Cohere Command モデル

InvokeModel または InvokeModelWithResponseStream (ストリーミング) を使用して、Cohere Commandモデルに推論呼び出しを行います。このとき、使用するモデルのモデル ID が必要になります。モデル ID を取得するには、「Amazon Bedrock でサポートされている基盤モデル」を参照してください。

リクエストとレスポンス

{
    "prompt": string,
    "temperature": float,
    "p": float,
    "k": float,
    "max_tokens": int,
    "stop_sequences": [string],
    "return_likelihoods": "GENERATION|ALL|NONE",
    "stream": boolean,
    "num_generations": int,
    "logit_bias": {token_id: bias},
    "truncate": "NONE|START|END"
}

コード例

この例は、Cohere Command モデルを呼び出す方法を示しています。

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to generate text using a Cohere model.
"""
import json
import logging
import boto3


from botocore.exceptions import ClientError

logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)


def generate_text(model_id, body):
    """
    Generate text using a Cohere 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 with Cohere model %s", model_id)

    accept = 'application/json'
    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 example.
    """

    logging.basicConfig(level=logging.INFO,
                        format="%(levelname)s: %(message)s")

    model_id = 'cohere.command-text-v14'
    prompt = """Summarize this dialogue: 
"Customer: Please connect me with a support agent.
AI: Hi there, how can I assist you today?
Customer: I forgot my password and lost access to the email affiliated to my account. Can you please help me?
AI: Yes of course. First I'll need to confirm your identity and then I can connect you with one of our support agents.
"""
    try:
        body = json.dumps({
            "prompt": prompt,
            "max_tokens": 200,
            "temperature": 0.6,
            "p": 1,
            "k": 0,
            "num_generations": 2,
            "return_likelihoods": "GENERATION"
        })
        response = generate_text(model_id=model_id,
                                 body=body)

        response_body = json.loads(response.get('body').read())
        generations = response_body.get('generations')

        for index, generation in enumerate(generations):

            print(f"Generation {index + 1}\n------------")
            print(f"Text:\n {generation['text']}\n")
            if 'likelihood' in generation:
                print(f"Likelihood:\n {generation['likelihood']}\n")
            
            print(f"Reason: {generation['finish_reason']}\n\n")

    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 with Cohere model {model_id}.")


if __name__ == "__main__":
    main()