GraphRAG Entity Extraction & Community

Microsoft GraphRAG (Knowledge Graph Entity-Relationship Extraction & Hierarchical Community Summarization) overcomes the fatal limitation of standard Vector RAG on holistic 'Global Sensemaking' queries (e.g. 'What are the overarching systemic supply-chain vulnerabilities across these 500 annual reports?'); the end-to-end pipeline spans: 1) Graph Indexing: using LLMs to extract Entities, Relationships, and Claims from unstructured text into a global property graph; 2) Hierarchical Community Detection (Leiden Algorithm): partitioning graph topology into multi-scale hierarchical subgraphs (from C0 macro themes to C2 micro cliques); 3) Pre-computing Community Summaries: synthesizing dense semantic reports for each cluster; 4) Dual-Mode Search: Global Search running parallel Map-Reduce over high-level community reports, and Local Search traversing multi-hop entity neighborhoods for precise factual retrieval.

Holistic enterprise knowledge intelligence, multi-hop financial beneficial ownership audits, and whole-codebase architecture synthesis.

Standard vector retrieval returns disconnected local chunks, failing completely on holistic summarization queries; GraphRAG unifies micro multi-hop entity paths with macro structural sensemaking.

Diagram the end-to-end GraphRAG pipeline from entity extraction, Leiden clustering, community summarization to Map-Reduce global search?

Why does standard Vector RAG fail completely on dataset-wide global synthesis queries where GraphRAG excels?

Explain why the Leiden community detection algorithm fixes Louvain's disconnected cluster pathologies in knowledge graph partitioning?

How to optimize GraphRAG indexing costs using smaller extraction models, prompt compression, and prompt caching?

How to combine Graph Traversal and Vector Retrieval into a hybrid search expansion pipeline in production?