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GraphRAG with LangChain & LangGraph: Neo4j and FalkorDB Guide
Large language models (LLMs) can write, summarize, and explain with impressive fluency — but they still have a reliability problem: they can sound confident while being wrong, especially when a question needs precise facts or depends on relationships hidden across multiple documents and systems. Retrieval-Augmented Generation (RAG) helps by fetching relevant text and feeding it into the model. But classic RAG is usually “vector-only”: it searches for passages that sound similar to the question.
That’s great for many tasks. Yet it often breaks down when the question is really about connections: who did what, in what order, through which dependency chain, and under which constraints. That’s the gap GraphRAG aims to fill by adding a knowledge graph (and graph queries) to the retrieval loop, so the model can ground its answer not just in similar text — but in structured relationships.
This article explains GraphRAG, then shows how two practical implementations — FalkorDB + LangChain + LangGraph and Neo4j + LangChain + LangGraph — use routing, query decomposition, vector search, and graph traversal to produce answers that are both more useful and easier to justify.
