Merge branch 'dev' into authentication
This commit is contained in:
gustavoschaedler
commit
2fa7810ff8
440 changed files with 47088 additions and 10916 deletions
|
|
@ -1,6 +1,6 @@
|
|||
# LangFlow Demo Codespace Readme
|
||||
# Langflow Demo Codespace Readme
|
||||
|
||||
These instructions will walk you through the process of running a LangFlow demo via GitHub Codespaces.
|
||||
These instructions will walk you through the process of running a Langflow demo via GitHub Codespaces.
|
||||
|
||||
## Setup
|
||||
|
||||
|
|
|
|||
47
.env.example
Normal file
47
.env.example
Normal file
|
|
@ -0,0 +1,47 @@
|
|||
# Description: Example of .env file
|
||||
# Usage: Copy this file to .env and change the values
|
||||
# according to your needs
|
||||
# Do not commit .env file to git
|
||||
# Do not change .env.example file
|
||||
|
||||
# Database URL
|
||||
# Postgres example: LANGFLOW_DATABASE_URL=postgresql://postgres:postgres@localhost:5432/langflow
|
||||
# SQLite example:
|
||||
LANGFLOW_DATABASE_URL=sqlite:///./langflow.db
|
||||
|
||||
# Cache type
|
||||
LANGFLOW_LANGCHAIN_CACHE=SQLiteCache
|
||||
|
||||
# Server host
|
||||
# Example: LANGFLOW_HOST=127.0.0.1
|
||||
LANGFLOW_HOST=
|
||||
|
||||
# Worker processes
|
||||
# Example: LANGFLOW_WORKERS=1
|
||||
LANGFLOW_WORKERS=
|
||||
|
||||
# Server port
|
||||
# Example: LANGFLOW_PORT=7860
|
||||
LANGFLOW_PORT=
|
||||
|
||||
# Logging level
|
||||
# Example: LANGFLOW_LOG_LEVEL=critical
|
||||
LANGFLOW_LOG_LEVEL=
|
||||
|
||||
# Path to the log file
|
||||
# Example: LANGFLOW_LOG_FILE=logs/langflow.log
|
||||
LANGFLOW_LOG_FILE=
|
||||
|
||||
# Path to the frontend directory containing build files
|
||||
# Example: LANGFLOW_FRONTEND_PATH=/path/to/frontend/build/files
|
||||
LANGFLOW_FRONTEND_PATH=
|
||||
|
||||
# Whether to open the browser after starting the server
|
||||
# Values: true, false
|
||||
# Example: LANGFLOW_OPEN_BROWSER=true
|
||||
LANGFLOW_OPEN_BROWSER=
|
||||
|
||||
# Whether to remove API keys from the projects saved in the database
|
||||
# Values: true, false
|
||||
# Example: LANGFLOW_REMOVE_API_KEYS=false
|
||||
LANGFLOW_REMOVE_API_KEYS=
|
||||
0
.githooks/pre-commit
Normal file → Executable file
0
.githooks/pre-commit
Normal file → Executable file
66
.github/workflows/codeql.yml
vendored
Normal file
66
.github/workflows/codeql.yml
vendored
Normal file
|
|
@ -0,0 +1,66 @@
|
|||
name: "CodeQL"
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [ 'dev', 'main' ]
|
||||
pull_request:
|
||||
# The branches below must be a subset of the branches above
|
||||
branches: [ 'dev' ]
|
||||
schedule:
|
||||
- cron: '17 2 * * 1'
|
||||
|
||||
jobs:
|
||||
analyze:
|
||||
name: Analyze
|
||||
runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }}
|
||||
timeout-minutes: ${{ (matrix.language == 'swift' && 120) || 360 }}
|
||||
permissions:
|
||||
actions: read
|
||||
contents: read
|
||||
security-events: write
|
||||
|
||||
strategy:
|
||||
fail-fast: false
|
||||
matrix:
|
||||
language: [ 'python', 'javascript' ]
|
||||
# CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python', 'ruby' ]
|
||||
# Use only 'java' to analyze code written in Java, Kotlin or both
|
||||
# Use only 'javascript' to analyze code written in JavaScript, TypeScript or both
|
||||
# Learn more about CodeQL language support at https://aka.ms/codeql-docs/language-support
|
||||
|
||||
steps:
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v3
|
||||
|
||||
# Initializes the CodeQL tools for scanning.
|
||||
- name: Initialize CodeQL
|
||||
uses: github/codeql-action/init@v2
|
||||
with:
|
||||
languages: ${{ matrix.language }}
|
||||
# If you wish to specify custom queries, you can do so here or in a config file.
|
||||
# By default, queries listed here will override any specified in a config file.
|
||||
# Prefix the list here with "+" to use these queries and those in the config file.
|
||||
|
||||
# For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
|
||||
# queries: security-extended,security-and-quality
|
||||
|
||||
|
||||
# Autobuild attempts to build any compiled languages (C/C++, C#, Go, Java, or Swift).
|
||||
# If this step fails, then you should remove it and run the build manually (see below)
|
||||
- name: Autobuild
|
||||
uses: github/codeql-action/autobuild@v2
|
||||
|
||||
# ℹ️ Command-line programs to run using the OS shell.
|
||||
# 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
|
||||
|
||||
# If the Autobuild fails above, remove it and uncomment the following three lines.
|
||||
# modify them (or add more) to build your code if your project, please refer to the EXAMPLE below for guidance.
|
||||
|
||||
# - run: |
|
||||
# echo "Run, Build Application using script"
|
||||
# ./location_of_script_within_repo/buildscript.sh
|
||||
|
||||
- name: Perform CodeQL Analysis
|
||||
uses: github/codeql-action/analyze@v2
|
||||
with:
|
||||
category: "/language:${{matrix.language}}"
|
||||
39
.github/workflows/deploy_gh-pages.yml
vendored
Normal file
39
.github/workflows/deploy_gh-pages.yml
vendored
Normal file
|
|
@ -0,0 +1,39 @@
|
|||
name: Deploy to GitHub Pages
|
||||
|
||||
on:
|
||||
push:
|
||||
branches:
|
||||
- main
|
||||
# Review gh actions docs if you want to further define triggers, paths, etc
|
||||
# https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#on
|
||||
|
||||
jobs:
|
||||
deploy:
|
||||
name: Deploy to GitHub Pages
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
- uses: actions/setup-node@v3
|
||||
with:
|
||||
node-version: 18
|
||||
cache: npm
|
||||
cache-dependency-path: ./docs/package-lock.json
|
||||
|
||||
- name: Install dependencies
|
||||
run: cd docs && npm install
|
||||
- name: Build website
|
||||
run: cd docs && npm run build
|
||||
|
||||
# Popular action to deploy to GitHub Pages:
|
||||
# Docs: https://github.com/peaceiris/actions-gh-pages#%EF%B8%8F-docusaurus
|
||||
- name: Deploy to GitHub Pages
|
||||
uses: peaceiris/actions-gh-pages@v3
|
||||
with:
