A document chat can answer “What does this report say about the launch?” yet stumble on “What are the top five themes across all these reports?” The second question needs evidence gathered and synthesized across a collection, not just the passage most similar to the wording of the prompt. That is a known weakness of some retrieval patterns—not evidence that RAG systems miss a fixed share of answers. Microsoft’s GraphRAG is designed to help with these cross-document and corpus-wide questions, at the cost of more indexing work and resources.
Why nearest-chunk retrieval can miss the answer
A conventional retrieval-augmented generation (RAG) system searches a collection for passages relevant to a question, then gives selected passages to a language model to answer from. In vector-based systems, that selection commonly relies on semantic similarity: passages that resemble the query are more likely to be retrieved.
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This is effective for direct lookups. If a document says when a product launched and the user asks for its launch date, a relevant passage is likely to match the question closely. But a query such as “What are the main themes in this dataset?” may have no single passage that resembles the question. The answer instead depends on identifying patterns across many documents and synthesizing them.
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There is a second challenge: a question may require joining facts that appear separately. Microsoft Research’s example asks first “What is Novorossiya?” and then “What has Novorossiya done?” Answering the follow-up well may require connecting the entity to related actions and evidence elsewhere in the corpus. A top-k retriever can return plausible individual chunks while still failing to assemble the full picture.
Microsoft’s 2024 paper describes global questions as a query-focused summarization task rather than explicit retrieval. That distinction matters: the documents can contain relevant information even when the retrieval method does not bring the right combination of passages into the model’s context.
How GraphRAG builds a richer index
GraphRAG adds structure during indexing so the system has more than standalone text chunks to work with. In its standard approach, a language model extracts entities and relationships from source documents, organizes related entities into communities, and generates summaries—called community reports—for those groups.
- Extract entities: identify people, places, organizations, concepts, and other named items in the source text.
- Extract relationships: record connections between entities, such as who acted, where an event occurred, or which concepts are related.
- Summarize entities and relationships: create descriptions that make the extracted structure useful for later queries.
- Build communities and reports: group related entities and prepare summaries at different levels of the resulting structure.
At query time, GraphRAG can use these graph and community structures to assemble context. Its documentation also describes optional claim extraction. The exact indexing workflow and configuration can vary; the important difference is that GraphRAG prepares cross-document structure before a user asks a question.
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Choose a query mode that matches the question
GraphRAG offers different search modes for different question shapes. Sending every question through the most expensive mode is usually unnecessary; a direct passage lookup remains a good fit for ordinary retrieval.
| Mode | Best fit | What it uses |
|---|---|---|
| Basic or vector search | A direct lookup whose answer is likely stated in a retrievable passage. | Retrieved text chunks; GraphRAG includes a basic vector RAG implementation for comparison. |
| Local search | A question centered on a particular person, organization, place, or other entity in the corpus. | Relevant graph-derived information together with raw source text chunks. |
| Global search | A question about the collection as a whole, such as its main or top themes. | Community reports processed in a map-reduce-style workflow. |
| DRIFT search | An entity-centered question that could benefit from broader community context and follow-up exploration. | Local search expanded with community context. |
A practical router can start with the question itself: “What does this document say?” points toward direct retrieval; “What has this entity done?” points toward local search; “What patterns recur across the dataset?” points toward global search. Use DRIFT when the initial local question needs broader context. The documentation describes these modes but does not prescribe a universal routing threshold, so validate the routing choices against the questions your users actually ask.
What Microsoft’s results do—and do not—show
Microsoft’s 2024 GraphRAG paper reports improved answer comprehensiveness and diversity over a conventional RAG baseline for a class of global sensemaking questions on datasets in the 1-million-token range. This is evidence for a particular task shape and evaluation, not a universal accuracy result. It does not establish that GraphRAG is better for every lookup, domain, or benchmark, nor that it eliminates incorrect answers.
The research blog discusses evaluation dimensions including comprehensiveness, support from source material and context, and diversity of viewpoints. Those dimensions are especially relevant when a useful answer must represent a broad corpus rather than retrieve one fact. They should not be confused with a general guarantee of correctness.
Costs, quality tradeoffs, and implementation status
Indexing takes more work
GraphRAG must run extraction and summarization steps before query time, so its richer index is not free. Microsoft warns that indexing can be expensive and recommends starting small. Its methods documentation estimates graph extraction at roughly 75% of indexing cost; that is an implementation estimate, not a dollar amount or a fixed comparison with every RAG system.
Global queries can use more resources
Global search processes community reports to build a corpus-wide response. More detailed lower-level reports may support more thorough answers, but can require more time and language-model resources. Route only questions that benefit from synthesis across the dataset to this mode when possible.
FastGraphRAG trades fidelity for lower cost
Microsoft also documents FastGraphRAG, which uses NLP noun-phrase extraction and co-occurrence rather than much of the standard approach’s LLM reasoning. Microsoft characterizes it as cheaper but noisier. It may be suitable for global summarization when high-fidelity graph exploration is not the main requirement; assess whether its extracted structure is adequate for your corpus before relying on it.
Validate generated structure and answers
Because GraphRAG’s entities, relationships, and reports are produced through extraction and summarization, omissions or mistakes in those stages can affect later answers. That is a pipeline risk to test, not a measured error rate established by the cited evaluations. Check representative answers against original source chunks and graph or community evidence. Global search also offers an option to include outside general knowledge; the documentation warns that doing so may increase hallucinations. Keep it disabled when corpus grounding is the priority unless there is a clear reason to enable it.
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As of October 2026, the GraphRAG repository describes the project as largely in maintenance mode and says it will not accept new pull requests or implement new features. It also describes the code as a demonstration rather than an officially supported Microsoft offering. This is a statement about the current implementation’s maintenance and support posture; it does not establish that the underlying method is abandoned or unusable. Recheck the repository before making a deployment decision because its status can change.
How to decide whether GraphRAG fits your corpus
Run a small pilot before replacing an existing RAG pipeline. Compare the current system and GraphRAG on a set of questions that includes both direct lookups and cross-document or corpus-wide synthesis.
Quick Recap
- Collect representative questions: include specific entity questions, fact lookups, and genuine “what themes or patterns recur?” questions.
- Compare modes fairly: test basic/vector retrieval for direct lookups, local search for entity-centered questions, and global search for whole-corpus synthesis. Include DRIFT if broader context is important.
- Check evidence coverage: inspect whether answers include the important supporting material from the corpus, not just whether the wording sounds plausible. For synthesis questions, assess whether distinct relevant themes or viewpoints are represented.
- Record resource use: track indexing effort and query-time latency and language-model usage for the modes you expect to run.
- Choose a route by task: keep simpler retrieval for questions it handles well; add GraphRAG where the pilot demonstrates value that justifies the extra cost and operational complexity.
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