What is the difference between GraphRAG and vector RAG?
Vector RAG retrieves passages by semantic similarity; GraphRAG retrieves structure. Vector RAG embeds chunks, stores them in an index like pgvector, Qdrant, or Weaviate, and returns the top-k nearest neighbours. GraphRAG first extracts entities and relationships into a knowledge graph, then retrieves connected subgraphs or pre-computed community summaries so the model reasons over how facts relate.
The distinction is about query shape, not sophistication:
- ▸Vector RAG answers *find passages that look like this question*. It is fast, cheap, and strong when the answer sits in one or two chunks.
- ▸GraphRAG answers *connect facts scattered across many documents*. Microsoft's open-source GraphRAG builds an entity graph, runs Leiden community detection, and summarises each community so the system can answer global, thematic questions.
- ▸Hybrid keeps vectors for local precision and adds a graph traversal step for multi-hop and aggregation queries.
If you have not yet fixed retrieval quality on the vector side, graphs will not save you. Start with the fundamentals in RAG Retrieval Quality at Scale: Fixing the Real Bottleneck.
When does GraphRAG actually beat vector RAG?
GraphRAG wins on multi-hop reasoning, global sensemaking, and entity-dense corpora where the answer requires connecting three or more facts that never co-occur in one chunk. On single-fact lookups it adds latency and cost with no accuracy gain. Match the method to the question, not to the hype cycle.
Patterns where graphs earn their keep:
- ▸Multi-hop questions such as *which suppliers of our top-revenue product are located in NIS2-regulated countries* need joins across documents that vector similarity never surfaces together.
- ▸Global or aggregation queries such as *what are the recurring themes across 4,000 support tickets* favour community summaries over 20 disconnected chunks.
- ▸Highly connected domains including compliance control mappings, org charts, fraud rings, drug-interaction data, and supply-chain dependency trees.
- ▸Corpora with heavy coreference where the same entity appears under many aliases; the graph resolves them once.
Vector RAG remains the right default for FAQ lookup, policy retrieval, and code search where relevance, not relationship, decides the answer.
What does the 2026 evidence say about GraphRAG accuracy?
Public reporting and 2026 benchmark write-ups converge on a narrow but real win: GraphRAG variants improve multi-hop and global-question accuracy, often materially, while offering little or no gain on single-hop factoids. Treat vendor headline numbers as directional and re-run the tests on your own corpus before you believe them.
What the current literature broadly shows:
- ▸On multi-hop benchmarks like HotpotQA, MuSiQue, and 2WikiMultiHopQA, graph-augmented retrievers (GraphRAG, HippoRAG, RAPTOR-style hierarchical summaries) tend to report higher exact-match and recall than flat vector baselines.
- ▸Microsoft's LazyGraphRAG, published in late 2024 and refined through 2025, was reported to cut indexing cost by orders of magnitude versus the original GraphRAG while keeping most of the answer-quality advantage on global queries.
- ▸Hybrid retrievers that fuse vector search with graph traversal typically beat either method alone, which is the more durable finding than any single leaderboard score.
Honesty check: numbers vary widely by dataset, chunking, and the extraction model used to build the graph. A gain on 2WikiMultiHopQA does not transfer to your closed-domain contracts. Build a small labelled evaluation set and measure faithfulness and answer accuracy directly, in the spirit of RAG in Production: The Retrieval Engineering Nobody Demos.
What does GraphRAG cost that vector RAG does not?
GraphRAG moves cost from query time to build time. Constructing the graph means running an LLM over your whole corpus to extract entities and relationships, then re-running it whenever documents change. That indexing bill, plus graph storage and traversal complexity, is the trade-off you buy multi-hop accuracy with.
Where the money and time actually go:
- ▸Extraction cost dominates. Every chunk is processed by a model to pull entities, relations, and claims. For a large corpus this can be thousands of dollars in tokens before you answer a single question.
- ▸Re-indexing on change is the operational tax people forget. A frequently updated knowledge base means repeated extraction; incremental graph updates are non-trivial.
