使用 Converse API 進行推論

text 欄位會對應至指定提示的字串。系統會將 text 欄位與其他在相同 ContentBlock 中指定的欄位一起解譯。

以下顯示 Message 物件，其 content 陣列僅包含文字 ContentBlock：

{
    "role": "user",
    "content": [
        {
            "text": "string"
        }
    ]
}

image 欄位會對應至 ImageBlock。傳遞 bytes 欄位中影像的原始位元組 (以 base64 編碼)。如果您使用 AWS SDK，則不需要對 base64 中的位元組進行編碼。

如果您排除 text 欄位，則模型會描述影像。

以下顯示範例 Message 物件，其 content 陣列僅包含影像 ContentBlock：

{
    "role": "user",
    "content": [
        {
            "image": {
                "format": "png",
                "source": {
                    "bytes": "image in bytes"
                }
            }
        }
    ]
}

您也可以指定 Amazon S3 URI (而不是直接在請求內文中傳遞位元組)。以下顯示具有內容陣列的範例 Message 物件，其中包含透過 Amazon S3 URI 傳遞的來源。

{
    "role": "user",
    "content": [
        {
            "image": {
                "format": "png",
                "source": {
                    "s3Location": {
                        "uri": "s3://amzn-s3-demo-bucket/myImage",
                        "bucketOwner": "111122223333"
                    }
                }
            }
        }
    ]
}

document 欄位對應至 DocumentBlock。如果您包含 DocumentBlock，請檢查請求是否符合下列限制：

使用文件時，您可以啟用 citations 標籤，這會在 API 呼叫的回應中提供文件特定的引文。如需詳細資訊，請參閱 DocumentBlock API。

以下顯示範例 Message 物件，其 content 陣列僅包含文件 ContentBlock 和必要的隨附文字 ContentBlock。

{
    "role": "user",
    "content": [
        {
            "text": "string"
        },
        {
            "document": {
                "format": "pdf",
                "name": "MyDocument",
                "source": {
                    "bytes": "document in bytes"
                }
            }
        }
    ]
}

您也可以指定 Amazon S3 URI (而不是直接在請求內文中傳遞位元組)。以下顯示具有內容陣列的範例 Message 物件，其中包含透過 Amazon S3 URI 傳遞的來源。

{
    "role": "user",
    "content": [
        {
            "text": "string"
        },
        {
            "document": {
                "format": "pdf",
                "name": "MyDocument",
                "source": {
                    "s3Location": {
                      "uri": "s3://amzn-s3-demo-bucket/myDocument",
                      "bucketOwner": "111122223333"
                    }
                }
            }
        }
    ]
}

video 欄位會對應至 VideoBlock 物件。傳遞 bytes 欄位中的原始位元組 (以 base64 編碼)。如果您使用 AWS SDK，則不需要對 base64 中的位元組進行編碼。

如果您未包含 text 欄位，模型會描述影片。

以下顯示範例 Message 物件，其 content 陣列僅包含影片 ContentBlock。

{
    "role": "user",
    "content": [
        {
            "video": {
                "format": "mp4",
                "source": {
                    "bytes": "video in bytes"
                }
            }
        }
    ]
}

您也可以指定 Amazon S3 URI (而不是直接在請求內文中傳遞位元組)。以下顯示具有內容陣列的範例 Message 物件，其中包含透過 Amazon S3 URI 傳遞的來源。

{
    "role": "user",
    "content": [
        {
            "video": {
                "format": "mp4",
                "source": {
                    "s3Location": {
                        "uri": "s3://amzn-s3-demo-bucket/myVideo",
                        "bucketOwner": "111122223333"
                    }
                }
            }
        }
    ]
}

您可以使用 cachePoint 欄位來使用提示快取，將快取檢查點新增為訊息中的區塊，以及隨附的提示。提示快取是一項功能，可讓您開始快取對話的內容，以節省成本和減少延遲。如需詳細資訊，請參閱提示快取可加快模型推論速度。

