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.devcontainer/demo/README.md
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@ -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

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.env.example Normal file
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@ -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=

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@ -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}}"

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@ -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.

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@ -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

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@ -10,7 +10,7 @@ on:
- "pyproject.toml"
env:
POETRY_VERSION: "1.4.0"
POETRY_VERSION: "1.5.1"
jobs:
if_release:

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.gitignore vendored
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@ -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/*

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@ -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

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"request": "launch",
"module": "uvicorn",
"args": [
"langflow.main:app",
"--factory",
"langflow.main:create_app",
"--port",
"7860",
"--reload",

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CONTRIBUTING.md
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@ -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.

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GCP_DEPLOYMENT.md
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@ -13,7 +13,7 @@ This script sets up a Debian-based VM with the Langflow package, Nginx, and the
## Spot/Preemptible Instance
[![Open in Cloud Shell - Spot Instance](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/genome21/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md)
[![Open in Cloud Shell - Spot Instance](https://gstatic.com/cloudssh/images/open-btn.svg)](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.

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Makefile
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@ -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

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README.md
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@ -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
[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](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
[![Deploy on Railway](https://railway.app/button.svg)](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>
[![Star History Chart](https://api.star-history.com/svg?repos=logspace-ai/langflow&type=Timeline)](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.

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# 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.

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module.exports = {
presets: [require.resolve('@docusaurus/core/lib/babel/preset')],
};

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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.

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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.

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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>

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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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import Admonition from '@theme/Admonition';
# LLMs
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An LLM stands for Large Language Model. It is a core component of Langflow and provides a standard interface for interacting with different LLMs from various providers such as OpenAI, Cohere, and HuggingFace. LLMs are used widely throughout Langflow, including in chains and agents. They can be used to generate text based on a given prompt (or input).
---
### Anthropic
Wrapper around Anthropic's large language models. Find out more at [Anthropic](https://www.anthropic.com).
- **anthropic_api_key:** Used to authenticate and authorize access to the Anthropic API.
- **anthropic_api_url:** Specifies the URL of the Anthropic API to connect to.
- **temperature:** Tunes the degree of randomness in text generations. Should be a non-negative value.
---
### ChatAnthropic
Wrapper around Anthropic's large language model used for chat-based interactions. Find out more at [Anthropic](https://www.anthropic.com).
- **anthropic_api_key:** Used to authenticate and authorize access to the Anthropic API.
- **anthropic_api_url:** Specifies the URL of the Anthropic API to connect to.
- **temperature:** Tunes the degree of randomness in text generations. Should be a non-negative value.
---
### CTransformers
The `CTransformers` component provides access to the Transformer models implemented in C/C++ using the [GGML](https://github.com/ggerganov/ggml) library.
:::info
Make sure to have the `ctransformers` python package installed. Learn more about installation, supported models, and usage [here](https://github.com/marella/ctransformers).
:::
**config:** Configuration for the Transformer models. Check out [config](https://github.com/marella/ctransformers#config). Defaults to:
```
{
"top_k": 40,
"top_p": 0.95,
"temperature": 0.8,
"repetition_penalty": 1.1,
"last_n_tokens": 64,
"seed": -1,
"max_new_tokens": 256,
"stop": null,
"stream": false,
"reset": true,
"batch_size": 8,
"threads": -1,
"context_length": -1,
"gpu_layers": 0
}
```
**model:** The path to a model file or directory or the name of a Hugging Face Hub model repo.
**model_file:** The name of the model file in the repo or directory.
**model_type:** Transformer model to be used. Learn more [here](https://github.com/marella/ctransformers).
---
### ChatOpenAI
Wrapper around [OpenAI's](https://openai.com) chat large language models. This component supports some of the LLMs (Large Language Models) available by OpenAI and is used for tasks such as chatbots, Generative Question-Answering (GQA), and summarization.
- **max_tokens:** The maximum number of tokens to generate in the completion. `-1` returns as many tokens as possible, given the prompt and the model's maximal context size – defaults to `256`.
- **model_kwargs:** Holds any model parameters valid for creating non-specified calls.
- **model_name:** Defines the OpenAI chat model to be used.
- **openai_api_base:** Used to specify the base URL for the OpenAI API. It is typically set to the API endpoint provided by the OpenAI service.
- **openai_api_key:** Key used to authenticate and access the OpenAI API.
- **temperature:** Tunes the degree of randomness in text generations. Should be a non-negative value – defaults to `0.7`.
---
### Cohere
Wrapper around [Cohere's](https://cohere.com) large language models.
- **cohere_api_key:** Holds the API key required to authenticate with the Cohere service.
- **max_tokens:** Maximum number of tokens to predict per generation – defaults to `256`.
- **temperature:** Tunes the degree of randomness in text generations. Should be a non-negative value – defaults to `0.75`.
---
### HuggingFaceHub
Wrapper around [HuggingFace](https://www.huggingface.co/models) models.
:::info
The HuggingFace Hub is an online platform that hosts over 120k models, 20k datasets, and 50k demo apps, all of which are open-source and publicly available. Discover more at [HuggingFace](http://www.huggingface.co).
:::
- **huggingfacehub_api_token:** Token needed to authenticate the API.
- **model_kwargs:** Keyword arguments to pass to the model.
- **repo_id:** Model name to use – defaults to `gpt2`.
- **task:** Task to call the model with. Should be a task that returns `generated_text` or `summary_text`.
---
### LlamaCpp
The `LlamaCpp` component provides access to the `llama.cpp` models.
:::info
Make sure to have the `llama.cpp` python package installed. Learn more about installation, supported models, and usage [here](https://github.com/ggerganov/llama.cpp).
:::
- **echo:** Whether to echo the prompt – defaults to `False`.
- **f16_kv:** Use half-precision for key/value cache – defaults to `True`.
- **last_n_tokens_size:** The number of tokens to look back at when applying the repeat_penalty. Defaults to `64`.
- **logits_all:** Return logits for all tokens, not just the last token Defaults to `False`.
- **logprobs:** The number of logprobs to return. If None, no logprobs are returned.
- **lora_base:** The path to the Llama LoRA base model.
- **lora_path:** The path to the Llama LoRA. If None, no LoRa is loaded.
- **max_tokens:** The maximum number of tokens to generate. Defaults to `256`.
- **model_path:** The path to the Llama model file.
- **n_batch:** Number of tokens to process in parallel. Should be a number between 1 and n_ctx. Defaults to `8`.
- **n_ctx:** Token context window. Defaults to `512`.
- **n_gpu_layers:** Number of layers to be loaded into GPU memory. Default None.
- **n_parts:**Number of parts to split the model into. If -1, the number of parts is automatically determined. Defaults to `-1`.
- **n_threads:** Number of threads to use. If None, the number of threads is automatically determined.
- **repeat_penalty:** The penalty to apply to repeated tokens. Defaults to `1.1`.
- **seed:** Seed. If -1, a random seed is used. Defaults to `-1`.
- **stop:** A list of strings to stop generation when encountered.
- **streaming:** Whether to stream the results, token by token. Defaults to `True`.
- **suffix:** A suffix to append to the generated text. If None, no suffix is appended.
- **tags:** Tags to add to the run trace.
- **temperature:** The temperature to use for sampling. Defaults to `0.8`.
- **top_k:** The top-k value to use for sampling. Defaults to `40`.
- **top_p:** The top-p value to use for sampling. Defaults to `0.95`.
- **use_mlock:** Force the system to keep the model in RAM. Defaults to `False`.
- **use_mmap:** Whether to keep the model loaded in RAM. Defaults to `True`.
- **verbose:** This parameter is used to control the level of detail in the output of the chain. When set to True, it will print out some internal states of the chain while it is being run, which can help debug and understand the chain's behavior. If set to False, it will suppress the verbose output. Defaults to `False`.
- **vocab_only:** Only load the vocabulary, no weights. Defaults to `False`.
---
### OpenAI
Wrapper around [OpenAI's](https://openai.com) large language models.
- **max_tokens:** The maximum number of tokens to generate in the completion. `-1` returns as many tokens as possible, given the prompt and the model's maximal context size – defaults to `256`.
- **model_kwargs:** Holds any model parameters valid for creating non-specified calls.
- **model_name:** Defines the OpenAI model to be used.
- **openai_api_base:** Used to specify the base URL for the OpenAI API. It is typically set to the API endpoint provided by the OpenAI service.
- **openai_api_key:** Key used to authenticate and access the OpenAI API.
- **temperature:** Tunes the degree of randomness in text generations. Should be a non-negative value – defaults to `0.7`.
---
### VertexAI
Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) large language models.
:::info
Vertex AI is a cloud computing platform offered by Google Cloud Platform (GCP). It provides access, management, and development of applications and services through global data centers. To use Vertex AI PaLM, you need to have the [google-cloud-aiplatform](https://pypi.org/project/google-cloud-aiplatform/) Python package installed and credentials configured for your environment.
:::
- **credentials:** The default custom credentials (google.auth.credentials.Credentials) to use.
- **location:** The default location to use when making API calls – defaults to `us-central1`.
- **max_output_tokens:** Token limit determines the maximum amount of text output from one prompt – defaults to `128`.
- **model_name:** The name of the Vertex AI large language model – defaults to `text-bison`.
- **project:** The default GCP project to use when making Vertex API calls.
- **request_parallelism:** The amount of parallelism allowed for requests issued to VertexAI models – defaults to `5`.
- **temperature:** Tunes the degree of randomness in text generations. Should be a non-negative value – defaults to `0`.
- **top_k:** How the model selects tokens for output, the next token is selected from – defaults to `40`.
- **top_p:** Tokens are selected from most probable to least until the sum of their – defaults to `0.95`.
- **tuned_model_name:** The name of a tuned model. If provided, model_name is ignored.
- **verbose:** This parameter is used to control the level of detail in the output of the chain. When set to True, it will print out some internal states of the chain while it is being run, which can help debug and understand the chain's behavior. If set to False, it will suppress the verbose output – defaults to `False`.

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import Admonition from '@theme/Admonition';
# Loaders
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import Admonition from '@theme/Admonition';
# Memories
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Memory is a concept in chat-based applications that allows the system to remember previous interactions. It helps in maintaining the context of the conversation and enables the system to understand new messages in relation to past messages.
---
### ConversationBufferMemory
The `ConversationBufferMemory` component is a type of memory system that plainly stores the last few inputs and outputs of a conversation.
**Params**
- **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.
- **memory_key:** Specifies the prompt variable name where the memory will store and retrieve the chat messages. It allows for the preservation of the conversation history throughout the interaction with the language model – defaults to `chat_history`.
- **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.
- **return_messages:** Determines whether the history should be returned as a string or as a list of messages. If `return_messages` is set to True, the history will be returned as a list of messages. If `return_messages` is set to False or not specified, the history will be returned as a string. The default is `False`.
---
### ConversationBufferWindowMemory
`ConversationBufferWindowMemory` is a variation of the `ConversationBufferMemory` that maintains a list of the recent interactions in a conversation. It only keeps the last K interactions in memory, which can be useful for maintaining a sliding window of the most recent interactions without letting the buffer get too large.
**Params**
- **input_key:** Used to specify the keys in the memory object where the input messages should be stored. It allows for the retrieval and manipulation of input messages.
- **memory_key:** Specifies the prompt variable name where the memory will store and retrieve the chat messages. It allows for the preservation of the conversation history throughout the interaction with the language model. Defaults to `chat_history`.
- **k:** Used to specify the number of interactions or messages that should be stored in the conversation buffer. It determines the size of the sliding window that keeps track of the most recent interactions.
- **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.
- **return_messages:** Determines whether the history should be returned as a string or as a list of messages. If `return_messages` is set to True, the history will be returned as a list of messages. If `return_messages` is set to False or not specified, the history will be returned as a string. The default is `False`.
---
### ConversationEntityMemory
The `ConversationEntityMemory` component incorporates intricate memory structures, specifically a key-value store, for entities referenced in a conversation. This facilitates the storage and retrieval of information related to entities that have been mentioned throughout the conversation.
**Params**
- **Entity Store:** Structure that stores information about specific entities mentioned in a conversation.
- **LLM:** Language Model to use in the `ConversationEntityMemory`.
- **chat_history_key:** Specify a unique identifier for the chat history data associated with a particular entity. This allows for organizing and accessing the chat history data for each entity within the conversation entity memory. Defaults to `history`
- **input_key:** Used to specify the keys in the memory object where the input messages should be stored. It allows for the retrieval and manipulation of input messages.
- **k:** Refers to the number of entities that can be stored in the memory. It determines the maximum number of entities that can be stored and retrieved from the memory object. Defaults to `10`
- **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.
- **return_messages:** Determines whether the history should be returned as a string or as a list of messages. If `return_messages` is set to True, the history will be returned as a list of messages. If `return_messages` is set to False or not specified, the history will be returned as a string. The default is `False`.
---
### ConversationKGMemory
`ConversationKGMemory` is a type of memory that uses a knowledge graph to recreate memory. It allows the extraction of entities and knowledge triplets from a new message, using previous messages as context.
**Params**
- **LLM:** Language Model to use in the `ConversationKGMemory`.
- **input_key:** Used to specify the keys in the memory object where the input messages should be stored. It allows for the retrieval and manipulation of input messages.
- **k:** Represents the number of previous conversation turns that will be stored in the memory. By setting "k" to 2, it means that the memory will retain the previous 2 conversation turns, allowing the model to access and utilize the information from those turns during the conversation. Defaults to `10`
- **memory_key:** Specifies the prompt variable name where the memory will store and retrieve the chat messages. It allows for the preservation of the conversation history throughout the interaction with the language model. Defaults to `chat_history`.
- **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.
- **return_messages:** Determines whether the history should be returned as a string or as a list of messages. If `return_messages` is set to True, the history will be returned as a list of messages. If `return_messages` is set to False or not specified, the history will be returned as a string. The default is `False`.
---
### ConversationSummaryMemory
The `ConversationSummaryMemory` is a memory component that creates a summary of the conversation over time. It condenses information from the conversation and stores the current summary in memory. It is particularly useful for longer conversations where keeping the entire message history in the prompt would take up too many tokens.
**Params**
- **LLM:** Language Model to use in the `ConversationSummaryMemory`.
- **input_key:** Used to specify the keys in the memory object where the input messages should be stored. It allows for the retrieval and manipulation of input messages.
- **memory_key:** Specifies the prompt variable name where the memory will store and retrieve the chat messages. It allows for the preservation of the conversation history throughout the interaction with the language model. Defaults to `chat_history`.
- **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.
- **return_messages:** Determines whether the history should be returned as a string or as a list of messages. If `return_messages` is set to True, the history will be returned as a list of messages. If `return_messages` is set to False or not specified, the history will be returned as a string. The default is `False`.
---
### PostgresChatMessageHistory
The `PostgresChatMessageHistory` is a memory component that allows for the storage and retrieval of chat message history using a PostgreSQL database. The connection to the PostgreSQL database is established using a connection string, which includes the necessary authentication and database information.
**Params**
- **connection_string:** Refers to a string that contains the necessary information to establish a connection to a PostgreSQL database. The `connection_string` typically includes details such as the username, password, host, port, and database name required to connect to the PostgreSQL database. Defaults to `postgresql://postgres:mypassword@localhost/chat_history`
- **session_id:** It is a unique identifier that is used to associate chat message history with a specific session or conversation.
- **table_name:** Refers to the name of the table in the PostgreSQL database where the chat message history will be stored. Defaults to `message_store`
---
### VectorRetrieverMemory
The `VectorRetrieverMemory` is a memory component that allows for the retrieval of vectors based on a given query. It is used to perform vector-based searches and retrievals.
**Params**
- **Retriever:** The retriever used to fetch documents.
- **input_key:** Used to specify the keys in the memory object where the input messages should be stored. It allows for the retrieval and manipulation of input messages.
- **memory_key:** Specifies the prompt variable name where the memory will store and retrieve the chat messages. It allows for the preservation of the conversation history throughout the interaction with the language model – defaults to `chat_history`.
- **return_messages:** Determines whether the history should be returned as a string or as a list of messages. If `return_messages` is set to True, the history will be returned as a list of messages. If `return_messages` is set to False or not specified, the history will be returned as a string – defaults to `False`.

