Build a production-ready RAG pipeline using Claude 3.5 Sonnet and Ragie.ai with a Streamlit UI in under 50 lines.
## 🖇️ RAG-as-a-Service with Claude 3.5 Sonnet 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. ### Features - 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 ### How to get Started? 1. Clone the GitHub repository ```bash git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git cd awesome-llm-apps/rag_tutorials/rag-as-a-service ``` 2. Install the required dependencies: ```bash pip install -r requirements.txt ``` 3. Get your Anthropic API and Ragie API Key - Sign up for an [Anthropic account](https://console.anthropic.com/) and get your API key - Sign up for an [Ragie account](https://www.ragie.ai/) and get your API key 4. Run the Streamlit app ```bash streamlit run rag_app.py ```