Langchain Rag

A comprehensive LangChain RAG implementation guide that covers the complete pipeline from document ingestion to response generation. Shows you how to load documents from PDFs, web pages, and directories, split them with RecursiveCharacterTextSplitter, embed with OpenAI, and store in Chroma, FAISS, or Pinecone. Includes practical examples for similarity search, retrieval configuration, and connecting everything into a working RAG system. The vector store comparison table is helpful for choosing between local development (Chroma), high performance (FAISS), and production deployment (Pinecone). Code examples are thorough and show both the basic patterns and real implementation details you'll need.

Install to Claude Code

npx -y skills add langchain-ai/langchain-skills --skill langchain-rag --agent claude-code