以下顯示範例 Message 物件，其 content 陣列包含文件 ContentBlock 和必要的隨附文字 ContentBlock，以及同時將文件和文字內容新增至快取的 cachePoint。

{
    "role": "user",
    "content": [
        {
            "text": "string"
        },
        {
            "document": {
                "format": "pdf",
                "name": "string",
                "source": {
                    "bytes": "document in bytes"
                }
            }
        },
        {
            "cachePoint": {
                "type": "default"
            }
        }
    ]
}

guardContent 欄位會對應至 GuardrailConverseContentBlock 物件。您可以使用此欄位，將要由 guardrailConfig 欄位中定義的防護機制評估的輸入設為目標。如果您未指定此欄位，防護機制會評估請求內文中的所有訊息。您可以在 GuardBlock 中傳遞下列類型的內容：

如需防護機制的使用詳細資訊，請參閱使用 Amazon Bedrock 防護機制偵測和篩選有害內容。

reasoningContent 欄位會對應至 ReasoningContentBlock。此區塊包含有關模型在隨附 ContentBlock 中產生回應所執行之推理的內容。

以下顯示 Message 物件，其 content 陣列僅包含 ReasoningContentBlock 和隨附的文字 ContentBlock。

{
    "role": "user",
    "content": [
        {
            "text": "string"
        },
        {
            "reasoningContent": {
                "reasoningText": {
                    "text": "string",
                    "signature": "string"
                }
                "redactedContent": "base64-encoded binary data object"
            }
        }
    ]
}

ReasoningContentBlock 包含用於在 reasoningText 欄位中產生隨附內容的推理，以及模型提供者在 redactedContent 欄位中基於信任和安全性原因加密之推理中的任何內容。

在 reasoningText 欄位中，text 欄位會描述推理。signature 欄位是對話中所有訊息的雜湊，可防止模型使用的推理遭到竄改。您必須在後續 Converse 請求中包含簽章和所有先前的訊息。如果變更任何訊息，回應會擲回錯誤。

包含模型要使用之工具的相關資訊。如需詳細資訊，請參閱使用工具完成 Amazon Bedrock 模型回應。

包含使用工具之模型結果相關資訊。如需詳細資訊，請參閱使用工具完成 Amazon Bedrock 模型回應。

此範例示範如何使用 Anthropic Claude 3 Sonnet 模型呼叫 Converse 操作。此範例示範如何傳送輸入文字、推論參數，以及模型特有的其他參數。程式碼會要求模型建立歌曲清單，以展開對話。然後，其會繼續對話，詢問歌曲是否來自英國的藝術家。

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to use the <noloc>Converse</noloc> API with Anthropic Claude 3 Sonnet (on demand).
"""

import logging
import boto3

from botocore.exceptions import ClientError


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


def generate_conversation(bedrock_client,
                          model_id,
                          system_prompts,
                          messages):
    """
    Sends messages to a model.
    Args:
        bedrock_client: The Boto3 Bedrock runtime client.
        model_id (str): The model ID to use.
        system_prompts (JSON) : The system prompts for the model to use.
        messages (JSON) : The messages to send to the model.

    Returns:
        response (JSON): The conversation that the model generated.

    """

    logger.info("Generating message with model %s", model_id)

    # Inference parameters to use.
    temperature = 0.5
    top_k = 200

    # Base inference parameters to use.
    inference_config = {"temperature": temperature}
    # Additional inference parameters to use.
    additional_model_fields = {"top_k": top_k}

    # Send the message.
    response = bedrock_client.converse(
        modelId=model_id,
        messages=messages,
        system=system_prompts,
        inferenceConfig=inference_config,
        additionalModelRequestFields=additional_model_fields
    )

    # Log token usage.
    token_usage = response['usage']
    logger.info("Input tokens: %s", token_usage['inputTokens'])
    logger.info("Output tokens: %s", token_usage['outputTokens'])
    logger.info("Total tokens: %s", token_usage['totalTokens'])
    logger.info("Stop reason: %s", response['stopReason'])

    return response

def main():
    """
    Entrypoint for Anthropic Claude 3 Sonnet example.
    """