|
||||
github_token: ${{ secrets.GITHUB_TOKEN }}
|
||||
# Build output to publish to the `gh-pages` branch:
|
||||
publish_dir: ./docs/build
|
||||
# The following lines assign commit authorship to the official
|
||||
# GH-Actions bot for deploys to `gh-pages` branch:
|
||||
# https://github.com/actions/checkout/issues/13#issuecomment-724415212
|
||||
# The GH actions bot is used by default if you didn't specify the two fields.
|
||||
# You can swap them out with your own user credentials.
|
||||
49
.github/workflows/pre-release.yml
vendored
Normal file
49
.github/workflows/pre-release.yml
vendored
Normal file
|
|
@ -0,0 +1,49 @@
|
|||
name: pre-release
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
types:
|
||||
- closed
|
||||
branches:
|
||||
- dev
|
||||
paths:
|
||||
- "pyproject.toml"
|
||||
|
||||
env:
|
||||
POETRY_VERSION: "1.5.1"
|
||||
|
||||
jobs:
|
||||
if_release:
|
||||
if: |
|
||||
${{ github.event.pull_request.merged == true }}
|
||||
&& ${{ contains(github.event.pull_request.labels.*.name, 'pre-release') }}
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
- name: Install poetry
|
||||
run: pipx install poetry==$POETRY_VERSION
|
||||
- name: Set up Python 3.10
|
||||
uses: actions/setup-python@v4
|
||||
with:
|
||||
python-version: "3.10"
|
||||
cache: "poetry"
|
||||
- name: Build project for distribution
|
||||
run: make build
|
||||
- name: Check Version
|
||||
id: check-version
|
||||
run: |
|
||||
echo version=$(poetry version --short) >> $GITHUB_OUTPUT
|
||||
- name: Create Release
|
||||
uses: ncipollo/release-action@v1
|
||||
with:
|
||||
artifacts: "dist/*"
|
||||
token: ${{ secrets.GITHUB_TOKEN }}
|
||||
draft: false
|
||||
generateReleaseNotes: true
|
||||
tag: v${{ steps.check-version.outputs.version }}
|
||||
commit: main
|
||||
- name: Publish to PyPI
|
||||
env:
|
||||
POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
|
||||
run: |
|
||||
poetry publish
|
||||
2
.github/workflows/release.yml
vendored
2
.github/workflows/release.yml
vendored
|
|
@ -10,7 +10,7 @@ on:
|
|||
- "pyproject.toml"
|
||||
|
||||
env:
|
||||
POETRY_VERSION: "1.4.0"
|
||||
POETRY_VERSION: "1.5.1"
|
||||
|
||||
jobs:
|
||||
if_release:
|
||||
|
|
|
|||
11
.gitignore
vendored
11
.gitignore
vendored
|
|
@ -1,3 +1,5 @@
|
|||
# This is to avoid Opencommit hook from getting pushed
|
||||
prepare-commit-msg
|
||||
# Logs
|
||||
logs
|
||||
*.log
|
||||
|
|
@ -242,3 +244,12 @@ dmypy.json
|
|||
# Poetry
|
||||
.testenv/*
|
||||
langflow.db
|
||||
|
||||
|
||||
.githooks/prepare-commit-msg
|
||||
.langchain.db
|
||||
|
||||
# docusaurus
|
||||
.docusaurus/
|
||||
|
||||
/tmp/*
|
||||
|
|
|
|||
31
.readthedocs.yaml
Normal file
31
.readthedocs.yaml
Normal file
|
|
@ -0,0 +1,31 @@
|
|||
# Read the Docs configuration file for Sphinx projects
|
||||
# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
|
||||
|
||||
# Required
|
||||
version: 2
|
||||
|
||||
# Set the OS, Python version and other tools you might need
|
||||
build:
|
||||
os: ubuntu-22.04
|
||||
tools:
|
||||
python: "3.11"
|
||||
# You can also specify other tool versions:
|
||||
# nodejs: "19"
|
||||
# rust: "1.64"
|
||||
# golang: "1.19"
|
||||
|
||||
# Build documentation in the "docs/" directory with Sphinx
|
||||
sphinx:
|
||||
configuration: docs/conf.py
|
||||
|
||||
# Optionally build your docs in additional formats such as PDF and ePub
|
||||
# formats:
|
||||
# - pdf
|
||||
# - epub
|
||||
|
||||
# Optional but recommended, declare the Python requirements required
|
||||
# to build your documentation
|
||||
# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
|
||||
# python:
|
||||
# install:
|
||||
# - requirements: docs/requirements.txt
|
||||
3
.vscode/launch.json
vendored
3
.vscode/launch.json
vendored
|
|
@ -6,7 +6,8 @@
|
|||
"request": "launch",
|
||||
"module": "uvicorn",
|
||||
"args": [
|
||||
"langflow.main:app",
|
||||
"--factory",
|
||||
"langflow.main:create_app",
|
||||
"--port",
|
||||
"7860",
|
||||
"--reload",
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
# Contributing to LangFlow
|
||||
# Contributing to Langflow
|
||||
|
||||
Hello there! We appreciate your interest in contributing to LangFlow.
|
||||
Hello there! We appreciate your interest in contributing to Langflow.
|
||||
As an open-source project in a rapidly developing field, we are extremely open
|
||||
to contributions, whether it be in the form of a new feature, improved infra, or better documentation.
|
||||
|
||||
|
|
@ -9,7 +9,7 @@ Please do not try to push directly to this repo unless you are a maintainer.
|
|||
|
||||
## 🗺️Contributing Guidelines
|
||||
|
||||
### 🚩GitHub Issues
|
||||
## 🚩GitHub Issues
|
||||
|
||||
Our [issues](https://github.com/logspace-ai/langflow/issues) page is kept up to date
|
||||
with bugs, improvements, and feature requests. There is a taxonomy of labels to help
|
||||
|
|
@ -33,18 +33,19 @@ so that more people can benefit from it.
|
|||
[collapses the content](https://developer.mozilla.org/en/docs/Web/HTML/Element/details)
|
||||
so it only becomes visible on click, making the issue easier to read and follow.
|
||||
|
||||
### Issue labels
|
||||
## Issue labels
|
||||
|
||||
[See this page](https://github.com/logspace-ai/langflow/labels) for an overview of
|
||||
the system we use to tag our issues and pull requests.
|
||||
|
||||
## Local development
|
||||
|
||||
### Local development
|
||||
You can develop LangFlow using docker compose, or locally.
|
||||
You can develop Langflow using docker compose, or locally.
|
||||
|
||||
We provide a .vscode/launch.json file for debugging the backend in VSCode, which is a lot faster than using docker compose.
|
||||
|
||||
Setting up hooks:
|
||||
|
||||
```bash
|
||||
make init
|
||||
```
|
||||
|
|
@ -53,30 +54,46 @@ This will install the pre-commit hooks, which will run `make format` on every co
|
|||
|
||||
It is advised to run `make lint` before pushing to the repository.
|
||||
|
||||
#### **Locally**
|
||||
Run locally by cloning the repository and installing the dependencies. We recommend using a virtual environment to isolate the dependencies from your system.
|
||||
## Run locally
|
||||
|
||||
Langflow can run locally by cloning the repository and installing the dependencies. We recommend using a virtual environment to isolate the dependencies from your system.
|
||||
|
||||
Before you start, make sure you have the following installed:
|
||||
- Poetry (>=1.4)
|
||||
- Node.js
|
||||
|
||||
For the backend, you will need to install the dependencies and start the development server.
|
||||
- Poetry (>=1.4)
|
||||
- Node.js
|
||||
|
||||
Then, in the root folder, install the dependencies and start the development server for the backend:
|
||||
|
||||
```bash
|
||||
make install_backend
|
||||
make backend
|
||||
```
|
||||
For the frontend, you will need to install the dependencies and start the development server.
|
||||
|
||||
And the frontend:
|
||||
|
||||
```bash
|
||||
make frontend
|
||||
```
|
||||
|
||||
## Docker compose
|
||||
|
||||
The following snippet will run the backend and frontend in separate containers. The frontend will be available at `localhost:3000` and the backend at `localhost:7860`.
|
||||
|
||||
#### **Docker compose**
|
||||
This will run the backend and frontend in separate containers. The frontend will be available at `localhost:3000` and the backend at `localhost:7860`.
|
||||
```bash
|
||||
docker compose up --build
|
||||
# or
|
||||
make dev build=1
|
||||
```
|
||||
|
||||
## Documentation
|
||||
|
||||
The documentation is built using [Docusaurus](https://docusaurus.io/). To run the documentation locally, run the following commands:
|
||||
|
||||
```bash
|
||||
cd docs
|
||||
npm install
|
||||
npm run start
|
||||
```
|
||||
|
||||
The documentation will be available at `localhost:3000` and all the files are located in the `docs/docs` folder.
|
||||
Once you are done with your changes, you can create a Pull Request to the `main` branch.