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import Admonition from "@theme/Admonition";
# Prompts
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A prompt refers to the input given to a language model. It is constructed from multiple components and can be parametrized using prompt templates. A prompt template is a reproducible way to generate prompts and allow for easy customization through input variables.
---
### PromptTemplate
The `PromptTemplate` component allows users to create prompts and define variables that provide control over instructing the model. The template can take in a set of variables from the end user and generates the prompt once the conversation is initiated.
<Admonition type="info">
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input of its own. Check out [Prompt
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- **template:** Template used to format an individual request.

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import Admonition from '@theme/Admonition';
# Retrievers
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A retriever is an interface that returns documents given an unstructured query. It is more general than a vector store and does not need to be able to store documents, only to return or retrieve them.
---
### MultiQueryRetriever
The `MultiQueryRetriever` component automates the process of generating multiple queries, retrieves relevant documents for each query, and combines the results to provide a more extensive and diverse set of potentially relevant documents. This approach enhances the effectiveness of the retrieval process and helps overcome the limitations of traditional distance-based retrieval methods.
**Params**
- **LLM:** Language Model to use in the `MultiQueryRetriever`.
- **Prompt:** Prompt to represent a schema for an LLM.
- **Retriever:** The retriever used to fetch documents.
- **parser_key:** This parameter is used to specify the key or attribute name of the parsed output that will be used for retrieval. It determines how the results from the language model are split into a list of queries. Defaults to `lines`, which means that the output from the language model will be split into a list of lines of text. This allows the retriever to retrieve relevant documents based on each line of text separately.

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import Admonition from '@theme/Admonition';
# Text Splitters
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A text splitter is a tool that divides a document or text into smaller chunks or segments. It is used to break down large texts into more manageable pieces for analysis or processing.
---
### CharacterTextSplitter
The `CharacterTextSplitter` is used to split a long text into smaller chunks based on a specified character. It splits the text by trying to keep paragraphs, sentences, and words together as long as possible, as these are semantically related pieces of text.
**Params**
- **Documents:** Input documents to split.
- **chunk_overlap:** Determines the number of characters that overlap between consecutive chunks when splitting text. It specifies how much of the previous chunk should be included in the next chunk.
For example, if the `chunk_overlap` is set to 20 and the `chunk_size` is set to 100, the splitter will create chunks of 100 characters each, but the last 20 characters of each chunk will overlap with the first 20 characters of the next chunk. This allows for a smoother transition between chunks and ensures that no information is lost – defaults to `200`.
- **chunk_size:** Determines the maximum number of characters in each chunk when splitting a text. It specifies the size or length of each chunk.
For example, if the chunk_size is set to 100, the splitter will create chunks of 100 characters each. If the text is longer than 100 characters, it will be divided into multiple chunks of equal size, except for the last chunk, which may be smaller if there are remaining characters –defaults to `1000`.
- **separator:** Specifies the character that will be used to split the text into chunks – defaults to `.`
---
### RecursiveCharacterTextSplitter
The `RecursiveCharacterTextSplitter` splits the text by trying to keep paragraphs, sentences, and words together as long as possible, similar to the `CharacterTextSplitter`. However, it also recursively splits the text into smaller chunks if the chunk size exceeds a specified threshold.
**Params**
- **Documents:** Input documents to split.
- **chunk_overlap:** Determines the number of characters that overlap between consecutive chunks when splitting text. It specifies how much of the previous chunk should be included in the next chunk.
- **chunk_size:** Determines the maximum number of characters in each chunk when splitting a text. It specifies the size or length of each chunk.
- **separator_type:** The parameter allows the user to split the code with multiple language support. It supports various languages such as Text, Ruby, Python, Solidity, Java, and more. Defaults to `Text`.
- **separators:** The `separators` in RecursiveCharacterTextSplitter are the characters used to split the text into chunks. The text splitter tries to create chunks based on splitting on the first character in the list of `separators`. If any chunks are too large, it moves on to the next character in the list and continues splitting. Defaults to `.`

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import Admonition from '@theme/Admonition';
# Toolkits
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import Admonition from '@theme/Admonition';
# Tools
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import Admonition from '@theme/Admonition';
# Utilities
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import Admonition from '@theme/Admonition';
# Vector Stores
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import Admonition from '@theme/Admonition';
# Wrappers
<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>
### TextRequestsWrapper
This component is designed to work with the Python Requests module, which is a popular tool for making web requests. Used to fetch data from a particular website.
**Params**
- **header:** specifies the headers to be included in the HTTP request. Defaults to `{'Authorization': 'Bearer <token>'}`.
Headers are key-value pairs that provide additional information about the request or the client making the request. They can be used to send authentication credentials, specify the content type of the request, set cookies, and more. They allow the client and the server to communicate additional information beyond the basic request.

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# Community
## 🤖 Join **Langflow** Discord server
Join us to ask questions and showcase your projects.
Let's bring together the building blocks of AI integration!
Langflow [Discord](https://discord.gg/EqksyE2EX9) server.
---
## 🐦 Stay tunned for **Langflow** on Twitter
Follow [@logspace_ai](https://twitter.com/logspace_ai) on **Twitter** to get the latest news about **Langflow**.
---
## ⭐️ Star **Langflow** on GitHub
You can "star" **Langflow** in [GitHub](https://github.com/logspace-ai/langflow).
By adding a star, other users will be able to find it more easily and see that it has been already useful for others.
---
## 👀 Watch the GitHub repository for releases
You can "watch" **Langflow** in [GitHub](https://github.com/logspace-ai/langflow).
If you select "Watching" instead of "Releases only" you will receive notifications when someone creates a new issue or question. You can also specify that you only want to be notified about new issues, discussions, PRs, etc.
Then you can try and help them solve those questions.
---
Thanks! 🚀

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# 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
with sorting and discovery of issues of interest.
If you're looking for help with your code, consider posting a question on the
[GitHub Discussions board](https://github.com/logspace-ai/langflow/discussions). Please
understand that we won't be able to provide individual support via email. We
also believe that help is much more valuable if it's **shared publicly**,
so that more people can benefit from it.
- **Describing your issue:** Try to provide as many details as possible. What
exactly goes wrong? _How_ is it failing? Is there an error?
"XY doesn't work" usually isn't that helpful for tracking down problems. Always
remember to include the code you ran and if possible, extract only the relevant
parts and don't just dump your entire script. This will make it easier for us to
reproduce the error.
- **Sharing long blocks of code or logs:** If you need to include long code,
logs or tracebacks, you can wrap them in `<details>` and `</details>`. This
[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
[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.

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# How to contribute?
👋 Hello there! We welcome contributions from developers of all levels to our open-source project on [GitHub](https://github.com/logspace-ai/langflow). If you'd like to contribute, please check our contributing guidelines and help make Langflow more accessible.
As an open-source project in a rapidly developing field, we are extremely open
to contributions, whether in the form of a new feature, improved infra, or better documentation.
To contribute to this project, please follow a ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow.
Please do not try to push directly to this repo unless you are a maintainer.
---
## Local development
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
```
This will install the pre-commit hooks, which will run `make format` on every commit.
It is advised to run `make lint` before pushing to the repository.
---
## 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
Then, in the root folder, install the dependencies and start the development server for the backend:
```bash
make backend
```
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`.
```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.

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# Deploy on Google Cloud Platform
## Run Langflow from a New Google Cloud Project
This guide will help you set up a Langflow development VM in a Google Cloud Platform project using Google Cloud Shell.
:::note
When Cloud Shell opens, be sure to select **Trust repo**. Some `gcloud` commands might not run in an ephemeral Cloud Shell environment.
:::
## Standard VM
[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial.md)
This script sets up a Debian-based VM with the Langflow package, Nginx, and the necessary configurations to run the Langflow Dev environment.
---
## Spot/Preemptible Instance
[![Open in Cloud Shell - Spot Instance](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/genome21/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.
---
## Pricing (approximate)
> For a more accurate breakdown of costs, please use the [**GCP Pricing Calculator**](https://cloud.google.com/products/calculator)
| Component | Regular Cost (Hourly) | Regular Cost (Monthly) | Spot/Preemptible Cost (Hourly) | Spot/Preemptible Cost (Monthly) | Notes |
| -------------- | --------------------- | ---------------------- | ------------------------------ | ------------------------------- | ----- |
| 100 GB Disk | - | $10/month | - | $10/month | Disk cost remains the same for both regular and Spot/Preemptible VMs |
| VM (n1-standard-4) | $0.15/hr | ~$108/month | ~$0.04/hr | ~$29/month | The VM cost can be significantly reduced using a Spot/Preemptible instance |
| **Total** | **$0.15/hr** | **~$118/month** | **~$0.04/hr** | **~$39/month** | Total costs for running the VM and disk 24/7 for an entire month |