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

    model_id = "anthropic.claude-3-sonnet-20240229-v1:0"

    # Setup the system prompts and messages to send to the model.
    system_prompts = [{"text": "You are an app that creates playlists for a radio station that plays rock and pop music. Only return song names and the artist."}]
    message_1 = {
        "role": "user",
        "content": [{"text": "Create a list of 3 pop songs."}]
    }
    message_2 = {
        "role": "user",
        "content": [{"text": "Make sure the songs are by artists from the United Kingdom."}]
    }
    messages = []

    try:

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

        # Start the conversation with the 1st message.
        messages.append(message_1)
        response = generate_conversation(
            bedrock_client, model_id, system_prompts, messages)

        # Add the response message to the conversation.
        output_message = response['output']['message']
        messages.append(output_message)

        # Continue the conversation with the 2nd message.
        messages.append(message_2)
        response = generate_conversation(
            bedrock_client, model_id, system_prompts, messages)

        output_message = response['output']['message']
        messages.append(output_message)

        # Show the complete conversation.
        for message in messages:
            print(f"Role: {message['role']}")
            for content in message['content']:
                print(f"Text: {content['text']}")
            print()

    except ClientError as err:
        message = err.response['Error']['Message']
        logger.error("A client error occurred: %s", message)
        print(f"A client error occured: {message}")

    else:
        print(
            f"Finished generating text with model {model_id}.")


if __name__ == "__main__":
    main()

此範例示範如何在訊息中傳送影像，以及請求模型描述影像。此範例使用 Converse 操作和 Anthropic Claude 3 Sonnet 模型。

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to send an image with the <noloc>Converse</noloc> API with an accompanying text prompt to Anthropic Claude 3 Sonnet (on demand).
"""

import logging
import boto3


from botocore.exceptions import ClientError


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


def generate_conversation(bedrock_client,
                          model_id,
                          input_text,
                          input_image):
    """
    Sends a message to a model.
    Args:
        bedrock_client: The Boto3 Bedrock runtime client.
        model_id (str): The model ID to use.
        input text : The text prompt accompanying the image.
        input_image : The path to the input image.

    Returns:
        response (JSON): The conversation that the model generated.

    """

    logger.info("Generating message with model %s", model_id)

    # Get image extension and read in image as bytes
    image_ext = input_image.split(".")[-1]
    with open(input_image, "rb") as f:
        image = f.read()

    message = {
        "role": "user",
        "content": [
            {
                "text": input_text
            },
            {
                "image": {
                    "format": image_ext,
                    "source": {
                        "bytes": image
                    }
                }
            }
        ]
    }

    messages = [message]

    # Send the message.
    response = bedrock_client.converse(
        modelId=model_id,
        messages=messages
    )

    return response


def main():
    """
    Entrypoint for Anthropic Claude 3 Sonnet example.
    """

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

    model_id = "anthropic.claude-3-sonnet-20240229-v1:0"
    input_text = "What's in this image?"
    input_image = "path/to/image"

    try:

        bedrock_client = boto3.client(service_name="bedrock-runtime")

        response = generate_conversation(
            bedrock_client, model_id, input_text, input_image)

        output_message = response['output']['message']

        print(f"Role: {output_message['role']}")

        for content in output_message['content']:
            print(f"Text: {content['text']}")

        token_usage = response['usage']
        print(f"Input tokens:  {token_usage['inputTokens']}")
        print(f"Output tokens:  {token_usage['outputTokens']}")
        print(f"Total tokens:  {token_usage['totalTokens']}")
        print(f"Stop reason: {response['stopReason']}")

    except ClientError as err:
        message = err.response['Error']['Message']
        logger.error("A client error occurred: %s", message)
        print(f"A client error occured: {message}")

    else:
        print(
            f"Finished generating text with model {model_id}.")


