Quick Answer: GraphRAG vs RAG comes down to retrieval: GraphRAG traverses a knowledge graph of entities and relationships, while vector RAG pulls text chunks by similarity. That lets GraphRAG answer multi-hop, "how are these connected" questions vector RAG often misses. Vector RAG wins on single-fact lookups and costs less to keep current, so add a graph only when relationships make questions hard.
Most enterprise knowledge bases start with vector RAG, which handles "what does the travel policy say" well. It struggles when the answer depends on how things connect. That gap is the real GraphRAG vs RAG question.
Below: how a knowledge graph changes retrieval, which questions it wins, what it takes to run, and a 20-question test for any knowledge base tool.
What is the difference between GraphRAG and RAG?
Standard RAG retrieves the text chunks whose embeddings sit closest to the question. GraphRAG first turns the corpus into a knowledge graph of entities and relationships, then retrieves by following those links, usually alongside ordinary vector search.
Microsoft Research's GraphRAG documentation calls vector-similarity retrieval "baseline RAG." It describes GraphRAG as a structured, hierarchical approach built on an extracted graph and cluster summaries.
| Vector RAG | GraphRAG | |
|---|---|---|
| Core unit | Text chunk plus its embedding | Entity, relationship and the source text behind them |
| Retrieval method | Nearest-neighbor similarity search | Walks from matched entities to their neighbors, reads cluster summaries, often adds vector search |
| Best questions | Single facts stated in one passage | Multi-hop, "how are these connected" and corpus-wide themes |
| Build effort | Chunk, embed, load a vector index | LLM extraction over every chunk, entity merging, schema design, clustering |
| Update effort | Re-embed the changed chunks | Re-extract changed content and repair the affected entities, edges and summaries |
| Explainability | Shows which chunks were used | Shows the entities and relationships behind an answer |
| Typical failure | Misses facts split across documents | Wrong or duplicate entities produce confident wrong links |
How does a knowledge graph change retrieval?
A knowledge graph changes retrieval from "find similar text" to "find the things named in the question, then walk to what they connect to." Implied relationships become explicit edges.
Microsoft's reference pipeline indexes in four steps:
- Split the corpus into TextUnits, which later serve as citable references.
- Have an LLM extract entities, relationships and key claims from each unit.
- Cluster the graph hierarchically with the Leiden algorithm.
- Summarize each community of related entities, from the bottom up.
At query time, Local Search fans out from a question's entities to their neighbors, Global Search reasons over community summaries, and Basic Search falls back to top-k vector retrieval.
Other knowledge graph RAG designs skip the summaries. AWS describes a pattern where an LLM loads entities into a graph database such as Amazon Neptune and each question becomes a graph query.
When does GraphRAG answer questions vector RAG misses?
GraphRAG wins when the answer is spread across documents and joined by a relationship, or when the question covers the whole corpus. In Microsoft's words, baseline RAG "struggles to connect the dots."
- Multi-hop. "Which customer contracts depend on the payment provider we're replacing?" needs three documents joined in sequence.
- Ownership and dependency. "Who owns the systems named in the last three outage reviews?" joins incident reports to an org chart. Similarity search finds the reports, not the owners.
- Corpus-wide. "What themes recur across 2,000 support escalations?" needs a view top-k retrieval can't assemble.
A systematic evaluation of RAG and GraphRAG ran both under one protocol. It found vector RAG stronger on single-hop, detail-oriented queries and GraphRAG stronger on multi-hop, reasoning-intensive ones, with no overall winner. On a vendor-run benchmark reported by AWS, Lettria's hybrid GraphRAG answered 80% of questions correctly against 50.83% for vector-only RAG.
When you shortlist AI knowledge base builders for chat and support, ask whether each retrieves only by similarity or can also follow relationships.
What does GraphRAG cost to build and maintain?
GraphRAG costs more than vector RAG at every stage: an LLM reads every chunk, someone owns the schema, and content changes ripple through the graph. The GraphRAG repository on GitHub warns that indexing "can be an expensive operation" and advises starting small.
- Extraction. Every text unit passes through an LLM, so index cost grows with corpus size. Microsoft also recommends tuning the extraction prompts.
- Schema and entity merging. Someone decides which entity types matter and merges duplicates, such as a vendor's legal name and its nickname in the ticket system.
- Storage. Microsoft's default parquet output suits a pilot. A graph database is one more system to run and secure beside the vector index (see the best vector databases for RAG).
