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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:
We're eager to provide personalized assistance when deploying your DocsGPT to a live environment.
- [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 [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 ](https://www.docker.com/ ) 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
< a href = "[https://github.com/arc53/DocsGPT/graphs/contributors](https://docsgpt.arc53.com/)" >
< img src = "https://contrib.rocks/image?repo=arc53/DocsGPT" / >
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Built with [🦜️🔗 LangChain ](https://github.com/hwchase17/langchain )
