Vector Search
Vector search finds matches by comparing numerical representations (embeddings) of a query and catalog items in a shared vector space, rather than comparing text directly.
Part of AI Search
Items and queries that are semantically similar end up positioned close together in that vector space, even if they don't share any literal words — which is what allows vector search to match a descriptive query to a product with none of the same keywords.