GraphRAG for Law: Building Legal Reasoning Agents
summary
GraphRAG uses Neo4j + APOC + native vector search to model statutes, sections and precedents as granular nodes with embeddings, combining semantic vectors, a classification layer with embeddings and graph traversals to return source-linked passages plus their referenced neighborhood. It contrasts pre-built vs on-the-fly graphs (pre-built yields auditable provenance; on-the-fly speeds ad hoc queries) and shows hybrid retrieval that surfaces precise provisions, flags missing facts, and benchmarks better traceability and consistency than raw LLM output.
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chapters (10)
- 0:00Introduction to LexRAG
- 1:20Limitations of raw LLMs
- 3:28GraphRAG build types
- 6:32Hybrid RAG approaches
- 8:29Legal data modeling
- 15:27The classification layer
- 19:17Retrieval recipes
- 24:05Graph traversal benefits
- 27:23Case law mapping demo
- 30:12Challenges and Q&A
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