Use vector RAG when your answer lives inside one passage; use GraphRAG when the answer lives in the connections between passages. Vector search retrieves text that looks similar to your question, which makes it fast and accurate for single-hop, detail-oriented lookups. GraphRAG retrieves by walking explicit entity-to-entity edges, which is the reliable way to answer multi-hop relationship questions and to summarize a whole corpus. A query like who reports to whose manager has no single chunk that contains the answer, so semantic similarity returns near-misses forever. The retrieval that answers it follows the edges. When you genuinely need both detail recall and relationship reasoning, a hybrid runs vector recall first and graph traversal second, trading added orchestration latency for accuracy gains on the queries vectors miss.
The rule in one line: vector RAG finds the right page. GraphRAG finds the right path.
How vector RAG retrieves: semantic similarity, fast, and blind to explicit relationships
Vector RAG converts every document chunk into a dense embedding, then matches your query embedding against that store by nearest-neighbor similarity. The system stuffs the top-k most similar chunks into the prompt and generates from them. This is why vector search is fast and why it scales: approximate nearest-neighbor indexes return results in tens of milliseconds even across large vector stores. It also explains the blind spot. Similarity measures surface meaning, not structure. The index has no concept of an entity, an edge, or a chain. It cannot follow a link because, to a vector store, there are no links, only points in space that happen to sit close together.
On the queries vector RAG was built for, it is hard to beat. A 2025 systematic evaluation found standard RAG reaching 64.78 F1 on the single-hop Natural Questions benchmark, ahead of the hierarchical RaptorRAG variant at 60.04, and roughly 71.7% precision against a local Community-GraphRAG at 69.5%. When the answer is a fact sitting inside one well-written passage, adding a graph layer mostly adds latency without buying accuracy.
Vector search will never answer who reports to whose manager, because no single chunk contains that answer. It is assembled from edges, not retrieved from text.
How GraphRAG retrieves: traverse entities and edges, answer multi-hop and global questions, show the path
GraphRAG first extracts a knowledge graph from your corpus: entities become nodes, relationships become typed edges, and source chunks stay linked to the nodes they mention. At query time, retrieval traverses that graph. Instead of grabbing the k most similar chunks, the system starts at the entities your question names and walks the edges between them, collecting the connected facts along the way. A multi-hop question such as actor, then character, then movie, then director resolves by following that exact path rather than hoping four loosely related chunks all land in the top-k.
Traversal buys two things similarity cannot. First, explainability: each answer traces back to specific nodes, edges, and source chunks, so the reasoning trail can be audited rather than trusted as a black box. Second, global sense-making. Microsoft's GraphRAG builds hierarchical community summaries over the graph, letting it answer corpus-wide questions like what are the main themes here, which no single chunk holds. Using those community summaries, Microsoft reported a roughly 70 to 80% win rate over a naive vector RAG baseline on the comprehensiveness and diversity of generated answers.
The questions only graphs answer: chains, hierarchies, and global sense-making
Three query shapes break vector search and define GraphRAG's territory. The first is the multi-hop chain, where the answer requires linking facts that never co-occur in one passage: which suppliers are two steps removed from a flagged vendor. The second is the hierarchy or reachability question: who is in this person's reporting line, what depends on this deprecated service, which clauses inherit from this master agreement. The third is global sense-making over the whole corpus, where the answer is a synthesis no chunk contains: what are the recurring risk themes across ten thousand contracts.
The pattern underneath all three: the answer is a property of the connections, not of any single node. Vector RAG can retrieve the nodes, but it discards exactly the structure the question is asking about. This is why graph methods pull ahead on several multi-hop benchmarks. On HotpotQA, the graph method HippoRAG2 reached 63.01 F1 against standard RAG's 60.04, and the same systematic evaluation concluded the two approaches show complementary behaviors rather than a consistent winner: detail to vectors, relationships to graphs.
The cost: graph construction and orchestration overhead, and the accuracy it buys
GraphRAG is not free, and the cost is mostly paid up front. Building the graph means running entity and relationship extraction over your corpus, designing an ontology, and resolving duplicate entities. That setup runs in weeks to months for a governed schema, against days to stand up a vector index. Maintenance differs too: vectors update by re-embedding changed documents, while graphs need schema governance and entity resolution as data shifts.
The payoff is accuracy on the queries vectors miss. Across finance, healthcare, aeronautics, and law, adding graph structure to RAG improved answer precision by up to 35%. Community summarization also cuts query-time cost: Microsoft's approach used 97% fewer tokens summarizing root-level communities than re-reading source text, so the upfront graph investment partly pays itself back at inference time. The honest framing: graphs cost more to build and maintain, and earn it back only when your queries are genuinely relationship-shaped.
Graphs charge you weeks of construction and a governance bill. They repay you only on queries where the answer lives in the edges.
