.svg){kind=icon}
Learn
A knowledge graph stores entities and the explicit, typed relationships between them so you can traverse connections, while a vector database stores numeric embeddings of content and retrieves items by similarity, so a knowledge graph answers relationship questions a vector database can only approximate.
Last updated: October 18, 2026
Why it matters to your mission. An analyst asks which network a facilitator belongs to and how its funding moves, and a similarity-only search returns the passages that read most like the question, not the connections the question is actually about. A knowledge graph stores the entities and the typed relationships between them, so the same question becomes a path the system can follow and the analyst can inspect. The outcome is relationship questions answered with an explicit, traceable chain back to source, rather than a ranked list of documents that look relevant.
A knowledge graph stores entities and the explicit, typed relationships between them so you can traverse connections, while a vector database stores numeric embeddings of content and retrieves items by similarity, so a knowledge graph answers relationship questions a vector database can only approximate. The two are often set against each other, but in most mission systems they are complementary layers, not competitors.
A vector database answers "what is most similar to this?" A knowledge graph answers "how is this connected to that, and through what?" Those are different questions, and intelligence work leans hard on the second. Similarity is how you find candidate material fast; relationships are how you turn that material into an understanding a decision can rest on.
Key takeaways
A vector database stores content as embeddings, numeric vectors produced by a model so that items with similar meaning land near each other in a high-dimensional space. A query is embedded the same way, and the database returns the stored vectors nearest to it. This is what powers semantic search and most retrieval-augmented generation: instead of matching keywords, the system matches meaning, so a report about a "vessel" can surface for a query about a "ship."
That similarity is the strength and the limit. A vector database is excellent at finding material that resembles a query, including across phrasing, synonyms, and languages. It does not, on its own, represent how two pieces of content are related. Two documents can sit far apart in embedding space and still describe the same person; two can sit close together and have nothing to do with each other. The database returns what looks similar, and leaves the question of how things connect to whatever reads the results.
A knowledge graph stores information as entities (people, units, vessels, organizations, places) connected by typed relationships, such as "commands," "financed by," or "co-located with." The structure itself carries meaning: the graph holds how the facts relate, not only the facts. That is the property flat retrieval loses. The 2025 survey Retrieval-Augmented Generation with Graphs (GraphRAG) makes the point directly: graphs, by their nature of nodes connected by edges, encode relational information that embedding-space retrieval does not preserve as well.
Because relationships are explicit, a knowledge graph can be traversed. From one entity you can follow its edges to the entities it connects to, and from those to the next, assembling a chain that no single document states outright. Each node and edge can carry a link back to the reporting it was built from, so the path an answer took is inspectable rather than implied. A knowledge graph does not replace similarity search; it adds the layer of structure that similarity search has no way to represent.
*Bottom line: a vector database retrieves content by numeric similarity and is fast at finding material that resembles a query; a knowledge graph stores explicit relationships between entities and is built to answer multi-step connection questions.*
The two differ in what they store, what they are good at, and the kind of question each one answers well. They are different layers: similarity for finding, structure for reasoning.
| Dimension | Vector database | Knowledge graph |
|---|---|---|
| What is stored | Numeric embeddings of content | Entities and the typed relationships between them |
| How retrieval works | Nearest-neighbor similarity to a query vector | Traversal of explicit edges from entity to entity |
| Best-fit question | Find material similar in meaning to this query | How is this entity connected to that one, and through what |
| Relationships | Implicit; not modeled, inferred from proximity | Explicit; modeled as typed, directed edges |
| Multi-hop reasoning | Weak; each result is retrieved in isolation | Native; the answer follows a chain across entities |
| Auditability of the path | Returns similar items; the connection is not shown | The traversal path through entities and edges is inspectable |
| Build and maintenance cost | Lower; embed and index the content | Higher; resolve entities and build and maintain the edges |
| Fit for multi-source intelligence | Strong for fast retrieval, blind to the network | Strong for reasoning over relationships across sources |
A knowledge graph is only as trustworthy as the entity resolution and fusion beneath it. A missing edge hides a connection; a wrong one invents a connection that is not there. That is why which records describe the same real-world entity has to be decided well before the graph is traversed, and why every edge needs a line back to the source that justifies it.
Yes, and most capable systems do. The two answer different halves of the same workflow. A vector database finds candidate material fast across unstructured, multi-source reporting, including across phrasing and language. A knowledge graph then turns the entities inside that material into an explicit network you can traverse and reason over. One narrows the haystack; the other shows how the pieces connect.
This pairing is what graph-based retrieval-augmented generation relies on. Plain retrieval-augmented generation, introduced by Lewis et al. in Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (NeurIPS, 2020), grounds a language model in retrieved text, commonly drawn from a vector database. Graph-based retrieval adds the structure: the model answers over an explicit subgraph of entities and relationships, so a multi-hop question like "who finances the network this person belongs to" can be answered by following edges rather than hoping the whole chain appears in one retrieved passage. Vector retrieval supplies the reach; the graph supplies the reasoning.
