Semantic Search

Semantic search works by converting both the query and the stored content into numerical vectors (embeddings) in the same mathematical space. The search then finds the stored vectors closest to the query vector, as measured by cosine similarity. A query for "cyberpunk street scene" will match an image tagged "neon rain-slicked alley, Blade Runner aesthetic" because the embedding model has learned these concepts are semantically related.

For AI asset libraries, semantic search is essential because creators describe their work in natural language that rarely matches the exact vocabulary in generation parameters. However, semantic search alone has limitations — it cannot enforce strict constraints like exact dates or specific tool names. This is why production systems typically combine semantic search with structured filters in a hybrid search architecture.