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:
We're eager to provide personalized assistance when deploying your DocsGPT to a live environment.
- [Get Support 👋](https://airtable.com/appdeaL0F1qV8Bl2C/shrrJF1Ll7btCJRbP)
- [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 [here](https://github.com/orgs/arc53/projects/2). Please don't hesitate to contribute or create issues, it helps us improve DocsGPT!
## Our Open-Source models optimized 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 stores for other libraries.
- Frontend - Frontend uses Vite and React.
## 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 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 two 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 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`](application/core/settings.py) if you want to see more config options.)
2. (optional) Create a Python virtual environment:
```commandline
python -m venv venv
. venv/bin/activate
```
3. Change to the `application/` subdir and install dependencies for the backend:
```commandline
pip install -r application/requirements.txt
```
4. Run the app using `flask run --host=0.0.0.0 --port=7091`.
5. 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 the `/frontend` folder.
2. Install dependencies by running `npm install`.
3. Run the app using `npm run dev`.
## Many Thanks To Our Contributors
Built with [🦜️🔗 LangChain](https://github.com/hwchase17/langchain)