DocsGPT/README.md

DocsGPT 🦖

Open-Source RAG Assistant

DocsGPT is an open-source genAI tool that helps users get reliable answers from any knowledge source, while avoiding hallucinations. It enables quick and reliable information retrieval, with tooling and agentic system capability built in.

☁️ Cloud Version • 💬 Discord • 📖 Guides
👫 Contribute • 🏠 Self-host • ⚡️ Quickstart

Key Features:

• 🗂️ Wide Format Support: Reads PDF, DOCX, CSV, XLSX, EPUB, MD, RST, HTML, MDX, JSON, PPTX, and images.
• 🌐 Web & Data Integration: Ingests from URLs, sitemaps, Reddit, GitHub and web crawlers.
• ✅ Reliable Answers: Get accurate, hallucination-free responses with source citations viewable in a clean UI.
• 🔗 Actionable Tooling: Connect to APIs, tools, and other services to enable LLM actions.
• 🧩 Pre-built Integrations: Use readily available HTML/React chat widgets, search tools, Discord/Telegram bots, and more.
• 🔌 Flexible Deployment: Works with major LLMs (OpenAI, Google, Anthropic) and local models (Ollama, llama_cpp).
• 🏢 Secure & Scalable: Run privately and securely with Kubernetes support, designed for enterprise-grade reliability.

Roadmap

• Full GoogleAI compatibility (Jan 2025)
• Add tools (Jan 2025)
• Anthropic Tool compatibility
• Add triggerable actions / tools
• Add oath2 authentication for tools and sources
• Manually updating chunks in the app UI
• Devcontainer for easy development
• Chatbots menu re-design to handle tools, scheduling, and more

You can find our full roadmap here. Please don't hesitate to contribute or create issues, it helps us improve DocsGPT!

Production Support / Help for Companies:

We're eager to provide personalized assistance when deploying your DocsGPT to a live environment.

Get a Demo 👋⁠

Send Email 📧

QuickStart

Note

Make sure you have Docker installed

On Mac OS or Linux, write:

./setup.sh

It will install all the dependencies and allow you to download the local model, use OpenAI or use our LLM API.

Otherwise, refer to this Guide for Windows:

1. Download and open this repository with git clone https://github.com/arc53/DocsGPT.git

2. Create a .env file in your root directory and set the env variables and VITE_API_STREAMING to true or false, depending on whether you want streaming answers or not. It should look like this inside:

   LLM_NAME=[docsgpt or openai or others] 
   VITE_API_STREAMING=true
   API_KEY=[if LLM_NAME is openai]
   

   See optional environment variables in the /.env-template and /application/.env_sample files.

3. Run ./run-with-docker-compose.sh.

4. Navigate to http://localhost:5173/.

To stop, just run Ctrl + C.

Development Environments

Spin up Mongo and Redis

For development, only two containers are used from docker-compose.yaml (by deleting all services except for Redis and Mongo). See file docker-compose-dev.yaml.

Run

docker compose -f docker-compose-dev.yaml build
docker compose -f docker-compose-dev.yaml up -d

Run the Backend

Note

Make sure you have Python 3.12 installed.

1. Export required environment variables or prepare a .env file in the project folder:
   • Copy .env-template and create .env.

(check out application/core/settings.py if you want to see more config options.)

2. (optional) Create a Python virtual environment: You can follow the Python official documentation for virtual environments.

a) On Mac OS and Linux

python -m venv venv
. venv/bin/activate

b) On Windows

python -m venv venv
 venv/Scripts/activate

3. Download embedding model and save it in the model/ folder: You can use the script below, or download it manually from here, unzip it and save it in the model/ folder.

wget https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip
unzip mpnet-base-v2.zip -d model
rm mpnet-base-v2.zip

4. Install dependencies for the backend:

pip install -r application/requirements.txt

5. Run the app using flask --app application/app.py run --host=0.0.0.0 --port=7091.
6. Start worker with celery -A application.app.celery worker -l INFO.

Start Frontend

Note

Make sure you have Node version 16 or higher.

1. Navigate to the /frontend folder.
2. Install the required packages husky and vite (ignore if already installed).

npm install husky -g
npm install vite -g

3. Install dependencies by running npm install --include=dev.
4. Run the app using npm run dev.

Contributing

Please refer to the CONTRIBUTING.md file for information about how to get involved. We welcome issues, questions, and pull requests.

Architecture

Project Structure

• Application - Flask app (main application).

• Extensions - Extensions, like react widget or discord bot.

• Frontend - Frontend uses Vite and React.

• Scripts - Miscellaneous scripts.

Code Of Conduct

We as members, contributors, and leaders, pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. Please refer to the CODE_OF_CONDUCT.md file for more information about contributing.

Many Thanks To Our Contributors⚡

License

The source code license is MIT, as described in the LICENSE file.

This project is supported by: