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v51

latestOpenAPI 3.1.0proprietaryraw.githubusercontent.com2026-07-21411711.1 MB

Embed API (v2)

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

Embeddings can be used to create text 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.

post/v2/embed

Headers

X-Client-Namestring

The name of the project that is making the request.

Request body

textsstring[]

An array of strings for the model to embed. Maximum number of texts per call is 96.

imagesstring[]

An array of image data URIs for the model to embed.

The image must be a valid data URI. The image must be in either image/jpeg, image/png, image/webp, or image/gif format.

Image embeddings are supported with Embed v3.0 and newer models.

For Embed v3.x models, the maximum number of images per call is 1, and each image has a maximum size of 5MB.

For Embed v4.0 and newer models, there is no limit on the number of images per call. The combined size of all images in the request must be at most 20MB.

modelstring required

ID of one of the available Embedding models.

input_type'search_document' | 'search_query' | 'classification' | 'clustering' | 'image' required

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.
max_tokensinteger

The maximum number of tokens to embed per input. If the input text is longer than this, it will be truncated according to the truncate parameter.

output_dimensioninteger

The number of dimensions of the output embedding. This is only available for embed-v4 and newer models. Possible values are 256, 512, 1024, and 1536. The default is 1536.

embedding_typesEmbeddingType[]

Specifies the types of embeddings you want to get back. Can be one or more of the following types.

  • "float": Use this when you want to get back the default float embeddings. Supported with all Embed models.
  • "int8": Use this when you want to get back signed int8 embeddings. Supported with Embed v3.0 and newer Embed models.
  • "uint8": Use this when you want to get back unsigned int8 embeddings. Supported with Embed v3.0 and newer Embed models.
  • "binary": Use this when you want to get back signed binary embeddings. Supported with Embed v3.0 and newer Embed models.
  • "ubinary": Use this when you want to get back unsigned binary embeddings. Supported with Embed v3.0 and newer Embed models.
  • "base64": Use this when you want to get back base64 embeddings. Supported with Embed v3.0 and newer Embed models.
truncate'NONE' | 'START' | 'END'

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.

priorityinteger

Controls how early the request is handled. Lower numbers indicate higher priority (default: 0, the highest). When the system is under load, higher-priority requests are processed first and are the least likely to be dropped.

Response

OK

response_type'embeddings_floats' | 'embeddings_by_type'
idstring required
textsstring[]

The text entries for which embeddings were returned.