DocsGPT 🦖
Open-Source RAG Assistant
DocsGPT is an open-source genAI tool that helps users get reliable answers from any knowledge source, while avoiding hallucinations. It enables quick and reliable information retrieval, with tooling and agentic system capability built in.
Key Features:
- 🗂️ Wide Format Support: Reads PDF, DOCX, CSV, XLSX, EPUB, MD, RST, HTML, MDX, JSON, PPTX, and images.
- 🌐 Web & Data Integration: Ingests from URLs, sitemaps, Reddit, GitHub and web crawlers.
- ✅ Reliable Answers: Get accurate, hallucination-free responses with source citations viewable in a clean UI.
- 🔗 Actionable Tooling: Connect to APIs, tools, and other services to enable LLM actions.
- 🧩 Pre-built Integrations: Use readily available HTML/React chat widgets, search tools, Discord/Telegram bots, and more.
- 🔌 Flexible Deployment: Works with major LLMs (OpenAI, Google, Anthropic) and local models (Ollama, llama_cpp).
- 🏢 Secure & Scalable: Run privately and securely with Kubernetes support, designed for enterprise-grade reliability.
## 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!
### Production Support / Help for Companies:
We're eager to provide personalized assistance when deploying your DocsGPT to a live environment.
[Get a Demo :wave:](https://www.docsgpt.cloud/contact)
[Send Email :email:](mailto:support@docsgpt.cloud?subject=DocsGPT%20support%2Fsolutions)
## Our Open-Source Models Optimized for DocsGPT:
| Name | Base Model | Requirements (or similar) |
| --------------------------------------------------------------------- | ----------- | ------------------------- |
| [Docsgpt-7b-mistral](https://huggingface.co/Arc53/docsgpt-7b-mistral) | Mistral-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.
## End to End AI Framework for Information Retrieval
## Useful Links
- :mag: :fire: [Cloud Version](https://app.docsgpt.cloud/)
- :speech_balloon: :tada: [Join our Discord](https://discord.gg/n5BX8dh8rU)
- :books: :sunglasses: [Guides](https://docs.docsgpt.cloud/)
- :couple: [Interested in contributing?](https://github.com/arc53/DocsGPT/blob/main/CONTRIBUTING.md)
- :file_folder: :rocket: [How to use any other documentation](https://docs.docsgpt.cloud/Guides/How-to-train-on-other-documentation)
- :house: :closed_lock_with_key: [How to host it locally (so all data will stay on-premises)](https://docs.docsgpt.cloud/Guides/How-to-use-different-LLM)
## Project Structure
- Application - Flask app (main application).
- Extensions - Chrome extension.
- Scripts - Script that creates similarity search index for other libraries.
- Frontend - Frontend uses Vite and React.
## QuickStart
> [!Note]
> Make sure you have [Docker](https://docs.docker.com/engine/install/) installed
On Mac OS or Linux, write:
`./setup.sh`
It will install all the dependencies and allow you to download the local model, use OpenAI or use our LLM API.
Otherwise, refer to this Guide for Windows:
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 variables and `VITE_API_STREAMING` to true or false, depending on whether you want streaming answers or not.
It should look like this inside:
```
LLM_NAME=[docsgpt or openai or others]
VITE_API_STREAMING=true
API_KEY=[if LLM_NAME is openai]
```
See optional environment variables in the [/.env-template](https://github.com/arc53/DocsGPT/blob/main/.env-template) and [/application/.env_sample](https://github.com/arc53/DocsGPT/blob/main/application/.env_sample) files.
3. Run [./run-with-docker-compose.sh](https://github.com/arc53/DocsGPT/blob/main/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](https://github.com/arc53/DocsGPT/blob/main/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
> [!Note]
> Make sure you have Python 3.12 installed.
1. Export required environment variables or prepare a `.env` file in the project folder:
- Copy [.env-template](https://github.com/arc53/DocsGPT/blob/main/application/.env-template) and create `.env`.
(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:
You can follow the [Python official documentation](https://docs.python.org/3/tutorial/venv.html) for virtual environments.
a) On Mac OS and Linux
```commandline
python -m venv venv
. venv/bin/activate
```
b) On Windows
```commandline
python -m venv venv
venv/Scripts/activate
```
3. Download embedding model and save it in the `model/` folder:
You can use the script below, or download it manually from [here](https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip), unzip it and save it in the `model/` folder.
```commandline
wget https://d3dg1063dc54p9.cloudfront.net/models/embeddings/mpnet-base-v2.zip
unzip mpnet-base-v2.zip -d model
rm mpnet-base-v2.zip
```
4. Install dependencies for the backend:
```commandline
pip install -r application/requirements.txt
```
5. Run the app using `flask --app application/app.py run --host=0.0.0.0 --port=7091`.
6. Start worker with `celery -A application.app.celery worker -l INFO`.
### Start Frontend
> [!Note]
> Make sure you have Node version 16 or higher.
1. Navigate to the [/frontend](https://github.com/arc53/DocsGPT/tree/main/frontend) folder.
2. Install the required packages `husky` and `vite` (ignore if already installed).
```commandline
npm install husky -g
npm install vite -g
```
3. Install dependencies by running `npm install --include=dev`.
4. Run the app using `npm run dev`.
## Contributing
Please refer to the [CONTRIBUTING.md](CONTRIBUTING.md) file for information about how to get involved. We welcome issues, questions, and pull requests.
## Code Of Conduct
We as members, contributors, and leaders, pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. Please refer to the [CODE_OF_CONDUCT.md](CODE_OF_CONDUCT.md) file for more information about contributing.
## Many Thanks To Our Contributors⚡
## License
The source code license is [MIT](https://opensource.org/license/mit/), as described in the [LICENSE](LICENSE) file.
This project is supported by:
