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.
### Production Support/ Help for companies:
When deploying your DocsGPT to a live environment, we're eager to provide personalized assistance.
- [Schedule Demo 👋](https://cal.com/arc53/docsgpt-demo-b2b?date=2023-10-04&month=2023-10)
- [Send Email ✉️](mailto:contact@arc53.com?subject=DocsGPT%20support%2Fsolutions)
### [🎉 Join the Hacktoberfest with DocsGPT and Earn a Free T-shirt! 🎉](https://github.com/arc53/DocsGPT/blob/main/HACKTOBERFEST.md)
## Roadmap
You can find our [Roadmap](https://github.com/orgs/arc53/projects/2) here. Please don't hesitate to contribute or create issues, it helps us make DocsGPT better!
## Our Open-Source models optimised for DocsGPT:
| Name | Base Model | Requirements (or similar) |
|-------------------|------------|----------------------------------------------------------|
| [Docsgpt-7b-falcon](https://huggingface.co/Arc53/docsgpt-7b-falcon) | Falcon-7b | 1xA10G gpu |
| [Docsgpt-14b](https://huggingface.co/Arc53/docsgpt-14b) | llama-2-14b | 2xA10 gpu's |
| [Docsgpt-40b-falcon](https://huggingface.co/Arc53/docsgpt-40b-falcon) | falcon-40b | 8xA10G gpu's |
If you don't have enough resources to run it you can use bitsnbytes to quantize
## Features
## Useful links
[Live preview](https://docsgpt.arc53.com/)
[Join Our Discord](https://discord.gg/n5BX8dh8rU)
[Guides](https://docs.docsgpt.co.uk/)
[Interested in contributing?](https://github.com/arc53/DocsGPT/blob/main/CONTRIBUTING.md)
[How to use any other documentation](https://docs.docsgpt.co.uk/Guides/How-to-train-on-other-documentation)
[How to host it locally (so all data will stay on-premises)](https://docs.docsgpt.co.uk/Guides/How-to-use-different-LLM)
## Project structure
- Application - Flask app (main application)
- Extensions - Chrome extension
- Scripts - Script that creates similarity search index and store for other libraries.
- Frontend - Frontend uses Vite and React
## QuickStart
Note: Make sure you have Docker installed
On Mac OS or Linux just write:
`./setup.sh`
It will install all the dependencies and give you an option to download 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 OPENAI_API_KEY with your OpenAI API key and VITE_API_STREAMING to true or false, depending on if 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
## Development environments
### Spin up mongo and redis
For development only 2 containers are used from docker-compose.yaml (by deleting all services except for Redis and Mongo).
See file [docker-compose-dev.yaml](./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
Make sure you have Python 3.10 or 3.11 installed.
1. Export required environment variables or prep .env file in application folder
Prepare .env file
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)
3. (optional) Create a Python virtual environment
```commandline
python -m venv venv
. venv/bin/activate
```
4. Change to `application/` subdir and install dependencies for the backend
```commandline
pip install -r application/requirements.txt
```
5. Run the app `flask run --host=0.0.0.0 --port=7091`
6. Start worker with `celery -A application.app.celery worker -l INFO`
### Start frontend
Make sure you have Node version 16 or higher.
1. Navigate to `/frontend` folder
2. Install dependencies
`npm install`
3. Run the app
`npm run dev`
## All Thanks To Our Contributors
Built with [🦜️🔗 LangChain](https://github.com/hwchase17/langchain)