chitralputhran/Advanced-RAG-LangGraph
📦 Open Source Projectchitralputhran
A robust RAG pipeline leveraging LangGraph and Tavily for intelligent, document-based Q&A with error handling.
This project serves as a comprehensive implementation of advanced RAG patterns using the LangGraph framework. Unlike standard RAG setups, this application utilizes a graph-based architecture to manage complex reasoning steps and conditional logic, ensuring that the AI can handle document retrieval, web search augmentation, and iterative refinement of answers. Key technical components include ChromaDB for efficient semantic search and retrieval, and Tavily for external knowledge integration. The application features a clean Streamlit interface, allowing users to interact with documents while observing the underlying agentic flow. The codebase emphasizes modularity, showcasing how to implement state management in LangGraph to track conversation history and retrieval context effectively. It is specifically designed to address common RAG failure points, such as irrelevant retrieval or hallucination, by incorporating validation steps within the graph workflow.
💡Highlights
- ├─LangGraph-based stateful workflow
- ├─Tavily search integration
- └─ChromaDB vector storage
🎯For
- ├─AI Engineers
- └─RAG Developers