Create Embeddings
POST/v1/embeddings
Authorization
Authorizationstringrequired
Bearer token for API authentication. Format: `Bearer YOUR_API_KEY`
Request Body
modelstringrequired
The model to use for creating embeddings.
Constraints
•Minimum length: 1•Maximum length: 500
inputstring | arrayrequired
Input
Constraints
•Minimum length: 1•Maximum length: 64000•Minimum items: 1•Maximum items: 256
dimensionsinteger
The number of dimensions to use for the embeddings.
Constraints
•Exclusive minimum: 0
promptstring
The prompt to use for the embedding creation.
Constraints
•Minimum length: 1•Maximum length: 32000
normalizedbooleandefault:
trueWhether to normalize the embeddings.
encoding_formatEncodingFormat | EncodingFormat[]default:
floatThe encoding format(s) of the embeddings. Can be a single format or a list of formats.
Response Body
modelstringrequired
The model used
dataEmbedding[] | MultiEncodingEmbedding[]required
The created embeddings.
objectenum
ObjectType
Possible values
listparsing_jobextraction_jobembeddingembedding_dictrank_resultfilevector_storevector_store.fileapi_keydata_sourcedata_source.connectorvector_store.histogramnormalizedbooleanrequired
Whether the embeddings are normalized.
encoding_formatEncodingFormat | EncodingFormat[]required
The encoding formats of the embeddings.
dimensionsintegerrequired
The number of dimensions used for the embeddings.
Question Answering
Question answering
Rerank Documents
Rerank different kind of documents for a given query.
Last updated: August 19, 2025