LangChain is a practical framework for assembling production LLM applications from reusable components. It addresses the integration burden in retrieval-augmented generation (RAG), where embedding queries, searching vectors, formatting context, calling models, handling failures, and validating responses otherwise require separate glue code. section 12.1.3
Its standardized building blocks cover the main stages of an LLM pipeline:
- Document loaders ingest PDFs, web pages, databases, and APIs through a unified interface. section 12.1.3
- Text splitters chunk material while preserving semantic boundaries; embedding models expose a consistent interface across hosted and local models. section 12.1.3
- Vector-store adapters work with FAISS, Pinecone, Chroma, and other retrieval systems, while LLM adapters cover OpenAI, Claude, Gemini, and local models. section 12.1.3
- Chains connect component outputs to subsequent inputs and provide reusable workflows for RAG, summarization, and question answering. section 12.1.3
Declarative RAG pipeline
A manual RAG function must manage embedding, similarity search, context formatting, prompt construction, model invocation, validation, retries, and errors. A LangChain pipeline instead creates a RetrievalQA chain with an LLM and a vector store retriever, then invokes it with a query and can return source documents. section 12.1.3
Production use extends beyond composition. Validate inputs before spending tokens, reject prompt-injection attempts early, trace retrieval and prompt behavior with tools such as Langfuse, and version prompts, embeddings, retrieval parameters, and model configuration independently. Use sequential LangChain chains for straightforward RAG; move to LangGraph when workflows require branching, parallel execution, or iterative refinement. section 12.5
