GraphRAG Explained 🔥 Building Smarter LLMs
summary
Built a runnable GraphRAG pipeline that converts PDF blog posts into chunked embeddings, extracts entities, and stores chunks plus relationships in Neo4j Aura, using Neo4j + LangChain + Python with GPT‑4o Mini for LLM tasks and text-ada-002 for cheap embedding tests. Demonstrates three retrievals: vector search, vector + Cypher hybrid, and text‑to‑Cypher, returns grounded answers (counts 21 blog posts), shows multi‑hop reasoning improves relevance, and notes occasional missing results so validation queries are necessary.
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chapters (22)
- 0:00GraphRAG introduction
- 1:44Traditional RAG limits
- 3:49Vector search basics
- 4:54GraphRAG multi-hop
- 5:57Knowledge graph building
- 7:23GraphRAG pipeline
- 8:26PDF to knowledge graph
- 10:12Entity extraction
- 11:39Code: knowledge graph
- 17:21Graph visualization
- 18:45Graph data retrieval
- 20:50GraphRAG retrieval
- 21:54Code: vector retrieval
- 23:59Testing vector retrieval
- 26:43Vector + Cypher
- 27:26GraphRAG query
- 29:10Multi-hop testing
- 31:17Text to Cypher
- 32:18Cypher results
- 33:20Key takeaways
- 34:24Neo4j resources
- 35:26Q&A
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