- ▸Storage and infrastructure add a graph database such as Neo4j or Memgraph alongside your vector store, plus the schema and traversal logic to maintain.
- ▸Latency rises when a query walks the graph and summarises subgraphs, versus a single approximate-nearest-neighbour lookup over an HNSW index.
LazyGraphRAG and on-demand extraction narrow this gap, but the rule holds: if your questions are mostly local, you are paying for reasoning power you will not use. The economics mirror those in The Real Cost of Running an LLM in Production in 2026.
How do you build a hybrid GraphRAG plus vector pipeline?
Keep vector search as the fast path and add graph retrieval only for queries that need it. Route simple lookups to a dense-plus-reranker pipeline; route multi-hop and global questions to graph traversal or community summaries; fuse both result sets before generation. This captures most of GraphRAG's accuracy without paying its cost on every query.
A pragmatic reference build:
- ▸Ingestion: chunk documents, embed with a strong model such as text-embedding-3-large or Cohere embed v3, and store vectors in pgvector or Qdrant.
- ▸Graph layer: extract entities and relations with an LLM into Neo4j, or use LlamaIndex PropertyGraphIndex, and pre-compute community summaries for global questions.
- ▸Router: a lightweight classifier or the agent itself decides whether a query is local (vector) or relational (graph), avoiding graph cost on trivial lookups.
- ▸Fusion and rerank: merge candidates and apply a cross-encoder reranker such as bge-reranker-v2 or Cohere Rerank so the generator sees the best evidence first.
- ▸Generation with citations: force the model to answer only from retrieved nodes and attach source IDs.
Choosing between a graph store, pgvector, and a dedicated vector database is its own decision; we cover the vector side in pgvector or a Dedicated Vector Database? A Production Guide.
Which questions still favour plain vector RAG?
Most enterprise questions do. If the answer lives in a single passage, plain vector RAG with a good reranker is faster, cheaper, and easier to operate. Reserve graphs for the minority of queries that genuinely span many documents. Over-engineering retrieval is a common and expensive mistake in 2026.
Stay on vectors when:
- ▸Users ask lookup questions answered by one policy, one ticket, or one code file.
- ▸Your corpus is small or loosely connected, so entity extraction yields a sparse, low-value graph.
- ▸Freshness matters and documents change constantly, making repeated graph extraction uneconomic.
- ▸You have no evaluation set yet; you cannot justify graph complexity you cannot measure.
The mature move is to instrument retrieval, find the failing query classes, and add graph capability only where the data says it helps. That is the same discipline behind RAG and Data Engineering in 2026: Your AI Is Only as Good as Your Data.
How do you evaluate GraphRAG before committing?
Build a labelled test set of real questions, tag each by hop count, and measure faithfulness and answer accuracy for vector, graph, and hybrid side by side. Only adopt graphs for the query classes where they beat vectors by a margin that justifies the indexing cost. Decide with numbers, not demos.
A concrete evaluation protocol:
- ▸Assemble 100 to 300 real questions from logs, and label each as single-hop, multi-hop, or global.
- ▸Run all three pipelines and score with a framework such as RAGAS or promptfoo, tracking faithfulness, context precision, and answer correctness.
- ▸Segment the results by hop count. Expect graphs to win multi-hop and global, and to tie or lose on single-hop.
- ▸Add cost and latency columns so accuracy is never read in isolation.
- ▸Re-test on drift. Graph quality degrades as extraction misses new entity types, so schedule periodic re-evaluation.
How TuniCyberLabs helps
We design and build production retrieval systems for EU and North African teams: vector baselines that actually rank, hybrid GraphRAG where multi-hop accuracy pays for itself, honest evaluation harnesses, and cost controls so you are not extracting a graph you never query. We integrate with pgvector, Qdrant, Neo4j, LlamaIndex, and your existing data platform, and we hand you the eval set, not just a demo.
Deciding whether knowledge graphs will move your accuracy? Talk to our AI engineering team and we will benchmark GraphRAG against your own corpus before you spend a euro on indexing.