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DocsGPT 🦖
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< strong > Open-Source Documentation Assistant< / strong >
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< strong > DocsGPT< / strong > is a cutting-edge open-source solution that streamlines the process of finding information in project documentation. With its integration of the powerful < strong > GPT< / strong > models, developers can easily ask questions about a project and receive accurate answers.
Say goodbye to time-consuming manual searches, and let < strong > DocsGPT< / strong > 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.
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< a href = "https://github.com/arc53/DocsGPT" > < / a >
< a href = "https://github.com/arc53/DocsGPT" > < / a >
< a href = "https://github.com/arc53/DocsGPT/blob/main/LICENSE" > < / a >
< a href = "https://discord.gg/n5BX8dh8rU" > < / a >
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### 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
```commandline
export CELERY_BROKER_URL=redis://localhost:6379/0
export CELERY_RESULT_BACKEND=redis://localhost:6379/1
export MONGO_URI=mongodb://localhost:27017/docsgpt
export FLASK_APP=application/app.py
export FLASK_DEBUG=true
```
2. Prepare .env file
Copy `.env_sample` and create `.env` with your OpenAI API token
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`
Built with [🦜️🔗 LangChain ](https://github.com/hwchase17/langchain )
