - traefik.constraint-label=${TRAEFIK_PUBLIC_TAG?Variable not set}
+ - traefik.http.routers.${STACK_NAME?Variable not set}-flower-http.rule=Host(`flower.${DOMAIN?Variable not set}`)
+ - traefik.http.routers.${STACK_NAME?Variable not set}-flower-http.entrypoints=http
+ - traefik.http.routers.${STACK_NAME?Variable not set}-flower-http.middlewares=${STACK_NAME?Variable not set}-https-redirect
+ - traefik.http.routers.${STACK_NAME?Variable not set}-flower-https.rule=Host(`flower.${DOMAIN?Variable not set}`)
+ - traefik.http.routers.${STACK_NAME?Variable not set}-flower-https.entrypoints=https
+ - traefik.http.routers.${STACK_NAME?Variable not set}-flower-https.tls=true
+ - traefik.http.routers.${STACK_NAME?Variable not set}-flower-https.tls.certresolver=le
+ - traefik.http.services.${STACK_NAME?Variable not set}-flower.loadbalancer.server.port=5555
+
+ frontend:
+ image: "ogabrielluiz/langflow_frontend:latest"
+ env_file:
+ - .env
+ # user: your-non-root-user
+ build:
+ context: ../src/frontend
+ dockerfile: Dockerfile
+ args:
+ - BACKEND_URL=http://backend:7860
+ restart: on-failure
+ deploy:
+ labels:
+ - traefik.enable=true
+ - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+ - traefik.http.routers.${STACK_NAME?Variable not set}-frontend-http.rule=PathPrefix(`/`)
+ - traefik.http.services.${STACK_NAME?Variable not set}-frontend.loadbalancer.server.port=80
+
+ broker:
+ # RabbitMQ management console
+ image: rabbitmq:3-management
+ environment:
+ - RABBITMQ_DEFAULT_USER=${RABBITMQ_DEFAULT_USER:-admin}
+ - RABBITMQ_DEFAULT_PASS=${RABBITMQ_DEFAULT_PASS:-admin}
+ volumes:
+ - rabbitmq_data:/etc/rabbitmq/
+ - rabbitmq_data:/var/lib/rabbitmq/
+ - rabbitmq_log:/var/log/rabbitmq/
+ ports:
+ - 5672:5672
+ - 15672:15672
+
+ prometheus:
+ image: prom/prometheus:v2.37.9
+ env_file:
+ - .env
+ volumes:
+ - ./prometheus.yml:/etc/prometheus/prometheus.yml
+ command:
+ - "--config.file=/etc/prometheus/prometheus.yml"
+ # ports:
+ # - 9090:9090
+ healthcheck:
+ test: "exit 0"
+ deploy:
+ labels:
+ - traefik.enable=true
+ - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+ - traefik.http.routers.${STACK_NAME?Variable not set}-prometheus-http.rule=PathPrefix(`/metrics`)
+ - traefik.http.services.${STACK_NAME?Variable not set}-prometheus.loadbalancer.server.port=9090
+
+ grafana:
+ image: grafana/grafana:8.2.6
+ env_file:
+ - .env
+ # ports:
+ # - 3000:3000
+ volumes:
+ - grafana_data:/var/lib/grafana
+ deploy:
+ labels:
+ - traefik.enable=true
+ - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+ - traefik.http.routers.${STACK_NAME?Variable not set}-grafana-http.rule=PathPrefix(`/grafana`)
+ - traefik.http.services.${STACK_NAME?Variable not set}-grafana.loadbalancer.server.port=3000
+
+ tests:
+ extends:
+ file: docker-compose.yml
+ service: backend
+ env_file:
+ - .env
+ build:
+ context: ../
+ dockerfile: base.Dockerfile
+ command: pytest -vv
+ healthcheck:
+ test: "exit 0"
+ # override deploy labels to avoid conflicts with the backend service
+ labels:
+ - traefik.enable=true
+ - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+ - traefik.http.routers.${STACK_NAME?Variable not set}-tests-http.rule=PathPrefix(`/api/v1`) || PathPrefix(`/docs`) || PathPrefix(`/health`)
+ - traefik.http.services.${STACK_NAME?Variable not set}-tests.loadbalancer.server.port=7861
+
+volumes:
+ grafana_data:
+ app-db-data:
+ rabbitmq_data:
+ rabbitmq_log:
+ pgadmin-data:
+
+networks:
+ traefik-public:
+ # Allow setting it to false for testing
+ external: false # ${TRAEFIK_PUBLIC_NETWORK_IS_EXTERNAL-true}
diff --git a/deploy/docker-compose.yml b/deploy/docker-compose.yml
new file mode 100644
index 000000000..4eda0dc37
--- /dev/null
+++ b/deploy/docker-compose.yml
@@ -0,0 +1,258 @@
+version: "3.8"
+
+services:
+ proxy:
+ image: traefik:v3.0
+ env_file:
+ - .env
+ networks:
+ - ${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+ - default
+ volumes:
+ - /var/run/docker.sock:/var/run/docker.sock
+ command:
+ # Enable Docker in Traefik, so that it reads labels from Docker services
+ - --providers.docker
+ # Add a constraint to only use services with the label for this stack
+ # from the env var TRAEFIK_TAG
+ - --providers.docker.constraints=Label(`traefik.constraint-label-stack`, `${TRAEFIK_TAG?Variable not set}`)
+ # Do not expose all Docker services, only the ones explicitly exposed
+ - --providers.docker.exposedbydefault=false
+ # Enable the access log, with HTTP requests
+ - --accesslog
+ # Enable the Traefik log, for configurations and errors
+ - --log
+ # Enable the Dashboard and API
+ - --api
+ deploy:
+ placement:
+ constraints:
+ - node.role == manager
+ labels:
+ # Enable Traefik for this service, to make it available in the public network
+ - traefik.enable=true
+ # Use the traefik-public network (declared below)
+ - traefik.docker.network=${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+ # Use the custom label "traefik.constraint-label=traefik-public"
+ # This public Traefik will only use services with this label
+ - traefik.constraint-label=${TRAEFIK_PUBLIC_TAG?Variable not set}
+ # traefik-http set up only to use the middleware to redirect to https
+ - traefik.http.middlewares.${STACK_NAME?Variable not set}-https-redirect.redirectscheme.scheme=https
+ - traefik.http.middlewares.${STACK_NAME?Variable not set}-https-redirect.redirectscheme.permanent=true
+ # Handle host with and without "www" to redirect to only one of them
+ # Uses environment variable DOMAIN
+ # To disable www redirection remove the Host() you want to discard, here and
+ # below for HTTPS
+ - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-http.rule=Host(`${DOMAIN?Variable not set}`) || Host(`www.${DOMAIN?Variable not set}`)
+ - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-http.entrypoints=http
+ # traefik-https the actual router using HTTPS
+ - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.rule=Host(`${DOMAIN?Variable not set}`) || Host(`www.${DOMAIN?Variable not set}`)
+ - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.entrypoints=https
+ - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.tls=true
+ # Use the "le" (Let's Encrypt) resolver created below
+ - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.tls.certresolver=le
+ # Define the port inside of the Docker service to use
+ - traefik.http.services.${STACK_NAME?Variable not set}-proxy.loadbalancer.server.port=80
+ # Handle domain with and without "www" to redirect to only one
+ # To disable www redirection remove the next line
+ - traefik.http.middlewares.${STACK_NAME?Variable not set}-www-redirect.redirectregex.regex=^https?://(www.)?(${DOMAIN?Variable not set})/(.*)
+ # Redirect a domain with www to non-www
+ # To disable it remove the next line
+ - traefik.http.middlewares.${STACK_NAME?Variable not set}-www-redirect.redirectregex.replacement=https://${DOMAIN?Variable not set}/$${3}
+ # Redirect a domain without www to www
+ # To enable it remove the previous line and uncomment the next
+ # - traefik.http.middlewares.${STACK_NAME}-www-redirect.redirectregex.replacement=https://www.${DOMAIN}/$${3}
+ # Middleware to redirect www, to disable it remove the next line
+ - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-https.middlewares=${STACK_NAME?Variable not set}-www-redirect
+ # Middleware to redirect www, and redirect HTTP to HTTPS
+ # to disable www redirection remove the section: ${STACK_NAME?Variable not set}-www-redirect,
+ - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-http.middlewares=${STACK_NAME?Variable not set}-www-redirect,${STACK_NAME?Variable not set}-https-redirect
+
+ backend: &backend
+ image: "ogabrielluiz/langflow:latest"
+ build:
+ context: ../
+ dockerfile: base.Dockerfile
+ depends_on:
+ - db
+ - broker
+ - result_backend
+ env_file:
+ - .env
+ volumes:
+ - ../:/app
+ - ./startup-backend.sh:/startup-backend.sh # Ensure the paths match
+ command: /startup-backend.sh # Fixed the path
+ healthcheck:
+ test: "exit 0"
+ deploy:
+ labels:
+ - traefik.enable=true
+ - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+ - traefik.http.routers.${STACK_NAME?Variable not set}-backend-http.rule=PathPrefix(`/api/v1`) || PathPrefix(`/docs`) || PathPrefix(`/health`)
+ - traefik.http.services.${STACK_NAME?Variable not set}-backend.loadbalancer.server.port=7860
+
+ db:
+ image: postgres:15.4
+ volumes:
+ - app-db-data:/var/lib/postgresql/data/pgdata
+ environment:
+ - PGDATA=/var/lib/postgresql/data/pgdata
+ deploy:
+ placement:
+ constraints:
+ - node.labels.app-db-data == true
+ healthcheck:
+ test: "exit 0"
+ env_file:
+ - .env
+
+ pgadmin:
+ image: dpage/pgadmin4
+ networks:
+ - ${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+ - default
+ volumes:
+ - pgadmin-data:/var/lib/pgadmin
+ env_file:
+ - .env
+ deploy:
+ labels:
+ - traefik.enable=true
+ - traefik.docker.network=${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+ - traefik.constraint-label=${TRAEFIK_PUBLIC_TAG?Variable not set}
+ - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-http.rule=Host(`pgadmin.${DOMAIN?Variable not set}`)
+ - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-http.entrypoints=http
+ - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-http.middlewares=${STACK_NAME?Variable not set}-https-redirect
+ - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-https.rule=Host(`pgadmin.${DOMAIN?Variable not set}`)
+ - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-https.entrypoints=https
+ - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-https.tls=true
+ - traefik.http.routers.${STACK_NAME?Variable not set}-pgadmin-https.tls.certresolver=le
+ - traefik.http.services.${STACK_NAME?Variable not set}-pgadmin.loadbalancer.server.port=5050
+
+ result_backend:
+ image: redis:6.2.5
+ env_file:
+ - .env
+ # ports:
+ # - 6379:6379
+ healthcheck:
+ test: "exit 0"
+
+ celeryworker:
+ <<: *backend
+ env_file:
+ - .env
+ build:
+ context: ../
+ dockerfile: base.Dockerfile
+ command: celery -A langflow.worker.celery_app worker --loglevel=INFO --concurrency=1 -n lf-worker@%h
+ healthcheck:
+ test: "exit 0"
+ deploy:
+ replicas: 1
+
+ flower:
+ <<: *backend
+ env_file:
+ - .env
+ networks:
+ - default
+ build:
+ context: ../
+ dockerfile: base.Dockerfile
+ environment:
+ - FLOWER_PORT=5555
+
+ command: /bin/sh -c "celery -A langflow.worker.celery_app --broker=${BROKER_URL?Variable not set} flower --port=5555"
+ deploy:
+ labels:
+ - traefik.enable=true
+ - traefik.docker.network=${TRAEFIK_PUBLIC_NETWORK?Variable not set}
+ - traefik.constraint-label=${TRAEFIK_PUBLIC_TAG?Variable not set}
+ - traefik.http.routers.${STACK_NAME?Variable not set}-flower-http.rule=Host(`flower.${DOMAIN?Variable not set}`)
+ - traefik.http.routers.${STACK_NAME?Variable not set}-flower-http.entrypoints=http
+ - traefik.http.routers.${STACK_NAME?Variable not set}-flower-http.middlewares=${STACK_NAME?Variable not set}-https-redirect
+ - traefik.http.routers.${STACK_NAME?Variable not set}-flower-https.rule=Host(`flower.${DOMAIN?Variable not set}`)
+ - traefik.http.routers.${STACK_NAME?Variable not set}-flower-https.entrypoints=https
+ - traefik.http.routers.${STACK_NAME?Variable not set}-flower-https.tls=true
+ - traefik.http.routers.${STACK_NAME?Variable not set}-flower-https.tls.certresolver=le
+ - traefik.http.services.${STACK_NAME?Variable not set}-flower.loadbalancer.server.port=5555
+
+ frontend:
+ image: "ogabrielluiz/langflow_frontend:latest"
+ env_file:
+ - .env
+ # user: your-non-root-user
+ build:
+ context: ../src/frontend
+ dockerfile: Dockerfile
+ args:
+ - BACKEND_URL=http://backend:7860
+ restart: on-failure
+ deploy:
+ labels:
+ - traefik.enable=true
+ - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+ - traefik.http.routers.${STACK_NAME?Variable not set}-frontend-http.rule=PathPrefix(`/`)
+ - traefik.http.services.${STACK_NAME?Variable not set}-frontend.loadbalancer.server.port=80
+
+ broker:
+ # RabbitMQ management console
+ image: rabbitmq:3-management
+ environment:
+ - RABBITMQ_DEFAULT_USER=${RABBITMQ_DEFAULT_USER:-admin}
+ - RABBITMQ_DEFAULT_PASS=${RABBITMQ_DEFAULT_PASS:-admin}
+ volumes:
+ - rabbitmq_data:/etc/rabbitmq/
+ - rabbitmq_data:/var/lib/rabbitmq/
+ - rabbitmq_log:/var/log/rabbitmq/
+ ports:
+ - 5672:5672
+ - 15672:15672
+
+ prometheus:
+ image: prom/prometheus:v2.37.9
+ env_file:
+ - .env
+ volumes:
+ - ./prometheus.yml:/etc/prometheus/prometheus.yml
+ command:
+ - "--config.file=/etc/prometheus/prometheus.yml"
+ # ports:
+ # - 9090:9090
+ healthcheck:
+ test: "exit 0"
+ deploy:
+ labels:
+ - traefik.enable=true
+ - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+ - traefik.http.routers.${STACK_NAME?Variable not set}-prometheus-http.rule=PathPrefix(`/metrics`)
+ - traefik.http.services.${STACK_NAME?Variable not set}-prometheus.loadbalancer.server.port=9090
+
+ grafana:
+ image: grafana/grafana:8.2.6
+ env_file:
+ - .env
+ # ports:
+ # - 3000:3000
+ volumes:
+ - grafana_data:/var/lib/grafana
+ deploy:
+ labels:
+ - traefik.enable=true
+ - traefik.constraint-label-stack=${TRAEFIK_TAG?Variable not set}
+ - traefik.http.routers.${STACK_NAME?Variable not set}-grafana-http.rule=PathPrefix(`/grafana`)
+ - traefik.http.services.${STACK_NAME?Variable not set}-grafana.loadbalancer.server.port=3000
+
+volumes:
+ grafana_data:
+ app-db-data:
+ rabbitmq_data:
+ rabbitmq_log:
+ pgadmin-data:
+
+networks:
+ traefik-public:
+ # Allow setting it to false for testing
+ external: false # ${TRAEFIK_PUBLIC_NETWORK_IS_EXTERNAL-true}
diff --git a/deploy/prometheus.yml b/deploy/prometheus.yml
new file mode 100644
index 000000000..1d53d5b5f
--- /dev/null
+++ b/deploy/prometheus.yml
@@ -0,0 +1,11 @@
+global:
+ scrape_interval: 15s
+ evaluation_interval: 15s
+
+scrape_configs:
+ - job_name: prometheus
+ static_configs:
+ - targets: ["prometheus:9090"]
+ - job_name: flower
+ static_configs:
+ - targets: ["flower:5555"]
diff --git a/deploy/startup-backend.sh b/deploy/startup-backend.sh
new file mode 100755
index 000000000..75c8002a5
--- /dev/null
+++ b/deploy/startup-backend.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+
+export LANGFLOW_DATABASE_URL="postgresql://${DB_USER}:${DB_PASSWORD}@${DB_HOST}:${DB_PORT}/${DB_NAME}"
+
+
+# Your command to start the backend
+
+# If the ENVIRONMENT variable is set to "development", then start the backend in development mode
+# else start the backend in production mode with guvicorn
+if [ "$ENVIRONMENT" = "development" ]; then
+ echo "Starting backend in development mode"
+ exec python -m uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --log-level ${LOG_LEVEL:-info} --workers 2 --reload
+else
+ echo "Starting backend in production mode"
+ exec langflow run --host 0.0.0.0 --port 7860 --log-level ${LOG_LEVEL:-info} --workers -1 --backend-only
+fi
+
diff --git a/dev.Dockerfile b/dev.Dockerfile
index b38929db2..ed3631516 100644
--- a/dev.Dockerfile
+++ b/dev.Dockerfile
@@ -15,4 +15,4 @@ COPY ./ ./
# Install dependencies
RUN poetry config virtualenvs.create false && poetry install --no-interaction --no-ansi
-CMD ["uvicorn", "langflow.main:app", "--host", "0.0.0.0", "--port", "5003", "--reload", "log-level", "debug"]
\ No newline at end of file
+CMD ["uvicorn", "--factory", "src.backend.langflow.main:create_app", "--host", "0.0.0.0", "--port", "7860", "--reload", "--log-level", "debug"]
diff --git a/docker-compose.debug.yml b/docker-compose.debug.yml
index 581bdc6da..c73ff2a60 100644
--- a/docker-compose.debug.yml
+++ b/docker-compose.debug.yml
@@ -1,28 +1,33 @@
-version: '3.4'
-
-services:
- backend:
- volumes:
- - ./:/app
- build:
- context: ./
- dockerfile: ./dev.Dockerfile
- command: ["sh", "-c", "pip install debugpy -t /tmp && python /tmp/debugpy --wait-for-client --listen 0.0.0.0:5678 -m uvicorn langflow.main:app --host 0.0.0.0 --port 7860 --reload"]
- ports:
- - 7860:7860
- - 5678:5678
- restart: on-failure
-
- frontend:
- build:
- context: ./src/frontend
- dockerfile: ./dev.Dockerfile
- args:
- - BACKEND_URL=http://backend:7860
- ports:
- - "3000:3000"
- volumes:
- - ./src/frontend/public:/home/node/app/public
- - ./src/frontend/src:/home/node/app/src
- - ./src/frontend/package.json:/home/node/app/package.json
- restart: on-failure
\ No newline at end of file
+version: "3.4"
+
+services:
+ backend:
+ volumes:
+ - ./:/app
+ build:
+ context: ./
+ dockerfile: ./dev.Dockerfile
+ command:
+ [
+ "sh",
+ "-c",
+ "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
+ - 5678:5678
+ restart: on-failure
+
+ frontend:
+ build:
+ context: ./src/frontend
+ dockerfile: ./dev.Dockerfile
+ args:
+ - BACKEND_URL=http://backend:7860
+ ports:
+ - "3000:3000"
+ volumes:
+ - ./src/frontend/public:/home/node/app/public
+ - ./src/frontend/src:/home/node/app/src
+ - ./src/frontend/package.json:/home/node/app/package.json
+ restart: on-failure
diff --git a/docker-compose.yml b/docker-compose.yml
index 755d0794d..13d39e1f3 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -1,4 +1,4 @@
-version: '3'
+version: "3"
services:
backend:
@@ -9,7 +9,7 @@ services:
- "7860:7860"
volumes:
- ./:/app
- command: bash -c "uvicorn langflow.main: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:
@@ -22,7 +22,7 @@ services:
ports:
- "3000:3000"
volumes:
- - ./src/frontend/public:/home/node/app/public
- - ./src/frontend/src:/home/node/app/src
- - ./src/frontend/package.json:/home/node/app/package.json
- restart: on-failure
\ No newline at end of file
+ - ./src/frontend/public:/home/node/app/public
+ - ./src/frontend/src:/home/node/app/src
+ - ./src/frontend/package.json:/home/node/app/package.json
+ restart: on-failure
diff --git a/docker_example/README.md b/docker_example/README.md
new file mode 100644
index 000000000..9e72dc645
--- /dev/null
+++ b/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/)
\ No newline at end of file
diff --git a/docker_example/docker-compose.yml b/docker_example/docker-compose.yml
index 40ceb2504..ffb033104 100644
--- a/docker_example/docker-compose.yml
+++ b/docker_example/docker-compose.yml
@@ -6,5 +6,5 @@ services:
context: .
dockerfile: Dockerfile
ports:
- - "5003:5003"
+ - "7860:7860"
command: langflow --host 0.0.0.0
diff --git a/docs/docs/components/custom.mdx b/docs/docs/components/custom.mdx
index ffa747c1b..90282a73e 100644
--- a/docs/docs/components/custom.mdx
+++ b/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.
+
Unlike Langchain types, base Python types do not add a
[handle](../guidelines/components) to the field by default. To add handles,
diff --git a/docs/docs/components/embeddings.mdx b/docs/docs/components/embeddings.mdx
index 644d91fee..5b134f08d 100644
--- a/docs/docs/components/embeddings.mdx
+++ b/docs/docs/components/embeddings.mdx
@@ -73,3 +73,25 @@ Used to load [OpenAIโs](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 โ defaults to `us-central1`.
+- **max_output_tokens:** Token limit determines the maximum amount of text output from one prompt โ defaults to `128`.
+- **model_name:** The name of the Vertex AI large language model โ 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 โ defaults to `5`.
+- **temperature:** Tunes the degree of randomness in text generations. Should be a non-negative value โ defaults to `0`.
+- **top_k:** How the model selects tokens for output, the next token is selected from โ defaults to `40`.
+- **top_p:** Tokens are selected from most probable to least until the sum of their โ 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 โ defaults to `False`.
\ No newline at end of file
diff --git a/docs/docs/components/llms.mdx b/docs/docs/components/llms.mdx
index ccca8fdd4..412f38681 100644
--- a/docs/docs/components/llms.mdx
+++ b/docs/docs/components/llms.mdx
@@ -195,4 +195,62 @@ Vertex AI is a cloud computing platform offered by Google Cloud Platform (GCP).
- **top_k:** How the model selects tokens for output, the next token is selected from โ defaults to `40`.
- **top_p:** Tokens are selected from most probable to least until the sum of their โ 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 โ defaults to `False`.
\ No newline at end of file
+- **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 โ 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 โ defaults to `us-central1`.
+- **max_output_tokens:** Token limit determines the maximum amount of text output from one prompt โ defaults to `128`.
+- **model_name:** The name of the Vertex AI large language model โ 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 โ defaults to `5`.
+- **temperature:** Tunes the degree of randomness in text generations. Should be a non-negative value โ defaults to `0`.
+- **top_k:** How the model selects tokens for output, the next token is selected from โ defaults to `40`.
+- **top_p:** Tokens are selected from most probable to least until the sum of their โ 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 โ defaults to `False`.
+
+---
+
+### QianfanLLMEndpoint
+
+Wrapper around [Baidu Qianfan](https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html) large language models.
+
+:::info
+The Qianfan Big Model Platform is a one-stop platform for enterprise developers to develop and operate large models and services. It provides data management based on ERNIE Bot's underlying model (Ernie Bot), automatic model customization and fine-tuning, and one-stop large-scale model customization services for cloud deployment of prediction services, and provides ERNIE Bot's enterprise level service API that can be quickly called, helping to implement the demand for generative AI applications in various industries.
+:::
+
+- **Model Name:** Model name. you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu preset models are mapping to an endpoint. `Model Name` will be ignored if `Endpoint` is set.
+- **Qianfan Ak:** which you could get from https://cloud.baidu.com/product/wenxinworkshop.
+- **Qianfan Sk:** which you could get from https://cloud.baidu.com/product/wenxinworkshop.
+- **Top p:** Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. The diversity of the output text is affected, and the larger the value, the stronger the diversity of the generated text - defaults to `0.8`.
+- **Temperature:** Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. Higher values make the output more random, while lower values make it more concentrated and deterministic - defaults to `0.95`.
+- **Penalty Score:** Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. By increasing the penalty for generated tokens, the phenomenon of duplicate generation is reduced. A higher value indicates a higher penalty - defaults to `1.0`.
+- **Endpoint:** Endpoint of the Qianfan LLM, required if custom model used.
+
+---
+
+### QianfanChatEndpoint
+
+Wrapper around [Baidu Qianfan](https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html) chat large language models. This component supports some of the LLMs (Large Language Models) available by Baidu qianfan and is used for tasks such as chatbots, Generative Question-Answering (GQA), and summarization.
+
+:::info
+The Qianfan Big Model Platform is a one-stop platform for enterprise developers to develop and operate large models and services. It provides data management based on ERNIE Bot's underlying model (Ernie Bot), automatic model customization and fine-tuning, and one-stop large-scale model customization services for cloud deployment of prediction services, and provides ERNIE Bot's enterprise level service API that can be quickly called, helping to implement the demand for generative AI applications in various industries.
+:::
+
+- **Model Name:** Model name. you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu preset models are mapping to an endpoint. `Model Name` will be ignored if `Endpoint` is set.
+- **Qianfan Ak:** which you could get from https://cloud.baidu.com/product/wenxinworkshop.
+- **Qianfan Sk:** which you could get from https://cloud.baidu.com/product/wenxinworkshop.
+- **Top p:** Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. The diversity of the output text is affected, and the larger the value, the stronger the diversity of the generated text - defaults to `0.8`.
+- **Temperature:** Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. Higher values make the output more random, while lower values make it more concentrated and deterministic - defaults to `0.95`.
+- **Penalty Score:** Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo. By increasing the penalty for generated tokens, the phenomenon of duplicate generation is reduced. A higher value indicates a higher penalty - defaults to `1.0`.
+- **Endpoint:** Endpoint of the Qianfan LLM, required if custom model used.
\ No newline at end of file
diff --git a/docs/docs/deployment/jina-deployment.md b/docs/docs/deployment/jina-deployment.md
index bf9df051d..e69de29bb 100644
--- a/docs/docs/deployment/jina-deployment.md
+++ b/docs/docs/deployment/jina-deployment.md
@@ -1,101 +0,0 @@
-# Deploy on Jina AI Cloud
-
-Langflow integrates with langchain-serve to provide a one-command deployment to [Jina AI Cloud](https://github.com/jina-ai/langchain-serve).
-
-Start by installing `langchain-serve` with
-
-```bash
-pip install -U langchain-serve
-```
-
-Then, run:
-
-```bash
-langflow --jcloud
-```
-
-```text
-๐ Langflow server successfully deployed on Jina AI Cloud ๐
-๐ Click on the link to open the server (please allow ~1-2 minutes for the server to startup): https://.wolf.jina.ai/
-๐ Read more about managing the server: https://github.com/jina-ai/langchain-serve
-```
-
-**Complete (example) output:**
-
-```text
- ๐ Deploying Langflow server on Jina AI Cloud
- โญโโโโโโโโโโโโโโโโโโโโโโโโโ ๐ Flow is available! โโโโโโโโโโโโโโโโโโโโโโโโโโโฎ
- โ โ
- โ ID langflow-e3dd8820ec โ
- โ Gateway (Websocket) wss://langflow-e3dd8820ec.wolf.jina.ai โ
- โ Dashboard https://dashboard.wolf.jina.ai/flow/e3dd8820ec โ
- โ โ
- โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
- โญโโโโโโโโโโโโโโโฌโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ
- โ App ID โ langflow-e3dd8820ec โ
- โโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค
- โ Phase โ Serving โ
- โโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค
- โ Endpoint โ wss://langflow-e3dd8820ec.wolf.jina.ai โ
- โโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค
- โ App logs โ dashboards.wolf.jina.ai โ
- โโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค
- โ Swagger UI โ https://langflow-e3dd8820ec.wolf.jina.ai/docs โ
- โโโโโโโโโโโโโโโโผโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโค
- โ OpenAPI JSON โ https://langflow-e3dd8820ec.wolf.jina.ai/openapi.json โ
- โฐโโโโโโโโโโโโโโโดโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
-
- ๐ Langflow server successfully deployed on Jina AI Cloud ๐
- ๐ Click on the link to open the server (please allow ~1-2 minutes for the server to startup): https://langflow-e3dd8820ec.wolf.jina.ai/
- ๐ Read more about managing the server: https://github.com/jina-ai/langchain-serve
- ```
-## API Usage (with python)
-
-You can use Langflow directly on your browser or the API endpoints on Jina AI Cloud to interact with the server.
-
-```python
-import requests
-
-BASE_API_URL = "https://langflow-e3dd8820ec.wolf.jina.ai/api/v1/predict"
-FLOW_ID = "864c4f98-2e59-468b-8e13-79cd8da07468"
-# You can tweak the flow by adding a tweaks dictionary
-# e.g {"OpenAI-XXXXX": {"model_name": "gpt-4"}}
-TWEAKS = {
-"ChatOpenAI-g4jEr": {},
-"ConversationChain-UidfJ": {}
-}
-
-def run_flow(message: str, flow_id: str, tweaks: dict = None) -> dict:
- """
- Run a flow with a given message and optional tweaks.
-
- :param message: The message to send to the flow
- :param flow_id: The ID of the flow to run
- :param tweaks: Optional tweaks to customize the flow
- :return: The JSON response from the flow
- """
- api_url = f"{BASE_API_URL}/{flow_id}"
-
- payload = {"message": message}
-
- if tweaks:
- payload["tweaks"] = tweaks
-
- response = requests.post(api_url, json=payload)
- return response.json()
-
-# Setup any tweaks you want to apply to the flow
-print(run_flow("Your message", flow_id=FLOW_ID, tweaks=TWEAKS))
- ```
-
- ```json
-{
- "result": "Great choice! Bangalore in the 1920s was a vibrant city with a rich cultural and political scene. Here are some suggestions for things to see and do:\n\n1. Visit the Bangalore Palace - built in 1887, this stunning palace is a perfect example of Tudor-style architecture. It was home to the Maharaja of Mysore and is now open to the public.\n\n2. Attend a performance at the Ravindra Kalakshetra - this cultural center was built in the 1920s and is still a popular venue for music and dance performances.\n\n3. Explore the neighborhoods of Basavanagudi and Malleswaram - both of these areas have retained much of their old-world charm and are great places to walk around and soak up the atmosphere.\n\n4. Check out the Bangalore Club - founded in 1868, this exclusive social club was a favorite haunt of the British expat community in the 1920s.\n\n5. Attend a meeting of the Indian National Congress - founded in 1885, the INC was a major force in the Indian independence movement and held many meetings and rallies in Bangalore in the 1920s.\n\nHope you enjoy your trip to 1920s Bangalore!"
-}
- ```
-
-:::info
-
-Read more about resource customization, cost, and management of Langflow apps on Jina AI Cloud in the **[langchain-serve](https://github.com/jina-ai/langchain-serve)** repository.
-
-:::
\ No newline at end of file
diff --git a/docs/docs/guidelines/api.mdx b/docs/docs/guidelines/api.mdx
new file mode 100644
index 000000000..97d2db76e
--- /dev/null
+++ b/docs/docs/guidelines/api.mdx
@@ -0,0 +1,147 @@
+import useBaseUrl from "@docusaurus/useBaseUrl";
+import ZoomableImage from "/src/theme/ZoomableImage.js";
+
+# API Keys
+
+## Introduction
+
+Langflow offers an API Key functionality that allows users to access their individual components and flows without going through traditional login authentication. The API Key is a user-specific token that can be included in the request's header or query parameter to authenticate API calls. The following documentation outlines how to generate, use, and manage these API Keys in Langflow.
+
+## Generating an API Key
+
+### Through Langflow UI
+
+{/* add image img/api-key.png */}
+
+ \
+ -H 'Content-Type: application/json'\
+ -H 'x-api-key: '\
+ -d '{"inputs": {"text":""}, "tweaks": {}}'
+```
+
+With Python using `requests`:
+
+```python
+import requests
+from typing import Optional
+
+BASE_API_URL = "http://localhost:3001/api/v1/process"
+FLOW_ID = "4441b773-0724-434e-9cee-19d995d8f2df"
+# You can tweak the flow by adding a tweaks dictionary
+# e.g {"OpenAI-XXXXX": {"model_name": "gpt-4"}}
+TWEAKS = {}
+
+def run_flow(inputs: dict,
+ flow_id: str,
+ tweaks: Optional[dict] = None,
+ apiKey: Optional[str] = None) -> dict:
+ """
+ Run a flow with a given message and optional tweaks.
+
+ :param message: The message to send to the flow
+ :param flow_id: The ID of the flow to run
+ :param tweaks: Optional tweaks to customize the flow
+ :return: The JSON response from the flow
+ """
+ api_url = f"{BASE_API_URL}/{flow_id}"
+
+ payload = {"inputs": inputs}
+ headers = {}
+
+ if tweaks:
+ payload["tweaks"] = tweaks
+ if apiKey:
+ headers = {"x-api-key": apiKey}
+
+ response = requests.post(api_url, json=payload, headers=headers)
+ return response.json()
+
+# Setup any tweaks you want to apply to the flow
+inputs = {"text":""}
+api_key = ""
+print(run_flow(inputs, flow_id=FLOW_ID, tweaks=TWEAKS, apiKey=api_key))
+```
+
+### Using the Query Parameter
+
+Alternatively, you can include the API key as a query parameter in the URL:
+
+```bash
+curl -X POST \
+ http://localhost:3000/api/v1/process/?x-api-key= \
+ -H 'Content-Type: application/json'\
+ -d '{"inputs": {"text":""}, "tweaks": {}}'
+```
+
+Or with Python:
+
+```python
+import requests
+
+BASE_API_URL = "http://localhost:3001/api/v1/process"
+FLOW_ID = "4441b773-0724-434e-9cee-19d995d8f2df"
+# You can tweak the flow by adding a tweaks dictionary
+# e.g {"OpenAI-XXXXX": {"model_name": "gpt-4"}}
+TWEAKS = {}
+
+def run_flow(inputs: dict,
+ flow_id: str,
+ tweaks: Optional[dict] = None,
+ apiKey: Optional[str] = None) -> dict:
+ """
+ Run a flow with a given message and optional tweaks.
+
+ :param message: The message to send to the flow
+ :param flow_id: The ID of the flow to run
+ :param tweaks: Optional tweaks to customize the flow
+ :return: The JSON response from the flow
+ """
+ api_url = f"{BASE_API_URL}/{flow_id}"
+
+ payload = {"inputs": inputs}
+ headers = {}
+
+ if tweaks:
+ payload["tweaks"] = tweaks
+ if apiKey:
+ api_url += f"?x-api-key={apiKey}"
+
+ response = requests.post(api_url, json=payload, headers=headers)
+ return response.json()
+
+# Setup any tweaks you want to apply to the flow
+inputs = {"text":""}
+api_key = ""
+print(run_flow(inputs, flow_id=FLOW_ID, tweaks=TWEAKS, apiKey=api_key))
+```
+
+## Security Considerations
+
+- **Visibility**: The API key won't be retrievable again through the UI for security reasons.
+- **Scope**: The key only allows access to the flows and components of the specific user to whom it was issued.
+
+## Revoking an API Key
+
+To revoke an API key, simply delete it from the UI. This will immediately invalidate the key and prevent it from being used again.
diff --git a/docs/docs/guidelines/async-api.mdx b/docs/docs/guidelines/async-api.mdx
new file mode 100644
index 000000000..c5473812e
--- /dev/null
+++ b/docs/docs/guidelines/async-api.mdx
@@ -0,0 +1,73 @@
+import Admonition from "@theme/Admonition";
+
+# Asynchronous Processing
+
+## Introduction
+
+Starting from version 0.5, Langflow introduces a new feature to its API: the _`sync`_ flag. This flag allows users to opt for asynchronous processing of their flows, freeing up resources and enabling better control over long-running tasks.
+This feature supports running tasks in a Celery worker queue and AnyIO task groups for now.
+
+
+ This is an experimental feature. The default behavior of the API is still
+ synchronous processing. The API may change in the future.
+
+
+## The _`sync`_ Flag
+
+The _`sync`_ flag can be included in the payload of your POST request to the _`/api/v1/process/`_ endpoint.
+When set to _`false`_, the API will initiate an asynchronous task instead of processing the flow synchronously.
+
+### API Request with _`sync`_ flag
+
+```bash
+curl -X POST \
+ http://localhost:3000/api/v1/process/ \
+ -H 'Content-Type: application/json' \
+ -H 'x-api-key: ' \
+ -d '{"inputs": {"text": ""}, "tweaks": {}, "sync": false}'
+```
+
+Response:
+
+```json
+{
+ "result": {
+ "output": "..."
+ },
+ "task": {
+ "id": "...",
+ "href": "api/v1/task/"
+ },
+ "session_id": "...",
+ "backend": "..." // celery or anyio
+}
+```
+
+## Checking Task Status
+
+You can check the status of an asynchronous task by making a GET request to the `/task/{task_id}` endpoint.
+
+```bash
+curl -X GET \
+ http://localhost:3000/api/v1/task/ \
+ -H 'x-api-key: '
+```
+
+### Response
+
+The endpoint will return the current status of the task and, if completed, the result of the task. Possible statuses include:
+
+- _`PENDING`_: The task is waiting for execution.
+- _`SUCCESS`_: The task has completed successfully.
+- _`FAILURE`_: The task has failed.
+
+Example response for a completed task:
+
+```json
+{
+ "status": "SUCCESS",
+ "result": {
+ "output": "..."
+ }
+}
+```
diff --git a/docs/docs/guidelines/custom-component.mdx b/docs/docs/guidelines/custom-component.mdx
index 816f8356b..4f18d0375 100644
--- a/docs/docs/guidelines/custom-component.mdx
+++ b/docs/docs/guidelines/custom-component.mdx
@@ -387,7 +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
```
diff --git a/docs/docs/guidelines/login.mdx b/docs/docs/guidelines/login.mdx
new file mode 100644
index 000000000..85ca1371f
--- /dev/null
+++ b/docs/docs/guidelines/login.mdx
@@ -0,0 +1,128 @@
+import ThemedImage from "@theme/ThemedImage";
+import useBaseUrl from "@docusaurus/useBaseUrl";
+import ZoomableImage from "/src/theme/ZoomableImage.js";
+import ReactPlayer from "react-player";
+import Admonition from "@theme/Admonition";
+
+# Sign up and Sign in
+
+## Introduction
+
+The login functionality in Langflow serves to authenticate users and protect sensitive routes in the application. Starting from version 0.5, Langflow introduces an enhanced login mechanism that is governed by a few environment variables. This allows new secure features.
+
+## Environment Variables
+
+The following environment variables are crucial in configuring the login settings:
+
+- _`LANGFLOW_AUTO_LOGIN`_: Determines whether Langflow should automatically log users in. Default is `True`.
+- _`LANGFLOW_SUPERUSER`_: The username of the superuser.
+- _`LANGFLOW_SUPERUSER_PASSWORD`_: The password for the superuser.
+- _`LANGFLOW_SECRET_KEY`_: A key used for encrypting the superuser's password.
+- _`LANGFLOW_NEW_USER_IS_ACTIVE`_: Determines whether new users are automatically activated. Default is `False`.
+
+All of these variables can be passed to the CLI command _`langflow run`_ through the _`--env-file`_ option. For example:
+
+```bash
+langflow run --env-file .env
+```
+
+
+ It is critical not to expose these environment variables in your code
+ repository. Always set them securely in your deployment environment, for
+ example, using Docker secrets, Kubernetes ConfigMaps/Secrets, or dedicated
+ secure environment configuration systems like AWS Secrets Manager.
+
+
+### _`LANGFLOW_AUTO_LOGIN`_
+
+By default, this variable is set to `True`. When enabled (`True`), Langflow operates as it did in versions prior to 0.5โautomatic login without requiring explicit user authentication.
+
+To disable automatic login and enforce user authentication:
+
+```bash
+export LANGFLOW_AUTO_LOGIN=False
+```
+
+### _`LANGFLOW_SUPERUSER`_ and _`LANGFLOW_SUPERUSER_PASSWORD`_
+
+These environment variables are only relevant when `LANGFLOW_AUTO_LOGIN` is set to `False`. They specify the username and password for the superuser, which is essential for administrative tasks.
+
+To create a superuser manually:
+
+```bash
+export LANGFLOW_SUPERUSER=admin
+export LANGFLOW_SUPERUSER_PASSWORD=securepassword
+```
+
+You can also use the CLI command `langflow superuser` to set up a superuser interactively.
+
+### _`LANGFLOW_SECRET_KEY`_
+
+This environment variable holds a secret key used for encrypting the superuser's password. Make sure to set this to a secure, randomly generated string.
+
+```bash
+export LANGFLOW_SECRET_KEY=randomly_generated_secure_key
+```
+
+### _`LANGFLOW_NEW_USER_IS_ACTIVE`_
+
+By default, this variable is set to `False`. When enabled (`True`), new users are automatically activated and can log in without requiring explicit activation by the superuser.
+
+## Command-Line Interface
+
+Langflow provides a command-line utility for managing superusers:
+
+```bash
+langflow superuser
+```
+
+This command prompts you to enter the username and password for the superuser, unless they are already set using environment variables.
+
+## Sign-up
+
+With _`LANGFLOW_AUTO_LOGIN`_ set to _`False`_, Langflow requires users to sign up before they can log in. The sign-up page is the default landing page when a user visits Langflow for the first time.
+
+
+ This implementation is still in development. Contributions are welcome!
+
+
+The Async API is an implementation of the Langflow API that uses [Celery](https://docs.celeryproject.org/en/stable/)
+to run the tasks asynchronously, using a message broker to send and receive messages, a result backend to store the results and a cache to store the task states and session data.
+
+### Configuration
+
+The folder _`./deploy`_ in the [Github repository](https://github.com/logspace-ai/langflow) contains a _`.env.example`_ file that can be used to configure a Langflow deployment.
+The file contains the variables required to configure a Celery worker queue, Redis cache and result backend and a RabbitMQ message broker.
+
+To set it up locally you can copy the file to _`.env`_ and run the following command:
+
+```bash
+docker compose up -d
+```
+
+This will set up the following containers:
+
+- Langflow API
+- Celery worker
+- RabbitMQ message broker
+- Redis cache
+- PostgreSQL database
+- PGAdmin
+- Flower
+- Traefik
+- Grafana
+- Prometheus
+
+### Testing
+
+To run the tests for the Async API, you can run the following command:
+
+```bash
+docker compose -f docker-compose.with_tests.yml up --exit-code-from tests tests result_backend broker celeryworker db --build
+```
diff --git a/docs/docs/guides/superuser.mdx b/docs/docs/guides/superuser.mdx
new file mode 100644
index 000000000..04e0f96af
--- /dev/null
+++ b/docs/docs/guides/superuser.mdx
@@ -0,0 +1,7 @@
+import ThemedImage from "@theme/ThemedImage";
+import useBaseUrl from "@docusaurus/useBaseUrl";
+import ZoomableImage from "/src/theme/ZoomableImage.js";
+import ReactPlayer from "react-player";
+
+Now, we need to explain what are the permissions the superuser gets. Once logged in, they can activate new users,
+edit them,
diff --git a/docs/docs/index.mdx b/docs/docs/index.mdx
index 7be04549c..f0fee5009 100644
--- a/docs/docs/index.mdx
+++ b/docs/docs/index.mdx
@@ -7,10 +7,11 @@ import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
{" "}
+
"]
maintainers = [
@@ -19,24 +19,24 @@ 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"
[tool.poetry.dependencies]
-pandas = "^2.0.0"
python = ">=3.9,<3.11"
-fastapi = "^0.100.0"
-uvicorn = "^0.22.0"
+fastapi = "^0.103.0"
+uvicorn = "^0.23.0"
beautifulsoup4 = "^4.12.2"
google-search-results = "^2.4.1"
google-api-python-client = "^2.79.0"
typer = "^0.9.0"
gunicorn = "^21.2.0"
-langchain = "^0.0.271"
+langchain = "^0.0.312"
openai = "^0.27.8"
-chromadb = "^0.3.0"
+pandas = "2.0.3"
+chromadb = "^0.3.21"
huggingface-hub = { version = "^0.16.0", extras = ["inference"] }
rich = "^13.5.0"
llama-cpp-python = { version = "~0.1.0", optional = true }
@@ -49,21 +49,19 @@ fake-useragent = "^1.2.1"
docstring-parser = "^0.15"
psycopg2-binary = "^2.9.6"
pyarrow = "^12.0.0"
-tiktoken = "~0.4.0"
+tiktoken = "~0.5.0"
wikipedia = "^1.4.0"
-langchain-serve = { version = ">0.0.51", optional = true }
qdrant-client = "^1.4.0"
websockets = "^10.3"
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 = "^4.21.0"
+cohere = "^4.27.0"
python-multipart = "^0.0.6"
sqlmodel = "^0.0.8"
faiss-cpu = "^1.7.4"
anthropic = "^0.3.0"
-
orjson = "3.9.3"
multiprocess = "^0.70.14"
cachetools = "^5.3.1"
@@ -78,15 +76,28 @@ psycopg = "^3.1.9"
psycopg-binary = "^3.1.9"
fastavro = "^1.8.0"
langchain-experimental = "^0.0.8"
+celery = { extras = ["redis"], version = "^5.3.1", optional = true }
+redis = { version = "^4.6.0", optional = true }
+flower = { version = "^2.0.0", optional = true }
+alembic = "^1.12.0"
+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"
+metal-sdk = "^2.2.0"
markupsafe = "^2.1.3"
extract-msg = "^0.45.0"
jq = "^1.6.0"
+boto3 = "^1.28.63"
+numexpr = "^2.8.6"
+qianfan = "0.0.5"
[tool.poetry.group.dev.dependencies]
+types-redis = "^4.6.0.5"
black = "^23.1.0"
ipykernel = "^6.21.2"
mypy = "^1.1.1"
@@ -100,11 +111,18 @@ 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"
+locust = "^2.16.1"
+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]
-deploy = ["langchain-serve"]
+deploy = ["langchain-serve", "celery", "redis", "flower"]
local = ["llama-cpp-python", "sentence-transformers", "ctransformers"]
all = ["deploy", "local"]
@@ -116,6 +134,7 @@ testpaths = ["tests", "integration"]
console_output_style = "progress"
filterwarnings = ["ignore::DeprecationWarning"]
log_cli = true
+markers = ["async_test"]
[tool.ruff]
diff --git a/scripts/deploy_langflow_gcp.sh b/scripts/gcp/deploy_langflow_gcp.sh
similarity index 100%
rename from scripts/deploy_langflow_gcp.sh
rename to scripts/gcp/deploy_langflow_gcp.sh
diff --git a/scripts/deploy_langflow_gcp_spot.sh b/scripts/gcp/deploy_langflow_gcp_spot.sh
similarity index 100%
rename from scripts/deploy_langflow_gcp_spot.sh
rename to scripts/gcp/deploy_langflow_gcp_spot.sh
diff --git a/scripts/walkthroughtutorial.md b/scripts/gcp/walkthroughtutorial.md
similarity index 100%
rename from scripts/walkthroughtutorial.md
rename to scripts/gcp/walkthroughtutorial.md
diff --git a/scripts/walkthroughtutorial_spot.md b/scripts/gcp/walkthroughtutorial_spot.md
similarity index 100%
rename from scripts/walkthroughtutorial_spot.md
rename to scripts/gcp/walkthroughtutorial_spot.md
diff --git a/src/backend/Dockerfile b/src/backend/Dockerfile
index 13f2dc62e..8257d89b0 100644
--- a/src/backend/Dockerfile
+++ b/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" ]
diff --git a/src/backend/langflow/__init__.py b/src/backend/langflow/__init__.py
index 5920369e2..d3afbb4af 100644
--- a/src/backend/langflow/__init__.py
+++ b/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_service
from langflow.processing.process import load_flow_from_json
from langflow.interface.custom.custom_component import CustomComponent
@@ -10,4 +12,4 @@ except metadata.PackageNotFoundError:
__version__ = ""
del metadata # optional, avoids polluting the results of dir(__package__)
-__all__ = ["load_flow_from_json", "cache_manager", "CustomComponent"]
+__all__ = ["load_flow_from_json", "cache_service", "CustomComponent"]
diff --git a/src/backend/langflow/__main__.py b/src/backend/langflow/__main__.py
index d5472c1c9..1bdcf97f2 100644
--- a/src/backend/langflow/__main__.py
+++ b/src/backend/langflow/__main__.py
@@ -1,23 +1,69 @@
+import platform
+import socket
import sys
import time
-import httpx
-from langflow.utils.util import get_number_of_workers
-from multiprocess import Process # type: ignore
-import platform
+import webbrowser
from pathlib import Path
from typing import Optional
-import socket
-from rich.panel import Panel
+
+import httpx
+import typer
+from dotenv import load_dotenv
+from langflow.main import setup_app
+from langflow.services.database.utils import session_getter
+from langflow.services.getters import get_db_service, get_settings_service
+from langflow.services.utils import initialize_services, initialize_settings_service
+from langflow.utils.logger import configure, logger
+from multiprocess import Process, cpu_count # type: ignore
from rich import box
from rich import print as rprint
-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
+from rich.panel import Panel
+from rich.table import Table
-app = typer.Typer()
+console = Console()
+
+app = typer.Typer(no_args_is_help=True)
+
+
+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 set_var_for_macos_issue():
+ # OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES
+ # we need to set this var is we are running on MacOS
+ # otherwise we get an error when running gunicorn
+
+ if platform.system() in ["Darwin"]:
+ import os
+
+ os.environ["OBJC_DISABLE_INITIALIZE_FORK_SAFETY"] = "YES"
+ # https://stackoverflow.com/questions/75747888/uwsgi-segmentation-fault-with-flask-python-app-behind-nginx-after-running-for-2 # noqa
+ os.environ["no_proxy"] = "*" # to avoid error with gunicorn
+ logger.debug("Set OBJC_DISABLE_INITIALIZE_FORK_SAFETY to YES to avoid error")
def update_settings(
@@ -30,74 +76,29 @@ def update_settings(
"""Update the settings from a config file."""
# Check for database_url in the environment variables
-
+ initialize_settings_service()
+ settings_service = get_settings_service()
if config:
logger.debug(f"Loading settings from {config}")
- settings.update_from_yaml(config, dev=dev)
+ settings_service.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_service.settings.update_settings(REMOVE_API_KEYS=remove_api_keys)
if cache:
logger.debug(f"Setting cache to {cache}")
- settings.update_settings(CACHE=cache)
+ settings_service.settings.update_settings(CACHE=cache)
if components_path:
logger.debug(f"Adding component path {components_path}")
- settings.update_settings(COMPONENTS_PATH=components_path)
-
-
-def serve_on_jcloud():
- """
- Deploy Langflow server on Jina AI Cloud
- """
- import asyncio
- from importlib.metadata import version as mod_version
-
- import click
-
- try:
- from lcserve.__main__ import serve_on_jcloud # type: ignore
- except ImportError:
- click.secho(
- "๐จ Please install langchain-serve to deploy Langflow server on Jina AI Cloud "
- "using `pip install langchain-serve`",
- fg="red",
- )
- return
-
- app_name = "langflow.lcserve:app"
- app_dir = str(Path(__file__).parent)
- version = mod_version("langflow")
- base_image = "jinaai+docker://deepankarm/langflow"
-
- click.echo("๐ Deploying Langflow server on Jina AI Cloud")
- app_id = asyncio.run(
- serve_on_jcloud(
- fastapi_app_str=app_name,
- app_dir=app_dir,
- uses=f"{base_image}:{version}",
- name="langflow",
- )
- )
- click.secho(
- "๐ Langflow server successfully deployed on Jina AI Cloud ๐", fg="green"
- )
- click.secho(
- "๐ Click on the link to open the server (please allow ~1-2 minutes for the server to startup): ",
- nl=False,
- fg="green",
- )
- click.secho(f"https://{app_id}.wolf.jina.ai/", fg="blue")
- click.secho("๐ Read more about managing the server: ", nl=False, fg="green")
- click.secho("https://github.com/jina-ai/langchain-serve", fg="blue")
+ settings_service.settings.update_settings(COMPONENTS_PATH=components_path)
@app.command()
-def serve(
+def run(
host: str = typer.Option(
"127.0.0.1", help="Host to bind the server to.", envvar="LANGFLOW_HOST"
),
workers: int = typer.Option(
- 2, help="Number of worker processes.", envvar="LANGFLOW_WORKERS"
+ 1, help="Number of worker processes.", envvar="LANGFLOW_WORKERS"
),
timeout: int = typer.Option(300, help="Worker timeout in seconds."),
port: int = typer.Option(7860, help="Port to listen on.", envvar="LANGFLOW_PORT"),
@@ -106,7 +107,9 @@ 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."
@@ -122,7 +125,6 @@ def serve(
help="Type of cache to use. (InMemoryCache, 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)"),
# This variable does not work but is set by the .env file
# and works with Pydantic
@@ -146,17 +148,22 @@ 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.
+ Run the Langflow.
"""
+
+ set_var_for_macos_issue()
# override env variables with .env file
+
if env_file:
load_dotenv(env_file, override=True)
- if jcloud:
- return serve_on_jcloud()
-
configure(log_level=log_level, log_file=log_file)
update_settings(
config,
@@ -167,7 +174,7 @@ 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)
@@ -175,10 +182,13 @@ def serve(
options = {
"bind": f"{host}:{port}",
"workers": get_number_of_workers(workers),
- "worker_class": "uvicorn.workers.UvicornWorker",
"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 +309,53 @@ 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."
+ ),
+ log_level: str = typer.Option(
+ "critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"
+ ),
+):
+ """
+ Create a superuser.
+ """
+ configure(log_level=log_level)
+ initialize_services()
+ db_service = get_db_service()
+ with session_getter(db_service) 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: User = session.query(User).filter(User.username == username).first()
+ if user is None or not user.is_superuser:
+ 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(True, help="Run migrations in test mode.")):
+ """
+ Run or test migrations.
+ """
+ initialize_services()
+ db_service = get_db_service()
+ if not test:
+ db_service.run_migrations()
+ results = db_service.run_migrations_test()
+ display_results(results)
+
+
def main():
app()
diff --git a/src/backend/langflow/alembic.ini b/src/backend/langflow/alembic.ini
new file mode 100644
index 000000000..379661422
--- /dev/null
+++ b/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
diff --git a/src/backend/langflow/alembic/README b/src/backend/langflow/alembic/README
new file mode 100644
index 000000000..98e4f9c44
--- /dev/null
+++ b/src/backend/langflow/alembic/README
@@ -0,0 +1 @@
+Generic single-database configuration.
\ No newline at end of file
diff --git a/src/backend/langflow/alembic/env.py b/src/backend/langflow/alembic/env.py
new file mode 100644
index 000000000..e606036f1
--- /dev/null
+++ b/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()
diff --git a/src/backend/langflow/alembic/script.py.mako b/src/backend/langflow/alembic/script.py.mako
new file mode 100644
index 000000000..6ce335109
--- /dev/null
+++ b/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"}
diff --git a/src/backend/langflow/alembic/versions/260dbcc8b680_adds_tables.py b/src/backend/langflow/alembic/versions/260dbcc8b680_adds_tables.py
new file mode 100644
index 000000000..53e049a05
--- /dev/null
+++ b/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 ###
diff --git a/src/backend/langflow/alembic/versions/67cc006d50bf_add_profile_image_column.py b/src/backend/langflow/alembic/versions/67cc006d50bf_add_profile_image_column.py
new file mode 100644
index 000000000..ca0aa0542
--- /dev/null
+++ b/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 ###
diff --git a/src/backend/langflow/alembic/versions/eb5866d51fd2_change_columns_to_be_nullable.py b/src/backend/langflow/alembic/versions/eb5866d51fd2_change_columns_to_be_nullable.py
new file mode 100644
index 000000000..cb126c926
--- /dev/null
+++ b/src/backend/langflow/alembic/versions/eb5866d51fd2_change_columns_to_be_nullable.py
@@ -0,0 +1,85 @@
+"""Change columns to be nullable
+
+Revision ID: eb5866d51fd2
+Revises: 67cc006d50bf
+Create Date: 2023-10-04 10:18:25.640458
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy import exc
+import sqlmodel # noqa: F401
+
+# revision identifiers, used by Alembic.
+revision: str = "eb5866d51fd2"
+down_revision: Union[str, None] = "67cc006d50bf"
+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! ###
+ connection = op.get_bind()
+ try:
+ op.drop_table("flowstyle")
+ with op.batch_alter_table("component", schema=None) as batch_op:
+ batch_op.drop_index("ix_component_frontend_node_id")
+ batch_op.drop_index("ix_component_name")
+ except exc.SQLAlchemyError:
+ connection.execute("ROLLBACK")
+ except Exception:
+ pass
+
+ try:
+ op.drop_table("component")
+ except exc.SQLAlchemyError:
+ connection.execute("ROLLBACK")
+ except Exception:
+ pass
+ # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+ # ### commands auto generated by Alembic - please adjust! ###
+ try:
+ op.create_table(
+ "component",
+ sa.Column("id", sa.CHAR(length=32), nullable=False),
+ sa.Column("frontend_node_id", sa.CHAR(length=32), nullable=False),
+ sa.Column("name", sa.VARCHAR(), nullable=False),
+ sa.Column("description", sa.VARCHAR(), nullable=True),
+ sa.Column("python_code", sa.VARCHAR(), nullable=True),
+ sa.Column("return_type", sa.VARCHAR(), nullable=True),
+ sa.Column("is_disabled", sa.BOOLEAN(), nullable=False),
+ sa.Column("is_read_only", sa.BOOLEAN(), nullable=False),
+ sa.Column("create_at", sa.DATETIME(), nullable=False),
+ sa.Column("update_at", sa.DATETIME(), nullable=False),
+ sa.PrimaryKeyConstraint("id"),
+ )
+ with op.batch_alter_table("component", schema=None) as batch_op:
+ batch_op.create_index("ix_component_name", ["name"], unique=False)
+ batch_op.create_index(
+ "ix_component_frontend_node_id", ["frontend_node_id"], unique=False
+ )
+ except Exception:
+ pass
+
+ try:
+ op.create_table(
+ "flowstyle",
+ sa.Column("color", sa.VARCHAR(), nullable=False),
+ sa.Column("emoji", sa.VARCHAR(), nullable=False),
+ sa.Column("flow_id", sa.CHAR(length=32), nullable=True),
+ sa.Column("id", sa.CHAR(length=32), nullable=False),
+ sa.ForeignKeyConstraint(
+ ["flow_id"],
+ ["flow.id"],
+ ),
+ sa.PrimaryKeyConstraint("id"),
+ sa.UniqueConstraint("id"),
+ )
+ except Exception:
+ pass
+ # ### end Alembic commands ###
diff --git a/src/backend/langflow/api/router.py b/src/backend/langflow/api/router.py
index b9c51c11e..dbaf20e75 100644
--- a/src/backend/langflow/api/router.py
+++ b/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)
diff --git a/src/backend/langflow/api/utils.py b/src/backend/langflow/api/utils.py
index 0fb53e541..c519fffed 100644
--- a/src/backend/langflow/api/utils.py
+++ b/src/backend/langflow/api/utils.py
@@ -59,33 +59,6 @@ def build_input_keys_response(langchain_object, artifacts):
return input_keys_response
-def merge_nested_dicts(dict1, dict2):
- for key, value in dict2.items():
- if isinstance(value, dict) and isinstance(dict1.get(key), dict):
- dict1[key] = merge_nested_dicts(dict1[key], value)
- 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) + ")"
diff --git a/src/backend/langflow/api/v1/__init__.py b/src/backend/langflow/api/v1/__init__.py
index f001152a9..9335a4607 100644
--- a/src/backend/langflow/api/v1/__init__.py
+++ b/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",
]
diff --git a/src/backend/langflow/api/v1/api_key.py b/src/backend/langflow/api/v1/api_key.py
new file mode 100644
index 000000000..7f5916d06
--- /dev/null
+++ b/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.getters 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
diff --git a/src/backend/langflow/api/v1/base.py b/src/backend/langflow/api/v1/base.py
index 71cac5412..f2c2f3f59 100644
--- a/src/backend/langflow/api/v1/base.py
+++ b/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 = {
diff --git a/src/backend/langflow/api/v1/callback.py b/src/backend/langflow/api/v1/callback.py
index 69dbf5082..787ca9680 100644
--- a/src/backend/langflow/api/v1/callback.py
+++ b/src/backend/langflow/api/v1/callback.py
@@ -1,55 +1,33 @@
import asyncio
+from uuid import UUID
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
-from langflow.api.v1.schemas import ChatResponse
+from langflow.api.v1.schemas import ChatResponse, PromptResponse
-from typing import Any, Dict, List, Union
-from fastapi import WebSocket
+from typing import Any, Dict, List, Optional
+from langflow.services.getters import get_chat_service
-from langchain.schema import AgentAction, LLMResult, AgentFinish
-from langflow.utils.logger import logger
+from langflow.utils.util import remove_ansi_escape_codes
+from langchain.schema import AgentAction, AgentFinish
+from loguru import logger
# https://github.com/hwchase17/chat-langchain/blob/master/callback.py
class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler):
"""Callback handler for streaming LLM responses."""
- def __init__(self, websocket: WebSocket):
- self.websocket = websocket
+ def __init__(self, client_id: str):
+ self.chat_service = get_chat_service()
+ self.client_id = client_id
+ self.websocket = self.chat_service.active_connections[self.client_id]
async def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
resp = ChatResponse(message=token, type="stream", intermediate_steps="")
await self.websocket.send_json(resp.dict())
- async def on_llm_start(
- self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
- ) -> Any:
- """Run when LLM starts running."""
-
- async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> Any:
- """Run when LLM ends running."""
-
- async def on_llm_error(
- self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
- ) -> Any:
- """Run when LLM errors."""
-
- async def on_chain_start(
- self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
- ) -> Any:
- """Run when chain starts running."""
-
- async def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> Any:
- """Run when chain ends running."""
-
- async def on_chain_error(
- self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
- ) -> Any:
- """Run when chain errors."""
-
async def on_tool_start(
self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
) -> Any:
@@ -95,8 +73,14 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler):
logger.error(f"Error sending response: {exc}")
async def on_tool_error(
- self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
- ) -> Any:
+ self,
+ error: BaseException,
+ *,
+ run_id: UUID,
+ parent_run_id: Optional[UUID] = None,
+ tags: Optional[List[str]] = None,
+ **kwargs: Any,
+ ) -> None:
"""Run when tool errors."""
async def on_text(self, text: str, **kwargs: Any) -> Any:
@@ -104,6 +88,14 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler):
# This runs when first sending the prompt
# to the LLM, adding it will send the final prompt
# to the frontend
+ if "Prompt after formatting" in text:
+ text = text.replace("Prompt after formatting:\n", "")
+ text = remove_ansi_escape_codes(text)
+ resp = PromptResponse(
+ prompt=text,
+ )
+ await self.websocket.send_json(resp.dict())
+ self.chat_service.chat_history.add_message(self.client_id, resp)
async def on_agent_action(self, action: AgentAction, **kwargs: Any):
log = f"Thought: {action.log}"
@@ -131,8 +123,10 @@ class AsyncStreamingLLMCallbackHandler(AsyncCallbackHandler):
class StreamingLLMCallbackHandler(BaseCallbackHandler):
"""Callback handler for streaming LLM responses."""
- def __init__(self, websocket):
- self.websocket = websocket
+ def __init__(self, client_id: str):
+ self.chat_service = get_chat_service()
+ self.client_id = client_id
+ self.websocket = self.chat_service.active_connections[self.client_id]
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
resp = ChatResponse(message=token, type="stream", intermediate_steps="")
diff --git a/src/backend/langflow/api/v1/chat.py b/src/backend/langflow/api/v1/chat.py
index e71ddf81f..0f277fbb7 100644
--- a/src/backend/langflow/api/v1/chat.py
+++ b/src/backend/langflow/api/v1/chat.py
@@ -1,57 +1,99 @@
-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 cachetools import LRUCache
+from langflow.services.auth.utils import get_current_active_user, get_current_user
+from langflow.services.cache.utils import update_build_status
+from loguru import logger
+from langflow.services.getters import get_chat_service, get_session, get_cache_service
+from sqlmodel import Session
+from langflow.services.chat.manager import ChatService
+from langflow.services.cache.manager import BaseCacheService
+
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_service: "ChatService" = Depends(get_chat_service),
+):
"""Websocket endpoint for chat."""
try:
- if client_id in chat_manager.in_memory_cache:
- await chat_manager.handle_websocket(client_id, websocket)
+ 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_service.cache_service:
+ await chat_service.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}")
+ logger.error(f"Websocket exrror: {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_service: "ChatService" = Depends(get_chat_service),
+ cache_service: "BaseCacheService" = Depends(get_cache_service),
+):
"""Initialize the build by storing graph data and returning a unique session ID."""
-
try:
if flow_id is None:
raise ValueError("No ID provided")
# Check if already building
if (
- flow_id in flow_data_store
- and flow_data_store[flow_id]["status"] == BuildStatus.IN_PROGRESS
+ flow_id in cache_service
+ and isinstance(cache_service[flow_id], dict)
+ and cache_service[flow_id].get("status") == BuildStatus.IN_PROGRESS
):
return InitResponse(flowId=flow_id)
# Delete from cache if already exists
- if flow_id in chat_manager.in_memory_cache:
- with chat_manager.in_memory_cache._lock:
- chat_manager.in_memory_cache.delete(flow_id)
- logger.debug(f"Deleted flow {flow_id} from cache")
- flow_data_store[flow_id] = {
+ if flow_id in chat_service.cache_service:
+ chat_service.cache_service.delete(flow_id)
+ logger.debug(f"Deleted flow {flow_id} from cache")
+ cache_service[flow_id] = {
"graph_data": graph_data,
"status": BuildStatus.STARTED,
+ "user_id": current_user.id,
}
return InitResponse(flowId=flow_id)
@@ -61,12 +103,14 @@ async def init_build(graph_data: dict, flow_id: str):
@router.get("/build/{flow_id}/status", response_model=BuiltResponse)
-async def build_status(flow_id: str):
- """Check the flow_id is in the flow_data_store."""
+async def build_status(
+ flow_id: str, cache_service: "BaseCacheService" = Depends(get_cache_service)
+):
+ """Check the flow_id is in the cache_service."""
try:
built = (
- flow_id in flow_data_store
- and flow_data_store[flow_id]["status"] == BuildStatus.SUCCESS
+ flow_id in cache_service
+ and cache_service[flow_id]["status"] == BuildStatus.SUCCESS
)
return BuiltResponse(
@@ -79,24 +123,29 @@ 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_service: "ChatService" = Depends(get_chat_service),
+ cache_service: "BaseCacheService" = Depends(get_cache_service),
+):
"""Stream the build process based on stored flow data."""
async def event_stream(flow_id):
final_response = {"end_of_stream": True}
artifacts = {}
try:
- if flow_id not in flow_data_store:
+ if flow_id not in cache_service:
error_message = "Invalid session ID"
yield str(StreamData(event="error", data={"error": error_message}))
return
- if flow_data_store[flow_id].get("status") == BuildStatus.IN_PROGRESS:
+ if cache_service[flow_id].get("status") == BuildStatus.IN_PROGRESS:
error_message = "Already building"
yield str(StreamData(event="error", data={"error": error_message}))
return
- graph_data = flow_data_store[flow_id].get("graph_data")
+ graph_data = cache_service[flow_id].get("graph_data")
+ cache_service[flow_id]["user_id"]
if not graph_data:
error_message = "No data provided"
@@ -109,7 +158,7 @@ async def stream_build(flow_id: str):
graph = Graph.from_payload(graph_data)
number_of_nodes = len(graph.nodes)
- flow_data_store[flow_id]["status"] = BuildStatus.IN_PROGRESS
+ update_build_status(cache_service, flow_id, BuildStatus.IN_PROGRESS)
for i, vertex in enumerate(graph.generator_build(), 1):
try:
@@ -117,7 +166,10 @@ async def stream_build(flow_id: str):
"log": f"Building node {vertex.vertex_type}",
}
yield str(StreamData(event="log", data=log_dict))
- vertex.build()
+ if vertex.is_task:
+ vertex = try_running_celery_task(vertex)
+ else:
+ vertex.build()
params = vertex._built_object_repr()
valid = True
logger.debug(f"Building node {str(vertex.vertex_type)}")
@@ -133,16 +185,20 @@ async def stream_build(flow_id: str):
logger.exception(exc)
params = str(exc)
valid = False
- flow_data_store[flow_id]["status"] = BuildStatus.FAILURE
+ update_build_status(cache_service, flow_id, BuildStatus.FAILURE)
- response = {
- "valid": valid,
- "params": params,
- "id": vertex.id,
- "progress": round(i / number_of_nodes, 2),
- }
+ vertex_id = (
+ vertex.parent_node_id if vertex.parent_is_top_level else vertex.id
+ )
+ if vertex_id in graph.top_level_nodes:
+ response = {
+ "valid": valid,
+ "params": params,
+ "id": vertex_id,
+ "progress": round(i / number_of_nodes, 2),
+ }
- yield str(StreamData(event="message", data=response))
+ yield str(StreamData(event="message", data=response))
langchain_object = graph.build()
# Now we need to check the input_keys to send them to the client
@@ -157,15 +213,15 @@ async def stream_build(flow_id: str):
"handle_keys": [],
}
yield str(StreamData(event="message", data=input_keys_response))
-
- chat_manager.set_cache(flow_id, langchain_object)
+ chat_service.set_cache(flow_id, langchain_object)
# We need to reset the chat history
- chat_manager.chat_history.empty_history(flow_id)
- flow_data_store[flow_id]["status"] = BuildStatus.SUCCESS
+ chat_service.chat_history.empty_history(flow_id)
+ update_build_status(cache_service, flow_id, BuildStatus.SUCCESS)
except Exception as exc:
logger.exception(exc)
logger.error("Error while building the flow: %s", exc)
- flow_data_store[flow_id]["status"] = BuildStatus.FAILURE
+
+ update_build_status(cache_service, flow_id, BuildStatus.FAILURE)
yield str(StreamData(event="error", data={"error": str(exc)}))
finally:
yield str(StreamData(event="message", data=final_response))
@@ -175,3 +231,19 @@ async def stream_build(flow_id: str):
except Exception as exc:
logger.error(f"Error streaming build: {exc}")
raise HTTPException(status_code=500, detail=str(exc))
+
+
+def try_running_celery_task(vertex):
+ # Try running the task in celery
+ # and set the task_id to the local vertex
+ # if it fails, run the task locally
+ try:
+ from langflow.worker import build_vertex
+
+ task = build_vertex.delay(vertex)
+ vertex.task_id = task.id
+ except Exception as exc:
+ logger.debug(f"Error running task in celery: {exc}")
+ vertex.task_id = None
+ vertex.build()
+ return vertex
diff --git a/src/backend/langflow/api/v1/components.py b/src/backend/langflow/api/v1/components.py
index 1e34da2aa..d2b39dfd2 100644
--- a/src/backend/langflow/api/v1/components.py
+++ b/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.database.base import get_session
+from langflow.services.database.models.component import Component, ComponentModel
+from langflow.services.getters import get_session
from sqlmodel import Session, select
from fastapi import APIRouter, Depends, HTTPException
from sqlalchemy.exc import IntegrityError
diff --git a/src/backend/langflow/api/v1/endpoints.py b/src/backend/langflow/api/v1/endpoints.py
index 2c109224b..9b1213c2c 100644
--- a/src/backend/langflow/api/v1/endpoints.py
+++ b/src/backend/langflow/api/v1/endpoints.py
@@ -1,89 +1,103 @@
from http import HTTPStatus
-from typing import Annotated, Optional
+from typing import Annotated, 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.database.models.flow import Flow
+
+from langflow.services.cache.utils import save_uploaded_file
+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.settings import settings
-
-from fastapi import APIRouter, Depends, HTTPException, UploadFile, Body
-
+from langflow.services.database.models.user.user import User
+from langflow.services.getters import (
+ get_session_service,
+ get_settings_service,
+ get_task_service,
+)
+from loguru import logger
+from fastapi import APIRouter, Depends, HTTPException, UploadFile, Body, status
+import sqlalchemy as sa
from langflow.interface.custom.custom_component import CustomComponent
from langflow.api.v1.schemas import (
ProcessResponse,
+ TaskResponse,
+ TaskStatusResponse,
UploadFileResponse,
CustomComponentCode,
)
-from langflow.api.utils import merge_nested_dicts_with_renaming
-from langflow.interface.types import (
- build_langchain_types_dict,
- build_langchain_template_custom_component,
- build_langchain_custom_component_list_from_path,
-)
+from langflow.services.getters import get_session
+
+try:
+ from langflow.worker import process_graph_cached_task
+except ImportError:
+
+ def process_graph_cached_task(*args, **kwargs):
+ raise NotImplementedError("Celery is not installed")
+
-from langflow.database.base import get_session
from sqlmodel import Session
+
+from langflow.services.task.manager import TaskService
+
# build router
router = APIRouter(tags=["Base"])
-@router.get("/all")
-def get_all():
+@router.get("/all", dependencies=[Depends(get_current_active_user)])
+def get_all(
+ settings_service=Depends(get_settings_service),
+):
+ from langflow.interface.types import get_all_types_dict
+
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 = {}
- if settings.COMPONENTS_PATH:
- logger.info(f"Building custom components from {settings.COMPONENTS_PATH}")
-
- custom_component_dicts = []
- processed_paths = []
- for path in 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:
- logger.debug(
- {key: len(value) for key, value in custom_component_dict.items()}
- )
- custom_components_from_file = merge_nested_dicts_with_renaming(
- custom_components_from_file, custom_component_dict
- )
-
- return merge_nested_dicts_with_renaming(
- native_components, custom_components_from_file
- )
+ try:
+ return get_all_types_dict(settings_service)
+ except Exception as exc:
+ raise HTTPException(status_code=500, detail=str(exc)) from exc
# For backwards compatibility we will keep the old endpoint
-@router.post("/predict/{flow_id}", response_model=ProcessResponse)
-@router.post("/process/{flow_id}", response_model=ProcessResponse)
-async def process_flow(
+@router.post(
+ "/predict/{flow_id}",
+ response_model=ProcessResponse,
+ dependencies=[Depends(api_key_security)],
+)
+@router.post(
+ "/process/{flow_id}",
+ response_model=ProcessResponse,
+)
+async def process(
+ 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
+ task_service: "TaskService" = Depends(get_task_service),
+ api_key_user: User = Depends(api_key_security),
+ sync: Annotated[bool, Body(embed=True)] = True, # noqa: F821
):
"""
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")
@@ -95,16 +109,94 @@ 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)
+ if sync:
+ task_id, result = await task_service.launch_and_await_task(
+ process_graph_cached_task
+ if task_service.use_celery
+ else process_graph_cached,
+ graph_data,
+ inputs,
+ clear_cache,
+ session_id,
+ )
+ if isinstance(result, dict) and "result" in result:
+ task_result = result["result"]
+ session_id = result["session_id"]
+ elif hasattr(result, "result") and hasattr(result, "session_id"):
+ task_result = result.result
+
+ session_id = result.session_id
+ else:
+ logger.warning(
+ "This is an experimental feature and may not work as expected."
+ "Please report any issues to our GitHub repository."
+ )
+ if session_id is None:
+ # Generate a session ID
+ session_id = get_session_service().generate_key(
+ session_id=session_id, data_graph=graph_data
+ )
+ task_id, task = await task_service.launch_task(
+ process_graph_cached_task
+ if task_service.use_celery
+ else process_graph_cached,
+ graph_data,
+ inputs,
+ clear_cache,
+ session_id,
+ )
+ task_result = task.status
+
+ if task_id:
+ task_response = TaskResponse(id=task_id, href=f"api/v1/task/{task_id}")
+ else:
+ task_response = None
+
return ProcessResponse(
- result=response,
+ result=task_result,
+ task=task_response,
+ session_id=session_id,
+ backend=task_service.backend_name,
)
+ 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)
raise HTTPException(status_code=500, detail=str(e)) from e
+@router.get("/task/{task_id}", response_model=TaskStatusResponse)
+async def get_task_status(task_id: str):
+ task_service = get_task_service()
+ task = task_service.get_task(task_id)
+ result = None
+ if task.ready():
+ result = task.result
+ if isinstance(result, dict) and "result" in result:
+ result = result["result"]
+ elif hasattr(result, "result"):
+ result = result.result
+
+ if task is None:
+ raise HTTPException(status_code=404, detail="Task not found")
+ return TaskStatusResponse(status=task.status, result=result)
+
+
@router.post(
"/upload/{flow_id}",
response_model=UploadFileResponse,
@@ -113,7 +205,7 @@ async def process_flow(
async def create_upload_file(file: UploadFile, flow_id: str):
# Cache file
try:
- file_path = save_uploaded_file(file.file, folder_name=flow_id)
+ file_path = save_uploaded_file(file, folder_name=flow_id)
return UploadFileResponse(
flowId=flow_id,
@@ -136,6 +228,10 @@ def get_version():
async def custom_component(
raw_code: CustomComponentCode,
):
+ from langflow.interface.types import (
+ build_langchain_template_custom_component,
+ )
+
extractor = CustomComponent(code=raw_code.code)
extractor.is_check_valid()
diff --git a/src/backend/langflow/api/v1/flow_styles.py b/src/backend/langflow/api/v1/flow_styles.py
deleted file mode 100644
index 40e292eb3..000000000
--- a/src/backend/langflow/api/v1/flow_styles.py
+++ /dev/null
@@ -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"}
diff --git a/src/backend/langflow/api/v1/flows.py b/src/backend/langflow/api/v1/flows.py
index 2ff57f4ef..9953db0db 100644
--- a/src/backend/langflow/api/v1/flows.py
+++ b/src/backend/langflow/api/v1/flows.py
@@ -1,19 +1,21 @@
from typing import List
from uuid import UUID
from fastapi.encoders import jsonable_encoder
-from langflow.settings import settings
+
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 langflow.services.getters import get_session
+from langflow.services.getters import get_settings_service
import orjson
-from sqlmodel import Session, select
+from sqlmodel import Session
from fastapi import APIRouter, Depends, HTTPException
from fastapi import File, UploadFile
@@ -23,48 +25,77 @@ 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)
-def read_flows(*, session: Session = Depends(get_session)):
+@router.get("/", response_model=list[FlowRead], status_code=200)
+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)
-def read_flow(*, session: Session = Depends(get_session), flow_id: UUID):
+@router.get("/{flow_id}", response_model=FlowRead, status_code=200)
+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):
- return flow
+ if user_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_service=Depends(get_settings_service),
):
"""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_service.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)
@@ -101,7 +143,10 @@ 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()
@@ -110,11 +155,19 @@ async def upload_file(
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)
diff --git a/src/backend/langflow/api/v1/login.py b/src/backend/langflow/api/v1/login.py
new file mode 100644
index 000000000..4dfc723b5
--- /dev/null
+++ b/src/backend/langflow/api/v1/login.py
@@ -0,0 +1,73 @@
+from sqlmodel import Session
+from fastapi import APIRouter, Depends, HTTPException, status
+from fastapi.security import OAuth2PasswordRequestForm
+
+from langflow.services.getters 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.getters import get_settings_service
+
+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)
+):
+ try:
+ user = authenticate_user(form_data.username, form_data.password, db)
+ except Exception as exc:
+ if isinstance(exc, HTTPException):
+ raise exc
+ raise HTTPException(
+ status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+ detail=str(exc),
+ ) from exc
+
+ if user:
+ 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_service=Depends(get_settings_service)
+):
+ if settings_service.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"},
+ )
diff --git a/src/backend/langflow/api/v1/schemas.py b/src/backend/langflow/api/v1/schemas.py
index de0348d38..37e7d712d 100644
--- a/src/backend/langflow/api/v1/schemas.py
+++ b/src/backend/langflow/api/v1/schemas.py
@@ -1,8 +1,12 @@
from enum import Enum
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
-from langflow.database.models.base import orjson_dumps
-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
@@ -43,10 +47,30 @@ class UpdateTemplateRequest(BaseModel):
template: dict
+class TaskResponse(BaseModel):
+ """Task response schema."""
+
+ id: Optional[str] = Field(None)
+ href: Optional[str] = Field(None)
+
+
class ProcessResponse(BaseModel):
"""Process response schema."""
- result: dict
+ result: Any
+ task: Optional[TaskResponse] = None
+ session_id: Optional[str] = None
+ backend: Optional[str] = None
+
+
+# TaskStatusResponse(
+# status=task.status, result=task.result if task.ready() else None
+# )
+class TaskStatusResponse(BaseModel):
+ """Task status response schema."""
+
+ status: str
+ result: Optional[Any] = None
class ChatMessage(BaseModel):
@@ -54,6 +78,7 @@ class ChatMessage(BaseModel):
is_bot: bool = False
message: Union[str, None, dict] = None
+ chatKey: Optional[str] = None
type: str = "human"
@@ -61,6 +86,7 @@ class ChatResponse(ChatMessage):
"""Chat response schema."""
intermediate_steps: str
+
type: str
is_bot: bool = True
files: list = []
@@ -72,6 +98,14 @@ class ChatResponse(ChatMessage):
return v
+class PromptResponse(ChatMessage):
+ """Prompt response schema."""
+
+ prompt: str
+ type: str = "prompt"
+ is_bot: bool = True
+
+
class FileResponse(ChatMessage):
"""File response schema."""
@@ -135,3 +169,32 @@ 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
diff --git a/src/backend/langflow/api/v1/users.py b/src/backend/langflow/api/v1/users.py
new file mode 100644
index 000000000..73c7346d9
--- /dev/null
+++ b/src/backend/langflow/api/v1/users.py
@@ -0,0 +1,169 @@
+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.getters import get_session, get_settings_service
+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),
+ settings_service=Depends(get_settings_service),
+) -> User:
+ """
+ Add a new user to the database.
+ """
+ new_user = User.from_orm(user)
+ try:
+ new_user.password = get_password_hash(user.password)
+ new_user.is_active = settings_service.auth_settings.NEW_USER_IS_ACTIVE
+ 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,
+ _: 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:
+ if not user.is_superuser:
+ raise HTTPException(
+ status_code=400, detail="You can't change your password here"
+ )
+ user_update.password = get_password_hash(user_update.password)
+
+ 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"}
diff --git a/src/backend/langflow/api/v1/validate.py b/src/backend/langflow/api/v1/validate.py
index e1c514aaa..65fb66bd2 100644
--- a/src/backend/langflow/api/v1/validate.py
+++ b/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)
diff --git a/src/backend/langflow/cache/__init__.py b/src/backend/langflow/cache/__init__.py
deleted file mode 100644
index 723aa9e18..000000000
--- a/src/backend/langflow/cache/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from langflow.cache.manager import cache_manager
-from langflow.cache.flow import InMemoryCache
-
-__all__ = [
- "cache_manager",
- "InMemoryCache",
-]
diff --git a/src/backend/langflow/cache/flow.py b/src/backend/langflow/cache/flow.py
deleted file mode 100644
index 6d8fee977..000000000
--- a/src/backend/langflow/cache/flow.py
+++ /dev/null
@@ -1,146 +0,0 @@
-import threading
-import time
-from collections import OrderedDict
-
-from langflow.cache.base import BaseCache
-
-
-class InMemoryCache(BaseCache):
- """
- A simple in-memory cache using an OrderedDict.
-
- This cache supports setting a maximum size and expiration time for cached items.
- When the cache is full, it uses a Least Recently Used (LRU) eviction policy.
- Thread-safe using a threading Lock.
-
- Attributes:
- max_size (int, optional): Maximum number of items to store in the cache.
- expiration_time (int, optional): Time in seconds after which a cached item expires. Default is 1 hour.
-
- Example:
-
- cache = InMemoryCache(max_size=3, expiration_time=5)
-
- # setting cache values
- cache.set("a", 1)
- cache.set("b", 2)
- cache["c"] = 3
-
- # getting cache values
- a = cache.get("a")
- b = cache["b"]
- """
-
- def __init__(self, max_size=None, expiration_time=60 * 60):
- """
- Initialize a new InMemoryCache instance.
-
- Args:
- max_size (int, optional): Maximum number of items to store in the cache.
- expiration_time (int, optional): Time in seconds after which a cached item expires. Default is 1 hour.
- """
- self._cache = OrderedDict()
- self._lock = threading.Lock()
- self.max_size = max_size
- self.expiration_time = expiration_time
-
- def get(self, key):
- """
- Retrieve an item from the cache.
-
- Args:
- key: The key of the item to retrieve.
-
- Returns:
- The value associated with the key, or None if the key is not found or the item has expired.
- """
- with self._lock:
- if key in self._cache:
- item = self._cache.pop(key)
- if (
- self.expiration_time is None
- or time.time() - item["time"] < self.expiration_time
- ):
- # Move the key to the end to make it recently used
- self._cache[key] = item
- return item["value"]
- else:
- self.delete(key)
- return None
-
- def set(self, key, value):
- """
- Add an item to the cache.
-
- If the cache is full, the least recently used item is evicted.
-
- Args:
- key: The key of the item.
- value: The value to cache.
- """
- with self._lock:
- if key in self._cache:
- # Remove existing key before re-inserting to update order
- self.delete(key)
- elif self.max_size and len(self._cache) >= self.max_size:
- # Remove least recently used item
- self._cache.popitem(last=False)
- self._cache[key] = {"value": value, "time": time.time()}
-
- def get_or_set(self, key, value):
- """
- Retrieve an item from the cache. If the item does not exist, set it with the provided value.
-
- Args:
- key: The key of the item.
- value: The value to cache if the item doesn't exist.
-
- Returns:
- The cached value associated with the key.
- """
- with self._lock:
- if key in self._cache:
- return self.get(key)
- self.set(key, value)
- return value
-
- def delete(self, key):
- """
- Remove an item from the cache.
-
- Args:
- key: The key of the item to remove.
- """
- # with self._lock:
- self._cache.pop(key, None)
-
- def clear(self):
- """
- Clear all items from the cache.
- """
- with self._lock:
- self._cache.clear()
-
- def __contains__(self, key):
- """Check if the key is in the cache."""
- return key in self._cache
-
- def __getitem__(self, key):
- """Retrieve an item from the cache using the square bracket notation."""
- return self.get(key)
-
- def __setitem__(self, key, value):
- """Add an item to the cache using the square bracket notation."""
- self.set(key, value)
-
- def __delitem__(self, key):
- """Remove an item from the cache using the square bracket notation."""
- self.delete(key)
-
- def __len__(self):
- """Return the number of items in the cache."""
- return len(self._cache)
-
- def __repr__(self):
- """Return a string representation of the InMemoryCache instance."""
- return f"InMemoryCache(max_size={self.max_size}, expiration_time={self.expiration_time})"
diff --git a/src/backend/langflow/components/agents/OpenAIConversationalAgent.py b/src/backend/langflow/components/agents/OpenAIConversationalAgent.py
index ff1a993b1..364abf962 100644
--- a/src/backend/langflow/components/agents/OpenAIConversationalAgent.py
+++ b/src/backend/langflow/components/agents/OpenAIConversationalAgent.py
@@ -79,4 +79,5 @@ class ConversationalAgent(CustomComponent):
memory=memory,
verbose=True,
return_intermediate_steps=True,
+ handle_parsing_errors=True,
)
diff --git a/src/backend/langflow/components/chains/PromptRunner.py b/src/backend/langflow/components/chains/PromptRunner.py
index 141941c38..96a64f6fc 100644
--- a/src/backend/langflow/components/chains/PromptRunner.py
+++ b/src/backend/langflow/components/chains/PromptRunner.py
@@ -1,7 +1,7 @@
from langflow import CustomComponent
from langchain.llms.base import BaseLLM
-from langchain import PromptTemplate
+from langchain.prompts import PromptTemplate
from langchain.schema import Document
@@ -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,
- llm: BaseLLM,
- prompt: PromptTemplate,
+ self, llm: BaseLLM, prompt: PromptTemplate, inputs: dict = {}
) -> 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
diff --git a/src/backend/langflow/components/llms/AmazonBedrock.py b/src/backend/langflow/components/llms/AmazonBedrock.py
new file mode 100644
index 000000000..04785db63
--- /dev/null
+++ b/src/backend/langflow/components/llms/AmazonBedrock.py
@@ -0,0 +1,45 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.llms.bedrock import Bedrock
+from langchain.llms.base import BaseLLM
+
+
+class AmazonBedrockComponent(CustomComponent):
+ display_name: str = "Amazon Bedrock"
+ description: str = "LLM model from Amazon Bedrock."
+
+ def build_config(self):
+ return {
+ "model_id": {
+ "display_name": "Model Id",
+ "options": [
+ "ai21.j2-grande-instruct",
+ "ai21.j2-jumbo-instruct",
+ "ai21.j2-mid",
+ "ai21.j2-mid-v1",
+ "ai21.j2-ultra",
+ "ai21.j2-ultra-v1",
+ "anthropic.claude-instant-v1",
+ "anthropic.claude-v1",
+ "anthropic.claude-v2",
+ "cohere.command-text-v14",
+ ],
+ },
+ "credentials_profile_name": {"display_name": "Credentials Profile Name"},
+ "streaming": {"display_name": "Streaming", "field_type": "bool"},
+ "code": {"show": False},
+ }
+
+ def build(
+ self,
+ model_id: str = "anthropic.claude-instant-v1",
+ credentials_profile_name: Optional[str] = None,
+ ) -> BaseLLM:
+ try:
+ output = Bedrock(
+ credentials_profile_name=credentials_profile_name,
+ model_id=model_id,
+ ) # type: ignore
+ except Exception as e:
+ raise ValueError("Could not connect to AmazonBedrock API.") from e
+ return output
diff --git a/src/backend/langflow/components/llms/BaiduQianfanChatEndpoints.py b/src/backend/langflow/components/llms/BaiduQianfanChatEndpoints.py
new file mode 100644
index 000000000..1ea679b47
--- /dev/null
+++ b/src/backend/langflow/components/llms/BaiduQianfanChatEndpoints.py
@@ -0,0 +1,92 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
+from langchain.llms.base import BaseLLM
+
+
+class QianfanChatEndpointComponent(CustomComponent):
+ display_name: str = "QianfanChatEndpoint"
+ description: str = (
+ "Baidu Qianfan chat models. Get more detail from "
+ "https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint."
+ )
+
+ def build_config(self):
+ return {
+ "model": {
+ "display_name": "Model Name",
+ "options": [
+ "ERNIE-Bot",
+ "ERNIE-Bot-turbo",
+ "BLOOMZ-7B",
+ "Llama-2-7b-chat",
+ "Llama-2-13b-chat",
+ "Llama-2-70b-chat",
+ "Qianfan-BLOOMZ-7B-compressed",
+ "Qianfan-Chinese-Llama-2-7B",
+ "ChatGLM2-6B-32K",
+ "AquilaChat-7B",
+ ],
+ "info": "https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint",
+ "required": True,
+ },
+ "qianfan_ak": {
+ "display_name": "Qianfan Ak",
+ "required": True,
+ "password": True,
+ "info": "which you could get from https://cloud.baidu.com/product/wenxinworkshop",
+ },
+ "qianfan_sk": {
+ "display_name": "Qianfan Sk",
+ "required": True,
+ "password": True,
+ "info": "which you could get from https://cloud.baidu.com/product/wenxinworkshop",
+ },
+ "top_p": {
+ "display_name": "Top p",
+ "field_type": "float",
+ "info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
+ "value": 0.8,
+ },
+ "temperature": {
+ "display_name": "Temperature",
+ "field_type": "float",
+ "info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
+ "value": 0.95,
+ },
+ "penalty_score": {
+ "display_name": "Penalty Score",
+ "field_type": "float",
+ "info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
+ "value": 1.0,
+ },
+ "endpoint": {
+ "display_name": "Endpoint",
+ "info": "Endpoint of the Qianfan LLM, required if custom model used.",
+ },
+ "code": {"show": False},
+ }
+
+ def build(
+ self,
+ model: str = "ERNIE-Bot-turbo",
+ qianfan_ak: Optional[str] = None,
+ qianfan_sk: Optional[str] = None,
+ top_p: Optional[float] = None,
+ temperature: Optional[float] = None,
+ penalty_score: Optional[float] = None,
+ endpoint: Optional[str] = None,
+ ) -> BaseLLM:
+ try:
+ output = QianfanChatEndpoint( # type: ignore
+ model=model,
+ qianfan_ak=qianfan_ak,
+ qianfan_sk=qianfan_sk,
+ top_p=top_p,
+ temperature=temperature,
+ penalty_score=penalty_score,
+ endpoint=endpoint,
+ )
+ except Exception as e:
+ raise ValueError("Could not connect to Baidu Qianfan API.") from e
+ return output # type: ignore
diff --git a/src/backend/langflow/components/llms/BaiduQianfanLLMEndpoints.py b/src/backend/langflow/components/llms/BaiduQianfanLLMEndpoints.py
new file mode 100644
index 000000000..786c4516b
--- /dev/null
+++ b/src/backend/langflow/components/llms/BaiduQianfanLLMEndpoints.py
@@ -0,0 +1,92 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint
+from langchain.llms.base import BaseLLM
+
+
+class QianfanLLMEndpointComponent(CustomComponent):
+ display_name: str = "QianfanLLMEndpoint"
+ description: str = (
+ "Baidu Qianfan hosted open source or customized models. "
+ "Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint"
+ )
+
+ def build_config(self):
+ return {
+ "model": {
+ "display_name": "Model Name",
+ "options": [
+ "ERNIE-Bot",
+ "ERNIE-Bot-turbo",
+ "BLOOMZ-7B",
+ "Llama-2-7b-chat",
+ "Llama-2-13b-chat",
+ "Llama-2-70b-chat",
+ "Qianfan-BLOOMZ-7B-compressed",
+ "Qianfan-Chinese-Llama-2-7B",
+ "ChatGLM2-6B-32K",
+ "AquilaChat-7B",
+ ],
+ "info": "https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint",
+ "required": True,
+ },
+ "qianfan_ak": {
+ "display_name": "Qianfan Ak",
+ "required": True,
+ "password": True,
+ "info": "which you could get from https://cloud.baidu.com/product/wenxinworkshop",
+ },
+ "qianfan_sk": {
+ "display_name": "Qianfan Sk",
+ "required": True,
+ "password": True,
+ "info": "which you could get from https://cloud.baidu.com/product/wenxinworkshop",
+ },
+ "top_p": {
+ "display_name": "Top p",
+ "field_type": "float",
+ "info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
+ "value": 0.8,
+ },
+ "temperature": {
+ "display_name": "Temperature",
+ "field_type": "float",
+ "info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
+ "value": 0.95,
+ },
+ "penalty_score": {
+ "display_name": "Penalty Score",
+ "field_type": "float",
+ "info": "Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo",
+ "value": 1.0,
+ },
+ "endpoint": {
+ "display_name": "Endpoint",
+ "info": "Endpoint of the Qianfan LLM, required if custom model used.",
+ },
+ "code": {"show": False},
+ }
+
+ def build(
+ self,
+ model: str = "ERNIE-Bot-turbo",
+ qianfan_ak: Optional[str] = None,
+ qianfan_sk: Optional[str] = None,
+ top_p: Optional[float] = None,
+ temperature: Optional[float] = None,
+ penalty_score: Optional[float] = None,
+ endpoint: Optional[str] = None,
+ ) -> BaseLLM:
+ try:
+ output = QianfanLLMEndpoint( # type: ignore
+ model=model,
+ qianfan_ak=qianfan_ak,
+ qianfan_sk=qianfan_sk,
+ top_p=top_p,
+ temperature=temperature,
+ penalty_score=penalty_score,
+ endpoint=endpoint,
+ )
+ except Exception as e:
+ raise ValueError("Could not connect to Baidu Qianfan API.") from e
+ return output # type: ignore
diff --git a/src/backend/langflow/components/llms/HuggingFaceEndpoints.py b/src/backend/langflow/components/llms/HuggingFaceEndpoints.py
index ea2b4f20b..0d28d5b9b 100644
--- a/src/backend/langflow/components/llms/HuggingFaceEndpoints.py
+++ b/src/backend/langflow/components/llms/HuggingFaceEndpoints.py
@@ -1,6 +1,6 @@
from typing import Optional
from langflow import CustomComponent
-from langchain.llms import HuggingFaceEndpoint
+from langchain.llms.huggingface_endpoint import HuggingFaceEndpoint
from langchain.llms.base import BaseLLM
@@ -13,7 +13,6 @@ class HuggingFaceEndpointsComponent(CustomComponent):
"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},
@@ -27,7 +26,7 @@ class HuggingFaceEndpointsComponent(CustomComponent):
def build(
self,
endpoint_url: str,
- task="text2text-generation",
+ task: str = "text2text-generation",
huggingfacehub_api_token: Optional[str] = None,
model_kwargs: Optional[dict] = None,
) -> BaseLLM:
@@ -36,6 +35,7 @@ class HuggingFaceEndpointsComponent(CustomComponent):
endpoint_url=endpoint_url,
task=task,
huggingfacehub_api_token=huggingfacehub_api_token,
+ model_kwargs=model_kwargs,
)
except Exception as e:
raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
diff --git a/src/backend/langflow/components/retrievers/AmazonKendra.py b/src/backend/langflow/components/retrievers/AmazonKendra.py
new file mode 100644
index 000000000..827945a51
--- /dev/null
+++ b/src/backend/langflow/components/retrievers/AmazonKendra.py
@@ -0,0 +1,48 @@
+from typing import Optional
+from langflow import CustomComponent
+from langchain.retrievers import AmazonKendraRetriever
+from langchain.schema import BaseRetriever
+
+
+class AmazonKendraRetrieverComponent(CustomComponent):
+ display_name: str = "Amazon Kendra Retriever"
+ description: str = "Retriever that uses the Amazon Kendra API."
+
+ def build_config(self):
+ return {
+ "index_id": {"display_name": "Index ID"},
+ "region_name": {"display_name": "Region Name"},
+ "credentials_profile_name": {"display_name": "Credentials Profile Name"},
+ "attribute_filter": {
+ "display_name": "Attribute Filter",
+ "field_type": "code",
+ },
+ "top_k": {"display_name": "Top K", "field_type": "int"},
+ "user_context": {
+ "display_name": "User Context",
+ "field_type": "code",
+ },
+ "code": {"show": False},
+ }
+
+ def build(
+ self,
+ index_id: str,
+ top_k: int = 3,
+ region_name: Optional[str] = None,
+ credentials_profile_name: Optional[str] = None,
+ attribute_filter: Optional[dict] = None,
+ user_context: Optional[dict] = None,
+ ) -> BaseRetriever:
+ try:
+ output = AmazonKendraRetriever(
+ index_id=index_id,
+ top_k=top_k,
+ region_name=region_name,
+ credentials_profile_name=credentials_profile_name,
+ attribute_filter=attribute_filter,
+ user_context=user_context,
+ ) # type: ignore
+ except Exception as e:
+ raise ValueError("Could not connect to AmazonKendra API.") from e
+ return output
diff --git a/src/backend/langflow/components/retrievers/MetalRetriever.py b/src/backend/langflow/components/retrievers/MetalRetriever.py
index 9b2d1e5dd..b105cd24f 100644
--- a/src/backend/langflow/components/retrievers/MetalRetriever.py
+++ b/src/backend/langflow/components/retrievers/MetalRetriever.py
@@ -14,7 +14,7 @@ class MetalRetrieverComponent(CustomComponent):
"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", "field_type": "code"},
+ "params": {"display_name": "Parameters"},
"code": {"show": False},
}
diff --git a/src/backend/langflow/components/textsplitters/LanguageRecursiveTextSplitter.py b/src/backend/langflow/components/textsplitters/LanguageRecursiveTextSplitter.py
index da7c0dd73..636eb427e 100644
--- a/src/backend/langflow/components/textsplitters/LanguageRecursiveTextSplitter.py
+++ b/src/backend/langflow/components/textsplitters/LanguageRecursiveTextSplitter.py
@@ -2,7 +2,6 @@ from typing import Optional
from langflow import CustomComponent
from langchain.text_splitter import Language
from langchain.schema import Document
-from langflow.utils.util import build_loader_repr_from_documents
class LanguageRecursiveTextSplitterComponent(CustomComponent):
@@ -78,5 +77,4 @@ class LanguageRecursiveTextSplitterComponent(CustomComponent):
)
docs = splitter.split_documents(documents)
- self.repr_value = build_loader_repr_from_documents(docs)
return docs
diff --git a/src/backend/langflow/components/textsplitters/RecursiveCharacterTextSplitter.py b/src/backend/langflow/components/textsplitters/RecursiveCharacterTextSplitter.py
index 58b061f2f..751d2518c 100644
--- a/src/backend/langflow/components/textsplitters/RecursiveCharacterTextSplitter.py
+++ b/src/backend/langflow/components/textsplitters/RecursiveCharacterTextSplitter.py
@@ -1,6 +1,7 @@
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):
@@ -74,6 +75,5 @@ class RecursiveCharacterTextSplitterComponent(CustomComponent):
)
docs = splitter.split_documents(documents)
- # self.repr_value = build_loader_repr_from_documents(docs)
- self.repr_value = separators
+ self.repr_value = build_loader_repr_from_documents(docs)
return docs
diff --git a/src/backend/langflow/components/utilities/GetRequest.py b/src/backend/langflow/components/utilities/GetRequest.py
index c8182ad0d..d5df32cca 100644
--- a/src/backend/langflow/components/utilities/GetRequest.py
+++ b/src/backend/langflow/components/utilities/GetRequest.py
@@ -1,6 +1,6 @@
from langflow import CustomComponent
from langchain.schema import Document
-from langflow.database.models.base import orjson_dumps
+from langflow.services.database.models.base import orjson_dumps
import requests
from typing import Optional
@@ -19,7 +19,6 @@ class GetRequest(CustomComponent):
},
"headers": {
"display_name": "Headers",
- "field_type": "code",
"info": "The headers to send with the request.",
},
"code": {"show": False},
diff --git a/src/backend/langflow/components/utilities/JSONDocumentBuilder.py b/src/backend/langflow/components/utilities/JSONDocumentBuilder.py
index e41b6b6af..26a2afd94 100644
--- a/src/backend/langflow/components/utilities/JSONDocumentBuilder.py
+++ b/src/backend/langflow/components/utilities/JSONDocumentBuilder.py
@@ -13,7 +13,7 @@
from langflow import CustomComponent
from langchain.schema import Document
-from langflow.database.models.base import orjson_dumps
+from langflow.services.database.models.base import orjson_dumps
class JSONDocumentBuilder(CustomComponent):
diff --git a/src/backend/langflow/components/utilities/PostRequest.py b/src/backend/langflow/components/utilities/PostRequest.py
index 9d5ab504f..6857f4866 100644
--- a/src/backend/langflow/components/utilities/PostRequest.py
+++ b/src/backend/langflow/components/utilities/PostRequest.py
@@ -1,6 +1,6 @@
from langflow import CustomComponent
from langchain.schema import Document
-from langflow.database.models.base import orjson_dumps
+from langflow.services.database.models.base import orjson_dumps
import requests
from typing import Optional
@@ -15,7 +15,6 @@ class PostRequest(CustomComponent):
"url": {"display_name": "URL", "info": "The URL to make the request to."},
"headers": {
"display_name": "Headers",
- "field_type": "code",
"info": "The headers to send with the request.",
},
"code": {"show": False},
diff --git a/src/backend/langflow/components/utilities/UpdateRequest.py b/src/backend/langflow/components/utilities/UpdateRequest.py
index 569dac013..d18c94a56 100644
--- a/src/backend/langflow/components/utilities/UpdateRequest.py
+++ b/src/backend/langflow/components/utilities/UpdateRequest.py
@@ -2,7 +2,7 @@ from typing import List, Optional
import requests
from langflow import CustomComponent
from langchain.schema import Document
-from langflow.database.models.base import orjson_dumps
+from langflow.services.database.models.base import orjson_dumps
class UpdateRequest(CustomComponent):
@@ -15,7 +15,7 @@ class UpdateRequest(CustomComponent):
"url": {"display_name": "URL", "info": "The URL to make the request to."},
"headers": {
"display_name": "Headers",
- "field_type": "code",
+ "field_type": "NestedDict",
"info": "The headers to send with the request.",
},
"code": {"show": False},
diff --git a/src/backend/langflow/components/vectorstores/Chroma.py b/src/backend/langflow/components/vectorstores/Chroma.py
new file mode 100644
index 000000000..c4b353b32
--- /dev/null
+++ b/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"
+ 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
+ )
diff --git a/src/backend/langflow/components/vectorstores/Vectara.py b/src/backend/langflow/components/vectorstores/Vectara.py
index 6edc69822..6f7fcc0bb 100644
--- a/src/backend/langflow/components/vectorstores/Vectara.py
+++ b/src/backend/langflow/components/vectorstores/Vectara.py
@@ -5,7 +5,6 @@ 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):
@@ -22,7 +21,6 @@ class VectaraComponent(CustomComponent):
"vectara_api_key": {"display_name": "Vectara API Key", "password": True},
"code": {"show": False},
"documents": {"display_name": "Documents"},
- "embedding": {"display_name": "Embedding"},
}
def build(
@@ -30,21 +28,21 @@ class VectaraComponent(CustomComponent):
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:
+ if documents 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,
+ source="langflow",
)
return Vectara(
vectara_customer_id=vectara_customer_id,
vectara_corpus_id=vectara_corpus_id,
vectara_api_key=vectara_api_key,
+ source="langflow",
)
diff --git a/src/backend/langflow/config.yaml b/src/backend/langflow/config.yaml
index da43403f2..421144a3a 100644
--- a/src/backend/langflow/config.yaml
+++ b/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:
- # documentation: "https://python.langchain.com/docs/modules/model_io/models/chat/integrations/google_vertex_ai_palm"
+ ChatVertexAI:
+ 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
@@ -263,8 +265,8 @@ retrievers:
# ZepRetriever:
# documentation: "https://python.langchain.com/docs/modules/data_connection/retrievers/integrations/zep_memorystore"
vectorstores:
- Chroma:
- documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma"
+ # Chroma:
+ # documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma"
Qdrant:
documentation: "https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/qdrant"
Weaviate:
diff --git a/src/backend/langflow/chat/__init__.py b/src/backend/langflow/core/__init__.py
similarity index 100%
rename from src/backend/langflow/chat/__init__.py
rename to src/backend/langflow/core/__init__.py
diff --git a/src/backend/langflow/core/celery_app.py b/src/backend/langflow/core/celery_app.py
new file mode 100644
index 000000000..ef3fc6545
--- /dev/null
+++ b/src/backend/langflow/core/celery_app.py
@@ -0,0 +1,11 @@
+from celery import Celery # type: ignore
+
+
+def make_celery(app_name: str, config: str) -> Celery:
+ celery_app = Celery(app_name)
+ celery_app.config_from_object(config)
+ celery_app.conf.task_routes = {"langflow.worker.tasks.*": {"queue": "langflow"}}
+ return celery_app
+
+
+celery_app = make_celery("langflow", "langflow.core.celeryconfig")
diff --git a/src/backend/langflow/core/celeryconfig.py b/src/backend/langflow/core/celeryconfig.py
new file mode 100644
index 000000000..35d51bba0
--- /dev/null
+++ b/src/backend/langflow/core/celeryconfig.py
@@ -0,0 +1,14 @@
+# celeryconfig.py
+import os
+
+langflow_redis_host = os.environ.get("LANGFLOW_REDIS_HOST")
+langflow_redis_port = os.environ.get("LANGFLOW_REDIS_PORT")
+if "BROKER_URL" in os.environ and "RESULT_BACKEND" in os.environ:
+ # RabbitMQ
+ broker_url = os.environ.get("BROKER_URL", "amqp://localhost")
+ result_backend = os.environ.get("RESULT_BACKEND", "redis://localhost:6379/0")
+elif langflow_redis_host and langflow_redis_port:
+ broker_url = f"redis://{langflow_redis_host}:{langflow_redis_port}/0"
+ result_backend = f"redis://{langflow_redis_host}:{langflow_redis_port}/0"
+# tasks should be json or pickle
+accept_content = ["json", "pickle"]
diff --git a/src/backend/langflow/database/base.py b/src/backend/langflow/database/base.py
deleted file mode 100644
index 13b887d8d..000000000
--- a/src/backend/langflow/database/base.py
+++ /dev/null
@@ -1,93 +0,0 @@
-from contextlib import contextmanager
-import os
-
-from sqlmodel import SQLModel, Session, create_engine
-from langflow.utils.logger import logger
-import sqlalchemy as sa
-
-
-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()
-
- @classmethod
- def teardown(cls):
- logger.debug("Tearing down database engine")
- if cls._instance is not None:
- cls._instance.dispose()
- cls._instance = None
-
-
-def create_db_and_tables():
- logger.debug("Creating database and tables")
- try:
- SQLModel.metadata.create_all(Engine.get())
- except sa.exc.OperationalError as exc:
- # Check if the error is because the table already exists
- if "already exists" in str(exc):
- logger.debug("Database and tables already exist")
- else:
- logger.error(f"Error creating database and tables: {exc}")
- raise RuntimeError("Error creating database and tables") from exc
- 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
diff --git a/src/backend/langflow/database/models/flow_style.py b/src/backend/langflow/database/models/flow_style.py
deleted file mode 100644
index fe53799fe..000000000
--- a/src/backend/langflow/database/models/flow_style.py
+++ /dev/null
@@ -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
diff --git a/src/backend/langflow/field_typing/__init__.py b/src/backend/langflow/field_typing/__init__.py
new file mode 100644
index 000000000..9a2161d3f
--- /dev/null
+++ b/src/backend/langflow/field_typing/__init__.py
@@ -0,0 +1,53 @@
+# LANGCHAIN_BASE_TYPES = {
+# "Chain": Chain,
+# "AgentExecutor": AgentExecutor,
+# "Tool": Tool,
+# "BaseLLM": BaseLLM,
+# "PromptTemplate": PromptTemplate,
+# "BaseLoader": BaseLoader,
+# "Document": Document,
+# "TextSplitter": TextSplitter,
+# "VectorStore": VectorStore,
+# "Embeddings": Embeddings,
+# "BaseRetriever": BaseRetriever,
+# "BaseOutputParser": BaseOutputParser,
+# "BaseMemory": BaseMemory,
+# "BaseChatMemory": BaseChatMemory,
+# }
+from .constants import (
+ Tool,
+ PromptTemplate,
+ Chain,
+ BaseChatMemory,
+ BaseLLM,
+ BaseLoader,
+ BaseMemory,
+ BaseOutputParser,
+ BaseRetriever,
+ VectorStore,
+ Embeddings,
+ TextSplitter,
+ Document,
+ AgentExecutor,
+ NestedDict,
+ Data,
+)
+
+__all__ = [
+ "NestedDict",
+ "Data",
+ "Tool",
+ "PromptTemplate",
+ "Chain",
+ "BaseChatMemory",
+ "BaseLLM",
+ "BaseLoader",
+ "BaseMemory",
+ "BaseOutputParser",
+ "BaseRetriever",
+ "VectorStore",
+ "Embeddings",
+ "TextSplitter",
+ "Document",
+ "AgentExecutor",
+]
diff --git a/src/backend/langflow/field_typing/constants.py b/src/backend/langflow/field_typing/constants.py
new file mode 100644
index 000000000..3ce429548
--- /dev/null
+++ b/src/backend/langflow/field_typing/constants.py
@@ -0,0 +1,50 @@
+from langchain.agents.agent import AgentExecutor
+from langchain.chains.base import Chain
+from langchain.document_loaders.base import BaseLoader
+from langchain.llms.base import BaseLLM
+from langchain.memory.chat_memory import BaseChatMemory
+from langchain.prompts import PromptTemplate
+from langchain.schema import BaseOutputParser, BaseRetriever, Document
+from langchain.schema.embeddings import Embeddings
+from langchain.schema.memory import BaseMemory
+from langchain.text_splitter import TextSplitter
+from langchain.tools import Tool
+from langchain.vectorstores.base import VectorStore
+from typing import Union, Dict
+
+# Type alias for more complex dicts
+NestedDict = Dict[str, Union[str, Dict]]
+
+
+class Data:
+ pass
+
+
+LANGCHAIN_BASE_TYPES = {
+ "Chain": Chain,
+ "AgentExecutor": AgentExecutor,
+ "Tool": Tool,
+ "BaseLLM": BaseLLM,
+ "PromptTemplate": PromptTemplate,
+ "BaseLoader": BaseLoader,
+ "Document": Document,
+ "TextSplitter": TextSplitter,
+ "VectorStore": VectorStore,
+ "Embeddings": Embeddings,
+ "BaseRetriever": BaseRetriever,
+ "BaseOutputParser": BaseOutputParser,
+ "BaseMemory": BaseMemory,
+ "BaseChatMemory": BaseChatMemory,
+}
+# Langchain base types plus Python base types
+CUSTOM_COMPONENT_SUPPORTED_TYPES = {
+ **LANGCHAIN_BASE_TYPES,
+ "str": str,
+ "int": int,
+ "float": float,
+ "bool": bool,
+ "list": list,
+ "dict": dict,
+ "NestedDict": NestedDict,
+ "Data": Data,
+}
diff --git a/src/backend/langflow/graph/edge/base.py b/src/backend/langflow/graph/edge/base.py
index dc7eab328..82714e395 100644
--- a/src/backend/langflow/graph/edge/base.py
+++ b/src/backend/langflow/graph/edge/base.py
@@ -1,22 +1,84 @@
-from langflow.utils.logger import logger
+from loguru import logger
from typing import TYPE_CHECKING
+from pydantic import BaseModel, Field
+from typing import List, Optional
if TYPE_CHECKING:
from langflow.graph.vertex.base import Vertex
+class SourceHandle(BaseModel):
+ baseClasses: List[str] = Field(
+ ..., description="List of base classes for the source handle."
+ )
+ dataType: str = Field(..., description="Data type for the source handle.")
+ id: str = Field(..., description="Unique identifier for the source handle.")
+
+
+class TargetHandle(BaseModel):
+ fieldName: str = Field(..., description="Field name for the target handle.")
+ id: str = Field(..., description="Unique identifier for the target handle.")
+ inputTypes: Optional[List[str]] = Field(
+ None, description="List of input types for the target handle."
+ )
+ type: str = Field(..., description="Type of the target handle.")
+
+
class Edge:
def __init__(self, source: "Vertex", target: "Vertex", edge: dict):
self.source: "Vertex" = source
self.target: "Vertex" = target
- self.source_handle = edge.get("sourceHandle", "")
- self.target_handle = edge.get("targetHandle", "")
- # 'BaseLoader;BaseOutputParser|documents|PromptTemplate-zmTlD'
- # target_param is documents
- self.target_param = self.target_handle.split("|")[1]
-
+ if data := edge.get("data", {}):
+ self._source_handle = data.get("sourceHandle", {})
+ self._target_handle = data.get("targetHandle", {})
+ self.source_handle: SourceHandle = SourceHandle(**self._source_handle)
+ self.target_handle: TargetHandle = TargetHandle(**self._target_handle)
+ self.target_param = self.target_handle.fieldName
+ # validate handles
+ self.validate_handles()
+ else:
+ # Logging here because this is a breaking change
+ logger.error("Edge data is empty")
+ self._source_handle = edge.get("sourceHandle", "")
+ self._target_handle = edge.get("targetHandle", "")
+ # 'BaseLoader;BaseOutputParser|documents|PromptTemplate-zmTlD'
+ # target_param is documents
+ self.target_param = self._target_handle.split("|")[1]
+ # Validate in __init__ to fail fast
self.validate_edge()
+ def validate_handles(self) -> None:
+ if self.target_handle.inputTypes is None:
+ self.valid_handles = (
+ self.target_handle.type in self.source_handle.baseClasses
+ )
+ else:
+ self.valid_handles = (
+ any(
+ baseClass in self.target_handle.inputTypes
+ for baseClass in self.source_handle.baseClasses
+ )
+ or self.target_handle.type in self.source_handle.baseClasses
+ )
+ if not self.valid_handles:
+ logger.debug(self.source_handle)
+ logger.debug(self.target_handle)
+ raise ValueError(
+ f"Edge between {self.source.vertex_type} and {self.target.vertex_type} "
+ f"has invalid handles"
+ )
+
+ def __setstate__(self, state):
+ self.source = state["source"]
+ self.target = state["target"]
+ self.target_param = state["target_param"]
+ self.source_handle = state.get("source_handle")
+ self.target_handle = state.get("target_handle")
+
+ def reset(self) -> None:
+ self.source._build_params()
+ self.target._build_params()
+
def validate_edge(self) -> None:
# Validate that the outputs of the source node are valid inputs
# for the target node
diff --git a/src/backend/langflow/graph/graph/base.py b/src/backend/langflow/graph/graph/base.py
index 99b4e2b3d..83976ae49 100644
--- a/src/backend/langflow/graph/graph/base.py
+++ b/src/backend/langflow/graph/graph/base.py
@@ -1,7 +1,8 @@
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.graph.utils import process_flow
from langflow.graph.vertex.base import Vertex
from langflow.graph.vertex.types import (
FileToolVertex,
@@ -10,7 +11,7 @@ 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
@@ -19,13 +20,29 @@ class Graph:
def __init__(
self,
- nodes: List[Dict[str, Union[str, Dict[str, Union[str, List[str]]]]]],
+ nodes: List[Dict],
edges: List[Dict[str, str]],
) -> None:
self._nodes = nodes
self._edges = edges
+ self.raw_graph_data = {"nodes": nodes, "edges": edges}
+
+ self.top_level_nodes = []
+ for node in self._nodes:
+ if node_id := node.get("id"):
+ self.top_level_nodes.append(node_id)
+
+ self._graph_data = process_flow(self.raw_graph_data)
+ self._nodes = self._graph_data["nodes"]
+ self._edges = self._graph_data["edges"]
self._build_graph()
+ def __setstate__(self, state):
+ self.__dict__.update(state)
+ for edge in self.edges:
+ edge.reset()
+ edge.validate_edge()
+
@classmethod
def from_payload(cls, payload: Dict) -> "Graph":
"""
@@ -44,10 +61,16 @@ class Graph:
edges = payload["edges"]
return cls(nodes, edges)
except KeyError as exc:
+ logger.exception(exc)
raise ValueError(
f"Invalid payload. Expected keys 'nodes' and 'edges'. Found {list(payload.keys())}"
) from exc
+ def __eq__(self, other: object) -> bool:
+ if not isinstance(other, Graph):
+ return False
+ return self.__repr__() == other.__repr__()
+
def _build_graph(self) -> None:
"""Builds the graph from the nodes and edges."""
self.nodes = self._build_vertices()
@@ -144,10 +167,10 @@ 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)
+ logger.debug("There are %s vertices in the graph", len(sorted_vertices))
yield from sorted_vertices
def get_node_neighbors(self, node: Vertex) -> Dict[Vertex, int]:
@@ -187,10 +210,12 @@ 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:
- return VERTEX_TYPE_MAP[node_type]
+ if node_type in lazy_load_vertex_dict.VERTEX_TYPE_MAP:
+ 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]:
@@ -202,7 +227,9 @@ class Graph:
node_lc_type: str = node_data["node"]["template"]["_type"] # type: ignore
VertexClass = self._get_vertex_class(node_type, node_lc_type)
- nodes.append(VertexClass(node))
+ vertex = VertexClass(node)
+ vertex.set_top_level(self.top_level_nodes)
+ nodes.append(vertex)
return nodes
diff --git a/src/backend/langflow/graph/graph/constants.py b/src/backend/langflow/graph/graph/constants.py
index 5e5c3b709..c9fea48b5 100644
--- a/src/backend/langflow/graph/graph/constants.py
+++ b/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]] = {
- **{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()},
-}
+class VertexTypesDict(LazyLoadDictBase):
+ def __init__(self):
+ self._all_types_dict = None
+
+ @property
+ def VERTEX_TYPE_MAP(self):
+ return self.all_types_dict
+
+ def _build_dict(self):
+ langchain_types_dict = self.get_type_dict()
+ return {
+ **langchain_types_dict,
+ "Custom": ["Custom Tool", "Python Function"],
+ }
+
+ 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()
diff --git a/src/backend/langflow/graph/graph/utils.py b/src/backend/langflow/graph/graph/utils.py
index e69de29bb..71b81fea1 100644
--- a/src/backend/langflow/graph/graph/utils.py
+++ b/src/backend/langflow/graph/graph/utils.py
@@ -0,0 +1,230 @@
+from collections import deque
+import copy
+
+
+def find_last_node(nodes, edges):
+ """
+ This function receives a flow and returns the last node.
+ """
+ return next((n for n in nodes if all(e["source"] != n["id"] for e in edges)), None)
+
+
+def add_parent_node_id(nodes, parent_node_id):
+ """
+ This function receives a list of nodes and adds a parent_node_id to each node.
