Enhancing the Accuracy of RAG Applications With Knowledge Graphs

Graph ML and GenAI Research from Neo4j have published a practical guide to constructing and retrieving information from knowledge graphs in RAG applications using Neo4j and LangChain. Graph retrieval-augmented generation (GraphRAG) combines the strengths of graph databases with vector search methods, enhancing the depth and contextuality of retrieved information. The authors provide a step-by-step tutorial on how to create a knowledge graph using LLMs, set up a Neo4j instance, ingest data, construct and retrieve graphs, and implement a hybrid retrieval approach that combines vector and keyword indexes with graph retrieval. The implementation includes an unstructured data retriever, a graph retriever, and a final retriever that integrates the two components. The authors aim to make knowledge graph generation more accessible and easier to use for RAG applications.