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https://github.com/redis-developer/redis-rag-workbench

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Host: GitHub
URL: https://github.com/redis-developer/redis-rag-workbench
Owner: redis-developer
License: mit
Created: 2024-06-20T20:34:25.000Z (7 months ago)
Default Branch: main
Last Pushed: 2024-11-20T17:22:54.000Z (about 2 months ago)
Last Synced: 2024-11-20T18:27:39.458Z (about 2 months ago)
Language: Python
Size: 582 KB
Stars: 4
Watchers: 2
Forks: 4
Open Issues: 9
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

redis-ai-resources - Redis RAG Workbench - based chatbot over a user-uploaded PDF. Toggle different settings and configurations to improve chatbot performance and quality. Utilizes RedisVL, LangChain, RAGAs, and more. | (Demos)

README

RAG Workbench

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
![Language](https://img.shields.io/github/languages/top/redis-developer/redis-rag-workbench)
![GitHub last commit](https://img.shields.io/github/last-commit/redis-developer/redis-rag-workbench)

🛠️ **Redis RAG Workbench** is a development playground for exploring Retrieval-Augmented Generation (RAG) techniques with Redis. Upload a PDF and begin building a RAG app to chat with the document, taking full advantage of Redis features like **vector search**, **semantic caching**, **LLM memory**, and more.

## Features

- Integration with Redis for vector storage and caching
- Support for various LLM models and reranking techniques
- Modular architecture for easy extension and customization (soon)

## Prerequisites

- Python >= 3.11 and [Poetry](https://python-poetry.org/docs/#installation)
- Redis Stack
```bash
docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest
```
- [OpenAI API key](https://platform.openai.com/)
- [Cohere API key](https://cohere.com/) (for optional reranking features)

## Installation

1. Clone the repository:
```bash
git clone https://github.com/redis-developer/redis-rag-workbench.git
cd redis-rag-workbench
```

2. Install the required dependencies with Poetry:
```bash
poetry install --no-root
```

3. Set up your environment variables by creating a `.env` file in the project root:
```env
REDIS_URL=your_redis_url
OPENAI_API_KEY=your_openai_api_key
COHERE_API_KEY=your_cohere_api_key
```

## Running the Application

To start the application, run:

```bash
poetry run uvicorn main:app --reload
```

> This will start the server, and you can access the workbench by navigating to `http://localhost:8000` in your web browser.

## Project Structure

- `main.py`: The entry point of the application
- `demos/`: Contains individual RAG demo implementations
- `shared_components/`: Reusable utilities and components
- `static/`: Static assets for the web interface

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.