+ """
+ for node in nodes:
+ node["parent_node_id"] = parent_node_id
+
+
+def ungroup_node(group_node_data, base_flow):
+ template, flow = (
+ group_node_data["node"]["template"],
+ group_node_data["node"]["flow"],
+ )
+ parent_node_id = group_node_data["id"]
+ g_nodes = flow["data"]["nodes"]
+ add_parent_node_id(g_nodes, parent_node_id)
+ g_edges = flow["data"]["edges"]
+
+ # Redirect edges to the correct proxy node
+ updated_edges = get_updated_edges(
+ base_flow, g_nodes, g_edges, group_node_data["id"]
+ )
+
+ # Update template values
+ update_template(template, g_nodes)
+
+ nodes = [
+ n for n in base_flow["nodes"] if n["id"] != group_node_data["id"]
+ ] + g_nodes
+ edges = (
+ [
+ e
+ for e in base_flow["edges"]
+ if e["target"] != group_node_data["id"]
+ and e["source"] != group_node_data["id"]
+ ]
+ + g_edges
+ + updated_edges
+ )
+
+ base_flow["nodes"] = nodes
+ base_flow["edges"] = edges
+
+ return nodes
+
+
+def process_flow(flow_object):
+ cloned_flow = copy.deepcopy(flow_object)
+ processed_nodes = set() # To keep track of processed nodes
+
+ def process_node(node):
+ node_id = node.get("id")
+
+ # If node already processed, skip
+ if node_id in processed_nodes:
+ return
+
+ if (
+ node.get("data")
+ and node["data"].get("node")
+ and node["data"]["node"].get("flow")
+ ):
+ process_flow(node["data"]["node"]["flow"]["data"])
+ new_nodes = ungroup_node(node["data"], cloned_flow)
+ # Add new nodes to the queue for future processing
+ nodes_to_process.extend(new_nodes)
+
+ # Mark node as processed
+ processed_nodes.add(node_id)
+
+ nodes_to_process = deque(cloned_flow["nodes"])
+
+ while nodes_to_process:
+ node = nodes_to_process.popleft()
+ process_node(node)
+
+ return cloned_flow
+
+
+def update_template(template, g_nodes):
+ """
+ Updates the template of a node in a graph with the given template.
+
+ Args:
+ template (dict): The new template to update the node with.
+ g_nodes (list): The list of nodes in the graph.
+
+ Returns:
+ None
+ """
+ for _, value in template.items():
+ if not value.get("proxy"):
+ continue
+ proxy_dict = value["proxy"]
+ field, id_ = proxy_dict["field"], proxy_dict["id"]
+ node_index = next((i for i, n in enumerate(g_nodes) if n["id"] == id_), -1)
+ if node_index != -1:
+ display_name = None
+ show = g_nodes[node_index]["data"]["node"]["template"][field]["show"]
+ advanced = g_nodes[node_index]["data"]["node"]["template"][field][
+ "advanced"
+ ]
+ if "display_name" in g_nodes[node_index]["data"]["node"]["template"][field]:
+ display_name = g_nodes[node_index]["data"]["node"]["template"][field][
+ "display_name"
+ ]
+ else:
+ display_name = g_nodes[node_index]["data"]["node"]["template"][field][
+ "name"
+ ]
+
+ g_nodes[node_index]["data"]["node"]["template"][field] = value
+ g_nodes[node_index]["data"]["node"]["template"][field]["show"] = show
+ g_nodes[node_index]["data"]["node"]["template"][field][
+ "advanced"
+ ] = advanced
+ g_nodes[node_index]["data"]["node"]["template"][field][
+ "display_name"
+ ] = display_name
+
+
+def update_target_handle(new_edge, g_nodes, group_node_id):
+ """
+ Updates the target handle of a given edge if it is a proxy node.
+
+ Args:
+ new_edge (dict): The edge to update.
+ g_nodes (list): The list of nodes in the graph.
+ group_node_id (str): The ID of the group node.
+
+ Returns:
+ dict: The updated edge.
+ """
+ target_handle = new_edge["data"]["targetHandle"]
+ if target_handle.get("proxy"):
+ proxy_id = target_handle["proxy"]["id"]
+ if node := next((n for n in g_nodes if n["id"] == proxy_id), None):
+ set_new_target_handle(proxy_id, new_edge, target_handle, node)
+ return new_edge
+
+
+def set_new_target_handle(proxy_id, new_edge, target_handle, node):
+ """
+ Sets a new target handle for a given edge.
+
+ Args:
+ proxy_id (str): The ID of the proxy.
+ new_edge (dict): The new edge to be created.
+ target_handle (dict): The target handle of the edge.
+ node (dict): The node containing the edge.
+
+ Returns:
+ None
+ """
+ new_edge["target"] = proxy_id
+ _type = target_handle.get("type")
+ if _type is None:
+ raise KeyError("The 'type' key must be present in target_handle.")
+
+ field = target_handle["proxy"]["field"]
+ new_target_handle = {
+ "fieldName": field,
+ "type": _type,
+ "id": proxy_id,
+ }
+ if node["data"]["node"].get("flow"):
+ new_target_handle["proxy"] = {
+ "field": node["data"]["node"]["template"][field]["proxy"]["field"],
+ "id": node["data"]["node"]["template"][field]["proxy"]["id"],
+ }
+ if input_types := target_handle.get("inputTypes"):
+ new_target_handle["inputTypes"] = input_types
+ new_edge["data"]["targetHandle"] = new_target_handle
+
+
+def update_source_handle(new_edge, g_nodes, g_edges):
+ """
+ Updates the source handle of a given edge to the last node in the flow data.
+
+ Args:
+ new_edge (dict): The edge to update.
+ flow_data (dict): The flow data containing the nodes and edges.
+
+ Returns:
+ dict: The updated edge with the new source handle.
+ """
+ last_node = copy.deepcopy(find_last_node(g_nodes, g_edges))
+ new_edge["source"] = last_node["id"]
+ new_source_handle = new_edge["data"]["sourceHandle"]
+ new_source_handle["id"] = last_node["id"]
+ new_edge["data"]["sourceHandle"] = new_source_handle
+ return new_edge
+
+
+def get_updated_edges(base_flow, g_nodes, g_edges, group_node_id):
+ """
+ Given a base flow, a list of graph nodes and a group node id, returns a list of updated edges.
+ An updated edge is an edge that has its target or source handle updated based on the group node id.
+
+ Args:
+ base_flow (dict): The base flow containing a list of edges.
+ g_nodes (list): A list of graph nodes.
+ group_node_id (str): The id of the group node.
+
+ Returns:
+ list: A list of updated edges.
+ """
+ updated_edges = []
+ for edge in base_flow["edges"]:
+ new_edge = copy.deepcopy(edge)
+ if new_edge["target"] == group_node_id:
+ new_edge = update_target_handle(new_edge, g_nodes, group_node_id)
+
+ if new_edge["source"] == group_node_id:
+ new_edge = update_source_handle(new_edge, g_nodes, g_edges)
+
+ if edge["target"] == group_node_id or edge["source"] == group_node_id:
+ updated_edges.append(new_edge)
+ return updated_edges
diff --git a/src/backend/langflow/graph/vertex/base.py b/src/backend/langflow/graph/vertex/base.py
index 71afc0c02..88a4db26e 100644
--- a/src/backend/langflow/graph/vertex/base.py
+++ b/src/backend/langflow/graph/vertex/base.py
@@ -1,9 +1,11 @@
import ast
+import pickle
from langflow.graph.utils import UnbuiltObject
+from langflow.graph.vertex.utils import is_basic_type
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
@@ -12,12 +14,19 @@ import types
from typing import Any, Dict, List, Optional
from typing import TYPE_CHECKING
+
if TYPE_CHECKING:
from langflow.graph.edge.base import Edge
class Vertex:
- def __init__(self, data: Dict, base_type: Optional[str] = None) -> None:
+ def __init__(
+ self,
+ data: Dict,
+ base_type: Optional[str] = None,
+ is_task: bool = False,
+ params: Optional[Dict] = None,
+ ) -> None:
self.id: str = data["id"]
self._data = data
self.edges: List["Edge"] = []
@@ -26,6 +35,66 @@ class Vertex:
self._built_object = UnbuiltObject()
self._built = False
self.artifacts: Dict[str, Any] = {}
+ self.task_id: Optional[str] = None
+ self.is_task = is_task
+ self.params = params or {}
+ self.parent_node_id: Optional[str] = self._data.get("parent_node_id")
+ self.parent_is_top_level = False
+
+ def reset_params(self):
+ for edge in self.edges:
+ if edge.source != self:
+ target_param = edge.target_param
+ if target_param in ["document", "texts"]:
+ # this means they got data and have already ingested it
+ # so we continue after removing the param
+ self.params.pop(target_param, None)
+ continue
+
+ if target_param in self.params and not is_basic_type(
+ self.params[target_param]
+ ):
+ # edge.source.params = {}
+ edge.source._build_params()
+ edge.source._built_object = UnbuiltObject()
+ edge.source._built = False
+
+ self.params[target_param] = edge.source
+
+ def __getstate__(self):
+ state_dict = self.__dict__.copy()
+ try:
+ # try pickling the built object
+ # if it fails, then we need to delete it
+ # and build it again
+ pickle.dumps(state_dict["_built_object"])
+ except Exception:
+ self.reset_params()
+ del state_dict["_built_object"]
+ del state_dict["_built"]
+ return state_dict
+
+ def __setstate__(self, state):
+ self._data = state["_data"]
+ self.params = state["params"]
+ self.base_type = state["base_type"]
+ self.is_task = state["is_task"]
+ self.edges = state["edges"]
+ self.id = state["id"]
+ self._parse_data()
+ if "_built_object" in state:
+ self._built_object = state["_built_object"]
+ self._built = state["_built"]
+ else:
+ self._built_object = UnbuiltObject()
+ self._built = False
+ self.artifacts: Dict[str, Any] = {}
+ self.task_id: Optional[str] = None
+ self.parent_node_id = state["parent_node_id"]
+ self.parent_is_top_level = state["parent_is_top_level"]
+
+ def set_top_level(self, top_level_nodes: List[str]) -> None:
+ self.parent_is_top_level = self.parent_node_id in top_level_nodes
def _parse_data(self) -> None:
self.data = self._data["data"]
@@ -63,11 +132,18 @@ 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
+ def get_task(self):
+ # using the task_id, get the task from celery
+ # and return it
+ from celery.result import AsyncResult # type: ignore
+
+ return AsyncResult(self.task_id)
+
def _build_params(self):
# sourcery skip: merge-list-append, remove-redundant-if
# Some params are required, some are optional
@@ -89,9 +165,11 @@ class Vertex:
for key, value in self.data["node"]["template"].items()
if isinstance(value, dict)
}
- params = {}
+ params = self.params.copy() if self.params else {}
for edge in self.edges:
+ if not hasattr(edge, "target_param"):
+ continue
param_key = edge.target_param
if param_key in template_dict:
if template_dict[param_key]["list"]:
@@ -102,6 +180,8 @@ class Vertex:
params[param_key] = edge.source
for key, value in template_dict.items():
+ if key in params:
+ continue
# Skip _type and any value that has show == False and is not code
# If we don't want to show code but we want to use it
if key == "_type" or (not value.get("show") and key != "code"):
@@ -112,9 +192,10 @@ class Vertex:
# Load the type in value.get('suffixes') using
# what is inside value.get('content')
# value.get('value') is the file name
- file_path = value.get("file_path")
-
- params[key] = file_path
+ if file_path := value.get("file_path"):
+ params[key] = file_path
+ else:
+ raise ValueError(f"File path not found for {self.vertex_type}")
elif value.get("type") in DIRECT_TYPES and params.get(key) is None:
if value.get("type") == "code":
try:
@@ -122,6 +203,29 @@ 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
+ elif value.get("type") == "int" and value.get("value") is not None:
+ try:
+ params[key] = int(value.get("value"))
+ except ValueError:
+ params[key] = value.get("value")
+ elif value.get("type") == "float" and value.get("value") is not None:
+ try:
+ params[key] = float(value.get("value"))
+ except ValueError:
+ params[key] = value.get("value")
else:
params[key] = value.get("value")
@@ -131,20 +235,21 @@ class Vertex:
else:
params.pop(key, None)
# Add _type to params
+ self._raw_params = 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._build_each_node_in_params_dict(user_id)
+ self._get_and_instantiate_class(user_id)
self._validate_built_object()
self._built = True
- def _build_each_node_in_params_dict(self):
+ def _build_each_node_in_params_dict(self, user_id=None):
"""
Iterates over each node in the params dictionary and builds it.
"""
@@ -153,9 +258,9 @@ class Vertex:
if value == self:
del self.params[key]
continue
- self._build_node_and_update_params(key, value)
+ self._build_node_and_update_params(key, value, user_id)
elif isinstance(value, list) and self._is_list_of_nodes(value):
- self._build_list_of_nodes_and_update_params(key, value)
+ self._build_list_of_nodes_and_update_params(key, value, user_id)
def _is_node(self, value):
"""
@@ -169,23 +274,45 @@ class Vertex:
"""
return all(self._is_node(node) for node in value)
- def _build_node_and_update_params(self, key, node):
+ def get_result(self, user_id=None, timeout=None) -> Any:
+ # Check if the Vertex was built already
+ if self._built:
+ return self._built_object
+
+ if self.is_task and self.task_id is not None:
+ task = self.get_task()
+ result = task.get(timeout=timeout)
+ if result is not None: # If result is ready
+ self._update_built_object_and_artifacts(result)
+ return self._built_object
+ else:
+ # Handle the case when the result is not ready (retry, throw exception, etc.)
+ pass
+
+ # If there's no task_id, build the vertex locally
+ self.build(user_id)
+ return self._built_object
+
+ 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.get_result(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.get_result(user_id)
if isinstance(built, list):
if key not in self.params:
self.params[key] = []
@@ -215,7 +342,7 @@ 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.
"""
@@ -226,11 +353,13 @@ 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:
+ logger.exception(exc)
raise ValueError(
- f"Error building node {self.vertex_type}: {str(exc)}"
+ f"Error building node {self.vertex_type}(ID:{self.id}): {str(exc)}"
) from exc
def _update_built_object_and_artifacts(self, result):
@@ -255,9 +384,9 @@ class Vertex:
raise ValueError(message)
- def build(self, force: bool = False) -> Any:
+ 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
@@ -269,7 +398,10 @@ class Vertex:
return f"Vertex(id={self.id}, data={self.data})"
def __eq__(self, __o: object) -> bool:
- return self.id == __o.id if isinstance(__o, Vertex) else False
+ try:
+ return self.id == __o.id if isinstance(__o, Vertex) else False
+ except AttributeError:
+ return False
def __hash__(self) -> int:
return id(self)
diff --git a/src/backend/langflow/graph/vertex/types.py b/src/backend/langflow/graph/vertex/types.py
index 9a2dc21c5..7609982a5 100644
--- a/src/backend/langflow/graph/vertex/types.py
+++ b/src/backend/langflow/graph/vertex/types.py
@@ -7,55 +7,68 @@ from langflow.interface.utils import extract_input_variables_from_prompt
class AgentVertex(Vertex):
- def __init__(self, data: Dict):
- super().__init__(data, base_type="agents")
+ def __init__(self, data: Dict, params: Optional[Dict] = None):
+ super().__init__(data, base_type="agents", params=params)
self.tools: List[Union[ToolkitVertex, ToolVertex]] = []
self.chains: List[ChainVertex] = []
+ def __getstate__(self):
+ state = super().__getstate__()
+ state["tools"] = self.tools
+ state["chains"] = self.chains
+ return state
+
+ def __setstate__(self, state):
+ self.tools = state["tools"]
+ self.chains = state["chains"]
+ super().__setstate__(state)
+
def _set_tools_and_chains(self) -> None:
for edge in self.edges:
+ if not hasattr(edge, "source"):
+ continue
source_node = edge.source
if isinstance(source_node, (ToolVertex, ToolkitVertex)):
self.tools.append(source_node)
elif isinstance(source_node, ChainVertex):
self.chains.append(source_node)
- def build(self, force: bool = False) -> Any:
+ def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any:
if not self._built or force:
self._set_tools_and_chains()
# First, build the tools
for tool_node in self.tools:
- tool_node.build()
+ tool_node.build(user_id=user_id)
# Next, build the chains and the rest
for chain_node in self.chains:
- chain_node.build(tools=self.tools)
+ chain_node.build(tools=self.tools, user_id=user_id)
- self._build()
+ self._build(user_id=user_id)
return self._built_object
class ToolVertex(Vertex):
- def __init__(self, data: Dict):
- super().__init__(data, base_type="tools")
+ def __init__(self, data: Dict, params: Optional[Dict] = None):
+ super().__init__(data, base_type="tools", params=params)
class LLMVertex(Vertex):
built_node_type = None
class_built_object = None
- def __init__(self, data: Dict):
- super().__init__(data, base_type="llms")
+ def __init__(self, data: Dict, params: Optional[Dict] = None):
+ super().__init__(data, base_type="llms", params=params)
- def build(self, force: bool = False) -> Any:
+ def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any:
# LLM is different because some models might take up too much memory
# or time to load. So we only load them when we need them.ร
if self.vertex_type == self.built_node_type:
return self.class_built_object
if not self._built or force:
- self._build()
+ self._build(user_id=user_id)
self.built_node_type = self.vertex_type
self.class_built_object = self._built_object
# Avoid deepcopying the LLM
@@ -64,39 +77,41 @@ class LLMVertex(Vertex):
class ToolkitVertex(Vertex):
- def __init__(self, data: Dict):
- super().__init__(data, base_type="toolkits")
+ def __init__(self, data: Dict, params=None):
+ super().__init__(data, base_type="toolkits", params=params)
class FileToolVertex(ToolVertex):
- def __init__(self, data: Dict):
- super().__init__(data)
+ def __init__(self, data: Dict, params=None):
+ super().__init__(data, params=params)
class WrapperVertex(Vertex):
def __init__(self, data: Dict):
super().__init__(data, base_type="wrappers")
- def build(self, force: bool = False) -> Any:
+ def build(self, force: bool = False, user_id=None, *args, **kwargs) -> Any:
if not self._built or force:
if "headers" in self.params:
self.params["headers"] = ast.literal_eval(self.params["headers"])
- self._build()
+ self._build(user_id=user_id)
return self._built_object
class DocumentLoaderVertex(Vertex):
- def __init__(self, data: Dict):
- super().__init__(data, base_type="documentloaders")
+ def __init__(self, data: Dict, params: Optional[Dict] = None):
+ super().__init__(data, base_type="documentloaders", params=params)
def _built_object_repr(self):
# This built_object is a list of documents. Maybe we should
# show how many documents are in the list?
if self._built_object:
- avg_length = sum(len(doc.page_content) for doc in self._built_object) / len(
- self._built_object
- )
+ avg_length = sum(
+ len(doc.page_content)
+ for doc in self._built_object
+ if hasattr(doc, "page_content")
+ ) / len(self._built_object)
return f"""{self.vertex_type}({len(self._built_object)} documents)
\nAvg. Document Length (characters): {int(avg_length)}
Documents: {self._built_object[:3]}..."""
@@ -104,14 +119,51 @@ class DocumentLoaderVertex(Vertex):
class EmbeddingVertex(Vertex):
- def __init__(self, data: Dict):
- super().__init__(data, base_type="embeddings")
+ def __init__(self, data: Dict, params: Optional[Dict] = None):
+ super().__init__(data, base_type="embeddings", params=params)
class VectorStoreVertex(Vertex):
- def __init__(self, data: Dict):
+ def __init__(self, data: Dict, params=None):
super().__init__(data, base_type="vectorstores")
+ self.params = params or {}
+
+ # VectorStores may contain databse connections
+ # so we need to define the __reduce__ method and the __setstate__ method
+ # to avoid pickling errors
+ def clean_edges_for_pickling(self):
+ # for each edge that has self as source
+ # we need to clear the _built_object of the target
+ # so that we don't try to pickle a database connection
+ for edge in self.edges:
+ if edge.source == self:
+ edge.target._built_object = None
+ edge.target._built = False
+ edge.target.params[edge.target_param] = self
+
+ def remove_docs_and_texts_from_params(self):
+ # remove documents and texts from params
+ # so that we don't try to pickle a database connection
+ self.params.pop("documents", None)
+ self.params.pop("texts", None)
+
+ def __getstate__(self):
+ # We want to save the params attribute
+ # and if "documents" or "texts" are in the params
+ # we want to remove them because they have already
+ # been processed.
+ params = self.params.copy()
+ params.pop("documents", None)
+ params.pop("texts", None)
+ self.clean_edges_for_pickling()
+
+ return super().__getstate__()
+
+ def __setstate__(self, state):
+ super().__setstate__(state)
+ self.remove_docs_and_texts_from_params()
+
class MemoryVertex(Vertex):
def __init__(self, data: Dict):
@@ -124,8 +176,8 @@ class RetrieverVertex(Vertex):
class TextSplitterVertex(Vertex):
- def __init__(self, data: Dict):
- super().__init__(data, base_type="textsplitters")
+ def __init__(self, data: Dict, params: Optional[Dict] = None):
+ super().__init__(data, base_type="textsplitters", params=params)
def _built_object_repr(self):
# This built_object is a list of documents. Maybe we should
@@ -148,16 +200,19 @@ class ChainVertex(Vertex):
def build(
self,
force: bool = False,
- tools: Optional[List[Union[ToolkitVertex, ToolVertex]]] = None,
+ user_id=None,
+ *args,
+ **kwargs,
) -> Any:
if not self._built or force:
# Check if the chain requires a PromptVertex
for key, value in self.params.items():
if isinstance(value, PromptVertex):
# Build the PromptVertex, passing the tools if available
+ tools = kwargs.get("tools", None)
self.params[key] = value.build(tools=tools, force=force)
- self._build()
+ self._build(user_id=user_id)
return self._built_object
@@ -169,7 +224,10 @@ class PromptVertex(Vertex):
def build(
self,
force: bool = False,
+ user_id=None,
tools: Optional[List[Union[ToolkitVertex, ToolVertex]]] = None,
+ *args,
+ **kwargs,
) -> Any:
if not self._built or force:
if (
@@ -180,7 +238,7 @@ class PromptVertex(Vertex):
# Check if it is a ZeroShotPrompt and needs a tool
if "ShotPrompt" in self.vertex_type:
tools = (
- [tool_node.build() for tool_node in tools]
+ [tool_node.build(user_id=user_id) for tool_node in tools]
if tools is not None
else []
)
@@ -205,10 +263,10 @@ class PromptVertex(Vertex):
self.params["input_variables"] = list(
set(self.params["input_variables"])
)
- else:
+ elif isinstance(self.params, dict):
self.params.pop("input_variables", None)
- self._build()
+ self._build(user_id=user_id)
return self._built_object
def _built_object_repr(self):
@@ -252,8 +310,13 @@ class OutputParserVertex(Vertex):
class CustomComponentVertex(Vertex):
def __init__(self, data: Dict):
- super().__init__(data, base_type="custom_components")
+ super().__init__(data, base_type="custom_components", is_task=True)
def _built_object_repr(self):
+ if self.task_id and self.is_task:
+ if task := self.get_task():
+ return str(task.info)
+ else:
+ return f"Task {self.task_id} is not running"
if self.artifacts and "repr" in self.artifacts:
return self.artifacts["repr"] or super()._built_object_repr()
diff --git a/src/backend/langflow/graph/vertex/utils.py b/src/backend/langflow/graph/vertex/utils.py
new file mode 100644
index 000000000..e1d439f4b
--- /dev/null
+++ b/src/backend/langflow/graph/vertex/utils.py
@@ -0,0 +1,5 @@
+from langflow.utils.constants import PYTHON_BASIC_TYPES
+
+
+def is_basic_type(obj):
+ return type(obj) in PYTHON_BASIC_TYPES
diff --git a/src/backend/langflow/interface/agents/base.py b/src/backend/langflow/interface/agents/base.py
index cc5214c0c..696f5afd0 100644
--- a/src/backend/langflow/interface/agents/base.py
+++ b/src/backend/langflow/interface/agents/base.py
@@ -5,9 +5,10 @@ from langchain.agents import types
from langflow.custom.customs import get_custom_nodes
from langflow.interface.agents.custom import CUSTOM_AGENTS
from langflow.interface.base import LangChainTypeCreator
-from langflow.settings import settings
+from langflow.services.getters import get_settings_service
+
from langflow.template.frontend_node.agents import AgentFrontendNode
-from langflow.utils.logger import logger
+from loguru import logger
from langflow.utils.util import build_template_from_class, build_template_from_method
@@ -53,13 +54,17 @@ class AgentCreator(LangChainTypeCreator):
# Now this is a generator
def to_list(self) -> List[str]:
names = []
+ settings_service = get_settings_service()
for _, agent in self.type_to_loader_dict.items():
agent_name = (
agent.function_name()
if hasattr(agent, "function_name")
else agent.__name__
)
- if agent_name in settings.AGENTS or settings.DEV:
+ if (
+ agent_name in settings_service.settings.AGENTS
+ or settings_service.settings.DEV
+ ):
names.append(agent_name)
return names
diff --git a/src/backend/langflow/interface/agents/custom.py b/src/backend/langflow/interface/agents/custom.py
index b4e2b9bac..735b27917 100644
--- a/src/backend/langflow/interface/agents/custom.py
+++ b/src/backend/langflow/interface/agents/custom.py
@@ -1,6 +1,6 @@
from typing import Any, List, Optional
-from langchain import LLMChain
+from langchain.chains.llm import LLMChain
from langchain.agents import (
AgentExecutor,
Tool,
diff --git a/src/backend/langflow/interface/agents/prebuilt.py b/src/backend/langflow/interface/agents/prebuilt.py
index 5b81b7713..ec4799a81 100644
--- a/src/backend/langflow/interface/agents/prebuilt.py
+++ b/src/backend/langflow/interface/agents/prebuilt.py
@@ -1,4 +1,4 @@
-from langchain import LLMChain
+from langchain.chains.llm import LLMChain
from langchain.agents import AgentExecutor, ZeroShotAgent
from langchain.agents.agent_toolkits.json.prompt import JSON_PREFIX, JSON_SUFFIX
from langchain.agents.agent_toolkits.json.toolkit import JsonToolkit
diff --git a/src/backend/langflow/interface/base.py b/src/backend/langflow/interface/base.py
index 76d859b1f..13dd05619 100644
--- a/src/backend/langflow/interface/base.py
+++ b/src/backend/langflow/interface/base.py
@@ -2,13 +2,14 @@ from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional, Type, Union
from langchain.chains.base import Chain
from langchain.agents import AgentExecutor
+from langflow.services.getters import get_settings_service
from pydantic import BaseModel
from langflow.template.field.base import TemplateField
from langflow.template.frontend_node.base import FrontendNode
from langflow.template.template.base import Template
-from langflow.utils.logger import logger
-from langflow.settings import settings
+from loguru import logger
+
# Assuming necessary imports for Field, Template, and FrontendNode classes
@@ -26,9 +27,12 @@ class LangChainTypeCreator(BaseModel, ABC):
@property
def docs_map(self) -> Dict[str, str]:
"""A dict with the name of the component as key and the documentation link as value."""
+ settings_service = get_settings_service()
if self.name_docs_dict is None:
try:
- type_settings = getattr(settings, self.type_name.upper())
+ type_settings = getattr(
+ settings_service.settings, self.type_name.upper()
+ )
self.name_docs_dict = {
name: value_dict["documentation"]
for name, value_dict in type_settings.items()
diff --git a/src/backend/langflow/interface/chains/base.py b/src/backend/langflow/interface/chains/base.py
index fe58397b2..b2d07b7e4 100644
--- a/src/backend/langflow/interface/chains/base.py
+++ b/src/backend/langflow/interface/chains/base.py
@@ -3,9 +3,10 @@ from typing import Any, Dict, List, Optional, Type
from langflow.custom.customs import get_custom_nodes
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.importing.utils import import_class
-from langflow.settings import settings
+from langflow.services.getters import get_settings_service
+
from langflow.template.frontend_node.chains import ChainFrontendNode
-from langflow.utils.logger import logger
+from loguru import logger
from langflow.utils.util import build_template_from_class, build_template_from_method
from langchain import chains
from langchain_experimental.sql import SQLDatabaseChain # type: ignore
@@ -30,6 +31,7 @@ class ChainCreator(LangChainTypeCreator):
@property
def type_to_loader_dict(self) -> Dict:
if self.type_dict is None:
+ settings_service = get_settings_service()
self.type_dict: dict[str, Any] = {
chain_name: import_class(f"langchain.chains.{chain_name}")
for chain_name in chains.__all__
@@ -43,7 +45,8 @@ class ChainCreator(LangChainTypeCreator):
self.type_dict = {
name: chain
for name, chain in self.type_dict.items()
- if name in settings.CHAINS or settings.DEV
+ if name in settings_service.settings.CHAINS
+ or settings_service.settings.DEV
}
return self.type_dict
diff --git a/src/backend/langflow/interface/custom/base.py b/src/backend/langflow/interface/custom/base.py
index 06e874fa7..45a3ea215 100644
--- a/src/backend/langflow/interface/custom/base.py
+++ b/src/backend/langflow/interface/custom/base.py
@@ -8,7 +8,7 @@ from langflow.interface.custom.custom_component import CustomComponent
from langflow.template.frontend_node.custom_components import (
CustomComponentFrontendNode,
)
-from langflow.utils.logger import logger
+from loguru import logger
# Assuming necessary imports for Field, Template, and FrontendNode classes
diff --git a/src/backend/langflow/interface/custom/constants.py b/src/backend/langflow/interface/custom/constants.py
index 39a1e9f3b..6df72500c 100644
--- a/src/backend/langflow/interface/custom/constants.py
+++ b/src/backend/langflow/interface/custom/constants.py
@@ -1,65 +1,33 @@
-from langchain import PromptTemplate
-from langchain.chains.base import Chain
-from langchain.document_loaders.base import BaseLoader
-from langchain.embeddings.base import Embeddings
-from langchain.llms.base import BaseLLM
-from langchain.schema import BaseRetriever, Document
-from langchain.text_splitter import TextSplitter
-from langchain.tools import Tool
-from langchain.vectorstores.base import VectorStore
-from langchain.schema import BaseOutputParser
-from langchain.schema.memory import BaseMemory
-from langchain.memory.chat_memory import BaseChatMemory
-from langchain.agents.agent import AgentExecutor
-
-LANGCHAIN_BASE_TYPES = {
- "Chain": Chain,
- "AgentExecutor": AgentExecutor,
- "Tool": Tool,
- "BaseLLM": BaseLLM,
- "PromptTemplate": PromptTemplate,
- "BaseLoader": BaseLoader,
- "Document": Document,
- "TextSplitter": TextSplitter,
- "VectorStore": VectorStore,
- "Embeddings": Embeddings,
- "BaseRetriever": BaseRetriever,
- "BaseOutputParser": BaseOutputParser,
- "BaseMemory": BaseMemory,
- "BaseChatMemory": BaseChatMemory,
-}
-
-# Langchain base types plus Python base types
-CUSTOM_COMPONENT_SUPPORTED_TYPES = {
- **LANGCHAIN_BASE_TYPES,
- "str": str,
- "int": int,
- "float": float,
- "bool": bool,
- "list": list,
- "dict": dict,
-}
-
-
DEFAULT_CUSTOM_COMPONENT_CODE = """from langflow import CustomComponent
-from langchain.llms.base import BaseLLM
-from langchain.chains import LLMChain
-from langchain import PromptTemplate
-from langchain.schema import Document
+from langflow.field_typing import (
+ Tool,
+ PromptTemplate,
+ Chain,
+ BaseChatMemory,
+ BaseLLM,
+ BaseLoader,
+ BaseMemory,
+ BaseOutputParser,
+ BaseRetriever,
+ VectorStore,
+ Embeddings,
+ TextSplitter,
+ Document,
+ AgentExecutor,
+ NestedDict,
+ Data,
+)
-import requests
-class YourComponent(CustomComponent):
+class Component(CustomComponent):
display_name: str = "Custom Component"
description: str = "Create any custom component you want!"
def build_config(self):
- return { "url": { "multiline": True, "required": True } }
+ return {"param": {"display_name": "Parameter"}}
+
+ def build(self, param: Data) -> Data:
+ return param
- def build(self, url: str, llm: BaseLLM, prompt: PromptTemplate) -> Document:
- response = requests.get(url)
- chain = LLMChain(llm=llm, prompt=prompt)
- result = chain.run(response.text[:300])
- return Document(page_content=str(result))
"""
diff --git a/src/backend/langflow/interface/custom/custom_component.py b/src/backend/langflow/interface/custom/custom_component.py
index 31f8433bc..4b889e77b 100644
--- a/src/backend/langflow/interface/custom/custom_component.py
+++ b/src/backend/langflow/interface/custom/custom_component.py
@@ -1,14 +1,16 @@
-from typing import Any, Callable, List, Optional
+from typing import Any, Callable, List, Optional, Union
+from uuid import UUID
from fastapi import HTTPException
-from langflow.interface.custom.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES
+from langflow.field_typing.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES
from langflow.interface.custom.component import Component
from langflow.interface.custom.directory_reader import DirectoryReader
+from langflow.services.getters import get_db_service
from langflow.interface.custom.utils import extract_inner_type
from langflow.utils import validate
-from langflow.database.base import session_getter
-from langflow.database.models.flow import Flow
+from langflow.services.database.utils import session_getter
+from langflow.services.database.models.flow import Flow
from pydantic import Extra
import yaml
@@ -21,6 +23,7 @@ class CustomComponent(Component, extra=Extra.allow):
function: Optional[Callable] = None
return_type_valid_list = list(CUSTOM_COMPONENT_SUPPORTED_TYPES.keys())
repr_value: Optional[Any] = ""
+ user_id: Optional[Union[UUID, str]] = None
def __init__(self, **data):
super().__init__(**data)
@@ -92,7 +95,23 @@ class CustomComponent(Component, extra=Extra.allow):
build_method = build_methods[0]
- return build_method["args"]
+ args = build_method["args"]
+ for arg in args:
+ if arg.get("type") == "prompt":
+ raise HTTPException(
+ status_code=400,
+ detail={
+ "error": "Type hint Error",
+ "traceback": (
+ "Prompt type is not supported in the build method."
+ " Try using PromptTemplate instead."
+ ),
+ },
+ )
+ elif not arg.get("type"):
+ # Set the type to Data
+ arg["type"] = "Data"
+ return args
@property
def get_function_entrypoint_return_type(self) -> List[str]:
@@ -173,22 +192,29 @@ class CustomComponent(Component, extra=Extra.allow):
return validate.create_function(self.code, self.function_entrypoint_name)
def load_flow(self, flow_id: str, tweaks: Optional[dict] = None) -> Any:
- from langflow.processing.process import build_sorted_vertices_with_caching
+ from langflow.processing.process import build_sorted_vertices
from langflow.processing.process import process_tweaks
- with session_getter() as session:
+ db_service = get_db_service()
+ with session_getter(db_service) as session:
graph_data = flow.data if (flow := session.get(Flow, flow_id)) else None
if not graph_data:
raise ValueError(f"Flow {flow_id} not found")
if tweaks:
graph_data = process_tweaks(graph_data=graph_data, tweaks=tweaks)
- return build_sorted_vertices_with_caching(graph_data)
+ return build_sorted_vertices(graph_data)
def list_flows(self, *, get_session: Optional[Callable] = None) -> List[Flow]:
- get_session = get_session or session_getter
- with get_session() as session:
- flows = session.query(Flow).all()
- return flows
+ if not self.user_id:
+ raise ValueError("Session is invalid")
+ try:
+ get_session = get_session or session_getter
+ db_service = get_db_service()
+ with get_session(db_service) as session:
+ flows = session.query(Flow).filter(Flow.user_id == self.user_id).all()
+ return flows
+ except Exception as e:
+ raise ValueError("Session is invalid") from e
def get_flow(
self,
@@ -199,12 +225,16 @@ class CustomComponent(Component, extra=Extra.allow):
get_session: Optional[Callable] = None,
) -> Flow:
get_session = get_session or session_getter
-
- with get_session() as session:
+ db_service = get_db_service()
+ with get_session(db_service) as session:
if flow_id:
flow = session.query(Flow).get(flow_id)
elif flow_name:
- flow = session.query(Flow).filter(Flow.name == flow_name).first()
+ flow = (
+ session.query(Flow)
+ .filter(Flow.name == flow_name)
+ .filter(Flow.user_id == self.user_id)
+ ).first()
else:
raise ValueError("Either flow_name or flow_id must be provided")
diff --git a/src/backend/langflow/interface/custom/directory_reader.py b/src/backend/langflow/interface/custom/directory_reader.py
index 44b2d4f1b..01b11a4a6 100644
--- a/src/backend/langflow/interface/custom/directory_reader.py
+++ b/src/backend/langflow/interface/custom/directory_reader.py
@@ -1,7 +1,7 @@
import os
import ast
import zlib
-from langflow.utils.logger import logger
+from loguru import logger
class CustomComponentPathValueError(ValueError):
diff --git a/src/backend/langflow/interface/document_loaders/base.py b/src/backend/langflow/interface/document_loaders/base.py
index ebae1e5a4..e2c379b67 100644
--- a/src/backend/langflow/interface/document_loaders/base.py
+++ b/src/backend/langflow/interface/document_loaders/base.py
@@ -1,10 +1,11 @@
from typing import Dict, List, Optional, Type
from langflow.interface.base import LangChainTypeCreator
+from langflow.services.getters import get_settings_service
from langflow.template.frontend_node.documentloaders import DocumentLoaderFrontNode
from langflow.interface.custom_lists import documentloaders_type_to_cls_dict
-from langflow.settings import settings
-from langflow.utils.logger import logger
+
+from loguru import logger
from langflow.utils.util import build_template_from_class
@@ -30,10 +31,12 @@ class DocumentLoaderCreator(LangChainTypeCreator):
return None
def to_list(self) -> List[str]:
+ settings_service = get_settings_service()
return [
documentloader.__name__
for documentloader in self.type_to_loader_dict.values()
- if documentloader.__name__ in settings.DOCUMENTLOADERS or settings.DEV
+ if documentloader.__name__ in settings_service.settings.DOCUMENTLOADERS
+ or settings_service.settings.DEV
]
diff --git a/src/backend/langflow/interface/embeddings/base.py b/src/backend/langflow/interface/embeddings/base.py
index 7572a06cc..d280cf1c1 100644
--- a/src/backend/langflow/interface/embeddings/base.py
+++ b/src/backend/langflow/interface/embeddings/base.py
@@ -2,10 +2,11 @@ from typing import Dict, List, Optional, Type
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.custom_lists import embedding_type_to_cls_dict
-from langflow.settings import settings
+from langflow.services.getters import get_settings_service
+
from langflow.template.frontend_node.base import FrontendNode
from langflow.template.frontend_node.embeddings import EmbeddingFrontendNode
-from langflow.utils.logger import logger
+from loguru import logger
from langflow.utils.util import build_template_from_class
@@ -32,10 +33,12 @@ class EmbeddingCreator(LangChainTypeCreator):
return None
def to_list(self) -> List[str]:
+ settings_service = get_settings_service()
return [
embedding.__name__
for embedding in self.type_to_loader_dict.values()
- if embedding.__name__ in settings.EMBEDDINGS or settings.DEV
+ if embedding.__name__ in settings_service.settings.EMBEDDINGS
+ or settings_service.settings.DEV
]
diff --git a/src/backend/langflow/interface/importing/utils.py b/src/backend/langflow/interface/importing/utils.py
index d07222dd1..44d72df42 100644
--- a/src/backend/langflow/interface/importing/utils.py
+++ b/src/backend/langflow/interface/importing/utils.py
@@ -3,7 +3,7 @@
import importlib
from typing import Any, Type
-from langchain import PromptTemplate
+from langchain.prompts import PromptTemplate
from langchain.agents import Agent
from langchain.base_language import BaseLanguageModel
from langchain.chains.base import Chain
@@ -144,6 +144,8 @@ def import_chain(chain: str) -> Type[Chain]:
if chain in CUSTOM_CHAINS:
return CUSTOM_CHAINS[chain]
+ if chain == "SQLDatabaseChain":
+ return import_class("langchain_experimental.sql.SQLDatabaseChain")
return import_class(f"langchain.chains.{chain}")
diff --git a/src/backend/langflow/interface/initialize/loading.py b/src/backend/langflow/interface/initialize/loading.py
index 1cce109ab..4492cca1b 100644
--- a/src/backend/langflow/interface/initialize/loading.py
+++ b/src/backend/langflow/interface/initialize/loading.py
@@ -1,7 +1,7 @@
import json
import orjson
-from typing import Any, Callable, Dict, Sequence, Type
-
+from typing import Any, Callable, Dict, Sequence, Type, TYPE_CHECKING
+from langchain.schema import Document
from langchain.agents import agent as agent_module
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.base import BaseToolkit
@@ -34,13 +34,29 @@ from langflow.utils import validate
from langchain.chains.base import Chain
from langchain.vectorstores.base import VectorStore
from langchain.document_loaders.base import BaseLoader
-from langflow.utils.logger import logger
+from loguru import logger
+
+if TYPE_CHECKING:
+ from langflow import CustomComponent
-def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any:
+def build_vertex_in_params(params: Dict) -> Dict:
+ from langflow.graph.vertex.base import Vertex
+
+ # If any of the values in params is a Vertex, we will build it
+ return {
+ key: value.build() if isinstance(value, Vertex) else value
+ for key, value in params.items()
+ }
+
+
+def instantiate_class(
+ node_type: str, base_type: str, params: Dict, user_id=None
+) -> Any:
"""Instantiate class from module type and key, and params"""
params = convert_params_to_sets(params)
params = convert_kwargs(params)
+
if node_type in CUSTOM_NODES:
if custom_node := CUSTOM_NODES.get(node_type):
if hasattr(custom_node, "initialize"):
@@ -48,7 +64,9 @@ def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any:
return custom_node(**params)
logger.debug(f"Instantiating {node_type} of type {base_type}")
class_object = import_by_type(_type=base_type, name=node_type)
- return instantiate_based_on_type(class_object, base_type, node_type, params)
+ return instantiate_based_on_type(
+ class_object, base_type, node_type, params, user_id=user_id
+ )
def convert_params_to_sets(params):
@@ -75,7 +93,7 @@ def convert_kwargs(params):
return params
-def instantiate_based_on_type(class_object, base_type, node_type, params):
+def instantiate_based_on_type(class_object, base_type, node_type, params, user_id):
if base_type == "agents":
return instantiate_agent(node_type, class_object, params)
elif base_type == "prompts":
@@ -89,11 +107,11 @@ def instantiate_based_on_type(class_object, base_type, node_type, params):
elif base_type == "toolkits":
return instantiate_toolkit(node_type, class_object, params)
elif base_type == "embeddings":
- return instantiate_embedding(class_object, params)
+ return instantiate_embedding(node_type, class_object, params)
elif base_type == "vectorstores":
return instantiate_vectorstore(class_object, params)
elif base_type == "documentloaders":
- return instantiate_documentloader(class_object, params)
+ return instantiate_documentloader(node_type, class_object, params)
elif base_type == "textsplitters":
return instantiate_textsplitter(class_object, params)
elif base_type == "utilities":
@@ -109,19 +127,19 @@ def instantiate_based_on_type(class_object, base_type, node_type, params):
elif base_type == "memory":
return instantiate_memory(node_type, class_object, params)
elif base_type == "custom_components":
- return instantiate_custom_component(node_type, class_object, params)
+ return instantiate_custom_component(node_type, class_object, params, user_id)
elif base_type == "wrappers":
return instantiate_wrapper(node_type, class_object, params)
else:
return class_object(**params)
-def instantiate_custom_component(node_type, class_object, params):
+def instantiate_custom_component(node_type, class_object, params, user_id):
# we need to make a copy of the params because we will be
# modifying it
params_copy = params.copy()
- class_object = get_function_custom(params_copy.pop("code"))
- custom_component = class_object()
+ class_object: "CustomComponent" = get_function_custom(params_copy.pop("code"))
+ custom_component = class_object(user_id=user_id)
built_object = custom_component.build(**params_copy)
return built_object, {"repr": custom_component.custom_repr()}
@@ -148,7 +166,7 @@ def instantiate_llm(node_type, class_object, params: Dict):
# This is a workaround so JinaChat works until streaming is implemented
# if "openai_api_base" in params and "jina" in params["openai_api_base"]:
# False if condition is True
- if node_type == "VertexAI":
+ if "VertexAI" in node_type:
return initialize_vertexai(class_object=class_object, params=params)
# max_tokens sometimes is a string and should be an int
if "max_tokens" in params:
@@ -262,9 +280,13 @@ def instantiate_toolkit(node_type, class_object: Type[BaseToolkit], params: Dict
return loaded_toolkit
-def instantiate_embedding(class_object, params: Dict):
+def instantiate_embedding(node_type, class_object, params: Dict):
params.pop("model", None)
params.pop("headers", None)
+
+ if "VertexAI" in node_type:
+ return initialize_vertexai(class_object=class_object, params=params)
+
try:
return class_object(**params)
except ValidationError:
@@ -278,6 +300,15 @@ def instantiate_embedding(class_object, params: Dict):
def instantiate_vectorstore(class_object: Type[VectorStore], params: Dict):
search_kwargs = params.pop("search_kwargs", {})
+ if search_kwargs == {"yourkey": "value"}:
+ search_kwargs = {}
+ # clean up docs or texts to have only documents
+ if "texts" in params:
+ params["documents"] = params.pop("texts")
+ if "documents" in params:
+ params["documents"] = [
+ doc for doc in params["documents"] if isinstance(doc, Document)
+ ]
if initializer := vecstore_initializer.get(class_object.__name__):
vecstore = initializer(class_object, params)
else:
@@ -292,7 +323,9 @@ def instantiate_vectorstore(class_object: Type[VectorStore], params: Dict):
return vecstore
-def instantiate_documentloader(class_object: Type[BaseLoader], params: Dict):
+def instantiate_documentloader(
+ node_type: str, class_object: Type[BaseLoader], params: Dict
+):
if "file_filter" in params:
# file_filter will be a string but we need a function
# that will be used to filter the files using file_filter
@@ -312,6 +345,11 @@ def instantiate_documentloader(class_object: Type[BaseLoader], params: Dict):
raise ValueError(
"The metadata you provided is not a valid JSON string."
) from exc
+
+ if node_type == "WebBaseLoader":
+ if web_path := params.pop("web_path", None):
+ params["web_paths"] = [web_path]
+
docs = class_object(**params).load()
# Now if metadata is an empty dict, we will not add it to the documents
if metadata:
diff --git a/src/backend/langflow/interface/initialize/utils.py b/src/backend/langflow/interface/initialize/utils.py
index 116673645..199626de5 100644
--- a/src/backend/langflow/interface/initialize/utils.py
+++ b/src/backend/langflow/interface/initialize/utils.py
@@ -1,6 +1,6 @@
import contextlib
import json
-from langflow.database.models.base import orjson_dumps
+from langflow.services.database.models.base import orjson_dumps
import orjson
from typing import Any, Dict, List
diff --git a/src/backend/langflow/interface/initialize/vector_store.py b/src/backend/langflow/interface/initialize/vector_store.py
index 233292626..59e7c0d3b 100644
--- a/src/backend/langflow/interface/initialize/vector_store.py
+++ b/src/backend/langflow/interface/initialize/vector_store.py
@@ -8,7 +8,7 @@ from langchain.vectorstores import (
SupabaseVectorStore,
MongoDBAtlasVectorSearch,
)
-
+from langchain.schema import Document
import os
import orjson
@@ -201,11 +201,16 @@ def initialize_chroma(class_object: Type[Chroma], params: dict):
if "texts" in params:
params["documents"] = params.pop("texts")
for doc in params["documents"]:
+ if not isinstance(doc, Document):
+ # remove any non-Document objects from the list
+ params["documents"].remove(doc)
+ continue
if doc.metadata is None:
doc.metadata = {}
for key, value in doc.metadata.items():
if value is None:
doc.metadata[key] = ""
+
chromadb = class_object.from_documents(**params)
if persist:
chromadb.persist()
diff --git a/src/backend/langflow/interface/listing.py b/src/backend/langflow/interface/listing.py
index fe3090f65..aa72e568e 100644
--- a/src/backend/langflow/interface/listing.py
+++ b/src/backend/langflow/interface/listing.py
@@ -1,47 +1,27 @@
-from langflow.interface.agents.base import agent_creator
-from langflow.interface.chains.base import chain_creator
-from langflow.interface.document_loaders.base import documentloader_creator
-from langflow.interface.embeddings.base import embedding_creator
-from langflow.interface.llms.base import llm_creator
-from langflow.interface.memories.base import memory_creator
-from langflow.interface.prompts.base import prompt_creator
-from langflow.interface.text_splitters.base import textsplitter_creator
-from langflow.interface.toolkits.base import toolkits_creator
-from langflow.interface.tools.base import tool_creator
-from langflow.interface.utilities.base import utility_creator
-from langflow.interface.vector_store.base import vectorstore_creator
-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 langflow.services.getters import get_settings_service
+from langflow.utils.lazy_load import LazyLoadDictBase
-def get_type_dict():
- return {
- "agents": agent_creator.to_list(),
- "prompts": prompt_creator.to_list(),
- "llms": llm_creator.to_list(),
- "tools": tool_creator.to_list(),
- "chains": chain_creator.to_list(),
- "memory": memory_creator.to_list(),
- "toolkits": toolkits_creator.to_list(),
- "wrappers": wrapper_creator.to_list(),
- "documentLoaders": documentloader_creator.to_list(),
- "vectorStore": vectorstore_creator.to_list(),
- "embeddings": embedding_creator.to_list(),
- "textSplitters": textsplitter_creator.to_list(),
- "utilities": utility_creator.to_list(),
- "outputParsers": output_parser_creator.to_list(),
- "retrievers": retriever_creator.to_list(),
- "custom_components": custom_component_creator.to_list(),
- }
+class AllTypesDict(LazyLoadDictBase):
+ def __init__(self):
+ self._all_types_dict = None
+
+ @property
+ def ALL_TYPES_DICT(self):
+ return self.all_types_dict
+
+ def _build_dict(self):
+ langchain_types_dict = self.get_type_dict()
+ return {
+ **langchain_types_dict,
+ "Custom": ["Custom Tool", "Python Function"],
+ }
+
+ def get_type_dict(self):
+ from langflow.interface.types import get_all_types_dict
+
+ settings_service = get_settings_service()
+ return get_all_types_dict(settings_service=settings_service)
-LANGCHAIN_TYPES_DICT = get_type_dict()
-
-# Now we'll build a dict with Langchain types and ours
-
-ALL_TYPES_DICT = {
- **LANGCHAIN_TYPES_DICT,
- "Custom": ["Custom Tool", "Python Function"],
-}
+lazy_load_dict = AllTypesDict()
diff --git a/src/backend/langflow/interface/llms/base.py b/src/backend/langflow/interface/llms/base.py
index 06aedd3cb..ffb7fa2f2 100644
--- a/src/backend/langflow/interface/llms/base.py
+++ b/src/backend/langflow/interface/llms/base.py
@@ -2,9 +2,10 @@ from typing import Dict, List, Optional, Type
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.custom_lists import llm_type_to_cls_dict
-from langflow.settings import settings
+from langflow.services.getters import get_settings_service
+
from langflow.template.frontend_node.llms import LLMFrontendNode
-from langflow.utils.logger import logger
+from loguru import logger
from langflow.utils.util import build_template_from_class
@@ -33,10 +34,12 @@ class LLMCreator(LangChainTypeCreator):
return None
def to_list(self) -> List[str]:
+ settings_service = get_settings_service()
return [
llm.__name__
for llm in self.type_to_loader_dict.values()
- if llm.__name__ in settings.LLMS or settings.DEV
+ if llm.__name__ in settings_service.settings.LLMS
+ or settings_service.settings.DEV
]
diff --git a/src/backend/langflow/interface/memories/base.py b/src/backend/langflow/interface/memories/base.py
index 9cd25381c..3f3658304 100644
--- a/src/backend/langflow/interface/memories/base.py
+++ b/src/backend/langflow/interface/memories/base.py
@@ -2,10 +2,11 @@ from typing import Dict, List, Optional, Type
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.custom_lists import memory_type_to_cls_dict
-from langflow.settings import settings
+from langflow.services.getters import get_settings_service
+
from langflow.template.frontend_node.base import FrontendNode
from langflow.template.frontend_node.memories import MemoryFrontendNode
-from langflow.utils.logger import logger
+from loguru import logger
from langflow.utils.util import build_template_from_class, build_template_from_method
from langflow.custom.customs import get_custom_nodes
@@ -48,10 +49,12 @@ class MemoryCreator(LangChainTypeCreator):
return None
def to_list(self) -> List[str]:
+ settings_service = get_settings_service()
return [
memory.__name__
for memory in self.type_to_loader_dict.values()
- if memory.__name__ in settings.MEMORIES or settings.DEV
+ if memory.__name__ in settings_service.settings.MEMORIES
+ or settings_service.settings.DEV
]
diff --git a/src/backend/langflow/interface/output_parsers/base.py b/src/backend/langflow/interface/output_parsers/base.py
index b5235ad58..06dfdf4eb 100644
--- a/src/backend/langflow/interface/output_parsers/base.py
+++ b/src/backend/langflow/interface/output_parsers/base.py
@@ -4,9 +4,10 @@ from langchain import output_parsers
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.importing.utils import import_class
-from langflow.settings import settings
+from langflow.services.getters import get_settings_service
+
from langflow.template.frontend_node.output_parsers import OutputParserFrontendNode
-from langflow.utils.logger import logger
+from loguru import logger
from langflow.utils.util import build_template_from_class, build_template_from_method
@@ -23,6 +24,7 @@ class OutputParserCreator(LangChainTypeCreator):
@property
def type_to_loader_dict(self) -> Dict:
if self.type_dict is None:
+ settings_service = get_settings_service()
self.type_dict = {
output_parser_name: import_class(
f"langchain.output_parsers.{output_parser_name}"
@@ -33,7 +35,8 @@ class OutputParserCreator(LangChainTypeCreator):
self.type_dict = {
name: output_parser
for name, output_parser in self.type_dict.items()
- if name in settings.OUTPUT_PARSERS or settings.DEV
+ if name in settings_service.settings.OUTPUT_PARSERS
+ or settings_service.settings.DEV
}
return self.type_dict
diff --git a/src/backend/langflow/interface/prompts/base.py b/src/backend/langflow/interface/prompts/base.py
index c062a4a35..29d3e8ba8 100644
--- a/src/backend/langflow/interface/prompts/base.py
+++ b/src/backend/langflow/interface/prompts/base.py
@@ -5,9 +5,10 @@ from langchain import prompts
from langflow.custom.customs import get_custom_nodes
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.importing.utils import import_class
-from langflow.settings import settings
+from langflow.services.getters import get_settings_service
+
from langflow.template.frontend_node.prompts import PromptFrontendNode
-from langflow.utils.logger import logger
+from loguru import logger
from langflow.utils.util import build_template_from_class
@@ -20,6 +21,7 @@ class PromptCreator(LangChainTypeCreator):
@property
def type_to_loader_dict(self) -> Dict:
+ settings_service = get_settings_service()
if self.type_dict is None:
self.type_dict = {
prompt_name: import_class(f"langchain.prompts.{prompt_name}")
@@ -34,7 +36,8 @@ class PromptCreator(LangChainTypeCreator):
self.type_dict = {
name: prompt
for name, prompt in self.type_dict.items()
- if name in settings.PROMPTS or settings.DEV
+ if name in settings_service.settings.PROMPTS
+ or settings_service.settings.DEV
}
return self.type_dict
diff --git a/src/backend/langflow/interface/retrievers/base.py b/src/backend/langflow/interface/retrievers/base.py
index 759cd5916..4ee40e659 100644
--- a/src/backend/langflow/interface/retrievers/base.py
+++ b/src/backend/langflow/interface/retrievers/base.py
@@ -4,9 +4,10 @@ from langchain import retrievers
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.importing.utils import import_class
-from langflow.settings import settings
+from langflow.services.getters import get_settings_service
+
from langflow.template.frontend_node.retrievers import RetrieverFrontendNode
-from langflow.utils.logger import logger
+from loguru import logger
from langflow.utils.util import build_template_from_method, build_template_from_class
@@ -48,10 +49,12 @@ class RetrieverCreator(LangChainTypeCreator):
return None
def to_list(self) -> List[str]:
+ settings_service = get_settings_service()
return [
retriever
for retriever in self.type_to_loader_dict.keys()
- if retriever in settings.RETRIEVERS or settings.DEV
+ if retriever in settings_service.settings.RETRIEVERS
+ or settings_service.settings.DEV
]
diff --git a/src/backend/langflow/interface/run.py b/src/backend/langflow/interface/run.py
index 97f47334e..63391204a 100644
--- a/src/backend/langflow/interface/run.py
+++ b/src/backend/langflow/interface/run.py
@@ -1,21 +1,9 @@
-from langflow.cache.utils import memoize_dict
+from typing import Dict, Tuple
from langflow.graph import Graph
-from langflow.utils.logger import logger
+from loguru import logger
-@memoize_dict(maxsize=10)
-def build_langchain_object_with_caching(data_graph):
- """
- Build langchain object from data_graph.
- """
-
- logger.debug("Building langchain object")
- graph = Graph.from_payload(data_graph)
- return graph.build()
-
-
-@memoize_dict(maxsize=10)
-def build_sorted_vertices_with_caching(data_graph):
+def build_sorted_vertices(data_graph) -> Tuple[Graph, Dict]:
"""
Build langchain object from data_graph.
