LangChain RAG

LangChain RAG is a ready-to-use framework template for Retrieval-Augmented Generation that combines a large language model with a vector store to justify each answer in the documents being inspected. The workflow consists of two scripted steps: extraction, where LangChain transforms a user query into an embedding, performs a similarity search against Chroma, Pinecone, or Qdrant, and returns the top-k fragments; and generation, where these fragments are embedded in a prompt template and fed to GPT-4, Claude, or another LLM. Built-in abstractions — RetrievalQA, ConversationalRetrievalChain, and RefineChain — handle fragmentation, citation formatting, flow tokens, and fallback models, so developers can hook up a full RAG pipeline in less than 40 lines of Python. Observability callbacks monitor latency and token consumption, while guardrails remove PII and enforce compliance. Because each layer follows LangChain’s plug-and-play interfaces, teams can share embedding models, vector databases, or LLMs without rewriting business logic, delivering actual, up-to-date chatbots, co-pilots, and search interfaces in days.