Embed

POST

This endpoint returns text and image embeddings. An embedding is a list of floating point numbers that captures semantic information about the content that it represents.

Embeddings can be used to create classifiers as well as empower semantic search. To learn more about embeddings, see the embedding page.

If you want to learn more how to use the embedding model, have a look at the Semantic Search Guide.

Headers

X-Client-NamestringOptional

The name of the project that is making the request.

Request

This endpoint expects an object.
textslist of stringsOptional

An array of strings for the model to embed. Maximum number of texts per call is 96. We recommend reducing the length of each text to be under 512 tokens for optimal quality.

imageslist of stringsOptional

An array of image data URIs for the model to embed. Maximum number of images per call is 1.

The image must be a valid data URI. The image must be in either image/jpeg or image/png format and has a maximum size of 5MB.

modelstringOptional

Defaults to embed-english-v2.0

The identifier of the model. Smaller “light” models are faster, while larger models will perform better. Custom models can also be supplied with their full ID.

Available models and corresponding embedding dimensions:

  • embed-english-v3.0 1024

  • embed-multilingual-v3.0 1024

  • embed-english-light-v3.0 384

  • embed-multilingual-light-v3.0 384

  • embed-english-v2.0 4096

  • embed-english-light-v2.0 1024

  • embed-multilingual-v2.0 768

input_typeenumOptional
Allowed values: search_documentsearch_queryclassificationclusteringimage

Specifies the type of input passed to the model. Required for embedding models v3 and higher.

  • "search_document": Used for embeddings stored in a vector database for search use-cases.
  • "search_query": Used for embeddings of search queries run against a vector DB to find relevant documents.
  • "classification": Used for embeddings passed through a text classifier.
  • "clustering": Used for the embeddings run through a clustering algorithm.
  • "image": Used for embeddings with image input.
embedding_typeslist of enumsOptional
Allowed values: floatint8uint8binaryubinary

Specifies the types of embeddings you want to get back. Not required and default is None, which returns the Embed Floats response type. Can be one or more of the following types.

  • "float": Use this when you want to get back the default float embeddings. Valid for all models.
  • "int8": Use this when you want to get back signed int8 embeddings. Valid for only v3 models.
  • "uint8": Use this when you want to get back unsigned int8 embeddings. Valid for only v3 models.
  • "binary": Use this when you want to get back signed binary embeddings. Valid for only v3 models.
  • "ubinary": Use this when you want to get back unsigned binary embeddings. Valid for only v3 models.
truncateenumOptionalDefaults to END
Allowed values: NONESTARTEND

One of NONE|START|END to specify how the API will handle inputs longer than the maximum token length.

Passing START will discard the start of the input. END will discard the end of the input. In both cases, input is discarded until the remaining input is exactly the maximum input token length for the model.

If NONE is selected, when the input exceeds the maximum input token length an error will be returned.

Response

This endpoint returns an object.
Embeddings Floats
OR
Embeddings by Type
Create an Embed Job
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