Jina AI Search Foundation Models API
**Embeddings** turn text, images, audio, video and PDFs into dense vectors for semantic search, RAG, clustering and classification. Task-specific variants (`retrieval.query`, `retrieval.passage`, `text-matching`, `clustering`, `classification`) are selected per request rather than per model, and `dimensions` truncates the output where a model supports it. **Reranking** scores a query against a candidate set and returns it reordered, which is more accurate than embedding similarity and slower — the usual shape is embeddings to retrieve, reranking to refine. Each endpoint's `model` field lists the models it accepts.