You are probably looking at the title of this blog post and saying to yourself, "I didn't know that you can build a RAG solution using other technologies besides a vector database." You aren't alone in this thought. There has been some great marketing out there for pushing vector databases as the only solution for RAG Agents; however, that couldn't be further from the truth. I am here to talk about one of many possible solutions when building a RAG Agent, and that option is using a Graph database. What… you say? Yes… and not only is it an option, but it's a unique option that solves 2 of the most significant problems with RAG Agents today: answer correctness and AI Governance.
Why now? Governance. Large enterprises and regulators want provenance, lineage, and repeatability. Graph architectures log provenance natively, align cleanly to data-governance controls, and make fairness audits feasible because the structure is visible… not embedded inside a 1,536-dimensional vector you can’t explain to your compliance team. In practice, this means auditable trails, policy-friendly retention and tiering, and operational safeguards.
Here’s the pivot: graphs tighten the retrieval stage so you can show your work… AND they do it without demanding exotic hardware. Graph-based retrieval and summarization run well on standard CPUs; you don’t need an expensive GPU to start getting faithful, auditable answers. With that foundation in place, let’s unpack how graph-based RAG differs from the vector-only pattern you’ve seen everywhere.
Differences Between Vector and Graph RAG
Vector-based RAG shines at semantic recall: you split documents into chunks, turn each chunk into a high-dimensional vector (an embedding), then find the k-NN (k-nearest neighbors) to drive your query by similarity. Think of each chunk as a dot in a huge coordinate system; dots that sit near your query "mean" something similar. That’s powerful for synonyms and paraphrases (for example, car vs. automobile), but it flattens everything else. The vector doesn’t know that page 3 depends on page 2, that "Jane Smith" is the same person as "J. Smith," or that a policy updated in 2025 supersedes the 2023 version. Chunk boundaries are arbitrary, cross-document links disappear, and time becomes a rumor.
A vector index can tell you how similar a chunk was (a numeric score), but not why it was relevant beyond "the math said so." When a question requires multi-hop reasoning (ie "Who led Project Atlas when the Q4 budget was approved?"), vector retrieval may surface a manager bio and a budget memo, but it carries no connective tissue proving that the person in one chunk is the same entity referenced in the other, or that the budget approval date falls within their tenure. Explainability takes the hit.
The LLM can try to bridge that gap, which increases the odds of confident nonsense, and audits become an archeology dig: different embedding models, preprocessing choices, or even chunking strategies can change which neighbors appear, hurting reproducibility. When Enterprises deploy these systems and need to explain how an AI solution arrived at their answer, they can't just say "trust us" or "it's in the embeddings" without offering any kind of proof. These implementations don’t fail because their embeddings are "bad"... They fail because the system can’t show its work.
Vector-based RAG is fantastic at finding passages that "feel" similar to your query, but it treats knowledge as a flat bag of chunks. That makes multi-hop reasoning opaque and auditing painful. Graph-based RAG flips the script: it stores relationships explicitly, so every answer can be traced through a concrete path: question → nodes/association → source document. That transparency is the difference between "trust me" and "prove it" in regulated environments.
Let’s be clear: this isn’t Graph versus Vector. It’s Graph for facts and relationships, plus Vector for semantic recall and domain "language." To understand this, you are going to have to continue reading.
Increased Answer Correctness
Hallucinations thrive when the model has a broad, fuzzy context and too much freedom to improvise. Graph-based RAG reduces the search space to a grounded subgraph centered on entities and relationships directly connected to the query. Answers get assembled from facts reachable along edges (what associations are called in the Graph world), not from "nearby-sounding" chunks. That structural constraint is why graph-guided retrieval tends to produce more faithful outputs and why studies report measurable drops in hallucinations when graphs drive the context that the LLM sees.
How it works in practice: extract and link entities, expand to a small subgraph, score the paths, and pass only that evidence (aka supporting data) to the model. Because the evidence set is tight and explicit, you could even run low-entropy decoding (e.g., temperature=0) without starving the model, which further improves consistency. Microsoft’s GraphRAG documentation/details describe this "subgraph then summarize" flow; decoding research shows why lower temperature reduces randomness in outputs. Together, the subgraph and conservative decoding narrow the model’s error surface. If you are interested in learning more about Graph-based RAG, I would invite you to take a look at the video above.
For a non-technical view, think of the graph as a bouncer at the door. It lets in only a few documents and facts connected to your question, then the LLM speaks over that short guest list. You’re not asking the model to "find the truth in the whole library"; you’re asking it to synthesize from a vetted list of facts. That’s why evaluations of GraphRAG show higher faithfulness and relevance than vector-only baselines… the answer is built from a smaller, cleaner slice of reality.