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ This script sets up a Debian-based VM with the Langflow package, Nginx, and the
|
|||
|
||||
## Spot/Preemptible Instance
|
||||
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/genome21/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md)
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md)
|
||||
|
||||
When running as a [spot (preemptible) instance](https://cloud.google.com/compute/docs/instances/preemptible), the code and VM will behave the same way as in a regular instance, executing the startup script to configure the environment, install necessary dependencies, and run the Langflow application. However, **due to the nature of spot instances, the VM may be terminated at any time if Google Cloud needs to reclaim the resources**. This makes spot instances suitable for fault-tolerant, stateless, or interruptible workloads that can handle unexpected terminations and restarts.
|
||||
|
||||
|
|
|
|||
9
Makefile
9
Makefile
|
|
@ -5,6 +5,8 @@ all: help
|
|||
init:
|
||||
@echo 'Installing pre-commit hooks'
|
||||
git config core.hooksPath .githooks
|
||||
@echo 'Making pre-commit hook executable'
|
||||
chmod +x .githooks/pre-commit
|
||||
@echo 'Installing backend dependencies'
|
||||
make install_backend
|
||||
@echo 'Installing frontend dependencies'
|
||||
|
|
@ -44,13 +46,18 @@ install_backend:
|
|||
|
||||
backend:
|
||||
make install_backend
|
||||
poetry run uvicorn langflow.main:app --port 7860 --reload --log-level debug
|
||||
poetry run uvicorn --factory src.backend.langflow.main:create_app --port 7860 --reload --log-level debug
|
||||
|
||||
build_and_run:
|
||||
echo 'Removing dist folder'
|
||||
rm -rf dist
|
||||
make build && poetry run pip install dist/*.tar.gz && poetry run langflow
|
||||
|
||||
build_and_install:
|
||||
echo 'Removing dist folder'
|
||||
rm -rf dist
|
||||
make build && poetry run pip install dist/*.tar.gz
|
||||
|
||||
build_frontend:
|
||||
cd src/frontend && CI='' npm run build
|
||||
cp -r src/frontend/build src/backend/langflow/frontend
|
||||
|
|
|
|||
242
README.md
242
README.md
|
|
@ -1,6 +1,6 @@
|
|||
<!-- Title -->
|
||||
|
||||
# ⛓️ LangFlow
|
||||
# ⛓️ Langflow
|
||||
|
||||
~ An effortless way to experiment and prototype [LangChain](https://github.com/hwchase17/langchain) pipelines ~
|
||||
|
||||
|
|
@ -14,36 +14,120 @@
|
|||
</p>
|
||||
|
||||
<p>
|
||||
<a href="https://discord.gg/FUhJnnJ9"><img alt="Discord Server" src="https://dcbadge.vercel.app/api/server/FUhJnnJ9?compact=true&style=flat"/></a>
|
||||
<a href="https://huggingface.co/spaces/Logspace/LangFlow"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="HuggingFace Spaces"></a>
|
||||
<a href="https://discord.gg/EqksyE2EX9"><img alt="Discord Server" src="https://dcbadge.vercel.app/api/server/EqksyE2EX9?compact=true&style=flat"/></a>
|
||||
<a href="https://huggingface.co/spaces/Logspace/Langflow"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="HuggingFace Spaces"></a>
|
||||
</p>
|
||||
|
||||
<a href="https://github.com/logspace-ai/langflow">
|
||||
<img width="100%" src="https://github.com/logspace-ai/langflow/blob/main/img/langflow-demo.gif?raw=true"></a>
|
||||
|
||||
|
||||
<p>
|
||||
</p>
|
||||
|
||||
## 📦 Installation
|
||||
# Table of Contents
|
||||
|
||||
- [⛓️ Langflow](#️-langflow)
|
||||
- [Table of Contents](#table-of-contents)
|
||||
- [📦 Installation](#-installation)
|
||||
- [Locally](#locally)
|
||||
- [HuggingFace Spaces](#huggingface-spaces)
|
||||
- [🖥️ Command Line Interface (CLI)](#️-command-line-interface-cli)
|
||||
- [Usage](#usage)
|
||||
- [Environment Variables](#environment-variables)
|
||||
- [Deployment](#deployment)
|
||||
- [Deploy Langflow on Google Cloud Platform](#deploy-langflow-on-google-cloud-platform)
|
||||
- [Deploy Langflow on Jina AI Cloud](#deploy-langflow-on-jina-ai-cloud)
|
||||
- [API Usage](#api-usage)
|
||||
- [Deploy on Railway](#deploy-on-railway)
|
||||
- [Deploy on Render](#deploy-on-render)
|
||||
- [🎨 Creating Flows](#-creating-flows)
|
||||
- [👋 Contributing](#-contributing)
|
||||
- [📄 License](#-license)
|
||||
|
||||
# 📦 Installation
|
||||
|
||||
### <b>Locally</b>
|
||||
You can install LangFlow from pip:
|
||||
|
||||
You can install Langflow from pip:
|
||||
|
||||
```shell
|
||||
# This installs the package without dependencies for local models
|
||||
pip install langflow
|
||||
```
|
||||
|
||||
To use local models (e.g llama-cpp-python) run:
|
||||
|
||||
```shell
|
||||
pip install langflow[local]
|
||||
```
|
||||
|
||||
This will install the following dependencies:
|
||||
|
||||
- [CTransformers](https://github.com/marella/ctransformers)
|
||||
- [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
|
||||
- [sentence-transformers](https://github.com/UKPLab/sentence-transformers)
|
||||
|
||||
You can still use models from projects like LocalAI
|
||||
|
||||
Next, run:
|
||||
|
||||
```shell
|
||||
python -m langflow
|
||||
```
|
||||
|
||||
or
|
||||
|
||||
```shell
|
||||
langflow
|
||||
langflow # or langflow --help
|
||||
```
|
||||
|
||||
### Deploy Langflow on Google Cloud Platform
|
||||
### HuggingFace Spaces
|
||||
|
||||
You can also check it out on [HuggingFace Spaces](https://huggingface.co/spaces/Logspace/Langflow) and run it in your browser! You can even clone it and have your own copy of Langflow to play with.
|
||||
|
||||
# 🖥️ Command Line Interface (CLI)
|
||||
|
||||
Langflow provides a command-line interface (CLI) for easy management and configuration.
|
||||
|
||||
## Usage
|
||||
|
||||
You can run the Langflow using the following command:
|
||||
|
||||
```shell
|
||||
langflow [OPTIONS]
|
||||
```
|
||||
|
||||
Each option is detailed below:
|
||||
|
||||
- `--help`: Displays all available options.
|
||||
- `--host`: Defines the host to bind the server to. Can be set using the `LANGFLOW_HOST` environment variable. The default is `127.0.0.1`.
|
||||
- `--workers`: Sets the number of worker processes. Can be set using the `LANGFLOW_WORKERS` environment variable. The default is `1`.
|
||||
- `--timeout`: Sets the worker timeout in seconds. The default is `60`.
