Embed API (v1)

POST/v1/embedEmbed API (v1)

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.

Parameters

X-Client-Namestringheader

The name of the project that is making the request.

Request body

application/json

object

embedding_typesarray of string

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. 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.

Show child attributes

imagesarray of string

An array of image data URIs for the model to embed. The image must be a valid [data URI](https://developer.mozilla.org/en-US/docs/Web/URI/Schemes/data). 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`.

minItems 1

Show child attributes

minItems 1

input_typestring

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.

one of "search_document", "search_query", "classification", "clustering", "image"

modelstring

ID of one of the available [Embedding models](https://docs.cohere.com/docs/cohere-embed).

textsarray of string

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

maxItems 96 · minItems 1

Show child attributes

maxItems 96 · minItems 1

truncatestring

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.

one of "NONE", "START", "END" · default "END"

Example request

{
  "embedding_types": [
    "base64"
  ],
  "images": [
    "string"
  ],
  "input_type": "classification",
  "model": "string",
  "texts": [
    "string"
  ],
  "truncate": "END"
}

Responses

Headers

X-API-Warning

The name of the project that is making the request.

value

oneOf · 2 options

Option 1objectEmbedFloatsResponse

embeddingsarray of array of numberrequired

An array of embeddings, where each embedding is an array of floats. The length of the `embeddings` array will be the same as the length of the original `texts` array.

Show child attributes

Show array items

idstringrequired

imagesarray of object

The image entries for which embeddings were returned.

Show child attributes

Show array items

bit_depthnumber · int64required

Bit depth of the image

formatstringrequired

Format of the image

heightnumber · int64required

Height of the image in pixels

widthnumber · int64required

Width of the image in pixels

metaobject

Show child attributes

api_versionobject

Show child attributes

is_deprecatedboolean

is_experimentalboolean

versionstringrequired

billed_unitsobject

Show child attributes

classificationsnumber

The number of billed classifications units.

image_tokensnumber

The number of billed image tokens.

imagesnumber

The number of billed images.

input_tokensnumber

The number of billed input tokens.

output_tokensnumber

The number of billed output tokens.

pagesnumber

The number of billed pages parsed.

search_unitsnumber

The number of billed search units.

cached_tokensnumber

The number of prompt tokens that hit the inference cache.

tokensobject

Show child attributes

input_tokensnumber

The number of tokens used as input to the model.

output_tokensnumber

The number of tokens produced by the model.

warningsarray of string

Show child attributes

response_typestring

one of "embeddings_floats", "embeddings_by_type"

textsarray of stringrequired

The text entries for which embeddings were returned.

Show child attributes

Option 2objectEmbedByTypeResponse

embeddingsobjectrequired

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.

Show child attributes

base64array of string

An array of base64 embeddings. Each string is the result of appending the float embedding bytes together and base64 encoding that.

Show child attributes

binaryarray of array of integer

An array of packed signed binary embeddings. The length of each binary embedding is 1/8 the length of the float embeddings of the provided model. Each value is between -128 and 127.

Show child attributes

Show array items

floatarray of array of number

An array of float embeddings.

Show child attributes

Show array items

int8array of array of integer

An array of signed int8 embeddings. Each value is between -128 and 127.

Show child attributes

Show array items

ubinaryarray of array of integer

An array of packed unsigned binary embeddings. The length of each binary embedding is 1/8 the length of the float embeddings of the provided model. Each value is between 0 and 255.

Show child attributes

Show array items

uint8array of array of integer

An array of unsigned int8 embeddings. Each value is between 0 and 255.

Show child attributes

Show array items

idstringrequired

imagesarray of object

The image entries for which embeddings were returned.

Show child attributes

Items: Image ↑

metaobjectApiMeta ↑

response_typestring

one of "embeddings_floats", "embeddings_by_type"

textsarray of string

The text entries for which embeddings were returned.

Show child attributes

Example response

{
  "embeddings": {
    "base64": [
      "string"
    ],
    "binary": [
      [
        0
      ]
    ],
    "float": [
      [
        0
      ]
    ],
    "int8": [
      [
        0
      ]
    ],
    "ubinary": [
      [
        0
      ]
    ],
    "uint8": [
      [
        0
      ]
    ]
  },
  "id": "string",
  "images": [
    {
      "bit_depth": 0,
      "format": "string",
      "height": 0,
      "width": 0
    }
  ],
  "meta": {
    "api_version": {
      "is_deprecated": true,
      "is_experimental": true,
      "version": "string"
    },
    "billed_units": {
      "classifications": 0,
      "image_tokens": 0,
      "images": 0,
      "input_tokens": 0,
      "output_tokens": 0,
      "pages": 0,
      "search_units": 0
    },
    "cached_tokens": 0,
    "tokens": {
      "input_tokens": 0,
      "output_tokens": 0
    },
    "warnings": [
      "string"
    ]
  },
  "response_type": "embeddings_by_type",
  "texts": [
    "string"
  ]
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

object

idstring

messagestring

Example response

{
  "id": "string",
  "message": "string"
}

Examples

X-Client-Nameheader parameter

curl -X POST 'https://api.cohere.com/v1/embed' \
  -H 'Content-Type: application/json' \
  -d '{"embedding_types":["base64"],"images":["string"],"input_type":"classification","model":"string","texts":["string"],"truncate":"END"}'

