teddynote-langchain-rag

To build a RAG pipeline with LangChain, you load and split documents into chunks, generate embeddings, store them in a vector store, and configure retrievers and chains. This framework handles the entire process from document loading to deployment and evaluation.

The best way to split documents for embedding is to break them into manageable chunks during the document loading phase. This ensures the embedding models process text efficiently and the vector store retrieves relevant information accurately.

To troubleshoot a LangChain RAG pipeline returning irrelevant results, evaluate the retrieval strategies, embedding models, and prompt designs. This framework provides specific evaluation and monitoring capabilities to identify and resolve pipeline issues.

Yes, you need Python libraries to process and analyze text data for LangChain retrieval chains. The framework requires these dependencies to handle document loading, splitting, embedding generation, and vector store operations effectively.

Yes, you can evaluate and monitor a RAG pipeline before production deployment. This framework includes evaluation and deployment features, allowing you to assess prompt designs, retrievers, and chains to ensure public-safe reasoning and optimal performance.