GraphRAG
GraphRAG uses a graph representation of entities and relationships to help retrieve or organize evidence for generation. The name includes a specific Microsoft research approach as well as broader graph-assisted designs; a useful description should state which graph, retrieval mechanism and evidence aggregation the implementation actually uses.
What it is
In Microsoft's GraphRAG approach, a language model extracts entities and relationships from source text, builds a graph and creates summaries of graph communities. Different query strategies can then use local entity context or community summaries to answer questions, including broad questions across a collection. Other graph-assisted RAG systems traverse curated relationships or combine graph results with vector search. These designs share the use of connected evidence but are not identical algorithms. GraphRAG differs from simply storing embeddings in a graph database: explicit connections influence what evidence is retrieved or how it is summarized.
What the work involves
The practitioner defines entity identity, relationship semantics and provenance, then validates the extracted graph before relying on it. Query tests should distinguish local factual lookup from global synthesis because they exercise different retrieval behavior. A comparison with ordinary lexical or dense RAG measures whether graph construction adds value. Useful artifacts include graph-building configuration, sampled extraction audits, query modes and answer traces back to original sources. Rebuilding or updating summaries also needs an operational policy when the underlying documents change.
Illustrative example
A collection of incident reports describes failures involving overlapping suppliers and components. A graph-assisted system links recurring entities and creates summaries of related incidents. A question about shared failure patterns can use those summaries, while a question about one component follows its local evidence. Reviewers check the resulting synthesis against the original reports, including reports that contradict the dominant pattern. The graph helps organize connections but does not permit a summary to turn a correlation into a verified cause.
Limits and common mistakes
Entity extraction and merging can introduce false links, and community summaries can lose minority evidence or qualifiers. Graph construction may be costly and updates complex. Broad synthesis quality does not establish superior performance for simple lookups. The label should therefore identify the actual method and workload, with separate checks for graph accuracy and answer support. A graph is an organized representation of available claims, not an automatic guarantee that those claims are correct.
Prerequisites
Related skills
- → is subcategory of: Retrieval-Augmented Generation
- ← is part of: Knowledge Graphs
Sources and further reading
- From Local to Global: A Graph RAG Approach to Query-Focused Summarization
Primary formulation of entity graphs, community summaries and global query-focused synthesis.
- Microsoft GraphRAG documentation
Official implementation documentation and query architecture.
Last updated: 2026-10-10