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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteGraphRAG adds an LLM-derived knowledge graph and summaries of related document communities to retrieval, giving a system another way to answer questions that depend on connections across a corpus. It is not a universal replacement for vector search: local, global, DRIFT, and basic vector retrieval suit different question scopes, and the extra indexing and operational work must earn its place on your data.
What GraphRAG adds to retrieval
Conventional vector retrieval finds text chunks that are semantically similar to a question. That is useful when the answer is likely to appear in a small number of passages. But a question such as “What are the main themes in the dataset?” may require combining evidence spread across many documents rather than finding one especially relevant passage.
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GraphRAG creates an indexed representation of a collection: entities and their relationships, communities of connected entities, and summaries of those communities. At query time, its search paths can use that structure alongside source text. The graph provides a route to related material; it does not make every extracted relationship correct or every generated answer reliable.
The Tool Desk
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The default pipeline described in Microsoft’s GraphRAG indexing documentation turns source documents into several linked forms of data. In broad order, it:
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- Loads documents and divides them into text chunks.
- Extracts entities and relationships from the text; optional claims can also be extracted.
- Creates embeddings for text chunks.
- Detects communities in the graph of entities and relationships.
- Generates reports summarizing those communities.
- Creates embeddings for entities and community reports.
The resulting index gives query methods more than one kind of context to draw on: original text units, graph elements, and community reports. Microsoft’s Knowledge Model describes these outputs independently of the underlying storage technology. The library also documents provider factories for models, readers, caches, loggers, storage, vector stores, and workflows, allowing implementation choices beyond its built-in integrations.
Which query path fits the question?
Choose a retrieval path based on the question’s scope, not on the assumption that a graph-based method is inherently more advanced or accurate. The GraphRAG query overview includes a basic vector-RAG mode specifically for comparison.
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| Path | What it uses | Good fit | Trade-off |
|---|---|---|---|
| Basic vector search | Top-k text-unit chunks selected by vector retrieval, supplied as context. | A focused question likely to be answered by a few relevant passages. | It does not use GraphRAG’s community-report synthesis; answer quality depends on whether retrieval surfaces the needed chunks. |
| Local search | Relevant information from the extracted knowledge graph combined with raw document chunks. | A question centered on particular entities in the corpus and their related information. | It depends on useful graph extraction as well as relevant source text. |
| Global search | Community reports processed in a map-reduce fashion. | A broad question about themes or patterns across the collection. | Microsoft describes it as resource-intensive; processing across reports can require substantial query-time resources. |
| DRIFT search | Community information added to the local-search process, with the query refined into detailed follow-up questions. | A local question where widening the starting point may help uncover relevant connections. | The overview describes the approach, but does not state a fixed cost or latency relative to the other paths. |
A specific question about a named organization may be a reasonable candidate for local or vector search. A question asking for the collection’s main themes is a better candidate for global search. For a changing corpus, a request such as “Catch me up on the last two weeks of updates” illustrates a broad synthesis task; Microsoft Research discussed this kind of keywordless query in its dynamic community selection work.
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Standard GraphRAG or FastGraphRAG?
The indexing method affects both the structure available to queries and the work required to build it. Microsoft’s indexing-method documentation describes a quality-cost trade-off rather than one method that fits every project.
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| Method | How it builds graph information | Trade-off and fit |
|---|---|---|
| Standard GraphRAG | Uses language-model processing for entity and relationship extraction and summaries. Community reports draw on entity and relationship descriptions, and optionally claims. | Produces richer entity and relationship descriptions, but requires more model processing. Prefer it when high-fidelity entities or graph exploration matter. |
| FastGraphRAG | Uses traditional NLP methods for some reasoning: noun phrases stand in for entities, and text-unit co-occurrence defines relationships. | Costs less to build but produces a noisier graph. It may fit summary-focused global search when reducing model cost matters more than graph fidelity. |
Microsoft estimates that graph extraction accounts for roughly 75% of indexing cost. That is the documentation’s estimate, not a universal ratio: actual expense depends on the corpus, configuration, models, and provider. The important decision is whether the extra structure improves the questions you actually need to answer enough to justify its construction and upkeep.
What the evidence does—and does not—show
In the 2024 paper From Local to Global: A Graph RAG Approach to Query-Focused Summarization, Edge and co-authors report improved comprehensiveness and diversity over a conventional RAG baseline for a class of global sensemaking questions on datasets around one million tokens. This is evidence for that task class and evaluation setup, not proof that GraphRAG outperforms vector retrieval on every corpus or question.
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A separate Microsoft Research post, published November 15, 2024, describes dynamic community selection: pruning irrelevant community-report branches before the resource-intensive map-reduce step. Its reported evaluation used 50 global questions over an AP News dataset, with an LLM evaluator assessing comprehensiveness, diversity, and empowerment. Those are details of that comparison, not a general benchmark guarantee or a cost figure for other deployments.
How to decide whether to adopt it
Run a comparison on representative questions from your own corpus. Include questions that need a passage-level answer, an entity-centered answer, and a synthesis across the collection. Compare basic vector retrieval with the GraphRAG paths that match those questions; judge the results on answer quality and the cost of producing them.
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- Query scope: Does the question need a few relevant chunks, graph connections around an entity, or synthesis across many communities?
- Graph fidelity: Do extracted entities and relationships reflect the domain accurately, or do noisy links mislead retrieval?
- Indexing expense: Can you afford the model work to build and refresh the graph, and does the resulting index improve useful answers?
- Query resources: Does global search’s map-reduce work fit your latency and compute constraints?
- Evaluation: Can you maintain a repeatable set of questions and criteria to detect regressions as data, prompts, or models change?
If your users mostly ask questions answered by a handful of passages, begin with vector retrieval and add graph indexing only if testing reveals a meaningful gap. If users regularly need broad synthesis across documents, evaluate global search, while accounting for its resource demands. Local and DRIFT search are relevant when questions focus on entities but may benefit from connected context.
Operational responsibilities matter
The GraphRAG repository describes the project as a research project in largely maintenance mode: it will not accept new feature work, though bug fixes and dependency updates—particularly CVEs—may be accepted. It also says the code is a demonstration, not an officially supported Microsoft offering, and warns that indexing can be expensive. Its README recommends tuning prompts to the data and includes migration and configuration guidance.
That project status makes ownership part of the architecture decision. Before adopting it, assign responsibility for version upgrades, prompt evaluation, cost monitoring, storage and workflow configuration, and production support. Treat the index and prompts as components that need evaluation when the corpus or implementation changes, not as a one-time setup.
Quick Recap
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