Amazon Titan Model teks

Model Amazon Titan Text mendukung parameter inferensi berikut.

Untuk informasi lebih lanjut tentang Titan Pedoman rekayasa prompt teks, lihat Titan Pedoman Rekayasa Teks Prompt.

Untuk informasi lebih lanjut tentang Titan model, lihatIkhtisar Amazon Titan model.

Permintaan dan tanggapan

Badan permintaan diteruskan di body bidang InvokeModelWithResponseStreampermintaan InvokeModelatau permintaan.

Contoh kode

Contoh berikut menunjukkan cara menjalankan inferensi dengan Amazon Titan Teks model Premier dengan PythonSDK.

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to create a list of action items from a meeting transcript
with the Amazon Titan Text model (on demand).
"""
import json
import logging
import boto3

from botocore.exceptions import ClientError


class ImageError(Exception):
    "Custom exception for errors returned by Amazon Titan Text models"

    def __init__(self, message):
        self.message = message


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


def generate_text(model_id, body):
    """
    Generate text using Amazon Titan Text models on demand.
    Args:
        model_id (str): The model ID to use.
        body (str) : The request body to use.
    Returns:
        response (json): The response from the model.
    """

    logger.info(
        "Generating text with Amazon Titan Text model %s", model_id)

    bedrock = boto3.client(service_name='bedrock-runtime')

    accept = "application/json"
    content_type = "application/json"

    response = bedrock.invoke_model(
        body=body, modelId=model_id, accept=accept, contentType=content_type
    )
    response_body = json.loads(response.get("body").read())

    finish_reason = response_body.get("error")

    if finish_reason is not None:
        raise ImageError(f"Text generation error. Error is {finish_reason}")

    logger.info(
        "Successfully generated text with Amazon Titan Text model %s", model_id)

    return response_body


def main():
    """
    Entrypoint for Amazon Titan Text model example.
    """
    try:
        logging.basicConfig(level=logging.INFO,
                            format="%(levelname)s: %(message)s")

        # You can replace the model_id with any other Titan Text Models
        # Titan Text Model family model_id is as mentioned below:
        # amazon.titan-text-premier-v1:0, amazon.titan-text-express-v1, amazon.titan-text-lite-v1
        model_id = 'amazon.titan-text-premier-v1:0'

        prompt = """Meeting transcript: Miguel: Hi Brant, I want to discuss the workstream  
            for our new product launch Brant: Sure Miguel, is there anything in particular you want
            to discuss? Miguel: Yes, I want to talk about how users enter into the product.
            Brant: Ok, in that case let me add in Namita. Namita: Hey everyone 
            Brant: Hi Namita, Miguel wants to discuss how users enter into the product.
            Miguel: its too complicated and we should remove friction.  
            for example, why do I need to fill out additional forms?  
            I also find it difficult to find where to access the product
            when I first land on the landing page. Brant: I would also add that
            I think there are too many steps. Namita: Ok, I can work on the
            landing page to make the product more discoverable but brant
            can you work on the additonal forms? Brant: Yes but I would need 
            to work with James from another team as he needs to unblock the sign up workflow.
            Miguel can you document any other concerns so that I can discuss with James only once?
            Miguel: Sure.
            From the meeting transcript above, Create a list of action items for each person. """

        body = json.dumps({
            "inputText": prompt,
            "textGenerationConfig": {
                "maxTokenCount": 3072,
                "stopSequences": [],
                "temperature": 0.7,
                "topP": 0.9
            }
        })

        response_body = generate_text(model_id, body)
        print(f"Input token count: {response_body['inputTextTokenCount']}")

        for result in response_body['results']:
            print(f"Token count: {result['tokenCount']}")
            print(f"Output text: {result['outputText']}")
            print(f"Completion reason: {result['completionReason']}")

    except ClientError as err:
        message = err.response["Error"]["Message"]
        logger.error("A client error occurred: %s", message)
        print("A client error occured: " +
              format(message))
    except ImageError as err:
        logger.error(err.message)
        print(err.message)

    else:
        print(
            f"Finished generating text with the Amazon Titan Text Premier model {model_id}.")


if __name__ == "__main__":
    main()

Contoh berikut menunjukkan cara menjalankan inferensi dengan Amazon Titan Text G1 - Express model dengan PythonSDK.

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to create a list of action items from a meeting transcript
with the Amazon &titan-text-express; model (on demand).
"""
import json
import logging
import boto3

from botocore.exceptions import ClientError


class ImageError(Exception):
    "Custom exception for errors returned by Amazon &titan-text-express; model"

    def __init__(self, message):
        self.message = message


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


def generate_text(model_id, body):
    """
    Generate text using Amazon &titan-text-express; model on demand.
    Args:
        model_id (str): The model ID to use.
        body (str) : The request body to use.
    Returns:
        response (json): The response from the model.
    """

    logger.info(
        "Generating text with Amazon &titan-text-express; model %s", model_id)

    bedrock = boto3.client(service_name='bedrock-runtime')

    accept = "application/json"
    content_type = "application/json"

    response = bedrock.invoke_model(
        body=body, modelId=model_id, accept=accept, contentType=content_type
    )
    response_body = json.loads(response.get("body").read())

    finish_reason = response_body.get("error")

    if finish_reason is not None:
        raise ImageError(f"Text generation error. Error is {finish_reason}")

    logger.info(
        "Successfully generated text with Amazon &titan-text-express; model %s", model_id)

    return response_body


def main():
    """
    Entrypoint for Amazon &titan-text-express; example.
    """
    try:
        logging.basicConfig(level=logging.INFO,
                            format="%(levelname)s: %(message)s")

        model_id = 'amazon.titan-text-express-v1'

        prompt = """Meeting transcript: Miguel: Hi Brant, I want to discuss the workstream  
            for our new product launch Brant: Sure Miguel, is there anything in particular you want
            to discuss? Miguel: Yes, I want to talk about how users enter into the product.
            Brant: Ok, in that case let me add in Namita. Namita: Hey everyone 
            Brant: Hi Namita, Miguel wants to discuss how users enter into the product.
            Miguel: its too complicated and we should remove friction.  
            for example, why do I need to fill out additional forms?  
            I also find it difficult to find where to access the product
            when I first land on the landing page. Brant: I would also add that
            I think there are too many steps. Namita: Ok, I can work on the
            landing page to make the product more discoverable but brant
            can you work on the additonal forms? Brant: Yes but I would need 
            to work with James from another team as he needs to unblock the sign up workflow.
            Miguel can you document any other concerns so that I can discuss with James only once?
            Miguel: Sure.
            From the meeting transcript above, Create a list of action items for each person. """

        body = json.dumps({
            "inputText": prompt,
            "textGenerationConfig": {
                "maxTokenCount": 4096,
                "stopSequences": [],
                "temperature": 0,
                "topP": 1
            }
        })

        response_body = generate_text(model_id, body)
        print(f"Input token count: {response_body['inputTextTokenCount']}")

        for result in response_body['results']:
            print(f"Token count: {result['tokenCount']}")
            print(f"Output text: {result['outputText']}")
            print(f"Completion reason: {result['completionReason']}")

    except ClientError as err:
        message = err.response["Error"]["Message"]
        logger.error("A client error occurred: %s", message)
        print("A client error occured: " +
              format(message))
    except ImageError as err:
        logger.error(err.message)
        print(err.message)

    else:
        print(
            f"Finished generating text with the Amazon &titan-text-express; model {model_id}.")


if __name__ == "__main__":
    main()