19f807b6c4
consisting of prompt and response |
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.github/workflows | ||
application | ||
extensions | ||
frontend | ||
scripts | ||
.gitignore | ||
CODE_OF_CONDUCT.md | ||
CONTRIBUTING.md | ||
docker-compose.yaml | ||
LICENSE | ||
Readme Logo.png | ||
README.md |
DocsGPT 🦖
Open-Source Documentation Assistant
DocsGPT is a cutting-edge open-source solution that streamlines the process of finding information in 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.
Roadmap
You can find our Roadmap here, please don't hesitate contributing or creating issues, it helps us make DocsGPT better!
Preview
Live preview
Join Our Discord
Project structure
-
Application - flask app (main application)
-
Extensions - chrome extension
-
Scripts - script that creates similarity search index and store for other libraries.
QuickStart
Please note: current vector database uses pandas Python documentation, thus responses will be related to it, if you want to use other docs please follow a guide below
- Navigate to
/application
folder - Install dependencies
pip install -r requirements.txt
- Prepare .env file Copy .env_sample and create .env with your openai api token
- Run the app
python app.py
To start frontend
- Navigate to
/frontend
folder - Install dependencies
npm install
- Run the app
npm run dev
Alternatively, you can use docker-compose to run the app via docker
- From the root folder run
docker-compose build && docker-compose up
How to install the Chrome extension
Guides
Interested in contributing?
How to use any other documentation
How to host it locally (so all data will stay on-premises)
Built with 🦜️🔗 LangChain