Redis RAG Workbench is a playground for exploring Retrieval-Augmented Generation (RAG) techniques using Redis. This project provides a collection of demos showcasing various RAG implementations and utilities.

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

• Python 3.11 or higher
• Redis server
• OpenAI API key
• Cohere API key (for certain reranking features)

1. Clone the repository:

   git clone https://github.com/your-username/redis-rag-workbench.git cd redis-rag-workbench

2. Create and activate a virtual environment:

   python -m venv .venv source .venv/bin/activate

3. Install Poetry (see detailed instructions at Poetry Installation):

   curl -sSL https://install.python-poetry.org | python3 -

4. Install the required dependencies:

   poetry install --no-root

5. Set up your environment variables by creating a .env file in the project root:

   REDIS_URL=your_redis_url OPENAI_API_KEY=your_openai_api_key COHERE_API_KEY=your_cohere_api_key

To start the application, run:

uvicorn main:app --reload

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

• 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

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

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