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)
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Clone the repository:
git clone https://github.com/your-username/redis-rag-workbench.git cd redis-rag-workbench
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Create and activate a virtual environment:
python -m venv .venv source .venv/bin/activate
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Install Poetry (see detailed instructions at Poetry Installation):
curl -sSL https://install.python-poetry.org | python3 -
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Install the required dependencies:
poetry install
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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 applicationdemos/
: Contains individual RAG demo implementationsshared_components/
: Reusable utilities and componentsstatic/
: 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.