Hands-On GraphRAG: Using Knowledge Graphs to Improve Retrieval Grounding | Neo4j | Martin O’Hanlon
VIEW ORIGINAL SLIDES: https://docs.google.com/presentation/... _____ Generative AI needs reliable grounding to be truly useful. This practical session introduces GraphRAG—combining knowledge graphs with RAG to add structure, relationships, and provenance to retrieved context. You will discover how to build a knowledge graph from unstructured text and see how graph context improves retrieval precision and explainability. You’ll see where vector search is effective, where it struggles, and how graph traversal can improve context. You will see how GraphRAG tools can be integrated into a simple LangChain agent. You will learn how graphs can help your GenAI project by providing richer and transparent context. _____ This video is a part of a conference series from Qdrant's #VectorSpaceDay 2025. Read about the full event recap here: https://qdrant.tech/blog/vector-space... and check out all other speaker presentations in this playlist: • Vector Space Day 2025

GraphRAG: The Marriage of Knowledge Graphs and RAG: Emil Eifrem

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Knowledge Graphs & GraphRAG: Techniques for Building Effective GenAI Applications: Zach Blumenthal

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Intro to GraphRAG — Zach Blumenfeld

Practical GraphRAG: Making LLMs smarter with Knowledge Graphs — Michael, Jesus, and Stephen, Neo4j

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Agentic GraphRAG: Simplifying Retrieval Across Structured & Unstructured Data — Zach Blumenfeld

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Graph RAG: Improving RAG with Knowledge Graphs

Temporal RAG: Real-Time Knowledge Graphs for AI Agents using Graphiti, Neo4j and LangGraph

