Retrieval-Augmented Generation (RAG) has become a cornerstone technique for grounding large language models in external knowledge, addressing issues like stale training data and hallucinations. This article provides a step-by-step walkthrough of building a Naive RAG pipeline using LangChain, a popular framework for orchestrating LLM workflows. The author demonstrates how to ingest documents, split them into chunks, generate embeddings, and store them in a vector database for efficient similarity search. The retrieval step then fetches relevant context, which is passed to the LLM to generate a grounded response. While the tutorial covers the essential components, it stays at a basic level, omitting advanced topics like query rewriting, reranking, or hybrid search strategies. For developers new to RAG, this serves as a clear entry point, but experienced practitioners may find it lacking in depth. The code examples are straightforward and can be adapted to various document types and vector stores, making it a practical reference for prototyping.
A practical guide to implementing a basic RAG pipeline with LangChain, covering document ingestion, vectorization, and retrieval generation to ground LLM responses in external data.