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.
The name of the project that is making the request.
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
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.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.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.
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.
An object with different embedding types. The length of each embedding type array will be the same as the length of the original texts
array.
The text entries for which embeddings were returned.