GraphRAG

GraphRAG retrieves facts by following relationships between entities rather than by matching text similarity alone. A vector search answers what a passage sounds like; a graph answers how two things are connected, by walking from a named entity across one or more relationships to the facts that matter. For a question that spans more than one hop, who reports to whom, which vendor is linked to which counterparty, a graph can reach an answer a similarity search never will.

Compliance and investigations questions are frequently relationship questions: ownership chains, reporting lines, shared addresses between entities that should be unrelated. Text search alone tends to answer these poorly, because the connection that matters is not written down as a sentence anywhere.

Answering what system does this person's employer use requires following a works-at relationship to an organization, then a uses relationship from that organization to a system, not a single similarity match against any one document.

In Dynamiq, a Knowledge Base can build a graph alongside its vector store, with entity and relationship types defined in an editable ontology, retrieval bounded by a beam-search traversal so it does not explode across a highly connected entity, and access control enforced on the graph's own edges, so a caller only ever sees facts drawn from documents they are entitled to see.