Build and deploy a production-ready Retrieval-Augmented Generation (RAG) service using Claude 3.5 Sonnet and Ragie.ai. This implementation allows you to create a document querying system with a user-friendly Streamlit interface in less than 50 lines of Python code.
- Production-ready RAG pipeline
- Integration with Claude 3.5 Sonnet for response generation
- Document upload from URLs
- Real-time document querying
- Support for both fast and accurate document processing modes
- Clone the GitHub repository
git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
cd rag-as-a-service- Install the required dependencies:
pip install -r requirements.txt- Get your Anthropic API and Ragie API Key
- Sign up for an Anthropic account and get your API key
- Sign up for an Ragie account and get your API key
- Run the Streamlit app
streamlit run rag_app.py