Updated Bedrock notes (#655)

Bedrock/converse/converse_document.py

@@ -0,0 +1,96 @@
+# 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).
+Source: https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html
+"""
+
+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()

Bedrock/converse/converse_image.py

@@ -0,0 +1,90 @@
+# 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).
+Source: https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html
+"""
+
+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()

Bedrock/converse/converse_text.py

@@ -0,0 +1,119 @@
+# 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).
+Source: https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html
+"""
+
+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()