GraphRAG vs. Vector RAG: When Relationships Beat Similarity
Vector RAG fails on multi-hop questions where the answer spans multiple documents. Microsoft GraphRAG and the Mixture-of-PageRanks method (arXiv:2412.06078) address this differently. Here is the decision matrix and when to use which.
Where vector RAG breaks ¶
Vector RAG works by embedding your documents and your query into the same space, then returning the chunks most similar to the query. This is effective when the answer lives in a single chunk and similarity is the right signal.
It breaks on multi-hop questions: "Which customers used both feature A and feature B, and what did they have in common?" No single chunk contains that answer. The relevant information is distributed across multiple documents, and the relationship between them is load-bearing. Cosine similarity to the query does not surface the relationship.
Graph-based retrieval is designed for this failure mode.
Two approaches to graph retrieval ¶
Microsoft GraphRAG
Microsoft's GraphRAG (microsoft.github.io/graphrag) builds a knowledge graph from your corpus at index time:
At query time, GraphRAG offers three search modes:
GraphRAG consistently outperforms vector RAG on global comprehension questions in Microsoft's benchmarks. The cost: index construction requires many LLM calls (entity extraction for every chunk in your corpus). For a 10,000-document corpus, this can cost $10-100+ in LLM API fees just to build the index.
Mixture-of-PageRanks (MixPR)
A December 2024 paper (arXiv:2412.06078) proposes a lighter alternative: Mixture-of-PageRanks (MixPR).
Instead of building a full knowledge graph at index time, MixPR constructs a sparse graph at query time using document-level co-citation and entity overlap. It then runs PageRank variants on this sparse graph to score nodes by importance relative to the query.
The key claim: MixPR matches or outperforms vector RAG on multi-hop questions without requiring LLM-powered entity extraction at index time. Construction cost is proportional to retrieval time, not corpus size. For corpora where relationships matter but upfront LLM indexing cost is prohibitive, this is the practical path.
Decision matrix ¶
| Signal | Vector RAG | GraphRAG | MixPR |
|---|---|---|---|
| Single-chunk answers | Excellent | Overkill | Overkill |
| Multi-hop relationships | Fails | Designed for this | Good |
| Global corpus synthesis | Poor | Excellent | Moderate |
| Index build cost | Embeddings only | High (LLM per chunk) | Query-time only |
| Query latency | Fast | Fast (pre-built graph) | Moderate (graph at query time) |
| Corpus size sweet spot | Any | Large (amortizes index cost) | Medium |
When to use which ¶
Vector RAG: Single-document Q&A, fact lookup, search over well-structured homogeneous content. If your users ask "find me the section about X," vector RAG is correct and GraphRAG is unnecessary overhead.
GraphRAG: Analyst-grade queries over heterogeneous document sets where entities and relationships are the unit of interest. Legal document analysis, research synthesis, customer data that spans multiple systems. Index construction cost is amortized over many queries.
MixPR: Multi-hop queries over medium-sized corpora where you cannot afford upfront LLM extraction. Also useful as a reranking layer on top of vector retrieval: retrieve broad vector candidates, then MixPR-score for relationship relevance.
The context injection problem both share ¶
Graph and vector retrieval share a downstream problem: the retrieved content still has to fit in a context window, and the LLM still has to attend to what matters.
GraphRAG community summaries can be verbose. MixPR-ranked documents still carry noise. Whether your retriever finds content by similarity or by graph centrality, what you inject into the LLM context determines answer quality more than which retrieval method you used.
Context compression at the injection layer (extracting the information relevant to the specific query from the retrieved set rather than injecting full chunks) is orthogonal to retrieval method and addresses the shared bottleneck.
What to build first ¶
If you are starting a RAG system:
Building GraphRAG first and paying the index cost upfront without evidence your queries need it is a common and expensive mistake.
Compress what you retrieve before it enters the context window →
Cite this¶
Researchers, analysts, or journalists referencing this post can use either format below — both are copyable.
@misc{graphrag-vs-vector-rag-when-relationships-beat-similarity-2026,
title = {GraphRAG vs. Vector RAG: When Relationships Beat Similarity},
author = {James Hollingsworth},
year = {2026},
month = {May},
url = {https://www.gotcontext.ai/blog/graphrag-vs-vector-rag-when-relationships-beat-similarity},
note = {gotcontext.ai engineering blog.},
}James Hollingsworth. (2026, May 8). GraphRAG vs. Vector RAG: When Relationships Beat Similarity. gotcontext.ai. Retrieved from https://www.gotcontext.ai/blog/graphrag-vs-vector-rag-when-relationships-beat-similarity.