Agentic RAG Meets Neo4j: Multi-Hop Reasoning Over Scientific Knowledge Graphs скачать в хорошем качестве

Agentic RAG Meets Neo4j: Multi-Hop Reasoning Over Scientific Knowledge Graphs

Traditional RAG systems often struggle with complex, multi-hop queries that require contextual understanding and structured exploration. In this talk, we’ll present INRAExplorer, a generative AI system powered by a multitool agent architecture and a Neo4j knowledge graph built from open-access scientific publications by INRAE (France’s National Research Institute for Agriculture, Food and Environment). Unlike typical RAG systems, INRAExplorer leverages Neo4j and other custom tools to enable exhaustive, iterative exploration of scientific data. The agent dynamically decides what tool to use as well as when and how to query the graph—retrieving all papers by a specific author, tracing collaboration networks, or answering multifaceted domain-specific questions. This talk will walk through the architecture, the knowledge graph construction process, and live examples showing how combining LLM agents with Neo4j unlocks truly interactive scientific discovery. Join this session to see how agentic RAG systems can turn domain-specific knowledge graphs into powerful tools for reasoning, research, and decision making. Speakers: Annabelle Blangero & Jean Lelong Resources: Get Started with Aura - https://bit.ly/3LOLrjh Deployment Center - https://bit.ly/4jOelM3 Ground AI Systems and Agents with Neo4j - https://bit.ly/4oVsnyb #nodes2025 #neo4j #graphdatabase #graphrag #knowledgegraph