const response = await fetch('https://api.cohere.com/v1/embed', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({"embedding_types":["base64"],"images":["string"],"input_type":"classification","model":"string","texts":["string"],"truncate":"END"}),
});
const data = await response.json();

const response = await fetch('https://api.cohere.com/v1/embed', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: "{\"embedding_types\":[\"base64\"],\"images\":[\"string\"],\"input_type\":\"classification\",\"model\":\"string\",\"texts\":[\"string\"],\"truncate\":\"END\"}" });
console.log(await response.json());

import requests

response = requests.request(
    "POST",
    "https://api.cohere.com/v1/embed",
    headers={"Content-Type": "application/json"},
    data='{"embedding_types":["base64"],"images":["string"],"input_type":"classification","model":"string","texts":["string"],"truncate":"END"}',
)
print(response.json())

package main

import (
	"context"
	"net/http"
	"strings"
)

func main() {
	ctx := context.Background()
	body := strings.NewReader(`{"embedding_types":["base64"],"images":["string"],"input_type":"classification","model":"string","texts":["string"],"truncate":"END"}`)
	req, err := http.NewRequestWithContext(ctx, "POST", "https://api.cohere.com/v1/embed", body)
	if err != nil {
		panic(err)
	}
	req.Header.Set("Content-Type", "application/json")
	resp, err := http.DefaultClient.Do(req)
	if err != nil {
		panic(err)
	}
	defer resp.Body.Close()
}

<?php
$request = curl_init("https://api.cohere.com/v1/embed");
curl_setopt($request, CURLOPT_CUSTOMREQUEST, "POST");
curl_setopt($request, CURLOPT_POSTFIELDS, "{\"embedding_types\":[\"base64\"],\"images\":[\"string\"],\"input_type\":\"classification\",\"model\":\"string\",\"texts\":[\"string\"],\"truncate\":\"END\"}");
curl_setopt($request, CURLOPT_HTTPHEADER, ['Content-Type: application/json']);
$response = curl_exec($request);
curl_close($request);

var request = HttpRequest.newBuilder(URI.create("https://api.cohere.com/v1/embed"))
    .method("POST", HttpRequest.BodyPublishers.noBody())
    .header("Content-Type", "application/json")
    .method("POST", HttpRequest.BodyPublishers.ofString("{\"embedding_types\":[\"base64\"],\"images\":[\"string\"],\"input_type\":\"classification\",\"model\":\"string\",\"texts\":[\"string\"],\"truncate\":\"END\"}"))
    .build();
var response = HttpClient.newHttpClient().send(request, HttpResponse.BodyHandlers.ofString());

require 'net/http'

uri = URI("https://api.cohere.com/v1/embed")
request = Net::HTTPGenericRequest.new("POST", true, true, uri)
request['Content-Type'] = 'application/json'
request.body = "{\"embedding_types\":[\"base64\"],\"images\":[\"string\"],\"input_type\":\"classification\",\"model\":\"string\",\"texts\":[\"string\"],\"truncate\":\"END\"}"
response = Net::HTTP.start(uri.hostname, uri.port, use_ssl: true) { |http| http.request(request) }

using var client = new HttpClient();
using var request = new HttpRequestMessage(HttpMethod.Post, "https://api.cohere.com/v1/embed");
request.Content = new StringContent("{\"embedding_types\":[\"base64\"],\"images\":[\"string\"],\"input_type\":\"classification\",\"model\":\"string\",\"texts\":[\"string\"],\"truncate\":\"END\"}", Encoding.UTF8, "application/json");
using var response = await client.SendAsync(request);

{
  "embeddings": {
    "base64": [
      "string"
    ],
    "binary": [
      [
        0
      ]
    ],
    "float": [
      [
        0
      ]
    ],
    "int8": [
      [
        0
      ]
    ],
    "ubinary": [
      [
        0
      ]
    ],
    "uint8": [
      [
        0
      ]
    ]
  },
  "id": "string",
  "images": [
    {
      "bit_depth": 0,
      "format": "string",
      "height": 0,
      "width": 0
    }
  ],
  "meta": {
    "api_version": {
      "is_deprecated": true,
      "is_experimental": true,
      "version": "string"
    },
    "billed_units": {
      "classifications": 0,
      "image_tokens": 0,
      "images": 0,
      "input_tokens": 0,
      "output_tokens": 0,
      "pages": 0,
      "search_units": 0
    },
    "cached_tokens": 0,
    "tokens": {
      "input_tokens": 0,
      "output_tokens": 0
    },
    "warnings": [
      "string"
    ]
  },
  "response_type": "embeddings_by_type",
  "texts": [
    "string"
  ]
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

{
  "id": "string",
  "message": "string"
}

Search documentation

/datasets

/connectors

/embed-jobs

/finetuning

/models

/audio/transcriptions

/batches

Operations

Embed API (v1)

Parameters

Request body

Responses

Headers

/datasets

/connectors

/embed-jobs

/finetuning

/models

/audio/transcriptions

/batches

Operations