"""
@@ -28,7 +16,7 @@ def build_sorted_vertices_with_caching(data_graph):
vertex.build()
if vertex.artifacts:
artifacts.update(vertex.artifacts)
- return graph.build(), artifacts
+ return graph, artifacts
def build_langchain_object(data_graph):
@@ -57,8 +45,12 @@ def get_memory_key(langchain_object):
"chat_history": "history",
"history": "chat_history",
}
- memory_key = langchain_object.memory.memory_key
- return mem_key_dict.get(memory_key)
+ # Check if memory_key attribute exists
+ if hasattr(langchain_object.memory, "memory_key"):
+ memory_key = langchain_object.memory.memory_key
+ return mem_key_dict.get(memory_key)
+ else:
+ return None # or some other default value or action
def update_memory_keys(langchain_object, possible_new_mem_key):
diff --git a/src/backend/langflow/interface/text_splitters/base.py b/src/backend/langflow/interface/text_splitters/base.py
index 787f20d82..4f337817f 100644
--- a/src/backend/langflow/interface/text_splitters/base.py
+++ b/src/backend/langflow/interface/text_splitters/base.py
@@ -1,10 +1,11 @@
from typing import Dict, List, Optional, Type
from langflow.interface.base import LangChainTypeCreator
+from langflow.services.getters import get_settings_service
from langflow.template.frontend_node.textsplitters import TextSplittersFrontendNode
from langflow.interface.custom_lists import textsplitter_type_to_cls_dict
-from langflow.settings import settings
-from langflow.utils.logger import logger
+
+from loguru import logger
from langflow.utils.util import build_template_from_class
@@ -30,10 +31,12 @@ class TextSplitterCreator(LangChainTypeCreator):
return None
def to_list(self) -> List[str]:
+ settings_service = get_settings_service()
return [
textsplitter.__name__
for textsplitter in self.type_to_loader_dict.values()
- if textsplitter.__name__ in settings.TEXTSPLITTERS or settings.DEV
+ if textsplitter.__name__ in settings_service.settings.TEXTSPLITTERS
+ or settings_service.settings.DEV
]
diff --git a/src/backend/langflow/interface/toolkits/base.py b/src/backend/langflow/interface/toolkits/base.py
index b7c165a4d..73ca4852f 100644
--- a/src/backend/langflow/interface/toolkits/base.py
+++ b/src/backend/langflow/interface/toolkits/base.py
@@ -4,8 +4,9 @@ from langchain.agents import agent_toolkits
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.importing.utils import import_class, import_module
-from langflow.settings import settings
-from langflow.utils.logger import logger
+from langflow.services.getters import get_settings_service
+
+from loguru import logger
from langflow.utils.util import build_template_from_class
@@ -29,13 +30,15 @@ class ToolkitCreator(LangChainTypeCreator):
@property
def type_to_loader_dict(self) -> Dict:
if self.type_dict is None:
+ settings_service = get_settings_service()
self.type_dict = {
toolkit_name: import_class(
f"langchain.agents.agent_toolkits.{toolkit_name}"
)
# if toolkit_name is not lower case it is a class
for toolkit_name in agent_toolkits.__all__
- if not toolkit_name.islower() and toolkit_name in settings.TOOLKITS
+ if not toolkit_name.islower()
+ and toolkit_name in settings_service.settings.TOOLKITS
}
return self.type_dict
diff --git a/src/backend/langflow/interface/tools/base.py b/src/backend/langflow/interface/tools/base.py
index 8c9158c05..a99025ff7 100644
--- a/src/backend/langflow/interface/tools/base.py
+++ b/src/backend/langflow/interface/tools/base.py
@@ -15,7 +15,8 @@ from langflow.interface.tools.constants import (
OTHER_TOOLS,
)
from langflow.interface.tools.util import get_tool_params
-from langflow.settings import settings
+from langflow.services.getters import get_settings_service
+
from langflow.template.field.base import TemplateField
from langflow.template.template.base import Template
from langflow.utils import util
@@ -66,6 +67,7 @@ class ToolCreator(LangChainTypeCreator):
@property
def type_to_loader_dict(self) -> Dict:
+ settings_service = get_settings_service()
if self.tools_dict is None:
all_tools = {}
@@ -74,7 +76,10 @@ class ToolCreator(LangChainTypeCreator):
tool_name = tool_params.get("name") or tool
- if tool_name in settings.TOOLS or settings.DEV:
+ if (
+ tool_name in settings_service.settings.TOOLS
+ or settings_service.settings.DEV
+ ):
if tool_name == "JsonSpec":
tool_params["path"] = tool_params.pop("dict_") # type: ignore
all_tools[tool_name] = {
diff --git a/src/backend/langflow/interface/types.py b/src/backend/langflow/interface/types.py
index c43a22e01..0046c84fb 100644
--- a/src/backend/langflow/interface/types.py
+++ b/src/backend/langflow/interface/types.py
@@ -1,10 +1,10 @@
import ast
import contextlib
from typing import Any, List
-from langflow.api.utils import merge_nested_dicts_with_renaming
+from langflow.api.utils import get_new_key
from langflow.interface.agents.base import agent_creator
from langflow.interface.chains.base import chain_creator
-from langflow.interface.custom.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES
+from langflow.field_typing.constants import CUSTOM_COMPONENT_SUPPORTED_TYPES
from langflow.interface.custom.utils import extract_inner_type
from langflow.interface.document_loaders.base import documentloader_creator
from langflow.interface.embeddings.base import embedding_creator
@@ -30,7 +30,7 @@ from langflow.template.frontend_node.custom_components import (
from langflow.interface.retrievers.base import retriever_creator
from langflow.interface.custom.directory_reader import DirectoryReader
-from langflow.utils.logger import logger
+from loguru import logger
from langflow.utils.util import get_base_classes
import re
@@ -288,6 +288,24 @@ def add_base_classes(frontend_node, return_types: List[str]):
frontend_node.get("base_classes").append(base_class)
+def add_output_types(frontend_node, return_types: List[str]):
+ """Add output types to the frontend node"""
+ for return_type in return_types:
+ if return_type not in CUSTOM_COMPONENT_SUPPORTED_TYPES or return_type is None:
+ raise HTTPException(
+ status_code=400,
+ detail={
+ "error": (
+ "Invalid return type should be one of: "
+ f"{list(CUSTOM_COMPONENT_SUPPORTED_TYPES.keys())}"
+ ),
+ "traceback": traceback.format_exc(),
+ },
+ )
+
+ frontend_node.get("output_types").append(return_type)
+
+
def build_langchain_template_custom_component(custom_component: CustomComponent):
"""Build a custom component template for the langchain"""
try:
@@ -303,9 +321,9 @@ def build_langchain_template_custom_component(custom_component: CustomComponent)
logger.debug("Updated attributes")
field_config = build_field_config(custom_component)
logger.debug("Built field config")
- add_extra_fields(
- frontend_node, field_config, custom_component.get_function_entrypoint_args
- )
+ entrypoint_args = custom_component.get_function_entrypoint_args
+
+ add_extra_fields(frontend_node, field_config, entrypoint_args)
logger.debug("Added extra fields")
frontend_node = add_code_field(
frontend_node, custom_component.code, field_config.get("code", {})
@@ -314,9 +332,14 @@ def build_langchain_template_custom_component(custom_component: CustomComponent)
add_base_classes(
frontend_node, custom_component.get_function_entrypoint_return_type
)
+ add_output_types(
+ frontend_node, custom_component.get_function_entrypoint_return_type
+ )
logger.debug("Added base classes")
return frontend_node
except Exception as exc:
+ if isinstance(exc, HTTPException):
+ raise exc
raise HTTPException(
status_code=400,
detail={
@@ -433,6 +456,24 @@ def build_invalid_menu(invalid_components):
return invalid_menu
+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 build_langchain_custom_component_list_from_path(path: str):
"""Build a list of custom components for the langchain from a given path"""
file_list = load_files_from_path(path)
@@ -446,3 +487,51 @@ def build_langchain_custom_component_list_from_path(path: str):
invalid_menu = build_invalid_menu(invalid_components)
return merge_nested_dicts_with_renaming(valid_menu, invalid_menu)
+
+
+def get_all_types_dict(settings_service):
+ 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: dict[str, Any] = {}
+ if settings_service.settings.COMPONENTS_PATH:
+ logger.info(
+ f"Building custom components from {settings_service.settings.COMPONENTS_PATH}"
+ )
+
+ custom_component_dicts = []
+ processed_paths = []
+ for path in settings_service.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_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
+ )
+
+ return merge_nested_dicts_with_renaming(
+ native_components, custom_components_from_file
+ )
+
+
+def merge_nested_dicts(dict1, dict2):
+ for key, value in dict2.items():
+ if isinstance(value, dict) and isinstance(dict1.get(key), dict):
+ dict1[key] = merge_nested_dicts(dict1[key], value)
+ else:
+ dict1[key] = value
+ return dict1
diff --git a/src/backend/langflow/interface/utilities/base.py b/src/backend/langflow/interface/utilities/base.py
index b0ee4d4be..bfc7cb11e 100644
--- a/src/backend/langflow/interface/utilities/base.py
+++ b/src/backend/langflow/interface/utilities/base.py
@@ -1,13 +1,14 @@
from typing import Dict, List, Optional, Type
-from langchain import SQLDatabase, utilities
+from langchain import utilities
from langflow.custom.customs import get_custom_nodes
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.importing.utils import import_class
-from langflow.settings import settings
+from langflow.services.getters import get_settings_service
+
from langflow.template.frontend_node.utilities import UtilitiesFrontendNode
-from langflow.utils.logger import logger
+from loguru import logger
from langflow.utils.util import build_template_from_class
@@ -26,16 +27,18 @@ class UtilityCreator(LangChainTypeCreator):
from the langchain.chains module and filtering them according to the settings.utilities list.
"""
if self.type_dict is None:
+ settings_service = get_settings_service()
self.type_dict = {
utility_name: import_class(f"langchain.utilities.{utility_name}")
for utility_name in utilities.__all__
}
- self.type_dict["SQLDatabase"] = SQLDatabase
+ self.type_dict["SQLDatabase"] = utilities.SQLDatabase
# Filter according to settings.utilities
self.type_dict = {
name: utility
for name, utility in self.type_dict.items()
- if name in settings.UTILITIES or settings.DEV
+ if name in settings_service.settings.UTILITIES
+ or settings_service.settings.DEV
}
return self.type_dict
diff --git a/src/backend/langflow/interface/utils.py b/src/backend/langflow/interface/utils.py
index e14d0e464..b28a660bf 100644
--- a/src/backend/langflow/interface/utils.py
+++ b/src/backend/langflow/interface/utils.py
@@ -8,8 +8,9 @@ import re
import yaml
from langchain.base_language import BaseLanguageModel
from PIL.Image import Image
-from langflow.utils.logger import logger
-from langflow.chat.config import ChatConfig
+from loguru import logger
+from langflow.services.chat.config import ChatConfig
+from langflow.services.getters import get_settings_service
def load_file_into_dict(file_path: str) -> dict:
@@ -35,7 +36,7 @@ def pil_to_base64(image: Image) -> str:
return img_str.decode("utf-8")
-def try_setting_streaming_options(langchain_object, websocket):
+def try_setting_streaming_options(langchain_object):
# If the LLM type is OpenAI or ChatOpenAI,
# set streaming to True
# First we need to find the LLM
@@ -63,13 +64,11 @@ def extract_input_variables_from_prompt(prompt: str) -> list[str]:
def setup_llm_caching():
"""Setup LLM caching."""
-
- from langflow.settings import settings
-
+ settings_service = get_settings_service()
try:
- set_langchain_cache(settings)
+ set_langchain_cache(settings_service.settings)
except ImportError:
- logger.warning(f"Could not import {settings.CACHE}. ")
+ logger.warning(f"Could not import {settings_service.settings.CACHE_TYPE}. ")
except Exception as exc:
logger.warning(f"Could not setup LLM caching. Error: {exc}")
@@ -81,7 +80,7 @@ def set_langchain_cache(settings):
if cache_type := os.getenv("LANGFLOW_LANGCHAIN_CACHE"):
try:
cache_class = import_class(
- f"langchain.cache.{cache_type or settings.CACHE}"
+ f"langchain.cache.{cache_type or settings.LANGCHAIN_CACHE}"
)
logger.debug(f"Setting up LLM caching with {cache_class.__name__}")
diff --git a/src/backend/langflow/interface/vector_store/base.py b/src/backend/langflow/interface/vector_store/base.py
index 4a937ba89..786031a6b 100644
--- a/src/backend/langflow/interface/vector_store/base.py
+++ b/src/backend/langflow/interface/vector_store/base.py
@@ -4,9 +4,10 @@ from langchain import vectorstores
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.importing.utils import import_class
-from langflow.settings import settings
+from langflow.services.getters import get_settings_service
+
from langflow.template.frontend_node.vectorstores import VectorStoreFrontendNode
-from langflow.utils.logger import logger
+from loguru import logger
from langflow.utils.util import build_template_from_method
@@ -43,10 +44,12 @@ class VectorstoreCreator(LangChainTypeCreator):
return None
def to_list(self) -> List[str]:
+ settings_service = get_settings_service()
return [
vectorstore
for vectorstore in self.type_to_loader_dict.keys()
- if vectorstore in settings.VECTORSTORES or settings.DEV
+ if vectorstore in settings_service.settings.VECTORSTORES
+ or settings_service.settings.DEV
]
diff --git a/src/backend/langflow/interface/wrappers/base.py b/src/backend/langflow/interface/wrappers/base.py
index 77e38f921..de631101a 100644
--- a/src/backend/langflow/interface/wrappers/base.py
+++ b/src/backend/langflow/interface/wrappers/base.py
@@ -1,9 +1,9 @@
from typing import Dict, List, Optional
-from langchain import requests, sql_database
+from langchain.utilities import requests, sql_database
from langflow.interface.base import LangChainTypeCreator
-from langflow.utils.logger import logger
+from loguru import logger
from langflow.utils.util import build_template_from_class, build_template_from_method
diff --git a/src/backend/langflow/jcloud.yml b/src/backend/langflow/jcloud.yml
deleted file mode 100644
index ffc1c9b8a..000000000
--- a/src/backend/langflow/jcloud.yml
+++ /dev/null
@@ -1,2 +0,0 @@
-instance: C4
-autoscale_min: 1
\ No newline at end of file
diff --git a/src/backend/langflow/lcserve.py b/src/backend/langflow/lcserve.py
deleted file mode 100644
index 87f69e014..000000000
--- a/src/backend/langflow/lcserve.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# This file is used by lc-serve to load the mounted app and serve it.
-
-import os
-
-# Use the JCLOUD_WORKSPACE for db URL if it's provided by JCloud.
-if "JCLOUD_WORKSPACE" in os.environ:
- os.environ[
- "LANGFLOW_DATABASE_URL"
- ] = f"sqlite:///{os.environ['JCLOUD_WORKSPACE']}/langflow.db"
-
-from langflow.main import setup_app
-from langflow.utils.logger import configure
-
-configure(log_level="DEBUG")
-app = setup_app()
diff --git a/src/backend/langflow/main.py b/src/backend/langflow/main.py
index 85e209062..9caa157d0 100644
--- a/src/backend/langflow/main.py
+++ b/src/backend/langflow/main.py
@@ -6,25 +6,25 @@ from fastapi.responses import FileResponse
from fastapi.staticfiles import StaticFiles
from langflow.api import router
-from langflow.database.base import create_db_and_tables, Engine
+
+
from langflow.interface.utils import setup_llm_caching
+from langflow.services.utils import initialize_services
from langflow.services.plugins.langfuse import LangfuseInstance
+from langflow.services.utils import (
+ teardown_services,
+)
from langflow.utils.logger import configure
def create_app():
"""Create the FastAPI app and include the router."""
+
configure()
app = FastAPI()
- origins = [
- "*",
- ]
-
- @app.get("/health")
- def get_health():
- return {"status": "OK"}
+ origins = ["*"]
app.add_middleware(
CORSMiddleware,
@@ -34,13 +34,19 @@ def create_app():
allow_headers=["*"],
)
+ @app.get("/health")
+ def health():
+ return {"status": "ok"}
+
app.include_router(router)
- app.on_event("startup")(Engine.update)
- app.on_event("startup")(create_db_and_tables)
+
+ app.on_event("startup")(initialize_services)
app.on_event("startup")(setup_llm_caching)
app.on_event("startup")(LangfuseInstance.update)
- app.on_event("shutdown")(Engine.teardown)
+
+ app.on_event("shutdown")(teardown_services)
app.on_event("shutdown")(LangfuseInstance.teardown)
+
return app
@@ -72,22 +78,25 @@ def get_static_files_dir():
return frontend_path / "frontend"
-def setup_app(static_files_dir: Optional[Path] = None) -> FastAPI:
+def setup_app(
+ static_files_dir: Optional[Path] = None, backend_only: bool = False
+) -> FastAPI:
"""Setup the FastAPI app."""
# get the directory of the current file
if not static_files_dir:
static_files_dir = get_static_files_dir()
- if not static_files_dir or not static_files_dir.exists():
+ if not backend_only and (not static_files_dir or not static_files_dir.exists()):
raise RuntimeError(f"Static files directory {static_files_dir} does not exist.")
app = create_app()
- setup_static_files(app, static_files_dir)
+ if not backend_only and static_files_dir is not None:
+ setup_static_files(app, static_files_dir)
return app
if __name__ == "__main__":
import uvicorn
- from langflow.utils.util import get_number_of_workers
+ from langflow.__main__ import get_number_of_workers
configure()
uvicorn.run(
diff --git a/src/backend/langflow/processing/base.py b/src/backend/langflow/processing/base.py
index bbd68a47b..6c56a59dd 100644
--- a/src/backend/langflow/processing/base.py
+++ b/src/backend/langflow/processing/base.py
@@ -4,8 +4,7 @@ from langflow.api.v1.callback import (
StreamingLLMCallbackHandler,
)
from langflow.processing.process import fix_memory_inputs, format_actions
-
-from langflow.utils.logger import logger
+from loguru import logger
from langchain.agents.agent import AgentExecutor
from langchain.callbacks.base import BaseCallbackHandler
@@ -35,7 +34,9 @@ def get_langfuse_callback(trace_id):
if langfuse := LangfuseInstance.get():
logger.debug("Langfuse credentials found")
try:
- trace = langfuse.trace(CreateTrace(id=trace_id))
+ trace = langfuse.trace(
+ CreateTrace(name="langflow-" + trace_id, id=trace_id)
+ )
return trace.getNewHandler()
except Exception as exc:
logger.error(f"Error initializing langfuse callback: {exc}")
diff --git a/src/backend/langflow/processing/process.py b/src/backend/langflow/processing/process.py
index b3db4d0e9..4c96e2fde 100644
--- a/src/backend/langflow/processing/process.py
+++ b/src/backend/langflow/processing/process.py
@@ -2,17 +2,20 @@ import json
from pathlib import Path
from langchain.schema import AgentAction
from langflow.interface.run import (
- build_sorted_vertices_with_caching,
+ build_sorted_vertices,
get_memory_key,
update_memory_keys,
)
-from langflow.utils.logger import logger
+from langflow.services.getters import get_session_service
+from loguru import logger
from langflow.graph import Graph
from langchain.chains.base import Chain
from langchain.vectorstores.base import VectorStore
from typing import Any, Dict, List, Optional, Tuple, Union
from langchain.schema import Document
+from pydantic import BaseModel
+
def fix_memory_inputs(langchain_object):
"""
@@ -65,7 +68,7 @@ def get_result_and_thought(langchain_object: Any, inputs: dict):
langchain_object.verbose = True
if hasattr(langchain_object, "return_intermediate_steps"):
- langchain_object.return_intermediate_steps = True
+ langchain_object.return_intermediate_steps = False
fix_memory_inputs(langchain_object)
@@ -86,44 +89,54 @@ def get_input_str_if_only_one_input(inputs: dict) -> Optional[str]:
return list(inputs.values())[0] if len(inputs) == 1 else None
-def process_graph_cached(
- data_graph: Dict[str, Any], inputs: Optional[dict] = None, clear_cache=False
-):
- """
- Process graph by extracting input variables and replacing ZeroShotPrompt
- with PromptTemplate,then run the graph and return the result and thought.
- """
- # Load langchain object
- if clear_cache:
- build_sorted_vertices_with_caching.clear_cache()
- logger.debug("Cleared cache")
- langchain_object, artifacts = build_sorted_vertices_with_caching(data_graph)
- logger.debug("Loaded LangChain object")
- if inputs is None:
- inputs = {}
+def get_build_result(data_graph, session_id):
+ # If session_id is provided, load the langchain_object from the session
+ # using build_sorted_vertices_with_caching.get_result_by_session_id
+ # if it returns something different than None, return it
+ # otherwise, build the graph and return the result
+ if session_id:
+ logger.debug(f"Loading LangChain object from session {session_id}")
+ result = build_sorted_vertices(data_graph=data_graph)
+ if result is not None:
+ logger.debug("Loaded LangChain object")
+ return result
- # Add artifacts to inputs
- # artifacts can be documents loaded when building
- # the flow
- for (
- key,
- value,
- ) in artifacts.items():
- if key not in inputs or not inputs[key]:
- inputs[key] = value
+ logger.debug("Building langchain object")
+ return build_sorted_vertices(data_graph)
+
+
+def load_langchain_object(
+ data_graph: Dict[str, Any], session_id: str
+) -> Tuple[Union[Chain, VectorStore], Dict[str, Any], str]:
+ langchain_object, artifacts = get_build_result(data_graph, session_id)
+ logger.debug("Loaded LangChain object")
if langchain_object is None:
- # Raise user facing error
raise ValueError(
"There was an error loading the langchain_object. Please, check all the nodes and try again."
)
- # Generate result and thought
+ return langchain_object, artifacts, session_id
+
+
+def process_inputs(inputs: Optional[dict], artifacts: Dict[str, Any]) -> dict:
+ if inputs is None:
+ inputs = {}
+
+ for key, value in artifacts.items():
+ if key not in inputs or not inputs[key]:
+ inputs[key] = value
+
+ return inputs
+
+
+def generate_result(langchain_object: Union[Chain, VectorStore], inputs: dict):
if isinstance(langchain_object, Chain):
if inputs is None:
raise ValueError("Inputs must be provided for a Chain")
logger.debug("Generating result and thought")
result = get_result_and_thought(langchain_object, inputs)
+
logger.debug("Generated result and thought")
elif isinstance(langchain_object, VectorStore):
result = langchain_object.search(**inputs)
@@ -136,6 +149,36 @@ def process_graph_cached(
return result
+class Result(BaseModel):
+ result: Any
+ session_id: str
+
+
+async def process_graph_cached(
+ data_graph: Dict[str, Any],
+ inputs: Optional[dict] = None,
+ clear_cache=False,
+ session_id=None,
+) -> Result:
+ session_service = get_session_service()
+ if clear_cache:
+ session_service.clear_session(session_id)
+ if session_id is None:
+ session_id = session_service.generate_key(
+ session_id=session_id, data_graph=data_graph
+ )
+ # Load the graph using SessionService
+ graph, artifacts = session_service.load_session(session_id, data_graph)
+ built_object = graph.build()
+ processed_inputs = process_inputs(inputs, artifacts)
+ result = generate_result(built_object, processed_inputs)
+ # langchain_object is now updated with the new memory
+ # we need to update the cache with the updated langchain_object
+ session_service.update_session(session_id, (graph, artifacts))
+
+ return Result(result=result, session_id=session_id)
+
+
def load_flow_from_json(
flow: Union[Path, str, dict], tweaks: Optional[dict] = None, build=True
):
diff --git a/src/backend/langflow/server.py b/src/backend/langflow/server.py
index a96f8b7e0..3a2943444 100644
--- a/src/backend/langflow/server.py
+++ b/src/backend/langflow/server.py
@@ -4,6 +4,8 @@ from gunicorn.app.base import BaseApplication # type: ignore
class LangflowApplication(BaseApplication):
def __init__(self, app, options=None):
self.options = options or {}
+
+ self.options["worker_class"] = "uvicorn.workers.UvicornWorker"
self.application = app
super().__init__()
diff --git a/src/backend/langflow/services/__init__.py b/src/backend/langflow/services/__init__.py
new file mode 100644
index 000000000..8ac74b5b9
--- /dev/null
+++ b/src/backend/langflow/services/__init__.py
@@ -0,0 +1,4 @@
+from .manager import service_manager
+from .schema import ServiceType
+
+__all__ = ["service_manager", "ServiceType"]
diff --git a/src/backend/langflow/database/__init__.py b/src/backend/langflow/services/auth/__init__.py
similarity index 100%
rename from src/backend/langflow/database/__init__.py
rename to src/backend/langflow/services/auth/__init__.py
diff --git a/src/backend/langflow/services/auth/factory.py b/src/backend/langflow/services/auth/factory.py
new file mode 100644
index 000000000..b44019289
--- /dev/null
+++ b/src/backend/langflow/services/auth/factory.py
@@ -0,0 +1,12 @@
+from langflow.services.factory import ServiceFactory
+from langflow.services.auth.service import AuthService
+
+
+class AuthServiceFactory(ServiceFactory):
+ name = "auth_service"
+
+ def __init__(self):
+ super().__init__(AuthService)
+
+ def create(self, settings_service):
+ return AuthService(settings_service)
diff --git a/src/backend/langflow/services/auth/service.py b/src/backend/langflow/services/auth/service.py
new file mode 100644
index 000000000..5b0acf8c6
--- /dev/null
+++ b/src/backend/langflow/services/auth/service.py
@@ -0,0 +1,12 @@
+from langflow.services.base import Service
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+ from langflow.services.settings.manager import SettingsService
+
+
+class AuthService(Service):
+ name = "auth_service"
+
+ def __init__(self, settings_service: "SettingsService"):
+ self.settings_service = settings_service
diff --git a/src/backend/langflow/services/auth/utils.py b/src/backend/langflow/services/auth/utils.py
new file mode 100644
index 000000000..f88a1cd12
--- /dev/null
+++ b/src/backend/langflow/services/auth/utils.py
@@ -0,0 +1,296 @@
+from datetime import datetime, timedelta, timezone
+from fastapi import Depends, HTTPException, Security, status
+from fastapi.security import APIKeyHeader, APIKeyQuery, OAuth2PasswordBearer
+from jose import JWTError, jwt
+from typing import Annotated, Coroutine, Optional, Union
+from uuid import UUID
+from langflow.services.database.models.api_key.api_key import ApiKey
+from langflow.services.database.models.api_key.crud import check_key
+from langflow.services.database.models.user.user import User
+from langflow.services.database.models.user.crud import (
+ get_user_by_id,
+ get_user_by_username,
+ update_user_last_login_at,
+)
+from langflow.services.getters import get_session, get_settings_service
+from sqlmodel import Session
+
+oauth2_login = OAuth2PasswordBearer(tokenUrl="api/v1/login")
+
+API_KEY_NAME = "x-api-key"
+
+api_key_query = APIKeyQuery(
+ name=API_KEY_NAME, scheme_name="API key query", auto_error=False
+)
+api_key_header = APIKeyHeader(
+ name=API_KEY_NAME, scheme_name="API key header", auto_error=False
+)
+
+
+# Source: https://github.com/mrtolkien/fastapi_simple_security/blob/master/fastapi_simple_security/security_api_key.py
+async def api_key_security(
+ query_param: str = Security(api_key_query),
+ header_param: str = Security(api_key_header),
+ db: Session = Depends(get_session),
+) -> Optional[User]:
+ settings_service = get_settings_service()
+ result: Optional[Union[ApiKey, User]] = None
+ if settings_service.auth_settings.AUTO_LOGIN:
+ # Get the first user
+ if not settings_service.auth_settings.SUPERUSER:
+ raise HTTPException(
+ status_code=status.HTTP_400_BAD_REQUEST,
+ detail="Missing first superuser credentials",
+ )
+
+ result = get_user_by_username(db, settings_service.auth_settings.SUPERUSER)
+
+ elif not query_param and not header_param:
+ raise HTTPException(
+ status_code=status.HTTP_403_FORBIDDEN,
+ detail="An API key must be passed as query or header",
+ )
+
+ elif query_param:
+ result = check_key(db, query_param)
+
+ else:
+ result = check_key(db, header_param)
+
+ if not result:
+ raise HTTPException(
+ status_code=status.HTTP_403_FORBIDDEN,
+ detail="Invalid or missing API key",
+ )
+ if isinstance(result, ApiKey):
+ return result.user
+ elif isinstance(result, User):
+ return result
+
+
+async def get_current_user(
+ token: Annotated[str, Depends(oauth2_login)],
+ db: Session = Depends(get_session),
+) -> User:
+ settings_service = get_settings_service()
+
+ credentials_exception = HTTPException(
+ status_code=status.HTTP_401_UNAUTHORIZED,
+ detail="Could not validate credentials",
+ headers={"WWW-Authenticate": "Bearer"},
+ )
+
+ if isinstance(token, Coroutine):
+ token = await token
+
+ if settings_service.auth_settings.SECRET_KEY is None:
+ raise credentials_exception
+
+ try:
+ payload = jwt.decode(
+ token,
+ settings_service.auth_settings.SECRET_KEY,
+ algorithms=[settings_service.auth_settings.ALGORITHM],
+ )
+ user_id: UUID = payload.get("sub") # type: ignore
+ token_type: str = payload.get("type") # type: ignore
+ if expires := payload.get("exp", None):
+ expires_datetime = datetime.fromtimestamp(expires, timezone.utc)
+ # TypeError: can't compare offset-naive and offset-aware datetimes
+ if datetime.now(timezone.utc) > expires_datetime:
+ raise credentials_exception
+
+ if user_id is None or token_type:
+ raise credentials_exception
+ except JWTError as e:
+ raise credentials_exception from e
+
+ user = get_user_by_id(db, user_id) # type: ignore
+ if user is None or not user.is_active:
+ raise credentials_exception
+ return user
+
+
+def get_current_active_user(current_user: Annotated[User, Depends(get_current_user)]):
+ if not current_user.is_active:
+ raise HTTPException(status_code=400, detail="Inactive user")
+ return current_user
+
+
+def get_current_active_superuser(
+ current_user: Annotated[User, Depends(get_current_user)]
+) -> User:
+ if not current_user.is_active:
+ raise HTTPException(status_code=401, detail="Inactive user")
+ if not current_user.is_superuser:
+ raise HTTPException(
+ status_code=400, detail="The user doesn't have enough privileges"
+ )
+ return current_user
+
+
+def verify_password(plain_password, hashed_password):
+ settings_service = get_settings_service()
+ return settings_service.auth_settings.pwd_context.verify(
+ plain_password, hashed_password
+ )
+
+
+def get_password_hash(password):
+ settings_service = get_settings_service()
+ return settings_service.auth_settings.pwd_context.hash(password)
+
+
+def create_token(data: dict, expires_delta: timedelta):
+ settings_service = get_settings_service()
+
+ to_encode = data.copy()
+ expire = datetime.now(timezone.utc) + expires_delta
+ to_encode["exp"] = expire
+
+ return jwt.encode(
+ to_encode,
+ settings_service.auth_settings.SECRET_KEY,
+ algorithm=settings_service.auth_settings.ALGORITHM,
+ )
+
+
+def create_super_user(
+ username: str,
+ password: str,
+ db: Session = Depends(get_session),
+) -> User:
+ super_user = get_user_by_username(db, username)
+
+ if not super_user:
+ super_user = User(
+ username=username,
+ password=get_password_hash(password),
+ is_superuser=True,
+ is_active=True,
+ last_login_at=None,
+ )
+
+ db.add(super_user)
+ db.commit()
+ db.refresh(super_user)
+
+ return super_user
+
+
+def create_user_longterm_token(db: Session = Depends(get_session)) -> dict:
+ settings_service = get_settings_service()
+ username = settings_service.auth_settings.SUPERUSER
+ password = settings_service.auth_settings.SUPERUSER_PASSWORD
+ if not username or not password:
+ raise HTTPException(
+ status_code=status.HTTP_400_BAD_REQUEST,
+ detail="Missing first superuser credentials",
+ )
+ super_user = create_super_user(db=db, username=username, password=password)
+
+ access_token_expires_longterm = timedelta(days=365)
+ access_token = create_token(
+ data={"sub": str(super_user.id)},
+ expires_delta=access_token_expires_longterm,
+ )
+
+ # Update: last_login_at
+ update_user_last_login_at(super_user.id, db)
+
+ return {
+ "access_token": access_token,
+ "refresh_token": None,
+ "token_type": "bearer",
+ }
+
+
+def create_user_api_key(user_id: UUID) -> dict:
+ access_token = create_token(
+ data={"sub": str(user_id), "role": "api_key"},
+ expires_delta=timedelta(days=365 * 2),
+ )
+
+ return {"api_key": access_token}
+
+
+def get_user_id_from_token(token: str) -> UUID:
+ try:
+ user_id = jwt.get_unverified_claims(token)["sub"]
+ return UUID(user_id)
+ except (KeyError, JWTError, ValueError):
+ return UUID(int=0)
+
+
+def create_user_tokens(
+ user_id: UUID, db: Session = Depends(get_session), update_last_login: bool = False
+) -> dict:
+ settings_service = get_settings_service()
+
+ access_token_expires = timedelta(
+ minutes=settings_service.auth_settings.ACCESS_TOKEN_EXPIRE_MINUTES
+ )
+ access_token = create_token(
+ data={"sub": str(user_id)},
+ expires_delta=access_token_expires,
+ )
+
+ refresh_token_expires = timedelta(
+ minutes=settings_service.auth_settings.REFRESH_TOKEN_EXPIRE_MINUTES
+ )
+ refresh_token = create_token(
+ data={"sub": str(user_id), "type": "rf"},
+ expires_delta=refresh_token_expires,
+ )
+
+ # Update: last_login_at
+ if update_last_login:
+ update_user_last_login_at(user_id, db)
+
+ return {
+ "access_token": access_token,
+ "refresh_token": refresh_token,
+ "token_type": "bearer",
+ }
+
+
+def create_refresh_token(refresh_token: str, db: Session = Depends(get_session)):
+ settings_service = get_settings_service()
+
+ try:
+ payload = jwt.decode(
+ refresh_token,
+ settings_service.auth_settings.SECRET_KEY,
+ algorithms=[settings_service.auth_settings.ALGORITHM],
+ )
+ user_id: UUID = payload.get("sub") # type: ignore
+ token_type: str = payload.get("type") # type: ignore
+
+ if user_id is None or token_type is None:
+ raise HTTPException(
+ status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid refresh token"
+ )
+
+ return create_user_tokens(user_id, db)
+
+ except JWTError as e:
+ raise HTTPException(
+ status_code=status.HTTP_401_UNAUTHORIZED,
+ detail="Invalid refresh token",
+ ) from e
+
+
+def authenticate_user(
+ username: str, password: str, db: Session = Depends(get_session)
+) -> Optional[User]:
+ user = get_user_by_username(db, username)
+
+ if not user:
+ return None
+
+ if not user.is_active:
+ if not user.last_login_at:
+ raise HTTPException(status_code=400, detail="Waiting for approval")
+ raise HTTPException(status_code=400, detail="Inactive user")
+
+ return user if verify_password(password, user.password) else None
diff --git a/src/backend/langflow/services/base.py b/src/backend/langflow/services/base.py
new file mode 100644
index 000000000..301771944
--- /dev/null
+++ b/src/backend/langflow/services/base.py
@@ -0,0 +1,12 @@
+from abc import ABC
+
+
+class Service(ABC):
+ name: str
+ ready: bool = False
+
+ def teardown(self):
+ pass
+
+ def set_ready(self):
+ self.ready = True
diff --git a/src/backend/langflow/services/cache/__init__.py b/src/backend/langflow/services/cache/__init__.py
new file mode 100644
index 000000000..3b122aa9e
--- /dev/null
+++ b/src/backend/langflow/services/cache/__init__.py
@@ -0,0 +1,9 @@
+from . import factory, manager
+from langflow.services.cache.manager import InMemoryCache
+
+
+__all__ = [
+ "factory",
+ "manager",
+ "InMemoryCache",
+]
diff --git a/src/backend/langflow/cache/base.py b/src/backend/langflow/services/cache/base.py
similarity index 85%
rename from src/backend/langflow/cache/base.py
rename to src/backend/langflow/services/cache/base.py
index 88cb3a1da..4eee6639e 100644
--- a/src/backend/langflow/cache/base.py
+++ b/src/backend/langflow/services/cache/base.py
@@ -1,11 +1,13 @@
import abc
-class BaseCache(abc.ABC):
+class BaseCacheService(abc.ABC):
"""
Abstract base class for a cache.
"""
+ name = "cache_service"
+
@abc.abstractmethod
def get(self, key):
"""
@@ -28,6 +30,16 @@ class BaseCache(abc.ABC):
value: The value to cache.
"""
+ @abc.abstractmethod
+ def upsert(self, key, value):
+ """
+ Add an item to the cache if it doesn't exist, or update it if it does.
+
+ Args:
+ key: The key of the item.
+ value: The value to cache.
+ """
+
@abc.abstractmethod
def delete(self, key):
"""
diff --git a/src/backend/langflow/services/cache/factory.py b/src/backend/langflow/services/cache/factory.py
new file mode 100644
index 000000000..f00ab239f
--- /dev/null
+++ b/src/backend/langflow/services/cache/factory.py
@@ -0,0 +1,35 @@
+from langflow.services.cache.manager import InMemoryCache, RedisCache, BaseCacheService
+from langflow.services.factory import ServiceFactory
+from langflow.utils.logger import logger
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+ from langflow.services.settings.manager import SettingsService
+
+
+class CacheServiceFactory(ServiceFactory):
+ def __init__(self):
+ super().__init__(BaseCacheService)
+
+ def create(self, settings_service: "SettingsService"):
+ # Here you would have logic to create and configure a CacheService
+ # based on the settings_service
+
+ if settings_service.settings.CACHE_TYPE == "redis":
+ logger.debug("Creating Redis cache")
+ redis_cache = RedisCache(
+ host=settings_service.settings.REDIS_HOST,
+ port=settings_service.settings.REDIS_PORT,
+ db=settings_service.settings.REDIS_DB,
+ expiration_time=settings_service.settings.REDIS_CACHE_EXPIRE,
+ )
+ if redis_cache.is_connected():
+ logger.debug("Redis cache is connected")
+ return redis_cache
+ logger.warning(
+ "Redis cache is not connected, falling back to in-memory cache"
+ )
+ return InMemoryCache()
+
+ elif settings_service.settings.CACHE_TYPE == "memory":
+ return InMemoryCache()
diff --git a/src/backend/langflow/services/cache/manager.py b/src/backend/langflow/services/cache/manager.py
new file mode 100644
index 000000000..da76a2b5c
--- /dev/null
+++ b/src/backend/langflow/services/cache/manager.py
@@ -0,0 +1,336 @@
+import threading
+import time
+from collections import OrderedDict
+from langflow.services.base import Service
+
+from langflow.services.cache.base import BaseCacheService
+
+import pickle
+
+from loguru import logger
+
+
+class InMemoryCache(BaseCacheService, Service):
+
+ """
+ A simple in-memory cache using an OrderedDict.
+
+ This cache supports setting a maximum size and expiration time for cached items.
+ When the cache is full, it uses a Least Recently Used (LRU) eviction policy.
+ Thread-safe using a threading Lock.
+
+ Attributes:
+ max_size (int, optional): Maximum number of items to store in the cache.
+ expiration_time (int, optional): Time in seconds after which a cached item expires. Default is 1 hour.
+
+ Example:
+
+ cache = InMemoryCache(max_size=3, expiration_time=5)
+
+ # setting cache values
+ cache.set("a", 1)
+ cache.set("b", 2)
+ cache["c"] = 3
+
+ # getting cache values
+ a = cache.get("a")
+ b = cache["b"]
+ """
+
+ def __init__(self, max_size=None, expiration_time=60 * 60):
+ """
+ Initialize a new InMemoryCache instance.
+
+ Args:
+ max_size (int, optional): Maximum number of items to store in the cache.
+ expiration_time (int, optional): Time in seconds after which a cached item expires. Default is 1 hour.
+ """
+ self._cache = OrderedDict()
+ self._lock = threading.RLock()
+ self.max_size = max_size
+ self.expiration_time = expiration_time
+
+ def get(self, key):
+ """
+ Retrieve an item from the cache.
+
+ Args:
+ key: The key of the item to retrieve.
+
+ Returns:
+ The value associated with the key, or None if the key is not found or the item has expired.
+ """
+ with self._lock:
+ return self._get_without_lock(key)
+
+ def _get_without_lock(self, key):
+ """
+ Retrieve an item from the cache without acquiring the lock.
+ """
+ if item := self._cache.get(key):
+ if (
+ self.expiration_time is None
+ or time.time() - item["time"] < self.expiration_time
+ ):
+ # Move the key to the end to make it recently used
+ self._cache.move_to_end(key)
+ # Check if the value is pickled
+ if isinstance(item["value"], bytes):
+ value = pickle.loads(item["value"])
+ else:
+ value = item["value"]
+ return value
+ else:
+ self.delete(key)
+ return None
+
+ def set(self, key, value, pickle=False):
+ """
+ Add an item to the cache.
+
+ If the cache is full, the least recently used item is evicted.
+
+ Args:
+ key: The key of the item.
+ value: The value to cache.
+ """
+ with self._lock:
+ if key in self._cache:
+ # Remove existing key before re-inserting to update order
+ self.delete(key)
+ elif self.max_size and len(self._cache) >= self.max_size:
+ # Remove least recently used item
+ self._cache.popitem(last=False)
+ # pickle locally to mimic Redis
+ if pickle:
+ value = pickle.dumps(value)
+
+ self._cache[key] = {"value": value, "time": time.time()}
+
+ def upsert(self, key, value):
+ """
+ Inserts or updates a value in the cache.
+ If the existing value and the new value are both dictionaries, they are merged.
+
+ Args:
+ key: The key of the item.
+ value: The value to insert or update.
+ """
+ with self._lock:
+ existing_value = self._get_without_lock(key)
+ if (
+ existing_value is not None
+ and isinstance(existing_value, dict)
+ and isinstance(value, dict)
+ ):
+ existing_value.update(value)
+ value = existing_value
+
+ self.set(key, value)
+
+ def get_or_set(self, key, value):
+ """
+ Retrieve an item from the cache. If the item does not exist,
+ set it with the provided value.
+
+ Args:
+ key: The key of the item.
+ value: The value to cache if the item doesn't exist.
+
+ Returns:
+ The cached value associated with the key.
+ """
+ with self._lock:
+ if key in self._cache:
+ return self.get(key)
+ self.set(key, value)
+ return value
+
+ def delete(self, key):
+ """
+ Remove an item from the cache.
+
+ Args:
+ key: The key of the item to remove.
+ """
+ with self._lock:
+ self._cache.pop(key, None)
+
+ def clear(self):
+ """
+ Clear all items from the cache.
+ """
+ with self._lock:
+ self._cache.clear()
+
+ def __contains__(self, key):
+ """Check if the key is in the cache."""
+ return key in self._cache
+
+ def __getitem__(self, key):
+ """Retrieve an item from the cache using the square bracket notation."""
+ return self.get(key)
+
+ def __setitem__(self, key, value):
+ """Add an item to the cache using the square bracket notation."""
+ self.set(key, value)
+
+ def __delitem__(self, key):
+ """Remove an item from the cache using the square bracket notation."""
+ self.delete(key)
+
+ def __len__(self):
+ """Return the number of items in the cache."""
+ return len(self._cache)
+
+ def __repr__(self):
+ """Return a string representation of the InMemoryCache instance."""
+ return f"InMemoryCache(max_size={self.max_size}, expiration_time={self.expiration_time})"
+
+
+class RedisCache(BaseCacheService, Service):
+ """
+ A Redis-based cache implementation.
+
+ This cache supports setting an expiration time for cached items.
+
+ Attributes:
+ expiration_time (int, optional): Time in seconds after which a cached item expires. Default is 1 hour.
+
+ Example:
+
+ cache = RedisCache(expiration_time=5)
+
+ # setting cache values
+ cache.set("a", 1)
+ cache.set("b", 2)
+ cache["c"] = 3
+
+ # getting cache values
+ a = cache.get("a")
+ b = cache["b"]
+ """
+
+ def __init__(self, host="localhost", port=6379, db=0, expiration_time=60 * 60):
+ """
+ Initialize a new RedisCache instance.
+
+ Args:
+ host (str, optional): Redis host.
+ port (int, optional): Redis port.
+ db (int, optional): Redis DB.
+ expiration_time (int, optional): Time in seconds after which a
+ ached item expires. Default is 1 hour.
+ """
+ try:
+ import redis
+ except ImportError as exc:
+ raise ImportError(
+ "RedisCache requires the redis-py package."
+ " Please install Langflow with the deploy extra: pip install langflow[deploy]"
+ ) from exc
+ logger.warning(
+ "RedisCache is an experimental feature and may not work as expected."
+ " Please report any issues to our GitHub repository."
+ )
+ self._client = redis.StrictRedis(host=host, port=port, db=db)
+ self.expiration_time = expiration_time
+
+ # check connection
+ def is_connected(self):
+ """
+ Check if the Redis client is connected.
+ """
+ import redis
+
+ try:
+ self._client.ping()
+ return True
+ except redis.exceptions.ConnectionError:
+ return False
+
+ def get(self, key):
+ """
+ Retrieve an item from the cache.
+
+ Args:
+ key: The key of the item to retrieve.
+
+ Returns:
+ The value associated with the key, or None if the key is not found.
+ """
+ value = self._client.get(key)
+ return pickle.loads(value) if value else None
+
+ def set(self, key, value):
+ """
+ Add an item to the cache.
+
+ Args:
+ key: The key of the item.
+ value: The value to cache.
+ """
+ try:
+ if pickled := pickle.dumps(value):
+ result = self._client.setex(key, self.expiration_time, pickled)
+ if not result:
+ raise ValueError("RedisCache could not set the value.")
+ except TypeError as exc:
+ raise TypeError(
+ "RedisCache only accepts values that can be pickled. "
+ ) from exc
+
+ def upsert(self, key, value):
+ """
+ Inserts or updates a value in the cache.
+ If the existing value and the new value are both dictionaries, they are merged.
+
+ Args:
+ key: The key of the item.
+ value: The value to insert or update.
+ """
+ existing_value = self.get(key)
+ if (
+ existing_value is not None
+ and isinstance(existing_value, dict)
+ and isinstance(value, dict)
+ ):
+ existing_value.update(value)
+ value = existing_value
+
+ self.set(key, value)
+
+ def delete(self, key):
+ """
+ Remove an item from the cache.
+
+ Args:
+ key: The key of the item to remove.
+ """
+ self._client.delete(key)
+
+ def clear(self):
+ """
+ Clear all items from the cache.
+ """
+ self._client.flushdb()
+
+ def __contains__(self, key):
+ """Check if the key is in the cache."""
+ return False if key is None else self._client.exists(key)
+
+ def __getitem__(self, key):
+ """Retrieve an item from the cache using the square bracket notation."""
+ return self.get(key)
+
+ def __setitem__(self, key, value):
+ """Add an item to the cache using the square bracket notation."""
+ self.set(key, value)
+
+ def __delitem__(self, key):
+ """Remove an item from the cache using the square bracket notation."""
+ self.delete(key)
+
+ def __repr__(self):
+ """Return a string representation of the RedisCache instance."""
+ return f"RedisCache(expiration_time={self.expiration_time})"
diff --git a/src/backend/langflow/cache/utils.py b/src/backend/langflow/services/cache/utils.py
similarity index 74%
rename from src/backend/langflow/cache/utils.py
rename to src/backend/langflow/services/cache/utils.py
index cd282d2e9..bd6b4fb0a 100644
--- a/src/backend/langflow/cache/utils.py
+++ b/src/backend/langflow/services/cache/utils.py
@@ -6,9 +6,14 @@ import os
import tempfile
from collections import OrderedDict
from pathlib import Path
-from typing import Any, Dict
+from typing import TYPE_CHECKING, Any, Dict
from appdirs import user_cache_dir
-from langflow.database.models.base import orjson_dumps
+from fastapi import UploadFile
+from langflow.api.v1.schemas import BuildStatus
+from langflow.services.database.models.base import orjson_dumps
+
+if TYPE_CHECKING:
+ pass
CACHE: Dict[str, Any] = {}
@@ -30,6 +35,7 @@ def create_cache_folder(func):
def memoize_dict(maxsize=128):
cache = OrderedDict()
+ hash_to_key = {} # Mapping from hash to cache key
def decorator(func):
@functools.wraps(func)
@@ -39,16 +45,29 @@ def memoize_dict(maxsize=128):
if key not in cache:
result = func(*args, **kwargs)
cache[key] = result
+ hash_to_key[hashed] = key # Store the mapping
if len(cache) > maxsize:
- cache.popitem(last=False)
+ oldest_key = next(iter(cache))
+ oldest_hash = oldest_key[1]
+ del cache[oldest_key]
+ del hash_to_key[oldest_hash]
else:
result = cache[key]
+
+ wrapper.session_id = hashed # Store hash in the wrapper
return result
def clear_cache():
cache.clear()
+ hash_to_key.clear()
+
+ def get_result_by_session_id(session_id):
+ key = hash_to_key.get(session_id)
+ return cache.get(key) if key is not None else None
wrapper.clear_cache = clear_cache # type: ignore
+ wrapper.get_result_by_session_id = get_result_by_session_id # type: ignore
+ wrapper.hash = None
wrapper.cache = cache # type: ignore
return wrapper
@@ -138,7 +157,7 @@ def save_binary_file(content: str, file_name: str, accepted_types: list[str]) ->
@create_cache_folder
-def save_uploaded_file(file, folder_name):
+def save_uploaded_file(file: UploadFile, folder_name):
"""
Save an uploaded file to the specified folder with a hash of its content as the file name.
@@ -151,6 +170,12 @@ def save_uploaded_file(file, folder_name):
"""
cache_path = Path(CACHE_DIR)
folder_path = cache_path / folder_name
+ filename = file.filename
+ if isinstance(filename, str) or isinstance(filename, Path):
+ file_extension = Path(filename).suffix
+ else:
+ file_extension = ""
+ file_object = file.file
# Create the folder if it doesn't exist
if not folder_path.exists():
@@ -159,22 +184,30 @@ def save_uploaded_file(file, folder_name):
# Create a hash of the file content
sha256_hash = hashlib.sha256()
# Reset the file cursor to the beginning of the file
- file.seek(0)
+ file_object.seek(0)
# Iterate over the uploaded file in small chunks to conserve memory
- while chunk := file.read(8192): # Read 8KB at a time (adjust as needed)
+ while chunk := file_object.read(8192): # Read 8KB at a time (adjust as needed)
sha256_hash.update(chunk)
# Use the hex digest of the hash as the file name
hex_dig = sha256_hash.hexdigest()
- file_name = hex_dig
+ file_name = f"{hex_dig}{file_extension}"
# Reset the file cursor to the beginning of the file
- file.seek(0)
+ file_object.seek(0)
# Save the file with the hash as its name
file_path = folder_path / file_name
with open(file_path, "wb") as new_file:
- while chunk := file.read(8192):
+ while chunk := file_object.read(8192):
new_file.write(chunk)
return file_path
+
+
+def update_build_status(cache_service, flow_id: str, status: BuildStatus):
+ cached_flow = cache_service[flow_id]
+ if cached_flow is None:
+ raise ValueError(f"Flow {flow_id} not found in cache")
+ cached_flow["status"] = status
+ cache_service[flow_id] = cached_flow
diff --git a/src/backend/langflow/database/models/__init__.py b/src/backend/langflow/services/chat/__init__.py
similarity index 100%
rename from src/backend/langflow/database/models/__init__.py
rename to src/backend/langflow/services/chat/__init__.py
diff --git a/src/backend/langflow/cache/manager.py b/src/backend/langflow/services/chat/cache.py
similarity index 96%
rename from src/backend/langflow/cache/manager.py
rename to src/backend/langflow/services/chat/cache.py
index 13b281008..f6247b540 100644
--- a/src/backend/langflow/cache/manager.py
+++ b/src/backend/langflow/services/chat/cache.py
@@ -1,5 +1,6 @@
from contextlib import contextmanager
from typing import Any, Awaitable, Callable, List, Optional
+from langflow.services.base import Service
import pandas as pd
from PIL import Image
@@ -49,9 +50,11 @@ class AsyncSubject:
await observer()
-class CacheManager(Subject):
+class CacheService(Subject, Service):
"""Manages cache for different clients and notifies observers on changes."""
+ name = "cache_service"
+
def __init__(self):
super().__init__()
self._cache = {}
@@ -147,4 +150,4 @@ class CacheManager(Subject):
return list(self.current_cache.values())[-1]
-cache_manager = CacheManager()
+cache_service = CacheService()
diff --git a/src/backend/langflow/chat/config.py b/src/backend/langflow/services/chat/config.py
similarity index 100%
rename from src/backend/langflow/chat/config.py
rename to src/backend/langflow/services/chat/config.py
diff --git a/src/backend/langflow/services/chat/factory.py b/src/backend/langflow/services/chat/factory.py
new file mode 100644
index 000000000..54af7fcca
--- /dev/null
+++ b/src/backend/langflow/services/chat/factory.py
@@ -0,0 +1,11 @@
+from langflow.services.chat.manager import ChatService
+from langflow.services.factory import ServiceFactory
+
+
+class ChatServiceFactory(ServiceFactory):
+ def __init__(self):
+ super().__init__(ChatService)
+
+ def create(self):
+ # Here you would have logic to create and configure a ChatService
+ return ChatService()
diff --git a/src/backend/langflow/chat/manager.py b/src/backend/langflow/services/chat/manager.py
similarity index 73%
rename from src/backend/langflow/chat/manager.py
rename to src/backend/langflow/services/chat/manager.py
index 78b8b2b19..59488d431 100644
--- a/src/backend/langflow/chat/manager.py
+++ b/src/backend/langflow/services/chat/manager.py
@@ -1,18 +1,19 @@
from collections import defaultdict
import uuid
from fastapi import WebSocket, status
+from starlette.websockets import WebSocketState
from langflow.api.v1.schemas import ChatMessage, ChatResponse, FileResponse
-from langflow.cache import cache_manager
-from langflow.cache.manager import Subject
-from langflow.chat.utils import process_graph
from langflow.interface.utils import pil_to_base64
-from langflow.utils.logger import logger
-
+from langflow.services.base import Service
+from langflow.services.chat.cache import Subject
+from langflow.services.chat.utils import process_graph
+from loguru import logger
+from .cache import cache_service
import asyncio
from typing import Any, Dict, List
-from langflow.cache.flow import InMemoryCache
+from langflow.services import service_manager, ServiceType
import orjson
@@ -43,18 +44,20 @@ class ChatHistory(Subject):
self.history[client_id] = []
-class ChatManager:
+class ChatService(Service):
+ name = "chat_service"
+
def __init__(self):
self.active_connections: Dict[str, WebSocket] = {}
self.connection_ids: Dict[str, str] = {}
self.chat_history = ChatHistory()
- self.cache_manager = cache_manager
- self.cache_manager.attach(self.update)
- self.in_memory_cache = InMemoryCache()
+ self.chat_cache = cache_service
+ self.chat_cache.attach(self.update)
+ self.cache_service = service_manager.get(ServiceType.CACHE_SERVICE)
def on_chat_history_update(self):
"""Send the last chat message to the client."""
- client_id = self.cache_manager.current_client_id
+ client_id = self.chat_cache.current_client_id
if client_id in self.active_connections:
chat_response = self.chat_history.get_history(
client_id, filter_messages=False
@@ -75,8 +78,8 @@ class ChatManager:
asyncio.run_coroutine_threadsafe(coroutine, loop)
def update(self):
- if self.cache_manager.current_client_id in self.active_connections:
- self.last_cached_object_dict = self.cache_manager.get_last()
+ if self.chat_cache.current_client_id in self.active_connections:
+ self.last_cached_object_dict = self.chat_cache.get_last()
# Add a new ChatResponse with the data
chat_response = FileResponse(
message=None,
@@ -86,11 +89,10 @@ class ChatManager:
)
self.chat_history.add_message(
- self.cache_manager.current_client_id, chat_response
+ self.chat_cache.current_client_id, chat_response
)
async def connect(self, client_id: str, websocket: WebSocket):
- await websocket.accept()
self.active_connections[client_id] = websocket
# This is to avoid having multiple clients with the same id
#! Temporary solution
@@ -124,7 +126,8 @@ class ChatManager:
):
# Process the graph data and chat message
chat_inputs = payload.pop("inputs", {})
- chat_inputs = ChatMessage(message=chat_inputs)
+ chatkey = payload.pop("chatKey", None)
+ chat_inputs = ChatMessage(message=chat_inputs, chatKey=chatkey)
self.chat_history.add_message(client_id, chat_inputs)
# graph_data = payload
@@ -139,9 +142,10 @@ class ChatManager:
result, intermediate_steps = await process_graph(
langchain_object=langchain_object,
chat_inputs=chat_inputs,
- websocket=self.active_connections[client_id],
+ client_id=client_id,
session_id=self.connection_ids[client_id],
)
+ self.set_cache(client_id, langchain_object)
except Exception as e:
# Log stack trace
logger.exception(e)
@@ -177,9 +181,15 @@ class ChatManager:
"""
Set the cache for a client.
"""
+ # client_id is the flow id but that already exists in the cache
+ # so we need to change it to something else
- self.in_memory_cache.set(client_id, langchain_object)
- return client_id in self.in_memory_cache
+ result_dict = {
+ "result": langchain_object,
+ "type": type(langchain_object),
+ }
+ self.cache_service.upsert(client_id, result_dict)
+ return client_id in self.cache_service
async def handle_websocket(self, client_id: str, websocket: WebSocket):
await self.connect(client_id, websocket)
@@ -192,33 +202,45 @@ class ChatManager:
while True:
json_payload = await websocket.receive_json()
- try:
+ if isinstance(json_payload, str):
payload = orjson.loads(json_payload)
- except Exception:
+ elif isinstance(json_payload, dict):
payload = json_payload
- if "clear_history" in payload:
+ if "clear_history" in payload and payload["clear_history"]:
self.chat_history.history[client_id] = []
continue
- with self.cache_manager.set_client_id(client_id):
- langchain_object = self.in_memory_cache.get(client_id)
- await self.process_message(client_id, payload, langchain_object)
+ with self.chat_cache.set_client_id(client_id):
+ if langchain_object := self.cache_service.get(client_id).get(
+ "result"
+ ):
+ await self.process_message(client_id, payload, langchain_object)
+ else:
+ raise RuntimeError(
+ f"Could not find a build result for client_id {client_id}"
+ )
except Exception as exc:
# Handle any exceptions that might occur
- logger.error(f"Error handling websocket: {exc}")
- await self.close_connection(
- client_id=client_id,
- code=status.WS_1011_INTERNAL_ERROR,
- reason=str(exc)[:120],
- )
- finally:
- try:
+ logger.exception(f"Error handling websocket: {exc}")
+ if websocket.client_state == WebSocketState.CONNECTED:
await self.close_connection(
client_id=client_id,
- code=status.WS_1000_NORMAL_CLOSURE,
- reason="Client disconnected",
+ code=status.WS_1011_INTERNAL_ERROR,
+ reason=str(exc)[:120],
)
+ elif websocket.client_state == WebSocketState.DISCONNECTED:
+ self.disconnect(client_id)
+
+ finally:
+ try:
+ # first check if the connection is still open
+ if websocket.client_state == WebSocketState.CONNECTED:
+ await self.close_connection(
+ client_id=client_id,
+ code=status.WS_1000_NORMAL_CLOSURE,
+ reason="Client disconnected",
+ )
except Exception as exc:
logger.error(f"Error closing connection: {exc}")
self.disconnect(client_id)
diff --git a/src/backend/langflow/chat/utils.py b/src/backend/langflow/services/chat/utils.py
similarity index 88%
rename from src/backend/langflow/chat/utils.py
rename to src/backend/langflow/services/chat/utils.py
index e11b38b64..85b86a801 100644
--- a/src/backend/langflow/chat/utils.py
+++ b/src/backend/langflow/services/chat/utils.py
@@ -1,17 +1,16 @@
-from fastapi import WebSocket
from langflow.api.v1.schemas import ChatMessage
from langflow.processing.base import get_result_and_steps
from langflow.interface.utils import try_setting_streaming_options
-from langflow.utils.logger import logger
+from loguru import logger
async def process_graph(
langchain_object,
chat_inputs: ChatMessage,
- websocket: WebSocket,
+ client_id: str,
session_id: str,
):
- langchain_object = try_setting_streaming_options(langchain_object, websocket)
+ langchain_object = try_setting_streaming_options(langchain_object)
logger.debug("Loaded langchain object")
if langchain_object is None:
@@ -30,7 +29,7 @@ async def process_graph(
result, intermediate_steps = await get_result_and_steps(
langchain_object,
chat_inputs.message,
- websocket=websocket,
+ client_id=client_id,
session_id=session_id,
)
logger.debug("Generated result and intermediate_steps")
diff --git a/src/backend/langflow/services/database/__init__.py b/src/backend/langflow/services/database/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/src/backend/langflow/services/database/factory.py b/src/backend/langflow/services/database/factory.py
new file mode 100644
index 000000000..3726f520b
--- /dev/null
+++ b/src/backend/langflow/services/database/factory.py
@@ -0,0 +1,17 @@
+from typing import TYPE_CHECKING
+from langflow.services.database.manager import DatabaseService
+from langflow.services.factory import ServiceFactory
+
+if TYPE_CHECKING:
+ from langflow.services.settings.manager import SettingsService
+
+
+class DatabaseServiceFactory(ServiceFactory):
+ def __init__(self):
+ super().__init__(DatabaseService)
+
+ def create(self, settings_service: "SettingsService"):
+ # Here you would have logic to create and configure a DatabaseService
+ if not settings_service.settings.DATABASE_URL:
+ raise ValueError("No database URL provided")
+ return DatabaseService(settings_service.settings.DATABASE_URL)
diff --git a/src/backend/langflow/services/database/manager.py b/src/backend/langflow/services/database/manager.py
new file mode 100644
index 000000000..7465c804f
--- /dev/null
+++ b/src/backend/langflow/services/database/manager.py
@@ -0,0 +1,240 @@
+from pathlib import Path
+from typing import TYPE_CHECKING
+from langflow.services.base import Service
+from langflow.services.database.models.user.crud import get_user_by_username
+from langflow.services.database.utils import Result, TableResults
+from langflow.services.getters import get_settings_service
+from sqlalchemy import inspect
+import sqlalchemy as sa
+from sqlalchemy.exc import OperationalError
+from sqlmodel import SQLModel, Session, create_engine
+from loguru import logger
+from alembic.config import Config
+from alembic import command
+from langflow.services.database import models # noqa
+
+if TYPE_CHECKING:
+ from sqlalchemy.engine import Engine
+
+
+class DatabaseService(Service):
+ name = "database_service"
+
+ def __init__(self, database_url: str):
+ self.database_url = database_url
+ # This file is in langflow.services.database.manager.py
+ # the ini is in langflow
+ langflow_dir = Path(__file__).parent.parent.parent
+ self.script_location = langflow_dir / "alembic"
+ self.alembic_cfg_path = langflow_dir / "alembic.ini"
+ self.engine = self._create_engine()
+
+ def _create_engine(self) -> "Engine":
+ """Create the engine for the database."""
