GraphRAG extends retrieval-augmented generation (RAG) with an LLM-built knowledge graph and summaries of related groups of information. That structure can help answer questions about themes or connections spread across a document collection—questions that ordinary vector retrieval may not resolve from a few similar passages. It also adds indexing work and can make some searches more resource-intensive, so GraphRAG is a specialized approach, not a universal replacement for standard RAG.
What limitation of ordinary RAG does GraphRAG address?
Baseline RAG commonly finds text chunks by vector similarity: it compares a question with indexed passages and gives selected passages to a language model to answer. This works well when the answer is likely to appear in a few passages that resemble the query. It can be less effective when a question calls for aggregating evidence across many documents or identifying a theme that no single passage states directly.
For example, “What are the main themes in this dataset?” asks for a corpus-wide synthesis. A query such as “Catch me up on the last two weeks of updates” may likewise require connecting multiple items rather than retrieving one best-matching chunk. Microsoft Research presents these as global sensemaking tasks: questions whose answers depend on understanding the collection as a whole.
GraphRAG is intended to help with that gap by preparing connected, summarized representations of the corpus before a question is asked. It does not remove the need to retrieve relevant evidence, and it does not guarantee a correct answer.
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How GraphRAG builds its graph and summaries
GraphRAG performs substantial work during indexing. In the documented pipeline, source text is divided into TextUnits; an LLM extracts entities, relationships, and key claims; and the resulting graph is organized into a hierarchy of communities using the Leiden clustering technique. The system then generates summaries of those communities, working from lower levels upward. These summaries and graph structures can be supplied as context when answering later questions.
- Prepare the source text. The corpus is divided into manageable TextUnits.
- Extract structure. An LLM identifies entities, links between them, and key claims in the text.
- Organize related information. Graph clustering groups connected entities into communities and a hierarchy.
- Summarize communities. The system generates reports that describe the contents and themes of those groups.
- Use the prepared context at query time. The selected search mode draws on graph information, community reports, raw text chunks, or a combination, depending on the question.
This means GraphRAG adds generated representations on top of source documents; the graph and reports are not themselves a guarantee that every relationship was extracted correctly or every summary is complete. Microsoft’s documentation also recommends tuning prompts for the dataset, since default prompts may not produce the best results for a particular corpus.
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Which GraphRAG search mode fits the question?
GraphRAG offers different ways to use its indexed structures. Choose by the scope of the question rather than assuming one mode is best for every query.
| Mode | Best fit | How it works and tradeoff |
|---|---|---|
| Global Search | Corpus-wide themes, aggregation, and holistic sensemaking | Uses community reports in a map-reduce process: it generates partial responses from reports and synthesizes them into a final answer. It can be resource-intensive, and the hierarchy level used to select reports affects detail, time, and LLM resource use. |
| Local Search | A question focused on a particular entity and related source material | Combines graph-derived entity information with raw document chunks, connecting structured context to the underlying text. |
| DRIFT Search | A local question that benefits from broader community context and iterative refinement | Starts with relevant community reports, generates follow-up questions, then refines the result through local search. |
| Basic Search | Questions suited to ordinary top-k vector retrieval, or comparisons with that approach | Provides a rudimentary vector-RAG baseline for comparing retrieval behavior. |
The hierarchy choice in Global Search has a practical effect: Microsoft GraphRAG documentation says, “The quality of the global search’s response can be heavily influenced by the level of the community hierarchy chosen for sourcing community reports.” Lower-level reports may support more thorough responses, but can require more time and LLM resources.
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What does the evidence show—and what does it not show?
In its April 2024 paper, “From Local to Global: A Graph RAG Approach to Query-Focused Summarization”, Microsoft Research reports substantial improvements in answer comprehensiveness and diversity over a conventional RAG baseline for a class of global sensemaking questions on datasets in the one-million-token range. This is a bounded finding about those task types and datasets, not a general benchmark score or proof that GraphRAG outperforms vector RAG on every question.
Microsoft Research’s later work, “GraphRAG: Improving global search via dynamic community selection” (November 15, 2024), explores dynamic community selection for global search. Neither result should be read as establishing a universal accuracy advantage or a specific cost ratio.
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What are the main tradeoffs?
- More preparation before querying. Graph extraction, clustering, and community summarization add an indexing pipeline compared with basic vector retrieval.
- Query cost and latency vary by mode. Global Search can consume substantial resources; the chosen community-report hierarchy can change answer detail, response time, and LLM use.
- Generated structure can carry errors. Entities, relationships, and summaries depend on model extraction and summarization. A graph does not eliminate retrieval errors or hallucinations.
- Prompts may need tuning. The official documentation cautions that defaults may not suit a specific dataset.
- Benefits depend on query type. Corpus-wide synthesis is a stronger fit for GraphRAG’s distinctive structure than every routine lookup. A local question may call for Local or DRIFT Search; a straightforward question may be adequately served by ordinary vector retrieval.
When should you consider GraphRAG?
Consider GraphRAG when users regularly need to discover themes, synthesize findings across documents, or follow relationships that span a corpus. Its community summaries and graph can offer a useful route to that broader context. For entity-specific questions, Local Search or DRIFT Search may be more suitable; for simple passage-level questions, Basic Search can serve as a lower-complexity option or comparison baseline.
Evaluate the system against the actual questions users ask. Compare answer comprehensiveness and diversity alongside indexing effort, query resources, latency, and prompt-tuning needs. The appropriate choice may be a combination of retrieval modes rather than one approach for every query.
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Availability and project context
Microsoft Research’s Project GraphRAG page lists GraphRAG and LazyGraphRAG technology as available through Microsoft Discovery, an agentic platform for scientific research built in Azure. That project-page statement describes an access route; it does not establish that every GraphRAG deployment is hosted there or that it is the preferred implementation.
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