Usually both, used for what each does well. Use vector retrieval when the task is to find: surface the reporting that resembles a query across a large, unstructured corpus, fast, without depending on exact wording. Reach for the knowledge graph when the question is about connections and the answer is a path, not a passage: how two entities are linked, who sits between them, how a network is structured and where its center of gravity sits.
Intelligence questions skew heavily toward the second kind. A ranked list of similar documents does not tell a commander how an adversary network is wired or which node holds it together. Those are relationship questions, and they are exactly where similarity-only retrieval returns confident-looking results that no analyst can trace. The practical pattern is to run both over the same governed data: similarity to reach the material, the graph to reason over the relationships, with every entity resolved first and every edge traceable back to its source.
Grounding is the reason retrieval exists at all: answer from a source you can check, not from a model's untraced memory. For government use, that bar is higher, because a fluent answer that cannot be inspected cannot be defended. The U.S. Government Accountability Office, examining generative AI across federal agencies in Artificial Intelligence: Generative AI Use and Management at Federal Agencies, GAO-25-107653 (2025), found agencies expanding generative-AI use while still working to manage risks that include misinformation and national-security exposure, and noted that existing federal policy can itself complicate safe adoption. The need it describes is governable, inspectable AI, not just capable AI.
This is where the distinction matters in practice. A vector database shows you the items it judged similar; it does not show how they connect, so the reasoning across them stays implicit. A knowledge graph makes the connections explicit, which means the path an answer followed, entity to edge to entity, can be inspected and tied back to the reporting behind each step. The demand is already in the field: the U.S. Army's Combined Arms Center has fielded an AI-powered knowledge platform to make its doctrine and institutional knowledge answerable rather than leaving soldiers to comb disconnected systems (see U.S. Army, CAC advances AI-powered knowledge platform, 2026). Turning a corpus into answers an operator can trust and trace is the need both layers serve, and the relationship questions are where explicit structure earns its place.
Torch.AI builds reasoning infrastructure the customer can own and govern, offered as a government-owned (GOTS) deployment when a mission requires it; whether a specific deployment is government-owned or commercial (COTS) depends on the system the customer installs and purchases. Two products map to this topic. NEXUS makes data meaningful, building the semantic representation, vectorization, and Knowledge Domains that turn fragmented, multi-source data into a form a model can retrieve over. HALO makes relationships understandable, fusing resolved entities into a graph and reasoning across it, which is the explicit structure a relationship question traverses. You can see how this is packaged as a capability on the Torch.AI software page.
The result is fast similarity retrieval and explicit relationship reasoning over the same governed data, with every entity resolved before it is linked and every relationship tied back to its source reporting and its original classification preserved. This is the systems of record versus systems of reason distinction at the center of Torch.AI's approach: the reasoning layer sits on top of the customer's existing systems of record, complementing them rather than replacing them, so the authoritative sources stay intact and interoperable instead of locked inside a proprietary model. For the broader picture, see the pillar on knowledge graphs and GraphRAG for intelligence analysis and how reusable Knowledge Domains turn a problem space into a durable analytical asset.
Evaluating a capability? The seven requirements above are the backbone of an evaluation you can score against. Bring them to a scoping call and we will walk each one against your environment: request a technical walkthrough.
A vector database stores content as numeric embeddings so that items with similar meaning sit near each other, and retrieves by returning the stored vectors nearest to a query. It powers semantic search and most retrieval-augmented generation by matching meaning rather than keywords.
A knowledge graph stores entities and the explicit, typed relationships between them, so the structure itself encodes how facts connect and can be traversed from one entity to the next, with each node and edge traceable back to its source.
A vector database retrieves content by numeric similarity and is fast at finding material that resembles a query; a knowledge graph stores explicit relationships between entities and is built to answer multi-step connection questions a vector database can only approximate.
Yes, and most capable systems do. Vector retrieval finds candidate material fast across unstructured sources, and the knowledge graph turns the entities inside it into an explicit network you can traverse. This pairing is what graph-based retrieval-augmented generation relies on.
Usually both. Use vector retrieval to find material across a large unstructured corpus, and the knowledge graph to answer relationship questions whose answer is a path across entities rather than a single passage. Intelligence questions skew heavily toward the relationship kind.
No. A vector database retrieves by similarity and does not model how items connect; a knowledge graph makes relationships explicit and traversable. They answer different questions, and in mission systems they are complementary layers rather than substitutes.
Describe the problem.
Define the constraints. We will structure the session.
No demo.
No slide deck.
Real work.
Describe the problem.
Define the constraints. We will structure the session.
No demo.
No slide deck.
Real work.
