Practical GraphRAG: Making LLMs smarter with Knowledge Graphs
summary
Shows a concrete GraphRAG pipeline: ingest PDFs, transcripts and web pages into a lexical graph, extract entities with LLM prompts, run graph algorithms for clustering and relevance, and use graph traversals plus domain retrievers to assemble context for an LLM. Reports lower token costs and better grounding, citing a three times accuracy improvement in one study and 28.6% faster per-issue resolution at LinkedIn, and demos use Google's ADK and the Neo4j knowledge graph builder.
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chapters (14)
- 0:00Introduction to graph RAG
- 1:23Limitations of standard RAG
- 3:12Benefits of knowledge graphs
- 4:22Research and industry impact
- 5:20Adoption and use cases
- 6:55Graph RAG phases and patterns
- 7:57The graph RAG pattern catalog
- 8:48Construction and extraction
- 12:49Retrieval methods
- 14:29Building the knowledge graph
- 16:00Explainability in retrieval
- 16:48Agentic graph RAG approach
- 17:50Python tools and workflows
- 19:04Key takeaways and conclusion
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