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# 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
🎉 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://<your-app>.wolf.jina.ai/
📖 Read more about managing the server: https://github.com/jina-ai/langchain-serve
```
**Complete (example) output:**
```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
```
## 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.
:::

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import Admonition from "@theme/Admonition";
# Buffer Memory
For certain applications, retaining past interactions is crucial. For that, chains and agents may accept a memory component as one of their input parameters. The `ConversationBufferMemory` component is one of them. It stores messages and extracts them into variables.
## ⛓️ Langflow Example
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/buffer-memory.png",
}}
/>
#### <a target="\_blank" href="json_files/Buffer_Memory.json" download>Download Flow</a>
<Admonition type="note" title="LangChain Components 🦜🔗">
- [`ConversationBufferMemory`](https://python.langchain.com/docs/modules/memory/how_to/buffer)
- [`ConversationChain`](https://python.langchain.com/docs/modules/chains/)
- [`ChatOpenAI`](https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai)
</Admonition>

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import Admonition from "@theme/Admonition";
# Conversation Chain
This example shows how to instantiate a simple `ConversationChain` component using a Language Model (LLM). Once the Node Status turns green 🟢, the chat will be ready to take in user messages. Here, we used `ChatOpenAI` to act as the required LLM input, but you can use any LLM for this purpose.
<Admonition type="info">
Make sure to always get the API key from the provider.
</Admonition>
## ⛓️ Langflow Example
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/basic-chat.png",
}}
/>
#### <a target="\_blank" href="json_files/Basic_Chat.json" download>Download Flow</a>
<Admonition type="note" title="LangChain Components 🦜🔗">
- [`ConversationChain`](https://python.langchain.com/docs/modules/chains/)
- [`ChatOpenAI`](https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai)
</Admonition>

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import Admonition from "@theme/Admonition";
# CSV Loader
The `VectoStoreAgent` component retrieves information from one or more vector stores. This example shows a `VectoStoreAgent` connected to a CSV file through the `Chroma` vector store. Process description:
- The `CSVLoader` loads a CSV file into a list of documents.
- The extracted data is then processed by the `CharacterTextSplitter`, which splits the text into small, meaningful chunks (usually sentences).
- These chunks feed the `Chroma` vector store, which converts them into vectors and stores them for fast indexing.
- Finally, the agent accesses the information of the vector store through the `VectorStoreInfo` tool.
<Admonition type="info">
The vector store is used for efficient semantic search, while
`VectorStoreInfo` carries information about it, such as its name and
description. Embeddings are a way to represent words, phrases, or any entities
in a vector space. Learn more about them
[here](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings).
</Admonition>
<Admonition type="tip">
Once you build this flow, ask questions about the data in the chat interface
(e.g., number of rows or columns).
</Admonition>
## ⛓️ Langflow Example
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/csv-loader.png",
}}
/>
#### <a target="\_blank" href="json_files/CSV_Loader.json" download>Download Flow</a>
<Admonition type="note" title="LangChain Components 🦜🔗">
- [`CSVLoader`](https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/csv)
- [`CharacterTextSplitter`](https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter)
- [`OpenAIEmbedding`](https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/openai)
- [`Chroma`](https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma)
- [`VectorStoreInfo`](https://python.langchain.com/docs/modules/data_connection/vectorstores/)
- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai)
- [`VectorStoreAgent`](https://python.langchain.com/docs/modules/agents/toolkits/vectorstore)
</Admonition>

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---
description: Custom Components
hide_table_of_contents: true
---
import ZoomableImage from "/src/theme/ZoomableImage.js";
import Admonition from "@theme/Admonition";
# FlowRunner Component
The CustomComponent class allows us to create components that interact with Langflow itself. In this example, we will make a component that runs other flows available in "My Collection".
<ZoomableImage
alt="Document Processor Component"
sources={{
light: "img/flow_runner.png",
}}
style={{
width: "30%",
margin: "0 auto",
display: "flex",
justifyContent: "center",
}}
/>
We will cover how to:
- List Collection flows using the _`list_flows`_ method.
- Load a flow using the _`load_flow`_ method.
- Configure a dropdown input field using the _`options`_ parameter.
<details open>
<summary>Example Code</summary>
```python
from langflow import CustomComponent
from langchain.schema import Document
class FlowRunner(CustomComponent):
display_name = "Flow Runner"
description = "Run other flows using a document as input."
def build_config(self):
flows = self.list_flows()
flow_names = [f.name for f in flows]
return {"flow_name": {"options": flow_names,
"display_name": "Flow Name",
},
"document": {"display_name": "Document"}
}
def build(self, flow_name: str, document: Document) -> Document:
# List the flows
flows = self.list_flows()
# Get the flow that matches the selected name
# You can also get the flow by id
# using self.get_flow(flow_id=flow_id)
tweaks = {}
flow = self.get_flow(flow_name=flow_name, tweaks=tweaks)
# Get the page_content from the document
if document and isinstance(document, list):
document = document[0]
page_content = document.page_content
# Use it in the flow
result = flow(page_content)
return Document(page_content=str(result))
```
</details>
<CH.Scrollycoding rows={20} className={""}>
```python
from langflow import CustomComponent
class MyComponent(CustomComponent):
display_name = "Custom Component"
description = "This is a custom component"
def build_config(self):
...
def build(self):
...
```
The typical structure of a Custom Component is composed of _`display_name`_ and _`description`_ attributes, _`build`_ and _`build_config`_ methods.
---
```python
from langflow import CustomComponent
# focus
class FlowRunner(CustomComponent):
# focus
display_name = "Flow Runner"
# focus
description = "Run other flows"
def build_config(self):
...
def build(self):
...
```
Let's start by defining our component's _`display_name`_ and _`description`_.
---
```python
from langflow import CustomComponent
# focus
from langchain.schema import Document
class FlowRunner(CustomComponent):
display_name = "Flow Runner"
description = "Run other flows using a document as input."
def build_config(self):
...
def build(self):
...
```
Second, we will import _`Document`_ from the [_langchain.schema_](https://docs.langchain.com/docs/components/schema/) module. This will be the return type of the _`build`_ method.
---
```python
from langflow import CustomComponent
# focus
from langchain.schema import Document
class FlowRunner(CustomComponent):
display_name = "Flow Runner"
description = "Run other flows using a document as input."
def build_config(self):
...
# focus
def build(self, flow_name: str, document: Document) -> Document:
...
```
Now, let's add the [parameters](focus://11[20:55]) and the [return type](focus://11[60:69]) to the _`build`_ method. The parameters added are:
- _`flow_name`_ is the name of the flow we want to run.
- _`document`_ is the input document to be passed to that flow.
- Since _`Document`_ is a Langchain type, it will add an input [handle](../guidelines/components) to the component ([see more](../components/custom)).
---
```python focus=13:14
from langflow import CustomComponent
from langchain.schema import Document
class FlowRunner(CustomComponent):
display_name = "Flow Runner"
description = "Run other flows using a document as input."
def build_config(self):
...
def build(self, flow_name: str, document: Document) -> Document:
# List the flows
flows = self.list_flows()
```
We can now start writing the _`build`_ method. Let's list available flows in "My Collection" using the _`list_flows`_ method.
---
```python focus=15:18
from langflow import CustomComponent
from langchain.schema import Document
class FlowRunner(CustomComponent):
display_name = "Flow Runner"
description = "Run other flows using a document as input."
def build_config(self):
...
def build(self, flow_name: str, document: Document) -> Document:
# List the flows
flows = self.list_flows()
# Get the flow that matches the selected name
# You can also get the flow by id
# using self.get_flow(flow_id=flow_id)
tweaks = {}
flow = self.get_flow(flow_name=flow_name, tweaks=tweaks)
```
And retrieve a flow that matches the selected name (we'll make a dropdown input field for the user to choose among flow names).
<Admonition type="caution">
From version 0.4.0, names are unique, which was not the case in previous
versions. This might lead to unexpected results if using flows with the same
name.
</Admonition>
---
```python
from langflow import CustomComponent
from langchain.schema import Document
class FlowRunner(CustomComponent):
display_name = "Flow Runner"
description = "Run other flows using a document as input."
def build_config(self):
...
def build(self, flow_name: str, document: Document) -> Document:
# List the flows
flows = self.list_flows()
# Get the flow that matches the selected name
# You can also get the flow by id
# using self.get_flow(flow_id=flow_id)
tweaks = {}
flow = self.get_flow(flow_name=flow_name, tweaks=tweaks)
```
You can load this flow using _`get_flow`_ and set a _`tweaks`_ dictionary to customize it. Find more about tweaks in our [features guidelines](../guidelines/features#code).
---
```python
from langflow import CustomComponent
from langchain.schema import Document
class FlowRunner(CustomComponent):
display_name = "Flow Runner"
description = "Run other flows using a document as input."
def build_config(self):
...
def build(self, flow_name: str, document: Document) -> Document:
# List the flows
flows = self.list_flows()
# Get the flow that matches the selected name
# You can also get the flow by id
# using self.get_flow(flow_id=flow_id)
tweaks = {}
flow = self.get_flow(flow_name=flow_name, tweaks=tweaks)
# Get the page_content from the document
if document and isinstance(document, list):
document = document[0]
page_content = document.page_content
# Use it in the flow
result = flow(page_content)
return Document(page_content=str(result))
```
We are using a _`Document`_ as input because it is a straightforward way to pass text data in Langflow (specifically because you can connect it to many [loaders](../components/loaders)).
Generally, a flow will take a string or a dictionary as input because that's what LangChain components expect.
In case you are passing a dictionary, you need to build it according to the needs of the flow you are using.
The content of a document can be extracted using the _`page_content`_ attribute, which is a string, and passed as an argument to the selected flow.
---
```python focus=9:16
from langflow import CustomComponent
from langchain.schema import Document
class FlowRunner(CustomComponent):
display_name = "Flow Runner"
description = "Run other flows using a document as input."
def build_config(self):
flows = self.list_flows()
flow_names = [f.name for f in flows]
return {"flow_name": {"options": flow_names,
"display_name": "Flow Name",
},
"document": {"display_name": "Document"}
}
def build(self, flow_name: str, document: Document) -> Document:
# List the flows
flows = self.list_flows()
# Get the flow that matches the selected name
# You can also get the flow by id
# using self.get_flow(flow_id=flow_id)
tweaks = {}
flow = self.get_flow(flow_name=flow_name, tweaks=tweaks)
# Get the page_content from the document
if document and isinstance(document, list):
document = document[0]
page_content = document.page_content
# Use it in the flow
result = flow(page_content)
return Document(page_content=str(result))
```
Finally, we can add field customizations through the _`build_config`_ method. Here we added the _`options`_ key to make the _`flow_name`_ field a dropdown menu. Check out the [custom component reference](../components/custom) for a list of available keys.
<Admonition type="caution">
Make sure that the field type is _`str`_ and _`options`_ values are strings.
</Admonition>
</CH.Scrollycoding>
Done! This is what our script and custom component looks like:
<div style={{
display: "flex",
justifyContent: "center",
}}>
<ZoomableImage
alt="Document Processor Code"
sources={{
light: "img/flow_runner_code.png",
}}
style={{
maxWidth: "100%",
margin: "0 auto",
display: "flex",
justifyContent: "center",
}}
/>
<ZoomableImage
alt="Document Processor Component"
sources={{
light: "img/flow_runner.png",
}}
style={{
width: "40%",
margin: "0 auto",
display: "flex",
justifyContent: "center",
}}
/>
</div>

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import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
# 📚 How to Upload Examples?
We welcome all examples that can help our community learn and explore Langflow's capabilities.
Langflow Examples is a repository on [GitHub](https://github.com/logspace-ai/langflow_examples) that contains examples of flows that people can use for inspiration and learning.
{" "}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/community-examples.png",
}}
style={{ width: "100%" }}
/>
To upload examples, please follow these steps:
1. **Create a Flow:** First, create a flow using Langflow. You can use any of the available templates or create a new flow from scratch.
2. **Export the Flow:** Once you have created a flow, export it as a JSON file. Make sure to give your file a descriptive name and include a brief description of what it does.
3. **Submit a Pull Request:** Finally, submit a pull request (PR) to the examples repo. Make sure to include your JSON file in the PR.
If your example uses any third-party libraries or packages, please include them in your PR and make sure that your example follows the [**⛓️ Langflow Code Of Conduct**](https://github.com/logspace-ai/langflow/blob/dev/CODE_OF_CONDUCT.md).

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import Admonition from "@theme/Admonition";
# MidJourney Prompt Chain
The `MidJourneyPromptChain` can be used to generate imaginative and detailed MidJourney prompts.
For example, type something like:
```bash
Dragon
```
And get a response such as:
```text
Imagine a mysterious forest, the trees are tall and ancient, their branches reaching up to the sky. Through the darkness, a dragon emerges from the shadows, its scales shimmering in the moonlight. Its wingspan is immense, and its eyes glow with a fierce intensity. It is a majestic and powerful creature, one that commands both respect and fear.
```
<Admonition type="tip">
Notice that the `ConversationSummaryMemory` stores a summary of the
conversation over time. Try using it to create better prompts as the
conversation goes on.
</Admonition>
## ⛓️ Langflow Example
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/midjourney-prompt-chain.png",
}}
/>
#### <a target="\_blank" href="json_files/MidJourney_Prompt_Chain.json" download>Download Flow</a>
<Admonition type="note" title="LangChain Components 🦜🔗">
- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai)
- [`ConversationSummaryMemory`](https://python.langchain.com/docs/modules/memory/how_to/summary)
</Admonition>

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import Admonition from "@theme/Admonition";
# Multiple Vector Stores
The example below shows an agent operating with two vector stores built upon different data sources.
The `TextLoader` loads a TXT file, while the `WebBaseLoader` pulls text from webpages into a document format to accessed downstream. The `Chroma` vector stores are created analogous to what we have demonstrated in our [CSV Loader](/examples/csv-loader.mdx) example. Finally, the `VectorStoreRouterAgent` constructs an agent that routes between the vector stores.
<Admonition type="info">
Get the TXT file used
[here](https://github.com/hwchase17/chat-your-data/blob/master/state_of_the_union.txt).
</Admonition>
URL used by the `WebBaseLoader`:
```text
https://pt.wikipedia.org/wiki/Harry_Potter
```
<Admonition type="tip">
When you build the flow, request information about one of the sources. The
agent should be able to use the correct source to generate a response.
</Admonition>
<Admonition type="info">
Learn more about Multiple Vector Stores
[here](https://python.langchain.com/docs/modules/agents/toolkits/vectorstore?highlight=Multiple%20Vector%20Stores#multiple-vectorstores).
</Admonition>
## ⛓️ Langflow Example
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/multiple-vectorstores.png",
}}
/>
#### <a target="\_blank" href="json_files/Multiple_Vector_Stores.json" download>Download Flow</a>
<Admonition type="note" title="LangChain Components 🦜🔗">
- [`WebBaseLoader`](https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/web_base)
- [`TextLoader`](https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/unstructured_file)
- [`CharacterTextSplitter`](https://python.langchain.com/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter)
- [`OpenAIEmbedding`](https://python.langchain.com/docs/modules/data_connection/text_embedding/integrations/openai)
- [`Chroma`](https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma)
- [`VectorStoreInfo`](https://python.langchain.com/docs/modules/data_connection/vectorstores/)
- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai)
- [`VectorStoreRouterToolkit`](https://python.langchain.com/docs/modules/agents/toolkits/vectorstore)
- [`VectorStoreRouterAgent`](https://python.langchain.com/docs/modules/agents/toolkits/vectorstore)
</Admonition>

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import Admonition from "@theme/Admonition";
# Python Function
Langflow allows you to create a customized tool using the `PythonFunction` connected to a `Tool` component. In this example, Regex is used in Python to validate a pattern.
```python
import re
def is_brazilian_zipcode(zipcode: str) -> bool:
pattern = r"\d{5}-?\d{3}"
# Check if the zip code matches the pattern
if re.match(pattern, zipcode):
return True
return False
```
<Admonition type="tip">
When a tool is called, it is often desirable to have its output returned
directly to the user. You can do this by setting the **return_direct** flag
for a tool to be True.
</Admonition>
The `AgentInitializer` component is a quick way to construct an agent from the model and tools.
<Admonition type="info">
The `PythonFunction` is a custom component that uses the LangChain 🦜🔗 tool
decorator. Learn more about it
[here](https://python.langchain.com/docs/modules/agents/tools/how_to/custom_tools).
</Admonition>
## ⛓️ Langflow Example
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/python-function.png",
}}
/>
#### <a target="\_blank" href="json_files/Python_Function.json" download>Download Flow</a>
<Admonition type="note" title="LangChain Components 🦜🔗">
- [`PythonFunctionTool`](https://python.langchain.com/docs/modules/agents/tools/how_to/custom_tools)
- [`ChatOpenAI`](https://python.langchain.com/docs/modules/model_io/models/chat/integrations/openai)
- [`AgentInitializer`](https://python.langchain.com/docs/modules/agents/)
</Admonition>

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import Admonition from "@theme/Admonition";
# Serp API Tool
The [Serp API](https://serpapi.com/) (Search Engine Results Page) allows developers to scrape results from search engines such as Google, Bing and Yahoo, and can be used as in Langflow through the `Search` component.
<Admonition type="info">
To use the Serp API, you first need to sign up [Serp
API](https://serpapi.com/) for an API key on the provider's website.
</Admonition>
Here, the `ZeroShotPrompt` component specifies a prompt template for the `ZeroShotAgent`. Set a _Prefix_ and _Suffix_ with rules for the agent to obey. In the example, we used default templates.
The `LLMChain` is a simple chain that takes in a prompt template, formats it with the user input, and returns the response from an LLM.
<Admonition type="tip">
In this example, we used [`ChatOpenAI`](https://platform.openai.com/) as the
LLM, but feel free to experiment with other Language Models!
</Admonition>
The `ZeroShotAgent` takes the `LLMChain` and the `Search` tool as inputs, using the tool to find information when necessary.
<Admonition type="info">
Learn more about the Serp API
[here](https://python.langchain.com/docs/modules/agents/tools/integrations/serpapi).
</Admonition>
## ⛓️ Langflow Example
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/serp-api-tool.png",
}}
/>
#### <a target="\_blank" href="json_files/SerpAPI_Tool.json" download>Download Flow</a>
<Admonition type="note" title="LangChain Components 🦜🔗">
- [`ZeroShotPrompt`](https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/)
- [`OpenAI`](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/openai)
- [`LLMChain`](https://python.langchain.com/docs/modules/chains/foundational/llm_chain)
- [`Search`](https://python.langchain.com/docs/modules/agents/tools/integrations/serpapi)
- [`ZeroShotAgent`](https://python.langchain.com/docs/modules/agents/how_to/custom_mrkl_agent)
</Admonition>

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import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
import ReactPlayer from "react-player";
# 🎨 Creating Flows
## Compose
Creating flows with Langflow is easy. Drag sidebar components onto the canvas and connect them together to create your pipeline. Langflow provides a range of [LangChain components](https://python.langchain.com/docs/modules/) to choose from, including LLMs, prompt serializers, agents, and chains.
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/langflow_canvas.png",
}}
/>
## Fork
The easiest way to start with Langflow is by forking a **community example**. Forking an example stores a copy in your project collection, allowing you to edit and save the modified version as a new flow.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ReactPlayer playing controls url="/videos/langflow_fork.mp4" />
</div>
## Build
Building a flow means validating if the components have prerequisites fulfilled and are properly instantiated. When a chat message is sent, the flow will run for the first time, executing the pipeline.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ReactPlayer playing controls url="/videos/langflow_build.mp4" />
</div>

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# 🤗 HuggingFace Spaces
A fully featured version of Langflow can be accessed via HuggingFace spaces with no installation required.
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
{" "}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/hugging-face.png",
}}
style={{ width: "100%" }}
/>
Check out Langflow on [HuggingFace Spaces](https://huggingface.co/spaces/Logspace/Langflow).

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# 📦 How to install?
## Installation
You can install Langflow from pip:
```bash
pip install langflow
```
Next, run:
```bash
langflow
```

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import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
import ReactPlayer from "react-player";
# Chat Interface
Langflow’s chat interface provides a user-friendly experience and functionality to interact with the model and customize the prompt. The sidebar brings options that allow users to view and edit pre-defined prompt variables. This feature facilitates quick experimentation by enabling the modification of variable values right in the chat.
{" "}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/chat_interface.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
Notice that editing variables in the chat interface take place temporarily and won’t change their original value in the components once the chat is closed.
{" "}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/chat_interface2.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
To view the complete prompt in its original, structured format, click the "Display Prompt" option. This feature lets you see the prompt exactly as it entered the model.
{" "}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/chat_interface3.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
In the chat interface, you can redefine which variable should be interpreted as the chat input. This gives you control over these inputs and allows dynamic and creative interactions.
{" "}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/chat_interface4.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>

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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";
# Chat Widget
<div style={{ marginBottom: "20px" }}>
The <b>Langflow Chat Widget</b> is a powerful web component that enables
communication with a Langflow project. This widget allows for a chat interface
embedding, allowing the integration of Langflow into web applications
effortlessly.
</div>
## Features
🌟 **Seamless Integration:** Easily integrate the Langflow Chat Widget into your website or web application with just a few lines of JavaScript.
🚀 **Interactive Chat Interface:** Engage your users with a user-friendly conversation, powered by Langflow's advanced language understanding capabilities.
🎛️ **Customizable Styling:** Customize the appearance of the chat widget to match your application's design and branding.
🌐 **Multilingual Support:** Communicate with users in multiple languages, opening up your application to a global audience.
---
## Usage
<div style={{ marginBottom: "20px" }}>
You can get the HTML code embedded with the chat by clicking the Code button
at the Sidebar after building a flow.
</div>
{" "}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/widget-sidebar.png"),
}}
style={{ width: "50%", maxWidth: "600px", margin: "0 auto" }}
/>
<div style={{ marginBottom: "20px" }}>
Clicking the Chat Widget HTML tab, you'll get the code to be inserted. Read
below to learn how to use it with HTML, React and Angular.
</div>
{" "}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/widget-code.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
---
### HTML
The Chat Widget can be embedded into any HTML page, inside a _`<body>`_ tag, as demonstrated in the video below.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ReactPlayer playing controls url="/videos/langflow_widget.mp4" />
</div>
---
### React
To embed the Chat Widget using React, you'll need to insert this _`<script>`_ tag into the React _index.html_ file, inside the _`<body>`_ tag:
```html
<script src="https://cdn.jsdelivr.net/gh/logspace-ai/langflow-embedded-chat@main/dist/build/static/js/bundle.min.js"></script>
```
Then, declare your Web Component and encapsulate it in a React component.
```jsx
declare global {
namespace JSX {
interface IntrinsicElements {
"langflow-chat": any;
}
}
}
export default function ChatWidget({ className }) {
return (
<div className={className}>
<langflow-chat
chat_inputs='{"your_key":"value"}'
chat_input_field="your_chat_key"
flow_id="your_flow_id"
host_url="langflow_url"
></langflow-chat>
</div>
);
}
```
Finally, you can place the component anywhere in your code to display the Chat Widget.
---
### Angular
To use it in Angular, first add this _`<script>`_ tag into the Angular _index.html_ file, inside the _`<body>`_ tag.
```html
<script src="https://cdn.jsdelivr.net/gh/logspace-ai/langflow-embedded-chat@main/dist/build/static/js/bundle.min.js"></script>
```
When you use a custom web component in an Angular template, the Angular compiler might show a warning when it doesn't recognize the custom elements by default. To suppress this warning, add _`CUSTOM_ELEMENTS_SCHEMA`_ to the module's _`@NgModule.schemas`_.
- Open the module file (it typically ends with _.module.ts_) where you'd add the _`langflow-chat`_ web component.
- Import _`CUSTOM_ELEMENTS_SCHEMA`_ at the top of the file:
```ts
import { NgModule, CUSTOM_ELEMENTS_SCHEMA } from "@angular/core";
```
- Add _`CUSTOM_ELEMENTS_SCHEMA`_ to the 'schemas' array inside the '@NgModule' decorator:
```ts
@NgModule({
declarations: [
// ... Other components and directives ...
],
imports: [
// ... Other imported modules ...
],
schemas: [CUSTOM_ELEMENTS_SCHEMA], // Add the CUSTOM_ELEMENTS_SCHEMA here
})
export class YourModule {}
```
In your Angular project, find the component belonging to the module where _`CUSTOM_ELEMENTS_SCHEMA`_ was added.
- Inside the template, add the _`langflow-chat`_ tag to include the Chat Widget in your component's view:
```jsx
<langflow-chat
chat_inputs='{"your_key":"value"}'
chat_input_field="your_chat_key"
flow_id="your_flow_id"
host_url="langflow_url"
></langflow-chat>
```
<Admonition type="info">
<ul>
<li>
_`CUSTOM_ELEMENTS_SCHEMA`_ is a built-in schema that allows Angular to
recognize custom elements.
</li>
<li>
Adding _`CUSTOM_ELEMENTS_SCHEMA`_ tells Angular to allow custom elements
in your templates, and it will suppress the warning related to unknown
elements like _`langflow-chat`_.
</li>
<li>
Notice that you can only use the Chat Widget in components that are part
of the module where you added _`CUSTOM_ELEMENTS_SCHEMA`_.
</li>
</ul>
</Admonition>
---
## Configuration
Use the widget API to customize your Chat Widget:
<Admonition type="caution">
Props with the type JSON need to be passed as Stringified JSONs, with the
format &#123;<span>"key":"value"</span>&#125;.
</Admonition>
| Prop | Type | Required | Description |
| --------------------- | ------- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| bot_message_style | JSON | No | Applies custom formatting to bot messages. |
| chat_input_field | String | Yes | Defines the type of the input field for chat messages. |
| chat_inputs | JSON | Yes | Determines the chat input elements and their respective values. |
| chat_output_key | String | No | Specifies which output to display if multiple outputs are available. |
| chat_position | String | No | Positions the chat window on the screen (options include: top-left, top-center, top-right, center-left, center-right, bottom-right, bottom-center, bottom-left). |
| chat_trigger_style | JSON | No | Styles the chat trigger button. |
| chat_window_style | JSON | No | Customizes the overall appearance of the chat window. |
| error_message_style | JSON | No | Sets the format for error messages within the chat window. |
| flow_id | String | Yes | Identifies the flow that the component is associated with. |
| height | Number | No | Sets the height of the chat window in pixels. |
| host_url | String | Yes | Specifies the URL of the host for chat component communication. |
| input_container_style | JSON | No | Applies styling to the container where chat messages are entered. |
| input_style | JSON | No | Sets the style for the chat input field. |
| online | Boolean | No | Toggles the online status of the chat component. |
| online_message | String | No | Sets a custom message to display when the chat component is online. |
| placeholder | String | No | Sets the placeholder text for the chat input field. |
| placeholder_sending | String | No | Sets the placeholder text to display while a message is being sent. |
| send_button_style | JSON | No | Sets the style for the send button in the chat window. |
| send_icon_style | JSON | No | Sets the style for the send icon in the chat window. |
| tweaks | JSON | No | Applies additional custom adjustments for the associated flow. |
| user_message_style | JSON | No | Determines the formatting for user messages in the chat window. |
| width | Number | No | Sets the width of the chat window in pixels. |
| window_title | String | No | Sets the title displayed in the chat window's header or title bar. |

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import ThemedImage from '@theme/ThemedImage';
import useBaseUrl from '@docusaurus/useBaseUrl';
import ZoomableImage from '/src/theme/ZoomableImage.js';
import ReactPlayer from 'react-player';
# Collection
A collection is a snapshot of the flows available in the database. You can download your entire collection for local storage and upload it anytime for future use.
<div style={{ marginBottom: '20px', display: 'flex', justifyContent: 'center' }}>
<ReactPlayer playing controls url='/videos/langflow_collection.mp4'
/>
</div>

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import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
import ReactPlayer from "react-player";
# Component
Components are the building blocks of the flows. They are made of inputs, outputs, and parameters that define their functionality, providing a convenient and straightforward way to compose LLM-based applications. Learn more about components and how they work in the LangChain [documentation](https://docs.langchain.com/docs/category/components) section.
### Component's Features
<div style={{ marginBottom: "20px" }}>
During the flow creation process, you will notice handles (colored circles)
