What is graph search?
A search method that retrieves information by matching and traversing nodes, relationships, properties, paths, and structural patterns in a graph.
Why is this important?
Graph search preserves explicit relationships that flat documents or vectors can obscure. It is useful for identity resolution, permissions, recommendations, fraud analysis, dependency tracing, customer context, and multi-hop reasoning.
How it works
A query identifies starting nodes and relationship patterns, applies property constraints, traverses eligible paths, and returns connected entities or aggregates. Systems can combine graph traversal with vector similarity.
Technical example
A company-brain query finds a customer, their company, open deals, recent conversations, assigned owner, unresolved issues, and related product usage through typed relationships.
Implementation notes
Define a stable ontology, control traversal depth, index entry properties, prevent path explosion, enforce permissions at retrieval time, preserve provenance, and use vector search only where semantic similarity adds value.
