Standard RAG retrieves text chunks that are semantically similar to a query. When the answer lives in a single passage, that works. When it requires connecting information scattered across dozens of documents — who supplies what to whom, which regulation affects which product line, how two research findings relate — chunk-based retrieval falls apart.
GraphRAG fixes this by building a knowledge graph over your data. Instead of treating documents as bags of isolated chunks, it maps entities and their relationships into a structured network. The retrieval step then traverses that graph, following connections the way an analyst would. Pioneered by Microsoft Research, GraphRAG inserts a knowledge graph layer between indexing and generation — and the difference is dramatic for relationship-heavy domains.
How GraphRAG Works
GraphRAG extends the standard RAG pipeline with graph construction and traversal.
Indexing Phase
- Entity and Relation Extraction: NER models identify key entities — people, organizations, concepts, products — in your documents. Relation extraction discovers the connections between them.
- Graph Construction: Entities become nodes. Relationships become edges. The whole thing goes into a graph database, forming a structured knowledge network.
- Community Detection: The graph gets partitioned into clusters of closely related entities. Each community receives an auto-generated summary capturing its key themes.
Query Phase
- The user's question is analyzed to identify seed entities.
- The system traverses the graph outward from those seeds, following relationships to gather connected context.
- Relevant subgraphs and community summaries get ranked and assembled into the prompt.
- The LLM generates a response grounded in both the graph structure and the underlying source text.
How GraphRAG Differs from Standard RAG
Standard RAG matches query vectors against chunk vectors. Effective for direct questions. But it can't connect dots across documents. GraphRAG adds structural understanding:
- Multi-hop reasoning: "Which suppliers are affected by the regulation change?" might require bridging regulatory text to supplier records to product catalogs. GraphRAG traverses those connections. Standard RAG would need the answer to exist in a single chunk.
- Relationship awareness: The knowledge graph explicitly represents how entities relate. Retrieval reflects real-world structure, not just textual similarity.
- Global summarization: Community summaries give the LLM a bird's-eye view of topic areas. Better answers to broad or exploratory questions — the kind where standard retrieval struggles.
- Explainability: Retrieved entity chains and relationship paths show why certain context was included. Debugging gets easier. Trust goes up.
Use Cases
- Enterprise Knowledge Management: Organizations where information is scattered across systems with interconnected concepts. GraphRAG navigates that web instead of searching it blindly.
- Legal and Regulatory Analysis: Trace relationships between regulations, entities, jurisdictions, and compliance requirements across large document collections.
- Scientific Research: Surface non-obvious connections across papers, datasets, and experiments. Literature review that actually connects findings instead of just listing them.
- Supply Chain Intelligence: Map dependencies between suppliers, components, products, and regions. Assess risk and ripple effects when something changes.
- Investigative Analysis: Follow chains between people, organizations, transactions, and events across unstructured data.
When GraphRAG Is Worth the Complexity
GraphRAG shines when your data is inherently relational. Product catalogs with category hierarchies and component dependencies. Organizational structures. Regulatory frameworks with cross-references. Narrative content linking multiple actors and events.
If most questions are answered by a single passage, standard RAG with a good vector database is simpler and usually sufficient. GraphRAG adds real complexity — in the indexing pipeline, in the graph infrastructure, in the retrieval logic. The investment needs to be justified by the nature of the data and the questions people actually ask.
GraphRAG in the AI Stack
GraphRAG pulls together techniques from knowledge graph engineering, NLP, and generative AI. It builds on graph databases (Neo4j, Amazon Neptune), entity extraction models, community detection algorithms, and the same LLM and embedding infrastructure used in standard RAG. Agentic approaches are a natural complement — an AI agent can decide whether a question needs graph traversal or simple vector search, routing to the right retrieval strategy dynamically.
As organizations push past basic Q&A toward systems that genuinely understand their data's structure, GraphRAG represents where RAG is heading.