attached to one or both sides of a component. These handles represent the
availability to connect to other components, while their colors are type hints
(hover over a handle to see connection details).
</div>
<div style={{ marginBottom: "20px" }}>
For example, if you select a <code>ConversationChain</code> component, you
will see orange <span style={{ color: "orange" }}>o</span> and purple{" "}
<span style={{ color: "purple" }}>o</span> input handles. They indicate that
this component accepts an LLM and a Memory component as inputs. The red
asterisk <span style={{ color: "red" }}>*</span> means that at least one input
of that type is required.
</div>
{" "}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/single-compenent.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
<div style={{ marginBottom: "20px" }}>
On the top right corner, you will find the component status icon 🔴. Make the
necessary connections, build the flow (⚡ zap icon on the bottom right of the
canvas) and once the validation is completed, the status of each validated
component should light green 🟢. Hover over the component status to reveal the
outputs going through it in case of success, or the detected error in case of
failure.
</div>
---
### Component's Parameters
Langflow components can be edited in the component settings button. Hide parameters to reduce complexity and keep the canvas clean and intuitive for experimentation.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ReactPlayer playing controls url="/videos/langflow_parameters.mp4" />
</div>

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---
description: Custom Components
hide_table_of_contents: true
---
import ZoomableImage from "/src/theme/ZoomableImage.js";
import Admonition from "@theme/Admonition";
# Custom Components
In Langflow, a Custom Component is a special component type that allows users to extend the platform's functionality by creating their own reusable and configurable components.
A Custom Component is created from a user-defined Python script that uses the _`CustomComponent`_ class provided by the Langflow library. These components can be as simple as a basic function that takes and returns a string or as complex as a combination of multiple sub-components and API calls.
Let's take a look at the basic rules and features. Then we'll go over an example.
## TL;DR
- Create a class that inherits from _`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.
- Use the _`build_config`_ method to customize how these fields look and behave.
- Set up a folder with your components to load them up in Langflow's sidebar.
Here is an example:
<div style={{
display: "flex",
justifyContent: "center",
}}>
<CH.Code linuNumbers={false}>
```python
from langflow import CustomComponent
from langchain.schema import Document
class DocumentProcessor(CustomComponent):
display_name = "Document Processor"
description = "This component processes a document"
def build_config(self) -> dict:
options = ["Uppercase", "Lowercase", "Titlecase"]
return {
"function": {"options": options,
"value": options[0]}}
def build(self, document: Document, function: str) -> Document:
if isinstance(document, list):
document = document[0]
page_content = document.page_content
if function == "Uppercase":
page_content = page_content.upper()
elif function == "Lowercase":
page_content = page_content.lower()
elif function == "Titlecase":
page_content = page_content.title()
self.repr_value = f"Result of {function} function: {page_content}"
return Document(page_content=page_content)
```
</CH.Code>
<ZoomableImage
alt="Document Processor Component"
sources={{
light: "img/document_processor.png",
}}
style={{
width: "40%",
margin: "0 auto",
display: "flex",
justifyContent: "center",
}}
/>
</div>
<Admonition type="tip">
Check out [FlowRunner Component](../examples/flow-runner) for a more complex
example.
</Admonition>
---
## Rules
The Python script for every Custom Component should follow a set of rules. Let's go over them one by one:
<CH.Scrollycoding rows={20} className={""}>
### Rule 1
The script must contain a **single class** that inherits from _`CustomComponent`_.
```python
from langflow import CustomComponent
from langchain.schema import Document
class MyComponent(CustomComponent):
display_name = "Custom Component"
description = "This is a custom component"
def build_config(self) -> dict:
...
def build(self, document: Document, function: str) -> Document:
...
```
---
### Rule 2
This class requires a _`build`_ method used to run the component and define its fields.
```python
from langflow import CustomComponent
from langchain.schema import Document
class MyComponent(CustomComponent):
display_name = "Custom Component"
description = "This is a custom component"
def build_config(self) -> dict:
...
# focus
# mark
def build(self) -> Document:
...
```
---
The [Return Type Annotation](https://docs.python.org/3/library/typing.html) of the _`build`_ method defines the component type (e.g., Chain, BaseLLM, or basic Python types). Check out all supported types in the [component reference](../components/custom).
```python
from langflow import CustomComponent
from langchain.schema import Document
class MyComponent(CustomComponent):
display_name = "Custom Component"
description = "This is a custom component"
def build_config(self) -> dict:
...
# focus[20:31]
# mark
def build(self) -> Document:
...
```
---
```python
from langflow import CustomComponent
from langchain.schema import Document
class MyComponent(CustomComponent):
display_name = "Custom Component"
description = "This is a custom component"
def build_config(self) -> dict:
...
def build(self) -> Document:
...
```
### Rule 3
The class can have a [_`build_config`_](focus://8) method, which defines configuration fields for the component. The [_`build_config`_](focus://8) method should always return a dictionary with specific keys representing the field names and their corresponding configurations. 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.
Check out the [component reference](../components/custom) for more details on the available field configurations.
---
```python
from langflow import CustomComponent
from langchain.schema import Document
class MyComponent(CustomComponent):
display_name = "Custom Component"
description = "This is a custom component"
def build_config(self) -> dict:
...
def build(self) -> Document:
...
```
## Example
Let's create a custom component that processes a document (_`langchain.schema.Document`_) using a simple function.
---
### Pick a display name
To start, let's choose a name for our component by adding a _`display_name`_ attribute. This name will appear on the canvas. The name of the class is not relevant, but let's call it _`DocumentProcessor`_.
```python
from langflow import CustomComponent
from langchain.schema import Document
# focus
class DocumentProcessor(CustomComponent):
# focus
display_name = "Document Processor"
description = "This is a custom component"
def build_config(self) -> dict:
...
def build(self) -> Document:
...
```
---
### Write a description
We can also write a description for it using a _`description`_ attribute.
```python
from langflow import CustomComponent
from langchain.schema import Document
class DocumentProcessor(CustomComponent):
display_name = "Document Processor"
description = "This component processes a document"
def build_config(self) -> dict:
...
def build(self) -> Document:
...
```
---
```python
from langflow import CustomComponent
from langchain.schema import Document
class DocumentProcessor(CustomComponent):
display_name = "Document Processor"
description = "This component processes a document"
def build_config(self) -> dict:
...
def build(self, document: Document, function: str) -> Document:
if isinstance(document, list):
document = document[0]
page_content = document.page_content
if function == "Uppercase":
page_content = page_content.upper()
elif function == "Lowercase":
page_content = page_content.lower()
elif function == "Titlecase":
page_content = page_content.title()
self.repr_value = f"Result of {function} function: {page_content}"
return Document(page_content=page_content)
```
### Add the build method
Here, the build method takes two input parameters: _`document`_, representing the input document to be processed, and _`function`_, a string representing the selected text transformation to be applied (either "Uppercase," "Lowercase," or "Titlecase"). The method processes the text content of the input Document based on the selected function.
The attribute _`repr_value`_ is used to display the result of the component on the canvas. It is optional and can be used to display any string value.
The return type is _`Document`_.
---
### Customize the component fields
The _`build_config`_ method is here defined to customize the component fields.
- _`options`_ determines that the field will be a dropdown menu. The list values and field type must be _`str`_.
- _`value`_ is the default option of the dropdown menu.
- _`display_name`_ is the name of the field to be displayed.
```python
from langflow import CustomComponent
from langchain.schema import Document
class DocumentProcessor(CustomComponent):
display_name = "Document Processor"
description = "This component processes a document"
def build_config(self) -> dict:
options = ["Uppercase", "Lowercase", "Titlecase"]
return {
"function": {"options": options,
"value": options[0],
"display_name": "Function"
},
"document": {"display_name": "Document"}
}
def build(self, document: Document, function: str) -> Document:
if isinstance(document, list):
document = document[0]
page_content = document.page_content
if function == "Uppercase":
page_content = page_content.upper()
elif function == "Lowercase":
page_content = page_content.lower()
elif function == "Titlecase":
page_content = page_content.title()
self.repr_value = f"Result of {function} function: {page_content}"
return Document(page_content=page_content)
```
</CH.Scrollycoding>
All done! This is what our script and brand-new custom component look like:
<div style={{
display: "flex",
justifyContent: "center",
}}>
<ZoomableImage
alt="Document Processor Code"
sources={{
light: "img/document_processor_code.png",
}}
style={{
maxWidth: "100%",
margin: "0 auto",
display: "flex",
justifyContent: "center",
}}
/>
<ZoomableImage
alt="Document Processor Component"
sources={{
light: "img/document_processor.png",
}}
style={{
width: "40%",
margin: "0 auto",
display: "flex",
justifyContent: "center",
}}
/>
</div>
---
## Loading Custom Components
For advanced customization, Langflow offers the option to create and load custom components outside of the standard interface. This process involves creating the desired components using a text editor and loading them using the Langflow CLI.
### Folder Structure
Create a folder that follows the same structural conventions as the [config.yaml](https://github.com/logspace-ai/langflow/blob/dev/src/backend/langflow/config.yaml) file. Inside this main directory, use a `custom_components` subdirectory for your custom components.
Inside `custom_components`, you can create a Python file for each component. Similarly, any custom agents should be housed in an `agents` subdirectory.
If you use a subdirectory name that is not in our config.yaml file, your component will appear in an `Other` category in the sidebar.
Your structure should look something like this:
```
.
└── custom_components
├── document_processor.py
└── ...
└── agents
└── ...
└── my_agents <-- Other category
└── ...
```
### Loading Custom Components
You can specify the path to your custom components using the _`--components-path`_ argument when running the Langflow CLI, as shown below:
```bash
langflow --components-path /path/to/components
```
Alternatively, you can set the `LANGFLOW_COMPONENTS_PATH` environment variable:
```bash
export LANGFLOW_COMPONENTS_PATH=/path/to/components
langflow
```
Langflow will attempt to load all of the components found in the specified directory. If a component fails to load due to errors in the component's code, Langflow will print an error message to the console but will continue loading the rest of the components.
### Interacting with Custom Components
Once your custom components have been loaded successfully, they will appear in Langflow's sidebar. From there, you can add them to your Langflow canvas for use. However, please note that components with errors will not be available for addition to the canvas. Always ensure your code is error-free before attempting to load components.
Remember, creating custom components allows you to extend the functionality of Langflow to better suit your unique needs. Happy coding!

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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";
# Features
<div style={{ marginBottom: "20px" }}>
When you click for New Project, you will see on the top left corner of the
screen, some options such as <strong>Import</strong>, <strong>Export</strong>,{" "}
<strong>Code</strong> and <strong>Save</strong>, as displayed in the image
below:
</div>
{" "}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/features.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
<div style={{ marginBottom: "20px" }}>
Further down, we will explain each of these options.
</div>
---
### Import and Export
Flows can be exported and imported as JSON files.
<Admonition type="caution">
Watch out for API keys being stored in local files.
</Admonition>
---
### Code
The Code button shows snippets to use your flow as a Python object or an API.
**Python Code**
Through the Langflow package, you can load a flow from a JSON file and use it as a LangChain object.
```py
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?")
```
**API**
Once you save a flow, the API endpoint is created with your latest changes. Click the "code" button to use that flow as an API. You can post-adjust component parameters using the global variable TWEAKS.
The example below shows a Python script making a POST request to a local API endpoint, which gets a prediction based on the message input.
<div
style={{ marginBottom: "20px", display: "flex", justifyContent: "center" }}
>
<ReactPlayer playing controls url="/videos/langflow_api.mp4" />
</div>

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import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
import ReactPlayer from "react-player";
# Prompt Customization
The prompt template allows users to create prompts and define variables that provide control over instructing the model.
{" "}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/prompt_customization.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
Variables can be used to define instructions, questions, context, inputs, or examples for the model and can be created with any chosen name in curly brackets, e.g., `{variable_name}`. They act as placeholders for parts of the text that can be easily modified.
{" "}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/prompt_customization2.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
Once inserted, these variables are immediately recognized as new fields in the prompt component. Here, you can define their values within the component itself or leave a field empty to be adjusted over the chat interface.
{" "}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/prompt_customization3.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
You can also use documents or output parsers as prompt variables. By plugging them into prompt handles, they’ll disable and feed that input field.
{" "}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/prompt_customization4.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>
With this, users can interact with documents, webpages, or any other type of content directly from the prompt, which allows for seamless integration of external resources with the language model.
If working with an interactive (chat-like) flow, remember to keep one of the input variables empty to behave as the chat input.
{" "}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: useBaseUrl("img/prompt_customization5.png"),
}}
style={{ width: "100%", maxWidth: "800px", margin: "0 auto" }}
/>

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import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
import ReactPlayer from "react-player";
# Building chatbots with System Message
## Overview
In this guide, we will modify the "Basic Chat with Prompt and History" example, integrating the ChatPromptTemplate with the SystemMessagePromptTemplate and HumanMessagePromptTemplate components. By following these steps, you'll be able to build a personalized chatbot that can interpret and respond based on user-defined System messages.
## Interactive Guide
<iframe