+ settings_service = get_settings_service()
+ if (
+ settings_service.settings.DATABASE_URL
+ and settings_service.settings.DATABASE_URL.startswith("sqlite")
+ ):
+ connect_args = {"check_same_thread": False}
+ else:
+ connect_args = {}
+ return create_engine(self.database_url, connect_args=connect_args)
+
+ def __enter__(self):
+ self._session = Session(self.engine)
+ return self._session
+
+ def __exit__(self, exc_type, exc_value, traceback):
+ if exc_type is not None: # If an exception has been raised
+ logger.error(
+ f"Session rollback because of exception: {exc_type.__name__} {exc_value}"
+ )
+ self._session.rollback()
+ else:
+ self._session.commit()
+ self._session.close()
+
+ def get_session(self):
+ with Session(self.engine) as session:
+ yield session
+
+ def migrate_flows_if_auto_login(self):
+ # if auto_login is enabled, we need to migrate the flows
+ # to the default superuser if they don't have a user id
+ # associated with them
+ settings_service = get_settings_service()
+ if settings_service.auth_settings.AUTO_LOGIN:
+ with Session(self.engine) as session:
+ flows = (
+ session.query(models.Flow)
+ .filter(models.Flow.user_id == None) # noqa
+ .all()
+ )
+ if flows:
+ logger.debug("Migrating flows to default superuser")
+ username = settings_service.auth_settings.SUPERUSER
+ user = get_user_by_username(session, username)
+ for flow in flows:
+ flow.user_id = user.id
+ session.commit()
+ logger.debug("Flows migrated successfully")
+
+ def check_schema_health(self) -> bool:
+ inspector = inspect(self.engine)
+
+ model_mapping = {
+ "flow": models.Flow,
+ "user": models.User,
+ "apikey": models.ApiKey,
+ # Add other SQLModel classes here
+ }
+
+ # To account for tables that existed in older versions
+ legacy_tables = ["flowstyle"]
+
+ for table, model in model_mapping.items():
+ expected_columns = list(model.__fields__.keys())
+
+ try:
+ available_columns = [
+ col["name"] for col in inspector.get_columns(table)
+ ]
+ except sa.exc.NoSuchTableError:
+ logger.error(f"Missing table: {table}")
+ return False
+
+ for column in expected_columns:
+ if column not in available_columns:
+ logger.error(f"Missing column: {column} in table {table}")
+ return False
+
+ for table in legacy_tables:
+ if table in inspector.get_table_names():
+ logger.warning(f"Legacy table exists: {table}")
+
+ return True
+
+ def init_alembic(self):
+ logger.info("Initializing alembic")
+ alembic_cfg = Config()
+ alembic_cfg.set_main_option("script_location", str(self.script_location))
+ alembic_cfg.set_main_option("sqlalchemy.url", self.database_url)
+ command.stamp(alembic_cfg, "head")
+ logger.info("Alembic initialized")
+
+ def run_migrations(self):
+ # First we need to check if alembic has been initialized
+ # If not, we need to initialize it
+ # if not self.script_location.exists(): # this is not the correct way to check if alembic has been initialized
+ # We need to check if the alembic_version table exists
+ # if not, we need to initialize alembic
+ with Session(self.engine) as session:
+ # If the table does not exist it throws an error
+ # so we need to catch it
+ try:
+ session.execute("SELECT * FROM alembic_version")
+ except Exception:
+ logger.info("Alembic not initialized")
+ try:
+ self.init_alembic()
+ except Exception as exc:
+ logger.error(f"Error initializing alembic: {exc}")
+ raise RuntimeError("Error initializing alembic") from exc
+ else:
+ logger.info("Alembic already initialized")
+
+ logger.info(f"Running DB migrations in {self.script_location}")
+ alembic_cfg = Config()
+ alembic_cfg.set_main_option("script_location", str(self.script_location))
+ alembic_cfg.set_main_option("sqlalchemy.url", self.database_url)
+ command.upgrade(alembic_cfg, "head")
+
+ def run_migrations_test(self):
+ # This method is used for testing purposes only
+ # We will check that all models are in the database
+ # and that the database is up to date with all columns
+ sql_models = [models.Flow, models.User, models.ApiKey]
+ return [
+ TableResults(sql_model.__tablename__, self.check_table(sql_model))
+ for sql_model in sql_models
+ ]
+
+ def check_table(self, model):
+ results = []
+ inspector = inspect(self.engine)
+ table_name = model.__tablename__
+ expected_columns = list(model.__fields__.keys())
+ try:
+ available_columns = [
+ col["name"] for col in inspector.get_columns(table_name)
+ ]
+ results.append(Result(name=table_name, type="table", success=True))
+ except sa.exc.NoSuchTableError:
+ logger.error(f"Missing table: {table_name}")
+ results.append(Result(name=table_name, type="table", success=False))
+
+ for column in expected_columns:
+ if column not in available_columns:
+ logger.error(f"Missing column: {column} in table {table_name}")
+ results.append(Result(name=column, type="column", success=False))
+ else:
+ results.append(Result(name=column, type="column", success=True))
+ return results
+
+ def create_db_and_tables(self):
+ from sqlalchemy import inspect
+
+ inspector = inspect(self.engine)
+ table_names = inspector.get_table_names()
+ current_tables = ["flow", "user", "apikey"]
+
+ if table_names and all(table in table_names for table in current_tables):
+ logger.debug("Database and tables already exist")
+ return
+
+ logger.debug("Creating database and tables")
+
+ for table in SQLModel.metadata.sorted_tables:
+ try:
+ table.create(self.engine, checkfirst=True)
+ except OperationalError as oe:
+ logger.warning(
+ f"Table {table} already exists, skipping. Exception: {oe}"
+ )
+ except Exception as exc:
+ logger.error(f"Error creating table {table}: {exc}")
+ raise RuntimeError(f"Error creating table {table}") from exc
+
+ # Now check if the required tables exist, if not, something went wrong.
+ inspector = inspect(self.engine)
+ table_names = inspector.get_table_names()
+ for table in current_tables:
+ if table not in 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."
+ )
+
+ logger.debug("Database and tables created successfully")
+
+ def teardown(self):
+ logger.debug("Tearing down database")
+ try:
+ settings_service = get_settings_service()
+ # remove the default superuser if auto_login is enabled
+ # using the SUPERUSER to get the user
+ if settings_service.auth_settings.AUTO_LOGIN:
+ logger.debug("Removing default superuser")
+ username = settings_service.auth_settings.SUPERUSER
+ with Session(self.engine) as session:
+ user = get_user_by_username(session, username)
+ session.delete(user)
+ session.commit()
+ logger.debug("Default superuser removed")
+
+ except Exception as exc:
+ logger.error(f"Error tearing down database: {exc}")
+
+ self.engine.dispose()
diff --git a/src/backend/langflow/services/database/models/__init__.py b/src/backend/langflow/services/database/models/__init__.py
new file mode 100644
index 000000000..3cc4231a3
--- /dev/null
+++ b/src/backend/langflow/services/database/models/__init__.py
@@ -0,0 +1,5 @@
+from .flow import Flow
+from .user import User
+from .api_key import ApiKey
+
+__all__ = ["Flow", "User", "ApiKey"]
diff --git a/src/backend/langflow/services/database/models/api_key/__init__.py b/src/backend/langflow/services/database/models/api_key/__init__.py
new file mode 100644
index 000000000..fbb8265b9
--- /dev/null
+++ b/src/backend/langflow/services/database/models/api_key/__init__.py
@@ -0,0 +1,3 @@
+from .api_key import ApiKey, ApiKeyCreate, UnmaskedApiKeyRead, ApiKeyRead
+
+__all__ = ["ApiKey", "ApiKeyCreate", "UnmaskedApiKeyRead", "ApiKeyRead"]
diff --git a/src/backend/langflow/services/database/models/api_key/api_key.py b/src/backend/langflow/services/database/models/api_key/api_key.py
new file mode 100644
index 000000000..684027ee2
--- /dev/null
+++ b/src/backend/langflow/services/database/models/api_key/api_key.py
@@ -0,0 +1,51 @@
+from pydantic import validator
+from sqlmodel import Field, Relationship
+from uuid import UUID, uuid4
+from typing import Optional, TYPE_CHECKING
+from datetime import datetime
+from langflow.services.database.models.base import SQLModelSerializable
+
+if TYPE_CHECKING:
+ from langflow.services.database.models.user import User
+
+
+class ApiKeyBase(SQLModelSerializable):
+ name: Optional[str] = Field(index=True)
+ created_at: datetime = Field(default_factory=datetime.utcnow)
+ last_used_at: Optional[datetime] = Field(default=None, nullable=True)
+ total_uses: int = Field(default=0)
+ is_active: bool = Field(default=True)
+
+
+class ApiKey(ApiKeyBase, table=True):
+ id: UUID = Field(default_factory=uuid4, primary_key=True, unique=True)
+
+ api_key: str = Field(index=True, unique=True)
+ # User relationship
+ # Delete API keys when user is deleted
+ user_id: UUID = Field(index=True, foreign_key="user.id")
+ user: "User" = Relationship(
+ back_populates="api_keys",
+ )
+
+
+class ApiKeyCreate(ApiKeyBase):
+ api_key: Optional[str] = None
+ user_id: Optional[UUID] = None
+
+
+class UnmaskedApiKeyRead(ApiKeyBase):
+ id: UUID
+ api_key: str = Field()
+ user_id: UUID = Field()
+
+
+class ApiKeyRead(ApiKeyBase):
+ id: UUID
+ api_key: str = Field()
+ user_id: UUID = Field()
+
+ @validator("api_key", always=True)
+ def mask_api_key(cls, v):
+ # This validator will always run, and will mask the API key
+ return f"{v[:8]}{'*' * (len(v) - 8)}"
diff --git a/src/backend/langflow/services/database/models/api_key/crud.py b/src/backend/langflow/services/database/models/api_key/crud.py
new file mode 100644
index 000000000..0e0ae6137
--- /dev/null
+++ b/src/backend/langflow/services/database/models/api_key/crud.py
@@ -0,0 +1,77 @@
+import datetime
+import secrets
+import threading
+from uuid import UUID
+from typing import List, Optional
+from sqlmodel import Session, select
+from langflow.services.database.models.api_key import (
+ ApiKey,
+ ApiKeyCreate,
+ UnmaskedApiKeyRead,
+ ApiKeyRead,
+)
+
+
+def get_api_keys(session: Session, user_id: UUID) -> List[ApiKeyRead]:
+ query = select(ApiKey).where(ApiKey.user_id == user_id)
+ api_keys = session.exec(query).all()
+ return [ApiKeyRead.from_orm(api_key) for api_key in api_keys]
+
+
+def create_api_key(
+ session: Session, api_key_create: ApiKeyCreate, user_id: UUID
+) -> UnmaskedApiKeyRead:
+ # Generate a random API key with 32 bytes of randomness
+ generated_api_key = f"sk-{secrets.token_urlsafe(32)}"
+
+ api_key = ApiKey(
+ api_key=generated_api_key,
+ name=api_key_create.name,
+ user_id=user_id,
+ )
+
+ session.add(api_key)
+ session.commit()
+ session.refresh(api_key)
+ unmasked = UnmaskedApiKeyRead.from_orm(api_key)
+ unmasked.api_key = generated_api_key
+ return unmasked
+
+
+def delete_api_key(session: Session, api_key_id: UUID) -> None:
+ api_key = session.get(ApiKey, api_key_id)
+ if api_key is None:
+ raise ValueError("API Key not found")
+ session.delete(api_key)
+ session.commit()
+
+
+def check_key(session: Session, api_key: str) -> Optional[ApiKey]:
+ """Check if the API key is valid."""
+ query = select(ApiKey).where(ApiKey.api_key == api_key)
+ api_key_object: Optional[ApiKey] = session.exec(query).first()
+ if api_key_object is not None:
+ threading.Thread(
+ target=update_total_uses,
+ args=(
+ session,
+ api_key_object,
+ ),
+ ).start()
+ return api_key_object
+
+
+def update_total_uses(session, api_key: ApiKey):
+ """Update the total uses and last used at."""
+ # This is running in a separate thread to avoid slowing down the request
+ # but session is not thread safe so we need to create a new session
+
+ with Session(session.get_bind()) as new_session:
+ new_api_key = new_session.get(ApiKey, api_key.id)
+ if new_api_key is None:
+ raise ValueError("API Key not found")
+ new_api_key.total_uses += 1
+ new_api_key.last_used_at = datetime.datetime.now(datetime.timezone.utc)
+ new_session.add(new_api_key)
+ new_session.commit()
+ return new_api_key
diff --git a/src/backend/langflow/database/models/base.py b/src/backend/langflow/services/database/models/base.py
similarity index 100%
rename from src/backend/langflow/database/models/base.py
rename to src/backend/langflow/services/database/models/base.py
diff --git a/src/backend/langflow/services/database/models/component/__init__.py b/src/backend/langflow/services/database/models/component/__init__.py
new file mode 100644
index 000000000..c787c3e04
--- /dev/null
+++ b/src/backend/langflow/services/database/models/component/__init__.py
@@ -0,0 +1,3 @@
+from .component import Component, ComponentModel
+
+__all__ = ["Component", "ComponentModel"]
diff --git a/src/backend/langflow/database/models/component.py b/src/backend/langflow/services/database/models/component/component.py
similarity index 92%
rename from src/backend/langflow/database/models/component.py
rename to src/backend/langflow/services/database/models/component/component.py
index bb2408cdb..5c4e6c13a 100644
--- a/src/backend/langflow/database/models/component.py
+++ b/src/backend/langflow/services/database/models/component/component.py
@@ -1,4 +1,4 @@
-from langflow.database.models.base import SQLModelSerializable, SQLModel
+from langflow.services.database.models.base import SQLModelSerializable, SQLModel
from sqlmodel import Field
from typing import Optional
from datetime import datetime
diff --git a/src/backend/langflow/services/database/models/flow/__init__.py b/src/backend/langflow/services/database/models/flow/__init__.py
new file mode 100644
index 000000000..7c7cc0172
--- /dev/null
+++ b/src/backend/langflow/services/database/models/flow/__init__.py
@@ -0,0 +1,3 @@
+from .flow import Flow, FlowCreate, FlowRead, FlowUpdate
+
+__all__ = ["Flow", "FlowCreate", "FlowRead", "FlowUpdate"]
diff --git a/src/backend/langflow/database/models/flow.py b/src/backend/langflow/services/database/models/flow/flow.py
similarity index 62%
rename from src/backend/langflow/database/models/flow.py
rename to src/backend/langflow/services/database/models/flow/flow.py
index f9e3aa249..e578f37c4 100644
--- a/src/backend/langflow/database/models/flow.py
+++ b/src/backend/langflow/services/database/models/flow/flow.py
@@ -1,23 +1,23 @@
# Path: src/backend/langflow/database/models/flow.py
-from langflow.database.models.base import SQLModelSerializable
+from langflow.services.database.models.base import SQLModelSerializable
from pydantic import validator
-from sqlmodel import Field, Relationship, JSON, Column
-from uuid import UUID, uuid4
-from typing import Dict, Optional
-# if TYPE_CHECKING:
-from langflow.database.models.flow_style import FlowStyle, FlowStyleRead
+from sqlmodel import Field, JSON, Column, Relationship
+from uuid import UUID, uuid4
+from typing import Dict, Optional, TYPE_CHECKING
+
+if TYPE_CHECKING:
+ from langflow.services.database.models.user import User
class FlowBase(SQLModelSerializable):
name: str = Field(index=True)
description: Optional[str] = Field(index=True)
- data: Optional[Dict] = Field(default=None)
+ data: Optional[Dict] = Field(default=None, nullable=True)
@validator("data")
def validate_json(v):
- # dict_keys(['description', 'name', 'id', 'data'])
if not v:
return v
if not isinstance(v, dict):
@@ -35,23 +35,17 @@ class FlowBase(SQLModelSerializable):
class Flow(FlowBase, table=True):
id: UUID = Field(default_factory=uuid4, primary_key=True, unique=True)
data: Optional[Dict] = Field(default=None, sa_column=Column(JSON))
- style: Optional["FlowStyle"] = Relationship(
- back_populates="flow",
- # use "uselist=False" to make it a one-to-one relationship
- sa_relationship_kwargs={"uselist": False},
- )
+ user_id: UUID = Field(index=True, foreign_key="user.id")
+ user: "User" = Relationship(back_populates="flows")
class FlowCreate(FlowBase):
- pass
+ user_id: Optional[UUID] = None
class FlowRead(FlowBase):
id: UUID
-
-
-class FlowReadWithStyle(FlowRead):
- style: Optional["FlowStyleRead"] = None
+ user_id: UUID = Field()
class FlowUpdate(SQLModelSerializable):
diff --git a/src/backend/langflow/services/database/models/user/__init__.py b/src/backend/langflow/services/database/models/user/__init__.py
new file mode 100644
index 000000000..da9170eb7
--- /dev/null
+++ b/src/backend/langflow/services/database/models/user/__init__.py
@@ -0,0 +1,8 @@
+from .user import User, UserCreate, UserRead, UserUpdate
+
+__all__ = [
+ "User",
+ "UserCreate",
+ "UserRead",
+ "UserUpdate",
+]
diff --git a/src/backend/langflow/services/database/models/user/crud.py b/src/backend/langflow/services/database/models/user/crud.py
new file mode 100644
index 000000000..36f03e684
--- /dev/null
+++ b/src/backend/langflow/services/database/models/user/crud.py
@@ -0,0 +1,59 @@
+from datetime import datetime, timezone
+from typing import Union
+from uuid import UUID
+from fastapi import Depends, HTTPException, status
+from langflow.services.database.models.user.user import User, UserUpdate
+from langflow.services.getters import get_session
+from sqlalchemy.exc import IntegrityError
+from sqlmodel import Session
+from typing import Optional
+
+from sqlalchemy.orm.attributes import flag_modified
+
+
+def get_user_by_username(db: Session, username: str) -> Union[User, None]:
+ return db.query(User).filter(User.username == username).first()
+
+
+def get_user_by_id(db: Session, id: UUID) -> Union[User, None]:
+ return db.query(User).filter(User.id == id).first()
+
+
+def update_user(
+ user_db: Optional[User], user: UserUpdate, db: Session = Depends(get_session)
+) -> User:
+ if not user_db:
+ raise HTTPException(status_code=404, detail="User not found")
+
+ # user_db_by_username = get_user_by_username(db, user.username) # type: ignore
+ # if user_db_by_username and user_db_by_username.id != user_id:
+ # raise HTTPException(status_code=409, detail="Username already exists")
+
+ user_data = user.dict(exclude_unset=True)
+ changed = False
+ for attr, value in user_data.items():
+ if hasattr(user_db, attr) and value is not None:
+ setattr(user_db, attr, value)
+ changed = True
+
+ if not changed:
+ raise HTTPException(
+ status_code=status.HTTP_304_NOT_MODIFIED, detail="Nothing to update"
+ )
+
+ user_db.updated_at = datetime.now(timezone.utc)
+ flag_modified(user_db, "updated_at")
+
+ try:
+ db.commit()
+ except IntegrityError as e:
+ db.rollback()
+ raise HTTPException(status_code=400, detail=str(e)) from e
+
+ return user_db
+
+
+def update_user_last_login_at(user_id: UUID, db: Session = Depends(get_session)):
+ user_data = UserUpdate(last_login_at=datetime.now(timezone.utc)) # type: ignore
+ user = get_user_by_id(db, user_id)
+ return update_user(user, user_data, db)
diff --git a/src/backend/langflow/services/database/models/user/user.py b/src/backend/langflow/services/database/models/user/user.py
new file mode 100644
index 000000000..b1f514531
--- /dev/null
+++ b/src/backend/langflow/services/database/models/user/user.py
@@ -0,0 +1,53 @@
+from langflow.services.database.models.base import SQLModel, SQLModelSerializable
+from sqlmodel import Field, Relationship
+
+
+from datetime import datetime
+from typing import Optional, TYPE_CHECKING
+from uuid import UUID, uuid4
+
+if TYPE_CHECKING:
+ from langflow.services.database.models.api_key import ApiKey
+ from langflow.services.database.models.flow import Flow
+
+
+class User(SQLModelSerializable, table=True):
+ id: UUID = Field(default_factory=uuid4, primary_key=True, unique=True)
+ username: str = Field(index=True, unique=True)
+ password: str = Field()
+ profile_image: Optional[str] = Field(default=None, nullable=True)
+ is_active: bool = Field(default=False)
+ is_superuser: bool = Field(default=False)
+ create_at: datetime = Field(default_factory=datetime.utcnow)
+ updated_at: datetime = Field(default_factory=datetime.utcnow)
+ last_login_at: Optional[datetime] = Field()
+ api_keys: list["ApiKey"] = Relationship(
+ back_populates="user",
+ sa_relationship_kwargs={"cascade": "delete"},
+ )
+ flows: list["Flow"] = Relationship(back_populates="user")
+
+
+class UserCreate(SQLModel):
+ username: str = Field()
+ password: str = Field()
+
+
+class UserRead(SQLModel):
+ id: UUID = Field(default_factory=uuid4)
+ username: str = Field()
+ profile_image: Optional[str] = Field()
+ is_active: bool = Field()
+ is_superuser: bool = Field()
+ create_at: datetime = Field()
+ updated_at: datetime = Field()
+ last_login_at: Optional[datetime] = Field()
+
+
+class UserUpdate(SQLModel):
+ username: Optional[str] = Field()
+ profile_image: Optional[str] = Field()
+ password: Optional[str] = Field()
+ is_active: Optional[bool] = Field()
+ is_superuser: Optional[bool] = Field()
+ last_login_at: Optional[datetime] = Field()
diff --git a/src/backend/langflow/services/database/utils.py b/src/backend/langflow/services/database/utils.py
new file mode 100644
index 000000000..b457b70e2
--- /dev/null
+++ b/src/backend/langflow/services/database/utils.py
@@ -0,0 +1,82 @@
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+from loguru import logger
+from contextlib import contextmanager
+from alembic.util.exc import CommandError
+from sqlmodel import Session
+
+if TYPE_CHECKING:
+ from langflow.services.database.manager import DatabaseService
+
+
+def initialize_database():
+ logger.debug("Initializing database")
+ from langflow.services import service_manager, ServiceType
+
+ database_service: "DatabaseService" = service_manager.get(
+ ServiceType.DATABASE_SERVICE
+ )
+ try:
+ database_service.create_db_and_tables()
+ except Exception as exc:
+ # if the exception involves tables already existing
+ # we can ignore it
+ if "already exists" not in str(exc):
+ logger.error(f"Error creating DB and tables: {exc}")
+ raise RuntimeError("Error creating DB and tables") from exc
+ try:
+ database_service.check_schema_health()
+ except Exception as exc:
+ logger.error(f"Error checking schema health: {exc}")
+ raise RuntimeError("Error checking schema health") from exc
+ try:
+ database_service.run_migrations()
+ except CommandError as exc:
+ # if "overlaps with other requested revisions" or "Can't locate revision identified by"
+ # are not in the exception, we can't handle it
+ if "overlaps with other requested revisions" not in str(
+ exc
+ ) and "Can't locate revision identified by" not in str(exc):
+ raise exc
+ # This means there's wrong revision in the DB
+ # We need to delete the alembic_version table
+ # and run the migrations again
+ logger.warning(
+ "Wrong revision in DB, deleting alembic_version table and running migrations again"
+ )
+ with session_getter(database_service) as session:
+ session.execute("DROP TABLE alembic_version")
+ database_service.run_migrations()
+ except Exception as exc:
+ # if the exception involves tables already existing
+ # we can ignore it
+ if "already exists" not in str(exc):
+ logger.error(f"Error running migrations: {exc}")
+ raise RuntimeError("Error running migrations") from exc
+ logger.debug("Database initialized")
+
+
+@contextmanager
+def session_getter(db_service: "DatabaseService"):
+ try:
+ session = Session(db_service.engine)
+ yield session
+ except Exception as e:
+ print("Session rollback because of exception:", e)
+ session.rollback()
+ raise
+ finally:
+ session.close()
+
+
+@dataclass
+class Result:
+ name: str
+ type: str
+ success: bool
+
+
+@dataclass
+class TableResults:
+ table_name: str
+ results: list[Result]
diff --git a/src/backend/langflow/services/factory.py b/src/backend/langflow/services/factory.py
new file mode 100644
index 000000000..c37f4e9c2
--- /dev/null
+++ b/src/backend/langflow/services/factory.py
@@ -0,0 +1,6 @@
+class ServiceFactory:
+ def __init__(self, service_class):
+ self.service_class = service_class
+
+ def create(self, *args, **kwargs):
+ raise NotImplementedError
diff --git a/src/backend/langflow/services/getters.py b/src/backend/langflow/services/getters.py
new file mode 100644
index 000000000..e88b998b5
--- /dev/null
+++ b/src/backend/langflow/services/getters.py
@@ -0,0 +1,48 @@
+from langflow.services import ServiceType, service_manager
+from typing import TYPE_CHECKING, Generator
+
+
+if TYPE_CHECKING:
+ from langflow.services.database.manager import DatabaseService
+ from langflow.services.settings.manager import SettingsService
+ from langflow.services.cache.manager import BaseCacheService
+ from langflow.services.session.manager import SessionService
+ from langflow.services.task.manager import TaskService
+ from langflow.services.chat.manager import ChatService
+ from sqlmodel import Session
+
+
+def get_settings_service() -> "SettingsService":
+ try:
+ return service_manager.get(ServiceType.SETTINGS_SERVICE)
+ except ValueError:
+ # initialize settings service
+ from langflow.services.manager import initialize_settings_service
+
+ initialize_settings_service()
+ return service_manager.get(ServiceType.SETTINGS_SERVICE)
+
+
+def get_db_service() -> "DatabaseService":
+ return service_manager.get(ServiceType.DATABASE_SERVICE)
+
+
+def get_session() -> Generator["Session", None, None]:
+ db_service = service_manager.get(ServiceType.DATABASE_SERVICE)
+ yield from db_service.get_session()
+
+
+def get_cache_service() -> "BaseCacheService":
+ return service_manager.get(ServiceType.CACHE_SERVICE)
+
+
+def get_session_service() -> "SessionService":
+ return service_manager.get(ServiceType.SESSION_SERVICE)
+
+
+def get_task_service() -> "TaskService":
+ return service_manager.get(ServiceType.TASK_SERVICE)
+
+
+def get_chat_service() -> "ChatService":
+ return service_manager.get(ServiceType.CHAT_SERVICE)
diff --git a/src/backend/langflow/services/manager.py b/src/backend/langflow/services/manager.py
new file mode 100644
index 000000000..10fd6b699
--- /dev/null
+++ b/src/backend/langflow/services/manager.py
@@ -0,0 +1,154 @@
+from langflow.services.schema import ServiceType
+from typing import TYPE_CHECKING, Dict, List, Optional
+from loguru import logger
+
+if TYPE_CHECKING:
+ from langflow.services.factory import ServiceFactory
+ from langflow.services.base import Service
+
+
+class ServiceManager:
+ """
+ Manages the creation of different services.
+ """
+
+ def __init__(self):
+ self.services: Dict[str, "Service"] = {}
+ self.factories = {}
+ self.dependencies = {}
+
+ def register_factory(
+ self,
+ service_factory: "ServiceFactory",
+ dependencies: Optional[List[ServiceType]] = None,
+ ):
+ """
+ Registers a new factory with dependencies.
+ """
+ if dependencies is None:
+ dependencies = []
+ service_name = service_factory.service_class.name
+ self.factories[service_name] = service_factory
+ self.dependencies[service_name] = dependencies
+
+ def get(self, service_name: ServiceType):
+ """
+ Get (or create) a service by its name.
+ """
+ if service_name not in self.services:
+ self._create_service(service_name)
+
+ return self.services[service_name]
+
+ def _create_service(self, service_name: ServiceType):
+ """
+ Create a new service given its name, handling dependencies.
+ """
+ logger.debug(f"Create service {service_name}")
+ self._validate_service_creation(service_name)
+
+ # Create dependencies first
+ for dependency in self.dependencies.get(service_name, []):
+ if dependency not in self.services:
+ self._create_service(dependency)
+
+ # Collect the dependent services
+ dependent_services = {
+ dep.value: self.services[dep]
+ for dep in self.dependencies.get(service_name, [])
+ }
+
+ # Create the actual service
+ self.services[service_name] = self.factories[service_name].create(
+ **dependent_services
+ )
+ self.services[service_name].set_ready()
+
+ def _validate_service_creation(self, service_name: ServiceType):
+ """
+ Validate whether the service can be created.
+ """
+ if service_name not in self.factories:
+ raise ValueError(
+ f"No factory registered for the service class '{service_name.name}'"
+ )
+
+ def update(self, service_name: ServiceType):
+ """
+ Update a service by its name.
+ """
+ if service_name in self.services:
+ logger.debug(f"Update service {service_name}")
+ self.services.pop(service_name, None)
+ self.get(service_name)
+
+ def teardown(self):
+ """
+ Teardown all the services.
+ """
+ for service in self.services.values():
+ if service is None:
+ continue
+ logger.debug(f"Teardown service {service.name}")
+ try:
+ service.teardown()
+ except Exception as exc:
+ logger.exception(exc)
+ self.services = {}
+ self.factories = {}
+ self.dependencies = {}
+
+
+service_manager = ServiceManager()
+
+
+def reinitialize_services():
+ """
+ Reinitialize all the services needed.
+ """
+
+ service_manager.update(ServiceType.SETTINGS_SERVICE)
+ service_manager.update(ServiceType.DATABASE_SERVICE)
+ service_manager.update(ServiceType.CACHE_SERVICE)
+ service_manager.update(ServiceType.CHAT_SERVICE)
+ service_manager.update(ServiceType.SESSION_SERVICE)
+ service_manager.update(ServiceType.AUTH_SERVICE)
+ service_manager.update(ServiceType.TASK_SERVICE)
+
+ # Test cache connection
+ service_manager.get(ServiceType.CACHE_SERVICE)
+ # Test database connection
+ service_manager.get(ServiceType.DATABASE_SERVICE)
+
+ # Test cache connection
+ service_manager.get(ServiceType.CACHE_SERVICE)
+ # Test database connection
+ service_manager.get(ServiceType.DATABASE_SERVICE)
+
+
+def initialize_settings_service():
+ """
+ Initialize the settings manager.
+ """
+ from langflow.services.settings import factory as settings_factory
+
+ service_manager.register_factory(settings_factory.SettingsServiceFactory())
+
+
+def initialize_session_service():
+ """
+ Initialize the session manager.
+ """
+ from langflow.services.session import factory as session_service_factory # type: ignore
+ from langflow.services.cache import factory as cache_factory
+
+ initialize_settings_service()
+
+ service_manager.register_factory(
+ cache_factory.CacheServiceFactory(), dependencies=[ServiceType.SETTINGS_SERVICE]
+ )
+
+ service_manager.register_factory(
+ session_service_factory.SessionServiceFactory(),
+ dependencies=[ServiceType.CACHE_SERVICE],
+ )
diff --git a/src/backend/langflow/services/plugins/langfuse.py b/src/backend/langflow/services/plugins/langfuse.py
index 6969e9185..7a1f60a48 100644
--- a/src/backend/langflow/services/plugins/langfuse.py
+++ b/src/backend/langflow/services/plugins/langfuse.py
@@ -1,3 +1,4 @@
+from langflow.services.getters import get_settings_service
from langflow.utils.logger import logger
### Temporary implementation
@@ -16,19 +17,27 @@ class LangfuseInstance:
@classmethod
def create(cls):
- logger.debug("Checking Langfuse credentials")
- from langflow.settings import settings
- from langfuse import Langfuse # type: ignore
+ try:
+ logger.debug("Creating Langfuse instance")
+ from langfuse import Langfuse # type: ignore
- if settings.LANGFUSE_PUBLIC_KEY and settings.LANGFUSE_SECRET_KEY:
- logger.debug("Langfuse credentials found")
- cls._instance = Langfuse(
- public_key=settings.LANGFUSE_PUBLIC_KEY,
- secret_key=settings.LANGFUSE_SECRET_KEY,
- host=settings.LANGFUSE_HOST,
- )
- else:
- logger.debug("No Langfuse credentials found")
+ settings_manager = get_settings_service()
+
+ if (
+ settings_manager.settings.LANGFUSE_PUBLIC_KEY
+ and settings_manager.settings.LANGFUSE_SECRET_KEY
+ ):
+ logger.debug("Langfuse credentials found")
+ cls._instance = Langfuse(
+ public_key=settings_manager.settings.LANGFUSE_PUBLIC_KEY,
+ secret_key=settings_manager.settings.LANGFUSE_SECRET_KEY,
+ host=settings_manager.settings.LANGFUSE_HOST,
+ )
+ else:
+ logger.debug("No Langfuse credentials found")
+ cls._instance = None
+ except ImportError:
+ logger.debug("Langfuse not installed")
cls._instance = None
@classmethod
diff --git a/src/backend/langflow/services/schema.py b/src/backend/langflow/services/schema.py
new file mode 100644
index 000000000..8b3b41fcb
--- /dev/null
+++ b/src/backend/langflow/services/schema.py
@@ -0,0 +1,16 @@
+from enum import Enum
+
+
+class ServiceType(str, Enum):
+ """
+ Enum for the different types of services that can be
+ registered with the service manager.
+ """
+
+ AUTH_SERVICE = "auth_service"
+ CACHE_SERVICE = "cache_service"
+ SETTINGS_SERVICE = "settings_service"
+ DATABASE_SERVICE = "database_service"
+ CHAT_SERVICE = "chat_service"
+ SESSION_SERVICE = "session_service"
+ TASK_SERVICE = "task_service"
diff --git a/src/backend/langflow/services/session/__init__.py b/src/backend/langflow/services/session/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/src/backend/langflow/services/session/factory.py b/src/backend/langflow/services/session/factory.py
new file mode 100644
index 000000000..9abe025a8
--- /dev/null
+++ b/src/backend/langflow/services/session/factory.py
@@ -0,0 +1,14 @@
+from typing import TYPE_CHECKING
+from langflow.services.session.manager import SessionService
+from langflow.services.factory import ServiceFactory
+
+if TYPE_CHECKING:
+ from langflow.services.cache.manager import BaseCacheService
+
+
+class SessionServiceFactory(ServiceFactory):
+ def __init__(self):
+ super().__init__(SessionService)
+
+ def create(self, cache_service: "BaseCacheService"):
+ return SessionService(cache_service)
diff --git a/src/backend/langflow/services/session/manager.py b/src/backend/langflow/services/session/manager.py
new file mode 100644
index 000000000..6bdebf6b3
--- /dev/null
+++ b/src/backend/langflow/services/session/manager.py
@@ -0,0 +1,47 @@
+from typing import TYPE_CHECKING
+from langflow.interface.run import build_sorted_vertices
+from langflow.services.base import Service
+from langflow.services.cache.utils import compute_dict_hash
+from langflow.services.session.utils import session_id_generator
+
+if TYPE_CHECKING:
+ from langflow.services.cache.base import BaseCacheService
+
+
+class SessionService(Service):
+ name = "session_service"
+
+ def __init__(self, cache_service):
+ self.cache_service: "BaseCacheService" = cache_service
+
+ def load_session(self, key, data_graph):
+ # Check if the data is cached
+ if key in self.cache_service:
+ return self.cache_service.get(key)
+
+ if key is None:
+ key = self.generate_key(session_id=None, data_graph=data_graph)
+
+ # If not cached, build the graph and cache it
+ graph, artifacts = build_sorted_vertices(data_graph)
+
+ self.cache_service.set(key, (graph, artifacts))
+
+ return graph, artifacts
+
+ def build_key(self, session_id, data_graph):
+ json_hash = compute_dict_hash(data_graph)
+ return f"{session_id}{':' if session_id else ''}{json_hash}"
+
+ def generate_key(self, session_id, data_graph):
+ # Hash the JSON and combine it with the session_id to create a unique key
+ if session_id is None:
+ # generate a 5 char session_id to concatenate with the json_hash
+ session_id = session_id_generator()
+ return self.build_key(session_id, data_graph=data_graph)
+
+ def update_session(self, session_id, value):
+ self.cache_service.set(session_id, value)
+
+ def clear_session(self, session_id):
+ self.cache_service.delete(session_id)
diff --git a/src/backend/langflow/services/session/utils.py b/src/backend/langflow/services/session/utils.py
new file mode 100644
index 000000000..374d85540
--- /dev/null
+++ b/src/backend/langflow/services/session/utils.py
@@ -0,0 +1,8 @@
+import random
+import string
+
+
+def session_id_generator(size=6):
+ return "".join(
+ random.SystemRandom().choices(string.ascii_uppercase + string.digits, k=size)
+ )
diff --git a/src/backend/langflow/services/settings/__init__.py b/src/backend/langflow/services/settings/__init__.py
new file mode 100644
index 000000000..2191bf2cc
--- /dev/null
+++ b/src/backend/langflow/services/settings/__init__.py
@@ -0,0 +1,3 @@
+from . import factory, manager
+
+__all__ = ["factory", "manager"]
diff --git a/src/backend/langflow/services/settings/auth.py b/src/backend/langflow/services/settings/auth.py
new file mode 100644
index 000000000..7ac7461a0
--- /dev/null
+++ b/src/backend/langflow/services/settings/auth.py
@@ -0,0 +1,99 @@
+from pathlib import Path
+from typing import Optional
+import secrets
+from langflow.services.settings.constants import (
+ DEFAULT_SUPERUSER,
+ DEFAULT_SUPERUSER_PASSWORD,
+)
+from langflow.services.settings.utils import read_secret_from_file, write_secret_to_file
+
+from pydantic import BaseSettings, Field, validator
+from passlib.context import CryptContext
+from loguru import logger
+
+
+class AuthSettings(BaseSettings):
+ # Login settings
+ CONFIG_DIR: str
+ SECRET_KEY: str = Field(
+ default="",
+ description="Secret key for JWT. If not provided, a random one will be generated.",
+ env="LANGFLOW_SECRET_KEY",
+ allow_mutation=False,
+ )
+ ALGORITHM: str = "HS256"
+ ACCESS_TOKEN_EXPIRE_MINUTES: int = 60
+ REFRESH_TOKEN_EXPIRE_MINUTES: int = 60 * 12
+
+ # API Key to execute /process endpoint
+ API_KEY_SECRET_KEY: Optional[
+ str
+ ] = "b82818e0ad4ff76615c5721ee21004b07d84cd9b87ba4d9cb42374da134b841a"
+ API_KEY_ALGORITHM: str = "HS256"
+ API_V1_STR: str = "/api/v1"
+
+ # If AUTO_LOGIN = True
+ # > The application does not request login and logs in automatically as a super user.
+ AUTO_LOGIN: bool = True
+ NEW_USER_IS_ACTIVE: bool = False
+ SUPERUSER: str = DEFAULT_SUPERUSER
+ SUPERUSER_PASSWORD: str = DEFAULT_SUPERUSER_PASSWORD
+
+ pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
+
+ class Config:
+ validate_assignment = True
+ extra = "ignore"
+ env_prefix = "LANGFLOW_"
+
+ def reset_credentials(self):
+ self.SUPERUSER = DEFAULT_SUPERUSER
+ self.SUPERUSER_PASSWORD = DEFAULT_SUPERUSER_PASSWORD
+
+ # If autologin is true, then we need to set the credentials to
+ # the default values
+ # so we need to validate the superuser and superuser_password
+ # fields
+ @validator("SUPERUSER", "SUPERUSER_PASSWORD", pre=True)
+ def validate_superuser(cls, value, values):
+ if values.get("AUTO_LOGIN"):
+ if value != DEFAULT_SUPERUSER:
+ value = DEFAULT_SUPERUSER
+ logger.debug("Resetting superuser to default value")
+ if values.get("SUPERUSER_PASSWORD") != DEFAULT_SUPERUSER_PASSWORD:
+ values["SUPERUSER_PASSWORD"] = DEFAULT_SUPERUSER_PASSWORD
+ logger.debug("Resetting superuser password to default value")
+
+ return value
+
+ return value
+
+ @validator("SECRET_KEY", pre=True)
+ def get_secret_key(cls, value, values):
+ config_dir = values.get("CONFIG_DIR")
+
+ if not config_dir:
+ logger.debug("No CONFIG_DIR provided, not saving secret key")
+ return value or secrets.token_urlsafe(32)
+
+ secret_key_path = Path(config_dir) / "secret_key"
+
+ if value:
+ logger.debug("Secret key provided")
+ write_secret_to_file(secret_key_path, value)
+ else:
+ logger.debug("No secret key provided, generating a random one")
+
+ if secret_key_path.exists():
+ value = read_secret_from_file(secret_key_path)
+ logger.debug("Loaded secret key")
+ if not value:
+ value = secrets.token_urlsafe(32)
+ write_secret_to_file(secret_key_path, value)
+ logger.debug("Saved secret key")
+ else:
+ value = secrets.token_urlsafe(32)
+ write_secret_to_file(secret_key_path, value)
+ logger.debug("Saved secret key")
+
+ return value
diff --git a/src/backend/langflow/settings.py b/src/backend/langflow/services/settings/base.py
similarity index 73%
rename from src/backend/langflow/settings.py
rename to src/backend/langflow/services/settings/base.py
index e28e90eea..14e3f9928 100644
--- a/src/backend/langflow/settings.py
+++ b/src/backend/langflow/services/settings/base.py
@@ -2,14 +2,16 @@ import contextlib
import json
import orjson
import os
+from shutil import copy2
from typing import Optional, List
from pathlib import Path
import yaml
from pydantic import BaseSettings, root_validator, validator
-from langflow.utils.logger import logger
+from loguru import logger
-BASE_COMPONENTS_PATH = str(Path(__file__).parent / "components")
+# BASE_COMPONENTS_PATH = str(Path(__file__).parent / "components")
+BASE_COMPONENTS_PATH = str(Path(__file__).parent.parent.parent / "components")
class Settings(BaseSettings):
@@ -30,18 +32,51 @@ class Settings(BaseSettings):
OUTPUT_PARSERS: dict = {}
CUSTOM_COMPONENTS: dict = {}
+ # Define the default LANGFLOW_DIR
+ CONFIG_DIR: Optional[str] = None
+
DEV: bool = False
DATABASE_URL: Optional[str] = None
- CACHE: Optional[str] = None
+ CACHE_TYPE: str = "memory"
REMOVE_API_KEYS: bool = False
COMPONENTS_PATH: List[str] = []
+ LANGCHAIN_CACHE: str = "InMemoryCache"
+
+ # Redis
+ REDIS_HOST: str = "localhost"
+ REDIS_PORT: int = 6379
+ REDIS_DB: int = 0
+ REDIS_CACHE_EXPIRE: int = 3600
LANGFUSE_SECRET_KEY: Optional[str] = None
LANGFUSE_PUBLIC_KEY: Optional[str] = None
LANGFUSE_HOST: Optional[str] = None
+ @validator("CONFIG_DIR", pre=True, allow_reuse=True)
+ def set_langflow_dir(cls, value):
+ if not value:
+ import appdirs
+
+ # Define the app name and author
+ app_name = "langflow"
+ app_author = "logspace"
+
+ # Get the cache directory for the application
+ cache_dir = appdirs.user_cache_dir(app_name, app_author)
+
+ # Create a .langflow directory inside the cache directory
+ value = Path(cache_dir)
+ value.mkdir(parents=True, exist_ok=True)
+
+ if isinstance(value, str):
+ value = Path(value)
+ if not value.exists():
+ value.mkdir(parents=True, exist_ok=True)
+
+ return str(value)
+
@validator("DATABASE_URL", pre=True)
- def set_database_url(cls, value):
+ def set_database_url(cls, value, values):
if not value:
logger.debug(
"No database_url provided, trying LANGFLOW_DATABASE_URL env variable"
@@ -51,7 +86,28 @@ class Settings(BaseSettings):
logger.debug("Using LANGFLOW_DATABASE_URL env variable.")
else:
logger.debug("No DATABASE_URL env variable, using sqlite database")
- value = "sqlite:///./langflow.db"
+ # Originally, we used sqlite:///./langflow.db
+ # so we need to migrate to the new format
+ # if there is a database in that location
+ if not values["CONFIG_DIR"]:
+ raise ValueError(
+ "CONFIG_DIR not set, please set it or provide a DATABASE_URL"
+ )
+
+ new_path = f"{values['CONFIG_DIR']}/langflow.db"
+ if Path("./langflow.db").exists():
+ if Path(new_path).exists():
+ logger.debug(f"Database already exists at {new_path}, using it")
+ else:
+ try:
+ logger.debug("Copying existing database to new location")
+ copy2("./langflow.db", new_path)
+ logger.debug(f"Copied existing database to {new_path}")
+ except Exception:
+ logger.error("Failed to copy database, using default path")
+ new_path = "./langflow.db"
+
+ value = f"sqlite:///{new_path}"
return value
@@ -176,6 +232,3 @@ def load_settings_from_yaml(file_path: str) -> Settings:
logger.debug(f"Loading {len(settings_dict[key])} {key} from {file_path}")
return Settings(**settings_dict)
-
-
-settings = load_settings_from_yaml("config.yaml")
diff --git a/src/backend/langflow/services/settings/constants.py b/src/backend/langflow/services/settings/constants.py
new file mode 100644
index 000000000..6cf7d4823
--- /dev/null
+++ b/src/backend/langflow/services/settings/constants.py
@@ -0,0 +1,2 @@
+DEFAULT_SUPERUSER = "langflow"
+DEFAULT_SUPERUSER_PASSWORD = "langflow"
diff --git a/src/backend/langflow/services/settings/factory.py b/src/backend/langflow/services/settings/factory.py
new file mode 100644
index 000000000..9202ae8c3
--- /dev/null
+++ b/src/backend/langflow/services/settings/factory.py
@@ -0,0 +1,15 @@
+from pathlib import Path
+from langflow.services.settings.manager import SettingsService
+from langflow.services.factory import ServiceFactory
+
+
+class SettingsServiceFactory(ServiceFactory):
+ def __init__(self):
+ super().__init__(SettingsService)
+
+ def create(self):
+ # Here you would have logic to create and configure a SettingsService
+ langflow_dir = Path(__file__).parent.parent.parent
+ return SettingsService.load_settings_from_yaml(
+ str(langflow_dir / "config.yaml")
+ )
diff --git a/src/backend/langflow/services/settings/manager.py b/src/backend/langflow/services/settings/manager.py
new file mode 100644
index 000000000..cdededcea
--- /dev/null
+++ b/src/backend/langflow/services/settings/manager.py
@@ -0,0 +1,44 @@
+from langflow.services.base import Service
+from langflow.services.settings.auth import AuthSettings
+from langflow.services.settings.base import Settings
+from loguru import logger
+import os
+import yaml
+
+
+class SettingsService(Service):
+ name = "settings_service"
+
+ def __init__(self, settings: Settings, auth_settings: AuthSettings):
+ super().__init__()
+ self.settings = settings
+ self.auth_settings = auth_settings
+
+ @classmethod
+ def load_settings_from_yaml(cls, file_path: str) -> "SettingsService":
+ # Check if a string is a valid path or a file name
+ if "/" not in file_path:
+ # Get current path
+ current_path = os.path.dirname(os.path.abspath(__file__))
+
+ file_path = os.path.join(current_path, file_path)
+
+ with open(file_path, "r") as f:
+ settings_dict = yaml.safe_load(f)
+ settings_dict = {k.upper(): v for k, v in settings_dict.items()}
+
+ for key in settings_dict:
+ if key not in Settings.__fields__.keys():
+ raise KeyError(f"Key {key} not found in settings")
+ logger.debug(
+ f"Loading {len(settings_dict[key])} {key} from {file_path}"
+ )
+
+ settings = Settings(**settings_dict)
+ if not settings.CONFIG_DIR:
+ raise ValueError("CONFIG_DIR must be set in settings")
+
+ auth_settings = AuthSettings(
+ CONFIG_DIR=settings.CONFIG_DIR,
+ )
+ return cls(settings, auth_settings)
diff --git a/src/backend/langflow/services/settings/utils.py b/src/backend/langflow/services/settings/utils.py
new file mode 100644
index 000000000..fae96ff28
--- /dev/null
+++ b/src/backend/langflow/services/settings/utils.py
@@ -0,0 +1,47 @@
+import os
+from pathlib import Path
+import platform
+
+from loguru import logger
+
+
+def set_secure_permissions(file_path):
+ if platform.system() in ["Linux", "Darwin"]: # Unix/Linux/Mac
+ os.chmod(file_path, 0o600)
+ elif platform.system() == "Windows":
+ import win32api
+ import win32con
+ import win32security
+
+ user, domain, _ = win32security.LookupAccountName("", win32api.GetUserName())
+ sd = win32security.GetFileSecurity(
+ file_path, win32security.DACL_SECURITY_INFORMATION
+ )
+ dacl = win32security.ACL()
+
+ # Set the new DACL for the file: read and write access for the owner, no access for everyone else
+ dacl.AddAccessAllowedAce(
+ win32security.ACL_REVISION,
+ win32con.GENERIC_READ | win32con.GENERIC_WRITE,
+ user,
+ )
+ sd.SetSecurityDescriptorDacl(1, dacl, 0)
+ win32security.SetFileSecurity(
+ file_path, win32security.DACL_SECURITY_INFORMATION, sd
+ )
+ else:
+ print("Unsupported OS")
+
+
+def write_secret_to_file(path: Path, value: str) -> None:
+ with path.open("wb") as f:
+ f.write(value.encode("utf-8"))
+ try:
+ set_secure_permissions(path)
+ except Exception:
+ logger.error("Failed to set secure permissions on secret key")
+
+
+def read_secret_from_file(path: Path) -> str:
+ with path.open("r") as f:
+ return f.read()
diff --git a/src/backend/langflow/services/task/__init__.py b/src/backend/langflow/services/task/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/src/backend/langflow/services/task/backends/__init__.py b/src/backend/langflow/services/task/backends/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/src/backend/langflow/services/task/backends/anyio.py b/src/backend/langflow/services/task/backends/anyio.py
new file mode 100644
index 000000000..49c9c0e4f
--- /dev/null
+++ b/src/backend/langflow/services/task/backends/anyio.py
@@ -0,0 +1,69 @@
+from typing import Any, Callable, Optional, Tuple
+import anyio
+from langflow.services.task.backends.base import TaskBackend
+from loguru import logger
+
+
+class AnyIOTaskResult:
+ def __init__(self, scope):
+ self._scope = scope
+ self._status = "PENDING"
+ self._result = None
+ self._exception = None
+
+ @property
+ def status(self) -> str:
+ if self._status == "DONE":
+ return "FAILURE" if self._exception is not None else "SUCCESS"
+ return self._status
+
+ @property
+ def result(self) -> Any:
+ return self._result
+
+ def ready(self) -> bool:
+ return self._status == "DONE"
+
+ async def run(self, func, *args, **kwargs):
+ try:
+ self._result = await func(*args, **kwargs)
+ except Exception as e:
+ self._exception = e
+ finally:
+ self._status = "DONE"
+
+
+class AnyIOBackend(TaskBackend):
+ name = "anyio"
+
+ def __init__(self):
+ self.tasks = {}
+
+ async def launch_task(
+ self, task_func: Callable[..., Any], *args: Any, **kwargs: Any
+ ) -> Tuple[Optional[str], Optional[AnyIOTaskResult]]:
+ """
+ Launch a new task in an asynchronous manner.
+
+ Parameters:
+ task_func: The asynchronous function to run.
+ *args: Positional arguments to pass to task_func.
+ **kwargs: Keyword arguments to pass to task_func.
+
+ Returns:
+ A tuple containing a unique task ID and the task result object.
+ """
+ async with anyio.create_task_group() as tg:
+ try:
+ task_result = AnyIOTaskResult(tg)
+ tg.start_soon(task_result.run, task_func, *args, **kwargs)
+ task_id = str(id(task_result))
+ self.tasks[task_id] = task_result
+ logger.info(f"Task {task_id} started.")
+ return task_id, task_result
+ except Exception as e:
+ logger.error(f"An error occurred while launching the task: {e}")
+ return None, None
+
+ def get_task(self, task_id: str) -> Any:
+ return self.tasks.get(task_id)
diff --git a/src/backend/langflow/services/task/backends/base.py b/src/backend/langflow/services/task/backends/base.py
new file mode 100644
index 000000000..ccbd9273b
--- /dev/null
+++ b/src/backend/langflow/services/task/backends/base.py
@@ -0,0 +1,12 @@
+from abc import ABC, abstractmethod
+from typing import Any, Callable
+
+
+class TaskBackend(ABC):
+ @abstractmethod
+ def launch_task(self, task_func: Callable[..., Any], *args: Any, **kwargs: Any):
+ pass
+
+ @abstractmethod
+ def get_task(self, task_id: str) -> Any:
+ pass
diff --git a/src/backend/langflow/services/task/backends/celery.py b/src/backend/langflow/services/task/backends/celery.py
new file mode 100644
index 000000000..eae985f3a
--- /dev/null
+++ b/src/backend/langflow/services/task/backends/celery.py
@@ -0,0 +1,25 @@
+from typing import Any, Callable
+from celery.result import AsyncResult # type: ignore
+from langflow.services.task.backends.base import TaskBackend
+from langflow.worker import celery_app
+
+
+class CeleryBackend(TaskBackend):
+ name = "celery"
+
+ def __init__(self):
+ self.celery_app = celery_app
+
+ def launch_task(
+ self, task_func: Callable[..., Any], *args: Any, **kwargs: Any
+ ) -> tuple[str, AsyncResult]:
+ # I need to type the delay method to make it easier
+ from celery import Task # type: ignore
+
+ if not hasattr(task_func, "delay"):
+ raise ValueError(f"Task function {task_func} does not have a delay method")
+ task: Task = task_func.delay(*args, **kwargs)
+ return task.id, AsyncResult(task.id, app=self.celery_app)
+
+ def get_task(self, task_id: str) -> Any:
+ return AsyncResult(task_id, app=self.celery_app)
diff --git a/src/backend/langflow/services/task/factory.py b/src/backend/langflow/services/task/factory.py
new file mode 100644
index 000000000..efb6ac24d
--- /dev/null
+++ b/src/backend/langflow/services/task/factory.py
@@ -0,0 +1,11 @@
+from langflow.services.task.manager import TaskService
+from langflow.services.factory import ServiceFactory
+
+
+class TaskServiceFactory(ServiceFactory):
+ def __init__(self):
+ super().__init__(TaskService)
+
+ def create(self):
+ # Here you would have logic to create and configure a TaskService
+ return TaskService()
diff --git a/src/backend/langflow/services/task/manager.py b/src/backend/langflow/services/task/manager.py
new file mode 100644
index 000000000..807505c3a
--- /dev/null
+++ b/src/backend/langflow/services/task/manager.py
@@ -0,0 +1,75 @@
+from typing import Any, Callable, Coroutine, Union
+from langflow.utils.logger import configure
+from loguru import logger
+from langflow.services.base import Service
+from langflow.services.task.backends.anyio import AnyIOBackend
+from langflow.services.task.backends.base import TaskBackend
+from langflow.services.task.utils import get_celery_worker_status
+
+
+def check_celery_availability():
+ try:
+ from langflow.worker import celery_app
+
+ status = get_celery_worker_status(celery_app)
+ logger.debug(f"Celery status: {status}")
+ except Exception as exc:
+ logger.debug(f"Celery not available: {exc}")
+ status = {"availability": None}
+ return status
+
+
+try:
+ configure()
+ status = check_celery_availability()
+
+ USE_CELERY = status.get("availability") is not None
+except ImportError:
+ USE_CELERY = False
+
+
+class TaskService(Service):
+ name = "task_service"
+
+ def __init__(self):
+ self.backend = self.get_backend()
+ self.use_celery = USE_CELERY
+
+ @property
+ def backend_name(self) -> str:
+ return self.backend.name
+
+ def get_backend(self) -> TaskBackend:
+ if USE_CELERY:
+ from langflow.services.task.backends.celery import CeleryBackend
+
+ logger.debug("Using Celery backend")
+ return CeleryBackend()
+ logger.debug("Using AnyIO backend")
+ return AnyIOBackend()
+
+ # In your TaskService class
+ async def launch_and_await_task(
+ self,
+ task_func: Callable[..., Any],
+ *args: Any,
+ **kwargs: Any,
+ ) -> Any:
+ if not self.use_celery:
+ return None, await task_func(*args, **kwargs)
+ if not hasattr(task_func, "apply"):
+ raise ValueError(f"Task function {task_func} does not have an apply method")
+ task = task_func.apply(args=args, kwargs=kwargs)
+ result = task.get()
+ return task.id, result
+
+ async def launch_task(
+ self, task_func: Callable[..., Any], *args: Any, **kwargs: Any
+ ) -> Any:
+ logger.debug(f"Launching task {task_func} with args {args} and kwargs {kwargs}")
+ logger.debug(f"Using backend {self.backend}")
+ task = self.backend.launch_task(task_func, *args, **kwargs)
+ return await task if isinstance(task, Coroutine) else task
+
+ def get_task(self, task_id: Union[int, str]) -> Any:
+ return self.backend.get_task(task_id)
diff --git a/src/backend/langflow/services/task/utils.py b/src/backend/langflow/services/task/utils.py
new file mode 100644
index 000000000..5dfb03b83
--- /dev/null
+++ b/src/backend/langflow/services/task/utils.py
@@ -0,0 +1,22 @@
+import contextlib
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+ with contextlib.suppress(ImportError):
+ from celery import Celery # type: ignore
+
+
+def get_celery_worker_status(app: "Celery"):
+ i = app.control.inspect()
+ availability = app.control.ping()
+ stats = i.stats()
+ registered_tasks = i.registered()
+ active_tasks = i.active()
+ scheduled_tasks = i.scheduled()
+ return {
+ "availability": availability,
+ "stats": stats,
+ "registered_tasks": registered_tasks,
+ "active_tasks": active_tasks,
+ "scheduled_tasks": scheduled_tasks,
+ }
diff --git a/src/backend/langflow/services/utils.py b/src/backend/langflow/services/utils.py
new file mode 100644
index 000000000..adbf072fe
--- /dev/null
+++ b/src/backend/langflow/services/utils.py
@@ -0,0 +1,217 @@
+from langflow.services.auth.utils import create_super_user, verify_password
+from langflow.services.database.utils import initialize_database
+from langflow.services.manager import service_manager
+from langflow.services.schema import ServiceType
+from langflow.services.settings.constants import (
+ DEFAULT_SUPERUSER,
+ DEFAULT_SUPERUSER_PASSWORD,
+)
+from sqlmodel import Session
+from .getters import get_db_service, get_session, get_settings_service
+from loguru import logger
+
+
+def get_factories_and_deps():
+ from langflow.services.database import factory as database_factory
+ from langflow.services.cache import factory as cache_factory
+ from langflow.services.chat import factory as chat_factory
+ from langflow.services.settings import factory as settings_factory
+ from langflow.services.auth import factory as auth_factory
+ from langflow.services.task import factory as task_factory
+ from langflow.services.session import factory as session_service_factory # type: ignore
+
+ return [
+ (settings_factory.SettingsServiceFactory(), []),
+ (
+ auth_factory.AuthServiceFactory(),
+ [ServiceType.SETTINGS_SERVICE],
+ ),
+ (
+ database_factory.DatabaseServiceFactory(),
+ [ServiceType.SETTINGS_SERVICE],
+ ),
+ (
+ cache_factory.CacheServiceFactory(),
+ [ServiceType.SETTINGS_SERVICE],
+ ),
+ (chat_factory.ChatServiceFactory(), []),
+ (task_factory.TaskServiceFactory(), []),
+ (
+ session_service_factory.SessionServiceFactory(),
+ [ServiceType.CACHE_SERVICE],
+ ),
+ ]
+
+
+def get_or_create_super_user(session: Session, username, password, is_default):
+ from langflow.services.database.models.user.user import User
+
+ user = session.query(User).filter(User.username == username).first()
+
+ if user and user.is_superuser:
+ return None # Superuser already exists
+
+ if user and is_default:
+ if user.is_superuser:
+ if verify_password(password, user.password):
+ return None
+ else:
+ # Superuser exists but password is incorrect
+ # which means that the user has changed the
+ # base superuser credentials.
