GraphRAG adds more than a graph database to retrieval-augmented generation: it builds a language-model-generated map of entities, relationships, communities, and summaries before a question is asked. That structure is most useful for questions that require synthesis across a corpus—such as “What are the main themes in the dataset?”—while ordinary vector retrieval may be a better fit for finding a passage about one specific fact.
What “naive RAG” does—and what GraphRAG changes
A basic retrieval-augmented generation system typically breaks documents into chunks, embeds those chunks, retrieves the ones most similar to a query, and gives them to a language model to answer. This works naturally when the answer is likely to be stated in a small number of passages. It can be less effective when a question asks for patterns spread across many documents: a nearest-neighbor search can return locally relevant chunks without providing a coherent view of the corpus as a whole.
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GraphRAG retains vector embeddings but adds an indexing stage that builds structured, summarized representations of the text. The graph is not a substitute for the source documents or the language model. It is an additional layer of organization used to retrieve and combine information in ways suited to different question scopes.
What the graph contains
In Microsoft’s documented standard indexing pipeline, source text is divided into text units, then a language model extracts entities and relationships. The pipeline can summarize those records, optionally extract claims, detect communities of related entities, and generate reports describing those communities. It also embeds text; the default pipeline stores tables as Parquet files and writes embeddings to a configured vector store.
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- Entities: Named people, organizations, places, or other concepts identified in the text.
- Relationships: Connections between entities, such as a person working for an organization or an event involving a location.
- Communities: Groups in the graph that capture clusters of related entities and connections.
- Community reports: Generated summaries of those groups, organized at levels of detail in a hierarchy.
- Text units and embeddings: Links back to original text and vector representations that support retrieval beyond graph records.
The practical addition is the combination of cross-document links and summaries. An entity record can connect mentions found in separate text units; a community report can give a compact overview of a broader cluster. A graph by itself does not produce this effect: the value depends on language-model extraction and summarization, the resulting hierarchy, and retrieval methods that use those structures.
How GraphRAG uses that structure to answer questions
Global questions: summarize themes across the corpus
A global question asks about the collection rather than one passage or named entity. “What are the main themes in the dataset?” is the canonical example. Microsoft Research also describes “Catch me up on the last two weeks of updates” as a global query that traditional RAG may struggle to answer.
For global search, Microsoft’s query documentation describes selecting community reports from a chosen level of the hierarchy and processing them through a map-reduce workflow. The model creates rated intermediate points from batches of reports, then filters and combines those points into a final response. This gives the model a precomputed corpus-level structure to work with rather than requiring it to infer all themes from a few independently retrieved chunks. More detailed, lower-level reports can produce more thorough answers, but processing more reports can increase runtime and model-resource use.
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Local questions: investigate an entity or a small part of the graph
For a question about one or a few named entities, local search can combine graph information with relevant original text chunks. That lets retrieval use connections around an entity while retaining access to source text. It is a different path from global search, not a claim that every entity lookup needs a graph.
DRIFT and basic vector search
DRIFT Search starts with community context and can use follow-up questions to broaden a local search, gathering a wider range of facts than a narrowly targeted lookup. GraphRAG also includes basic vector search, which provides a useful direct comparison with a chunk-retrieval approach. A GraphRAG deployment can therefore offer multiple retrieval modes rather than forcing every question through global summaries.
Where GraphRAG is most likely to help
- Cross-document synthesis: Questions about recurring themes, developments, or connections distributed across a collection can benefit from community summaries.
- Broad questions without obvious keywords: A reader may ask for an overview without knowing which entity or phrase to search for. Precomputed reports can help surface an organized answer where nearest-neighbor search over individual chunks may miss the broader pattern.
- Questions that need both context and evidence: Local retrieval can use graph neighbors while bringing in original text, allowing a response to draw on structure and source passages.
The original GraphRAG paper evaluated global sensemaking questions on datasets in the approximate one-million-token range and reported improvements in answer comprehensiveness and diversity over a naive RAG baseline for that class of questions. That is evidence for a particular workload, not a general performance guarantee for other corpora, models, or retrieval tasks.
What the extra indexing work costs
Standard GraphRAG shifts a substantial share of work to the time when the index is built. Its LLM-assisted stages include entity and relationship extraction, summaries of those records, and community-report generation. Microsoft’s methods documentation estimates graph extraction at roughly 75% of indexing cost. This is a documentation estimate, not a universal bill or pricing guarantee; actual cost depends on the corpus, model, configuration, and refresh schedule.
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The project repository warns that indexing can be expensive and recommends starting small. It also states that the code is a demonstration, not an officially supported Microsoft offering, and recommends prompt tuning rather than assuming defaults will produce the best results. These are factors to account for in an adoption decision; they do not establish that GraphRAG cannot be used in production.
