Use the Converse API

The text field maps to a string specifying the prompt. The text field is interpreted alongside other fields that are specified in the same ContentBlock.

The following shows a Message object with a content array containing only a text ContentBlock:

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

The image field maps to an ImageBlock. Pass the raw bytes, encoded in base64, for an image in the bytes field. If you use an AWS SDK, you don't need to encode the bytes in base64.

If you exclude the text field, the model will describe the image.

The following shows a Message object with a content array contaning only an image ContentBlock:

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

The document field maps to an DocumentBlock. If you include a DocumentBlock, check that your request conforms to the following restrictions:

The following shows a Message object with a content array containing only a document ContentBlock and a required accompanying text ContentBlock.

{
    "role": "user",
    "content": [
        {
            "text": "string"
        },
        {
            "document": {
                "format": "pdf | csv | doc | docx | xls | xlsx | html | txt | md",
                "name": "string",
                "source": {
                    "bytes": "document in bytes"
                }
            }
        }
    ]
}

This example shows how to call the Converse operation with the Anthropic Claude 3 Sonnet model. The example shows how to send the input text, inference parameters, and additional parameters that are unique to the model. The code starts a conversation by asking the model to create a list of songs. It then continues the conversation by asking that the songs are by artists from the United Kingdom.

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to use the Converse 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()

This example shows how to send an image as part of a message and requests that the model describe the image. The example uses Converse operation and the Anthropic Claude 3 Sonnet model.

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to send an image with the Converse API 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 input message.
        input_image : The input image.

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

    """

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

    # Message to send.

    with open(input_image, "rb") as f:
        image = f.read()

    message = {
        "role": "user",
        "content": [
            {
                "text": input_text
            },
            {
                    "image": {
                        "format": 'png',
                        "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()

This example shows how to send a document as part of a message and requests that the model describe the contents of the document. The example uses Converse operation and the Anthropic Claude 3 Sonnet model.

# 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):
    """
    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 : The input document.

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

    """

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

    # Message to send.

    message = {
        "role": "user",
        "content": [
            {
                "text": input_text
            },
            {
                "document": {
                    "name": "MyDocument",
                    "format": "txt",
                    "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/to/document.pdf'

    try:

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


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

        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()

This example shows how to call the ConverseStream operation with the Anthropic Claude 3 Sonnet model. The example shows how to send the input text, inference parameters, and additional parameters that are unique to the model.

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to use the Converse 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()