LangChain

RAG is a design pattern, not a product. LangChain supports it out of the box. This guide shows a production-ready RAG setup in LangChain with architecture, retrieval choices, runnable code, evaluation metrics, and trade-offs from my client projects. TL;DR # Short answer: LangChain doesn’t “contain” RAG; it provides the building blocks to implement RAG cleanly. You wire up chunking, embeddings, vector store, and a retrieval-aware prompt chain. What you get below: Architecture diagram, runnable code (LangChain 0.2+), evaluation harness, parameter trade-offs, and when to avoid LangChain for leaner stacks. Related deep dives: Foundations of RAG → RAG for Knowledge-Intensive Tasks. Lightweight pipelines → LightRAG: Lean RAG with Benchmarks. Who should read this # You’re building an internal knowledge assistant, support bot, or compliance Q&A system. You need answers that cite real documents with predictable latency and cost. You want a minimal, maintainable RAG in LangChain with evaluation, not a toy demo. The problem I solved in production # When I implemented an extractive summarizer for financial and compliance reports, two pain points surfaced:

Want LightRAG’s lean retrieval with LangChain’s chain ecosystem? Here’s how to wrap LightRAG as a LangChain-compatible retriever — keeping retrieval explicit and fast while using LangChain for everything downstream. TL;DR Implement BaseRetriever._get_relevant_documents to make any retriever LangChain-compatible. LightRAG’s FAISS retrieval slots straight into LangChain chains, LCEL, and agents. Use this pattern when migrating an existing LangChain pipeline to leaner retrieval incrementally. For full LangChain pipelines without constraints, the standard LangChain retriever is fine. Why Combine LightRAG with LangChain # LightRAG gives you minimal, fast retrieval. LangChain gives you chains, agents, and tooling. Sometimes you want both: