# Enhancing AI Retrieval with Knowledge Graphs: A Deep Dive into GraphRAG

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Text version: https://stenobird.com/podcast/ai-engineering-podcast/enhancing-ai-retrieval-with-knowledge-graphs-a-deep-dive-into-graphrag.md
Podcast: [AI Engineering Podcast](https://stenobird.com/podcast/ai-engineering-podcast)
Published: 2024-09-10T01:32:14+00:00
Episode link: https://www.aiengineeringpodcast.com/graphrag-knowledge-graph-semantic-retrieval-episode-37
Audio file: https://op3.dev/e/dts.podtrac.com/redirect.mp3/serve.podhome.fm/episode/f6ff0caa-931b-4c08-bfdd-08dc7f5cd336/6386144015864977238b355c65-8ba8-4d98-a951-ba90dc34a2a9v1.mp3
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JSON: https://stenobird.com/v1/public/podcasts/ai-engineering-podcast/episodes/enhancing-ai-retrieval-with-knowledge-graphs-a-deep-dive-into-graphrag
Duration seconds: 3546

## Resource

Summary In this episode of the AI Engineering podcast, Philip Rathle, CTO of Neo4J, talks about the intersection of knowledge graphs and AI retrieval systems, specifically Retrieval Augmented Generation (RAG). He delves into GraphRAG, a novel approach that combines knowledge graphs with vector-based similarity search to enhance generative AI models. Philip explains how GraphRAG works by integrating a graph database for structured data storage, providing more accurate and explainable AI responses, and addressing limitations of traditional retrieval systems. The conversation covers technical aspects such as data modeling, entity extraction, and ontology use cases, as well as the infrastructure and workflow required to support GraphRAG, setting the stage for innovative applications across various industries. Announcements Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems Your host is Tobias Macey and today I'm interviewing Philip Rathle about the application of knowledge graphs in AI retrieval systems Interview Introduction How did you get involved in machine learning? Can you describe what GraphRAG is? What are the capabilities that graph structures offer beyond vector/similarity-based retrieval methods of prompting? What are some examples of the ways that semantic limitations of nearest-neighbor vector retrieval fail to provide relevant results? What are the technical requirements to implement graph-augmented retrieval? What are the concrete ways in which the embedding and retrieval steps of a typical RAG pipeline need to be modified to account for the addition of the graph? Many tutorials for building vector-based knowledge repositories skip over considerations around data modeling. For…

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