Enhancing Retrieval-Augmented Generation with GraphRAG Patterns in Neo4j скачать в хорошем качестве

Enhancing Retrieval-Augmented Generation with GraphRAG Patterns in Neo4j

Traditional RAG systems rely on vector similarity searches that often fail to capture the rich relationships within interconnected data, limiting their ability to provide contextually accurate responses. This session will introduce GraphRAG as a solution, demonstrating how Neo4j’s graph-native architecture enables relationship-aware retrieval through techniques like multi-hop reasoning, temporal traversals, and hybrid vector-graph searches. Attendees will learn how these methods overcome the shortcomings of conventional RAG by preserving semantic connections—whether in legal citations, biomedical interactions, or enterprise knowledge graphs—delivering more precise and explainable results. During the session, you will explore practical implementations of GraphRAG patterns in Neo4j, including contextual subgraph retrieval and dynamic node weighting, while discovering how to optimize Cypher queries for seamless LLM integration. The presenter will share real-world benchmarks showing a 20–35% improvement in retrieval precision over traditional RAG, along with strategies to address challenges like graph indexing and structured prompt engineering. By the end of the session, you will understand how to apply these techniques to transform unstructured searches into intelligent, relationship-driven explorations—equipping you to build next-generation semantic search systems. Speaker: Amit R 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