- Re-indexing. New documents mean new extraction runs and stale summaries.
- Skills. AWS lists graph modeling, graph queries, prompt engineering and LLM workflow maintenance.
The arXiv study also reports higher retrieval latency and storage footprint.
How do you evaluate GraphRAG against vector RAG?
Run both pipelines on the same questions, documents, model and prompt, then compare results by question type. One blended accuracy score hides the difference.
- Tag real user queries single-hop, multi-hop or corpus-wide.
- Hold chunking, the answering model and the prompt constant, as the arXiv benchmark did.
- Score correctness, citation accuracy, latency and cost per answer.
- Record index build time and refresh effort.
- Test a hybrid that routes by question type. The arXiv authors found that selecting or combining the two methods improved question answering.
Treat LLM-as-a-judge scores with care: the same study found judges sensitive to the order candidate summaries appear in, so randomize it. Agentic retrieval loops, where an agent decides what to fetch next, are a separate design axis, covered in our agentic RAG vs traditional RAG comparison.
How should you test a knowledge base tool on connected questions?
Give every tool the same 20 questions from your own documents, with known answers and sources. Weight the pack toward connected questions, where tools differ.
| Question type | Count | Example | A pass means |
|---|---|---|---|
| Single fact (control) | 5 | "What is the notice period in the standard vendor contract?" | Right answer, right source cited |
| Two-document hop | 5 | "Which region hosts the service that processes refunds?" | Joins both documents and cites both |
| Ownership and dependency | 4 | "Who owns every system that depends on the billing database?" | Complete list, no invented owners |
| Corpus-wide | 3 | "What are the top recurring causes in this year's incident reviews?" | Themes traceable to several reports |
| Change over time | 3 | "Which policy replaced the old remote-work policy, and what changed?" | Uses the current version and names the old one |
Score each answer correct, partial or wrong, and check every cited source. For a chat agent, an honest "I don't know" beats a confident wrong answer.
Teams comparing knowledge base tools for chat agents, for example Confluence AI against the knowledge suite from Origins AI (originshq.com), should run this pack on both before comparing features. Atlassian's Rovo, which runs in Confluence, can already draw on a graph. Atlassian's developer documentation says its Teamwork Graph holds objects and relationships from Atlassian and connected tools, carries each object's permissions, and can be used by Rovo Search, Chat and agents.
So test how each tool answers your connected questions, with documents loaded where it reads them in production. For deployment and model differences, see our comparison of the Origins AI Velocity AI Suite and Confluence AI.
What mistakes should you avoid when adopting GraphRAG?
The costliest mistake is building a graph for content that doesn't need one. If one passage answers most questions, vector RAG matches it for far less effort.
- No schema owner. Without someone deciding entity types and merging duplicates, the graph drifts into three versions of every team and vendor.
- Stale graphs. Skipped re-extraction leaves edges pointing at retired systems and people who've changed roles.
- No hybrid fallback. Global search can lose query-specific detail, so keep vector search.
- Skipping evals. Without a per-type baseline, nobody knows whether the graph helped.
- Ignoring permissions. A cluster summary can blend documents with different access rights, so check access on the sources.
How Origins AI's Velocity AI Suite fits connected questions
Origins AI describes the Velocity AI Suite on its product page as a modular enterprise AI suite for chat, voice, retrieval and embedded AI, including knowledge assistants that answer questions from internal documents and databases. Its Knowledge Foundation layer covers data intake, document intelligence, knowledge structuring and retrieval indexing, and Origins AI reports support for 1,900+ data sources and 91+ document formats.
Three documented pieces matter for connected questions:
- Structured and unstructured sources together. The page lists SQL, Postgres and MongoDB next to documents, tickets and wikis, where ownership facts often live.
- Your choice of stack. It lists Pinecone, Chroma and Weaviate, plus bring-your-own models from OpenAI, Anthropic or open-source providers.
- One knowledge layer, several front ends. Chat, voice, embedded assistants, and APIs and webhooks draw on the same knowledge.
The page also lists private storage options for every deployment model and hands-on implementation support. Run the 20-question pack during scoping.
Talk to an engineer
Want to see how your knowledge base handles connected questions? Book a call and bring 20 real questions, written with the test pack above.
Written by Apoorva Kumar, Co-Founder & CEO, Origins AI.