|
||||
- `--port`: Sets the port to listen on. Can be set using the `LANGFLOW_PORT` environment variable. The default is `7860`.
|
||||
- `--config`: Defines the path to the configuration file. The default is `config.yaml`.
|
||||
- `--env-file`: Specifies the path to the .env file containing environment variables. The default is `.env`.
|
||||
- `--log-level`: Defines the logging level. Can be set using the `LANGFLOW_LOG_LEVEL` environment variable. The default is `critical`.
|
||||
- `--components-path`: Specifies the path to the directory containing custom components. Can be set using the `LANGFLOW_COMPONENTS_PATH` environment variable. The default is `langflow/components`.
|
||||
- `--log-file`: Specifies the path to the log file. Can be set using the `LANGFLOW_LOG_FILE` environment variable. The default is `logs/langflow.log`.
|
||||
- `--cache`: Selects the type of cache to use. Options are `InMemoryCache` and `SQLiteCache`. Can be set using the `LANGFLOW_LANGCHAIN_CACHE` environment variable. The default is `SQLiteCache`.
|
||||
- `--jcloud/--no-jcloud`: Toggles the option to deploy on Jina AI Cloud. The default is `no-jcloud`.
|
||||
- `--dev/--no-dev`: Toggles the development mode. The default is `no-dev`.
|
||||
- `--database-url`: Sets the database URL to connect to. If not provided, a local SQLite database will be used. Can be set using the `LANGFLOW_DATABASE_URL` environment variable.
|
||||
- `--path`: Specifies the path to the frontend directory containing build files. This option is for development purposes only. Can be set using the `LANGFLOW_FRONTEND_PATH` environment variable.
|
||||
- `--open-browser/--no-open-browser`: Toggles the option to open the browser after starting the server. Can be set using the `LANGFLOW_OPEN_BROWSER` environment variable. The default is `open-browser`.
|
||||
- `--remove-api-keys/--no-remove-api-keys`: Toggles the option to remove API keys from the projects saved in the database. Can be set using the `LANGFLOW_REMOVE_API_KEYS` environment variable. The default is `no-remove-api-keys`.
|
||||
- `--install-completion [bash|zsh|fish|powershell|pwsh]`: Installs completion for the specified shell.
|
||||
- `--show-completion [bash|zsh|fish|powershell|pwsh]`: Shows completion for the specified shell, allowing you to copy it or customize the installation.
|
||||
|
||||
### Environment Variables
|
||||
|
||||
You can configure many of the CLI options using environment variables. These can be exported in your operating system or added to a `.env` file and loaded using the `--env-file` option.
|
||||
|
||||
A sample `.env` file named `.env.example` is included with the project. Copy this file to a new file named `.env` and replace the example values with your actual settings. If you're setting values in both your OS and the `.env` file, the `.env` settings will take precedence.
|
||||
|
||||
# Deployment
|
||||
|
||||
## Deploy Langflow on Google Cloud Platform
|
||||
|
||||
Follow our step-by-step guide to deploy Langflow on Google Cloud Platform (GCP) using Google Cloud Shell. The guide is available in the [**Langflow in Google Cloud Platform**](GCP_DEPLOYMENT.md) document.
|
||||
|
||||
|
|
@ -51,16 +135,17 @@ Alternatively, click the **"Open in Cloud Shell"** button below to launch Google
|
|||
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md)
|
||||
|
||||
|
||||
### Deploy Langflow on [Jina AI Cloud](https://github.com/jina-ai/langchain-serve)
|
||||
## Deploy Langflow on [Jina AI Cloud](https://github.com/jina-ai/langchain-serve)
|
||||
|
||||
Langflow integrates with langchain-serve to provide a one-command deployment to Jina AI Cloud.
|
||||
|
||||
Start by installing `langchain-serve` with
|
||||
Start by installing `langchain-serve` with
|
||||
|
||||
```bash
|
||||
pip install langflow[deploy]
|
||||
# or
|
||||
pip install -U langchain-serve
|
||||
```
|
||||
```
|
||||
|
||||
Then, run:
|
||||
|
||||
|
|
@ -77,33 +162,33 @@ langflow --jcloud
|
|||
<details>
|
||||
<summary>Show complete (example) output</summary>
|
||||
|
||||
```text
|
||||
🚀 Deploying Langflow server on Jina AI Cloud
|
||||
╭───────────────────────── 🎉 Flow is available! ──────────────────────────╮
|
||||
│ │
|
||||
│ ID langflow-e3dd8820ec │
|
||||
│ Gateway (Websocket) wss://langflow-e3dd8820ec.wolf.jina.ai │
|
||||
│ Dashboard https://dashboard.wolf.jina.ai/flow/e3dd8820ec │
|
||||
│ │
|
||||
╰──────────────────────────────────────────────────────────────────────────╯
|
||||
╭──────────────┬──────────────────────────────────────────────────────────────────────────────╮
|
||||
│ App ID │ langflow-e3dd8820ec │
|
||||
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
|
||||
│ Phase │ Serving │
|
||||
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
|
||||
│ Endpoint │ wss://langflow-e3dd8820ec.wolf.jina.ai │
|
||||
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
|
||||
│ App logs │ dashboards.wolf.jina.ai │
|
||||
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
|
||||
│ Swagger UI │ https://langflow-e3dd8820ec.wolf.jina.ai/docs │
|
||||
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
|
||||
│ OpenAPI JSON │ https://langflow-e3dd8820ec.wolf.jina.ai/openapi.json │
|
||||
╰──────────────┴──────────────────────────────────────────────────────────────────────────────╯
|
||||
```text
|
||||
🚀 Deploying Langflow server on Jina AI Cloud
|
||||
╭───────────────────────── 🎉 Flow is available! ──────────────────────────╮
|
||||
│ │
|
||||
│ ID langflow-e3dd8820ec │
|
||||
│ Gateway (Websocket) wss://langflow-e3dd8820ec.wolf.jina.ai │
|
||||
│ Dashboard https://dashboard.wolf.jina.ai/flow/e3dd8820ec │
|
||||
│ │
|
||||
╰──────────────────────────────────────────────────────────────────────────╯
|
||||
╭──────────────┬──────────────────────────────────────────────────────────────────────────────╮
|
||||
│ App ID │ langflow-e3dd8820ec │
|
||||
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
|
||||
│ Phase │ Serving │
|
||||
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
|
||||
│ Endpoint │ wss://langflow-e3dd8820ec.wolf.jina.ai │
|
||||
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
|
||||
│ App logs │ dashboards.wolf.jina.ai │
|
||||
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
|
||||
│ Swagger UI │ https://langflow-e3dd8820ec.wolf.jina.ai/docs │
|
||||
├──────────────┼──────────────────────────────────────────────────────────────────────────────┤
|
||||
│ OpenAPI JSON │ https://langflow-e3dd8820ec.wolf.jina.ai/openapi.json │
|
||||
╰──────────────┴──────────────────────────────────────────────────────────────────────────────╯
|
||||
|
||||
🎉 Langflow server successfully deployed on Jina AI Cloud 🎉
|
||||
🔗 Click on the link to open the server (please allow ~1-2 minutes for the server to startup): https://langflow-e3dd8820ec.wolf.jina.ai/
|
||||
📖 Read more about managing the server: https://github.com/jina-ai/langchain-serve
|
||||
```
|
||||
🎉 Langflow server successfully deployed on Jina AI Cloud 🎉
|
||||
🔗 Click on the link to open the server (please allow ~1-2 minutes for the server to startup): https://langflow-e3dd8820ec.wolf.jina.ai/
|
||||
📖 Read more about managing the server: https://github.com/jina-ai/langchain-serve
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
|
@ -114,41 +199,64 @@ You can use Langflow directly on your browser, or use the API endpoints on Jina
|
|||
<details>
|
||||
<summary>Show API usage (with python)</summary>
|
||||
|
||||
```python
|
||||
import json
|
||||
import requests
|
||||
```python
|
||||
import requests
|
||||
|
||||
FLOW_PATH = "Time_traveller.json"
|
||||
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": {}
|
||||
}
|
||||
|
||||
# HOST = 'http://localhost:7860'
|
||||
HOST = 'https://langflow-f1ed20e309.wolf.jina.ai'
|
||||
API_URL = f'{HOST}/predict'
|
||||
def run_flow(message: str, flow_id: str, tweaks: dict = None) -> dict:
|
||||
"""
|
||||
Run a flow with a given message and optional tweaks.
