GraphRAG Explained: How Knowledge Graphs Improve AI Retrieval
What GraphRAG is and when it helps: entity and relationship extraction, knowledge graph construction, community summaries, local and global search, costs, limitations and when standard RAG is enough.
Quick answer
GraphRAG builds a knowledge graph from your documents (entities such as people, organizations, products and events, and the relationships between them) and uses it for retrieval. Microsoft's GraphRAG also groups related entities into communities and writes summaries of each, enabling 'global' answers about themes across a whole corpus as well as 'local' answers about specific entities. It helps with relationship and corpus-wide questions, but indexing is expensive and harder to keep fresh, so most document Q&A should start with well-tuned standard RAG.
Where This Fits
Standard retrieval techniques are covered in the RAG guide, hybrid search and reranking. Organizational concerns such as permissions apply equally to graphs; see enterprise RAG architecture.
What Problem Does GraphRAG Solve?
Chunk-based retrieval answers 'what does the travel policy say about hotels?' well, because the answer lives in a few passages. It struggles with 'what themes come up across 400 customer interviews?' or 'which suppliers are linked to quality incidents at more than one plant?', because the answer is spread across many documents and depends on relationships. GraphRAG makes those relationships explicit and pre-summarizes groups of related information.
How GraphRAG Works
| Stage | What happens |
|---|---|
| Extraction | A language model or NLP pipeline identifies entities, relationships and claims in each text unit |
| Graph construction | Entities become nodes and relationships edges, merged across documents |
| Community detection | Clustering finds groups of closely connected entities |
| Summarization | A model writes reports for each community, often at several levels |
| Query | Local search uses entity neighbourhoods; global search uses community reports; hybrid modes combine them |
Local, Global and Combined Search
Microsoft's GraphRAG describes local search for questions about specific entities (it gathers an entity's neighbours, relationships and source text) and global search for broad questions (it uses community reports across the dataset). Microsoft Research has also described DRIFT search, which combines the two, and LazyGraphRAG, which defers summarization to query time to cut indexing cost. These are evolving research-led tools, so test them on your own corpus.
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Costs and Freshness
Building a graph with LLM extraction processes the whole corpus, often several times (extraction, merging, summaries), so indexing costs far exceed embedding a corpus. When documents change, the affected parts of the graph and summaries must be updated. For fast-changing content, this can be a deciding factor against GraphRAG or in favour of lazier variants.
When GraphRAG Is a Good Fit
- Research, investigation and due-diligence corpora with many interlinked entities
- Questions about themes, patterns and connections rather than single facts
- Relatively stable document collections
- Value in exploring the graph visually, not just answering questions
- Domains with clear entity types (companies, compounds, components, cases)
When to Stay With Standard RAG
Stay with chunk-based retrieval when questions are mostly factual lookups, content changes daily, budgets are tight, or evaluation shows standard RAG already performs well. Improving parsing, chunking, hybrid search and reranking is cheaper and often closes most gaps.
Advantages and Limitations
| Advantages | Limitations |
|---|---|
| Answers corpus-wide and relationship questions | Expensive indexing with LLM extraction |
| Explicit entities aid explanation and exploration | Extraction errors propagate into the graph |
| Community summaries support global questions | Harder to keep fresh as documents change |
| Can combine with vector retrieval | More components to build, evaluate and secure |
How to Evaluate Whether You Need GraphRAG
- 1. Collect questions and label them as factual, relational or thematic
- 2. Build a strong standard RAG baseline with hybrid search and reranking
- 3. Measure where it fails
- 4. Prototype GraphRAG on a subset of the corpus
- 5. Compare answer quality, cost and update effort
- 6. Adopt it only for the question types where it clearly helps
Building a Graph: Practical Choices
- Entity types: define the types that matter (organizations, products, components, sites, incidents) rather than extracting everything
- Relationship types: supplies, located at, caused, mentions; constrain them to keep the graph usable
- Entity resolution: merge variants of the same entity ('ACME Ltd', 'Acme Limited') with rules and review
- Provenance: link every node and edge to source text for citations
- Storage: files and tables for batch analysis, a graph database such as Neo4j for interactive queries
- Tooling: Microsoft's open-source GraphRAG library implements extraction, communities and search modes; graph databases and frameworks offer their own GraphRAG integrations
Combining Graph and Vector Retrieval
In practice, graphs and vectors work together. A query can use vector search to find relevant entities or passages, then expand through graph relationships to related entities and their sources, or use community summaries for broad questions and chunk retrieval for specifics. A router or agent can choose the mode per question. Evaluate each combination against your standard RAG baseline, as described in the RAG guide.
Worked Example
An illustrative scenario, not a client case: a manufacturer's quality team asks which component suppliers appear in incidents across several plants. Standard RAG returns individual incident reports but cannot summarize connections. A graph built from incident reports links suppliers, components, plants and failure modes; global queries over community summaries surface recurring patterns, while day-to-day policy questions continue to use standard RAG.
Common Mistakes
- Adopting GraphRAG before a strong standard baseline exists
- Underestimating indexing cost and refresh effort
- Not checking extraction quality
- Ignoring permissions in graph data
- Using graphs for simple factual Q&A
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Conclusion
GraphRAG is a powerful option for relationship and thematic questions, not a default. Build a strong standard RAG baseline first and add a graph where evaluation shows it pays. Related: RAG guide and enterprise RAG architecture.
Common questions
An approach to retrieval-augmented generation that builds a knowledge graph of entities and relationships from documents, often with summaries of related groups of entities, and uses that structure to retrieve information for a language model.