Microsoft's graph-based retrieval augmented generation for complex document understanding and multi-hop reasoning.
Microsoft's approach to AI-powered document search using knowledge graphs — understands relationships between concepts for deeper answers.
GraphRAG is Microsoft Research's open-source framework that enhances traditional RAG by building a knowledge graph from source documents before retrieval. Instead of simple vector similarity search, GraphRAG uses LLMs to extract entities and relationships from text, constructs a graph structure, detects communities using the Leiden algorithm, and generates summaries at multiple levels of abstraction.
The system operates in two modes: Local Search for specific questions that can be answered from nearby graph neighborhoods, and Global Search for holistic questions that require synthesizing information across the entire dataset. Global Search is GraphRAG's killer feature — it handles questions like 'What are the main themes in this dataset?' that traditional RAG completely fails at because no single chunk contains the answer.
The indexing pipeline is thorough but computationally expensive. It chunks documents, extracts entities and relationships via LLM calls, builds a graph, runs community detection, and generates community summaries at multiple hierarchy levels. This preprocessing cost is the main tradeoff — you're trading upfront compute for dramatically better retrieval quality on complex queries.
GraphRAG integrates with Azure OpenAI and OpenAI APIs for the LLM backbone, uses vector stores for embedding-based retrieval alongside the graph, and outputs structured artifacts (Parquet files) that can be inspected and debugged. The framework supports customizable prompts for entity extraction, allowing domain-specific tuning.
For enterprise knowledge management, research corpora, legal document analysis, and any domain where relationships between entities matter as much as the entities themselves, GraphRAG represents a significant leap over vanilla RAG. Microsoft has published extensive benchmarks showing 20-70% improvements in answer comprehensiveness for global sensemaking queries. The open-source version on GitHub has rapidly gained adoption, with the community building integrations for Neo4j, LlamaIndex, and LangChain.
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Uses LLMs to extract entities, relationships, and claims from documents, building a structured knowledge graph that captures semantic connections traditional chunking misses.
Use Case:
Analyzing a corpus of research papers to understand how different concepts and findings relate across publications.
Synthesizes answers from community summaries across the entire dataset, enabling holistic questions that vanilla RAG cannot handle.
Use Case:
Asking 'What are the key regulatory trends?' across thousands of policy documents.
Combines graph neighborhood traversal with vector similarity for precise, context-rich answers to specific questions.
Use Case:
Finding detailed information about a specific entity and all its relationships within the knowledge base.
Applies the Leiden algorithm to identify clusters of related entities, generating hierarchical summaries at multiple abstraction levels.
Use Case:
Automatically organizing a large knowledge base into thematic groups for exploration.
Entity and relationship extraction prompts can be tuned for specific domains, improving accuracy for specialized corpora.
Use Case:
Configuring extraction for medical literature to focus on drug interactions, symptoms, and treatment protocols.
Produces inspectable Parquet files containing entities, relationships, communities, and summaries for debugging and analysis.
Use Case:
Auditing the knowledge graph to verify extraction quality before deploying to production.
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View Pricing Options →Enterprise knowledge management
Research corpus analysis
Legal document understanding
Complex multi-hop question answering
We believe in transparent reviews. Here's what GraphRAG doesn't handle well:
Traditional RAG retrieves relevant text chunks via vector similarity. GraphRAG first builds a knowledge graph capturing entities and relationships, then uses graph structure plus community summaries for retrieval, enabling multi-hop reasoning and global sensemaking.
GraphRAG makes many LLM calls during indexing for entity extraction and summarization. For a 1M token corpus, expect roughly 5-10x the token cost of the source material. The tradeoff is dramatically better retrieval quality.
Yes, GraphRAG supports any OpenAI-compatible API endpoint, so you can use Ollama, vLLM, or other local inference servers to reduce cost.
GraphRAG supports incremental indexing, allowing you to add new documents without reprocessing the entire corpus, though full re-indexing may be needed for optimal community detection.
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