Embed text
Turns text into 1024-dimensional multilingual vectors (`@cf/baai/bge-m3`). Use it to fill a `float[]` field of your documents so `searchSemantic` and `searchByImage` can match them; the same model embeds queries and image descriptions.
Full-key operation
Turns text into 1024-dimensional multilingual vectors (@cf/baai/bge-m3). Use it to fill a float[] field of your documents so searchSemantic and searchByImage can match them; the same model embeds queries and image descriptions.
Authorization
api_key_header Every request carries an organization API key. A key has one of two scopes. A full key reaches every operation in this document and belongs on your server. A search key is the one to ship in a browser widget or a storefront page: it reaches only the operations marked x-nova-key-scope: search — searching, federated search, recommendations, event ingestion, and the visitor-facing AI operations (semantic and image search, voice transcription and voice search, assistant chat, and the visitor's own conversation). It cannot write documents or read or change configuration; any other operation answers 403. AI operations are paid in credits from the organization's wallet whichever key calls them (402 when the balance runs out), and a search key needs X-NOVA-SESSION-ID on them: the session is what a conversation belongs to, and what the per-session allowance (429) is counted against. A key of either scope may additionally carry restrictions set in the console: an allowlist of collections (403 elsewhere), a forced filter_by per collection that is ANDed into every search (document-level access control), an allowlist of fields (include_fields is limited to it and a request naming any other field in query_by, facet_by, sort_by, filter_by or highlight_fields answers 400), an IP/CIDR allowlist and an Origin/Referer allowlist (403), and a per-key rate limit (429 with Retry-After).
In: header
Request Body
application/json
TypeScript Definitions
Use the request body type in TypeScript.
Response Body
application/json
application/json
application/json
application/json
application/json
application/json
curl -X POST "https://example.com/embeddings" \ -H "Content-Type: application/json" \ -d '{ "input": [ "string" ] }'{ "model": "string", "num_dim": 0, "data": [ [ 0 ] ]}Stream the bytes of a large upload PUT
Sends the image bytes for an asset created with `createMediaUpload`. The body is the raw file (`Content-Type` image/png, image/jpeg, image/webp or image/gif; `Content-Length` required, up to 100 MB); it is streamed to storage without buffering. Returns 202 once stored and the embedding job is queued; 409 if the asset already has content.
Search a collection by meaning POST
Embeds the query text and returns the nearest documents by `vector_field`. Works in any language the embedding model understands; combine with `filter_by` for facets.