|
||||
|
||||
def predict(message):
|
||||
with open(FLOW_PATH, "r") as f:
|
||||
json_data = json.load(f)
|
||||
payload = {'exported_flow': json_data, 'message': message}
|
||||
response = requests.post(API_URL, json=payload)
|
||||
return response.json()
|
||||
: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}
|
||||
|
||||
predict('Take me to 1920s Bangalore')
|
||||
```
|
||||
if tweaks:
|
||||
payload["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!"
|
||||
}
|
||||
```
|
||||
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!"
|
||||
}
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
> 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
|
||||
|
||||
## 🎨 Creating Flows
|
||||
[](https://railway.app/template/Emy2sU?referralCode=MnPSdg)
|
||||
|
||||
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.
|
||||
## Deploy on Render
|
||||
|
||||
<a href="https://render.com/deploy?repo=https://github.com/logspace-ai/langflow/tree/main">
|
||||
<img src="https://render.com/images/deploy-to-render-button.svg" alt="Deploy to Render" />
|
||||
</a>
|
||||
|
||||
# 🎨 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.
|
||||
|
||||
Explore by editing prompt parameters, link chains and agents, track an agent's thought process, and export your flow.
|
||||
|
||||
|
|
@ -161,18 +269,20 @@ from langflow import load_flow_from_json
|
|||
|
||||
flow = load_flow_from_json("path/to/flow.json")
|
||||
# Now you can use it like any chain
|
||||
flow("Hey, have you heard of LangFlow?")
|
||||
flow("Hey, have you heard of Langflow?")
|
||||
```
|
||||
|
||||
# 👋 Contributing
|
||||
|
||||
## 👋 Contributing
|
||||
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.
|
||||
|
||||
We welcome contributions from developers of all levels to our open-source project on GitHub. If you'd like to contribute, please check our [contributing guidelines](./CONTRIBUTING.md) and help make LangFlow more accessible.
|
||||
Join our [Discord](https://discord.com/invite/EqksyE2EX9) server to ask questions, make suggestions and showcase your projects! 🦾
|
||||
|
||||
<p>
|
||||
</p>
|
||||
|
||||
[](https://star-history.com/#logspace-ai/langflow&Date)
|
||||
|
||||
# 📄 License
|
||||
|
||||
## 📄 License
|
||||
|
||||
LangFlow is released under the MIT License. See the LICENSE file for details.
|
||||
Langflow is released under the MIT License. See the LICENSE file for details.
|
||||
|
|
|
|||
41
docs/README.md
Normal file
41
docs/README.md
Normal file
|
|
@ -0,0 +1,41 @@
|
|||
# Website
|
||||
|
||||
This website is built using [Docusaurus 2](https://docusaurus.io/), a modern static website generator.
|
||||
|
||||
### Installation
|
||||
|
||||
```
|
||||
$ yarn
|
||||
```
|
||||
|
||||
### Local Development
|
||||
|
||||
```
|
||||
$ yarn start
|
||||
```
|
||||
|
||||
This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
|
||||
|
||||
### Build
|
||||
|
||||
```
|
||||
$ yarn build
|
||||
```
|
||||
|
||||
This command generates static content into the `build` directory and can be served using any static contents hosting service.
|
||||
|
||||
### Deployment
|
||||
|
||||
Using SSH:
|
||||
|
||||
```
|
||||
$ USE_SSH=true yarn deploy
|
||||
```
|
||||
|
||||
Not using SSH:
|
||||
|
||||
```
|
||||
$ GIT_USER=<Your GitHub username> yarn deploy
|
||||
```
|
||||
|
||||
If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch.
|
||||
3
docs/babel.config.js
Normal file
3
docs/babel.config.js
Normal file
|
|
@ -0,0 +1,3 @@
|
|||
module.exports = {
|
||||
presets: [require.resolve('@docusaurus/core/lib/babel/preset')],
|
||||
};
|
||||
91
docs/docs/components/agents.mdx
Normal file
91
docs/docs/components/agents.mdx
Normal file
|
|
@ -0,0 +1,91 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
|
||||
# Agents
|
||||
|
||||
<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
|
||||
<p>
|
||||
We appreciate your understanding as we polish our documentation – it may contain some rough edges. Share your feedback or report issues to help us improve! 🛠️📝
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
|
||||
Agents are components that use reasoning to make decisions and take actions, designed to autonomously perform tasks or provide services with some degree of “freedom” (or agency). They combine the power of LLM chaining processes with access to external tools such as APIs to interact with applications and accomplish tasks.
|
||||
|
||||
---
|
||||
|
||||
### AgentInitializer
|
||||
|
||||
The `AgentInitializer` component is a quick way to construct a zero-shot agent from a language model (LLM) and tools.
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the `AgentInitializer`.
|
||||
- **Memory:** Used to add memory functionality to an agent. It allows the agent to store and retrieve information from previous conversations.
|
||||
- **Tools:** Tools that the agent will have access to.
|
||||
- **Agent:** The type of agent to be instantiated. Current supported: `zero-shot-react-description`, `react-docstore`, `self-ask-with-search,conversational-react-description` and `openai-functions`.
|
||||
|
||||
---
|
||||
|
||||
### CSVAgent
|
||||
|
||||
A `CSVAgent` is an agent that is designed to interact with CSV (Comma-Separated Values) files. CSV files are a common format for storing tabular data, where each row represents a record and each column represents a field. The CSV agent can perform various tasks, such as reading and writing CSV files, processing the data, and generating tables. It can extract information from the CSV file, manipulate the data, and perform operations like filtering, sorting, and aggregating.
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the `CSVAgent`.
|
||||
- **path:** The file path to the CSV data.
|
||||
|
||||
---
|
||||
|
||||
### JSONAgent
|
||||
|
||||
The `JSONAgent` deals with JSON (JavaScript Object Notation) data. Similar to the CSVAgent, it works with a language model (LLM) and a toolkit designed for JSON manipulation. This agent can iteratively explore a JSON blob to find the information needed to answer the user's question. It can list keys, get values, and navigate through the structure of the JSON object.
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the `JSONAgent`.
|
||||
- **Toolkit:** Toolkit that the agent will have access to.
|
||||
|
||||
---
|
||||
|
||||
### SQLAgent
|
||||
|
||||
A `SQLAgent` is an agent that is designed to interact with SQL databases. It is capable of performing various tasks, such as querying the database, retrieving data, and executing SQL statements. The agent can provide information about the structure of the database, including the tables and their schemas. It can also perform operations like inserting, updating, and deleting data in the database. The SQL agent is a helpful tool for managing and working with SQL databases efficiently.
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the `SQLAgent`.
|
||||
- **database_uri:** A string representing the connection URI for the SQL database.
|
||||
|
||||
---
|
||||
|
||||
### VectorStoreAgent
|
||||
|
||||
The `VectorStoreAgent` is designed to work with a vector store – a data structure used for storing and querying vector-based representations of data. The `VectorStoreAgent` can query the vector store to find relevant information based on user inputs.