src="https://app.tango.us/app/embed/f05bb3a7-4ceb-4b61-921f-628563b562f6?iframe=true"
sandbox="allow-scripts allow-top-navigation-by-user-activation allow-popups allow-same-origin"
security="restricted"
title="Step-by-Step Instructions to Customize and Build a Chatbot with custom System Message"
width="100%"
height="800px"
referrerpolicy="strict-origin-when-cross-origin"
frameborder="0"
webkitallowfullscreen="webkitallowfullscreen"
mozallowfullscreen="mozallowfullscreen"
allowfullscreen="allowfullscreen"
></iframe>
## Step-by-Step Instructions
1. Navigate to the "Community Examples" section.
2. Locate the "Basic Chat with Prompt and History" example, and click on "Fork Example".
3. Once in the editor, find the "PromptTemplate" component and remove it.
4. Now, add these three components: ChatPromptTemplate, SystemMessagePromptTemplate, and HumanMessagePromptTemplate.
> **Note:** Remember to set the model to gpt-3.5-turbo-0613 or the most up-to-date version. The latest models have improved capabilities to comprehend System messages.
5. Open the "Prompt" field on the SystemMessagePromptTemplate component.
6. Enter the text: _`You are a {role} that {behavior}.`_
7. Save your changes by clicking on "Check & Save".
8. Define the 'role' variable by typing "obedient assistant".
9. Next, navigate to the HumanMessagePromptTemplate and open the "Prompt" field.
10. Here, simply enter `{input}`.
11. Save these changes by clicking on "Check & Save".
12. Now, you should see your flow populated with the variables you defined.
13. In the Memory component, set the 'Input Key' to "input".
> **Tip:** When using a Memory component with multiple variables, it's crucial to specify which variable should be used to generate the conversation history.
14. Click on the "Build" button to implement your changes.
15. Open the chat interface to test your modifications.
16. You should now be able to see and use the defined variables in the chat interface.
17. Click on 'role' to examine the variable you established in the canvas.
18. Now, let's define the 'behavior' variable.
19. Enter the text: "writes the word 'Langflow' at the end of every sentence."
20. Test your chatbot by typing "How can you help me?"
21. If everything was set up correctly, your chatbot should respond appropriately, following the defined behavior.
22. Congratulations! You have successfully customized and built your chatbot.
By following these instructions, you have created a dynamic chatbot capable of understanding and responding based on custom system messages, enhancing the user experience and interaction. Enjoy your personalized assistant!

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import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
import ReactPlayer from "react-player";
# Integrating documents with prompt variables
## Overview
This guide takes you through the process of augmenting the "Basic Chat with Prompt and History" example. You'll learn how to embed documents as context into the PromptTemplate component utilizing a WebBaseLoader.
## Interactive Guide
<iframe
src="https://app.tango.us/app/embed/76578e84-a700-4b3b-a6ef-8a0350472781?iframe=true"
sandbox="allow-scripts allow-top-navigation-by-user-activation allow-popups allow-same-origin"
security="restricted"
title="Loading a document into a PromptTemplate variable"
width="100%"
height="800px"
referrerpolicy="strict-origin-when-cross-origin"
frameborder="0"
webkitallowfullscreen="webkitallowfullscreen"
mozallowfullscreen="mozallowfullscreen"
allowfullscreen="allowfullscreen"
></iframe>
## Step-by-Step Instructions
1. Start by navigating to the "Community Examples" section.
2. Find the "Basic Chat with Prompt and History" example and click on "Fork Example".
3. In the editor, open the "Template" field.
4. Here, introduce the `{context}` variable, placing it somewhere before the "Current conversation:" text.
5. Once done, save your changes by clicking on "Check & Save".
6. Next, open the search bar and type "web".
7. Drag and drop a WebBaseLoader (or any other loader of your choice) onto the canvas.
8. Connect this loader to the `{context}` variable that we just added.
9. In the "Web Page" field, enter "https://langflow.org/how-upload-examples".
10. Now, click on "ConversationBufferMemory".
11. In the "Input Key" field, enter "text" to define the Chat variable.
> **Tip:** When defining more than one variable and using a Memory component, it's crucial to specify which variable should be used to create the conversation history.
12. Click on the "Build" button to implement your changes.
13. Open the chat interface to test your modifications.
14. Try asking something like, "How do I upload examples?"
15. Click on "Display Prompt" to view your template.
16. Now, you can see what the model used as a basis to generate its response.
By following these instructions, you have successfully loaded a document into a PromptTemplate variable, allowing for more enriched and context-aware chat responses. This customization enhances user interaction by integrating relevant document content into the chat flow.

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# 👋 Welcome to Langflow
Langflow is an easy way to prototype [LangChain](https://github.com/hwchase17/langchain) flows. The drag-and-drop feature allows quick and effortless experimentation, while the built-in chat interface facilitates real-time interaction. It provides options to edit prompt parameters, create chains and agents, track thought processes, and export flows.
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
import ZoomableImage from "/src/theme/ZoomableImage.js";
{" "}
<ZoomableImage
alt="Docusaurus themed image"
sources={{
light: "img/new_langflow2.gif",
}}
style={{ width: "100%" }}
/>

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const lightCodeTheme = require("prism-react-renderer/themes/github");
const { remarkCodeHike } = require("@code-hike/mdx");
// With JSDoc @type annotations, IDEs can provide config autocompletion
/** @type {import('@docusaurus/types').DocusaurusConfig} */
module.exports = {
title: "Langflow Documentation",
tagline: "Langflow is a GUI for LangChain, designed with react-flow",
favicon: "img/favicon.ico",
url: "https://logspace-ai.github.io",
baseUrl: "/",
onBrokenLinks: "throw",
onBrokenMarkdownLinks: "warn",
organizationName: "logspace-ai",
projectName: "langflow",
trailingSlash: false,
customFields: {
mendableAnonKey: process.env.MENDABLE_ANON_KEY,
},
i18n: {
defaultLocale: "en",
locales: ["en"],
},
presets: [
[
"@docusaurus/preset-classic",
/** @type {import('@docusaurus/preset-classic').Options} */
({
docs: {
beforeDefaultRemarkPlugins: [
[
remarkCodeHike,
{
theme: "github-light",
showCopyButton: true,
lineNumbers: true,
},
],
],
routeBasePath: "/",
sidebarPath: require.resolve("./sidebars.js"),
path: "docs",
// sidebarPath: 'sidebars.js',
},
theme: {
customCss: [
require.resolve("@code-hike/mdx/styles.css"),
require.resolve("./src/css/custom.css"),
],
},
}),
],
],
plugins: [
["docusaurus-node-polyfills", { excludeAliases: ["console"] }],
"docusaurus-plugin-image-zoom",
// ....
async function myPlugin(context, options) {
return {
name: "docusaurus-tailwindcss",
configurePostCss(postcssOptions) {
// Appends TailwindCSS and AutoPrefixer.
postcssOptions.plugins.push(require("tailwindcss"));
postcssOptions.plugins.push(require("autoprefixer"));
return postcssOptions;
},
};
},
],
themes: ["mdx-v2"],
themeConfig:
/** @type {import('@docusaurus/preset-classic').ThemeConfig} */
({
navbar: {
hideOnScroll: true,
title: "Langflow",
logo: {
alt: "Langflow",
src: "img/chain.png",
},
items: [
// right
{
position: "right",
href: "https://github.com/logspace-ai/langflow",
position: "right",
className: "header-github-link",
target: "_blank",
rel: null,
},
{
position: "right",
href: "https://twitter.com/logspace_ai",
position: "right",
className: "header-twitter-link",
target: "_blank",
rel: null,
},
{
position: "right",
href: "https://discord.gg/EqksyE2EX9",
position: "right",
className: "header-discord-link",
target: "_blank",
rel: null,
},
],
},
tableOfContents: {
minHeadingLevel: 2,
maxHeadingLevel: 5,
},
colorMode: {
defaultMode: "light",
disableSwitch: true,
respectPrefersColorScheme: false,
},
announcementBar: {
content:
'⭐️ If you like ⛓️Langflow, star it on <a target="_blank" rel="noopener noreferrer" href="https://github.com/logspace-ai/langflow">GitHub</a>! ⭐️',
backgroundColor: "#E8EBF1", //Mustard Yellow #D19900 #D4B20B - Salmon #E9967A
textColor: "#1C1E21",
isCloseable: false,
},
footer: {
links: [],
copyright: `Copyright © ${new Date().getFullYear()} Logspace.`,
},
zoom: {
selector: ".markdown :not(a) > img:not(.no-zoom)",
background: {
light: "rgba(240, 240, 240, 0.9)",
},
config: {},
},
// prism: {
// theme: require("prism-react-renderer/themes/dracula"),
// },
docs: {
sidebar: {
hideable: true,
},
},
}),
};

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declare module '*.module.scss' {
const classes: { readonly [key: string]: string };
export default classes;
}
declare module '@theme/*';
declare module '@components/*';
declare module '@docusaurus/*';

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{
"name": "docusaurus",
"version": "0.0.0",
"private": true,
"scripts": {
"docusaurus": "docusaurus",
"start": "docusaurus start",
"build": "docusaurus build",
"swizzle": "docusaurus swizzle",
"deploy": "docusaurus deploy",
"clear": "docusaurus clear",
"serve": "docusaurus serve",
"write-translations": "docusaurus write-translations",
"write-heading-ids": "docusaurus write-heading-ids"
},
"dependencies": {
"@babel/preset-react": "^7.22.3",
"@code-hike/mdx": "^0.9.0",
"@docusaurus/core": "2.4.1",
"@docusaurus/plugin-ideal-image": "^2.4.1",
"@docusaurus/preset-classic": "2.4.1",
"@docusaurus/theme-classic": "^2.4.1",
"@docusaurus/theme-search-algolia": "^2.4.1",
"@mdx-js/react": "^2.3.0",
"@mendable/search": "^0.0.114",
"@pbe/react-yandex-maps": "^1.2.4",
"@prismicio/client": "^7.0.1",
"@uiball/loaders": "^1.2.6",
"autoprefixer": "^10.4.14",
"clsx": "^1.2.1",
"docusaurus-plugin-image-zoom": "^0.1.4",
"docusaurus-theme-mdx-v2": "^0.1.2",
"jquery": "^3.7.0",
"medium-zoom": "^1.0.8",
"node-fetch": "^3.3.1",
"path-browserify": "^1.0.1",
"postcss": "^8.4.24",
"prism-react-renderer": "^1.3.5",
"react": "^17.0.2",
"react-dom": "^17.0.2",
"react-images": "^0.6.7",
"react-medium-image-zoom": "^5.1.6",
"react-player": "^2.12.0",
"react-transition-group": "^4.4.5",
"remark-parse": "^10.0.2",
"swizzle": "^1.1.0",
"tailwindcss": "^3.3.2"
},
"devDependencies": {
"@docusaurus/module-type-aliases": "2.4.1",
"css-loader": "^6.8.1",
"docusaurus-node-polyfills": "^1.0.0",
"node-sass": "^9.0.0",
"sass": "^1.62.1",
"ts-loader": "^9.4.3"
},
"browserslist": {
"production": [
">0.5%",
"not dead",
"not op_mini all"
],
"development": [
"last 1 chrome version",
"last 1 firefox version",
"last 1 safari version"
]
},
"engines": {
"node": ">=16.14"
}
}

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module.exports = function(context, options) {
return {
name: 'loaders',
configureWebpack(config, isServer) {
return {
module: {
rules: [
{
test: /\.(gif|png|jpe?g|svg)$/i,
exclude: /\.(mdx?)$/i,
use: ['file-loader', { loader: 'image-webpack-loader' }],
},
],
},
};
},
};
};

98
docs/sidebars.js Normal file
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module.exports = {
docs: [
{
type: "category",
label: "Getting Started",
collapsed: false,
items: [
"index",
"getting-started/installation",
"getting-started/hugging-face-spaces",
"getting-started/creating-flows",
],
},
{
type: "category",
label: "Guidelines",
collapsed: false,
items: [
"guidelines/components",
"guidelines/features",
"guidelines/collection",
"guidelines/prompt-customization",
"guidelines/chat-interface",
"guidelines/chat-widget",
"guidelines/custom-component",
],
},
{
type: "category",
label: "Component Reference",
collapsed: false,
items: [
"components/agents",
"components/chains",
"components/custom",
"components/embeddings",
"components/llms",
"components/loaders",
"components/memories",
"components/prompts",
"components/retrievers",
"components/text-splitters",
"components/toolkits",
"components/tools",
"components/vector-stores",
"components/wrappers",
],
},
{
type: "category",
label: "Step-by-Step Guides",
collapsed: false,
items: ["guides/loading_document", "guides/chatprompttemplate_guide"],
},
// {
// type: 'category',
// label: 'Components',
// collapsed: false,
// items: [
// 'components/agents', 'components/chains', 'components/loaders', 'components/embeddings', 'components/llms',
// 'components/memories', 'components/prompts','components/text-splitters', 'components/toolkits', 'components/tools',
// 'components/utilities', 'components/vector-stores', 'components/wrappers',
// ],
// },
{
type: "category",
label: "Examples",
collapsed: false,
items: [
"examples/flow-runner",
"examples/conversation-chain",
"examples/buffer-memory",
"examples/midjourney-prompt-chain",
"examples/csv-loader",
"examples/serp-api-tool",
"examples/multiple-vectorstores",
"examples/python-function",
"examples/how-upload-examples",
],
},
{
type: "category",
label: "Deployment",
collapsed: false,
items: ["deployment/gcp-deployment", "deployment/jina-deployment"],
},
{
type: "category",
label: "Contributing",
collapsed: false,
items: [
"contributing/how-contribute",
"contributing/github-issues",
"contributing/community",
],
},
],
};

4
docs/spell_add.sh Normal file
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for line in $(cat spell_check_results.txt); do
echo "Adding $line to cspell.config.yaml"
echo " - $line" >> cspell.config.yaml
done

26
docs/spell_check.sh Normal file
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# Using cspell, we'll loop over each subdirectory inside ./docs and check every mdx file for spelling errors.
# If there is an error, we'll write the word to an output file
# prep
if [ -f spell_check_results.txt ]; then
rm spell_check_results.txt
fi
cd docs
# first check, over the mdx files in the root directory
find . -maxdepth 1 -type f -name "*.mdx" -exec cspell --words-only {} \; >> ../output.txt
# loop over each subdirectory and any directories inside
for dir in */; do
find $dir -type d -exec cspell --words-only {}/*.mdx \; >> ../output.txt
done
# loop over each line in the output file and prune duplications
cd ../
awk '!a[$0]++' output.txt > spell_check_results.txt
rm output.txt
# check the number of lines in spell_check_results.txt
lines=$(wc -l < spell_check_results.txt)
echo "There are $lines spelling errors or unknown words."

210
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/**
* Any CSS included here will be global. The classic template
* bundles Infima by default. Infima is a CSS framework designed to
* work well for content-centric websites.
*/
:root {
--ifm-background-color: var(--token-primary-bg-c);
--ifm-navbar-link-hover-color: initial;
--ifm-navbar-padding-vertical: 0;
--ifm-navbar-item-padding-vertical: 0;
--ifm-font-family-base: -apple-system, BlinkMacSystemFont, Inter, Helvetica,
Arial, sans-serif, "Apple Color Emoji", "Segoe UI emoji";
--ifm-font-family-monospace: "SFMono-Regular", "Roboto Mono", Consolas,
"Liberation Mono", Menlo, Courier, monospace;
}
.theme-doc-sidebar-item-category.menu__list-item:not(:first-child) {
margin-top: 1.5rem !important;
}
.docusaurus-highlight-code-line {
background-color: rgba(0, 0, 0, 0.1);
display: block;
margin: 0 calc(-1 * var(--ifm-pre-padding));
padding: 0 var(--ifm-pre-padding);
}
.diagonal-box {
transform: skewY(-6deg);
}
.diagonal-content {
transform: skewY(6deg);
}
[class^="announcementBar"] {
z-index: 10;
}
.showcase {
background-color: #fff;
}
.showcase-border {
border-color: rgba(243, 244, 246, 1);
}
.text-description {
color: rgba(107, 114, 128, 1);
}
p {
text-align: justify;
}
/* apply */
#hero-apply {
z-index: -1;
background-image: linear-gradient(
var(--ifm-footer-background-color),
var(--ifm-navbar-background-color)
);
}
.apply-form {
background-image: linear-gradient(#fff, #f5f5fa);
max-width: 600px;
}
.apply-text {
color: #36395a;
}
/* index */
#hero {
background-image: linear-gradient(
var(--ifm-footer-background-color),
var(--ifm-navbar-background-color)
);
}
/**
* Hero component title overrides to match other heading styles
*/
.hero-title {
color: rgb(28, 30, 33);
font-family: var(--ifm-heading-font-family);
}
h1 {
font-size: 26px;
}
h2 {
font-size: 22px;
}
h3 {
font-size: 18px;
}
body {
font-size: 16px;
}
.docsearch-logo {
color: #21243d;
}
.apply-button:hover {
color: #fff;
}
/* GitHub */
.header-github-link:hover {
opacity: 0.6;
}
.header-github-link:before {
content: "";
width: 24px;
height: 24px;
display: flex;
