GenAI & BeyondConference50min
Agentic GraphRAG: AI’s Logical Edge
This talk demonstrates how combining RAG and GraphRAG retrieval models in agent architectures enhances AI’s ability to solve complex, domain-specific problems by leveraging graph relationships for improved accuracy, explainability, and logical reasoning in data analysis, planning, and retrieval, highlighting practical advantages over traditional retrieval approaches.
Stephen ChinNeo4j
talkDetail.whenAndWhere
Wednesday, October 8, 17:50-18:40
Room 6
talks.roomOccupancytalks.noOccupancyInfo
AI models are getting tasked to do increasingly complex and industry specific tasks where different retrieval approaches provide distinct advantages in accuracy, explainability, and cost to execute. GraphRAG retrieval models have become a powerful tool to solve domain specific problems where answers require logical reasoning and correlation that can be aided by graph relationships and proximity algorithms. We will demonstrate how an agent architecture combining RAG and GraphRAG retrieval patterns can bridge the gap in data analysis, strategic planning, and retrieval to solve complex domain specific problems.
Stephen Chin
Stephen Chin is VP of Developer Relations at Neo4j, conference chair of the LF AI & Data Foundation, and author of numerous titles including the upcoming GraphRAG: The Definitive Guide for O'Reilly. He has given keynotes and main stage talks at numerous conferences around the world including AI Engineer Summit, AI DevSummit, Devoxx, DevNexus, JNation, JavaOne, Shift, Joker, swampUP, and GIDS. Stephen is an avid motorcyclist who has done evangelism tours in Europe, Japan, and Brazil, interviewing developers in their natural habitat. When he is not traveling, he enjoys teaching kids how to do AI, embedded, and robot programming together with his daughters.
talkDetail.shareFeedback
talkDetail.signInRequired
talkDetail.signInToFeedbackDescription
occupancy.title
talkDetail.signInRequired
occupancy.signInToVoteDescription
comments.speakerNotEnabledComments