Auditable Inference & Enterprise Compliance
Graphs make provenance first-class. Instead of burying "where did this come from?" inside a dense vector, a graph records sources, activities, and agents as nodes and edges you can query: answer → claim → evidence → document. If you align those edges to a standard like W3C PROV (wasDerivedFrom, used, wasAssociatedWith), your explanation path becomes portable across tools and reports. That’s the difference between a hand-wavy "trust the embeddings" and a clickable trail your risk team can read.
Compliance cares about repeatability and logs. Graph databases bring the boring but critical plumbing: ACID transactions and write-ahead transaction logs for every change (great for reconstructing state at time-of-answer), plus DB audit logs to record who queried what and when. Pair that with policy controls in your platform (log export to an observability stack, retention windows, immutability), and you get a verifiable audit trail from prompt to payload. This maps cleanly to governance frameworks that call for traceability and documentation, and to regulatory obligations that require logging and technical documentation.
This is a governance-aligned logging blueprint. With a proper implementation, a graph-based RAG system can emit every item below:
- Query metadata: timestamp, user/app, policy tags.
- Retrieval proof: subgraph ID, node/edge IDs, k-hop radius, and the exact query that produced them.
- Evidence snapshot: URIs and content hashes of cited documents.
- Model card at inference time: model name/version, weights hash, safety/guard config.
- Decoding parameters: temperature/top-p/top-k/seed (so the same prompt can be re-run).
- Output record: final answer, citations, and an exported PROV graph.
- System state: index versions (graph + vector), config commit SHAs, and storage tier used for each artifact.
These artifacts let you replay the inference, prove lineage, and answer "what changed?" with evidence… not vibes.
For a non-technical view, vectors and LLMs lean on sampling to sound human (temperature, top-k/top-p add randomness), so identical questions can produce different phrasings. With a graph, you constrain what the model can say (the vetted subgraph), then you can reduce or remove randomness (e.g., temperature → 0) and still get fluent answers. The bouncer controls the guest list; the MC doesn’t have to improvise.
More Than the Sum Of The Parts
You can deploy graph and vector RAG independently or run them together when you need both explainability and broad semantic recall.
- Graph-first (no vector): Use a knowledge graph to extract entities/relations, carve a subgraph, and answer from that evidence. This excels in regulated Q&A, policies, and "show-your-work" workflows where provenance, time-scoping, and identity resolution matter.
- Vector-first (no graph): Use embeddings to recover paraphrases and fuzzy matches at speed. This is great for exploratory search, synonyms, and "find me similar" use cases where recall breadth beats precision.
- Hybrid: Let the graph gate the corpus (subgraph of interest), then invoke vector recall when the subgraph is sparse or confidence dips. Orchestrate a graph → vector → fuse pipeline: entity-link → subgraph → semantic recall → dedupe/score → constrained generation with PROV exports.
The strongest public signal that this hybrid approach works at scale comes from BlackRock and NVIDIA’s HybridRAG research paper. They pair graph traversals for grounded evidence with vector recall as a fallback, and report 96% factual faithfulness on financial-filings Q&A. The key idea is simple: let the graph carve a clean subgraph, then let vector search recover semantically similar snippets when the graph alone is too sparse. The result is high faithfulness and strong relevance in a domain where getting the answer wrong actually costs money. Below is a talk by Mitesh Pate, PhD at NVIDIA that was just published in July 2025 that explains this in further detail.
If you’re wondering about implementation details and performance, the short answer is: architecture and storage choices close the gap. As a leader in storage, NetApp can help with these solutions and architectural decisions. A lean open-source baseline can run well on commodity gear; an enterprise version build adds acceleration (tiered storage, caching, snapshots, policy enforcement) to push latency down and governance up. Both of these guides are in this GitHub repo: https://github.com/davidvonthenen/graph-rag-guide/
In the end, choose the retrieval method that matches your risk profile (graph for defensibility, vector for understanding) and layer in hybrid based on the requirements and use cases of the solution. The payoff is a system that’s explainable when it must be and expansive when it needs to be.
Final Thoughts
Enterprises win when answers are both searchable and defensible. Graphs provide the spine for truth and traceability; vectors extend your reach. Choose the mode per use case, and don’t be afraid to evolve from vector-only to hybrid (or even graph-only to hybrid) as traffic patterns and audits inform the design.
Graph-based RAG isn’t a silver bullet; it’s the backbone you use when truth and traceability matter. Use the graph to frame the answer (entities, relationships, and provenance), and you reduce hallucinations and make every step auditable. Keep vectors in the loop to "speak the language" of your domain, so the model retrieves semantically relevant phrasing without losing the factual spine.
Bottom line: for enterprise solutions where correctness and auditing are important, use Graph RAG as your default retrieval frame and adopt Hybrid RAG when you have the GPU budget and engineering resources to support it.