+ # This means that the user has already created
+ # a superuser and changed the password in the UI
+ # so we don't need to do anything.
+ logger.debug(
+ "Superuser exists but password is incorrect. "
+ "This means that the user has changed the "
+ "base superuser credentials."
+ )
+ return None
+ else:
+ logger.debug(
+ "User with superuser credentials exists but is not a superuser."
+ )
+ return None
+
+ if user:
+ if verify_password(password, user.password):
+ raise ValueError(
+ "User with superuser credentials exists but is not a superuser."
+ )
+ else:
+ raise ValueError("Incorrect superuser credentials")
+
+ if is_default:
+ logger.debug("Creating default superuser.")
+ else:
+ logger.debug("Creating superuser.")
+ try:
+ return create_super_user(username, password, db=session)
+ except Exception as exc:
+ if "UNIQUE constraint failed: user.username" in str(exc):
+ # This is to deal with workers running this
+ # at startup and trying to create the superuser
+ # at the same time.
+ logger.debug("Superuser already exists.")
+ return None
+
+
+def setup_superuser(settings_service, session: Session):
+ if settings_service.auth_settings.AUTO_LOGIN:
+ logger.debug("AUTO_LOGIN is set to True. Creating default superuser.")
+
+ username = settings_service.auth_settings.SUPERUSER
+ password = settings_service.auth_settings.SUPERUSER_PASSWORD
+
+ is_default = (username == DEFAULT_SUPERUSER) and (
+ password == DEFAULT_SUPERUSER_PASSWORD
+ )
+
+ try:
+ user = get_or_create_super_user(
+ session=session, username=username, password=password, is_default=is_default
+ )
+ if user is not None:
+ logger.debug("Superuser created successfully.")
+ except Exception as exc:
+ logger.exception(exc)
+ raise RuntimeError(
+ "Could not create superuser. Please create a superuser manually."
+ ) from exc
+ finally:
+ settings_service.auth_settings.reset_credentials()
+
+
+def teardown_superuser(settings_service, session):
+ """
+ Teardown the superuser.
+ """
+ # If AUTO_LOGIN is True, we will remove the default superuser
+ # from the database.
+
+ if settings_service.auth_settings.AUTO_LOGIN:
+ try:
+ logger.debug("AUTO_LOGIN is set to True. Removing default superuser.")
+ username = settings_service.auth_settings.SUPERUSER
+ from langflow.services.database.models.user.user import User
+
+ user = session.query(User).filter(User.username == username).first()
+ if user and user.is_superuser:
+ session.delete(user)
+ session.commit()
+ logger.debug("Default superuser removed successfully.")
+ else:
+ logger.debug("Default superuser not found.")
+ except Exception as exc:
+ logger.exception(exc)
+ raise RuntimeError("Could not remove default superuser.") from exc
+
+
+def teardown_services():
+ """
+ Teardown all the services.
+ """
+ try:
+ teardown_superuser(get_settings_service(), next(get_session()))
+ except Exception as exc:
+ logger.exception(exc)
+ try:
+ service_manager.teardown()
+ except Exception as exc:
+ logger.exception(exc)
+
+
+def initialize_settings_service():
+ """
+ Initialize the settings manager.
+ """
+ from langflow.services.settings import factory as settings_factory
+
+ service_manager.register_factory(settings_factory.SettingsServiceFactory())
+
+
+def initialize_session_service():
+ """
+ Initialize the session manager.
+ """
+ from langflow.services.session import factory as session_service_factory # type: ignore
+ from langflow.services.cache import factory as cache_factory
+
+ initialize_settings_service()
+
+ service_manager.register_factory(
+ cache_factory.CacheServiceFactory(), dependencies=[ServiceType.SETTINGS_SERVICE]
+ )
+
+ service_manager.register_factory(
+ session_service_factory.SessionServiceFactory(),
+ dependencies=[ServiceType.CACHE_SERVICE],
+ )
+
+
+def initialize_services():
+ """
+ Initialize all the services needed.
+ """
+ for factory, dependencies in get_factories_and_deps():
+ try:
+ service_manager.register_factory(factory, dependencies=dependencies)
+ except Exception as exc:
+ logger.exception(exc)
+ raise RuntimeError(
+ "Could not initialize services. Please check your settings."
+ ) from exc
+
+ # Test cache connection
+ service_manager.get(ServiceType.CACHE_SERVICE)
+ # Setup the superuser
+ initialize_database()
+ setup_superuser(
+ service_manager.get(ServiceType.SETTINGS_SERVICE), next(get_session())
+ )
+ try:
+ get_db_service().migrate_flows_if_auto_login()
+ except Exception as exc:
+ logger.error(f"Error migrating flows: {exc}")
+ raise RuntimeError("Error migrating flows") from exc
diff --git a/src/backend/langflow/template/frontend_node/agents.py b/src/backend/langflow/template/frontend_node/agents.py
index 63c8a4d5e..b3f9b4545 100644
--- a/src/backend/langflow/template/frontend_node/agents.py
+++ b/src/backend/langflow/template/frontend_node/agents.py
@@ -189,7 +189,7 @@ class InitializeAgentNode(FrontendNode):
),
TemplateField(
field_type="Tool",
- required=False,
+ required=True,
show=True,
name="tools",
is_list=True,
diff --git a/src/backend/langflow/template/frontend_node/base.py b/src/backend/langflow/template/frontend_node/base.py
index fe19b5652..4f8b6ad7e 100644
--- a/src/backend/langflow/template/frontend_node/base.py
+++ b/src/backend/langflow/template/frontend_node/base.py
@@ -44,7 +44,7 @@ class FieldFormatters(BaseModel):
class FrontendNode(BaseModel):
template: Template
- description: str
+ description: Optional[str] = None
base_classes: List[str]
name: str = ""
display_name: str = ""
@@ -140,13 +140,16 @@ class FrontendNode(BaseModel):
@staticmethod
def handle_dict_type(field: TemplateField, _type: str) -> str:
"""Handles 'dict' type by replacing it with 'code' or 'file' based on the field name."""
- if "dict" in _type.lower():
- if field.name == "dict_":
- field.field_type = "file"
- field.suffixes = [".json", ".yaml", ".yml"]
- field.file_types = ["json", "yaml", "yml"]
- else:
- field.field_type = "code"
+ if "dict" in _type.lower() and field.name == "dict_":
+ field.field_type = "file"
+ field.suffixes = [".json", ".yaml", ".yml"]
+ field.file_types = ["json", "yaml", "yml"]
+ elif (
+ _type.startswith("Dict")
+ or _type.startswith("Mapping")
+ or _type.startswith("dict")
+ ):
+ field.field_type = "dict"
return _type
@staticmethod
@@ -161,7 +164,7 @@ class FrontendNode(BaseModel):
) -> None:
"""Handles specific field values for certain fields."""
if key == "headers":
- field.value = """{'Authorization': 'Bearer '}"""
+ field.value = """{"Authorization": "Bearer "}"""
FrontendNode._handle_model_specific_field_values(field, key, name)
FrontendNode._handle_api_key_specific_field_values(field, key, name)
@@ -240,24 +243,10 @@ class FrontendNode(BaseModel):
"description",
}
- @staticmethod
- def replace_dict_with_code_or_file(
- field: TemplateField, _type: str, key: str
- ) -> str:
- """Replaces 'dict' type with 'code' or 'file'."""
- if "dict" in _type.lower():
- if key == "dict_":
- field.field_type = "file"
- field.suffixes = [".json", ".yaml", ".yml"]
- field.file_types = ["json", "yaml", "yml"]
- else:
- field.field_type = "code"
- return field.field_type
-
@staticmethod
def set_field_default_value(field: TemplateField, value: dict, key: str) -> None:
"""Sets the field value with the default value if present."""
if "default" in value:
field.value = value["default"]
if key == "headers":
- field.value = """{'Authorization': 'Bearer '}"""
+ field.value = """{"Authorization": "Bearer "}"""
diff --git a/src/backend/langflow/template/frontend_node/constants.py b/src/backend/langflow/template/frontend_node/constants.py
index 8800a3755..a213a2744 100644
--- a/src/backend/langflow/template/frontend_node/constants.py
+++ b/src/backend/langflow/template/frontend_node/constants.py
@@ -65,4 +65,11 @@ INPUT_KEY_INFO = """The variable to be used as Chat Input when more than one var
OUTPUT_KEY_INFO = """The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)"""
-CLASSES_TO_REMOVE = ["Serializable", "BaseModel", "object", "Runnable", "Generic"]
+CLASSES_TO_REMOVE = [
+ "RunnableSerializable",
+ "Serializable",
+ "BaseModel",
+ "object",
+ "Runnable",
+ "Generic",
+]
diff --git a/src/backend/langflow/template/frontend_node/custom_components.py b/src/backend/langflow/template/frontend_node/custom_components.py
index 4f36a1c9f..e239775bc 100644
--- a/src/backend/langflow/template/frontend_node/custom_components.py
+++ b/src/backend/langflow/template/frontend_node/custom_components.py
@@ -2,6 +2,7 @@ from langflow.template.field.base import TemplateField
from langflow.template.frontend_node.base import FrontendNode
from langflow.template.template.base import Template
from langflow.interface.custom.constants import DEFAULT_CUSTOM_COMPONENT_CODE
+from typing import Optional
class CustomComponentFrontendNode(FrontendNode):
@@ -24,7 +25,7 @@ class CustomComponentFrontendNode(FrontendNode):
)
],
)
- description: str = "Create any custom component you want!"
+ description: Optional[str] = None
base_classes: list[str] = []
def to_dict(self):
diff --git a/src/backend/langflow/template/frontend_node/documentloaders.py b/src/backend/langflow/template/frontend_node/documentloaders.py
index bb78d8855..0be2ebe98 100644
--- a/src/backend/langflow/template/frontend_node/documentloaders.py
+++ b/src/backend/langflow/template/frontend_node/documentloaders.py
@@ -30,7 +30,6 @@ class DocumentLoaderFrontNode(FrontendNode):
"UnstructuredEmailLoader": build_file_field(
suffixes=[".eml"], fileTypes=["eml"]
),
- "SlackDirectoryLoader": build_file_field(suffixes=[".zip"], fileTypes=["zip"]),
"EverNoteLoader": build_file_field(suffixes=[".xml"], fileTypes=["xml"]),
"FacebookChatLoader": build_file_field(suffixes=[".json"], fileTypes=["json"]),
"BSHTMLLoader": build_file_field(suffixes=[".html"], fileTypes=["html"]),
@@ -105,7 +104,30 @@ class DocumentLoaderFrontNode(FrontendNode):
advanced=False,
)
)
-
+ elif self.template.type_name in {"SlackDirectoryLoader"}:
+ self.template.add_field(
+ TemplateField(
+ field_type="file",
+ required=True,
+ show=True,
+ name="zip_path",
+ value="",
+ display_name="Path to zip file",
+ suffixes=[".zip"],
+ file_types=["zip"],
+ )
+ )
+ self.template.add_field(
+ TemplateField(
+ field_type="str",
+ required=False,
+ show=True,
+ name="workspace_url",
+ value="",
+ display_name="Workspace URL",
+ advanced=False,
+ )
+ )
elif self.template.type_name in self.file_path_templates:
self.template.add_field(self.file_path_templates[self.template.type_name])
elif self.template.type_name in {
@@ -148,11 +170,11 @@ class DocumentLoaderFrontNode(FrontendNode):
# add a metadata field of type dict
self.template.add_field(
TemplateField(
- field_type="code",
- required=True,
+ field_type="dict",
+ required=False,
show=True,
name="metadata",
- value="{}",
+ value={},
display_name="Metadata",
multiline=False,
)
diff --git a/src/backend/langflow/template/frontend_node/embeddings.py b/src/backend/langflow/template/frontend_node/embeddings.py
index d466b11a4..665328e78 100644
--- a/src/backend/langflow/template/frontend_node/embeddings.py
+++ b/src/backend/langflow/template/frontend_node/embeddings.py
@@ -5,6 +5,47 @@ from langflow.template.frontend_node.base import FrontendNode
class EmbeddingFrontendNode(FrontendNode):
+ def add_extra_fields(self) -> None:
+ if "VertexAI" in self.template.type_name:
+ # Add credentials field which should of type file.
+ self.template.add_field(
+ TemplateField(
+ field_type="file",
+ required=False,
+ show=True,
+ name="credentials",
+ value="",
+ suffixes=[".json"],
+ file_types=["json"],
+ )
+ )
+
+ @staticmethod
+ def format_vertex_field(field: TemplateField, name: str):
+ if "VertexAI" in name:
+ advanced_fields = [
+ "verbose",
+ "top_p",
+ "top_k",
+ "max_output_tokens",
+ ]
+ if field.name in advanced_fields:
+ field.advanced = True
+ show_fields = [
+ "verbose",
+ "project",
+ "location",
+ "credentials",
+ "max_output_tokens",
+ "model_name",
+ "temperature",
+ "top_p",
+ "top_k",
+ ]
+
+ if field.name in show_fields:
+ field.show = True
+
@staticmethod
def format_jina_fields(field: TemplateField):
if "jina" in field.name:
@@ -41,10 +82,36 @@ class EmbeddingFrontendNode(FrontendNode):
@staticmethod
def format_field(field: TemplateField, name: Optional[str] = None) -> None:
FrontendNode.format_field(field, name)
+ if name and "vertex" in name.lower():
+ EmbeddingFrontendNode.format_vertex_field(field, name)
field.advanced = not field.required
field.show = True
if field.name == "headers":
field.show = False
+ if field.name == "model_kwargs":
+ field.field_type = "dict"
+ field.advanced = True
+ field.show = True
+ elif field.name in [
+ "model_name",
+ "temperature",
+ "model_file",
+ "model_type",
+ "deployment_name",
+ "credentials",
+ ]:
+ field.advanced = False
+ field.show = True
+ if field.name == "credentials":
+ field.field_type = "file"
+ if name == "VertexAI" and field.name not in [
+ "callbacks",
+ "client",
+ "stop",
+ "tags",
+ "cache",
+ ]:
+ field.show = True
# Format Jina fields
EmbeddingFrontendNode.format_jina_fields(field)
diff --git a/src/backend/langflow/template/frontend_node/formatter/field_formatters.py b/src/backend/langflow/template/frontend_node/formatter/field_formatters.py
index 7987b134a..a67387df7 100644
--- a/src/backend/langflow/template/frontend_node/formatter/field_formatters.py
+++ b/src/backend/langflow/template/frontend_node/formatter/field_formatters.py
@@ -145,7 +145,7 @@ class HeadersDefaultValueFormatter(FieldFormatter):
def format(self, field: TemplateField, name: Optional[str] = None) -> None:
key = field.name
if key == "headers":
- field.value = """{'Authorization': 'Bearer '}"""
+ field.value = """{"Authorization": "Bearer "}"""
class DictCodeFileFormatter(FieldFormatter):
@@ -153,10 +153,13 @@ class DictCodeFileFormatter(FieldFormatter):
key = field.name
value = field.to_dict()
_type = value["type"]
- if "dict" in _type.lower():
- if key == "dict_":
- field.field_type = "file"
- field.suffixes = [".json", ".yaml", ".yml"]
- field.file_types = ["json", "yaml", "yml"]
- else:
- field.field_type = "code"
+ if "dict" in _type.lower() and key == "dict_":
+ field.field_type = "file"
+ field.suffixes = [".json", ".yaml", ".yml"]
+ field.file_types = ["json", "yaml", "yml"]
+ elif (
+ _type.startswith("Dict")
+ or _type.startswith("Mapping")
+ or _type.startswith("dict")
+ ):
+ field.field_type = "dict"
diff --git a/src/backend/langflow/template/frontend_node/llms.py b/src/backend/langflow/template/frontend_node/llms.py
index fdf0a2b5b..b8e007a27 100644
--- a/src/backend/langflow/template/frontend_node/llms.py
+++ b/src/backend/langflow/template/frontend_node/llms.py
@@ -1,5 +1,5 @@
from typing import Optional
-from langflow.database.models.base import orjson_dumps
+from langflow.services.database.models.base import orjson_dumps
from langflow.template.field.base import TemplateField
from langflow.template.frontend_node.base import FrontendNode
@@ -131,7 +131,7 @@ class LLMFrontendNode(FrontendNode):
if display_name := display_names_dict.get(field.name):
field.display_name = display_name
if field.name == "model_kwargs":
- field.field_type = "code"
+ field.field_type = "dict"
field.advanced = True
field.show = True
elif field.name in [
diff --git a/src/backend/langflow/template/frontend_node/prompts.py b/src/backend/langflow/template/frontend_node/prompts.py
index da5d2a300..f0ebc35aa 100644
--- a/src/backend/langflow/template/frontend_node/prompts.py
+++ b/src/backend/langflow/template/frontend_node/prompts.py
@@ -15,6 +15,7 @@ from langflow.template.template.base import Template
class PromptFrontendNode(FrontendNode):
@staticmethod
def format_field(field: TemplateField, name: Optional[str] = None) -> None:
+ FrontendNode.format_field(field, name)
# if field.field_type == "StringPromptTemplate"
# change it to str
PROMPT_FIELDS = [
@@ -43,6 +44,7 @@ class PromptFrontendNode(FrontendNode):
# All prompt fields should be password=False
field.password = False
+ field.dynamic = True
class PromptTemplateNode(FrontendNode):
diff --git a/src/backend/langflow/template/frontend_node/utilities.py b/src/backend/langflow/template/frontend_node/utilities.py
index fa0b55332..a5adb219d 100644
--- a/src/backend/langflow/template/frontend_node/utilities.py
+++ b/src/backend/langflow/template/frontend_node/utilities.py
@@ -1,6 +1,6 @@
import ast
from typing import Optional
-from langflow.database.models.base import orjson_dumps
+from langflow.services.database.models.base import orjson_dumps
from langflow.template.field.base import TemplateField
from langflow.template.frontend_node.base import FrontendNode
@@ -21,5 +21,4 @@ class UtilitiesFrontendNode(FrontendNode):
field.field_type = "str"
if isinstance(field.value, dict):
- field.field_type = "code"
field.value = orjson_dumps(field.value)
diff --git a/src/backend/langflow/template/frontend_node/vectorstores.py b/src/backend/langflow/template/frontend_node/vectorstores.py
index 23c293437..73e9aaaca 100644
--- a/src/backend/langflow/template/frontend_node/vectorstores.py
+++ b/src/backend/langflow/template/frontend_node/vectorstores.py
@@ -56,7 +56,7 @@ class VectorStoreFrontendNode(FrontendNode):
# Add search_kwargs field
extra_field = TemplateField(
name="search_kwargs",
- field_type="code",
+ field_type="NestedDict",
required=False,
placeholder="",
show=True,
diff --git a/src/backend/langflow/utils/constants.py b/src/backend/langflow/utils/constants.py
index e473d855b..0c97b56a2 100644
--- a/src/backend/langflow/utils/constants.py
+++ b/src/backend/langflow/utils/constants.py
@@ -48,4 +48,16 @@ def python_function(text: str) -> str:
return text
"""
-DIRECT_TYPES = ["str", "bool", "code", "int", "float", "Any", "prompt"]
+
+PYTHON_BASIC_TYPES = [str, bool, int, float, tuple, list, dict, set]
+DIRECT_TYPES = [
+ "str",
+ "bool",
+ "dict",
+ "int",
+ "float",
+ "Any",
+ "prompt",
+ "code",
+ "NestedDict",
+]
diff --git a/src/backend/langflow/utils/lazy_load.py b/src/backend/langflow/utils/lazy_load.py
new file mode 100644
index 000000000..df0130acc
--- /dev/null
+++ b/src/backend/langflow/utils/lazy_load.py
@@ -0,0 +1,15 @@
+class LazyLoadDictBase:
+ def __init__(self):
+ self._all_types_dict = None
+
+ @property
+ def all_types_dict(self):
+ if self._all_types_dict is None:
+ self._all_types_dict = self._build_dict()
+ return self._all_types_dict
+
+ def _build_dict(self):
+ raise NotImplementedError
+
+ def get_type_dict(self):
+ raise NotImplementedError
diff --git a/src/backend/langflow/utils/logger.py b/src/backend/langflow/utils/logger.py
index deb0f75ca..b08621410 100644
--- a/src/backend/langflow/utils/logger.py
+++ b/src/backend/langflow/utils/logger.py
@@ -1,30 +1,69 @@
-import logging
+from typing import Optional
+from loguru import logger
from pathlib import Path
-
from rich.logging import RichHandler
-
-logger = logging.getLogger("langflow")
+import os
+import orjson
+import appdirs
-def configure(log_level: str = "DEBUG", log_file: Path = None): # type: ignore
- log_format = "%(asctime)s - %(levelname)s - %(message)s"
- log_level_value = getattr(logging, log_level.upper(), logging.INFO)
+VALID_LOG_LEVELS = ["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]
- logging.basicConfig(
- level=log_level_value,
- format=log_format,
- datefmt="[%X]",
- handlers=[RichHandler(rich_tracebacks=True)],
+
+def serialize(record):
+ subset = {
+ "timestamp": record["time"].timestamp(),
+ "message": record["message"],
+ "level": record["level"].name,
+ "module": record["module"],
+ }
+ return orjson.dumps(subset)
+
+
+def patching(record):
+ record["extra"]["serialized"] = serialize(record)
+
+
+def configure(log_level: Optional[str] = None, log_file: Optional[Path] = None):
+ if os.getenv("LANGFLOW_LOG_LEVEL") in VALID_LOG_LEVELS and log_level is None:
+ log_level = os.getenv("LANGFLOW_LOG_LEVEL")
+ if log_level is None:
+ log_level = "INFO"
+ # Human-readable
+ log_format = (
+ "{time:YYYY-MM-DD HH:mm:ss} - "
+ "{level: <8} - {module} - {message} "
)
- if log_file:
- log_file = Path(log_file)
- log_file.parent.mkdir(parents=True, exist_ok=True)
+ # log_format = log_format_dev if log_level.upper() == "DEBUG" else log_format_prod
+ logger.remove() # Remove default handlers
+ logger.patch(patching)
+ # Configure loguru to use RichHandler
+ logger.configure(
+ handlers=[
+ {
+ "sink": RichHandler(rich_tracebacks=True, markup=True),
+ "format": log_format,
+ "level": log_level.upper(),
+ }
+ ]
+ )
- file_handler = logging.FileHandler(log_file)
- file_handler.setFormatter(logging.Formatter(log_format))
- logger.addHandler(file_handler)
+ if not log_file:
+ cache_dir = Path(appdirs.user_cache_dir("langflow"))
+ log_file = cache_dir / "langflow.log"
- logger.info(f"Logger set up with log level: {log_level_value}({log_level})")
+ log_file = Path(log_file)
+ log_file.parent.mkdir(parents=True, exist_ok=True)
+
+ logger.add(
+ sink=str(log_file),
+ level=log_level.upper(),
+ format=log_format,
+ rotation="10 MB", # Log rotation based on file size
+ serialize=True,
+ )
+
+ logger.debug(f"Logger set up with log level: {log_level}")
if log_file:
logger.info(f"Log file: {log_file}")
diff --git a/src/backend/langflow/utils/util.py b/src/backend/langflow/utils/util.py
index 4760ebc2d..c23c559e4 100644
--- a/src/backend/langflow/utils/util.py
+++ b/src/backend/langflow/utils/util.py
@@ -4,15 +4,17 @@ import importlib
from functools import wraps
from typing import List, Optional, Dict, Any, Union
-from docstring_parser import parse # type: ignore
+from docstring_parser import parse
from langflow.template.frontend_node.constants import FORCE_SHOW_FIELDS
from langflow.utils import constants
-from langflow.utils.logger import logger
-from multiprocess import cpu_count # type: ignore
from langchain.schema import Document
+def remove_ansi_escape_codes(text):
+ return re.sub(r"\x1b\[[0-9;]*[a-zA-Z]", "", text)
+
+
def build_template_from_function(
name: str, type_to_loader_dict: Dict, add_function: bool = False
):
@@ -189,7 +191,9 @@ def get_base_classes(cls):
"""Get the base classes of a class.
These are used to determine the output of the nodes.
"""
- if bases := cls.__bases__:
+
+ if hasattr(cls, "__bases__") and cls.__bases__:
+ bases = cls.__bases__
result = []
for base in bases:
if any(type in base.__module__ for type in ["pydantic", "abc"]):
@@ -266,6 +270,9 @@ def format_dict(
_type: Union[str, type] = get_type(value)
+ if "BaseModel" in str(_type):
+ continue
+
_type = remove_optional_wrapper(_type)
_type = check_list_type(_type, value)
_type = replace_mapping_with_dict(_type)
@@ -275,8 +282,6 @@ def format_dict(
value["password"] = is_password_field(key)
value["multiline"] = is_multiline_field(key)
- replace_dict_type_with_code(value)
-
if key == "dict_":
set_dict_file_attributes(value)
@@ -406,14 +411,6 @@ def is_multiline_field(key: str) -> bool:
}
-def replace_dict_type_with_code(value: Dict[str, Any]) -> None:
- """
- Replaces the type value with 'code' if the type is a dict.
- """
- if "dict" in value["type"].lower():
- value["type"] = "code"
-
-
def set_dict_file_attributes(value: Dict[str, Any]) -> None:
"""
Sets the file attributes for the 'dict_' key.
@@ -436,7 +433,7 @@ def set_headers_value(value: Dict[str, Any]) -> None:
"""
Sets the value for the 'headers' key.
"""
- value["value"] = """{'Authorization': 'Bearer '}"""
+ value["value"] = """{"Authorization": "Bearer "}"""
def add_options_to_field(
@@ -458,13 +455,6 @@ def add_options_to_field(
value["value"] = options_map[class_name][0]
-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 build_loader_repr_from_documents(documents: List[Document]) -> str:
if documents:
avg_length = sum(len(doc.page_content) for doc in documents) / len(documents)
diff --git a/src/backend/langflow/worker.py b/src/backend/langflow/worker.py
new file mode 100644
index 000000000..2eeba14a5
--- /dev/null
+++ b/src/backend/langflow/worker.py
@@ -0,0 +1,62 @@
+from langflow.core.celery_app import celery_app
+from typing import Any, Dict, Optional
+from typing import TYPE_CHECKING
+
+from celery.exceptions import SoftTimeLimitExceeded # type: ignore
+from langflow.processing.process import (
+ Result,
+ generate_result,
+ process_inputs,
+)
+from langflow.services.manager import initialize_session_service
+from langflow.services.getters import get_session_service
+
+if TYPE_CHECKING:
+ from langflow.graph.vertex.base import Vertex
+
+
+@celery_app.task(acks_late=True)
+def test_celery(word: str) -> str:
+ return f"test task return {word}"
+
+
+@celery_app.task(bind=True, soft_time_limit=30, max_retries=3)
+def build_vertex(self, vertex: "Vertex") -> "Vertex":
+ """
+ Build a vertex
+ """
+ try:
+ vertex.task_id = self.request.id
+ vertex.build()
+ return vertex
+ except SoftTimeLimitExceeded as e:
+ raise self.retry(
+ exc=SoftTimeLimitExceeded("Task took too long"), countdown=2
+ ) from e
+
+
+@celery_app.task(acks_late=True)
+def process_graph_cached_task(
+ data_graph: Dict[str, Any],
+ inputs: Optional[dict] = None,
+ clear_cache=False,
+ session_id=None,
+) -> Dict[str, Any]:
+ initialize_session_service()
+ session_service = get_session_service()
+ if clear_cache:
+ session_service.clear_session(session_id)
+ if session_id is None:
+ session_id = session_service.generate_key(
+ session_id=session_id, data_graph=data_graph
+ )
+ # Load the graph using SessionService
+ graph, artifacts = session_service.load_session(session_id, data_graph)
+ built_object = graph.build()
+ processed_inputs = process_inputs(inputs, artifacts)
+ result = generate_result(built_object, processed_inputs)
+ # langchain_object is now updated with the new memory
+ # we need to update the cache with the updated langchain_object
+ session_service.update_session(session_id, (graph, artifacts))
+
+ return Result(result=result, session_id=session_id).dict()
diff --git a/src/frontend/.dockerignore b/src/frontend/.dockerignore
new file mode 100644
index 000000000..ca5762007
--- /dev/null
+++ b/src/frontend/.dockerignore
@@ -0,0 +1,2 @@
+**/node_modules
+**/build
\ No newline at end of file
diff --git a/src/frontend/.github/workflows/playwright.yml b/src/frontend/.github/workflows/playwright.yml
new file mode 100644
index 000000000..90b6b700d
--- /dev/null
+++ b/src/frontend/.github/workflows/playwright.yml
@@ -0,0 +1,27 @@
+name: Playwright Tests
+on:
+ push:
+ branches: [ main, master ]
+ pull_request:
+ branches: [ main, master ]
+jobs:
+ test:
+ timeout-minutes: 60
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v3
+ - uses: actions/setup-node@v3
+ with:
+ node-version: 18
+ - name: Install dependencies
+ run: npm ci
+ - name: Install Playwright Browsers
+ run: npx playwright install --with-deps
+ - name: Run Playwright tests
+ run: npx playwright test
+ - uses: actions/upload-artifact@v3
+ if: always()
+ with:
+ name: playwright-report
+ path: playwright-report/
+ retention-days: 30
diff --git a/src/frontend/.gitignore b/src/frontend/.gitignore
index 4d29575de..9dfae2bb0 100644
--- a/src/frontend/.gitignore
+++ b/src/frontend/.gitignore
@@ -21,3 +21,6 @@
npm-debug.log*
yarn-debug.log*
yarn-error.log*
+/test-results/
+/playwright-report/*/
+/playwright/.cache/
diff --git a/src/frontend/Dockerfile b/src/frontend/Dockerfile
index 010811e7b..024eab562 100644
--- a/src/frontend/Dockerfile
+++ b/src/frontend/Dockerfile
@@ -1,10 +1,16 @@
-FROM node:14-alpine as frontend_build
-ARG BACKEND
+FROM node:20-alpine as frontend_build
+ARG BACKEND_URL
WORKDIR /app
-COPY . /app
+
+COPY ./package.json ./package-lock.json ./tsconfig.json ./vite.config.ts ./index.html ./tailwind.config.js ./postcss.config.js ./prettier.config.js /app/
RUN npm install
+COPY ./src /app/src
RUN npm run build
FROM nginx
COPY --from=frontend_build /app/build/ /usr/share/nginx/html
-COPY /nginx.conf /etc/nginx/conf.d/default.conf
\ No newline at end of file
+COPY /nginx.conf /etc/nginx/conf.d/default.conf
+COPY start-nginx.sh /start-nginx.sh
+RUN chmod +x /start-nginx.sh
+ENV BACKEND_URL=$BACKEND_URL
+CMD ["/start-nginx.sh"]
\ No newline at end of file
diff --git a/src/frontend/harFiles/langflow.har b/src/frontend/harFiles/langflow.har
new file mode 100644
index 000000000..edac6806e
--- /dev/null
+++ b/src/frontend/harFiles/langflow.har
@@ -0,0 +1,229 @@
+{
+ "log": {
+ "version": "1.2",
+ "creator": {
+ "name": "Playwright",
+ "version": "1.37.1"
+ },
+ "browser": {
+ "name": "chromium",
+ "version": "116.0.5845.82"
+ },
+ "entries": [
+ {
+ "startedDateTime": "2023-08-31T14:55:35.502Z",
+ "time": 0.727,
+ "request": {
+ "method": "GET",
+ "url": "http://localhost:3000/api/v1/auto_login",
+ "httpVersion": "HTTP/1.1",
+ "cookies": [],
+ "headers": [
+ { "name": "Accept", "value": "application/json, text/plain, */*" },
+ { "name": "Accept-Encoding", "value": "gzip, deflate, br" },
+ { "name": "Accept-Language", "value": "en-US,en;q=0.9" },
+ { "name": "Connection", "value": "keep-alive" },
+ { "name": "Host", "value": "localhost:3000" },
+ { "name": "Referer", "value": "http://localhost:3000/" },
+ { "name": "Sec-Fetch-Dest", "value": "empty" },
+ { "name": "Sec-Fetch-Mode", "value": "cors" },
+ { "name": "Sec-Fetch-Site", "value": "same-origin" },
+ { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36" },
+ { "name": "sec-ch-ua", "value": "\"Not)A;Brand\";v=\"24\", \"Chromium\";v=\"116\"" },
+ { "name": "sec-ch-ua-mobile", "value": "?0" },
+ { "name": "sec-ch-ua-platform", "value": "\"Linux\"" }
+ ],
+ "queryString": [],
+ "headersSize": -1,
+ "bodySize": -1
+ },
+ "response": {
+ "status": 200,
+ "statusText": "OK",
+ "httpVersion": "HTTP/1.1",
+ "cookies": [],
+ "headers": [
+ { "name": "Access-Control-Allow-Origin", "value": "*" },
+ { "name": "connection", "value": "close" },
+ { "name": "content-length", "value": "227" },
+ { "name": "content-type", "value": "application/json" },
+ { "name": "date", "value": "Thu, 31 Aug 2023 14:55:34 GMT" },
+ { "name": "server", "value": "uvicorn" }
+ ],
+ "content": {
+ "size": -1,
+ "mimeType": "application/json",
+ "text": "{\"access_token\":\"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJiNWIxYjFhZC05M2VjLTRjMjgtYWMxNy01OGMxNDQ0MWI1ZGQiLCJleHAiOjE3MjUwMjk3MzV9.8qhEtryWqA9RbwEP2s20umKdVE7J7jHGoogXZqxLNWk\",\"refresh_token\":null,\"token_type\":\"bearer\"}"
+ },
+ "headersSize": -1,
+ "bodySize": -1,
+ "redirectURL": ""
+ },
+ "cache": {},
+ "timings": { "send": -1, "wait": -1, "receive": 0.727 }
+ },
+ {
+ "startedDateTime": "2023-08-31T14:55:35.586Z",
+ "time": 0.719,
+ "request": {
+ "method": "GET",
+ "url": "http://localhost:3000/api/v1/flows/",
+ "httpVersion": "HTTP/1.1",
+ "cookies": [],
+ "headers": [
+ { "name": "Accept", "value": "application/json, text/plain, */*" },
+ { "name": "Accept-Encoding", "value": "gzip, deflate, br" },
+ { "name": "Accept-Language", "value": "en-US,en;q=0.9" },
+ { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJiNWIxYjFhZC05M2VjLTRjMjgtYWMxNy01OGMxNDQ0MWI1ZGQiLCJleHAiOjE3MjUwMjk3MzV9.8qhEtryWqA9RbwEP2s20umKdVE7J7jHGoogXZqxLNWk" },
+ { "name": "Connection", "value": "keep-alive" },
+ { "name": "Cookie", "value": "access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJiNWIxYjFhZC05M2VjLTRjMjgtYWMxNy01OGMxNDQ0MWI1ZGQiLCJleHAiOjE3MjUwMjk3MzV9.8qhEtryWqA9RbwEP2s20umKdVE7J7jHGoogXZqxLNWk; refresh_token=auto" },
+ { "name": "Host", "value": "localhost:3000" },
+ { "name": "Referer", "value": "http://localhost:3000/" },
+ { "name": "Sec-Fetch-Dest", "value": "empty" },
+ { "name": "Sec-Fetch-Mode", "value": "cors" },
+ { "name": "Sec-Fetch-Site", "value": "same-origin" },
+ { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36" },
+ { "name": "sec-ch-ua", "value": "\"Not)A;Brand\";v=\"24\", \"Chromium\";v=\"116\"" },
+ { "name": "sec-ch-ua-mobile", "value": "?0" },
+ { "name": "sec-ch-ua-platform", "value": "\"Linux\"" }
+ ],
+ "queryString": [],
+ "headersSize": -1,
+ "bodySize": -1
+ },
+ "response": {
+ "status": 200,
+ "statusText": "OK",
+ "httpVersion": "HTTP/1.1",
+ "cookies": [],
+ "headers": [
+ { "name": "Access-Control-Allow-Origin", "value": "*" },
+ { "name": "connection", "value": "close" },
+ { "name": "content-length", "value": "2" },
+ { "name": "content-type", "value": "application/json" },
+ { "name": "date", "value": "Thu, 31 Aug 2023 14:55:34 GMT" },
+ { "name": "server", "value": "uvicorn" }
+ ],
+ "content": {
+ "size": -1,
+ "mimeType": "application/json",
+ "text": "[]"
+ },
+ "headersSize": -1,
+ "bodySize": -1,
+ "redirectURL": ""
+ },
+ "cache": {},
+ "timings": { "send": -1, "wait": -1, "receive": 0.719 }
+ },
+ {
+ "startedDateTime": "2023-08-31T14:55:35.586Z",
+ "time": 1.031,
+ "request": {
+ "method": "GET",
+ "url": "http://localhost:3000/api/v1/all",
+ "httpVersion": "HTTP/1.1",
+ "cookies": [],
+ "headers": [
+ { "name": "Accept", "value": "application/json, text/plain, */*" },
+ { "name": "Accept-Encoding", "value": "gzip, deflate, br" },
+ { "name": "Accept-Language", "value": "en-US,en;q=0.9" },
+ { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJiNWIxYjFhZC05M2VjLTRjMjgtYWMxNy01OGMxNDQ0MWI1ZGQiLCJleHAiOjE3MjUwMjk3MzV9.8qhEtryWqA9RbwEP2s20umKdVE7J7jHGoogXZqxLNWk" },
+ { "name": "Connection", "value": "keep-alive" },
+ { "name": "Cookie", "value": "access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJiNWIxYjFhZC05M2VjLTRjMjgtYWMxNy01OGMxNDQ0MWI1ZGQiLCJleHAiOjE3MjUwMjk3MzV9.8qhEtryWqA9RbwEP2s20umKdVE7J7jHGoogXZqxLNWk; refresh_token=auto" },
+ { "name": "Host", "value": "localhost:3000" },
+ { "name": "Referer", "value": "http://localhost:3000/" },
+ { "name": "Sec-Fetch-Dest", "value": "empty" },
+ { "name": "Sec-Fetch-Mode", "value": "cors" },
+ { "name": "Sec-Fetch-Site", "value": "same-origin" },
+ { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36" },
+ { "name": "sec-ch-ua", "value": "\"Not)A;Brand\";v=\"24\", \"Chromium\";v=\"116\"" },
+ { "name": "sec-ch-ua-mobile", "value": "?0" },
+ { "name": "sec-ch-ua-platform", "value": "\"Linux\"" }
+ ],
+ "queryString": [],
+ "headersSize": -1,
+ "bodySize": -1
+ },
+ "response": {
+ "status": 200,
+ "statusText": "OK",
+ "httpVersion": "HTTP/1.1",
+ "cookies": [],
+ "headers": [
+ { "name": "Access-Control-Allow-Origin", "value": "*" },
+ { "name": "connection", "value": "close" },
+ { "name": "content-length", "value": "251307" },
+ { "name": "content-type", "value": "application/json" },
+ { "name": "date", "value": "Thu, 31 Aug 2023 14:55:34 GMT" },
+ { "name": "server", "value": "uvicorn" }
+ ],
+ "content": {
+ "size": -1,
+ "mimeType": "application/json",
+ "text": "{\"chains\":{\"ConversationChain\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain.callbacks.base.BaseCallbackHandler\",\"list\":true},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":{\"_type\":\"default\"},\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLLMOutputParser\",\"list\":false},\"prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":{\"input_variables\":[\"history\",\"input\"],\"input_types\":{},\"output_parser\":null,\"partial_variables\":{},\"template\":\"The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\\n\\nCurrent conversation:\\n{history}\\nHuman: 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is a chain you can use to have a conversation with a character from a series.\",\"base_classes\":[\"LLMChain\",\"BaseCustomChain\",\"Chain\",\"ConversationChain\",\"SeriesCharacterChain\",\"function\"],\"display_name\":\"SeriesCharacterChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"MidJourneyPromptChain\":{\"template\":{\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"_type\":\"MidJourneyPromptChain\"},\"description\":\"MidJourneyPromptChain is a chain you can use to generate new MidJourney prompts.\",\"base_classes\":[\"LLMChain\",\"BaseCustomChain\",\"Chain\",\"ConversationChain\",\"MidJourneyPromptChain\"],\"display_name\":\"MidJourneyPromptChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"TimeTravelGuideChain\":{\"template\":{\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"_type\":\"TimeTravelGuideChain\"},\"description\":\"Time travel guide chain.\",\"base_classes\":[\"LLMChain\",\"BaseCustomChain\",\"TimeTravelGuideChain\",\"Chain\",\"ConversationChain\"],\"display_name\":\"TimeTravelGuideChain\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"PromptRunner\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nfrom langchain.llms.base import BaseLLM\\nfrom langchain.prompts import PromptTemplate\\nfrom langchain.schema import Document\\n\\n\\nclass PromptRunner(CustomComponent):\\n display_name: str = \\\"Prompt Runner\\\"\\n description: str = \\\"Run a Chain with the given PromptTemplate\\\"\\n beta = True\\n field_config = {\\n \\\"llm\\\": {\\\"display_name\\\": \\\"LLM\\\"},\\n \\\"prompt\\\": {\\n \\\"display_name\\\": \\\"Prompt Template\\\",\\n \\\"info\\\": \\\"Make sure the prompt has all variables filled.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self, llm: BaseLLM, prompt: PromptTemplate, inputs: dict = {}\\n ) -> Document:\\n chain = prompt | llm\\n # The input is an empty dict because the prompt is already filled\\n result = chain.invoke(input=inputs)\\n if hasattr(result, \\\"content\\\"):\\n result = result.content\\n self.repr_value = result\\n return Document(page_content=str(result))\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"inputs\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"inputs\",\"display_name\":\"inputs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLLM\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"prompt\",\"display_name\":\"Prompt Template\",\"advanced\":false,\"dynamic\":false,\"info\":\"Make sure the prompt has all variables filled.\",\"type\":\"PromptTemplate\",\"list\":false}},\"description\":\"Run a Chain with the given PromptTemplate\",\"base_classes\":[\"Document\"],\"display_name\":\"Prompt Runner\",\"custom_fields\":{\"inputs\":null,\"llm\":null,\"prompt\":null},\"output_types\":[\"PromptRunner\"],\"documentation\":\"\",\"beta\":true,\"error\":null}},\"agents\":{\"ZeroShotAgent\":{\"template\":{\"callback_manager\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callback_manager\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseCallbackManager\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"AgentOutputParser\",\"list\":false},\"tools\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseTool\",\"list\":true},\"format_instructions\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"Use the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [{tool_names}]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\",\"password\":false,\"name\":\"format_instructions\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"input_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"Answer the following questions as best you can. You have access to the following tools:\",\"password\":false,\"name\":\"prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"suffix\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"Begin!\\n\\nQuestion: {input}\\nThought:{agent_scratchpad}\",\"password\":false,\"name\":\"suffix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"ZeroShotAgent\"},\"description\":\"Construct an agent from an LLM and tools.\",\"base_classes\":[\"Agent\",\"BaseSingleActionAgent\",\"ZeroShotAgent\",\"function\"],\"display_name\":\"ZeroShotAgent\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/agents/how_to/custom_mrkl_agent\",\"beta\":false,\"error\":null},\"JsonAgent\":{\"template\":{\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"toolkit\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"toolkit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseToolkit\",\"list\":false},\"_type\":\"json_agent\"},\"description\":\"Construct a json agent from an LLM and tools.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"JsonAgent\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/agents/toolkits/openapi\",\"beta\":false,\"error\":null},\"CSVAgent\":{\"template\":{\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".csv\"],\"password\":false,\"name\":\"path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"csv\"],\"file_path\":null},\"_type\":\"csv_agent\"},\"description\":\"Construct a CSV agent from a CSV and tools.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"CSVAgent\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/agents/toolkits/csv\",\"beta\":false,\"error\":null},\"AgentInitializer\":{\"template\":{\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMemory\",\"list\":false},\"tools\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Tool\",\"list\":true},\"agent\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"zero-shot-react-description\",\"password\":false,\"options\":[\"zero-shot-react-description\",\"react-docstore\",\"self-ask-with-search\",\"conversational-react-description\",\"openai-functions\",\"openai-multi-functions\"],\"name\":\"agent\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"_type\":\"initialize_agent\"},\"description\":\"Construct a zero shot agent from an LLM and tools.\",\"base_classes\":[\"AgentExecutor\",\"function\"],\"display_name\":\"AgentInitializer\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/agents/agent_types/\",\"beta\":false,\"error\":null},\"VectorStoreAgent\":{\"template\":{\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"vectorstoreinfo\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"vectorstoreinfo\",\"display_name\":\"Vector Store Info\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"VectorStoreInfo\",\"list\":false},\"_type\":\"vectorstore_agent\"},\"description\":\"Construct an agent from a Vector Store.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"VectorStoreAgent\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"VectorStoreRouterAgent\":{\"template\":{\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"vectorstoreroutertoolkit\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"vectorstoreroutertoolkit\",\"display_name\":\"Vector Store Router Toolkit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"VectorStoreRouterToolkit\",\"list\":false},\"_type\":\"vectorstorerouter_agent\"},\"description\":\"Construct an agent from a Vector Store Router.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"VectorStoreRouterAgent\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"SQLAgent\":{\"template\":{\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"display_name\":\"LLM\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"database_uri\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"database_uri\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"sql_agent\"},\"description\":\"Construct an SQL agent from an LLM and tools.\",\"base_classes\":[\"AgentExecutor\"],\"display_name\":\"SQLAgent\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"OpenAIConversationalAgent\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom typing import Optional\\nfrom langchain.prompts import SystemMessagePromptTemplate\\nfrom langchain.tools import Tool\\nfrom langchain.schema.memory import BaseMemory\\nfrom langchain.chat_models import ChatOpenAI\\n\\nfrom langchain.agents.agent import AgentExecutor\\nfrom langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent\\nfrom langchain.memory.token_buffer import ConversationTokenBufferMemory\\nfrom langchain.prompts.chat import MessagesPlaceholder\\nfrom langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import (\\n _get_default_system_message,\\n)\\n\\n\\nclass ConversationalAgent(CustomComponent):\\n display_name: str = \\\"OpenAI Conversational Agent\\\"\\n description: str = \\\"Conversational Agent that can use OpenAI's function calling API\\\"\\n\\n def build_config(self):\\n openai_function_models = [\\n \\\"gpt-3.5-turbo-0613\\\",\\n \\\"gpt-3.5-turbo-16k-0613\\\",\\n \\\"gpt-4-0613\\\",\\n \\\"gpt-4-32k-0613\\\",\\n ]\\n return {\\n \\\"tools\\\": {\\\"is_list\\\": True, \\\"display_name\\\": \\\"Tools\\\"},\\n \\\"memory\\\": {\\\"display_name\\\": \\\"Memory\\\"},\\n \\\"system_message\\\": {\\\"display_name\\\": \\\"System Message\\\"},\\n \\\"max_token_limit\\\": {\\\"display_name\\\": \\\"Max Token Limit\\\"},\\n \\\"model_name\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"options\\\": openai_function_models,\\n \\\"value\\\": openai_function_models[0],\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model_name: str,\\n openai_api_key: str,\\n tools: Tool,\\n openai_api_base: Optional[str] = None,\\n memory: Optional[BaseMemory] = None,\\n system_message: Optional[SystemMessagePromptTemplate] = None,\\n max_token_limit: int = 2000,\\n ) -> AgentExecutor:\\n llm = ChatOpenAI(\\n model=model_name,\\n openai_api_key=openai_api_key,\\n openai_api_base=openai_api_base,\\n )\\n if not memory:\\n memory_key = \\\"chat_history\\\"\\n memory = ConversationTokenBufferMemory(\\n memory_key=memory_key,\\n return_messages=True,\\n output_key=\\\"output\\\",\\n llm=llm,\\n max_token_limit=max_token_limit,\\n )\\n else:\\n memory_key = memory.memory_key # type: ignore\\n\\n _system_message = system_message or _get_default_system_message()\\n prompt = OpenAIFunctionsAgent.create_prompt(\\n system_message=_system_message, # type: ignore\\n extra_prompt_messages=[MessagesPlaceholder(variable_name=memory_key)],\\n )\\n agent = OpenAIFunctionsAgent(\\n llm=llm, tools=tools, prompt=prompt # type: ignore\\n )\\n return AgentExecutor(\\n agent=agent,\\n tools=tools, # type: ignore\\n memory=memory,\\n verbose=True,\\n return_intermediate_steps=True,\\n handle_parsing_errors=True,\\n )\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"max_token_limit\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":2000,\"password\":false,\"name\":\"max_token_limit\",\"display_name\":\"Max Token Limit\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"memory\",\"display_name\":\"Memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseMemory\",\"list\":false},\"model_name\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-3.5-turbo-0613\",\"password\":false,\"options\":[\"gpt-3.5-turbo-0613\",\"gpt-3.5-turbo-16k-0613\",\"gpt-4-0613\",\"gpt-4-32k-0613\"],\"name\":\"model_name\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"openai_api_base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_key\",\"display_name\":\"openai_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"system_message\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"system_message\",\"display_name\":\"System Message\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"SystemMessagePromptTemplate\",\"list\":false},\"tools\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"tools\",\"display_name\":\"Tools\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Tool\",\"list\":true}},\"description\":\"Conversational Agent that can use OpenAI's function calling API\",\"base_classes\":[\"Chain\",\"AgentExecutor\"],\"display_name\":\"OpenAI Conversational Agent\",\"custom_fields\":{\"max_token_limit\":null,\"memory\":null,\"model_name\":null,\"openai_api_base\":null,\"openai_api_key\":null,\"system_message\":null,\"tools\":null},\"output_types\":[\"OpenAIConversationalAgent\"],\"documentation\":\"\",\"beta\":true,\"error\":null}},\"prompts\":{\"ChatMessagePromptTemplate\":{\"template\":{\"additional_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"additional_kwargs\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\\nYou are a helpful assistant that talks casually about life in general.\\nYou are a good listener and you can talk about anything.\\n\",\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false},\"role\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"role\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"ChatMessagePromptTemplate\"},\"description\":\"Chat message prompt template.\",\"base_classes\":[\"BaseStringMessagePromptTemplate\",\"ChatMessagePromptTemplate\",\"BaseMessagePromptTemplate\"],\"display_name\":\"ChatMessagePromptTemplate\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/msg_prompt_templates\",\"beta\":false,\"error\":null},\"ChatPromptTemplate\":{\"template\":{\"messages\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"messages\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseMessagePromptTemplate\",\"list\":true},\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false},\"input_types\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"input_variables\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true},\"partial_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"ChatPromptTemplate\"},\"description\":\"A prompt template for chat models.\",\"base_classes\":[\"BaseChatPromptTemplate\",\"BasePromptTemplate\",\"ChatPromptTemplate\"],\"display_name\":\"ChatPromptTemplate\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/how_to/prompts\",\"beta\":false,\"error\":null},\"HumanMessagePromptTemplate\":{\"template\":{\"additional_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"additional_kwargs\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\\nYou are a helpful assistant that talks casually about life in general.\\nYou are a good listener and you can talk about anything.\\n\",\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false},\"_type\":\"HumanMessagePromptTemplate\"},\"description\":\"Human message prompt template. This is a message sent from the user.\",\"base_classes\":[\"BaseStringMessagePromptTemplate\",\"HumanMessagePromptTemplate\",\"BaseMessagePromptTemplate\"],\"display_name\":\"HumanMessagePromptTemplate\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/how_to/prompts\",\"beta\":false,\"error\":null},\"PromptTemplate\":{\"template\":{\"output_parser\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"output_parser\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"BaseOutputParser\",\"list\":false},\"input_types\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_types\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"input_variables\":{\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"input_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":true},\"partial_variables\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"partial_variables\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"template\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"prompt\",\"list\":false},\"template_format\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"f-string\",\"password\":false,\"name\":\"template_format\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"str\",\"list\":false},\"validate_template\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":true,\"password\":false,\"name\":\"validate_template\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"PromptTemplate\"},\"description\":\"A prompt template for a language model.\",\"base_classes\":[\"StringPromptTemplate\",\"BasePromptTemplate\",\"PromptTemplate\"],\"display_name\":\"PromptTemplate\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/\",\"beta\":false,\"error\":null},\"SystemMessagePromptTemplate\":{\"template\":{\"additional_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"additional_kwargs\",\"advanced\":true,\"dynamic\":true,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"prompt\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"\\nYou are a helpful assistant that talks casually about life in general.\\nYou are a good listener and you can talk about 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models.\",\"base_classes\":[\"_AnthropicCommon\",\"BaseLanguageModel\",\"BaseChatModel\",\"ChatAnthropic\",\"BaseLLM\"],\"display_name\":\"ChatAnthropic\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/anthropic\",\"beta\":false,\"error\":null},\"ChatOpenAI\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain.callbacks.base.BaseCallbackHandler\",\"list\":true},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":6,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"max_tokens\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"gpt-3.5-turbo-0613\",\"password\":false,\"options\":[\"gpt-3.5-turbo-0613\",\"gpt-3.5-turbo\",\"gpt-3.5-turbo-16k-0613\",\"gpt-3.5-turbo-16k\",\"gpt-4-0613\",\"gpt-4-32k-0613\",\"gpt-4\",\"gpt-4-32k\"],\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"n\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":1,\"password\":false,\"name\":\"n\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"openai_api_base\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"openai_api_base\",\"display_name\":\"OpenAI API Base\",\"advanced\":false,\"dynamic\":false,\"info\":\"\\nThe base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.\\n\",\"type\":\"str\",\"list\":false},\"openai_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":true,\"name\":\"openai_api_key\",\"display_name\":\"OpenAI API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_organization\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_organization\",\"display_name\":\"OpenAI Organization\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"openai_proxy\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"openai_proxy\",\"display_name\":\"OpenAI Proxy\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"request_timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.7,\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"tiktoken_model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tiktoken_model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatOpenAI\"},\"description\":\"`OpenAI` Chat large language models API.\",\"base_classes\":[\"BaseLanguageModel\",\"BaseChatModel\",\"ChatOpenAI\",\"BaseLLM\"],\"display_name\":\"ChatOpenAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai\",\"beta\":false,\"error\":null},\"ChatVertexAI\":{\"template\":{\"callbacks\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"callbacks\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"langchain.callbacks.base.BaseCallbackHandler\",\"list\":true},\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"ForwardRef(\\\"'_LanguageModel'\\\")\",\"list\":false},\"credentials\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".json\"],\"password\":false,\"name\":\"credentials\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"json\"],\"file_path\":null},\"cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"location\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"us-central1\",\"password\":false,\"name\":\"location\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"max_output_tokens\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":128,\"password\":false,\"name\":\"max_output_tokens\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"max_retries\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":6,\"password\":false,\"name\":\"max_retries\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"model_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"chat-bison\",\"password\":false,\"name\":\"model_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"project\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"project\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"request_parallelism\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"request_parallelism\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"stop\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"stop\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"streaming\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"streaming\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"tags\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tags\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.0,\"password\":false,\"name\":\"temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"top_k\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":40,\"password\":false,\"name\":\"top_k\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"top_p\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.95,\"password\":false,\"name\":\"top_p\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"verbose\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"verbose\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ChatVertexAI\"},\"description\":\"`Vertex AI` Chat large language models API.\",\"base_classes\":[\"_VertexAICommon\",\"BaseChatModel\",\"BaseLanguageModel\",\"_VertexAIBase\",\"ChatVertexAI\",\"BaseLLM\"],\"display_name\":\"ChatVertexAI\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/models/chat/integrations/google_vertex_ai_palm\",\"beta\":false,\"error\":null},\"HuggingFaceEndpoints\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.llms.huggingface_endpoint import HuggingFaceEndpoint\\nfrom langchain.llms.base import BaseLLM\\n\\n\\nclass HuggingFaceEndpointsComponent(CustomComponent):\\n display_name: str = \\\"Hugging Face Inference API\\\"\\n description: str = \\\"LLM model from Hugging Face Inference API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"endpoint_url\\\": {\\\"display_name\\\": \\\"Endpoint URL\\\", \\\"password\\\": True},\\n \\\"task\\\": {\\n \\\"display_name\\\": \\\"Task\\\",\\n \\\"options\\\": [\\\"text2text-generation\\\", \\\"text-generation\\\", \\\"summarization\\\"],\\n },\\n \\\"huggingfacehub_api_token\\\": {\\\"display_name\\\": \\\"API token\\\", \\\"password\\\": True},\\n \\\"model_kwargs\\\": {\\n \\\"display_name\\\": \\\"Model Keyword Arguments\\\",\\n \\\"field_type\\\": \\\"code\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n endpoint_url: str,\\n task: str = \\\"text2text-generation\\\",\\n huggingfacehub_api_token: Optional[str] = None,\\n model_kwargs: Optional[dict] = None,\\n ) -> BaseLLM:\\n try:\\n output = HuggingFaceEndpoint(\\n endpoint_url=endpoint_url,\\n task=task,\\n huggingfacehub_api_token=huggingfacehub_api_token,\\n model_kwargs=model_kwargs,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to HuggingFace Endpoints API.\\\") from e\\n return output\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"endpoint_url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"endpoint_url\",\"display_name\":\"Endpoint URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"huggingfacehub_api_token\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"huggingfacehub_api_token\",\"display_name\":\"API token\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"model_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"model_kwargs\",\"display_name\":\"Model Keyword Arguments\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"task\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"text2text-generation\",\"password\":false,\"options\":[\"text2text-generation\",\"text-generation\",\"summarization\"],\"name\":\"task\",\"display_name\":\"Task\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true}},\"description\":\"LLM model from Hugging Face Inference API.\",\"base_classes\":[\"BaseLanguageModel\",\"BaseLLM\"],\"display_name\":\"Hugging Face Inference API\",\"custom_fields\":{\"endpoint_url\":null,\"huggingfacehub_api_token\":null,\"model_kwargs\":null,\"task\":null},\"output_types\":[\"HuggingFaceEndpoints\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"BaiduQianfanChatEndpoints\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint\\nfrom langchain.llms.base import BaseLLM\\n\\n\\nclass QianfanChatEndpointComponent(CustomComponent):\\n display_name: str = \\\"QianfanChatEndpoint\\\"\\n description: str = (\\n \\\"Baidu Qianfan chat models. Get more detail from \\\"\\n \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\\\"\\n )\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"options\\\": [\\n \\\"ERNIE-Bot\\\",\\n \\\"ERNIE-Bot-turbo\\\",\\n \\\"BLOOMZ-7B\\\",\\n \\\"Llama-2-7b-chat\\\",\\n \\\"Llama-2-13b-chat\\\",\\n \\\"Llama-2-70b-chat\\\",\\n \\\"Qianfan-BLOOMZ-7B-compressed\\\",\\n \\\"Qianfan-Chinese-Llama-2-7B\\\",\\n \\\"ChatGLM2-6B-32K\\\",\\n \\\"AquilaChat-7B\\\",\\n ],\\n \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\",\\n \\\"required\\\": True,\\n },\\n \\\"qianfan_ak\\\": {\\n \\\"display_name\\\": \\\"Qianfan Ak\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"qianfan_sk\\\": {\\n \\\"display_name\\\": \\\"Qianfan Sk\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top p\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.8,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.95,\\n },\\n \\\"penalty_score\\\": {\\n \\\"display_name\\\": \\\"Penalty Score\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 1.0,\\n },\\n \\\"endpoint\\\": {\\n \\\"display_name\\\": \\\"Endpoint\\\",\\n \\\"info\\\": \\\"Endpoint of the Qianfan LLM, required if custom model used.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model: str = \\\"ERNIE-Bot-turbo\\\",\\n qianfan_ak: Optional[str] = None,\\n qianfan_sk: Optional[str] = None,\\n top_p: Optional[float] = None,\\n temperature: Optional[float] = None,\\n penalty_score: Optional[float] = None,\\n endpoint: Optional[str] = None,\\n ) -> BaseLLM:\\n try:\\n output = QianfanChatEndpoint( # type: ignore\\n model=model,\\n qianfan_ak=qianfan_ak,\\n qianfan_sk=qianfan_sk,\\n top_p=top_p,\\n temperature=temperature,\\n penalty_score=penalty_score,\\n endpoint=endpoint,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Baidu Qianfan API.\\\") from e\\n return output # type: ignore\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"endpoint\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint\",\"display_name\":\"Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Qianfan LLM, required if custom model used.\",\"type\":\"str\",\"list\":false},\"model\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"ERNIE-Bot-turbo\",\"password\":false,\"options\":[\"ERNIE-Bot\",\"ERNIE-Bot-turbo\",\"BLOOMZ-7B\",\"Llama-2-7b-chat\",\"Llama-2-13b-chat\",\"Llama-2-70b-chat\",\"Qianfan-BLOOMZ-7B-compressed\",\"Qianfan-Chinese-Llama-2-7B\",\"ChatGLM2-6B-32K\",\"AquilaChat-7B\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"type\":\"str\",\"list\":true},\"penalty_score\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1.0,\"password\":false,\"name\":\"penalty_score\",\"display_name\":\"Penalty Score\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"type\":\"float\",\"list\":false},\"qianfan_ak\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"qianfan_ak\",\"display_name\":\"Qianfan Ak\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\",\"type\":\"str\",\"list\":false},\"qianfan_sk\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"qianfan_sk\",\"display_name\":\"Qianfan Sk\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\",\"type\":\"str\",\"list\":false},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.95,\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"type\":\"float\",\"list\":false},\"top_p\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.8,\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"type\":\"float\",\"list\":false}},\"description\":\"Baidu Qianfan chat models. Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint.\",\"base_classes\":[\"BaseLanguageModel\",\"BaseLLM\"],\"display_name\":\"QianfanChatEndpoint\",\"custom_fields\":{\"endpoint\":null,\"model\":null,\"penalty_score\":null,\"qianfan_ak\":null,\"qianfan_sk\":null,\"temperature\":null,\"top_p\":null},\"output_types\":[\"BaiduQianfanChatEndpoints\"],\"documentation\":\"\",\"beta\":true,\"error\":null},\"BaiduQianfanLLMEndpoints\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint\\nfrom langchain.llms.base import BaseLLM\\n\\n\\nclass QianfanLLMEndpointComponent(CustomComponent):\\n display_name: str = \\\"QianfanLLMEndpoint\\\"\\n description: str = (\\n \\\"Baidu Qianfan hosted open source or customized models. \\\"\\n \\\"Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\"\\n )\\n\\n def build_config(self):\\n return {\\n \\\"model\\\": {\\n \\\"display_name\\\": \\\"Model Name\\\",\\n \\\"options\\\": [\\n \\\"ERNIE-Bot\\\",\\n \\\"ERNIE-Bot-turbo\\\",\\n \\\"BLOOMZ-7B\\\",\\n \\\"Llama-2-7b-chat\\\",\\n \\\"Llama-2-13b-chat\\\",\\n \\\"Llama-2-70b-chat\\\",\\n \\\"Qianfan-BLOOMZ-7B-compressed\\\",\\n \\\"Qianfan-Chinese-Llama-2-7B\\\",\\n \\\"ChatGLM2-6B-32K\\\",\\n \\\"AquilaChat-7B\\\",\\n ],\\n \\\"info\\\": \\\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\\\",\\n \\\"required\\\": True,\\n },\\n \\\"qianfan_ak\\\": {\\n \\\"display_name\\\": \\\"Qianfan Ak\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"qianfan_sk\\\": {\\n \\\"display_name\\\": \\\"Qianfan Sk\\\",\\n \\\"required\\\": True,\\n \\\"password\\\": True,\\n \\\"info\\\": \\\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\\\",\\n },\\n \\\"top_p\\\": {\\n \\\"display_name\\\": \\\"Top p\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.8,\\n },\\n \\\"temperature\\\": {\\n \\\"display_name\\\": \\\"Temperature\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 0.95,\\n },\\n \\\"penalty_score\\\": {\\n \\\"display_name\\\": \\\"Penalty Score\\\",\\n \\\"field_type\\\": \\\"float\\\",\\n \\\"info\\\": \\\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\\\",\\n \\\"value\\\": 1.0,\\n },\\n \\\"endpoint\\\": {\\n \\\"display_name\\\": \\\"Endpoint\\\",\\n \\\"info\\\": \\\"Endpoint of the Qianfan LLM, required if custom model used.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n model: str = \\\"ERNIE-Bot-turbo\\\",\\n qianfan_ak: Optional[str] = None,\\n qianfan_sk: Optional[str] = None,\\n top_p: Optional[float] = None,\\n temperature: Optional[float] = None,\\n penalty_score: Optional[float] = None,\\n endpoint: Optional[str] = None,\\n ) -> BaseLLM:\\n try:\\n output = QianfanLLMEndpoint( # type: ignore\\n model=model,\\n qianfan_ak=qianfan_ak,\\n qianfan_sk=qianfan_sk,\\n top_p=top_p,\\n temperature=temperature,\\n penalty_score=penalty_score,\\n endpoint=endpoint,\\n )\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Baidu Qianfan API.\\\") from e\\n return output # type: ignore\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"endpoint\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"endpoint\",\"display_name\":\"Endpoint\",\"advanced\":false,\"dynamic\":false,\"info\":\"Endpoint of the Qianfan LLM, required if custom model used.\",\"type\":\"str\",\"list\":false},\"model\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"ERNIE-Bot-turbo\",\"password\":false,\"options\":[\"ERNIE-Bot\",\"ERNIE-Bot-turbo\",\"BLOOMZ-7B\",\"Llama-2-7b-chat\",\"Llama-2-13b-chat\",\"Llama-2-70b-chat\",\"Qianfan-BLOOMZ-7B-compressed\",\"Qianfan-Chinese-Llama-2-7B\",\"ChatGLM2-6B-32K\",\"AquilaChat-7B\"],\"name\":\"model\",\"display_name\":\"Model Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"type\":\"str\",\"list\":true},\"penalty_score\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1.0,\"password\":false,\"name\":\"penalty_score\",\"display_name\":\"Penalty Score\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"type\":\"float\",\"list\":false},\"qianfan_ak\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"qianfan_ak\",\"display_name\":\"Qianfan Ak\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\",\"type\":\"str\",\"list\":false},\"qianfan_sk\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"qianfan_sk\",\"display_name\":\"Qianfan Sk\",\"advanced\":false,\"dynamic\":false,\"info\":\"which you could get from https://cloud.baidu.com/product/wenxinworkshop\",\"type\":\"str\",\"list\":false},\"temperature\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.95,\"password\":false,\"name\":\"temperature\",\"display_name\":\"Temperature\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"type\":\"float\",\"list\":false},\"top_p\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":0.8,\"password\":false,\"name\":\"top_p\",\"display_name\":\"Top p\",\"advanced\":false,\"dynamic\":false,\"info\":\"Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo\",\"type\":\"float\",\"list\":false}},\"description\":\"Baidu Qianfan hosted open source or customized models. Get more detail from https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint\",\"base_classes\":[\"BaseLanguageModel\",\"BaseLLM\"],\"display_name\":\"QianfanLLMEndpoint\",\"custom_fields\":{\"endpoint\":null,\"model\":null,\"penalty_score\":null,\"qianfan_ak\":null,\"qianfan_sk\":null,\"temperature\":null,\"top_p\":null},\"output_types\":[\"BaiduQianfanLLMEndpoints\"],\"documentation\":\"\",\"beta\":true,\"error\":null}},\"memories\":{\"ConversationBufferMemory\":{\"template\":{\"chat_memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMessageHistory\",\"list\":false},\"ai_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"AI\",\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"human_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"Human\",\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"input_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"type\":\"str\",\"list\":false},\"memory_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"output_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"type\":\"str\",\"list\":false},\"return_messages\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ConversationBufferMemory\"},\"description\":\"Buffer for storing conversation memory.\",\"base_classes\":[\"BaseMemory\",\"BaseChatMemory\",\"ConversationBufferMemory\"],\"display_name\":\"ConversationBufferMemory\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/buffer\",\"beta\":false,\"error\":null},\"ConversationBufferWindowMemory\":{\"template\":{\"chat_memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMessageHistory\",\"list\":false},\"ai_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"AI\",\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"human_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"Human\",\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"input_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"type\":\"str\",\"list\":false},\"k\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"memory_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"output_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"type\":\"str\",\"list\":false},\"return_messages\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ConversationBufferWindowMemory\"},\"description\":\"Buffer for storing conversation memory inside a limited size window.\",\"base_classes\":[\"BaseMemory\",\"ConversationBufferWindowMemory\",\"BaseChatMemory\"],\"display_name\":\"ConversationBufferWindowMemory\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/memory/how_to/buffer_window\",\"beta\":false,\"error\":null},\"ConversationEntityMemory\":{\"template\":{\"chat_memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMessageHistory\",\"list\":false},\"entity_extraction_prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"entity_extraction_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false},\"entity_store\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"entity_store\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseEntityStore\",\"list\":false},\"entity_summarization_prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"entity_summarization_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"ai_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"AI\",\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chat_history_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"history\",\"password\":false,\"name\":\"chat_history_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"entity_cache\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"entity_cache\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"human_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"Human\",\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"input_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"type\":\"str\",\"list\":false},\"k\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"output_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"type\":\"str\",\"list\":false},\"return_messages\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ConversationEntityMemory\"},\"description\":\"Entity extractor & summarizer memory.\",\"base_classes\":[\"BaseMemory\",\"ConversationEntityMemory\",\"BaseChatMemory\"],\"display_name\":\"ConversationEntityMemory\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/memory/integrations/entity_memory_with_sqlite\",\"beta\":false,\"error\":null},\"ConversationKGMemory\":{\"template\":{\"chat_memory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chat_memory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseChatMessageHistory\",\"list\":false},\"entity_extraction_prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"entity_extraction_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false},\"kg\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"kg\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"NetworkxEntityGraph\",\"list\":false},\"knowledge_extraction_prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"knowledge_extraction_prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BasePromptTemplate\",\"list\":false},\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLanguageModel\",\"list\":false},\"summary_message_cls\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"summary_message_cls\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Type[langchain.schema.messages.BaseMessage]\",\"list\":false},\"ai_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"AI\",\"password\":false,\"name\":\"ai_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"human_prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"Human\",\"password\":false,\"name\":\"human_prefix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"input_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"input_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Input when more than one variable is available.\",\"type\":\"str\",\"list\":false},\"k\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"k\",\"display_name\":\"Memory Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"memory_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"chat_history\",\"password\":false,\"name\":\"memory_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"output_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"output_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"The variable to be used as Chat Output (e.g. answer in a ConversationalRetrievalChain)\",\"type\":\"str\",\"list\":false},\"return_messages\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"return_messages\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"ConversationKGMemory\"},\"description\":\"Knowledge graph conversation 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Key\\\", \\\"password\\\": True},\\n \\\"code\\\": {\\\"advanced\\\": True},\\n }\\n\\n def build(\\n self,\\n metaphor_api_key: str,\\n use_autoprompt: bool = True,\\n search_num_results: int = 5,\\n similar_num_results: int = 5,\\n ) -> Union[Tool, BaseToolkit]:\\n # If documents, then we need to create a Vectara instance using .from_documents\\n client = Metaphor(api_key=metaphor_api_key)\\n\\n @tool\\n def search(query: str):\\n \\\"\\\"\\\"Call search engine with a query.\\\"\\\"\\\"\\n return client.search(\\n query, use_autoprompt=use_autoprompt, num_results=search_num_results\\n )\\n\\n @tool\\n def get_contents(ids: List[str]):\\n \\\"\\\"\\\"Get contents of a webpage.\\n\\n The ids passed in should be a list of ids as fetched from `search`.\\n \\\"\\\"\\\"\\n return client.get_contents(ids)\\n\\n @tool\\n def find_similar(url: str):\\n \\\"\\\"\\\"Get search results similar to a given URL.\\n\\n The url passed in should be a URL returned from `search`\\n \\\"\\\"\\\"\\n return client.find_similar(url, num_results=similar_num_results)\\n\\n return [search, get_contents, find_similar] # type: ignore\\n\",\"password\":false,\"name\":\"code\",\"advanced\":true,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"metaphor_api_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"metaphor_api_key\",\"display_name\":\"Metaphor API 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Pinecone wrapper from raw documents.\",\"base_classes\":[\"Pinecone\",\"VectorStore\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Pinecone\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/pinecone\",\"beta\":false,\"error\":null},\"Qdrant\":{\"template\":{\"documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":true},\"embedding\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Embeddings\",\"list\":false},\"hnsw_config\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"hnsw_config\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"common_types.HnswConfigDiff\",\"list\":false},\"init_from\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"init_from\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"common_types.InitFrom\",\"list\":false},\"optimizers_config\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"optimizers_config\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"common_types.OptimizersConfigDiff\",\"list\":false},\"quantization_config\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"quantization_config\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"common_types.QuantizationConfig\",\"list\":false},\"wal_config\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"wal_config\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"common_types.WalConfigDiff\",\"list\":false},\"api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"batch_size\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":64,\"password\":false,\"name\":\"batch_size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"collection_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"collection_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"content_payload_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"page_content\",\"password\":false,\"name\":\"content_payload_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"distance_func\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"Cosine\",\"password\":false,\"name\":\"distance_func\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"force_recreate\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"force_recreate\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"grpc_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":6334,\"password\":false,\"name\":\"grpc_port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"host\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"https\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"https\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"ids\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"ids\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"location\":{\"required\":false,\"placeholder\":\":memory:\",\"show\":true,\"multiline\":false,\"value\":\":memory:\",\"password\":false,\"name\":\"location\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"metadata_payload_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"metadata\",\"password\":false,\"name\":\"metadata_payload_key\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"metadatas\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadatas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":true},\"on_disk\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"on_disk\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"on_disk_payload\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"on_disk_payload\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"path\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"path\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":6333,\"password\":false,\"name\":\"port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"prefer_grpc\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"prefer_grpc\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"prefix\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"prefix\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"replication_factor\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"replication_factor\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"search_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{}\",\"password\":false,\"name\":\"search_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"NestedDict\",\"list\":false},\"shard_number\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"shard_number\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"timeout\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"timeout\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"float\",\"list\":false},\"url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"vector_name\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"vector_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"write_consistency_factor\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"write_consistency_factor\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"_type\":\"Qdrant\"},\"description\":\"Construct Qdrant wrapper from a list of texts.\",\"base_classes\":[\"VectorStore\",\"Qdrant\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Qdrant\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/qdrant\",\"beta\":false,\"error\":null},\"SupabaseVectorStore\":{\"template\":{\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"supabase.client.Client\",\"list\":false},\"documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":true},\"embedding\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Embeddings\",\"list\":false},\"ids\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"ids\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"metadatas\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadatas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":true},\"query_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"query_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"search_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{}\",\"password\":false,\"name\":\"search_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"NestedDict\",\"list\":false},\"supabase_service_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":true,\"name\":\"supabase_service_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"supabase_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"supabase_url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"table_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"table_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"SupabaseVectorStore\"},\"description\":\"Return VectorStore initialized from texts and embeddings.\",\"base_classes\":[\"SupabaseVectorStore\",\"VectorStore\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Supabase\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/supabase\",\"beta\":false,\"error\":null},\"Weaviate\":{\"template\":{\"client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"weaviate.Client\",\"list\":false},\"documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":true},\"embedding\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Embeddings\",\"list\":false},\"relevance_score_fn\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"relevance_score_fn\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Callable[[float], float]\",\"list\":false},\"batch_size\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"batch_size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"by_text\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"by_text\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"client_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{}\",\"password\":false,\"name\":\"client_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"code\",\"list\":false},\"index_name\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"index_name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"metadatas\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"metadatas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":true},\"search_kwargs\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{}\",\"password\":false,\"name\":\"search_kwargs\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"NestedDict\",\"list\":false},\"text_key\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"text\",\"password\":true,\"name\":\"text_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"weaviate_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"weaviate_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"weaviate_url\":{\"required\":true,\"placeholder\":\"http://localhost:8080\",\"show\":true,\"multiline\":false,\"value\":\"http://localhost:8080\",\"password\":false,\"name\":\"weaviate_url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"Weaviate\"},\"description\":\"Construct Weaviate wrapper from raw documents.\",\"base_classes\":[\"Weaviate\",\"VectorStore\",\"BaseRetriever\",\"VectorStoreRetriever\"],\"display_name\":\"Weaviate\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/weaviate\",\"beta\":false,\"error\":null},\"Vectara\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional, Union\\nfrom langflow import CustomComponent\\n\\nfrom langchain.vectorstores import Vectara\\nfrom langchain.schema import Document\\nfrom langchain.vectorstores.base import VectorStore\\nfrom langchain.schema import BaseRetriever\\n\\n\\nclass VectaraComponent(CustomComponent):\\n display_name: str = \\\"Vectara\\\"\\n description: str = \\\"Implementation of Vector Store using Vectara\\\"\\n documentation = (\\n \\\"https://python.langchain.com/docs/integrations/vectorstores/vectara\\\"\\n )\\n beta = True\\n # api key should be password = True\\n field_config = {\\n \\\"vectara_customer_id\\\": {\\\"display_name\\\": \\\"Vectara Customer ID\\\"},\\n \\\"vectara_corpus_id\\\": {\\\"display_name\\\": \\\"Vectara Corpus ID\\\"},\\n \\\"vectara_api_key\\\": {\\\"display_name\\\": \\\"Vectara API Key\\\", \\\"password\\\": True},\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\"},\\n }\\n\\n def build(\\n self,\\n vectara_customer_id: str,\\n vectara_corpus_id: str,\\n vectara_api_key: str,\\n documents: Optional[Document] = None,\\n ) -> Union[VectorStore, BaseRetriever]:\\n # If documents, then we need to create a Vectara instance using .from_documents\\n if documents is not None:\\n return Vectara.from_documents(\\n documents=documents, # type: ignore\\n vectara_customer_id=vectara_customer_id,\\n vectara_corpus_id=vectara_corpus_id,\\n vectara_api_key=vectara_api_key,\\n source=\\\"langflow\\\",\\n )\\n\\n return Vectara(\\n vectara_customer_id=vectara_customer_id,\\n vectara_corpus_id=vectara_corpus_id,\\n vectara_api_key=vectara_api_key,\\n source=\\\"langflow\\\",\\n )\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":false},\"vectara_api_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"vectara_api_key\",\"display_name\":\"Vectara API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"vectara_corpus_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"vectara_corpus_id\",\"display_name\":\"Vectara Corpus ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"vectara_customer_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"vectara_customer_id\",\"display_name\":\"Vectara Customer ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Implementation of Vector Store using Vectara\",\"base_classes\":[\"VectorStore\",\"BaseRetriever\"],\"display_name\":\"Vectara\",\"custom_fields\":{\"documents\":null,\"vectara_api_key\":null,\"vectara_corpus_id\":null,\"vectara_customer_id\":null},\"output_types\":[\"Vectara\"],\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/vectara\",\"beta\":true,\"error\":null},\"Chroma (1)\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional, Union\\nfrom langflow import CustomComponent\\n\\nfrom langchain.vectorstores import Chroma\\nfrom langchain.schema import Document\\nfrom langchain.vectorstores.base import VectorStore\\nfrom langchain.schema import BaseRetriever\\nfrom langchain.embeddings.base import Embeddings\\nimport chromadb # type: ignore\\n\\n\\nclass ChromaComponent(CustomComponent):\\n \\\"\\\"\\\"\\n A custom component for implementing a Vector Store using Chroma.\\n \\\"\\\"\\\"\\n\\n display_name: str = \\\"Chroma (Custom Component)\\\"\\n description: str = \\\"Implementation of Vector Store using Chroma\\\"\\n documentation = \\\"https://python.langchain.com/docs/integrations/vectorstores/chroma\\\"\\n beta = True\\n\\n def build_config(self):\\n \\\"\\\"\\\"\\n Builds the configuration for the component.\\n\\n Returns:\\n - dict: A dictionary containing the configuration options for the component.\\n \\\"\\\"\\\"\\n return {\\n \\\"collection_name\\\": {\\\"display_name\\\": \\\"Collection Name\\\", \\\"value\\\": \\\"langflow\\\"},\\n \\\"persist\\\": {\\\"display_name\\\": \\\"Persist\\\"},\\n \\\"persist_directory\\\": {\\\"display_name\\\": \\\"Persist Directory\\\"},\\n \\\"code\\\": {\\\"show\\\": False, \\\"display_name\\\": \\\"Code\\\"},\\n \\\"documents\\\": {\\\"display_name\\\": \\\"Documents\\\", \\\"is_list\\\": True},\\n \\\"embedding\\\": {\\\"display_name\\\": \\\"Embedding\\\"},\\n \\\"chroma_server_cors_allow_origins\\\": {\\n \\\"display_name\\\": \\\"Server CORS Allow Origins\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"chroma_server_host\\\": {\\\"display_name\\\": \\\"Server Host\\\", \\\"advanced\\\": True},\\n \\\"chroma_server_port\\\": {\\\"display_name\\\": \\\"Server Port\\\", \\\"advanced\\\": True},\\n \\\"chroma_server_grpc_port\\\": {\\n \\\"display_name\\\": \\\"Server gRPC Port\\\",\\n \\\"advanced\\\": True,\\n },\\n \\\"chroma_server_ssl_enabled\\\": {\\n \\\"display_name\\\": \\\"Server SSL Enabled\\\",\\n \\\"advanced\\\": True,\\n },\\n }\\n\\n def build(\\n self,\\n collection_name: str,\\n persist: bool,\\n chroma_server_ssl_enabled: bool,\\n persist_directory: Optional[str] = None,\\n embedding: Optional[Embeddings] = None,\\n documents: Optional[Document] = None,\\n chroma_server_cors_allow_origins: Optional[str] = None,\\n chroma_server_host: Optional[str] = None,\\n chroma_server_port: Optional[int] = None,\\n chroma_server_grpc_port: Optional[int] = None,\\n ) -> Union[VectorStore, BaseRetriever]:\\n \\\"\\\"\\\"\\n Builds the Vector Store or BaseRetriever object.\\n\\n Args:\\n - collection_name (str): The name of the collection.\\n - persist_directory (Optional[str]): The directory to persist the Vector Store to.\\n - chroma_server_ssl_enabled (bool): Whether to enable SSL for the Chroma server.\\n - persist (bool): Whether to persist the Vector Store or not.\\n - embedding (Optional[Embeddings]): The embeddings to use for the Vector Store.\\n - documents (Optional[Document]): The documents to use for the Vector Store.\\n - chroma_server_cors_allow_origins (Optional[str]): The CORS allow origins for the Chroma server.\\n - chroma_server_host (Optional[str]): The host for the Chroma server.\\n - chroma_server_port (Optional[int]): The port for the Chroma server.\\n - chroma_server_grpc_port (Optional[int]): The gRPC port for the Chroma server.\\n\\n Returns:\\n - Union[VectorStore, BaseRetriever]: The Vector Store or BaseRetriever object.\\n \\\"\\\"\\\"\\n\\n # Chroma settings\\n chroma_settings = None\\n\\n if chroma_server_host is not None:\\n chroma_settings = chromadb.config.Settings(\\n chroma_server_cors_allow_origins=chroma_server_cors_allow_origins\\n or None,\\n chroma_server_host=chroma_server_host,\\n chroma_server_port=chroma_server_port or None,\\n chroma_server_grpc_port=chroma_server_grpc_port or None,\\n chroma_server_ssl_enabled=chroma_server_ssl_enabled,\\n )\\n\\n # If documents, then we need to create a Chroma instance using .from_documents\\n if documents is not None and embedding is not None:\\n return Chroma.from_documents(\\n documents=documents, # type: ignore\\n persist_directory=persist_directory if persist else None,\\n collection_name=collection_name,\\n embedding=embedding,\\n client_settings=chroma_settings,\\n )\\n\\n return Chroma(\\n persist_directory=persist_directory, client_settings=chroma_settings\\n )\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"chroma_server_cors_allow_origins\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_cors_allow_origins\",\"display_name\":\"Server CORS Allow Origins\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_grpc_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_grpc_port\",\"display_name\":\"Server gRPC Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"chroma_server_host\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_host\",\"display_name\":\"Server Host\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"chroma_server_port\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"chroma_server_port\",\"display_name\":\"Server Port\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"chroma_server_ssl_enabled\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"chroma_server_ssl_enabled\",\"display_name\":\"Server SSL Enabled\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"collection_name\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"langflow\",\"password\":false,\"name\":\"collection_name\",\"display_name\":\"Collection Name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"documents\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":true},\"embedding\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"embedding\",\"display_name\":\"Embedding\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Embeddings\",\"list\":false},\"persist\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"persist\",\"display_name\":\"Persist\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"persist_directory\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"persist_directory\",\"display_name\":\"Persist Directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Implementation of Vector Store using Chroma\",\"base_classes\":[\"VectorStore\",\"BaseRetriever\"],\"display_name\":\"Chroma (Custom Component)\",\"custom_fields\":{\"chroma_server_cors_allow_origins\":null,\"chroma_server_grpc_port\":null,\"chroma_server_host\":null,\"chroma_server_port\":null,\"chroma_server_ssl_enabled\":null,\"collection_name\":null,\"documents\":null,\"embedding\":null,\"persist\":null,\"persist_directory\":null},\"output_types\":[\"Chroma\"],\"documentation\":\"https://python.langchain.com/docs/integrations/vectorstores/chroma\",\"beta\":true,\"error\":null}},\"documentloaders\":{\"AZLyricsLoader\":{\"template\":{\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"web_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web 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webpages.\",\"base_classes\":[\"Document\"],\"display_name\":\"AZLyricsLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/azlyrics\",\"beta\":false,\"error\":null},\"AirbyteJSONLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".json\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"AirbyteJSONLoader\"},\"description\":\"Load local `Airbyte` json files.\",\"base_classes\":[\"Document\"],\"display_name\":\"AirbyteJSONLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/airbyte_json\",\"beta\":false,\"error\":null},\"BSHTMLLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".html\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"html\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"BSHTMLLoader\"},\"description\":\"Load `HTML` files and parse them with `beautiful soup`.\",\"base_classes\":[\"Document\"],\"display_name\":\"BSHTMLLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/html\",\"beta\":false,\"error\":null},\"CSVLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".csv\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CSVLoader\"},\"description\":\"Load a `CSV` file into a list of Documents.\",\"base_classes\":[\"Document\"],\"display_name\":\"CSVLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv\",\"beta\":false,\"error\":null},\"CoNLLULoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".csv\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"csv\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"CoNLLULoader\"},\"description\":\"Load `CoNLL-U` files.\",\"base_classes\":[\"Document\"],\"display_name\":\"CoNLLULoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/conll-u\",\"beta\":false,\"error\":null},\"CollegeConfidentialLoader\":{\"template\":{\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"web_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"CollegeConfidentialLoader\"},\"description\":\"Load `College Confidential` webpages.\",\"base_classes\":[\"Document\"],\"display_name\":\"CollegeConfidentialLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/college_confidential\",\"beta\":false,\"error\":null},\"DirectoryLoader\":{\"template\":{\"glob\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"**/*.txt\",\"password\":false,\"name\":\"glob\",\"display_name\":\"glob\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"load_hidden\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"False\",\"password\":false,\"name\":\"load_hidden\",\"display_name\":\"Load hidden files\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"max_concurrency\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"max_concurrency\",\"display_name\":\"Max concurrency\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"path\",\"display_name\":\"Local directory\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"recursive\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"True\",\"password\":false,\"name\":\"recursive\",\"display_name\":\"Recursive\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"silent_errors\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"False\",\"password\":false,\"name\":\"silent_errors\",\"display_name\":\"Silent errors\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"use_multithreading\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"True\",\"password\":false,\"name\":\"use_multithreading\",\"display_name\":\"Use multithreading\",\"advanced\":true,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"DirectoryLoader\"},\"description\":\"Load from a directory.\",\"base_classes\":[\"Document\"],\"display_name\":\"DirectoryLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/file_directory\",\"beta\":false,\"error\":null},\"EverNoteLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".xml\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"xml\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"EverNoteLoader\"},\"description\":\"Load from `EverNote`.\",\"base_classes\":[\"Document\"],\"display_name\":\"EverNoteLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/evernote\",\"beta\":false,\"error\":null},\"FacebookChatLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".json\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"json\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"FacebookChatLoader\"},\"description\":\"Load `Facebook Chat` messages directory 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URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"file_filter\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"file_filter\",\"display_name\":\"File extensions (comma-separated)\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"repo_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"repo_path\",\"display_name\":\"Path to repository\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"GitLoader\"},\"description\":\"Load `Git` repository 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directory.\",\"base_classes\":[\"Document\"],\"display_name\":\"ReadTheDocsLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/readthedocs_documentation\",\"beta\":false,\"error\":null},\"SRTLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".srt\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"srt\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"SRTLoader\"},\"description\":\"Load `.srt` (subtitle) files.\",\"base_classes\":[\"Document\"],\"display_name\":\"SRTLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/subtitle\",\"beta\":false,\"error\":null},\"SlackDirectoryLoader\":{\"template\":{\"zip_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".zip\"],\"password\":false,\"name\":\"zip_path\",\"display_name\":\"Path to zip file\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"zip\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"workspace_url\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"workspace_url\",\"display_name\":\"Workspace URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"SlackDirectoryLoader\"},\"description\":\"Load from a `Slack` directory dump.\",\"base_classes\":[\"Document\"],\"display_name\":\"SlackDirectoryLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/slack\",\"beta\":false,\"error\":null},\"TextLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".txt\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"txt\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"TextLoader\"},\"description\":\"Load text file.\",\"base_classes\":[\"Document\"],\"display_name\":\"TextLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/\",\"beta\":false,\"error\":null},\"UnstructuredEmailLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".eml\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"eml\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"UnstructuredEmailLoader\"},\"description\":\"Load email files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredEmailLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/email\",\"beta\":false,\"error\":null},\"UnstructuredHTMLLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".html\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"html\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"UnstructuredHTMLLoader\"},\"description\":\"Load `HTML` files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredHTMLLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/html\",\"beta\":false,\"error\":null},\"UnstructuredMarkdownLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".md\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"md\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"UnstructuredMarkdownLoader\"},\"description\":\"Load `Markdown` files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredMarkdownLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/how_to/markdown\",\"beta\":false,\"error\":null},\"UnstructuredPowerPointLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".pptx\",\".ppt\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"pptx\",\"ppt\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"UnstructuredPowerPointLoader\"},\"description\":\"Load `Microsoft PowerPoint` files using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredPowerPointLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/microsoft_powerpoint\",\"beta\":false,\"error\":null},\"UnstructuredWordDocumentLoader\":{\"template\":{\"file_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"suffixes\":[\".docx\",\".doc\"],\"password\":false,\"name\":\"file_path\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"file\",\"list\":false,\"fileTypes\":[\"docx\",\"doc\"],\"file_path\":null},\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"_type\":\"UnstructuredWordDocumentLoader\"},\"description\":\"Load `Microsoft Word` file using `Unstructured`.\",\"base_classes\":[\"Document\"],\"display_name\":\"UnstructuredWordDocumentLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/microsoft_word\",\"beta\":false,\"error\":null},\"WebBaseLoader\":{\"template\":{\"metadata\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{},\"password\":false,\"name\":\"metadata\",\"display_name\":\"Metadata\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"web_path\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"web_path\",\"display_name\":\"Web Page\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"WebBaseLoader\"},\"description\":\"Load HTML pages using `urllib` and parse them with `BeautifulSoup'.\",\"base_classes\":[\"Document\"],\"display_name\":\"WebBaseLoader\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/web_base\",\"beta\":false,\"error\":null}},\"textsplitters\":{\"CharacterTextSplitter\":{\"template\":{\"documents\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":true},\"chunk_overlap\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":200,\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"chunk_size\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1000,\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"separator\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"\\\\n\",\"password\":false,\"name\":\"separator\",\"display_name\":\"Separator\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"CharacterTextSplitter\"},\"description\":\"Splitting text that looks at characters.\",\"base_classes\":[\"Document\"],\"display_name\":\"CharacterTextSplitter\",\"custom_fields\":{},\"output_types\":[\"Document\"],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter\",\"beta\":false,\"error\":null},\"LanguageRecursiveTextSplitter\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.text_splitter import Language\\nfrom langchain.schema import Document\\n\\n\\nclass LanguageRecursiveTextSplitterComponent(CustomComponent):\\n display_name: str = \\\"Language Recursive Text Splitter\\\"\\n description: str = \\\"Split text into chunks of a specified length based on language.\\\"\\n documentation: str = \\\"https://docs.langflow.org/components/text-splitters#languagerecursivetextsplitter\\\"\\n\\n def build_config(self):\\n options = [x.value for x in Language]\\n return {\\n \\\"documents\\\": {\\n \\\"display_name\\\": \\\"Documents\\\",\\n \\\"info\\\": \\\"The documents to split.\\\",\\n },\\n \\\"separator_type\\\": {\\n \\\"display_name\\\": \\\"Separator Type\\\",\\n \\\"info\\\": \\\"The type of separator to use.\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"options\\\": options,\\n \\\"value\\\": \\\"Python\\\",\\n },\\n \\\"separators\\\": {\\n \\\"display_name\\\": \\\"Separators\\\",\\n \\\"info\\\": \\\"The characters to split on.\\\",\\n \\\"is_list\\\": True,\\n },\\n \\\"chunk_size\\\": {\\n \\\"display_name\\\": \\\"Chunk Size\\\",\\n \\\"info\\\": \\\"The maximum length of each chunk.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 1000,\\n },\\n \\\"chunk_overlap\\\": {\\n \\\"display_name\\\": \\\"Chunk Overlap\\\",\\n \\\"info\\\": \\\"The amount of overlap between chunks.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 200,\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n documents: list[Document],\\n chunk_size: Optional[int] = 1000,\\n chunk_overlap: Optional[int] = 200,\\n separator_type: Optional[str] = \\\"Python\\\",\\n ) -> list[Document]:\\n \\\"\\\"\\\"\\n Split text into chunks of a specified length.\\n\\n Args:\\n separators (list[str]): The characters to split on.\\n chunk_size (int): The maximum length of each chunk.\\n chunk_overlap (int): The amount of overlap between chunks.\\n length_function (function): The function to use to calculate the length of the text.\\n\\n Returns:\\n list[str]: The chunks of text.\\n \\\"\\\"\\\"\\n from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\\n # Make sure chunk_size and chunk_overlap are ints\\n if isinstance(chunk_size, str):\\n chunk_size = int(chunk_size)\\n if isinstance(chunk_overlap, str):\\n chunk_overlap = int(chunk_overlap)\\n\\n splitter = RecursiveCharacterTextSplitter.from_language(\\n language=Language(separator_type),\\n chunk_size=chunk_size,\\n chunk_overlap=chunk_overlap,\\n )\\n\\n docs = splitter.split_documents(documents)\\n return docs\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"chunk_overlap\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":200,\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"The amount of overlap between chunks.\",\"type\":\"int\",\"list\":false},\"chunk_size\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1000,\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum length of each chunk.\",\"type\":\"int\",\"list\":false},\"documents\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"The documents to split.\",\"type\":\"Document\",\"list\":true},\"separator_type\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"Python\",\"password\":false,\"options\":[\"cpp\",\"go\",\"java\",\"kotlin\",\"js\",\"ts\",\"php\",\"proto\",\"python\",\"rst\",\"ruby\",\"rust\",\"scala\",\"swift\",\"markdown\",\"latex\",\"html\",\"sol\",\"csharp\"],\"name\":\"separator_type\",\"display_name\":\"Separator Type\",\"advanced\":false,\"dynamic\":false,\"info\":\"The type of separator to use.\",\"type\":\"str\",\"list\":true}},\"description\":\"Split text into chunks of a specified length based on language.\",\"base_classes\":[\"Document\"],\"display_name\":\"Language Recursive Text Splitter\",\"custom_fields\":{\"chunk_overlap\":null,\"chunk_size\":null,\"documents\":null,\"separator_type\":null},\"output_types\":[\"LanguageRecursiveTextSplitter\"],\"documentation\":\"https://docs.langflow.org/components/text-splitters#languagerecursivetextsplitter\",\"beta\":true,\"error\":null},\"RecursiveCharacterTextSplitter\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.schema import Document\\nfrom langflow.utils.util import build_loader_repr_from_documents\\n\\n\\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\\n display_name: str = \\\"Recursive Character Text Splitter\\\"\\n description: str = \\\"Split text into chunks of a specified length.\\\"\\n documentation: str = \\\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\\\"\\n\\n def build_config(self):\\n return {\\n \\\"documents\\\": {\\n \\\"display_name\\\": \\\"Documents\\\",\\n \\\"info\\\": \\\"The documents to split.\\\",\\n },\\n \\\"separators\\\": {\\n \\\"display_name\\\": \\\"Separators\\\",\\n \\\"info\\\": 'The characters to split on.\\\\nIf left empty defaults to [\\\"\\\\\\\\n\\\\\\\\n\\\", \\\"\\\\\\\\n\\\", \\\" \\\", \\\"\\\"].',\\n \\\"is_list\\\": True,\\n },\\n \\\"chunk_size\\\": {\\n \\\"display_name\\\": \\\"Chunk Size\\\",\\n \\\"info\\\": \\\"The maximum length of each chunk.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 1000,\\n },\\n \\\"chunk_overlap\\\": {\\n \\\"display_name\\\": \\\"Chunk Overlap\\\",\\n \\\"info\\\": \\\"The amount of overlap between chunks.\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"value\\\": 200,\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self,\\n documents: list[Document],\\n separators: Optional[list[str]] = None,\\n chunk_size: Optional[int] = 1000,\\n chunk_overlap: Optional[int] = 200,\\n ) -> list[Document]:\\n \\\"\\\"\\\"\\n Split text into chunks of a specified length.\\n\\n Args:\\n separators (list[str]): The characters to split on.\\n chunk_size (int): The maximum length of each chunk.\\n chunk_overlap (int): The amount of overlap between chunks.\\n length_function (function): The function to use to calculate the length of the text.\\n\\n Returns:\\n list[str]: The chunks of text.\\n \\\"\\\"\\\"\\n from langchain.text_splitter import RecursiveCharacterTextSplitter\\n\\n if separators == \\\"\\\":\\n separators = None\\n elif separators:\\n # check if the separators list has escaped characters\\n # if there are escaped characters, unescape them\\n separators = [x.encode().decode(\\\"unicode-escape\\\") for x in separators]\\n\\n # Make sure chunk_size and chunk_overlap are ints\\n if isinstance(chunk_size, str):\\n chunk_size = int(chunk_size)\\n if isinstance(chunk_overlap, str):\\n chunk_overlap = int(chunk_overlap)\\n splitter = RecursiveCharacterTextSplitter(\\n separators=separators,\\n chunk_size=chunk_size,\\n chunk_overlap=chunk_overlap,\\n )\\n\\n docs = splitter.split_documents(documents)\\n self.repr_value = build_loader_repr_from_documents(docs)\\n return docs\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"chunk_overlap\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":200,\"password\":false,\"name\":\"chunk_overlap\",\"display_name\":\"Chunk Overlap\",\"advanced\":false,\"dynamic\":false,\"info\":\"The amount of overlap between chunks.\",\"type\":\"int\",\"list\":false},\"chunk_size\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":1000,\"password\":false,\"name\":\"chunk_size\",\"display_name\":\"Chunk Size\",\"advanced\":false,\"dynamic\":false,\"info\":\"The maximum length of each chunk.\",\"type\":\"int\",\"list\":false},\"documents\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"documents\",\"display_name\":\"Documents\",\"advanced\":false,\"dynamic\":false,\"info\":\"The documents to split.\",\"type\":\"Document\",\"list\":true},\"separators\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"separators\",\"display_name\":\"Separators\",\"advanced\":false,\"dynamic\":false,\"info\":\"The characters to split on.\\nIf left empty defaults to [\\\"\\\\n\\\\n\\\", \\\"\\\\n\\\", \\\" \\\", \\\"\\\"].\",\"type\":\"str\",\"list\":true}},\"description\":\"Split text into chunks of a specified length.\",\"base_classes\":[\"Document\"],\"display_name\":\"Recursive Character Text Splitter\",\"custom_fields\":{\"chunk_overlap\":null,\"chunk_size\":null,\"documents\":null,\"separators\":null},\"output_types\":[\"RecursiveCharacterTextSplitter\"],\"documentation\":\"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\",\"beta\":true,\"error\":null}},\"utilities\":{\"BingSearchAPIWrapper\":{\"template\":{\"bing_search_url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"bing_search_url\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"bing_subscription_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"bing_subscription_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"k\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"_type\":\"BingSearchAPIWrapper\"},\"description\":\"Wrapper for Bing Search API.\",\"base_classes\":[\"BingSearchAPIWrapper\"],\"display_name\":\"BingSearchAPIWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"GoogleSearchAPIWrapper\":{\"template\":{\"google_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"google_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"google_cse_id\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"google_cse_id\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"k\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"search_engine\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"search_engine\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"siterestrict\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"siterestrict\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"GoogleSearchAPIWrapper\"},\"description\":\"Wrapper for Google Search API.\",\"base_classes\":[\"GoogleSearchAPIWrapper\"],\"display_name\":\"GoogleSearchAPIWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"GoogleSerperAPIWrapper\":{\"template\":{\"aiosession\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"aiosession\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"ClientSession\",\"list\":false},\"gl\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"us\",\"password\":false,\"name\":\"gl\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"hl\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"en\",\"password\":false,\"name\":\"hl\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"k\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"result_key_for_type\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"{\\n \\\"news\\\": \\\"news\\\",\\n \\\"places\\\": \\\"places\\\",\\n \\\"images\\\": \\\"images\\\",\\n \\\"search\\\": \\\"organic\\\"\\n}\",\"password\":true,\"name\":\"result_key_for_type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"serper_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"serper_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"tbs\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"tbs\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"type\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"search\",\"password\":false,\"options\":[\"news\",\"search\",\"places\",\"images\"],\"name\":\"type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"_type\":\"GoogleSerperAPIWrapper\"},\"description\":\"Wrapper around the Serper.dev Google Search API.\",\"base_classes\":[\"GoogleSerperAPIWrapper\"],\"display_name\":\"GoogleSerperAPIWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"SearxSearchWrapper\":{\"template\":{\"aiosession\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"aiosession\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"categories\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"categories\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"engines\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"engines\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":true},\"headers\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"{\\\"Authorization\\\": \\\"Bearer \\\"}\",\"password\":false,\"name\":\"headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"k\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":10,\"password\":false,\"name\":\"k\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"params\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"params\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"query_suffix\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"query_suffix\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"searx_host\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"\",\"password\":false,\"name\":\"searx_host\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"unsecure\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"unsecure\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"_type\":\"SearxSearchWrapper\"},\"description\":\"Wrapper for Searx API.\",\"base_classes\":[\"SearxSearchWrapper\"],\"display_name\":\"SearxSearchWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"SerpAPIWrapper\":{\"template\":{\"aiosession\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"aiosession\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"ClientSession\",\"list\":false},\"params\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"{\\n \\\"engine\\\": \\\"google\\\",\\n \\\"google_domain\\\": \\\"google.com\\\",\\n \\\"gl\\\": \\\"us\\\",\\n \\\"hl\\\": \\\"en\\\"\\n}\",\"password\":false,\"name\":\"params\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false},\"search_engine\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"search_engine\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"serpapi_api_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"serpapi_api_key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"SerpAPIWrapper\"},\"description\":\"Wrapper around SerpAPI.\",\"base_classes\":[\"SerpAPIWrapper\"],\"display_name\":\"SerpAPIWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"WikipediaAPIWrapper\":{\"template\":{\"doc_content_chars_max\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":4000,\"password\":false,\"name\":\"doc_content_chars_max\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"lang\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":\"en\",\"password\":false,\"name\":\"lang\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"load_all_available_meta\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":false,\"password\":false,\"name\":\"load_all_available_meta\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"bool\",\"list\":false},\"top_k_results\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"value\":3,\"password\":false,\"name\":\"top_k_results\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"int\",\"list\":false},\"wiki_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"wiki_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"_type\":\"WikipediaAPIWrapper\"},\"description\":\"Wrapper around WikipediaAPI.\",\"base_classes\":[\"WikipediaAPIWrapper\"],\"display_name\":\"WikipediaAPIWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"WolframAlphaAPIWrapper\":{\"template\":{\"wolfram_alpha_appid\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"wolfram_alpha_appid\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"wolfram_client\":{\"required\":false,\"placeholder\":\"\",\"show\":false,\"multiline\":false,\"password\":false,\"name\":\"wolfram_client\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Any\",\"list\":false},\"_type\":\"WolframAlphaAPIWrapper\"},\"description\":\"Wrapper for Wolfram Alpha.\",\"base_classes\":[\"WolframAlphaAPIWrapper\"],\"display_name\":\"WolframAlphaAPIWrapper\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":false,\"error\":null},\"GetRequest\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.schema import Document\\nfrom langflow.services.database.models.base import orjson_dumps\\nimport requests\\nfrom typing import Optional\\n\\n\\nclass GetRequest(CustomComponent):\\n display_name: str = \\\"GET Request\\\"\\n description: str = \\\"Make a GET request to the given URL.\\\"\\n output_types: list[str] = [\\\"Document\\\"]\\n documentation: str = \\\"https://docs.langflow.org/components/utilities#get-request\\\"\\n beta = True\\n field_config = {\\n \\\"url\\\": {\\n \\\"display_name\\\": \\\"URL\\\",\\n \\\"info\\\": \\\"The URL to make the request to\\\",\\n \\\"is_list\\\": True,\\n },\\n \\\"headers\\\": {\\n \\\"display_name\\\": \\\"Headers\\\",\\n \\\"info\\\": \\\"The headers to send with the request.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"timeout\\\": {\\n \\\"display_name\\\": \\\"Timeout\\\",\\n \\\"field_type\\\": \\\"int\\\",\\n \\\"info\\\": \\\"The timeout to use for the request.\\\",\\n \\\"value\\\": 5,\\n },\\n }\\n\\n def get_document(\\n self, session: requests.Session, url: str, headers: Optional[dict], timeout: int\\n ) -> Document:\\n try:\\n response = session.get(url, headers=headers, timeout=int(timeout))\\n try:\\n response_json = response.json()\\n result = orjson_dumps(response_json, indent_2=False)\\n except Exception:\\n result = response.text\\n self.repr_value = result\\n return Document(\\n page_content=result,\\n metadata={\\n \\\"source\\\": url,\\n \\\"headers\\\": headers,\\n \\\"status_code\\\": response.status_code,\\n },\\n )\\n except requests.Timeout:\\n return Document(\\n page_content=\\\"Request Timed Out\\\",\\n metadata={\\\"source\\\": url, \\\"headers\\\": headers, \\\"status_code\\\": 408},\\n )\\n except Exception as exc:\\n return Document(\\n page_content=str(exc),\\n metadata={\\\"source\\\": url, \\\"headers\\\": headers, \\\"status_code\\\": 500},\\n )\\n\\n def build(\\n self,\\n url: str,\\n headers: Optional[dict] = None,\\n timeout: int = 5,\\n ) -> list[Document]:\\n if headers is None:\\n headers = {}\\n urls = url if isinstance(url, list) else [url]\\n with requests.Session() as session:\\n documents = [self.get_document(session, u, headers, timeout) for u in urls]\\n self.repr_value = documents\\n return documents\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"headers\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"type\":\"dict\",\"list\":false},\"timeout\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":5,\"password\":false,\"name\":\"timeout\",\"display_name\":\"Timeout\",\"advanced\":false,\"dynamic\":false,\"info\":\"The timeout to use for the request.\",\"type\":\"int\",\"list\":false},\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to\",\"type\":\"str\",\"list\":true}},\"description\":\"Make a GET request to the given URL.\",\"base_classes\":[\"Document\"],\"display_name\":\"GET Request\",\"custom_fields\":{\"headers\":null,\"timeout\":null,\"url\":null},\"output_types\":[\"GetRequest\"],\"documentation\":\"https://docs.langflow.org/components/utilities#get-request\",\"beta\":true,\"error\":null},\"PostRequest\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\nfrom langchain.schema import Document\\nfrom langflow.services.database.models.base import orjson_dumps\\nimport requests\\nfrom typing import Optional\\n\\n\\nclass PostRequest(CustomComponent):\\n display_name: str = \\\"POST Request\\\"\\n description: str = \\\"Make a POST request to the given URL.\\\"\\n output_types: list[str] = [\\\"Document\\\"]\\n documentation: str = \\\"https://docs.langflow.org/components/utilities#post-request\\\"\\n beta = True\\n field_config = {\\n \\\"url\\\": {\\\"display_name\\\": \\\"URL\\\", \\\"info\\\": \\\"The URL to make the request to.\\\"},\\n \\\"headers\\\": {\\n \\\"display_name\\\": \\\"Headers\\\",\\n \\\"info\\\": \\\"The headers to send with the request.