A practical guide to constructing and retrieving information from knowledge graphs in RAG applications with Neo4j and LangChain.
Image created by DALL-E. Graph retrieval-augmented generation (GraphRAG ) is gaining momentum and becoming 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.
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Read it: Enhancing the Accuracy of RAG Applications With Knowledge Graphs