background: url("data:image/svg+xml,%3Csvg viewBox='0 0 24 24' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath d='M12 .297c-6.63 0-12 5.373-12 12 0 5.303 3.438 9.8 8.205 11.385.6.113.82-.258.82-.577 0-.285-.01-1.04-.015-2.04-3.338.724-4.042-1.61-4.042-1.61C4.422 18.07 3.633 17.7 3.633 17.7c-1.087-.744.084-.729.084-.729 1.205.084 1.838 1.236 1.838 1.236 1.07 1.835 2.809 1.305 3.495.998.108-.776.417-1.305.76-1.605-2.665-.3-5.466-1.332-5.466-5.93 0-1.31.465-2.38 1.235-3.22-.135-.303-.54-1.523.105-3.176 0 0 1.005-.322 3.3 1.23.96-.267 1.98-.399 3-.405 1.02.006 2.04.138 3 .405 2.28-1.552 3.285-1.23 3.285-1.23.645 1.653.24 2.873.12 3.176.765.84 1.23 1.91 1.23 3.22 0 4.61-2.805 5.625-5.475 5.92.42.36.81 1.096.81 2.22 0 1.606-.015 2.896-.015 3.286 0 .315.21.69.825.57C20.565 22.092 24 17.592 24 12.297c0-6.627-5.373-12-12-12'/%3E%3C/svg%3E")
no-repeat;
}
/* Twitter */
.header-twitter-link:hover {
opacity: 0.6;
}
.header-twitter-link::before {
content: "";
width: 24px;
height: 24px;
display: flex;
background: url("/logos/twitter.svg");
background-size: contain;
}
/* Discord */
.header-discord-link:hover {
opacity: 0.6;
}
.header-discord-link::before {
content: "";
width: 24px;
height: 24px;
display: flex;
background: url("/logos/discord.svg");
background-size: contain;
}
/* Images */
.image-rendering-crisp {
image-rendering: crisp-edges;
/* alias for google chrome */
image-rendering: -webkit-optimize-contrast;
}
.image-rendering-pixel {
image-rendering: pixelated;
}
.img-center {
display: flex;
justify-content: center;
width: 100%;
}
.resized-image {
width: 400px;
}
/* Reduce width on mobile for Mendable Search */
@media (max-width: 767px) {
.mendable-search {
width: 200px;
}
}
@media (max-width: 500px) {
.mendable-search {
width: 150px;
}
}
@media (max-width: 380px) {
.mendable-search {
width: 140px;
}
}
/*
.ch-scrollycoding {
gap: 10rem !important;
} */
.ch-scrollycoding-content {
max-width: 55% !important;
min-width: 40% !important;
}
.ch-scrollycoding-sticker {
max-width: 60% !important;
min-width: 45% !important;
}
.ch-scrollycoding-step-content {
min-height: 70px;
}

23
docs/src/pages/index.module.css Normal file
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/**
* CSS files with the .module.css suffix will be treated as CSS modules
* and scoped locally.
*/
.heroBanner {
padding: 4rem 0;
text-align: center;
position: relative;
overflow: hidden;
}
@media screen and (max-width: 996px) {
.heroBanner {
padding: 2rem;
}
}
.buttons {
display: flex;
align-items: center;
justify-content: center;
}

7
docs/src/pages/markdown-page.md Normal file
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---
title: Markdown page example
---
# Markdown page example
You don't need React to write simple standalone pages.

51
docs/src/theme/Footer.js Normal file
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import React from "react";
import Footer from "@theme-original/Footer";
import { MendableFloatingButton } from "@mendable/search";
import useDocusaurusContext from "@docusaurus/useDocusaurusContext";
export default function FooterWrapper(props) {
const iconSpan1 = React.createElement(
"img",
{
src: "/img/chain.png",
style: { width: "40px" },
},
null
);
const icon = React.createElement(
"div",
{
style: {
color: "#000000",
fontSize: "22px",
width: "48px",
height: "48px",
margin: "0px",
padding: "0px",
display: "flex",
alignItems: "center",
justifyContent: "center",
textAlign: "center",
},
},
[iconSpan1]
);
const {
siteConfig: { customFields },
} = useDocusaurusContext();
const mendableFloatingButton = React.createElement(MendableFloatingButton, {
floatingButtonStyle: { color: "#000000", backgroundColor: "#f6f6f6" },
anon_key: customFields.mendableAnonKey, // Mendable Search Public ANON key, ok to be public
showSimpleSearch: true,
icon: icon,
});
return (
<>
<Footer />
{mendableFloatingButton}
</>
);
}

5
docs/src/theme/SearchBar.js Normal file
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// By default, the classic theme does not provide any SearchBar implementation
// If you swizzled this, it is your responsibility to provide an implementation
// Tip: swizzle the SearchBar from the Algolia theme for inspiration:
// npm run swizzle @docusaurus/theme-search-algolia SearchBar
export {default} from '@docusaurus/Noop';

57
docs/src/theme/ZoomableImage.js Normal file
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import React, { useState, useEffect } from "react";
import ThemedImage from "@theme/ThemedImage";
import useBaseUrl from "@docusaurus/useBaseUrl";
const ZoomableImage = ({ alt, sources, style }) => {
// add style here
const [isFullscreen, setIsFullscreen] = useState(false);
const toggleFullscreen = () => {
setIsFullscreen(!isFullscreen);
};
const handleKeyPress = (event) => {
if (event.key === "Escape") {
setIsFullscreen(false);
}
};
useEffect(() => {
if (isFullscreen) {
document.addEventListener("keydown", handleKeyPress);
} else {
document.removeEventListener("keydown", handleKeyPress);
}
return () => {
document.removeEventListener("keydown", handleKeyPress);
};
}, [isFullscreen]);
// Default style
const defaultStyle = {
width: "50%",
margin: "0 auto",
display: "flex",
justifyContent: "center",
};
return (
<div
className={`zoomable-image ${isFullscreen ? "fullscreen" : ""}`}
onClick={toggleFullscreen}
style={{ ...defaultStyle, ...style }}
>
<ThemedImage
className="zoomable-image-inner"
alt={alt}
sources={{
light: useBaseUrl(sources.light),
dark: useBaseUrl(sources.dark),
}}
/>
</div>
);
};
export default ZoomableImage;

0
docs/static/.nojekyll vendored Normal file
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1
docs/static/CNAME vendored Normal file
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langflow.org

101
docs/static/data/organizations-100.csv vendored Normal file
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Index,Organization Id,Name,Website,Country,Description,Founded,Industry,Number of employees
1,FAB0d41d5b5d22c,Ferrell LLC,https://price.net/,Papua New Guinea,Horizontal empowering knowledgebase,1990,Plastics,3498
2,6A7EdDEA9FaDC52,"Mckinney, Riley and Day",http://www.hall-buchanan.info/,Finland,User-centric system-worthy leverage,2015,Glass / Ceramics / Concrete,4952
3,0bFED1ADAE4bcC1,Hester Ltd,http://sullivan-reed.com/,China,Switchable scalable moratorium,1971,Public Safety,5287
4,2bFC1Be8a4ce42f,Holder-Sellers,https://becker.com/,Turkmenistan,De-engineered systemic artificial intelligence,2004,Automotive,921
5,9eE8A6a4Eb96C24,Mayer Group,http://www.brewer.com/,Mauritius,Synchronized needs-based challenge,1991,Transportation,7870
6,cC757116fe1C085,Henry-Thompson,http://morse.net/,Bahamas,Face-to-face well-modulated customer loyalty,1992,Primary / Secondary Education,4914
7,219233e8aFF1BC3,Hansen-Everett,https://www.kidd.org/,Pakistan,Seamless disintermediate collaboration,2018,Publishing Industry,7832
8,ccc93DCF81a31CD,Mcintosh-Mora,https://www.brooks.com/,Heard Island and McDonald Islands,Centralized attitude-oriented capability,1970,Import / Export,4389
9,0B4F93aA06ED03e,Carr Inc,http://ross.com/,Kuwait,Distributed impactful customer loyalty,1996,Plastics,8167
10,738b5aDe6B1C6A5,Gaines Inc,http://sandoval-hooper.com/,Uzbekistan,Multi-lateral scalable protocol,1997,Outsourcing / Offshoring,9698
11,AE61b8Ffebbc476,Kidd Group,http://www.lyons.com/,Bouvet Island (Bouvetoya),Proactive foreground paradigm,2001,Primary / Secondary Education,7473
12,eb3B7D06cCdD609,Crane-Clarke,https://www.sandoval.com/,Denmark,Front-line clear-thinking encryption,2014,Food / Beverages,9011
13,8D0c29189C9798B,"Keller, Campos and Black",https://www.garner.info/,Liberia,Ameliorated directional emulation,2020,Museums / Institutions,2862
14,D2c91cc03CA394c,Glover-Pope,http://www.silva.biz/,United Arab Emirates,Persevering contextually-based approach,2013,Medical Practice,9079
15,C8AC1eaf9C036F4,Pacheco-Spears,https://aguilar.com/,Sweden,Secured logistical synergy,1984,Maritime,769
16,b5D10A14f7a8AfE,Hodge-Ayers,http://www.archer-elliott.com/,Honduras,Future-proofed radical implementation,1990,Facilities Services,8508
17,68139b5C4De03B4,"Bowers, Guerra and Krause",http://www.carrillo-nicholson.com/,Uganda,De-engineered transitional strategy,1972,Primary / Secondary Education,6986
18,5c2EffEfdba2BdF,Mckenzie-Melton,http://montoya-thompson.com/,Hong Kong,Reverse-engineered heuristic alliance,1998,Investment Management / Hedge Fund / Private Equity,4589
19,ba179F19F7925f5,Branch-Mann,http://www.lozano.com/,Botswana,Adaptive intangible frame,1999,Architecture / Planning,7961
20,c1Ce9B350BAc66b,Weiss and Sons,https://barrett.com/,Korea,Sharable optimal functionalities,2011,Plastics,5984
21,8de40AC4e6EaCa4,"Velez, Payne and Coffey",http://burton.com/,Luxembourg,Mandatory coherent synergy,1986,Wholesale,5010
22,Aad86a4F0385F2d,Harrell LLC,http://www.frey-rosario.com/,Guadeloupe,Reverse-engineered mission-critical moratorium,2018,Construction,2185
23,22aC3FFd64fD703,"Eaton, Reynolds and Vargas",http://www.freeman.biz/,Monaco,Self-enabling multi-tasking process improvement,2014,Luxury Goods / Jewelry,8987
24,5Ec4C272bCf085c,Robbins-Cummings,http://donaldson-wilkins.com/,Belgium,Organic non-volatile hierarchy,1991,Pharmaceuticals,5038
25,5fDBeA8BB91a000,Jenkins Inc,http://www.kirk.biz/,South Africa,Front-line systematic help-desk,2002,Insurance,1215
26,dFfD6a6F9AC2d9C,"Greene, Benjamin and Novak",http://www.kent.net/,Romania,Centralized leadingedge moratorium,2012,Museums / Institutions,4941
27,4B217cC5a0674C5,"Dickson, Richmond and Clay",http://everett.com/,Czech Republic,Team-oriented tangible complexity,1980,Real Estate / Mortgage,3122
28,88b1f1cDcf59a37,Prince-David,http://thompson.com/,Christmas Island,Virtual holistic methodology,1970,Banking / Mortgage,1046
29,f9F7bBCAEeC360F,Ayala LLC,http://www.zhang.com/,Philippines,Open-source zero administration hierarchy,2021,Legal Services,7664
30,7Cb3AeFcE4Ba31e,Rivas Group,https://hebert.org/,Australia,Open-architected well-modulated capacity,1998,Logistics / Procurement,4155
31,ccBcC32adcbc530,"Sloan, Mays and Whitehead",http://lawson.com/,Chad,Face-to-face high-level conglomeration,1997,Civil Engineering,365
32,f5afd686b3d05F5,"Durham, Allen and Barnes",http://chan-stafford.org/,Zimbabwe,Synergistic web-enabled framework,1993,Mechanical or Industrial Engineering,6135
33,38C6cfC5074Fa5e,Fritz-Franklin,http://www.lambert.com/,Nepal,Automated 4thgeneration website,1972,Hospitality,4516
34,5Cd7efccCcba38f,Burch-Ewing,http://cline.net/,Taiwan,User-centric 4thgeneration system engine,1981,Venture Capital / VC,7443
35,9E6Acb51e3F9d6F,"Glass, Barrera and Turner",https://dunlap.com/,Kyrgyz Republic,Multi-channeled 3rdgeneration open system,2020,Utilities,2610
36,4D4d7E18321eaeC,Pineda-Cox,http://aguilar.org/,Bolivia,Fundamental asynchronous capability,2010,Human Resources / HR,1312
37,485f5d06B938F2b,"Baker, Mccann and Macdonald",http://www.anderson-barker.com/,Kenya,Cross-group user-facing focus group,2013,Legislative Office,1638
38,19E3a5Bf6dBDc4F,Cuevas-Moss,https://dodson-castaneda.net/,Guatemala,Extended human-resource intranet,1994,Music,9995
39,6883A965c7b68F7,Hahn PLC,http://newman.com/,Belarus,Organic logistical leverage,2012,Electrical / Electronic Manufacturing,3715
40,AC5B7AA74Aa4A2E,"Valentine, Ferguson and Kramer",http://stuart.net/,Jersey,Centralized secondary time-frame,1997,Non - Profit / Volunteering,3585
41,decab0D5027CA6a,Arroyo Inc,https://www.turner.com/,Grenada,Managed demand-driven website,2006,Writing / Editing,9067
42,dF084FbBb613eea,Walls LLC,http://www.reese-vasquez.biz/,Cape Verde,Self-enabling fresh-thinking installation,1989,Investment Management / Hedge Fund / Private Equity,1678
43,A2D89Ab9bCcAd4e,"Mitchell, Warren and Schneider",https://fox.biz/,Trinidad and Tobago,Enhanced intangible time-frame,2021,Capital Markets / Hedge Fund / Private Equity,3816
44,77aDc905434a49f,Prince PLC,https://www.watts.com/,Sweden,Profit-focused coherent installation,2016,Individual / Family Services,7645
45,235fdEFE2cfDa5F,Brock-Blackwell,http://www.small.com/,Benin,Secured foreground emulation,1986,Online Publishing,7034
46,1eD64cFe986BBbE,Walton-Barnett,https://ashley-schaefer.com/,Western Sahara,Right-sized clear-thinking flexibility,2001,Luxury Goods / Jewelry,1746
47,CbBbFcdd0eaE2cF,Bartlett-Arroyo,https://cruz.com/,Northern Mariana Islands,Realigned didactic function,1976,Civic / Social Organization,3987
48,49aECbDaE6aBD53,"Wallace, Madden and Morris",http://www.blevins-fernandez.biz/,Germany,Persistent real-time customer loyalty,2016,Pharmaceuticals,9443
49,7b3fe6e7E72bFa4,Berg-Sparks,https://cisneros-love.com/,Canada,Stand-alone static implementation,1974,Arts / Crafts,2073
50,c6DedA82A8aef7E,Gonzales Ltd,http://bird.com/,Tonga,Managed human-resource policy,1988,Consumer Goods,9069
51,7D9FBF85cdC3871,Lawson and Sons,https://www.wong.com/,French Southern Territories,Compatible analyzing intranet,2021,Arts / Crafts,3527
52,7dd18Fb7cB07b65,"Mcguire, Mcconnell and Olsen",https://melton-briggs.com/,Korea,Profound client-server frame,1988,Printing,8445
53,EF5B55FadccB8Fe,Charles-Phillips,https://bowman.com/,Cote d'Ivoire,Monitored client-server implementation,2012,Mental Health Care,3450
54,f8D4B99e11fAF5D,Odom Ltd,https://www.humphrey-hess.com/,Cote d'Ivoire,Advanced static process improvement,2012,Management Consulting,1825
55,e24D21BFd3bF1E5,Richard PLC,https://holden-coleman.net/,Mayotte,Object-based optimizing model,1971,Broadcast Media,4942
56,B9BdfEB6D3Ca44E,Sampson Ltd,https://blevins.com/,Cayman Islands,Intuitive local adapter,2005,Farming,1418
57,2a74D6f3D3B268e,"Cherry, Le and Callahan",https://waller-delacruz.biz/,Nigeria,Universal human-resource collaboration,2017,Entertainment / Movie Production,7202
58,Bf3F3f62c8aBC33,Cherry PLC,https://www.avila.info/,Marshall Islands,Persistent tertiary website,1980,Plastics,8245
59,aeBe26B80a7a23c,Melton-Nichols,https://kennedy.com/,Palau,User-friendly clear-thinking productivity,2021,Legislative Office,8741
60,aAeb29ad43886C6,Potter-Walsh,http://thomas-french.org/,Turkey,Optional non-volatile open system,2008,Human Resources / HR,6923
61,bD1bc6bB6d1FeD3,Freeman-Chen,https://mathis.com/,Timor-Leste,Phased next generation adapter,1973,International Trade / Development,346
62,EB9f456e8b7022a,Soto Group,https://norris.info/,Vietnam,Enterprise-wide executive installation,1988,Business Supplies / Equipment,9097
63,Dfef38C51D8DAe3,"Poole, Cruz and Whitney",https://reed.info/,Reunion,Balanced analyzing groupware,1978,Marketing / Advertising / Sales,2992
64,055ffEfB2Dd95B0,Riley Ltd,http://wiley.com/,Brazil,Optional exuding superstructure,1986,Textiles,9315
65,cBfe4dbAE1699da,"Erickson, Andrews and Bailey",https://www.hobbs-grant.com/,Eritrea,Vision-oriented secondary project,2014,Consumer Electronics,7829
66,fdFbecbadcdCdf1,"Wilkinson, Charles and Arroyo",http://hunter-mcfarland.com/,United States Virgin Islands,Assimilated 24/7 archive,1996,Building Materials,602
67,5DCb8A5a5ca03c0,Floyd Ltd,http://www.whitney.com/,Falkland Islands (Malvinas),Function-based fault-tolerant concept,2017,Public Relations / PR,2911
68,ce57DCbcFD6d618,Newman-Galloway,https://www.scott.com/,Luxembourg,Enhanced foreground collaboration,1987,Information Technology / IT,3934
69,5aaD187dc929371,Frazier-Butler,https://www.daugherty-farley.info/,Northern Mariana Islands,Persistent interactive circuit,1972,Outsourcing / Offshoring,5130
70,902D7Ac8b6d476b,Newton Inc,https://www.richmond-manning.info/,Netherlands Antilles,Fundamental stable info-mediaries,1976,Military Industry,563
71,32BB9Ff4d939788,Duffy-Levy,https://www.potter.com/,Guernsey,Diverse exuding installation,1982,Wireless,6146
72,adcB0afbE58bAe3,Wagner LLC,https://decker-esparza.com/,Uruguay,Reactive attitude-oriented toolset,1987,International Affairs,6874
73,dfcA1c84AdB61Ac,Mccall-Holmes,http://www.dean.com/,Benin,Object-based value-added database,2009,Legal Services,696
74,208044AC2fe52F3,Massey LLC,https://frazier.biz/,Suriname,Configurable zero administration Graphical User Interface,1986,Accounting,5004
75,f3C365f0c1A0623,Hicks LLC,http://alvarez.biz/,Pakistan,Quality-focused client-server Graphical User Interface,1970,Computer Software / Engineering,8480
76,ec5Bdd3CBAfaB93,"Cole, Russell and Avery",http://www.blankenship.com/,Mongolia,De-engineered fault-tolerant challenge,2000,Law Enforcement,7012
77,DDB19Be7eeB56B4,Cummings-Rojas,https://simon-pearson.com/,Svalbard & Jan Mayen Islands,User-centric modular customer loyalty,2012,Financial Services,7529
78,dd6CA3d0bc3cAfc,"Beasley, Greene and Mahoney",http://www.petersen-lawrence.com/,Togo,Extended content-based methodology,1976,Religious Institutions,869
79,A0B9d56e61070e3,"Beasley, Sims and Allison",http://burke.info/,Latvia,Secured zero tolerance hub,1972,Facilities Services,6182
80,cBa7EFe5D05Adaf,Crawford-Rivera,https://black-ramirez.org/,Cuba,Persevering exuding budgetary management,1999,Online Publishing,7805
