Create an Embed Job

This API launches an async Embed job for a Dataset of type embed-input. The result of a completed embed job is new Dataset of type embed-output, which contains the original text entries and the corresponding embeddings.

Request

This endpoint expects an object.

modelstringRequiredformat: "string"

ID of the embedding model.

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

ID of the embedding model. 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

dataset_idstringRequired

ID of a Dataset. The Dataset must be of type embed-input and must have a validation status Validated

input_typeenumRequired

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 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.

Allowed values:

namestringOptional

The name of the embed job.

embedding_typeslist of enumsOptional

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 v3 and newer model versions.
"uint8": Use this when you want to get back unsigned int8 embeddings. Valid for v3 and newer model versions.
"binary": Use this when you want to get back signed binary embeddings. Valid for v3 and newer model versions.
"ubinary": Use this when you want to get back unsigned binary embeddings. Valid for v3 and newer model versions.

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 v3 and newer model versions. * `"uint8"`: Use this when you want to get back unsigned int8 embeddings. Valid for v3 and newer model versions. * `"binary"`: Use this when you want to get back signed binary embeddings. Valid for v3 and newer model versions. * `"ubinary"`: Use this when you want to get back unsigned binary embeddings. Valid for v3 and newer model versions.

truncateenumOptionalDefaults to END

One of 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.

Allowed values:

Response

job_idstring

metaobject or null

Errors

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 v3 and newer model versions.
"uint8": Use this when you want to get back unsigned int8 embeddings. Valid for v3 and newer model versions.
"binary": Use this when you want to get back signed binary embeddings. Valid for v3 and newer model versions.
"ubinary": Use this when you want to get back unsigned binary embeddings. Valid for v3 and newer model versions.

1	import cohere
2
3	co = cohere.Client()
4
5	# start an embed job
6	job = co.embed_jobs.create(
7	dataset_id="my-dataset-id", input_type="search_document", model="embed-v4.0"
8	)
9
10	# poll the server until the job is complete
11	response = co.wait(job)
12
13	print(response)

1	{
2	"job_id": "foo",
3	"meta": {
4	"api_version": {
5	"version": "foo",
6	"is_deprecated": true,
7	"is_experimental": true
8	},
9	"billed_units": {
10	"images": 42,
11	"input_tokens": 42,
12	"output_tokens": 42,
13	"search_units": 42,
14	"classifications": 42
15	},
16	"tokens": {
17	"input_tokens": 42,
18	"output_tokens": 42
19	},
20	"cached_tokens": 42,
21	"warnings": [
22	"foo"
23	]
24	}
25	}

Headers

Request

Response

Errors