Open-Source Documentation Assistant

DocsGPT is a cutting-edge open-source solution that streamlines the process of finding information in the project documentation. With its integration of the powerful GPT models, developers can easily ask questions about a project and receive accurate answers.

Say goodbye to time-consuming manual searches, and let DocsGPT help you quickly find the information you need. Try it out and see how it revolutionizes your project documentation experience. Contribute to its development and be a part of the future of AI-powered assistance.

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

• Book Demo 👋
• Send Email 📧

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

If you don't have enough resources to run it, you can use bitsnbytes to quantize.

• 🔍 🔥 Live preview

• 💬 🎉 Join our Discord

• 📚 😎 Guides

• 👫 Interested in contributing?

• 📁 🚀 How to use any other documentation

• 🏠 🔐 How to host it locally (so all data will stay on-premises)

• Application - Flask app (main application).

• Extensions - Chrome extension.

• Scripts - Script that creates similarity search index for other libraries.

• Frontend - Frontend uses Vite and React.

On Mac OS or Linux, write:

./setup.sh

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

Otherwise, refer to this Guide:

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 variable API_KEY with your OpenAI API key and VITE_API_STREAMING to true or false, depending on whether you want streaming answers or not. It should look like this inside:

   API_KEY=Yourkey
   VITE_API_STREAMING=true
   

   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.

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

1. Export required environment variables or prepare a .env file in the /application folder:
   • Copy .env_sample and create .env with your OpenAI API token for the API_KEY and EMBEDDINGS_KEY fields.

(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. Change to the application/ subdir by the command cd application/ and install dependencies for the backend:

pip install -r application/requirements.txt

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

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.

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

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.

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

Built with 🐦 🔗 LangChain