Semantic/vector search

Search using vector similarity for natural language queries. Converts the query text into an embedding and finds the most visually or semantically similar assets.

Best for: "a dreamy watercolor landscape", "product photo on white background", "dark moody portrait". For structured filtering use /assets/search with grammar syntax, or /assets/search-enhanced for automatic routing.