Cohere Embed model

Anda membuat permintaan inferensi ke sebuah Embed model dengan InvokeModelAnda memerlukan ID model untuk model yang ingin Anda gunakan. Untuk mendapatkan ID model, lihatModel pondasi yang didukung di Amazon Bedrock.

Permintaan dan Tanggapan

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

Contoh kode

Contoh ini menunjukkan cara memanggil Cohere Embed Englishmodel.

# Copyright Amazon.com, 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.
    """

    logger.info(
        "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
    )

    logger.info("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"
    embedding_types = ["int8", "float"]

    try:

        body = json.dumps({
            "texts": [
                text1,
                text2],
            "input_type": input_type,
            "embedding_types": embedding_types}
        )
        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()