Graph RAG vs Vector RAG: When Each Actually Wins
Vector RAG handles ~70% of retrieval well. Graph RAG is the missing layer for multi-hop, entity-aware, and structured queries. An honest comparison.
By Tulasinath Reddy · May 28, 2026 · Tags: Graph RAG Vector Search Comparison
"Just use vector search" was the consensus advice for retrieval-augmented generation in 2023. Two years later, the limits are obvious: vector RAG nails surface-level matching and fails everywhere that relationships matter. Graph RAG is the answer to those failures — but it's not a replacement for vector search. The most accurate systems run both.
This post is an honest comparison: where vector retrieval still wins, where graph retrieval is the only thing that works, and how to decide for your own pipeline.
What vector RAG is good at
Vector RAG turns every document chunk into an embedding and answers queries by finding the chunks whose embeddings sit closest to the query embedding. It's fast, simple, and brutally effective when:
- The answer lives in a single passage — definitions, single-step instructions, glossary lookups.
- The query is semantically similar to the answer's surface form ("how do I reset my password" matches a doc that literally describes resetting passwords).
- The corpus is mostly unstructured prose — blog posts, support tickets, transcripts.
For these workloads, throwing more clever retrieval at the problem is overkill. Cosine similarity over a good embedding model gets you 70–80% recall with ~30 lines of code.
Where vector RAG quietly fails
The cracks appear the moment the question requires composition — combining facts that live in different documents. Three failure modes that show up in every real production RAG system:
1. Multi-hop questions
"Which engineers reviewed the PR that introduced the bug fixed in v2.3.1?" There is no single chunk that contains all three entities (PR, bug, version). Vector search retrieves the chunks that mention each individually and hopes the LLM stitches them together. Most of the time, it hallucinates the join.
2. Relationship-aware filtering
"Show me all customers who are on the Pro plan AND have a Salesforce integration AND haven't logged in this quarter." Vector embeddings smush these constraints into a single semantic blob; recall on the third constraint drops sharply because it's the least lexically prominent.
3. Entity disambiguation
"Apple" the company versus the fruit. Vector search has no concept of typed entities — it sees similar text contexts and conflates them. Graphs anchor each mention to a typed node so the retrieval layer never confuses the two.
What graph RAG adds
Graph RAG extracts entities and relationships from your documents at ingest time and stores them alongside the embeddings. At query time, retrieval becomes a hybrid operation: find candidate chunks by vector similarity, then walk the graph to surface chunks that are relationally adjacent, even when their embeddings don't match the query directly.
Concretely, this gets you three things vector-only systems can't:
- Multi-hop traversal. The retrieval engine can follow typed edges (
:authored_by,:references,:depends_on) across multiple documents to reach an answer that no single chunk contains. - Citation anchoring. Every answer is pinned to a specific node + edge path, so "where did this come from?" has a deterministic answer instead of a top-K guess.
- Constrained retrieval. Filters become first-class —
nodes WHERE plan = 'Pro' AND HAS_EDGE(integration, 'Salesforce')— instead of being smuggled through prompt language.
Side by side
| Capability | Vector RAG | Graph RAG |
|---|---|---|
| Single-passage retrieval | Excellent | Excellent (uses the same vectors) |
| Multi-hop reasoning | Poor — relies on the LLM to join | Native — traversal at retrieval time |
| Entity disambiguation | None | Typed nodes per entity |
| Structured filters | Approximated via prompt | First-class edge/property queries |
| Setup cost | Low — embed and index | Higher — entity extraction + graph build at ingest |
| Per-query latency | ~10–30ms typical | ~15–60ms typical (traversal adds a few ms) |
| Token cost per answer | Higher — LLM does the joining work in-context | Lower — fewer chunks needed, less context wasted |
When to use which
- Vector only: small corpus (under 10k documents), prose-heavy content, low query complexity. A glossary chatbot, a docs search.
- Graph only: almost never — graphs without vectors miss semantic similarity entirely. Don't do this.
- Hybrid (recommended for production): retrieve candidate chunks by vector similarity, walk the graph to expand the candidate set, then rerank by fused relevance. This is the architecture VertexRAG ships.
How to decide for your own pipeline
Before adopting graph RAG, score your current vector pipeline against a representative question set. Look specifically at the multi-hop questions and structured filters — that's where graph adds the most. Anything below ~0.8 RAGAS faithfulness on those slices is a strong signal that retrieval, not the LLM, is the bottleneck.
Our free RAG Evaluation tool gives you a 0–100 readiness score in 30 seconds based on your content — and the breakdown tells you exactly which dimension (entity density, structure, coherence) you're underweighted on. If you score low on entity density, graph RAG will help disproportionately.
The honest short version
Vector RAG is a solved problem. Graph RAG isn't a different solution — it's the missing layer on top of vector RAG that handles everything semantics-alone can't see. Pick hybrid, score honestly, and don't over-engineer the easy half of your corpus.
If you want to try it without standing up a vector DB and a graph store yourself, the VertexRAG API does both in one call — pay only for the queries you actually run.
Read more on the VertexRAG blog or try the Graph RAG API.