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Start with retrieval. If the system is not finding the passage that contains the answer, a better graph will not fix that. Relationship modeling earns its place when a question depends on connecting entities across passages, following a chain of facts, or summarizing themes across a whole corpus. GraphRAG is the best-documented way to add that layer, but it is not a replacement for vector search. Its own documentation keeps a basic vector mode alongside the graph-informed modes. The practical design is a routed or hybrid system, with graph methods adopted only for the question types where a baseline shows a gap.
Diagnose which failure you have
Weak answers from a RAG system have different causes, and each cause points to a different fix.
- The relevant passage is missing from the results. The text exists in the corpus but does not appear among the retrieved chunks. Fix retrieval first: chunk boundaries, the embedding model, query rewriting, reranking, and metadata filters. A graph does not help a system that cannot find the paragraph in the first place.
- The passages are retrieved, but the answer requires joining them. Each fact is findable on its own, yet the question asks how they relate, such as which organization was tied to a decision through an intermediary named in a different document. This multi-hop case is where relationship modeling becomes relevant.
- The question asks about the whole corpus. Questions such as “what themes recur across these reports” have no single answer passage. Similarity search returns the chunks closest to the query; it does not summarize the collection. The failure is structural, not a ranking problem.
What relationship modeling means here
In GraphRAG, relationship modeling means using an LLM to extract entities and the relationships between them from source documents, then organizing the result into a graph that the query step can draw on. Microsoft Research’s overview, “GraphRAG: Unlocking LLM discovery on narrative private data,” published February 13, 2024, describes the approach as using an LLM to create a knowledge graph from a private dataset and using that graph to help prepare context for answers. Its examples covered relationship discovery and questions about themes across a dataset.
How the index is built
- Source documents are split into TextUnits, the text chunks that later serve as raw evidence.
- An LLM extracts entities, relationships, and claims from the TextUnits.
- The extracted graph is clustered hierarchically into communities.
- An LLM generates a community summary for each cluster. Global questions are answered from these summaries.
Graph construction adds indexing work and cost. The official documentation recommends prompt tuning, because the extraction prompts determine which entities and relationships end up in the graph.
Query modes: graph search does not replace vector search
The official GraphRAG documentation describes four query modes. They are not interchangeable, because each draws on different material.
| Mode | What it draws on | Caveat |
|---|---|---|
| Basic vector search | Ordinary vector retrieval over the text | Uses no graph, so it carries no relationship context. |
| Local search | Extracted graph information combined with raw text chunks | Quality depends on entity extraction being accurate; the documentation positions it for entity-focused questions. |
| Global search | Community reports built from the graph’s community summaries | The official documentation describes it as resource-intensive. |
| DRIFT search | Graph-based community context | Not separately characterized in the sources cited here. |
Match the workload to a starting point
- A direct question answerable from one passage: evaluate basic vector or other passage retrieval first. It needs no graph construction.
- A question centered on a named entity and its nearby facts: evaluate local search, which keeps raw source text in the answer context alongside graph information.
- A multi-hop question linking facts across documents: evaluate graph-informed or hybrid retrieval. GraphRAG-Bench classifies this kind of question under complex reasoning.
- A question about themes or patterns across the corpus: evaluate global search over community reports, and budget for its resource use.
What the evidence supports
The evidence is task-specific. It does not show that graph methods win in general, and it does not show that vector methods do.
Microsoft’s 2024 evaluation
Microsoft’s initial comparison used an LLM as grader and scored answers on qualitative measures: comprehensiveness, source context, and diversity. GraphRAG improved on those measures in its pairwise evaluation, while faithfulness was similar to baseline RAG. Read this as an early evaluation by the system’s developers, covering their own approach. It is not evidence that every GraphRAG system outperforms every vector system.
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Han et al., “RAG vs. GraphRAG: A Systematic Evaluation and Key Insights” (arXiv:2502.11371), by authors affiliated with Michigan State University, the University of Oregon, and Meta, compares RAG and GraphRAG on question answering and query-based summarization. Its abstract reports distinct strengths across tasks and considers ways to combine those strengths. The practical reading is that the right choice depends on the task, and a combined design is a legitimate option to test.
The benchmark question and what it has found
GraphRAG-Bench, introduced on June 6, 2025, asks: “Is GraphRAG really effective, and in which scenarios do graph structures provide measurable benefits for RAG systems?” It covers fact retrieval, complex reasoning, contextual summarization, and creative generation, and it evaluates across construction, retrieval, and generation. Its project page notes that recent studies find GraphRAG can underperform vanilla RAG on many real-world tasks.
The survey framing
The survey “Graph Retrieval-Augmented Generation: A Survey” (arXiv:2408.08921) describes three stages: graph-based indexing, graph-guided retrieval, and graph-enhanced generation. It is useful vocabulary and background on why relational structure can matter, but it does not establish a universal production recommendation.
Cost and operational burden
The official GraphRAG repository states: “GraphRAG indexing can be an expensive operation, please read all of the documentation to understand the process and costs involved, and start small.”
The sources cited here give no universal dollar budget, latency target, or performance percentage. Cost depends on corpus size, model choice, and extraction prompts, so measure it on your own data. The axes that matter most are:
- Indexing cost: the LLM work to extract and summarize the corpus, which you repeat as documents change.
- Query cost: global search is described as resource-intensive, so route only the questions that need it.
- Maintenance: graph structures and community summaries must stay consistent with the source documents.
- Traceability: confirm that answers can be traced back to source text, which local search supports through its raw chunks.
A start-small sequence
- Build a vector RAG baseline on the same corpus and with the same answer requirements you will use in production.
- Write representative questions and tag each one by workload: single passage, entity-centered, multi-hop, or corpus-wide.
- Index a subset of the corpus with the graph method, and record indexing time and cost.
- Compare answers against the baseline on completeness, faithfulness, source traceability, and cost, separately for each workload tag.
- Expand the graph index only to the workloads where the gain holds.
Check the project status before adopting it
The official repository describes GraphRAG as largely in maintenance mode. It states that the project will not accept new feature work and is not an officially supported Microsoft offering, though bug fixes and dependency updates may continue. Project status can change, so check the repository before you commit. If you adopt GraphRAG, plan to own its operation, including dependency upgrades, rather than assuming a supported product roadmap.
Design the routing, and troubleshoot a weak graph result
A routed design sends each question to the method that fits its workload: vector retrieval for single-passage lookups, local search for entity questions, and global search for corpus-level themes. The sources do not prescribe how to classify incoming questions. A rule set based on question patterns or a small classifier are both plausible, and each needs its own evaluation.
If a graph-informed answer is worse than your vector baseline on questions it should handle, check these causes in order:
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Quick Recap
- Extraction quality. If key entities or relationships are missing from the graph, the graph cannot connect them. Review extraction output and tune the prompts.
- Mode mismatch. Global search on a specific fact question returns thematic summaries rather than the detail you need.
- Workload mismatch. If most questions are single-passage lookups, the vector baseline may already be the right tool.
- Source grounding. If answers look complete but cite no source text, check whether local search with raw chunks is being used.
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