Key features
- Entity extraction: APAC entity and relationship mining across document corpus
- Community detection: APAC topical cluster identification for hierarchical indexing
- Global search: APAC corpus-level synthesis questions beyond vector RAG capability
- Local search: APAC entity subgraph + vector hybrid for specific questions
- Hierarchical summaries: APAC multi-level document corpus understanding
- Open-source: MIT licensed from Microsoft Research for APAC deployment
Best for
- APAC AI teams building knowledge applications over large document corpora where questions require synthesis across many documents rather than retrieval from individual chunks — particularly APAC regulatory intelligence, scientific research, and competitive analysis applications needing corpus-level reasoning.
Limitations to know
- ! High indexing cost — LLM extraction on every APAC document makes large corpora expensive
- ! Indexing time — APAC large corpora require hours to days to fully index
- ! Overkill for APAC simple factual retrieval — standard vector RAG is faster and cheaper
About GraphRAG
Microsoft GraphRAG is an open-source graph-based RAG system — building structured knowledge graphs from APAC document corpora with entity extraction, relationship mapping, community detection, and hierarchical summarization to enable LLMs to answer complex queries requiring global knowledge synthesis across APAC documents. APAC AI research teams and enterprise knowledge management applications use GraphRAG when questions require understanding relationships across entire document corpora rather than similarity to individual chunks.
GraphRAG's indexing pipeline processes APAC documents through multiple stages: entity and relationship extraction (LLM identifies APAC entities and how they relate), community detection (identifies which entities cluster together topically), and community summarization (LLM generates summaries for each APAC entity community at multiple granularity levels). This multi-level indexing creates a hierarchical knowledge structure from APAC document sets.
GraphRAG's query modes address different APAC reasoning needs: local search combines entity subgraph traversal with vector similarity for specific APAC entity questions ('What are MAS's specific requirements for AI in credit decisions?'), while global search aggregates community summaries to answer holistic questions about APAC document corpora ('What are the major themes across all APAC AI governance regulations in 2026?'). The global search capability is GraphRAG's unique contribution — standard vector RAG cannot answer corpus-level APAC synthesis questions.
GraphRAG is computationally expensive — APAC indexing runs LLM extraction on every APAC document and requires significant token budget for large APAC corpora. APAC teams should estimate indexing costs before running GraphRAG on large APAC document sets. For APAC regulatory intelligence, competitive research, and scientific literature review where corpus-level synthesis is the primary use case, GraphRAG's quality improvement justifies the APAC indexing cost.
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