Cohere Embed models - Amazon Bedrock

Cohere Embed models

You make inference requests to an Embed model with InvokeModel You need the model ID for the model that you want to use. To get the model ID, see Amazon Bedrock model IDs.


Amazon Bedrock doesn't support streaming responses from Cohere Embed models.

Request and Response


The Cohere Embed models have the following inference parameters.

{ "texts":[string], "input_type": "search_document|search_query|classification|clustering", "truncate": "NONE|START|END" }

The following are required parameters.

  • texts – An array of strings for the model to embed. For optimal performance, we recommend reducing the length of each text to less than 512 tokens. 1 token is about 4 characters.

    The following are text per call and character limits.

    Texts per call

    Minimum Maximum

    0 texts

    96 texts


    Minimum Maximum

    0 characters

    2048 characters

  • input_type – Prepends special tokens to differentiate each type from one another. You should not mix different types together, except when mixing types for for search and retrieval. In this case, embed your corpus with the search_document type and embedded queries with type search_query type.

    • search_document – In search use-cases, use search_document when you encode documents for embeddings that you store in a vector database.

    • search_query – Use search_query when querying your vector DB to find relevant documents.

    • classification – Use classification when using embeddings as an input to a text classifier.

    • clustering – Use clustering to cluster the embeddings.

The following are optional parameters:

  • truncate – Specifies how the API handles inputs longer than the maximum token length. Use one of the following:

    • NONE – (Default) Returns an error when the input exceeds the maximum input token length.

    • START – Discards the start of the input.

    • END – Discards the end of the input.

    If you specify START or END, the model discards the input until the remaining input is exactly the maximum input token length for the model.

For more information, see in the Cohere documentation.


The body response from a call to InvokeModel is the following:

{ "embeddings": [ [ <array of 1024 floats> ] ], "id": string, "response_type" : "embeddings_floats, "texts": [string] }

The body response has the following fields:

  • id – An identifier for the response.

  • response_type – The response type. This value is always embeddings_floats.

  • embeddings – An array of embeddings, where each embedding is an array of floats with 1024 elements. The length of the embeddings array will be the same as the length of the original texts array.

  • texts – An array containing the text entries for which embeddings were returned.

For more information, see

Code example

This examples shows how to call the Cohere Embed English model.

# Copyright, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: Apache-2.0 """ Shows how to generate text embeddings using the Cohere Embed English model. """ import json import logging import boto3 from botocore.exceptions import ClientError logger = logging.getLogger(__name__) logging.basicConfig(level=logging.INFO) def generate_text_embeddings(model_id, body): """ Generate text embedding by using the Cohere Embed model. Args: model_id (str): The model ID to use. body (str) : The reqest body to use. Returns: dict: The response from the model. """ "Generating text emdeddings with the Cohere Embed model %s", model_id) accept = '*/*' content_type = 'application/json' bedrock = boto3.client(service_name='bedrock-runtime') response = bedrock.invoke_model( body=body, modelId=model_id, accept=accept, contentType=content_type )"Successfully generated text with Cohere model %s", model_id) return response def main(): """ Entrypoint for Cohere Embed example. """ logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") model_id = 'cohere.embed-english-v3' text1 = "hello world" text2 = "this is a test" input_type = "search_document" try: body = json.dumps({ "texts": [ text1, text2], "input_type": input_type} ) response = generate_text_embeddings(model_id=model_id, body=body) response_body = json.loads(response.get('body').read()) print(f"ID: {response_body.get('id')}") print(f"Response type: {response_body.get('response_type')}") print("Embeddings") for i, embedding in enumerate(response_body.get('embeddings')): print(f"\tEmbedding {i}") print(*embedding) print("Texts") for i, text in enumerate(response_body.get('texts')): print(f"\tText {i}: {text}") except ClientError as err: message = err.response["Error"]["Message"] logger.error("A client error occurred: %s", message) print("A client error occured: " + format(message)) else: print( f"Finished generating text embeddings with Cohere model {model_id}.") if __name__ == "__main__": main()