What problem does it solve? Building a RAG system requires correctly wiring together document loading, chunking, embedding, vector storage, and retrieval, and small mistakes like mismatched embedding models or bad chunk sizes silently degrade answer quality. ## Core Features & Use Cases - Complete RAG Pipeline: Load documents, split them with RecursiveCharacterTextSplitter, embed with OpenAI, store in a vector database, and retrieve context for LLM generation in Python or TypeScript. - Vector Store Guidance: Compare InMemory, FAISS, Chroma, and Pinecone with persistence and production trade-offs, plus similarity, MMR, and metadata-filtered search. - Common Pitfall Fixes: Correct patterns for chunk size and overlap, persistent storage, consistent embedding models, FAISS deserialization, and dimension mismatches. - Use Case: Build a documentation Q&A agent that loads PDF and web pages, indexes them in Chroma, and answers user questions through a LangChain agent with a retrieval tool. ## Quick Start Use the langchain-rag skill to build a RAG pipeline that loads my PDF documents, stores embeddings in Chroma, and answers questions about them.
