GraphRAG Knowledge Graph Search

GraphRAG (Microsoft's Knowledge Graph Augmented RAG architecture) is a next-generation retrieval paradigm fusing LLM information extraction with Hierarchical Community Detection; during indexing, LLMs extract Entities, Relationships, and Claims from raw corpora to synthesize a global Knowledge Graph; the Leiden algorithm partitions the graph into multi-level hierarchical communities, with LLMs authoring pre-computed Community Summaries; during querying, it supports both entity-centric Local Search and corpus-spanning Map-Reduce Global Search.

High-level thematic questions (e.g., 'What are the top 3 structural themes across the entire dataset?'), multi-hop entity relationship reasoning, and holistic enterprise audits.

Traditional vector RAG is confined to localized passage matching, completely incapable of answering holistic global sense-making questions spanning thousands of documents; GraphRAG's pre-computed graph community summaries unlock dataset-wide synthesis.

Detail the dual query modes in GraphRAG: Local Search (entity neighborhood traversal) vs Global Search (community summary Map-Reduce)?

Explain the role and advantages of the Leiden community detection algorithm in GraphRAG hierarchical graph partitioning?

Analyze the massive LLM token ingestion costs of GraphRAG and challenges surrounding incremental graph updates?

How do native graph databases (Neo4j / Memgraph) store GraphRAG entities and optimize Cypher graph traversal queries?

Compare GraphRAG vs vector RAG benchmarks in domain-specific tasks (e.g., drug interactions, financial anti-money laundering)?