The hybrid pattern: vector for broad recall, graph for relationship verification
Most production systems that need both do not choose, they sequence. The common pattern runs vector search first for broad recall, pulling the candidate chunks and entities that look relevant, then hands those entities to graph traversal to verify and complete the relationships. Vector recall casts the wide net. The graph then confirms which connections actually hold and fills in the multi-hop links similarity alone would miss, and it supplies the audit trail the vector step cannot.
The tradeoff is orchestration latency. Adding a graph traversal stage after vector recall runs two retrieval passes and merges their results before generation, so it costs more time per query than vector recall alone. For a chatbot answering simple lookups, that overhead is wasted. For a system fielding relationship and multi-hop questions, the same overhead buys the precision gains that separate a confident wrong answer from a traceable correct one. Route by query type so you only pay it when it earns its keep.
| Dimension | Vector RAG | GraphRAG |
|---|---|---|
| Retrieval method | Nearest-neighbor similarity over embeddings | Traversal of entity nodes and typed edges |
| Best query type | Single-hop, detail, fact-in-one-passage | Multi-hop chains, hierarchies, global summaries |
| Query latency | Tens of milliseconds at scale | Higher; traverses connection paths |
| Setup time | Days to index | Weeks to months for governed schema |
| Explainability | Similarity score, hard to trace | Auditable node-and-edge reasoning path |
| Single-hop accuracy | Strong (about 65 F1, 72% precision on NQ) | Comparable or slightly lower |
| Global summaries | Weak; no corpus-wide synthesis | Strong; 70 to 80% win rate over naive RAG |
| Maintenance | Re-embed changed documents | Schema governance, entity resolution |
A decision rule: classify your query type before you pick the retriever
Pick the retriever by query shape, not by hype. Ask whether the answer sits inside a single passage or emerges from connections between passages. If a competent reader could answer from one retrieved chunk, vector RAG is the right tool: faster, cheaper, and competitive or better on those single-hop benchmarks. If answering requires chaining facts, walking a hierarchy, or synthesizing across the corpus, that is graph territory, and similarity search will keep returning plausible near-misses.
Here is what most coverage frames wrong. The popular framing treats this as a contest with a winner, but the systematic evaluation found the two methods complementary, not ranked. The decision that actually moves your accuracy is not vector versus graph; it is whether you built a clean graph at all. Classify queries at the front door: route detail and lookup questions to the vector path, route relationship and sense-making questions to the graph path, and reserve the hybrid pipeline for traffic that mixes both. Then weigh the failure mode nobody advertises. A graph is only as good as its construction, and the common collapse is not the retrieval method but a thin or ungoverned graph, where entity resolution and schema quality decide whether traversal returns truth or noise.
Classify the query before you choose the index. Detail goes to vectors, relationships go to graphs, mixed traffic goes to both.
Where a memory layer fits the retrieval stack
Both retrieval styles assume something persistent to retrieve from, which is where an external memory layer fits below the retriever. MemX, an external AI memory layer built by Neural Forge Technologies, stores and serves the facts and history an assistant accumulates across sessions, so vector recall and graph traversal both run against a durable, structured store instead of a single conversation window. MemX is private by architecture: per-user isolation, encryption at rest, and key management through Google Cloud KMS. The retrieval pattern still follows the same rule above. The memory layer just keeps the entities and relationships your graph or vector store query persistent long enough to be worth retrieving.
01Is GraphRAG always better than vector RAG?
No. They are complementary. Vector RAG matches or beats GraphRAG on single-hop, detail-oriented queries and is far faster and cheaper to run. GraphRAG wins on multi-hop chains, hierarchy questions, and corpus-wide summaries. Choose by query shape, not by which sounds more advanced.
02When should I use a knowledge graph instead of vector search?
Use a graph when the answer depends on connections between items rather than the content of one item: reporting chains, dependency trees, multi-step lookups, or global themes across a corpus. If a single retrieved passage could answer the question, vector search is the faster, cheaper choice.
03How much accuracy does GraphRAG add?
It depends on query type. Adding graph structure improved answer precision by up to 35% across finance, healthcare, aeronautics, and law in one study. For corpus-wide summaries, Microsoft's community-summary approach won 70 to 80% of comparisons against a naive vector baseline. On single-hop queries, the gain is near zero.
04What does a hybrid RAG pipeline cost in latency?
Running vector recall then graph traversal adds latency because two retrieval passes run before generation. That overhead is wasted on simple lookups but worthwhile on relationship questions, where the graph step buys accuracy and an audit trail that vector search alone cannot provide. Route by query type so you pay it only when it helps.
05Why can't vector search answer multi-hop questions?
Vector search returns chunks similar to your query, but multi-hop answers are not contained in any single chunk; they are assembled from connections between chunks. Embeddings encode surface meaning, not explicit links, so the structure the question asks about is exactly what the index discards.
The durable takeaway is a classification step, not a winner. Decide whether each query's answer lives in a passage or in the relationships between passages, then send it to the retriever built for that shape. Vector RAG owns detail and speed, GraphRAG owns relationships and global reasoning, and a routed hybrid earns its added orchestration cost only on the mixed traffic that needs both.
