Neither Graph RAG nor Vector RAG is automatically better for time-sensitive questions. A vector index can retrieve recent evidence if it is updated and the query finds the right passage. Graph retrieval can help connect people, events, and changing relationships across documents. Either can return stale answers if its evidence is stale; freshness depends on how time is represented, updates are processed, and retrieval is scoped to the date the question asks about.
What is the difference between Graph RAG and Vector RAG?
In a common text-based retrieval-augmented generation (RAG) pipeline, documents are split into passages, embedded, and retrieved according to semantic similarity. Graph-oriented RAG extracts entities and relationships from source text and uses graph-derived context to retrieve or organize evidence. It is a family of designs, not one fixed architecture: implementations may combine graph traversal with vector search, full-text search, or generated summaries. A survey of GraphRAG describes this difference at indexing time: text-based RAG vectorizes chunks directly, while GraphRAG first decomposes source text into a graph and then builds an index (GraphRAG survey).
| Approach | What retrieval makes available | Most natural fit |
|---|---|---|
| Vector RAG | Passages whose meaning is similar to the query | Questions answerable from one or a few relevant passages |
| Graph RAG | Explicit entities and relationships, often alongside source text or vector results | Questions that require connecting entities, events, or facts spread across documents |
This is a distinction in emphasis, not a requirement to choose only one mechanism. Microsoft’s GraphRAG documentation includes a basic top-k vector mode as well as graph-oriented query modes.
Does Graph RAG handle changing facts better?
Only when the system is designed to represent and retrieve change correctly. A graph can make relationships across time explicit, but the word “graph” does not guarantee temporal awareness. If new or corrected evidence has not been ingested—or if an old claim is not distinguished from the one that replaced it—the graph may be stale or misleading. A current vector index can also surface new evidence, provided ingestion and retrieval keep it available and select it for the question.
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For a changing fact, the system should be able to distinguish at least when the fact was true from when the system learned or stored it. That distinction matters for questions such as “Who holds the role now?” versus “Who held it in 2022?” It also helps avoid treating a correction as if the earlier statement had never existed.
What temporal Graph RAG research shows—and does not show
A paper published on 15 October 2025, RAG Meets Temporal Graphs: Time-Sensitive Modeling and Retrieval for Evolving Knowledge, proposes a bi-level temporal graph: timestamped knowledge-graph relations plus a hierarchical time graph. Its design describes incremental extraction and merging of new temporal facts, time-scoped subgraph retrieval, and a dataset called ECT-QA for assessing time-sensitive and incremental-update behavior. The paper’s abstract reports better performance than its evaluated baselines, but that result does not establish a universal win across production systems, corpora, costs, or freshness targets.
When should I use Graph RAG instead of vector search?
Start with the shape of the question. If the answer is likely contained in a small number of passages, vector retrieval is a sensible baseline. If it depends on following relationships—such as identifying a predecessor to a current officeholder or comparing changes across policy versions—graph-derived context may help. For questions that need both semantic matching and relationships, test a hybrid rather than assuming either technique must stand alone.
Microsoft’s GraphRAG documentation describes local search as combining graph-derived information with raw text chunks, and global search as broadening the starting point through graph summaries. It also documents basic vector search for comparing query modes (Microsoft GraphRAG query overview). Those modes illustrate different retrieval paths; they do not determine which will perform best on your corpus.
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How do I keep a RAG system’s answers up to date?
- Keep the source of truth current. Identify where corrections and new facts enter the system; retrieval cannot return evidence the pipeline has not received.
- Record time and provenance with evidence. Retain the source, relevant date or period, and when the system ingested the information. For changing claims, preserve the old and new versions rather than silently overwriting history when past-state questions matter.
- Refresh the retrieval index after changes. Measure how long it takes for a changed source to become retrievable. Confirm whether updates are incremental or require broader reprocessing.
- Scope retrieval to the question’s time. Treat “as of today,” “in 2022,” and “what changed between versions?” as different retrieval requirements. A time filter or temporal graph helps only if its timestamps mean what the question needs them to mean.
- Return inspectable evidence. Keep source passages and dates available with the answer so a reader or downstream system can check which claim supports it.
How should you compare the approaches for a real workload?
Use representative questions and the same changing source material for both candidates. Include direct lookups, questions about past states, questions about what changed, and questions that require linking entities across documents. A systematic evaluation paper provides benchmark-oriented context, but its existence is not evidence that one architecture wins in every setting (systematic evaluation of RAG).
| Evaluation axis | What to verify |
|---|---|
| Time semantics | Can the system distinguish when a fact applied from when it was learned or stored, and answer “as of” questions? |
| Freshness and update latency | How soon after a source changes is the correct version retrievable? Do updates require broad reprocessing? |
| Question shape | Does the answer need one passage, or a chain of entities and events across documents? |
| Evidence and conflicts | Can the system expose dated source evidence and keep conflicting or superseded claims distinct? |
| Operational cost | What are the extraction, indexing, storage, update, and query costs at the intended scale? |
| Answer quality and reliability | Measure temporal correctness, retrieval recall, answer faithfulness, latency, and stability after updates on the same test set. |
Do not collapse these measures into a single accuracy score: a system might answer well before an update but take too long to reflect it, or retrieve the right source yet misstate its time scope. Set the freshness target and acceptable operating cost for your workload, then compare the candidates against those criteria. No numeric latency, cost, or accuracy winner is established by the cited material.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the costs and project-status caveats?
Graph-oriented retrieval can add entity and relationship extraction, graph construction, entity resolution, and maintenance as source documents change. Microsoft explicitly warns that GraphRAG indexing can be expensive. Its repository describes the project as largely in maintenance mode, with bug fixes and dependency updates but no new features planned; this is the status of that specific project, not all Graph RAG implementations. Check the official repository for its current status before adopting it, since project plans can change.
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