Extraction and reports are generated representations, not ground truth. Errors or omissions in source documents, prompts, and model output can flow into the graph and summaries. The system’s usefulness therefore depends on corpus quality, configuration, query type, and evaluation against a suitable baseline; the cited documentation does not quantify a general error rate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What published cost and quality figures do—and do not—show
The results below come from distinct Microsoft Research evaluations and documentation. Their scopes should remain attached to the numbers; in particular, the global-search token comparison is between two GraphRAG configurations, not between GraphRAG and naive RAG.
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| Figure | What it describes | Scope and limitation |
|---|---|---|
| About 1 million tokens | The approximate dataset size range in the original paper’s evaluation of global sensemaking questions. | Microsoft Research authors, 2024; the paper reports improved comprehensiveness and diversity against naive RAG for the evaluated question class, not all retrieval tasks. |
| Roughly 75% of indexing cost | Estimated share attributed to graph extraction. | Microsoft GraphRAG methods documentation, accessed 2026; an estimate, not a universal deployment cost. |
| 50 global questions | Question count in a dynamic-versus-static global-search evaluation using an AP News dataset. | Microsoft Research, 2024; an LLM evaluator assessed comprehensiveness, diversity, and empowerment. |
| 77% lower average total token cost | Dynamic global search at community level 1 compared with static global search at level 1. | Microsoft Research, 2024; reported for that experiment, with similar judged quality and no statistically significant difference across the three metrics. |
| About 1,500 versus 470 reports | Reports processed in the map-reduce step: static level-1 search versus the average selected by dynamic level-1 search. | Microsoft Research, 2024; counts from the reported AP News experiment. |
| 34% higher average cost at level 3 | Dynamic search continuing to community level 3 compared with static level-1 search. | Microsoft Research, 2024; the evaluation also reported significant win rates for comprehensiveness and empowerment in that comparison. |
The dynamic-search study illustrates a trade-off inside global GraphRAG: selecting fewer reports can lower token use, while searching deeper into the hierarchy can cost more and may improve some evaluated qualities. It does not establish that GraphRAG as a whole is cheaper than naive RAG or that one setting is best for every corpus.
How to choose fairly between GraphRAG and simpler retrieval
A 2025 systematic evaluation by researchers affiliated with Michigan State University, the University of Oregon, and Meta compared RAG and GraphRAG for question answering and query-based summarization. Its abstract describes different strengths across tasks and evaluation perspectives, and treats broader real-world applicability as unsettled. Use that work as a reason to test against your workload, not as evidence that either approach wins universally.
- Question scope: If users usually ask for one fact or entity, local retrieval or basic vector search may be sufficient. If they routinely ask for trends or themes across many documents, test global search.
- Indexing budget and update frequency: Estimate acceptable upfront model calls and tokens, and how often the corpus changes. Richer extraction costs more; a frequently refreshed corpus can make that recurring work especially important.
- Quality criteria: For synthesis, assess completeness and diversity; also test factual support and usefulness for the application. A broad answer is not valuable if its claims cannot be supported by the underlying documents.
- Operational burden: Account for extraction quality, prompt tuning, community-report hierarchy choices, vector-store configuration, and index refreshes. Their impact depends on the deployment.
- Baseline fairness: Compare approaches using the same corpus, questions, language model, context budget, and evaluation method. Otherwise, a measured difference may reflect setup choices rather than the graph.
A practical way to pilot GraphRAG
- Choose a small, representative corpus. Include the kinds of documents and update patterns expected in real use, but avoid paying to index the full collection before you know whether graph structure helps.
- Write representative questions in advance. Include single-entity lookups, passage-oriented questions, and broad synthesis prompts such as “What are the main themes in the dataset?”
- Run basic, local, and global retrieval where appropriate. Compare each mode on questions it is designed to answer; do not expect global search to be the best path for every fact lookup.
- Judge answers against source documents. Record whether important themes were covered, whether claims are supported, and whether the answer is useful—not just whether it sounds comprehensive.
- Measure the indexing and query trade-offs. Track model usage, refresh effort, runtime, and any configuration changes needed to get reliable output before expanding the index.
What GraphRAG actually adds
GraphRAG adds a generated semantic map and summaries to a retrieval system that still uses text and vectors. Entity relationships support connected, local exploration; communities and reports make corpus-level synthesis more tractable. The strongest case is a workload with recurring questions about themes, developments, or connections across many documents. For routine passage lookup, the additional indexing may not justify itself. The right choice is the one that performs better on representative questions under the same corpus, model, and evaluation conditions.
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