81,Ea3f6D52Ec73563,Montes-Hensley,https://krueger.org/,Liechtenstein,Multi-tiered secondary productivity,2009,Printing,8433
82,bC0CEd48A8000E0,Velazquez-Odom,https://stokes.com/,Djibouti,Streamlined 6thgeneration function,2002,Alternative Dispute Resolution,4044
83,c89b9b59BC4baa1,Eaton-Morales,https://www.reeves-graham.com/,Micronesia,Customer-focused explicit frame,1990,Capital Markets / Hedge Fund / Private Equity,7013
84,FEC51bce8421a7b,"Roberson, Pennington and Palmer",http://www.keith-fisher.com/,Cameroon,Adaptive bi-directional hierarchy,1993,Telecommunications,5571
85,e0E8e27eAc9CAd5,"George, Russo and Guerra",https://drake.com/,Sweden,Centralized non-volatile capability,1989,Military Industry,2880
86,B97a6CF9bf5983C,Davila Inc,https://mcconnell.info/,Cocos (Keeling) Islands,Profit-focused dedicated frame,2017,Consumer Electronics,2215
87,a0a6f9b3DbcBEb5,Mays-Preston,http://www.browning-key.com/,Mali,User-centric heuristic focus group,2006,Military Industry,5786
88,8cC1bDa330a5871,Pineda-Morton,https://www.carr.com/,United States Virgin Islands,Grass-roots methodical info-mediaries,1991,Printing,6168
89,ED889CB2FE9cbd3,Huang and Sons,https://www.bolton.com/,Eritrea,Re-contextualized dynamic hierarchy,1981,Semiconductors,7484
90,F4Dc1417BC6cb8f,Gilbert-Simon,https://www.bradford.biz/,Burundi,Grass-roots radical parallelism,1973,Newspapers / Journalism,1927
91,7ABc3c7ecA03B34,Sampson-Griffith,http://hendricks.org/,Benin,Multi-layered composite paradigm,1972,Textiles,3881
92,4e0719FBE38e0aB,Miles-Dominguez,http://www.turner.com/,Gibraltar,Organized empowering forecast,1996,Civic / Social Organization,897
93,dEbDAAeDfaed00A,Rowe and Sons,https://www.simpson.org/,El Salvador,Balanced multimedia knowledgebase,1978,Facilities Services,8172
94,61BDeCfeFD0cEF5,"Valenzuela, Holmes and Rowland",https://www.dorsey.net/,Taiwan,Persistent tertiary focus group,1999,Transportation,1483
95,4e91eD25f486110,"Best, Wade and Shepard",https://zimmerman.com/,Zimbabwe,Innovative background definition,1991,Gambling / Casinos,4873
96,0a0bfFbBbB8eC7c,Holmes Group,https://mcdowell.org/,Ethiopia,Right-sized zero tolerance focus group,1975,Photography,2988
97,BA6Cd9Dae2Efd62,Good Ltd,http://duffy.com/,Anguilla,Reverse-engineered composite moratorium,1971,Consumer Services,4292
98,E7df80C60Abd7f9,Clements-Espinoza,http://www.flowers.net/,Falkland Islands (Malvinas),Progressive modular hub,1991,Broadcast Media,236
99,AFc285dbE2fEd24,Mendez Inc,https://www.burke.net/,Kyrgyz Republic,User-friendly exuding migration,1993,Education Management,339
100,e9eB5A60Cef8354,Watkins-Kaiser,http://www.herring.com/,Togo,Synergistic background access,2009,Financial Services,2785
1 Index Organization Id Name Website Country Description Founded Industry Number of employees
2 1 FAB0d41d5b5d22c Ferrell LLC https://price.net/ Papua New Guinea Horizontal empowering knowledgebase 1990 Plastics 3498
3 2 6A7EdDEA9FaDC52 Mckinney, Riley and Day http://www.hall-buchanan.info/ Finland User-centric system-worthy leverage 2015 Glass / Ceramics / Concrete 4952
4 3 0bFED1ADAE4bcC1 Hester Ltd http://sullivan-reed.com/ China Switchable scalable moratorium 1971 Public Safety 5287
5 4 2bFC1Be8a4ce42f Holder-Sellers https://becker.com/ Turkmenistan De-engineered systemic artificial intelligence 2004 Automotive 921
6 5 9eE8A6a4Eb96C24 Mayer Group http://www.brewer.com/ Mauritius Synchronized needs-based challenge 1991 Transportation 7870
7 6 cC757116fe1C085 Henry-Thompson http://morse.net/ Bahamas Face-to-face well-modulated customer loyalty 1992 Primary / Secondary Education 4914
8 7 219233e8aFF1BC3 Hansen-Everett https://www.kidd.org/ Pakistan Seamless disintermediate collaboration 2018 Publishing Industry 7832
9 8 ccc93DCF81a31CD Mcintosh-Mora https://www.brooks.com/ Heard Island and McDonald Islands Centralized attitude-oriented capability 1970 Import / Export 4389
10 9 0B4F93aA06ED03e Carr Inc http://ross.com/ Kuwait Distributed impactful customer loyalty 1996 Plastics 8167
11 10 738b5aDe6B1C6A5 Gaines Inc http://sandoval-hooper.com/ Uzbekistan Multi-lateral scalable protocol 1997 Outsourcing / Offshoring 9698
12 11 AE61b8Ffebbc476 Kidd Group http://www.lyons.com/ Bouvet Island (Bouvetoya) Proactive foreground paradigm 2001 Primary / Secondary Education 7473
13 12 eb3B7D06cCdD609 Crane-Clarke https://www.sandoval.com/ Denmark Front-line clear-thinking encryption 2014 Food / Beverages 9011
14 13 8D0c29189C9798B Keller, Campos and Black https://www.garner.info/ Liberia Ameliorated directional emulation 2020 Museums / Institutions 2862
15 14 D2c91cc03CA394c Glover-Pope http://www.silva.biz/ United Arab Emirates Persevering contextually-based approach 2013 Medical Practice 9079
16 15 C8AC1eaf9C036F4 Pacheco-Spears https://aguilar.com/ Sweden Secured logistical synergy 1984 Maritime 769
17 16 b5D10A14f7a8AfE Hodge-Ayers http://www.archer-elliott.com/ Honduras Future-proofed radical implementation 1990 Facilities Services 8508
18 17 68139b5C4De03B4 Bowers, Guerra and Krause http://www.carrillo-nicholson.com/ Uganda De-engineered transitional strategy 1972 Primary / Secondary Education 6986
19 18 5c2EffEfdba2BdF Mckenzie-Melton http://montoya-thompson.com/ Hong Kong Reverse-engineered heuristic alliance 1998 Investment Management / Hedge Fund / Private Equity 4589
20 19 ba179F19F7925f5 Branch-Mann http://www.lozano.com/ Botswana Adaptive intangible frame 1999 Architecture / Planning 7961
21 20 c1Ce9B350BAc66b Weiss and Sons https://barrett.com/ Korea Sharable optimal functionalities 2011 Plastics 5984
22 21 8de40AC4e6EaCa4 Velez, Payne and Coffey http://burton.com/ Luxembourg Mandatory coherent synergy 1986 Wholesale 5010
23 22 Aad86a4F0385F2d Harrell LLC http://www.frey-rosario.com/ Guadeloupe Reverse-engineered mission-critical moratorium 2018 Construction 2185
24 23 22aC3FFd64fD703 Eaton, Reynolds and Vargas http://www.freeman.biz/ Monaco Self-enabling multi-tasking process improvement 2014 Luxury Goods / Jewelry 8987
25 24 5Ec4C272bCf085c Robbins-Cummings http://donaldson-wilkins.com/ Belgium Organic non-volatile hierarchy 1991 Pharmaceuticals 5038
26 25 5fDBeA8BB91a000 Jenkins Inc http://www.kirk.biz/ South Africa Front-line systematic help-desk 2002 Insurance 1215
27 26 dFfD6a6F9AC2d9C Greene, Benjamin and Novak http://www.kent.net/ Romania Centralized leadingedge moratorium 2012 Museums / Institutions 4941
28 27 4B217cC5a0674C5 Dickson, Richmond and Clay http://everett.com/ Czech Republic Team-oriented tangible complexity 1980 Real Estate / Mortgage 3122
29 28 88b1f1cDcf59a37 Prince-David http://thompson.com/ Christmas Island Virtual holistic methodology 1970 Banking / Mortgage 1046
30 29 f9F7bBCAEeC360F Ayala LLC http://www.zhang.com/ Philippines Open-source zero administration hierarchy 2021 Legal Services 7664
31 30 7Cb3AeFcE4Ba31e Rivas Group https://hebert.org/ Australia Open-architected well-modulated capacity 1998 Logistics / Procurement 4155
32 31 ccBcC32adcbc530 Sloan, Mays and Whitehead http://lawson.com/ Chad Face-to-face high-level conglomeration 1997 Civil Engineering 365
33 32 f5afd686b3d05F5 Durham, Allen and Barnes http://chan-stafford.org/ Zimbabwe Synergistic web-enabled framework 1993 Mechanical or Industrial Engineering 6135
34 33 38C6cfC5074Fa5e Fritz-Franklin http://www.lambert.com/ Nepal Automated 4thgeneration website 1972 Hospitality 4516
35 34 5Cd7efccCcba38f Burch-Ewing http://cline.net/ Taiwan User-centric 4thgeneration system engine 1981 Venture Capital / VC 7443
36 35 9E6Acb51e3F9d6F Glass, Barrera and Turner https://dunlap.com/ Kyrgyz Republic Multi-channeled 3rdgeneration open system 2020 Utilities 2610
37 36 4D4d7E18321eaeC Pineda-Cox http://aguilar.org/ Bolivia Fundamental asynchronous capability 2010 Human Resources / HR 1312
38 37 485f5d06B938F2b Baker, Mccann and Macdonald http://www.anderson-barker.com/ Kenya Cross-group user-facing focus group 2013 Legislative Office 1638
39 38 19E3a5Bf6dBDc4F Cuevas-Moss https://dodson-castaneda.net/ Guatemala Extended human-resource intranet 1994 Music 9995
40 39 6883A965c7b68F7 Hahn PLC http://newman.com/ Belarus Organic logistical leverage 2012 Electrical / Electronic Manufacturing 3715
41 40 AC5B7AA74Aa4A2E Valentine, Ferguson and Kramer http://stuart.net/ Jersey Centralized secondary time-frame 1997 Non - Profit / Volunteering 3585
42 41 decab0D5027CA6a Arroyo Inc https://www.turner.com/ Grenada Managed demand-driven website 2006 Writing / Editing 9067
43 42 dF084FbBb613eea Walls LLC http://www.reese-vasquez.biz/ Cape Verde Self-enabling fresh-thinking installation 1989 Investment Management / Hedge Fund / Private Equity 1678
44 43 A2D89Ab9bCcAd4e Mitchell, Warren and Schneider https://fox.biz/ Trinidad and Tobago Enhanced intangible time-frame 2021 Capital Markets / Hedge Fund / Private Equity 3816
45 44 77aDc905434a49f Prince PLC https://www.watts.com/ Sweden Profit-focused coherent installation 2016 Individual / Family Services 7645
46 45 235fdEFE2cfDa5F Brock-Blackwell http://www.small.com/ Benin Secured foreground emulation 1986 Online Publishing 7034
47 46 1eD64cFe986BBbE Walton-Barnett https://ashley-schaefer.com/ Western Sahara Right-sized clear-thinking flexibility 2001 Luxury Goods / Jewelry 1746
48 47 CbBbFcdd0eaE2cF Bartlett-Arroyo https://cruz.com/ Northern Mariana Islands Realigned didactic function 1976 Civic / Social Organization 3987
49 48 49aECbDaE6aBD53 Wallace, Madden and Morris http://www.blevins-fernandez.biz/ Germany Persistent real-time customer loyalty 2016 Pharmaceuticals 9443
50 49 7b3fe6e7E72bFa4 Berg-Sparks https://cisneros-love.com/ Canada Stand-alone static implementation 1974 Arts / Crafts 2073
51 50 c6DedA82A8aef7E Gonzales Ltd http://bird.com/ Tonga Managed human-resource policy 1988 Consumer Goods 9069
52 51 7D9FBF85cdC3871 Lawson and Sons https://www.wong.com/ French Southern Territories Compatible analyzing intranet 2021 Arts / Crafts 3527
53 52 7dd18Fb7cB07b65 Mcguire, Mcconnell and Olsen https://melton-briggs.com/ Korea Profound client-server frame 1988 Printing 8445
54 53 EF5B55FadccB8Fe Charles-Phillips https://bowman.com/ Cote d'Ivoire Monitored client-server implementation 2012 Mental Health Care 3450
55 54 f8D4B99e11fAF5D Odom Ltd https://www.humphrey-hess.com/ Cote d'Ivoire Advanced static process improvement 2012 Management Consulting 1825
56 55 e24D21BFd3bF1E5 Richard PLC https://holden-coleman.net/ Mayotte Object-based optimizing model 1971 Broadcast Media 4942
57 56 B9BdfEB6D3Ca44E Sampson Ltd https://blevins.com/ Cayman Islands Intuitive local adapter 2005 Farming 1418
58 57 2a74D6f3D3B268e Cherry, Le and Callahan https://waller-delacruz.biz/ Nigeria Universal human-resource collaboration 2017 Entertainment / Movie Production 7202
59 58 Bf3F3f62c8aBC33 Cherry PLC https://www.avila.info/ Marshall Islands Persistent tertiary website 1980 Plastics 8245
60 59 aeBe26B80a7a23c Melton-Nichols https://kennedy.com/ Palau User-friendly clear-thinking productivity 2021 Legislative Office 8741
61 60 aAeb29ad43886C6 Potter-Walsh http://thomas-french.org/ Turkey Optional non-volatile open system 2008 Human Resources / HR 6923
62 61 bD1bc6bB6d1FeD3 Freeman-Chen https://mathis.com/ Timor-Leste Phased next generation adapter 1973 International Trade / Development 346
63 62 EB9f456e8b7022a Soto Group https://norris.info/ Vietnam Enterprise-wide executive installation 1988 Business Supplies / Equipment 9097
64 63 Dfef38C51D8DAe3 Poole, Cruz and Whitney https://reed.info/ Reunion Balanced analyzing groupware 1978 Marketing / Advertising / Sales 2992
65 64 055ffEfB2Dd95B0 Riley Ltd http://wiley.com/ Brazil Optional exuding superstructure 1986 Textiles 9315
66 65 cBfe4dbAE1699da Erickson, Andrews and Bailey https://www.hobbs-grant.com/ Eritrea Vision-oriented secondary project 2014 Consumer Electronics 7829
67 66 fdFbecbadcdCdf1 Wilkinson, Charles and Arroyo http://hunter-mcfarland.com/ United States Virgin Islands Assimilated 24/7 archive 1996 Building Materials 602
68 67 5DCb8A5a5ca03c0 Floyd Ltd http://www.whitney.com/ Falkland Islands (Malvinas) Function-based fault-tolerant concept 2017 Public Relations / PR 2911
69 68 ce57DCbcFD6d618 Newman-Galloway https://www.scott.com/ Luxembourg Enhanced foreground collaboration 1987 Information Technology / IT 3934
70 69 5aaD187dc929371 Frazier-Butler https://www.daugherty-farley.info/ Northern Mariana Islands Persistent interactive circuit 1972 Outsourcing / Offshoring 5130
71 70 902D7Ac8b6d476b Newton Inc https://www.richmond-manning.info/ Netherlands Antilles Fundamental stable info-mediaries 1976 Military Industry 563
72 71 32BB9Ff4d939788 Duffy-Levy https://www.potter.com/ Guernsey Diverse exuding installation 1982 Wireless 6146
73 72 adcB0afbE58bAe3 Wagner LLC https://decker-esparza.com/ Uruguay Reactive attitude-oriented toolset 1987 International Affairs 6874
74 73 dfcA1c84AdB61Ac Mccall-Holmes http://www.dean.com/ Benin Object-based value-added database 2009 Legal Services 696
75 74 208044AC2fe52F3 Massey LLC https://frazier.biz/ Suriname Configurable zero administration Graphical User Interface 1986 Accounting 5004
76 75 f3C365f0c1A0623 Hicks LLC http://alvarez.biz/ Pakistan Quality-focused client-server Graphical User Interface 1970 Computer Software / Engineering 8480
77 76 ec5Bdd3CBAfaB93 Cole, Russell and Avery http://www.blankenship.com/ Mongolia De-engineered fault-tolerant challenge 2000 Law Enforcement 7012
78 77 DDB19Be7eeB56B4 Cummings-Rojas https://simon-pearson.com/ Svalbard & Jan Mayen Islands User-centric modular customer loyalty 2012 Financial Services 7529
79 78 dd6CA3d0bc3cAfc Beasley, Greene and Mahoney http://www.petersen-lawrence.com/ Togo Extended content-based methodology 1976 Religious Institutions 869
80 79 A0B9d56e61070e3 Beasley, Sims and Allison http://burke.info/ Latvia Secured zero tolerance hub 1972 Facilities Services 6182
81 80 cBa7EFe5D05Adaf Crawford-Rivera https://black-ramirez.org/ Cuba Persevering exuding budgetary management 1999 Online Publishing 7805
82 81 Ea3f6D52Ec73563 Montes-Hensley https://krueger.org/ Liechtenstein Multi-tiered secondary productivity 2009 Printing 8433
83 82 bC0CEd48A8000E0 Velazquez-Odom https://stokes.com/ Djibouti Streamlined 6thgeneration function 2002 Alternative Dispute Resolution 4044
84 83 c89b9b59BC4baa1 Eaton-Morales https://www.reeves-graham.com/ Micronesia Customer-focused explicit frame 1990 Capital Markets / Hedge Fund / Private Equity 7013
85 84 FEC51bce8421a7b Roberson, Pennington and Palmer http://www.keith-fisher.com/ Cameroon Adaptive bi-directional hierarchy 1993 Telecommunications 5571
86 85 e0E8e27eAc9CAd5 George, Russo and Guerra https://drake.com/ Sweden Centralized non-volatile capability 1989 Military Industry 2880
87 86 B97a6CF9bf5983C Davila Inc https://mcconnell.info/ Cocos (Keeling) Islands Profit-focused dedicated frame 2017 Consumer Electronics 2215
88 87 a0a6f9b3DbcBEb5 Mays-Preston http://www.browning-key.com/ Mali User-centric heuristic focus group 2006 Military Industry 5786
89 88 8cC1bDa330a5871 Pineda-Morton https://www.carr.com/ United States Virgin Islands Grass-roots methodical info-mediaries 1991 Printing 6168
90 89 ED889CB2FE9cbd3 Huang and Sons https://www.bolton.com/ Eritrea Re-contextualized dynamic hierarchy 1981 Semiconductors 7484
91 90 F4Dc1417BC6cb8f Gilbert-Simon https://www.bradford.biz/ Burundi Grass-roots radical parallelism 1973 Newspapers / Journalism 1927
92 91 7ABc3c7ecA03B34 Sampson-Griffith http://hendricks.org/ Benin Multi-layered composite paradigm 1972 Textiles 3881
93 92 4e0719FBE38e0aB Miles-Dominguez http://www.turner.com/ Gibraltar Organized empowering forecast 1996 Civic / Social Organization 897
94 93 dEbDAAeDfaed00A Rowe and Sons https://www.simpson.org/ El Salvador Balanced multimedia knowledgebase 1978 Facilities Services 8172
95 94 61BDeCfeFD0cEF5 Valenzuela, Holmes and Rowland https://www.dorsey.net/ Taiwan Persistent tertiary focus group 1999 Transportation 1483
96 95 4e91eD25f486110 Best, Wade and Shepard https://zimmerman.com/ Zimbabwe Innovative background definition 1991 Gambling / Casinos 4873
97 96 0a0bfFbBbB8eC7c Holmes Group https://mcdowell.org/ Ethiopia Right-sized zero tolerance focus group 1975 Photography 2988
98 97 BA6Cd9Dae2Efd62 Good Ltd http://duffy.com/ Anguilla Reverse-engineered composite moratorium 1971 Consumer Services 4292
99 98 E7df80C60Abd7f9 Clements-Espinoza http://www.flowers.net/ Falkland Islands (Malvinas) Progressive modular hub 1991 Broadcast Media 236
100 99 AFc285dbE2fEd24 Mendez Inc https://www.burke.net/ Kyrgyz Republic User-friendly exuding migration 1993 Education Management 339
101 100 e9eB5A60Cef8354 Watkins-Kaiser http://www.herring.com/ Togo Synergistic background access 2009 Financial Services 2785

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