Run example Amazon Bedrock API requests through the AWS SDK for Python (Boto3)

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

Prerequisites

Test that your permissions and access keys are set up properly for Amazon Bedrock, using the Amazon Bedrock role that you created. These examples assume that you have configured your environment with your access keys. Note the following:

If you don't specify your credentials in your environment, you can specify them when creating a client for Amazon Bedrock operations. To do so, include the aws_access_key_id, aws_secret_access_key, and (if you're using short-term credentials) aws_session_token arguments when you create the client.

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:

# Use the ListFoundationModels API to show the models that are available in your region.
import boto3
             
# Create an &BR; client in the &region-us-east-1; Region.
bedrock = boto3.client(
    service_name="bedrock"
)

bedrock.list_foundation_models()

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 text response with InvokeModel

The following example runs the InvokeModel operation using an Amazon Bedrock client. InvokeModel 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 generate a text response with the operation:

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

import boto3
import json

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"

# Define the prompt for the model.
prompt = "Describe the purpose of a 'hello world' program in one line."

# Format the request payload using the model's native structure.
native_request = {
    "inputText": prompt,
    "textGenerationConfig": {
        "maxTokenCount": 512,
        "temperature": 0.5,
        "topP": 0.9
    },
}

# Convert the native request to JSON.
request = json.dumps(native_request)

try:
    # Invoke the model with the request.
    response = brt.invoke_model(modelId=model_id, body=request)

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

# Decode the response body.
model_response = json.loads(response["body"].read())

# Extract and print the response text.
response_text = model_response["results"][0]["outputText"]
print(response_text)

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

Submit a text prompt to a model and generate a text response with Converse

The following example runs the Converse operation using an Amazon Bedrock client. We recommend using Converse operation over InvokeModel when supported, because it unifies the inference request across Amazon Bedrock models and simplifies the management of multi-turn conversations. Run the following SDK for Python script to create an Amazon Bedrock runtime client and generate a text response with 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.