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 semantic routing.

Prerequisites

1. Make sure you have the following tools available:

   • Docker
   • Python >= 3.11 and Poetry
   • OpenAI API key
   • Cohere API key (for optional reranking features)

2. Clone the repository:

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

3. 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

Start the workbench

In the root of the repository, run the following to spin up the docker compose stack:

docker compose -f docker-compose.yml up

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

The first time the application runs, it will have to download model weights from huggingface and may take a few minutes.

Project Structure

• main.py: The entry point of the application
• demos/: Contains workbench demo implementation
• 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.