Amazon BedRock

# sonet 
model_id = 'anthropic.claude-3-sonnet-20240229-v1:0'
# Haiku 
model_id = anthropic.claude-3-haiku-20240307-v1:0

body=json.dumps(
        {
            "anthropic_version": "bedrock-2023-05-31",
            "max_tokens": max_tokens,
            "system": system_prompt,
            "messages": messages
        }  
    ) 

import boto3
import json
import logging

from botocore.exceptions import ClientError

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

def generate_message(bedrock_runtime, model_id, system_prompt, messages, max_tokens):

    body=json.dumps(
        {
            "anthropic_version": "bedrock-2023-05-31",
            "max_tokens": max_tokens,
            "system": system_prompt,
            "messages": messages
        }  
    )  

    response = bedrock_runtime.invoke_model(body=body, modelId=model_id)
    response_body = json.loads(response.get('body').read())
   
    return response_body

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

    try:

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

        model_id = 'anthropic.claude-3-sonnet-20240229-v1:0'
        system_prompt = "Please respond only with emoji."
        max_tokens = 1000

        # Prompt with user turn only.
        user_message =  {"role": "user", "content": "Hello World"}
        messages = [user_message]

        response = generate_message (bedrock_runtime, model_id, system_prompt, messages, max_tokens)
        print("User turn only.")
        print(json.dumps(response, indent=4))

        # Prompt with both user turn and prefilled assistant response.
        #Anthropic Claude continues by using the prefilled assistant text.
        assistant_message =  {"role": "assistant", "content": "<emoji>"}
        messages = [user_message, assistant_message]
        response = generate_message(bedrock_runtime, model_id,system_prompt, messages, max_tokens)
        print("User turn and prefilled assistant response.")
        print(json.dumps(response, indent=4))

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

if __name__ == "__main__":
    main()

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
"""
Shows how to run a multimodal prompt with Anthropic Claude (on demand) and InvokeModel.
"""

import json
import logging
import base64
import boto3

from botocore.exceptions import ClientError


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


def run_multi_modal_prompt(bedrock_runtime, model_id, messages, max_tokens):
    """
    Invokes a model with a multimodal prompt.
    Args:
        bedrock_runtime: The Amazon Bedrock boto3 client.
        model_id (str): The model ID to use.
        messages (JSON) : The messages to send to the model.
        max_tokens (int) : The maximum  number of tokens to generate.
    Returns:
        None.
    """



    body = json.dumps(
        {
            "anthropic_version": "bedrock-2023-05-31",
            "max_tokens": max_tokens,
             "messages": messages
        }
    )

    response = bedrock_runtime.invoke_model(
        body=body, modelId=model_id)
    response_body = json.loads(response.get('body').read())

    return response_body


def main():
    """
    Entrypoint for Anthropic Claude multimodal prompt example.
    """

    try:

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

        model_id = 'anthropic.claude-3-sonnet-20240229-v1:0'
        max_tokens = 1000
        input_image = "/path/to/image"
        input_text = "What's in this image?"

 
        # Read reference image from file and encode as base64 strings.
        with open(input_image, "rb") as image_file:
            content_image = base64.b64encode(image_file.read()).decode('utf8')

        message = {"role": "user",
             "content": [
                {"type": "image", "source": {"type": "base64",
                    "media_type": "image/jpeg", "data": content_image}},
                {"type": "text", "text": input_text}
                ]}

    
        messages = [message]

        response = run_multi_modal_prompt(
            bedrock_runtime, model_id, messages, max_tokens)
        print(json.dumps(response, indent=4))

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


if __name__ == "__main__":
    main()