Run example Amazon Bedrock API requests using an Amazon SageMaker AI notebook

This section guides you through trying out some common operations in Amazon Bedrock with an Amazon SageMaker AI notebook to test that your Amazon Bedrock role permissions are set up properly. Before you run the following examples, you should check that you have fulfilled the following prerequisites:

Prerequisites

After you open up your SageMaker AI notebook, you can try out the following examples:

List the foundation models that Amazon Bedrock has to offer

The following example runs the ListFoundationModels operation using an Amazon Bedrock client. ListFoundationModels lists the foundation models (FMs) that are available in Amazon Bedrock in your Region. Run the following SDK for Python script to create an Amazon Bedrock client and test the ListFoundationModels operation:

"""
Lists the available Amazon Bedrock models in an &AWS-Region;.
"""
import logging
import json
import boto3


from botocore.exceptions import ClientError


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


def list_foundation_models(bedrock_client):
    """
    Gets a list of available Amazon Bedrock foundation models.

    :return: The list of available bedrock foundation models.
    """

    try:
        response = bedrock_client.list_foundation_models()
        models = response["modelSummaries"]
        logger.info("Got %s foundation models.", len(models))
        return models

    except ClientError:
        logger.error("Couldn't list foundation models.")
        raise


def main():
    """Entry point for the example. Change aws_region to the &AWS-Region;
    that you want to use."""
   
    aws_region = "us-east-1"

    bedrock_client = boto3.client(service_name="bedrock", region_name=aws_region)
    
    fm_models = list_foundation_models(bedrock_client)
    for model in fm_models:
        print(f"Model: {model["modelName"]}")
        print(json.dumps(model, indent=2))
        print("---------------------------\n")
    
    logger.info("Done.")

if __name__ == "__main__":
    main()

If the script is successful, the response returns a list of foundation models that are available in Amazon Bedrock.

Submit a text prompt to a model and generate a response

The following example runs the Converse operation using an Amazon Bedrock client. Converse lets you submit a prompt to generate a model response. Run the following SDK for Python script to create an Amazon Bedrock runtime client and test the Converse operation:

# Use the Conversation API to send a text message to Amazon Titan Text G1 - Express.

import boto3
from botocore.exceptions import ClientError

# Create an Amazon Bedrock Runtime client.
brt = boto3.client("bedrock-runtime")

# Set the model ID, e.g., Amazon Titan Text G1 - Express.
model_id = "amazon.titan-text-express-v1"

# Start a conversation with the user message.
user_message = "Describe the purpose of a 'hello world' program in one line."
conversation = [
    {
        "role": "user",
        "content": [{"text": user_message}],
    }
]

try:
    # Send the message to the model, using a basic inference configuration.
    response = brt.converse(
        modelId=model_id,
        messages=conversation,
        inferenceConfig={"maxTokens": 512, "temperature": 0.5, "topP": 0.9},
    )

    # Extract and print the response text.
    response_text = response["output"]["message"]["content"][0]["text"]
    print(response_text)

except (ClientError, Exception) as e:
    print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
    exit(1)

If the command is successful, the response returns the text generated by the model in response to the prompt.