A practical guide to constructing and retrieving information from knowledge graphs in RAG applications with Neo4j and LangChain

Graph retrieval augmented generation (Graph RAG) is gaining momentum and emerging as a powerful addition to traditional vector search retrieval methods. This approach leverages the structured nature of graph databases, which organize data as nodes and relationships, to enhance the depth and contextuality of retrieved information.