<overview Retrieval Augmented Generation (RAG) enhances LLM responses by fetching relevant context from external knowledge sources. Pipeline: 1. Index : Load → Split → Embed → Store 2. Retrieve : Query → Embed → Search → Return docs 3. Generate : Docs + Query → LLM → Response Key Components: - Document Loaders : Ingest data from files, web, databases - Text Splitters : Break documents into chunks - Embeddings : Convert text to vectors - Vector Stores : Store and search embeddings </overview <vectorstore-selection | Vector Store | Use Case | Persistence | |--------------|----------|-----------…