if __name__ == "__main__":
    main()

此範例示範如何在訊息中傳送文件，以及請求模型描述文件的內容。此範例使用 Converse 操作和 Anthropic Claude 3 Sonnet 模型。

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to send an document as part of a message to Anthropic Claude 3 Sonnet (on demand).
"""

import logging
import boto3


from botocore.exceptions import ClientError


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


def generate_message(bedrock_client,
                     model_id,
                     input_text,
                     input_document_path):
    """
    Sends a message to a model.
    Args:
        bedrock_client: The Boto3 Bedrock runtime client.
        model_id (str): The model ID to use.
        input text : The input message.
        input_document_path : The path to the input document.

    Returns:
        response (JSON): The conversation that the model generated.

    """

    logger.info("Generating message with model %s", model_id)

    # Get format from path and read the path
    input_document_format = input_document_path.split(".")[-1]
    with open(input_document_path, 'rb') as input_document_file:
        input_document = input_document_file.read()

    # Message to send.
    message = {
        "role": "user",
        "content": [
            {
                "text": input_text
            },
            {
                "document": {
                    "name": "MyDocument",
                    "format": input_document_format,
                    "source": {
                        "bytes": input_document
                    }
                }
            }
        ]
    }

    messages = [message]

    # Send the message.
    response = bedrock_client.converse(
        modelId=model_id,
        messages=messages
    )

    return response


def main():
    """
    Entrypoint for Anthropic Claude 3 Sonnet example.
    """

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

    model_id = "anthropic.claude-3-sonnet-20240229-v1:0"
    input_text = "What's in this document?"
    input_document_path = "path/to/document"

    try:

        bedrock_client = boto3.client(service_name="bedrock-runtime")


        response = generate_message(
            bedrock_client, model_id, input_text, input_document_path)

        output_message = response['output']['message']

        print(f"Role: {output_message['role']}")

        for content in output_message['content']:
            print(f"Text: {content['text']}")

        token_usage = response['usage']
        print(f"Input tokens:  {token_usage['inputTokens']}")
        print(f"Output tokens:  {token_usage['outputTokens']}")
        print(f"Total tokens:  {token_usage['totalTokens']}")
        print(f"Stop reason: {response['stopReason']}")

    except ClientError as err:
        message = err.response['Error']['Message']
        logger.error("A client error occurred: %s", message)
        print(f"A client error occured: {message}")

    else:
        print(
            f"Finished generating text with model {model_id}.")


if __name__ == "__main__":
    main()

此範例示範如何使用 Anthropic Claude 3 Sonnet 模型呼叫 ConverseStream 操作。此範例示範如何傳送輸入文字、推論參數，以及模型特有的其他參數。

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to use the <noloc>Converse</noloc> API to stream a response from Anthropic Claude 3 Sonnet (on demand).
"""

import logging
import boto3


from botocore.exceptions import ClientError


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


def stream_conversation(bedrock_client,
                    model_id,
                    messages,
                    system_prompts,
                    inference_config,
                    additional_model_fields):
    """
    Sends messages to a model and streams the response.
    Args:
        bedrock_client: The Boto3 Bedrock runtime client.
        model_id (str): The model ID to use.
        messages (JSON) : The messages to send.
        system_prompts (JSON) : The system prompts to send.
        inference_config (JSON) : The inference configuration to use.
        additional_model_fields (JSON) : Additional model fields to use.

    Returns:
        Nothing.

