Giving LLMs a Map: Building Smarter GenAI with GraphRAG

Generative AI is powerful, but without the right data and retrieval strategies, results can quickly break down. This session will explore how GraphRAG combines knowledge graphs with retrieval-augmented generation to deliver more accurate, context-rich AI applications. Through live demos and code, we will walk through building a GenAI solution end to end using Neo4j and Python. Learn how to construct knowledge graphs from unstructured and structured data, make key design decisions around schema and chunking, and implement multiple retrieval strategies??"including vector search, vector plus Cypher, and text-to-Cypher approaches. Then, pull all these skills together in a conversational agent built with Neo4j and LangChain. Come to this session and leave with practical techniques for designing knowledge graphs, choosing the right retriever for a use case, and applying GraphRAG patterns you can adapt to your own GenAI projects.

Speaker: jennifer Reif, Neo4j