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"document\\\": {\\\"display_name\\\": \\\"Document\\\"},\\n }\\n\\n def post_document(\\n self,\\n session: requests.Session,\\n document: Document,\\n url: str,\\n headers: Optional[dict] = None,\\n ) -> Document:\\n try:\\n response = session.post(url, headers=headers, data=document.page_content)\\n try:\\n response_json = response.json()\\n result = orjson_dumps(response_json, indent_2=False)\\n except Exception:\\n result = response.text\\n self.repr_value = result\\n return Document(\\n page_content=result,\\n metadata={\\n \\\"source\\\": url,\\n \\\"headers\\\": headers,\\n \\\"status_code\\\": response,\\n },\\n )\\n except Exception as exc:\\n return Document(\\n page_content=str(exc),\\n metadata={\\n \\\"source\\\": url,\\n \\\"headers\\\": headers,\\n \\\"status_code\\\": 500,\\n },\\n )\\n\\n def build(\\n self,\\n document: Document,\\n url: str,\\n headers: Optional[dict] = None,\\n ) -> list[Document]:\\n if headers is None:\\n headers = {}\\n\\n if not isinstance(document, list) and isinstance(document, Document):\\n documents: list[Document] = [document]\\n elif isinstance(document, list) and all(\\n isinstance(doc, Document) for doc in document\\n ):\\n documents = document\\n else:\\n raise ValueError(\\\"document must be a Document or a list of Documents\\\")\\n\\n with requests.Session() as session:\\n documents = [\\n self.post_document(session, doc, url, headers) for doc in documents\\n ]\\n self.repr_value = documents\\n return documents\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"document\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":false},\"headers\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"type\":\"dict\",\"list\":false},\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to.\",\"type\":\"str\",\"list\":false}},\"description\":\"Make a POST request to the given URL.\",\"base_classes\":[\"Document\"],\"display_name\":\"POST Request\",\"custom_fields\":{\"document\":null,\"headers\":null,\"url\":null},\"output_types\":[\"PostRequest\"],\"documentation\":\"https://docs.langflow.org/components/utilities#post-request\",\"beta\":true,\"error\":null},\"UpdateRequest\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import List, Optional\\nimport requests\\nfrom langflow import CustomComponent\\nfrom langchain.schema import Document\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass UpdateRequest(CustomComponent):\\n display_name: str = \\\"Update Request\\\"\\n description: str = \\\"Make a PATCH request to the given URL.\\\"\\n output_types: list[str] = [\\\"Document\\\"]\\n documentation: str = \\\"https://docs.langflow.org/components/utilities#update-request\\\"\\n beta = True\\n field_config = {\\n \\\"url\\\": {\\\"display_name\\\": \\\"URL\\\", \\\"info\\\": \\\"The URL to make the request to.\\\"},\\n \\\"headers\\\": {\\n \\\"display_name\\\": \\\"Headers\\\",\\n \\\"field_type\\\": \\\"NestedDict\\\",\\n \\\"info\\\": \\\"The headers to send with the request.\\\",\\n },\\n \\\"code\\\": {\\\"show\\\": False},\\n \\\"document\\\": {\\\"display_name\\\": \\\"Document\\\"},\\n \\\"method\\\": {\\n \\\"display_name\\\": \\\"Method\\\",\\n \\\"field_type\\\": \\\"str\\\",\\n \\\"info\\\": \\\"The HTTP method to use.\\\",\\n \\\"options\\\": [\\\"PATCH\\\", \\\"PUT\\\"],\\n \\\"value\\\": \\\"PATCH\\\",\\n },\\n }\\n\\n def update_document(\\n self,\\n session: requests.Session,\\n document: Document,\\n url: str,\\n headers: Optional[dict] = None,\\n method: str = \\\"PATCH\\\",\\n ) -> Document:\\n try:\\n if method == \\\"PATCH\\\":\\n response = session.patch(\\n url, headers=headers, data=document.page_content\\n )\\n elif method == \\\"PUT\\\":\\n response = session.put(url, headers=headers, data=document.page_content)\\n else:\\n raise ValueError(f\\\"Unsupported method: {method}\\\")\\n try:\\n response_json = response.json()\\n result = orjson_dumps(response_json, indent_2=False)\\n except Exception:\\n result = response.text\\n self.repr_value = result\\n return Document(\\n page_content=result,\\n metadata={\\n \\\"source\\\": url,\\n \\\"headers\\\": headers,\\n \\\"status_code\\\": response.status_code,\\n },\\n )\\n except Exception as exc:\\n return Document(\\n page_content=str(exc),\\n metadata={\\\"source\\\": url, \\\"headers\\\": headers, \\\"status_code\\\": 500},\\n )\\n\\n def build(\\n self,\\n method: str,\\n document: Document,\\n url: str,\\n headers: Optional[dict] = None,\\n ) -> List[Document]:\\n if headers is None:\\n headers = {}\\n\\n if not isinstance(document, list) and isinstance(document, Document):\\n documents: list[Document] = [document]\\n elif isinstance(document, list) and all(\\n isinstance(doc, Document) for doc in document\\n ):\\n documents = document\\n else:\\n raise ValueError(\\\"document must be a Document or a list of Documents\\\")\\n\\n with requests.Session() as session:\\n documents = [\\n self.update_document(session, doc, url, headers, method)\\n for doc in documents\\n ]\\n self.repr_value = documents\\n return documents\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"document\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":false},\"headers\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"headers\",\"display_name\":\"Headers\",\"advanced\":false,\"dynamic\":false,\"info\":\"The headers to send with the request.\",\"type\":\"NestedDict\",\"list\":false},\"method\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"PATCH\",\"password\":false,\"options\":[\"PATCH\",\"PUT\"],\"name\":\"method\",\"display_name\":\"Method\",\"advanced\":false,\"dynamic\":false,\"info\":\"The HTTP method to use.\",\"type\":\"str\",\"list\":true},\"url\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"url\",\"display_name\":\"URL\",\"advanced\":false,\"dynamic\":false,\"info\":\"The URL to make the request to.\",\"type\":\"str\",\"list\":false}},\"description\":\"Make a PATCH request to the given URL.\",\"base_classes\":[\"Document\"],\"display_name\":\"Update Request\",\"custom_fields\":{\"document\":null,\"headers\":null,\"method\":null,\"url\":null},\"output_types\":[\"UpdateRequest\"],\"documentation\":\"https://docs.langflow.org/components/utilities#update-request\",\"beta\":true,\"error\":null},\"JSONDocumentBuilder\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"### JSON Document Builder\\n\\n# Build a Document containing a JSON object using a key and another Document page content.\\n\\n# **Params**\\n\\n# - **Key:** The key to use for the JSON object.\\n# - **Document:** The Document page to use for the JSON object.\\n\\n# **Output**\\n\\n# - **Document:** The Document containing the JSON object.\\n\\nfrom langflow import CustomComponent\\nfrom langchain.schema import Document\\nfrom langflow.services.database.models.base import orjson_dumps\\n\\n\\nclass JSONDocumentBuilder(CustomComponent):\\n display_name: str = \\\"JSON Document Builder\\\"\\n description: str = \\\"Build a Document containing a JSON object using a key and another Document page content.\\\"\\n output_types: list[str] = [\\\"Document\\\"]\\n beta = True\\n documentation: str = (\\n \\\"https://docs.langflow.org/components/utilities#json-document-builder\\\"\\n )\\n\\n field_config = {\\n \\\"key\\\": {\\\"display_name\\\": \\\"Key\\\"},\\n \\\"document\\\": {\\\"display_name\\\": \\\"Document\\\"},\\n }\\n\\n def build(\\n self,\\n key: str,\\n document: Document,\\n ) -> Document:\\n documents = None\\n if isinstance(document, list):\\n documents = [\\n Document(\\n page_content=orjson_dumps({key: doc.page_content}, indent_2=False)\\n )\\n for doc in document\\n ]\\n elif isinstance(document, Document):\\n documents = Document(\\n page_content=orjson_dumps({key: document.page_content}, indent_2=False)\\n )\\n else:\\n raise TypeError(\\n f\\\"Expected Document or list of Documents, got {type(document)}\\\"\\n )\\n self.repr_value = documents\\n return documents\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"document\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"document\",\"display_name\":\"Document\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"Document\",\"list\":false},\"key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"key\",\"display_name\":\"Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false}},\"description\":\"Build a Document containing a JSON object using a key and another Document page content.\",\"base_classes\":[\"Document\"],\"display_name\":\"JSON Document Builder\",\"custom_fields\":{\"document\":null,\"key\":null},\"output_types\":[\"JSONDocumentBuilder\"],\"documentation\":\"https://docs.langflow.org/components/utilities#json-document-builder\",\"beta\":true,\"error\":null}},\"output_parsers\":{\"ResponseSchema\":{\"template\":{\"description\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"password\":false,\"name\":\"description\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"name\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"name\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"type\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"string\",\"password\":false,\"name\":\"type\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"ResponseSchema\"},\"description\":\"A schema for a response from a structured output parser.\",\"base_classes\":[\"ResponseSchema\"],\"display_name\":\"ResponseSchema\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/output_parsers/structured\",\"beta\":false,\"error\":null},\"StructuredOutputParser\":{\"template\":{\"response_schemas\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"response_schemas\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"ResponseSchema\",\"list\":true},\"_type\":\"StructuredOutputParser\"},\"description\":\"\",\"base_classes\":[\"BaseLLMOutputParser\",\"BaseOutputParser\",\"StructuredOutputParser\"],\"display_name\":\"StructuredOutputParser\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/model_io/output_parsers/structured\",\"beta\":false,\"error\":null}},\"retrievers\":{\"MultiQueryRetriever\":{\"template\":{\"llm\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"llm\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseLLM\",\"list\":false},\"prompt\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":{\"input_variables\":[\"question\"],\"input_types\":{},\"output_parser\":null,\"partial_variables\":{},\"template\":\"You are an AI language model assistant. Your task is \\n to generate 3 different versions of the given user \\n question to retrieve relevant documents from a vector database. \\n By generating multiple perspectives on the user question, \\n your goal is to help the user overcome some of the limitations \\n of distance-based similarity search. Provide these alternative \\n questions separated by newlines. Original question: {question}\",\"template_format\":\"f-string\",\"validate_template\":true,\"_type\":\"prompt\"},\"password\":false,\"name\":\"prompt\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"PromptTemplate\",\"list\":false},\"retriever\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"retriever\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"BaseRetriever\",\"list\":false},\"parser_key\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"value\":\"lines\",\"password\":false,\"name\":\"parser_key\",\"display_name\":\"Parser Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"_type\":\"MultiQueryRetriever\"},\"description\":\"Initialize from llm using default template.\",\"base_classes\":[\"MultiQueryRetriever\",\"BaseRetriever\"],\"display_name\":\"MultiQueryRetriever\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"https://python.langchain.com/docs/modules/data_connection/retrievers/how_to/MultiQueryRetriever\",\"beta\":false,\"error\":null},\"MetalRetriever\":{\"template\":{\"code\":{\"dynamic\":true,\"required\":true,\"placeholder\":\"\",\"show\":false,\"multiline\":true,\"value\":\"from typing import Optional\\nfrom langflow import CustomComponent\\nfrom langchain.retrievers import MetalRetriever\\nfrom langchain.schema import BaseRetriever\\nfrom metal_sdk.metal import Metal # type: ignore\\n\\n\\nclass MetalRetrieverComponent(CustomComponent):\\n display_name: str = \\\"Metal Retriever\\\"\\n description: str = \\\"Retriever that uses the Metal API.\\\"\\n\\n def build_config(self):\\n return {\\n \\\"api_key\\\": {\\\"display_name\\\": \\\"API Key\\\", \\\"password\\\": True},\\n \\\"client_id\\\": {\\\"display_name\\\": \\\"Client ID\\\", \\\"password\\\": True},\\n \\\"index_id\\\": {\\\"display_name\\\": \\\"Index ID\\\"},\\n \\\"params\\\": {\\\"display_name\\\": \\\"Parameters\\\"},\\n \\\"code\\\": {\\\"show\\\": False},\\n }\\n\\n def build(\\n self, api_key: str, client_id: str, index_id: str, params: Optional[dict] = None\\n ) -> BaseRetriever:\\n try:\\n metal = Metal(api_key=api_key, client_id=client_id, index_id=index_id)\\n except Exception as e:\\n raise ValueError(\\\"Could not connect to Metal API.\\\") from e\\n return MetalRetriever(client=metal, params=params or {})\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\",\"api_key\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"api_key\",\"display_name\":\"API Key\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"client_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":true,\"name\":\"client_id\",\"display_name\":\"Client ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"index_id\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"index_id\",\"display_name\":\"Index ID\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"str\",\"list\":false},\"params\":{\"required\":false,\"placeholder\":\"\",\"show\":true,\"multiline\":false,\"password\":false,\"name\":\"params\",\"display_name\":\"Parameters\",\"advanced\":false,\"dynamic\":false,\"info\":\"\",\"type\":\"dict\",\"list\":false}},\"description\":\"Retriever that uses the Metal API.\",\"base_classes\":[\"BaseRetriever\"],\"display_name\":\"Metal Retriever\",\"custom_fields\":{\"api_key\":null,\"client_id\":null,\"index_id\":null,\"params\":null},\"output_types\":[\"MetalRetriever\"],\"documentation\":\"\",\"beta\":true,\"error\":null}},\"custom_components\":{\"CustomComponent\":{\"template\":{\"code\":{\"required\":true,\"placeholder\":\"\",\"show\":true,\"multiline\":true,\"value\":\"from langflow import CustomComponent\\n\\nfrom langflow.field_typing import (\\n Tool,\\n PromptTemplate,\\n Chain,\\n BaseChatMemory,\\n BaseLLM,\\n BaseLoader,\\n BaseMemory,\\n BaseOutputParser,\\n BaseRetriever,\\n VectorStore,\\n Embeddings,\\n TextSplitter,\\n Document,\\n AgentExecutor,\\n NestedDict,\\n Data,\\n)\\n\\n\\nclass Component(CustomComponent):\\n display_name: str = \\\"Custom Component\\\"\\n description: str = \\\"Create any custom component you want!\\\"\\n\\n def build_config(self):\\n return {\\\"param\\\": {\\\"display_name\\\": \\\"Parameter\\\"}}\\n\\n def build(self, param: Data) -> Data:\\n return param\\n\\n\",\"password\":false,\"name\":\"code\",\"advanced\":false,\"dynamic\":true,\"info\":\"\",\"type\":\"code\",\"list\":false},\"_type\":\"CustomComponent\"},\"description\":null,\"base_classes\":[],\"display_name\":\"Custom Component\",\"custom_fields\":{},\"output_types\":[],\"documentation\":\"\",\"beta\":true,\"error\":null}}}"
+ },
+ "headersSize": -1,
+ "bodySize": -1,
+ "redirectURL": ""
+ },
+ "cache": {},
+ "timings": { "send": -1, "wait": -1, "receive": 1.031 }
+ },
+ {
+ "startedDateTime": "2023-08-31T14:55:36.131Z",
+ "time": 0.743,
+ "request": {
+ "method": "GET",
+ "url": "http://localhost:3000/api/v1/flows/",
+ "httpVersion": "HTTP/1.1",
+ "cookies": [],
+ "headers": [
+ { "name": "Accept", "value": "application/json, text/plain, */*" },
+ { "name": "Accept-Encoding", "value": "gzip, deflate, br" },
+ { "name": "Accept-Language", "value": "en-US,en;q=0.9" },
+ { "name": "Authorization", "value": "Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJiNWIxYjFhZC05M2VjLTRjMjgtYWMxNy01OGMxNDQ0MWI1ZGQiLCJleHAiOjE3MjUwMjk3MzV9.8qhEtryWqA9RbwEP2s20umKdVE7J7jHGoogXZqxLNWk" },
+ { "name": "Connection", "value": "keep-alive" },
+ { "name": "Cookie", "value": "access_token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJiNWIxYjFhZC05M2VjLTRjMjgtYWMxNy01OGMxNDQ0MWI1ZGQiLCJleHAiOjE3MjUwMjk3MzV9.8qhEtryWqA9RbwEP2s20umKdVE7J7jHGoogXZqxLNWk; refresh_token=auto" },
+ { "name": "Host", "value": "localhost:3000" },
+ { "name": "Referer", "value": "http://localhost:3000/" },
+ { "name": "Sec-Fetch-Dest", "value": "empty" },
+ { "name": "Sec-Fetch-Mode", "value": "cors" },
+ { "name": "Sec-Fetch-Site", "value": "same-origin" },
+ { "name": "User-Agent", "value": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36" },
+ { "name": "sec-ch-ua", "value": "\"Not)A;Brand\";v=\"24\", \"Chromium\";v=\"116\"" },
+ { "name": "sec-ch-ua-mobile", "value": "?0" },
+ { "name": "sec-ch-ua-platform", "value": "\"Linux\"" }
+ ],
+ "queryString": [],
+ "headersSize": -1,
+ "bodySize": -1
+ },
+ "response": {
+ "status": 200,
+ "statusText": "OK",
+ "httpVersion": "HTTP/1.1",
+ "cookies": [],
+ "headers": [
+ { "name": "Access-Control-Allow-Origin", "value": "*" },
+ { "name": "connection", "value": "close" },
+ { "name": "content-length", "value": "2" },
+ { "name": "content-type", "value": "application/json" },
+ { "name": "date", "value": "Thu, 31 Aug 2023 14:55:35 GMT" },
+ { "name": "server", "value": "uvicorn" }
+ ],
+ "content": {
+ "size": -1,
+ "mimeType": "application/json",
+ "text": "[]"
+ },
+ "headersSize": -1,
+ "bodySize": -1,
+ "redirectURL": ""
+ },
+ "cache": {},
+ "timings": { "send": -1, "wait": -1, "receive": 0.743 }
+ }
+ ]
+ }
+ }
\ No newline at end of file
diff --git a/src/frontend/nginx.conf b/src/frontend/nginx.conf
index df665f5b6..cabf31c7a 100644
--- a/src/frontend/nginx.conf
+++ b/src/frontend/nginx.conf
@@ -1,18 +1,20 @@
server {
- gzip on;
- gzip_comp_level 2;
- gzip_min_length 1000;
- gzip_types text/xml text/css;
- gzip_http_version 1.1;
- gzip_vary on;
- gzip_disable "MSIE [4-6] \.";
+ gzip on;
+ gzip_comp_level 2;
+ gzip_min_length 1000;
+ gzip_types text/xml text/css;
+ gzip_http_version 1.1;
+ gzip_vary on;
+ gzip_disable "MSIE [4-6] \.";
- listen 80;
+ listen 80;
- location / {
- root /usr/share/nginx/html;
- index index.html index.htm;
- try_files $uri $uri/ /index.html =404;
- }
-
+ location / {
+ root /usr/share/nginx/html;
+ index index.html index.htm;
+ try_files $uri $uri/ /index.html =404;
+ }
+
+
+ include /etc/nginx/extra-conf.d/*.conf;
}
diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json
index 0923fc54c..0bf66bdca 100644
--- a/src/frontend/package-lock.json
+++ b/src/frontend/package-lock.json
@@ -1,97 +1,102 @@
{
"name": "langflow",
"version": "0.1.2",
- "lockfileVersion": 3,
+ "lockfileVersion": 2,
"requires": true,
"packages": {
"": {
"name": "langflow",
"version": "0.1.2",
"dependencies": {
- "@emotion/react": "^11.10.5",
- "@emotion/styled": "^11.10.5",
- "@headlessui/react": "^1.7.10",
- "@heroicons/react": "^2.0.15",
- "@mui/material": "^5.11.9",
+ "@emotion/react": "^11.11.1",
+ "@emotion/styled": "^11.11.0",
+ "@headlessui/react": "^1.7.17",
+ "@heroicons/react": "^2.0.18",
+ "@mui/material": "^5.14.7",
"@radix-ui/react-accordion": "^1.1.2",
"@radix-ui/react-checkbox": "^1.0.4",
"@radix-ui/react-dialog": "^1.0.4",
"@radix-ui/react-dropdown-menu": "^2.0.5",
+ "@radix-ui/react-form": "^0.0.3",
"@radix-ui/react-icons": "^1.3.0",
"@radix-ui/react-label": "^2.0.2",
"@radix-ui/react-menubar": "^1.0.3",
"@radix-ui/react-popover": "^1.0.6",
"@radix-ui/react-progress": "^1.0.3",
+ "@radix-ui/react-select": "^1.2.2",
"@radix-ui/react-separator": "^1.0.3",
"@radix-ui/react-slot": "^1.0.2",
"@radix-ui/react-switch": "^1.0.3",
"@radix-ui/react-tabs": "^1.0.4",
"@radix-ui/react-tooltip": "^1.0.6",
- "@tabler/icons-react": "^2.18.0",
- "@tailwindcss/forms": "^0.5.3",
+ "@tabler/icons-react": "^2.32.0",
+ "@tailwindcss/forms": "^0.5.6",
"@tailwindcss/line-clamp": "^0.4.4",
"@types/axios": "^0.14.0",
"accordion": "^3.0.2",
- "ace-builds": "^1.16.0",
+ "ace-builds": "^1.24.1",
"add": "^2.0.6",
"ansi-to-html": "^0.7.2",
- "axios": "^1.3.2",
+ "axios": "^1.5.0",
"base64-js": "^1.5.1",
- "class-variance-authority": "^0.6.0",
+ "class-variance-authority": "^0.6.1",
"clsx": "^1.2.1",
- "dompurify": "^3.0.4",
- "esbuild": "^0.17.18",
+ "dompurify": "^3.0.5",
+ "esbuild": "^0.17.19",
"lodash": "^4.17.21",
"lucide-react": "^0.233.0",
+ "moment": "^2.29.4",
"react": "^18.2.0",
"react-ace": "^10.1.0",
"react-cookie": "^4.1.1",
"react-dom": "^18.2.0",
- "react-error-boundary": "^4.0.2",
- "react-icons": "^4.8.0",
+ "react-error-boundary": "^4.0.11",
+ "react-icons": "^4.10.1",
"react-laag": "^2.0.5",
"react-markdown": "^8.0.7",
- "react-router-dom": "^6.8.1",
+ "react-router-dom": "^6.15.0",
"react-syntax-highlighter": "^15.5.0",
- "react-tabs": "^6.0.0",
- "react-tooltip": "^5.13.1",
- "reactflow": "^11.5.5",
- "rehype-mathjax": "^4.0.2",
+ "react-tabs": "^6.0.2",
+ "react-tooltip": "^5.21.1",
+ "react18-json-view": "^0.2.3",
+ "reactflow": "^11.9.2",
+ "rehype-mathjax": "^4.0.3",
"remark-gfm": "^3.0.1",
"remark-math": "^5.1.1",
- "shadcn-ui": "^0.2.2",
+ "shadcn-ui": "^0.2.3",
"short-unique-id": "^4.4.4",
"switch": "^0.0.0",
"table": "^6.8.1",
- "tailwind-merge": "^1.13.0",
- "tailwindcss-animate": "^1.0.5",
+ "tailwind-merge": "^1.14.0",
+ "tailwindcss-animate": "^1.0.7",
"uuid": "^9.0.0",
"vite-plugin-svgr": "^3.2.0",
"web-vitals": "^2.1.4"
},
"devDependencies": {
+ "@playwright/test": "^1.38.0",
"@swc/cli": "^0.1.62",
- "@swc/core": "^1.3.62",
+ "@swc/core": "^1.3.80",
"@tailwindcss/typography": "^0.5.9",
- "@testing-library/jest-dom": "^5.16.5",
+ "@testing-library/jest-dom": "^5.17.0",
"@testing-library/react": "^13.4.0",
"@testing-library/user-event": "^13.5.0",
"@types/jest": "^27.5.2",
- "@types/lodash": "^4.14.194",
- "@types/node": "^16.18.12",
- "@types/react": "^18.0.28",
- "@types/react-dom": "^18.0.11",
- "@types/uuid": "^9.0.1",
- "@vitejs/plugin-react-swc": "^3.0.0",
- "autoprefixer": "^10.4.14",
- "daisyui": "^3.1.1",
- "postcss": "^8.4.23",
+ "@types/lodash": "^4.14.197",
+ "@types/node": "^16.18.46",
+ "@types/react": "^18.2.21",
+ "@types/react-dom": "^18.2.7",
+ "@types/uuid": "^9.0.2",
+ "@vitejs/plugin-react-swc": "^3.3.2",
+ "autoprefixer": "^10.4.15",
+ "daisyui": "^3.6.3",
+ "postcss": "^8.4.29",
"prettier": "^2.8.8",
- "prettier-plugin-organize-imports": "^3.2.2",
+ "prettier-plugin-organize-imports": "^3.2.3",
"prettier-plugin-tailwindcss": "^0.3.0",
- "tailwindcss": "^3.3.2",
- "typescript": "^5.0.2",
- "vite": "^4.3.9"
+ "tailwindcss": "^3.3.3",
+ "typescript": "^5.2.2",
+ "vite": "^4.4.9"
}
},
"node_modules/@adobe/css-tools": {
@@ -214,29 +219,29 @@
}
},
"node_modules/@babel/compat-data": {
- "version": "7.22.9",
- "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.22.9.tgz",
- "integrity": "sha512-5UamI7xkUcJ3i9qVDS+KFDEK8/7oJ55/sJMB1Ge7IEapr7KfdfV/HErR+koZwOfd+SgtFKOKRhRakdg++DcJpQ==",
+ "version": "7.23.2",
+ "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.23.2.tgz",
+ "integrity": "sha512-0S9TQMmDHlqAZ2ITT95irXKfxN9bncq8ZCoJhun3nHL/lLUxd2NKBJYoNGWH7S0hz6fRQwWlAWn/ILM0C70KZQ==",
"engines": {
"node": ">=6.9.0"
}
},
"node_modules/@babel/core": {
- "version": "7.22.17",
- "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.22.17.tgz",
- "integrity": "sha512-2EENLmhpwplDux5PSsZnSbnSkB3tZ6QTksgO25xwEL7pIDcNOMhF5v/s6RzwjMZzZzw9Ofc30gHv5ChCC8pifQ==",
+ "version": "7.23.2",
+ "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.23.2.tgz",
+ "integrity": "sha512-n7s51eWdaWZ3vGT2tD4T7J6eJs3QoBXydv7vkUM06Bf1cbVD2Kc2UrkzhiQwobfV7NwOnQXYL7UBJ5VPU+RGoQ==",
"dependencies": {
"@ampproject/remapping": "^2.2.0",
"@babel/code-frame": "^7.22.13",
- "@babel/generator": "^7.22.15",
+ "@babel/generator": "^7.23.0",
"@babel/helper-compilation-targets": "^7.22.15",
- "@babel/helper-module-transforms": "^7.22.17",
- "@babel/helpers": "^7.22.15",
- "@babel/parser": "^7.22.16",
+ "@babel/helper-module-transforms": "^7.23.0",
+ "@babel/helpers": "^7.23.2",
+ "@babel/parser": "^7.23.0",
"@babel/template": "^7.22.15",
- "@babel/traverse": "^7.22.17",
- "@babel/types": "^7.22.17",
- "convert-source-map": "^1.7.0",
+ "@babel/traverse": "^7.23.2",
+ "@babel/types": "^7.23.0",
+ "convert-source-map": "^2.0.0",
"debug": "^4.1.0",
"gensync": "^1.0.0-beta.2",
"json5": "^2.2.3",
@@ -250,6 +255,11 @@
"url": "https://opencollective.com/babel"
}
},
+ "node_modules/@babel/core/node_modules/convert-source-map": {
+ "version": "2.0.0",
+ "resolved": "https://registry.npmjs.org/convert-source-map/-/convert-source-map-2.0.0.tgz",
+ "integrity": "sha512-Kvp459HrV2FEJ1CAsi1Ku+MY3kasH19TFykTz2xWmMeq6bk2NU3XXvfJ+Q61m0xktWwt+1HSYf3JZsTms3aRJg=="
+ },
"node_modules/@babel/core/node_modules/semver": {
"version": "6.3.1",
"resolved": "https://registry.npmjs.org/semver/-/semver-6.3.1.tgz",
@@ -259,11 +269,11 @@
}
},
"node_modules/@babel/generator": {
- "version": "7.22.15",
- "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.22.15.tgz",
- "integrity": "sha512-Zu9oWARBqeVOW0dZOjXc3JObrzuqothQ3y/n1kUtrjCoCPLkXUwMvOo/F/TCfoHMbWIFlWwpZtkZVb9ga4U2pA==",
+ "version": "7.23.0",
+ "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.23.0.tgz",
+ "integrity": "sha512-lN85QRR+5IbYrMWM6Y4pE/noaQtg4pNiqeNGX60eqOfo6gtEj6uw/JagelB8vVztSd7R6M5n1+PQkDbHbBRU4g==",
"dependencies": {
- "@babel/types": "^7.22.15",
+ "@babel/types": "^7.23.0",
"@jridgewell/gen-mapping": "^0.3.2",
"@jridgewell/trace-mapping": "^0.3.17",
"jsesc": "^2.5.1"
@@ -309,20 +319,20 @@
"integrity": "sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g=="
},
"node_modules/@babel/helper-environment-visitor": {
- "version": "7.22.5",
- "resolved": "https://registry.npmjs.org/@babel/helper-environment-visitor/-/helper-environment-visitor-7.22.5.tgz",
- "integrity": "sha512-XGmhECfVA/5sAt+H+xpSg0mfrHq6FzNr9Oxh7PSEBBRUb/mL7Kz3NICXb194rCqAEdxkhPT1a88teizAFyvk8Q==",
+ "version": "7.22.20",
+ "resolved": "https://registry.npmjs.org/@babel/helper-environment-visitor/-/helper-environment-visitor-7.22.20.tgz",
+ "integrity": "sha512-zfedSIzFhat/gFhWfHtgWvlec0nqB9YEIVrpuwjruLlXfUSnA8cJB0miHKwqDnQ7d32aKo2xt88/xZptwxbfhA==",
"engines": {
"node": ">=6.9.0"
}
},
"node_modules/@babel/helper-function-name": {
- "version": "7.22.5",
- "resolved": "https://registry.npmjs.org/@babel/helper-function-name/-/helper-function-name-7.22.5.tgz",
- "integrity": "sha512-wtHSq6jMRE3uF2otvfuD3DIvVhOsSNshQl0Qrd7qC9oQJzHvOL4qQXlQn2916+CXGywIjpGuIkoyZRRxHPiNQQ==",
+ "version": "7.23.0",
+ "resolved": "https://registry.npmjs.org/@babel/helper-function-name/-/helper-function-name-7.23.0.tgz",
+ "integrity": "sha512-OErEqsrxjZTJciZ4Oo+eoZqeW9UIiOcuYKRJA4ZAgV9myA+pOXhhmpfNCKjEH/auVfEYVFJ6y1Tc4r0eIApqiw==",
"dependencies": {
- "@babel/template": "^7.22.5",
- "@babel/types": "^7.22.5"
+ "@babel/template": "^7.22.15",
+ "@babel/types": "^7.23.0"
},
"engines": {
"node": ">=6.9.0"
@@ -351,15 +361,15 @@
}
},
"node_modules/@babel/helper-module-transforms": {
- "version": "7.22.17",
- "resolved": "https://registry.npmjs.org/@babel/helper-module-transforms/-/helper-module-transforms-7.22.17.tgz",
- "integrity": "sha512-XouDDhQESrLHTpnBtCKExJdyY4gJCdrvH2Pyv8r8kovX2U8G0dRUOT45T9XlbLtuu9CLXP15eusnkprhoPV5iQ==",
+ "version": "7.23.0",
+ "resolved": "https://registry.npmjs.org/@babel/helper-module-transforms/-/helper-module-transforms-7.23.0.tgz",
+ "integrity": "sha512-WhDWw1tdrlT0gMgUJSlX0IQvoO1eN279zrAUbVB+KpV2c3Tylz8+GnKOLllCS6Z/iZQEyVYxhZVUdPTqs2YYPw==",
"dependencies": {
- "@babel/helper-environment-visitor": "^7.22.5",
+ "@babel/helper-environment-visitor": "^7.22.20",
"@babel/helper-module-imports": "^7.22.15",
"@babel/helper-simple-access": "^7.22.5",
"@babel/helper-split-export-declaration": "^7.22.6",
- "@babel/helper-validator-identifier": "^7.22.15"
+ "@babel/helper-validator-identifier": "^7.22.20"
},
"engines": {
"node": ">=6.9.0"
@@ -399,9 +409,9 @@
}
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@@ -1281,15 +1291,14 @@
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@@ -1360,11 +1369,11 @@
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@@ -1373,11 +1382,12 @@
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@@ -1390,8 +1400,7 @@
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@@ -1431,6 +1440,21 @@
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@@ -1440,6 +1464,14 @@
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@@ -1623,19 +1655,19 @@
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@@ -2459,11 +2669,11 @@
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@@ -2702,9 +2913,9 @@
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@@ -2744,6 +2955,9 @@
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"url": "https://github.com/sponsors/gregberge"
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@@ -2787,13 +3001,14 @@
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},
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@@ -2803,16 +3018,16 @@
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+ "resolved": "https://registry.npmjs.org/zustand/-/zustand-4.4.4.tgz",
+ "integrity": "sha512-5UTUIAiHMNf5+mFp7/AnzJXS7+XxktULFN0+D1sCiZWyX7ZG+AQpqs2qpYrynRij4QvoDdCD+U+bmg/cG3Ucxw==",
+ "requires": {
+ "use-sync-external-store": "1.2.0"
+ }
+ },
+ "zwitch": {
+ "version": "2.0.4",
+ "resolved": "https://registry.npmjs.org/zwitch/-/zwitch-2.0.4.tgz",
+ "integrity": "sha512-bXE4cR/kVZhKZX/RjPEflHaKVhUVl85noU3v6b8apfQEc1x4A+zBxjZ4lN8LqGd6WZ3dl98pY4o717VFmoPp+A=="
+ }
}
}
diff --git a/src/frontend/package.json b/src/frontend/package.json
index e3814c1ad..0be0c9260 100644
--- a/src/frontend/package.json
+++ b/src/frontend/package.json
@@ -3,63 +3,67 @@
"version": "0.1.2",
"private": true,
"dependencies": {
- "@emotion/react": "^11.10.5",
- "@emotion/styled": "^11.10.5",
- "@headlessui/react": "^1.7.10",
- "@heroicons/react": "^2.0.15",
- "@mui/material": "^5.11.9",
+ "@emotion/react": "^11.11.1",
+ "@emotion/styled": "^11.11.0",
+ "@headlessui/react": "^1.7.17",
+ "@heroicons/react": "^2.0.18",
+ "@mui/material": "^5.14.7",
"@radix-ui/react-accordion": "^1.1.2",
"@radix-ui/react-checkbox": "^1.0.4",
"@radix-ui/react-dialog": "^1.0.4",
"@radix-ui/react-dropdown-menu": "^2.0.5",
+ "@radix-ui/react-form": "^0.0.3",
"@radix-ui/react-icons": "^1.3.0",
"@radix-ui/react-label": "^2.0.2",
"@radix-ui/react-menubar": "^1.0.3",
"@radix-ui/react-popover": "^1.0.6",
"@radix-ui/react-progress": "^1.0.3",
+ "@radix-ui/react-select": "^1.2.2",
"@radix-ui/react-separator": "^1.0.3",
"@radix-ui/react-slot": "^1.0.2",
"@radix-ui/react-switch": "^1.0.3",
"@radix-ui/react-tabs": "^1.0.4",
"@radix-ui/react-tooltip": "^1.0.6",
- "@tabler/icons-react": "^2.18.0",
- "@tailwindcss/forms": "^0.5.3",
+ "@tabler/icons-react": "^2.32.0",
+ "@tailwindcss/forms": "^0.5.6",
"@tailwindcss/line-clamp": "^0.4.4",
"@types/axios": "^0.14.0",
"accordion": "^3.0.2",
- "ace-builds": "^1.16.0",
+ "ace-builds": "^1.24.1",
"add": "^2.0.6",
"ansi-to-html": "^0.7.2",
- "axios": "^1.3.2",
+ "axios": "^1.5.0",
"base64-js": "^1.5.1",
- "class-variance-authority": "^0.6.0",
+ "class-variance-authority": "^0.6.1",
"clsx": "^1.2.1",
- "dompurify": "^3.0.4",
- "esbuild": "^0.17.18",
+ "dompurify": "^3.0.5",
+ "esbuild": "^0.17.19",
"lodash": "^4.17.21",
"lucide-react": "^0.233.0",
+ "moment": "^2.29.4",
"react": "^18.2.0",
"react-ace": "^10.1.0",
"react-cookie": "^4.1.1",
"react-dom": "^18.2.0",
- "react-error-boundary": "^4.0.2",
- "react-icons": "^4.8.0",
+ "react-error-boundary": "^4.0.11",
+ "react-icons": "^4.10.1",
"react-laag": "^2.0.5",
"react-markdown": "^8.0.7",
- "react-router-dom": "^6.8.1",
+ "react-router-dom": "^6.15.0",
"react-syntax-highlighter": "^15.5.0",
- "react-tabs": "^6.0.0",
- "react-tooltip": "^5.13.1",
- "reactflow": "^11.5.5",
- "rehype-mathjax": "^4.0.2",
+ "react-tabs": "^6.0.2",
+ "react-tooltip": "^5.21.1",
+ "react18-json-view": "^0.2.3",
+ "reactflow": "^11.9.2",
+ "rehype-mathjax": "^4.0.3",
"remark-gfm": "^3.0.1",
"remark-math": "^5.1.1",
- "shadcn-ui": "^0.2.2",
+ "shadcn-ui": "^0.2.3",
"short-unique-id": "^4.4.4",
"switch": "^0.0.0",
"table": "^6.8.1",
- "tailwind-merge": "^1.13.0",
- "tailwindcss-animate": "^1.0.5",
+ "tailwind-merge": "^1.14.0",
+ "tailwindcss-animate": "^1.0.7",
"uuid": "^9.0.0",
"vite-plugin-svgr": "^3.2.0",
"web-vitals": "^2.1.4"
@@ -69,7 +73,8 @@
"start": "vite",
"build": "vite build",
"serve": "vite preview",
- "format": "npx prettier --write \"**/*.{js,jsx,ts,tsx,json,md}\""
+ "format": "npx prettier --write \"./**/*.{js,jsx,ts,tsx,json,md}\"",
+ "type-check": "tsc --noEmit --pretty --project tsconfig.json && vite"
},
"eslintConfig": {
"extends": [
@@ -91,27 +96,28 @@
},
"proxy": "http://127.0.0.1:7860",
"devDependencies": {
+ "@playwright/test": "^1.38.0",
"@swc/cli": "^0.1.62",
- "@swc/core": "^1.3.62",
+ "@swc/core": "^1.3.80",
"@tailwindcss/typography": "^0.5.9",
- "@testing-library/jest-dom": "^5.16.5",
+ "@testing-library/jest-dom": "^5.17.0",
"@testing-library/react": "^13.4.0",
"@testing-library/user-event": "^13.5.0",
"@types/jest": "^27.5.2",
- "@types/lodash": "^4.14.194",
- "@types/node": "^16.18.12",
- "@types/react": "^18.0.28",
- "@types/react-dom": "^18.0.11",
- "@types/uuid": "^9.0.1",
- "@vitejs/plugin-react-swc": "^3.0.0",
- "autoprefixer": "^10.4.14",
- "daisyui": "^3.1.1",
- "postcss": "^8.4.23",
+ "@types/lodash": "^4.14.197",
+ "@types/node": "^16.18.46",
+ "@types/react": "^18.2.21",
+ "@types/react-dom": "^18.2.7",
+ "@types/uuid": "^9.0.2",
+ "@vitejs/plugin-react-swc": "^3.3.2",
+ "autoprefixer": "^10.4.15",
+ "daisyui": "^3.6.3",
+ "postcss": "^8.4.29",
"prettier": "^2.8.8",
- "prettier-plugin-organize-imports": "^3.2.2",
+ "prettier-plugin-organize-imports": "^3.2.3",
"prettier-plugin-tailwindcss": "^0.3.0",
- "tailwindcss": "^3.3.2",
- "typescript": "^5.0.2",
- "vite": "^4.3.9"
+ "tailwindcss": "^3.3.3",
+ "typescript": "^5.2.2",
+ "vite": "^4.4.9"
}
}
diff --git a/src/frontend/playwright-report/index.html b/src/frontend/playwright-report/index.html
new file mode 100644
index 000000000..d65e584e4
--- /dev/null
+++ b/src/frontend/playwright-report/index.html
@@ -0,0 +1,18 @@
+
+
+
+ Document
+
+
+
+
+
\ No newline at end of file
diff --git a/src/frontend/playwright.config.ts b/src/frontend/playwright.config.ts
new file mode 100644
index 000000000..8f7490b9e
--- /dev/null
+++ b/src/frontend/playwright.config.ts
@@ -0,0 +1,86 @@
+import { defineConfig, devices } from "@playwright/test";
+
+/**
+ * Read environment variables from file.
+ * https://github.com/motdotla/dotenv
+ */
+// require('dotenv').config();
+
+/**
+ * See https://playwright.dev/docs/test-configuration.
+ */
+export default defineConfig({
+ testDir: "./tests",
+ /* Run tests in files in parallel */
+ fullyParallel: true,
+ /* Fail the build on CI if you accidentally left test.only in the source code. */
+ forbidOnly: !!process.env.CI,
+ /* Retry on CI only */
+ retries: process.env.CI ? 2 : 0,
+ /* Opt out of parallel tests on CI. */
+ workers: process.env.CI ? 1 : undefined,
+ /* Reporter to use. See https://playwright.dev/docs/test-reporters */
+ reporter: [
+ ["html", { open: "never", outputFolder: "playwright-report/test-results" }],
+ ],
+ /* Shared settings for all the projects below. See https://playwright.dev/docs/api/class-testoptions. */
+ use: {
+ /* Base URL to use in actions like `await page.goto('/')`. */
+ // baseURL: "http://127.0.0.1:3000",
+
+ /* Collect trace when retrying the failed test. See https://playwright.dev/docs/trace-viewer */
+ trace: "on-first-retry",
+ },
+
+ /* Configure projects for major browsers */
+ projects: [
+ {
+ name: "chromium",
+ use: { ...devices["Desktop Chrome"] },
+ },
+
+ {
+ name: "firefox",
+ use: { ...devices["Desktop Firefox"] },
+ },
+
+ {
+ name: "webkit",
+ use: { ...devices["Desktop Safari"] },
+ },
+
+ /* Test against mobile viewports. */
+ // {
+ // name: 'Mobile Chrome',
+ // use: { ...devices['Pixel 5'] },
+ // },
+ // {
+ // name: 'Mobile Safari',
+ // use: { ...devices['iPhone 12'] },
+ // },
+
+ /* Test against branded browsers. */
+ // {
+ // name: 'Microsoft Edge',
+ // use: { ...devices['Desktop Edge'], channel: 'msedge' },
+ // },
+ // {
+ // name: 'Google Chrome',
+ // use: { ...devices['Desktop Chrome'], channel: 'chrome' },
+ // },
+ ],
+
+ /* Run your local dev server before starting the tests */
+ // webServer: [
+ // {
+ // command: "npm run backend",
+ // reuseExistingServer: !process.env.CI,
+ // timeout: 120 * 1000,
+ // },
+ // {
+ // command: "npm run start",
+ // url: "http://127.0.0.1:3000",
+ // reuseExistingServer: !process.env.CI,
+ // },
+ // ],
+});
diff --git a/src/frontend/public/favicon.ico b/src/frontend/public/favicon.ico
index 0612ba9b3..3bc197aa8 100644
Binary files a/src/frontend/public/favicon.ico and b/src/frontend/public/favicon.ico differ
diff --git a/src/frontend/run-tests.sh b/src/frontend/run-tests.sh
new file mode 100755
index 000000000..76e2fd756
--- /dev/null
+++ b/src/frontend/run-tests.sh
@@ -0,0 +1,78 @@
+#!/bin/bash
+
+# Default value for the --ui flag
+ui=false
+
+# Parse command-line arguments
+while [[ $# -gt 0 ]]; do
+ key="$1"
+ case $key in
+ --ui)
+ ui=true
+ shift
+ ;;
+ *)
+ echo "Unknown option: $key"
+ exit 1
+ ;;
+ esac
+ shift
+done
+
+# Function to forcibly terminate a process by port
+terminate_process_by_port() {
+ port="$1"
+ echo "Terminating process on port: $port"
+ fuser -k -n tcp "$port" # Forcefully terminate processes using the specified port
+ echo "Process terminated."
+}
+
+# Trap signals to ensure cleanup on script termination
+trap 'terminate_process_by_port 7860; terminate_process_by_port 3000' EXIT
+
+# install playwright if there is not installed yet
+npx playwright install
+
+# Navigate to the project root directory (where the Makefile is located)
+cd ../../
+
+# Start the frontend using 'make frontend' in the background
+make frontend &
+
+# Give some time for the frontend to start (adjust sleep duration as needed)
+sleep 10
+
+# Navigate to the test directory
+cd src/frontend
+
+# Run frontend only Playwright tests with or without UI based on the --ui flag
+if [ "$ui" = true ]; then
+ PLAYWRIGHT_HTML_REPORT=playwright-report/onlyFront npx playwright test tests/onlyFront --ui --project=chromium
+else
+ PLAYWRIGHT_HTML_REPORT=playwright-report/onlyFront npx playwright test tests/onlyFront --project=chromium
+fi
+
+# Navigate back to the project root directory
+cd ../../
+
+# Start the backend using 'make backend' in the background
+make backend &
+
+# Give some time for the backend to start (adjust sleep duration as needed)
+sleep 25
+
+# Navigate back to the test directory
+cd src/frontend
+
+# Run Playwright tests with or without UI based on the --ui flag
+if [ "$ui" = true ]; then
+ PLAYWRIGHT_HTML_REPORT=playwright-report/e2e npx playwright test tests/end-to-end --ui --project=chromium
+else
+ PLAYWRIGHT_HTML_REPORT=playwright-report/e2e npx playwright test tests/end-to-end --project=chromium
+fi
+
+npx playwright show-report
+
+# After the tests are finished, you can add cleanup or teardown logic here if needed
+
+# The trap will automatically terminate processes by port on script exit
diff --git a/src/frontend/set_proxy.sh b/src/frontend/set_proxy.sh
old mode 100755
new mode 100644
diff --git a/src/frontend/src/App.css b/src/frontend/src/App.css
index be7173d7f..a7a8bad51 100644
--- a/src/frontend/src/App.css
+++ b/src/frontend/src/App.css
@@ -3,45 +3,85 @@
@tailwind utilities;
.App {
- text-align: center;
+ text-align: center;
+}
+
+.react-flow__node {
+ width: auto;
+ height: auto;
+ border-radius: auto;
+ min-width: inherit;
}
.App-logo {
- height: 40vmin;
- pointer-events: none;
+ height: 40vmin;
+ pointer-events: none;
}
@media (prefers-reduced-motion: no-preference) {
- .App-logo {
- animation: App-logo-spin infinite 20s linear;
- }
+ .App-logo {
+ animation: App-logo-spin infinite 20s linear;
+ }
}
.App-header {
- background-color: #282c34;
- min-height: 100vh;
- display: flex;
- flex-direction: column;
- align-items: center;
- justify-content: center;
- font-size: calc(10px + 2vmin);
- color: white;
+ background-color: #282c34;
+ min-height: 100vh;
+ display: flex;
+ flex-direction: column;
+ align-items: center;
+ justify-content: center;
+ font-size: calc(10px + 2vmin);
+ color: white;
}
.App-link {
- color: #61dafb;
+ color: #61dafb;
}
@keyframes App-logo-spin {
- from {
- transform: rotate(0deg);
- }
- to {
- transform: rotate(360deg);
- }
+ from {
+ transform: rotate(0deg);
+ }
+ to {
+ transform: rotate(360deg);
+ }
}
@font-face {
- font-family: text-security-disc;
- src: url("assets/text-security-disc.woff") format("woff");
+ font-family: text-security-disc;
+ src: url("assets/text-security-disc.woff") format("woff");
+}
+
+.json-view {
+ height: 370px !important;
+ background-color: #2c2c2c !important;
+ border-radius: 10px !important;
+ padding: 10px !important;
+}
+
+.jv-indent {
+ overflow-y: auto !important;
+ max-height: 310px !important;
+ border-radius: 10px;
+}
+
+.jv-indent::-webkit-scrollbar {
+ width: 8px !important;
+ height: 8px !important;
+ border-radius: 10px;
+}
+
+.jv-indent::-webkit-scrollbar-track {
+ background-color: #f1f1f1 !important;
+ border-radius: 10px;
+}
+
+.jv-indent::-webkit-scrollbar-thumb {
+ background-color: #ccc !important;
+ border-radius: 999px !important;
+}
+
+.jv-indent::-webkit-scrollbar-thumb:hover {
+ background-color: #bbb !important;
}
diff --git a/src/frontend/src/App.tsx b/src/frontend/src/App.tsx
index 2109222ed..a4c36787d 100644
--- a/src/frontend/src/App.tsx
+++ b/src/frontend/src/App.tsx
@@ -1,6 +1,6 @@
import _ from "lodash";
import { useContext, useEffect, useState } from "react";
-import { useLocation } from "react-router-dom";
+import { useLocation, useNavigate } from "react-router-dom";
import "reactflow/dist/style.css";
import "./App.css";
@@ -16,8 +16,8 @@ import {
FETCH_ERROR_MESSAGE,
} from "./constants/constants";
import { alertContext } from "./contexts/alertContext";
+import { FlowsContext } from "./contexts/flowsContext";
import { locationContext } from "./contexts/locationContext";
-import { TabsContext } from "./contexts/tabsContext";
import { typesContext } from "./contexts/typesContext";
import Router from "./routes";
@@ -30,7 +30,7 @@ export default function App() {
setShowSideBar(true);
setIsStackedOpen(true);
}, [location.pathname, setCurrent, setIsStackedOpen, setShowSideBar]);
- const { hardReset } = useContext(TabsContext);
+ const { hardReset } = useContext(FlowsContext);
const {
errorData,
@@ -42,8 +42,11 @@ export default function App() {
successData,
successOpen,
setSuccessOpen,
+ setErrorData,
loading,
+ setLoading,
} = useContext(alertContext);
+ const navigate = useNavigate();
const { fetchError } = useContext(typesContext);
// Initialize state variable for the list of alerts
diff --git a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx
index dca4866d1..d47b7bcda 100644
--- a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx
+++ b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx
@@ -1,8 +1,15 @@
import { cloneDeep } from "lodash";
-import React, { useContext, useEffect, useRef, useState } from "react";
+import React, {
+ ReactNode,
+ useContext,
+ useEffect,
+ useRef,
+ useState,
+} from "react";
import { Handle, Position, useUpdateNodeInternals } from "reactflow";
import ShadTooltip from "../../../../components/ShadTooltipComponent";
import CodeAreaComponent from "../../../../components/codeAreaComponent";
+import DictComponent from "../../../../components/dictComponent";
import Dropdown from "../../../../components/dropdownComponent";
import FloatComponent from "../../../../components/floatComponent";
import IconComponent from "../../../../components/genericIconComponent";
@@ -10,14 +17,22 @@ import InputComponent from "../../../../components/inputComponent";
import InputFileComponent from "../../../../components/inputFileComponent";
import InputListComponent from "../../../../components/inputListComponent";
import IntComponent from "../../../../components/intComponent";
+import KeypairListComponent from "../../../../components/keypairListComponent";
import PromptAreaComponent from "../../../../components/promptComponent";
import TextAreaComponent from "../../../../components/textAreaComponent";
import ToggleShadComponent from "../../../../components/toggleShadComponent";
+import { Button } from "../../../../components/ui/button";
import { TOOLTIP_EMPTY } from "../../../../constants/constants";
-import { TabsContext } from "../../../../contexts/tabsContext";
+import { FlowsContext } from "../../../../contexts/flowsContext";
import { typesContext } from "../../../../contexts/typesContext";
import { ParameterComponentType } from "../../../../types/components";
-import { isValidConnection } from "../../../../utils/reactflowUtils";
+import {
+ convertObjToArray,
+ convertValuesToNumbers,
+ hasDuplicateKeys,
+ isValidConnection,
+ scapedJSONStringfy,
+} from "../../../../utils/reactflowUtils";
import {
nodeColors,
nodeIconsLucide,
@@ -38,15 +53,18 @@ export default function ParameterComponent({
required = false,
optionalHandle = null,
info = "",
-}: ParameterComponentType) {
- const ref = useRef(null);
- const refHtml = useRef(null);
- const infoHtml = useRef(null);
+ proxy,
+ showNode,
+ index = "",
+}: ParameterComponentType): JSX.Element {
+ const ref = useRef(null);
+ const refHtml = useRef(null);
+ const infoHtml = useRef(null);
const updateNodeInternals = useUpdateNodeInternals();
const [position, setPosition] = useState(0);
- const { setTabsState, tabId, save, flows } = useContext(TabsContext);
+ const { setTabsState, tabId, flows } = useContext(FlowsContext);
- const flow = flows.find((f) => f.id === tabId).data?.nodes ?? null;
+ const flow = flows.find((flow) => flow.id === tabId)?.data?.nodes ?? null;
// Update component position
useEffect(() => {
@@ -60,18 +78,28 @@ export default function ParameterComponent({
updateNodeInternals(data.id);
}, [data.id, position, updateNodeInternals]);
- const { reactFlowInstance } = useContext(typesContext);
+ const groupedEdge = useRef(null);
+
+ const { reactFlowInstance, setFilterEdge } = useContext(typesContext);
let disabled =
- reactFlowInstance?.getEdges().some((e) => e.targetHandle === id) ?? false;
+ reactFlowInstance
+ ?.getEdges()
+ .some((edge) => edge.targetHandle === scapedJSONStringfy(id)) ?? false;
const { data: myData } = useContext(typesContext);
- const handleOnNewValue = (newValue: any) => {
+ const handleOnNewValue = (
+ newValue: string | string[] | boolean | Object[]
+ ): void => {
let newData = cloneDeep(data);
- newData.node.template[name].value = newValue;
+ newData.node!.template[name].value = newValue;
setData(newData);
// Set state to pending
- setTabsState((prev) => {
+ //@ts-ignore
+ setTabsState((prev: TabsState) => {
+ if (!prev[tabId]) {
+ return prev;
+ }
return {
...prev,
[tabId]: {
@@ -84,12 +112,14 @@ export default function ParameterComponent({
renderTooltips();
};
+ const [errorDuplicateKey, setErrorDuplicateKey] = useState(false);
+
useEffect(() => {
- if (name === "openai_api_base") console.log(info);
+ // @ts-ignore
infoHtml.current = (
-
- {info.split("\n").map((line, i) => (
-
+
+ {info.split("\n").map((line, index) => (
+
{line}
))}
@@ -98,56 +128,68 @@ export default function ParameterComponent({
}, [info]);
function renderTooltips() {
- let groupedObj = groupByFamily(myData, tooltipTitle, left, flow);
+ let groupedObj: any = groupByFamily(myData, tooltipTitle!, left, flow!);
+ groupedEdge.current = groupedObj;
if (groupedObj && groupedObj.length > 0) {
- refHtml.current = groupedObj.map((item, i) => {
+ //@ts-ignore
+ refHtml.current = groupedObj.map((item, index) => {
const Icon: any =
nodeIconsLucide[item.family] ?? nodeIconsLucide["unknown"];
return (
-
0 ? "mt-2 flex items-center" : "flex items-center"
+
+ {index === 0 && (
+
+ {left
+ ? "Avaliable input components:"
+ : "Avaliable output components:"}
+
)}
- >
-
0 ? "mt-2 flex items-center" : "mt-3 flex items-center"
+ )}
>
-
-
-
- {nodeNames[item.family] ?? "Other"}
-
- {" "}
- {item.type === "" ? "" : " - "}
- {item.type.split(", ").length > 2
- ? item.type.split(", ").map((el, i) => (
-
-
- {i === item.type.split(", ").length - 1
- ? el
- : (el += `, `)}
-
-
- ))
- : item.type}
+ >
+
+ {nodeNames[item.family] ?? "Other"}{" "}
+
+ {" "}
+ {item.type === "" ? "" : " - "}
+ {item.type.split(", ").length > 2
+ ? item.type.split(", ").map((el, index) => (
+
+
+ {index === item.type.split(", ").length - 1
+ ? el
+ : (el += `, `)}
+
+
+ ))
+ : item.type}
+
-
+
);
});
} else {
+ //@ts-ignore
refHtml.current =
{TOOLTIP_EMPTY} ;
}
}
@@ -156,7 +198,56 @@ export default function ParameterComponent({
renderTooltips();
}, [tooltipTitle, flow]);
- return (
+ return !showNode ? (
+ left &&
+ (type === "str" ||
+ type === "bool" ||
+ type === "float" ||
+ type === "code" ||
+ type === "prompt" ||
+ type === "file" ||
+ type === "int" ||
+ type === "dict" ||
+ type === "NestedDict") &&
+ !optionalHandle ? (
+ <>
+ ) : (
+
+
+
+
+ isValidConnection(connection, reactFlowInstance!)
+ }
+ className={classNames(
+ left ? "my-12 -ml-0.5 " : " my-12 -mr-0.5 ",
+ "h-3 w-3 rounded-full border-2 bg-background"
+ )}
+ style={{
+ borderColor: color,
+ top: position,
+ }}
+ onClick={() => {
+ setFilterEdge(groupedEdge.current);
+ }}
+ >
+
+
+
+ )
+ ) : (
- {title}
+ {proxy ? (
+
{proxy.id}}>
+ {title}
+
+ ) : (
+ title
+ )}
{required ? " *" : ""}
{info !== "" && (
@@ -192,40 +289,53 @@ export default function ParameterComponent({
type === "code" ||
type === "prompt" ||
type === "file" ||
- type === "int") &&
+ type === "int" ||
+ type === "dict" ||
+ type === "NestedDict") &&
!optionalHandle ? (
<>
) : (
-
-
- isValidConnection(connection, reactFlowInstance)
- }
- className={classNames(
- left ? "-ml-0.5 " : "-mr-0.5 ",
- "h-3 w-3 rounded-full border-2 bg-background"
- )}
- style={{
- borderColor: color,
- top: position,
- }}
- >
-
+
+
+
+
+ isValidConnection(connection, reactFlowInstance!)
+ }
+ className={classNames(
+ left ? "-ml-0.5 " : "-mr-0.5 ",
+ "h-3 w-3 rounded-full border-2 bg-background"
+ )}
+ style={{
+ borderColor: color,
+ top: position,
+ }}
+ onClick={() => {
+ setFilterEdge(groupedEdge.current);
+ }}
+ >
+
+
+
)}
{left === true &&
type === "str" &&
- !data.node.template[name].options ? (
+ !data.node?.template[name].options ? (
- {data.node.template[name].list ? (
+ {data.node?.template[name].list ? (
- ) : data.node.template[name].multiline ? (
+ ) : data.node?.template[name].multiline ? (
) : (
)}
@@ -254,10 +366,11 @@ export default function ParameterComponent({
) : left === true && type === "bool" ? (
{
- handleOnNewValue(t);
+ enabled={data.node?.template[name].value ?? false}
+ setEnabled={(isEnabled) => {
+ handleOnNewValue(isEnabled);
}}
size="large"
/>
@@ -266,13 +379,13 @@ export default function ParameterComponent({
) : left === true &&
type === "str" &&
- data.node.template[name].options ? (
+ data.node?.template[name].options ? (
{
data.node = nodeClass;
}}
nodeClass={data.node}
disabled={disabled}
- value={data.node.template[name].value ?? ""}
+ value={data.node?.template[name].value ?? ""}
onChange={handleOnNewValue}
+ id={"code-input-" + index}
/>
) : left === true && type === "file" ? (
{
- data.node.template[name].file_path = t;
- save();
+ fileTypes={data.node?.template[name].fileTypes}
+ suffixes={data.node?.template[name].suffixes}
+ onFileChange={(filePath: string) => {
+ data.node!.template[name].file_path = filePath;
}}
>
@@ -311,13 +429,15 @@ export default function ParameterComponent({
) : left === true && type === "prompt" ? (
{
data.node = nodeClass;
@@ -327,10 +447,50 @@ export default function ParameterComponent({
}}
nodeClass={data.node}
disabled={disabled}
- value={data.node.template[name].value ?? ""}
+ value={data.node?.template[name].value ?? ""}
onChange={(e) => {
handleOnNewValue(e);
}}
+ id={"prompt-input-" + index}
+ />
+
+ ) : left === true && type === "NestedDict" ? (
+
+ {
+ data.node!.template[name].value = newValue;
+ handleOnNewValue(newValue);
+ }}
+ />
+
+ ) : left === true && type === "dict" ? (
+
+ {
+ const valueToNumbers = convertValuesToNumbers(newValue);
+ data.node!.template[name].value = valueToNumbers;
+ setErrorDuplicateKey(hasDuplicateKeys(valueToNumbers));
+ handleOnNewValue(valueToNumbers);
+ }}
/>
) : (
diff --git a/src/frontend/src/CustomNodes/GenericNode/index.tsx b/src/frontend/src/CustomNodes/GenericNode/index.tsx
index d72e46d23..81d99c8d5 100644
--- a/src/frontend/src/CustomNodes/GenericNode/index.tsx
+++ b/src/frontend/src/CustomNodes/GenericNode/index.tsx
@@ -4,29 +4,85 @@ import { NodeToolbar, useUpdateNodeInternals } from "reactflow";
import ShadTooltip from "../../components/ShadTooltipComponent";
import Tooltip from "../../components/TooltipComponent";
import IconComponent from "../../components/genericIconComponent";
+import InputComponent from "../../components/inputComponent";
+import { Textarea } from "../../components/ui/textarea";
import { useSSE } from "../../contexts/SSEContext";
-import { TabsContext } from "../../contexts/tabsContext";
+import { FlowsContext } from "../../contexts/flowsContext";
import { typesContext } from "../../contexts/typesContext";
import NodeToolbarComponent from "../../pages/FlowPage/components/nodeToolbarComponent";
+import { validationStatusType } from "../../types/components";
import { NodeDataType } from "../../types/flow";
-import { cleanEdges } from "../../utils/reactflowUtils";
+import {
+ cleanEdges,
+ handleKeyDown,
+ scapedJSONStringfy,
+} from "../../utils/reactflowUtils";
import { nodeColors, nodeIconsLucide } from "../../utils/styleUtils";
-import { classNames, toTitleCase } from "../../utils/utils";
+import { classNames, getFieldTitle } from "../../utils/utils";
import ParameterComponent from "./components/parameterComponent";
export default function GenericNode({
data: olddata,
+ xPos,
+ yPos,
selected,
}: {
data: NodeDataType;
selected: boolean;
-}) {
+ xPos: number;
+ yPos: number;
+}): JSX.Element {
const [data, setData] = useState(olddata);
- const { updateFlow, flows, tabId } = useContext(TabsContext);
+ const { updateFlow, flows, tabId } = useContext(FlowsContext);
const updateNodeInternals = useUpdateNodeInternals();
- const { types, deleteNode, reactFlowInstance } = useContext(typesContext);
+ const { types, deleteNode, reactFlowInstance, setFilterEdge, getFilterEdge } =
+ useContext(typesContext);
const name = nodeIconsLucide[data.type] ? data.type : types[data.type];
- const [validationStatus, setValidationStatus] = useState(null);
+ const [inputName, setInputName] = useState(true);
+ const [nodeName, setNodeName] = useState(data.node!.display_name);
+ const [inputDescription, setInputDescription] = useState(false);
+ const [nodeDescription, setNodeDescription] = useState(
+ data.node?.description!
+ );
+ const [validationStatus, setValidationStatus] =
+ useState(null);
+ const [showNode, setShowNode] = useState(true);
+ const [handles, setHandles] = useState([]);
+ let numberOfInputs: boolean[] = [];
+
+ function countHandles(): void {
+ numberOfInputs = Object.keys(data.node!.template)
+ .filter((templateField) => templateField.charAt(0) !== "_")
+ .map((templateCamp) => {
+ const { template } = data.node!;
+ if (template[templateCamp].input_types) return true;
+ if (!template[templateCamp].show) return false;
+ switch (template[templateCamp].type) {
+ case "str":
+ return false;
+ case "bool":
+ return false;
+ case "float":
+ return false;
+ case "code":
+ return false;
+ case "prompt":
+ return false;
+ case "file":
+ return false;
+ case "int":
+ return false;
+ default:
+ return true;
+ }
+ });
+ setHandles(numberOfInputs);
+ }
+
+ useEffect(() => {
+ countHandles();
+ }, []);
+
// State for outline color
const { sseData, isBuilding } = useSSE();
useEffect(() => {
@@ -41,15 +97,22 @@ export default function GenericNode({
nodes: flow.data.nodes,
},
updateEdge: (edge) => {
- flow.data.edges = edge;
+ flow.data!.edges = edge;
reactFlowInstance.setEdges(edge);
updateNodeInternals(data.id);
},
});
updateFlow(flow);
}
+ countHandles();
}, [data]);
+ useEffect(() => {
+ setTimeout(() => {
+ updateNodeInternals(data.id);
+ }, 300);
+ }, [showNode]);
+
// New useEffect to watch for changes in sseData and update validation status
useEffect(() => {
const relevantData = sseData[data.id];
@@ -60,182 +123,393 @@ export default function GenericNode({
setValidationStatus(null);
}
}, [sseData, data.id]);
-
return (
<>
- {data.node.beta && (
+ {data.node?.beta && showNode && (
)}
-
-
-
-
-
-
- {data.node.display_name}
-
-
-
-
-
-
-
Building...
- ) : !validationStatus ? (
-
- Build{" "}
-
- ) : (
-
- {typeof validationStatus.params === "string"
- ? validationStatus.params
- .split("\n")
- .map((line, index) =>
{line}
)
- : ""}
-
-
{data.node.description}
-
- <>
- {Object.keys(data.node.template)
- .filter((t) => t.charAt(0) !== "_")
- .map((t: string, idx) => (
-
- {data.node.template[t].show &&
- !data.node.template[t].advanced ? (
-
+ iconColor={`${nodeColors[types[data.type]]}`}
+ />
+ {showNode && (
+
+ {data.node?.flow && inputName ? (
+
+ {
+ setInputName(false);
+ if (nodeName.trim() !== "") {
+ setNodeName(nodeName);
+ data.node!.display_name = nodeName;
+ } else {
+ setNodeName(data.node!.display_name);
+ }
+ }}
+ value={nodeName}
+ onChange={setNodeName}
+ password={false}
+ blurOnEnter={true}
+ />
+
) : (
- <>
+
+ setInputName(true)}
+ >
+ {data.node?.display_name}
+
+
)}
- ))}
-
- {" "}
-
0
- ? data.node.output_types.join("|")
- : data.type
- }
- tooltipTitle={data.node.base_classes.join("\n")}
- id={[data.type, data.id, ...data.node.base_classes].join("|")}
- type={data.node.base_classes.join("|")}
- left={false}
- />
-
+
+ {!showNode && (
+ <>
+ {Object.keys(data.node!.template)
+ .filter((templateField) => templateField.charAt(0) !== "_")
+ .map(
+ (templateField: string, idx) =>
+ data.node!.template[templateField].show &&
+ !data.node!.template[templateField].advanced && (
+
+ )
+ )}
+
0
+ ? data.node.output_types.join("|")
+ : data.type
+ }
+ tooltipTitle={data.node?.base_classes.join("\n")}
+ id={{
+ baseClasses: data.node!.base_classes,
+ id: data.id,
+ dataType: data.type,
+ }}
+ type={data.node?.base_classes.join("|")}
+ left={false}
+ showNode={showNode}
+ />
+
+ )}
+
+
+
Building...
+ ) : !validationStatus ? (
+
+ Build{" "}
+
+ ) : (
+
+ {typeof validationStatus.params === "string"
+ ? validationStatus.params
+ .split("\n")
+ .map((line: string, index: number) => (
+
{line}
+ ))
+ : ""}
+
+
+
+ {data.node?.description !== "" &&
+ showNode &&
+ data.node?.flow &&
+ inputDescription ? (
+
@@ -486,35 +539,37 @@ export default function FormModal({
))
: null}
- {tabsState[id.current].formKeysData.memory_keys.map((i, k) => (
-
-
-
- {i}
-
-
-
{}}
- size="small"
- disabled={true}
- />
+ {tabsState[id.current].formKeysData.memory_keys!.map(
+ (key, index) => (
+
+
+
+ {key}
+
+
+ {}}
+ size="small"
+ disabled={true}
+ />
+
+
+
+ )
+ )}
@@ -534,14 +589,14 @@ export default function FormModal({
{chatHistory.length > 0 ? (
- chatHistory.map((c, i) => (
+ chatHistory.map((chat, index) => (
([]);
const { setErrorData, setSuccessData, setNoticeData } =
useContext(alertContext);
const ref = useRef();
const divRef = useRef(null);
const divRefPrompt = useRef(null);
- function checkVariables(valueToCheck) {
+ function checkVariables(valueToCheck: string): void {
const regex = /\{([^{}]+)\}/g;
- const matches = [];
+ const matches: string[] = [];
let match;
while ((match = regex.exec(valueToCheck))) {
matches.push(`{${match[1]}}`);
}
- let invalid_chars = [];
- let fixed_variables = [];
+ let invalid_chars: string[] = [];
+ let fixed_variables: string[] = [];
let input_variables = matches;
for (let variable of input_variables) {
let new_var = variable;
@@ -99,7 +88,7 @@ export default function GenericModal({
useEffect(() => {
setInputValue(value);
- }, [value]);
+ }, [value, modalOpen]);
const coloredContent = (inputValue || "")
.replace(/");
- const TextAreaContentView = () => {
- return (
- {
- setIsEdit(true);
- }}
- suppressWarning={true}
- />
- );
- };
-
- function getClassByNumberLength() {
+ function getClassByNumberLength(): string {
let sumOfCaracteres: number = 0;
wordsHighlight.forEach((element) => {
sumOfCaracteres = sumOfCaracteres + element.replace(/[{}]/g, "").length;
@@ -135,19 +111,35 @@ export default function GenericModal({
postValidatePrompt(field_name, inputValue, nodeClass!)
.then((apiReturn) => {
+ // if field_name is an empty string, then we need to set it
+ // to the first key of the custom_fields object
+ if (field_name === "") {
+ console.log(apiReturn.data?.frontend_node?.custom_fields);
+ field_name = Array.isArray(
+ apiReturn.data?.frontend_node?.custom_fields?.[""]
+ )
+ ? apiReturn.data?.frontend_node?.custom_fields?.[""][0] ?? ""
+ : apiReturn.data?.frontend_node?.custom_fields?.[""] ?? "";
+ }
if (apiReturn.data) {
- setValue(inputValue);
- apiReturn.data.frontend_node["template"]["template"]["value"] =
- inputValue;
- setNodeClass!(apiReturn?.data?.frontend_node);
-
let inputVariables = apiReturn.data.input_variables ?? [];
if (inputVariables && inputVariables.length === 0) {
setIsEdit(true);
setNoticeData({
title: "Your template does not have any variables.",
});
+ setModalOpen(false);
+ if (
+ JSON.stringify(apiReturn.data?.frontend_node) !==
+ JSON.stringify({})
+ )
+ setNodeClass!(apiReturn.data?.frontend_node);
setModalOpen(closeModal);
+ setValue(inputValue);
+ if (field_name !== "") {
+ apiReturn.data.frontend_node["template"][field_name]["value"] =
+ inputValue;
+ }
} else {
setIsEdit(false);
setSuccessData({
@@ -156,8 +148,13 @@ export default function GenericModal({
if (
JSON.stringify(apiReturn.data?.frontend_node) !==
JSON.stringify({})
- ) {
- setModalOpen(closeModal);
+ )
+ setNodeClass!(apiReturn.data?.frontend_node);
+ setModalOpen(closeModal);
+ setValue(inputValue);
+ if (field_name !== "") {
+ apiReturn.data.frontend_node["template"][field_name]["value"] =
+ inputValue;
}
}
} else {
@@ -168,16 +165,15 @@ export default function GenericModal({
}
})
.catch((error) => {
+ console.log(error);
setIsEdit(true);
return setErrorData({
title: "There is something wrong with this prompt, please review it",
- list: [error?.response?.data?.detail],
+ list: [error.toString()],
});
});
}
- const [modalOpen, setModalOpen] = useState(false);
-
return (
{}}
@@ -214,8 +210,9 @@ export default function GenericModal({
"flex h-full w-full"
)}
>
- {type === TypeModal.PROMPT && isEdit ? (
+ {type === TypeModal.PROMPT && isEdit && !readonly ? (