    """

    logger.info("Streaming messages with model %s", model_id)

    response = bedrock_client.converse_stream(
        modelId=model_id,
        messages=messages,
        system=system_prompts,
        inferenceConfig=inference_config,
        additionalModelRequestFields=additional_model_fields
    )

    stream = response.get('stream')
    if stream:
        for event in stream:

            if 'messageStart' in event:
                print(f"\nRole: {event['messageStart']['role']}")

            if 'contentBlockDelta' in event:
                print(event['contentBlockDelta']['delta']['text'], end="")

            if 'messageStop' in event:
                print(f"\nStop reason: {event['messageStop']['stopReason']}")

            if 'metadata' in event:
                metadata = event['metadata']
                if 'usage' in metadata:
                    print("\nToken usage")
                    print(f"Input tokens: {metadata['usage']['inputTokens']}")
                    print(
                        f":Output tokens: {metadata['usage']['outputTokens']}")
                    print(f":Total tokens: {metadata['usage']['totalTokens']}")
                if 'metrics' in event['metadata']:
                    print(
                        f"Latency: {metadata['metrics']['latencyMs']} milliseconds")


def main():
    """
    Entrypoint for streaming message API response example.
    """

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

    model_id = "anthropic.claude-3-sonnet-20240229-v1:0"
    system_prompt = """You are an app that creates playlists for a radio station
      that plays rock and pop music. Only return song names and the artist."""

    # Message to send to the model.
    input_text = "Create a list of 3 pop songs."

    message = {
        "role": "user",
        "content": [{"text": input_text}]
    }
    messages = [message]
    
    # System prompts.
    system_prompts = [{"text" : system_prompt}]

    # inference parameters to use.
    temperature = 0.5
    top_k = 200
    # Base inference parameters.
    inference_config = {
        "temperature": temperature
    }
    # Additional model inference parameters.
    additional_model_fields = {"top_k": top_k}

    try:
        bedrock_client = boto3.client(service_name='bedrock-runtime')

        stream_conversation(bedrock_client, model_id, messages,
                        system_prompts, inference_config, additional_model_fields)

    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 streaming messages with model {model_id}.")


if __name__ == "__main__":
    main()

此範例示範如何在訊息中傳送影片，以及請求模型描述影片。此範例使用 Converse 操作和 Amazon Nova Pro 模型。

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to send a video with the <noloc>Converse</noloc> API to Amazon Nova Pro (on demand).
"""

import logging
import boto3


from botocore.exceptions import ClientError


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


def generate_conversation(bedrock_client,
                          model_id,
                          input_text,
                          input_video):
    """
    Sends a message to a model.
    Args:
        bedrock_client: The Boto3 Bedrock runtime client.
        model_id (str): The model ID to use.
        input text : The input message.
        input_video : The input video.

    Returns:
        response (JSON): The conversation that the model generated.

    """

    logger.info("Generating message with model %s", model_id)

    # Message to send.

    with open(input_video, "rb") as f:
        video = f.read()

    message = {
        "role": "user",
        "content": [
            {
                "text": input_text
            },
            {
                    "video": {
                        "format": 'mp4',
                        "source": {
                            "bytes": video
                        }
                    }
            }
        ]
    }

    messages = [message]

    # Send the message.
    response = bedrock_client.converse(
        modelId=model_id,
        messages=messages
    )

    return response


def main():
    """
    Entrypoint for Amazon Nova Pro example.
    """

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

    model_id = "amazon.nova-pro-v1:0"
    input_text = "What's in this video?"
    input_video = "path/to/video"

    try:

        bedrock_client = boto3.client(service_name="bedrock-runtime")

        response = generate_conversation(
            bedrock_client, model_id, input_text, input_video)

        output_message = response['output']['message']

        print(f"Role: {output_message['role']}")

        for content in output_message['content']:
            print(f"Text: {content['text']}")

        token_usage = response['usage']
        print(f"Input tokens:  {token_usage['inputTokens']}")
        print(f"Output tokens:  {token_usage['outputTokens']}")
        print(f"Total tokens:  {token_usage['totalTokens']}")
        print(f"Stop reason: {response['stopReason']}")

    except ClientError as err:
        message = err.response['Error']['Message']
        logger.error("A client error occurred: %s", message)
        print(f"A client error occured: {message}")

    else:
        print(
            f"Finished generating text with model {model_id}.")


if __name__ == "__main__":
    main()