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the `VectorStoreAgent`.
|
||||
- **Vector Store Info:** `VectorStoreInfo` to use in the `VectorStoreAgent`.
|
||||
|
||||
---
|
||||
|
||||
### VectorStoreRouterAgent
|
||||
|
||||
The `VectorStoreRouterAgent` is a custom agent that takes a vector store router as input. It is typically used when there’s a need to retrieve information from multiple vector stores. These can be connected through a `VectorStoreRouterToolkit` and sent over to the `VectorStoreRouterAgent`. An agent configured with multiple vector stores can route queries to the appropriate store based on the context.
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the `VectorStoreRouterAgent`.
|
||||
- **Vector Store Router Toolkit:** `VectorStoreRouterToolkit` to use in the `VectorStoreRouterAgent`.
|
||||
|
||||
---
|
||||
|
||||
### ZeroShotAgent
|
||||
|
||||
The `ZeroShotAgent` is an agent that uses the ReAct framework to determine which tool to use based solely on the tool's description. It can be configured with any number of tools and requires a description for each tool. The agent is designed to be the most general-purpose action agent. It uses an `LLMChain` to determine which actions to take and in what order.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Allowed Tools:** Tools that the agent will have access to.
|
||||
- **LLM Chain:** LLM Chain to be used by the agent.
|
||||
148
docs/docs/components/chains.mdx
Normal file
148
docs/docs/components/chains.mdx
Normal file
|
|
@ -0,0 +1,148 @@
|
|||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Chains
|
||||
|
||||
<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
|
||||
<p>
|
||||
We appreciate your understanding as we polish our documentation – it may
|
||||
contain some rough edges. Share your feedback or report issues to help us
|
||||
improve! 🛠️📝
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
Chains, in the context of language models, refer to a series of calls made to a language model. It allows for the output of one call to be used as the input for another call. Different types of chains allow for different levels of complexity. Chains are useful for creating pipelines and executing specific scenarios.
|
||||
|
||||
---
|
||||
|
||||
### CombineDocsChain
|
||||
|
||||
The `CombineDocsChain` incorporates methods to combine or aggregate loaded documents for question-answering functionality.
|
||||
|
||||
<Admonition type="info">
|
||||
|
||||
Works as a proxy of LangChain’s [documents](https://python.langchain.com/docs/modules/chains/document/) chains generated by the `load_qa_chain` function.
|
||||
|
||||
</Admonition>
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the chain.
|
||||
- **chain_type:** The chain type to be used. Each one of them applies a different “combination strategy”.
|
||||
|
||||
- **stuff**: The stuff [documents](https://python.langchain.com/docs/modules/chains/document/stuff) chain (“stuff" as in "to stuff" or "to fill") is the most straightforward of _the_ document chains. It takes a list of documents, inserts them all into a prompt, and passes that prompt to an LLM. This chain is well-suited for applications where documents are small and only a few are passed in for most calls.
|
||||
- **map_reduce**: The map-reduce [documents](https://python.langchain.com/docs/modules/chains/document/map_reduce) chain first applies an LLM chain to each document individually (the Map step), treating the chain output as a new document. It then passes all the new documents to a separate combined documents chain to get a single output (the Reduce step). It can optionally first compress or collapse the mapped documents to make sure that they fit in the combined documents chain (which will often pass them to an LLM). This compression step is performed recursively if necessary.
|
||||
- **map_rerank**: The map re-rank [documents](https://python.langchain.com/docs/modules/chains/document/map_rerank) chain runs an initial prompt on each document that not only tries to complete a task but also gives a score for how certain it is in its answer. The highest-scoring response is returned.
|
||||
- **refine**: The refine [documents](https://python.langchain.com/docs/modules/chains/document/refine) chain constructs a response by looping over the input documents and iteratively updating its answer. For each document, it passes all non-document inputs, the current document, and the latest intermediate answer to an LLM chain to get a new answer.
|
||||
|
||||
Since the Refine chain only passes a single document to the LLM at a time, it is well-suited for tasks that require analyzing more documents than can fit in the model's context. The obvious tradeoff is that this chain will make far more LLM calls than, for example, the Stuff documents chain. There are also certain tasks that are difficult to accomplish iteratively. For example, the Refine chain can perform poorly when documents frequently cross-reference one another or when a task requires detailed information from many documents.
|
||||
|
||||
---
|
||||
|
||||
### ConversationChain
|
||||
|
||||
The `ConversationChain` is a straightforward chain for interactive conversations with a language model, making it ideal for chatbots or virtual assistants. It allows for dynamic conversations, question-answering, and complex dialogues.
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the chain.
|
||||
- **Memory:** Default memory store.
|
||||
- **input_key:** Used to specify the key under which the user input will be stored in the conversation memory. It allows you to provide the user's input to the chain for processing and generating a response.
|
||||
- **output_key:** Used to specify the key under which the generated response will be stored in the conversation memory. It allows you to retrieve the response using the specified key.
|
||||
- **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 be helpful for debugging and understanding the chain's behavior. If set to False, it will suppress the verbose output — defaults to `False`.
|
||||
|
||||
---
|
||||
|
||||
### ConversationalRetrievalChain
|
||||
|
||||
The `ConversationalRetrievalChain` extracts information and provides answers by combining document search and question-answering abilities.
|
||||
|
||||
<Admonition type="info">
|
||||
|
||||
A retriever is a component that finds documents based on a query. It doesn't store the documents themselves, but it returns the ones that match the query.
|
||||
|
||||
</Admonition >
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the chain.
|
||||
- **Memory:** Default memory store.
|
||||
- **Retriever:** The retriever used to fetch relevant documents.
|
||||
- **chain_type:** The chain type to be used. Each one of them applies a different “combination strategy”.
|
||||
|
||||
- **stuff**: The stuff [documents](https://python.langchain.com/docs/modules/chains/document/stuff) chain (“stuff" as in "to stuff" or "to fill") is the most straightforward of _the_ document chains. It takes a list of documents, inserts them all into a prompt, and passes that prompt to an LLM. This chain is well-suited for applications where documents are small and only a few are passed in for most calls.
|
||||
- **map_reduce**: The map-reduce [documents](https://python.langchain.com/docs/modules/chains/document/map_reduce) chain first applies an LLM chain to each document individually (the Map step), treating the chain output as a new document. It then passes all the new documents to a separate combined documents chain to get a single output (the Reduce step). It can optionally first compress or collapse the mapped documents to make sure that they fit in the combined documents chain (which will often pass them to an LLM). This compression step is performed recursively if necessary.
|
||||
- **map_rerank**: The map re-rank [documents](https://python.langchain.com/docs/modules/chains/document/map_rerank) chain runs an initial prompt on each document that not only tries to complete a task but also gives a score for how certain it is in its answer. The highest-scoring response is returned.
|
||||
- **refine**: The refine [documents](https://python.langchain.com/docs/modules/chains/document/refine) chain constructs a response by looping over the input documents and iteratively updating its answer. For each document, it passes all non-document inputs, the current document, and the latest intermediate answer to an LLM chain to get a new answer.
|
||||
|
||||
Since the Refine chain only passes a single document to the LLM at a time, it is well-suited for tasks that require analyzing more documents than can fit in the model's context. The obvious tradeoff is that this chain will make far more LLM calls than, for example, the Stuff documents chain. There are also certain tasks that are difficult to accomplish iteratively. For example, the Refine chain can perform poorly when documents frequently cross-reference one another or when a task requires detailed information from many documents.
|
||||
|
||||
- **return_source_documents:** Used to specify whether or not to include the source documents that were used to answer the question in the output. When set to `True`, source documents will be included in the output along with the generated answer. This can be useful for providing additional context or references to the user — defaults to `True`.
|
||||
- **verbose:** Whether or not to run in verbose mode. In verbose mode, intermediate logs will be printed to the console — defaults to `False`.
|
||||
|
||||
---
|
||||
|
||||
### LLMChain
|
||||
|
||||
The `LLMChain` is a straightforward chain that adds functionality around language models. It combines a prompt template with a language model. To use it, create input variables to format the prompt template. The formatted prompt is then sent to the language model, and the generated output is returned as the result of the `LLMChain`.
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the chain.
|
||||
- **Memory:** Default memory store.
|
||||
- **Prompt**: Prompt template object to use in the chain.
|
||||
- **output_key:** This parameter is used to specify which key in the LLM output dictionary should be returned as the final output. By default, the `LLMChain` returns both the input and output key values — defaults to `text`.
|
||||
- **verbose:** Whether or not to run in verbose mode. In verbose mode, intermediate logs will be printed to the console — defaults to `False`.
|
||||
|
||||
---
|
||||
|
||||
### LLMMathChain
|
||||
|
||||
The `LLMMathChain` combines a language model (LLM) and a math calculation component. It allows the user to input math problems and get the corresponding solutions.
|
||||
|
||||
The `LLMMathChain` works by using the language model with an `LLMChain` to understand the input math problem and generate a math expression. It then passes this expression to the math component, which evaluates it and returns the result.
|
||||
|
||||
**Params**
|
||||
|
||||
- **LLM:** Language Model to use in the chain.
|
||||
- **LLMChain:** LLM Chain to use in the chain.
|
||||
- **Memory:** Default memory store.
|
||||
- **input_key:** Used to specify the input value for the mathematical calculation. It allows you to provide the specific values or variables that you want to use in the calculation — defaults to `question`.
|
||||
- **output_key:** Used to specify the key under which the output of the mathematical calculation will be stored. It allows you to retrieve the result of the calculation using the specified key — defaults to `answer`.
|
||||
- **verbose:** Whether or not to run in verbose mode. In verbose mode, intermediate logs will be printed to the console — defaults to `False`.
|
||||
|
||||
---
|
||||
|
||||
### RetrievalQA
|
||||
|
||||
`RetrievalQA` is a chain used to find relevant documents or information to answer a given query. The retriever is responsible for returning the relevant documents based on the query, and the QA component then extracts the answer from those documents. The retrieval QA system combines the capabilities of both the retriever and the QA component to provide accurate and relevant answers to user queries.
|
||||
|
||||
<Admonition type="info">
|
||||
|
||||
A retriever is a component that finds documents based on a query. It doesn't store the documents themselves, but it returns the ones that match the query.
|
||||
|
||||
</Admonition >
|
||||
|
||||
**Params**
|
||||
|
||||
- **Combine Documents Chain:** Chain to use to combine the documents.
|
||||
- **Memory:** Default memory store.
|
||||
- **Retriever:** The retriever used to fetch relevant documents.
|
||||
- **input_key:** This parameter is used to specify the key in the input data that contains the question. It is used to retrieve the question from the input data and pass it to the question-answering model for generating the answer — defaults to `query`.
|
||||
- **output_key:** This parameter is used to specify the key in the output data where the generated answer will be stored. It is used to retrieve the answer from the output data after the question-answering model has generated it — defaults to `result`.
|
||||
- **return_source_documents:** Used to specify whether or not to include the source documents that were used to answer the question in the output. When set to `True`, source documents will be included in the output along with the generated answer. This can be useful for providing additional context or references to the user — defaults to `True`.
|
||||
- **verbose:** Whether or not to run in verbose mode. In verbose mode, intermediate logs will be printed to the console — defaults to `False`.
|
||||
|
||||
---
|
||||
|
||||
### SQLDatabaseChain
|
||||
|
||||
The `SQLDatabaseChain` finds answers to questions using a SQL database. It works by using the language model to understand the SQL query and generate the corresponding SQL code. It then passes the SQL code to the SQL database component, which executes the query on the database and returns the result.
|
||||
|
||||
**Params**
|
||||
|
||||
- **Db:** SQL Database to connect to.
|
||||
- **LLM:** Language Model to use in the chain.
|
||||
- **Prompt:** Prompt template to translate natural language to SQL.
|
||||
92
docs/docs/components/custom.mdx
Normal file
92
docs/docs/components/custom.mdx
Normal file
|
|
@ -0,0 +1,92 @@
|
|||
import Admonition from "@theme/Admonition";
|
||||
|
||||
# Custom Components
|
||||
|
||||
Used to create a custom component, a special type of Langflow component that allows users to extend the functionality of the platform by creating their own reusable and configurable components from a Python script.
|
||||
|
||||
To use a custom component, follow these steps:
|
||||
|
||||
- Create a class that inherits from _`langflow.CustomComponent`_ and contains a _`build`_ method.
|
||||
- Use arguments with [Type Annotations (or Type Hints)](https://docs.python.org/3/library/typing.html) of the _`build`_ method to create component fields.
|
||||
- If applicable, use the _`build_config`_ method to customize how these fields look and behave.
|
||||
|
||||
<Admonition type="info" label="Tip">
|
||||
|
||||
For an in-depth explanation of custom components, their rules, and applications, make sure to read [Custom Component guidelines](../guidelines/custom-component).
|
||||
|
||||
</Admonition>
|
||||
|
||||
**Params**
|
||||
|
||||
- **Code:** The Python code to define the component.
|
||||
|
||||
## The CustomComponent Class
|
||||
|
||||
The CustomComponent class serves as the foundation for creating custom components. By inheriting this class, users can create new, configurable components, tailored to their specific requirements.
|
||||
|
||||
**Methods**
|
||||
|
||||
- **build**: This method is required within a Custom Component class. It defines the component's functionality and specifies how it processes input data to produce output data. This method is called when the component is built (i.e., when you click the _Build_ ⚡ button in the canvas).
|
||||
|
||||
The type annotations of the _`build`_ instance method are used to create the fields of the component.
|
||||
|
||||
| Supported Types |
|
||||
| --------------------------------------------------------- |
|
||||
| _`str`_, _`int`_, _`float`_, _`bool`_, _`list`_, _`dict`_ |
|
||||
| _`langchain.chains.base.Chain`_ |
|
||||
| _`langchain.PromptTemplate`_ |
|
||||
| _`langchain.llms.base.BaseLLM`_ |
|
||||
| _`langchain.Tool`_ |
|
||||
| _`langchain.document_loaders.base.BaseLoader`_ |
|
||||
| _`langchain.schema.Document`_ |
|
||||
| _`langchain.text_splitters.TextSplitter`_ |
|
||||
| _`langchain.vectorstores.base.VectorStore`_ |
|
||||
| _`langchain.embeddings.base.Embeddings`_ |
|
||||
| _`langchain.schema.BaseRetriever`_ |
|
||||
|
||||
<Admonition type="info">
|
||||
Unlike Langchain types, base Python types do not add a
|
||||
[handle](../guidelines/components) to the field by default. To add handles,
|
||||
use the _`input_types`_ key in the _`build_config`_ method.
|
||||
</Admonition>
|
||||
|
||||
- **build_config**: Used to define the configuration fields of the component (if applicable). It should always return a dictionary with specific keys representing the field names and corresponding configurations. This method is called when the code is processed (i.e., when you click _Check and Save_ in the code editor). It must follow the format described below:
|
||||
|
||||
- Top-level keys are field names.
|
||||
- Their values are also of type _`dict`_. They specify the behavior of the generated fields.
|
||||
|
||||
Below are the available keys used to configure component fields:
|
||||
|
||||
| Key | Description |
|
||||
| -------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| _`field_type: str`_ | The type of the field (can be any of the types supported by the _`build`_ method). |
|
||||
| _`is_list: bool`_ | If the field can be a list of values, meaning that the user can manually add more inputs to the same field. |
|
||||
| _`options: List[str]`_ | When defined, the field becomes a dropdown menu where a list of strings defines the options to be displayed. If the _`value`_ attribute is set to one of the options, that option becomes default. For this parameter to work, _`field_type`_ should invariably be _`str`_. |
|
||||
| _`multiline: bool`_ | Defines if a string field opens a text editor. Useful for longer texts. |
|
||||
| _`input_types: List[str]`_ | Used when you want a _`str`_ field to have connectable handles. |
|
||||
| _`display_name: str`_ | Defines the name of the field. |
|
||||
| _`advanced: bool`_ | Hide the field in the canvas view (displayed component settings only). Useful when a field is for advanced users. |
|
||||
| _`password: bool`_ | To mask the input text. Useful to hide sensitive text (e.g. API keys). |
|
||||
| _`required: bool`_ | Makes the field required. |
|
||||
| _`info: str`_ | Adds a tooltip to the field. |
|
||||
| _`file_types: List[str]`_ | This is a requirement if the _`field_type`_ is _file_. Defines which file types will be accepted. For example, _json_, _yaml_ or _yml_. |
|
||||
|
||||
- The CustomComponent class also provides helpful methods for specific tasks (e.g., to load and use other flows from the Langflow platform):
|
||||
|
||||
| Method Name | Description |
|
||||
| -------------- | ------------------------------------------------------------------- |
|
||||
| _`list_flows`_ | Returns a list of Flow objects with an _`id`_ and a _`name`_. |
|
||||
| _`get_flow`_ | Returns a Flow object. Parameters are _`flow_name`_ or _`flow_id`_. |
|
||||
| _`load_flow`_ | Loads a flow from a given _`id`_. |
|
||||
|
||||
- Useful attributes:
|
||||
|
||||
| Attribute Name | Description |
|
||||
| -------------- | ----------------------------------------------------------------------------- |
|
||||
| _`repr_value`_ | Displays the value it receives in the _`build`_ method. Useful for debugging. |
|
||||
|
||||
<Admonition type="info" label="Tip">
|
||||
|
||||
Check out the [FlowRunner](../examples/flow-runner) example to understand how to call a flow from a custom component.
|
||||
|
||||
</Admonition>
|
||||
75
docs/docs/components/embeddings.mdx
Normal file
75
docs/docs/components/embeddings.mdx
Normal file
|
|
@ -0,0 +1,75 @@
|
|||
import Admonition from '@theme/Admonition';
|
||||
|
||||
# Embeddings
|
||||
|
||||
<Admonition type="caution" icon="🚧" title="ZONE UNDER CONSTRUCTION">
|
||||
<p>
|
||||
We appreciate your understanding as we polish our documentation – it may contain some rough edges. Share your feedback or report issues to help us improve! 🛠️📝
|
||||
</p>
|
||||
</Admonition>
|
||||
|
||||
Embeddings are vector representations of text that capture the semantic meaning of the text. They are created using text embedding models and allow us to think about the text in a vector space, enabling us to perform tasks like semantic search, where we look for pieces of text that are most similar in the vector space.
|
||||
|
||||
---
|
||||
|
||||
### CohereEmbeddings
|
||||
|
||||
Used to load [Cohere’s](https://cohere.com/) embedding models.
|
||||
|
||||
**Params**
|
||||
|
||||
- **cohere_api_key:** Holds the API key required to authenticate with the Cohere service.
|
||||
|
||||
- **model:** The language model used for embedding text documents and performing queries —defaults to `embed-english-v2.0`.
|
||||
|
||||
- **truncate:** Used to specify whether or not to truncate the input text. Truncation is useful when dealing with long texts that exceed the model's maximum input length. By truncating the text, the user can ensure that it fits within the model's constraints.
|
||||
|
||||
---
|
||||
|
||||
### HuggingFaceEmbeddings
|
||||
|
||||
Used to load [HuggingFace’s](https://huggingface.co) embedding models.
|
||||
|
||||
**Params**
|
||||
|
||||
- **cache_folder:** Used to specify the folder where the embeddings will be cached. When embeddings are computed for a text, they can be stored in the cache folder so that they can be reused later without the need to recompute them. This can improve the performance of the application by avoiding redundant computations.
|
||||
|
||||
- **encode_kwargs:** Used to pass additional keyword arguments to the encoding method of the underlying HuggingFace model. These keyword arguments can be used to customize the encoding process, such as specifying the maximum length of the input sequence or enabling truncation or padding.
|
||||
|
||||
- **model_kwargs:** Used to customize the behavior of the model, such as specifying the model architecture, the tokenizer, or any other model-specific configuration options. By using `model_kwargs`, the user can configure the HuggingFace model according to specific needs and preferences.
|
||||
|
||||
- **model_name:** Used to specify the name or identifier of the HuggingFace model that will be used for generating embeddings. It allows users to choose a specific pre-trained model from the Hugging Face model hub — defaults to `sentence-transformers/all-mpnet-base-v2`.
|
||||
|
||||
---
|
||||
|
||||
### OpenAIEmbeddings
|
||||
|
||||
Used to load [OpenAI’s](https://openai.com/) embedding models.
|
||||
|
||||
**Params**
|
||||
|
||||
- **chunk_size:** Determines the maximum size of each chunk of text that is processed for embedding. If any of the incoming text chunks exceeds `chunk_size` characters, it will be split into multiple chunks of size `chunk_size` or less before being embedded — defaults to `1000`.
|
||||
|
||||
- **deployment:** Used to specify the deployment name or identifier of the text embedding model. It allows the user to choose a specific deployment of the model to use for embedding. When the deployment is provided, this can be useful when the user has multiple deployments of the same model with different configurations or versions — defaults to `text-embedding-ada-002`.
|
||||
|
||||
- **embedding_ctx_length:** This parameter determines the maximum context length for the text embedding model. It specifies the number of tokens that the model considers when generating embeddings for a piece of text — defaults to `8191` (this means that the model will consider up to 8191 tokens when generating embeddings).
|
||||
|
||||
- **max_retries:** Determines the maximum number of times to retry a request if the model provider returns an error from their API — defaults to `6`.
|
||||
|
||||
- **model:** Defines which pre-trained text embedding model to use — defaults to `text-embedding-ada-002`.
|
||||
|
||||
- **openai_api_base:** Refers to the base URL for the Azure OpenAI resource. It is used to configure the API to connect to the Azure OpenAI service. The base URL can be found in the Azure portal under the user Azure OpenAI resource.
|
||||
|
||||
- **openai_api_key:** Is used to authenticate and authorize access to the OpenAI service.
|
||||
|
||||
- **openai_api_type:** Is used to specify the type of OpenAI API being used, either the regular OpenAI API or the Azure OpenAI API. This parameter allows the `OpenAIEmbeddings` class to connect to the appropriate API service.
|
||||
|
||||
- **openai_api_version:** Is used to specify the version of the OpenAI API being used. This parameter allows the `OpenAIEmbeddings` class to connect to the appropriate version of the OpenAI API service.
|
||||
|
||||
- **openai_organization:** Is used to specify the organization associated with the OpenAI API key. If not provided, the default organization associated with the API key will be used.
|
||||
|
||||
- **openai_proxy:** Proxy enables better budgeting and cost management for making OpenAI API calls, including more transparency into pricing.
|
||||
|
||||
- **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.
|
||||
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