Bedrock

Install

To use BedrockConverseModel, you need to either install pydantic-ai, or install pydantic-ai-slim with the bedrock optional group:

pipuv

pip install "pydantic-ai-slim[bedrock]"

uv add "pydantic-ai-slim[bedrock]"

Configuration

To use AWS Bedrock, you'll need an AWS account with Bedrock enabled and appropriate credentials. You can use either AWS credentials directly or a pre-configured boto3 client.

BedrockModelName contains a list of available Bedrock models, including models from Anthropic, Amazon, Cohere, Meta, and Mistral.

Environment variables

You can set your AWS credentials as environment variables (among other options):

export AWS_ACCESS_KEY_ID='your-access-key'
export AWS_SECRET_ACCESS_KEY='your-secret-key'
export AWS_DEFAULT_REGION='us-east-1'  # or your preferred region

You can then use BedrockConverseModel by name:

from pydantic_ai import Agent

agent = Agent('bedrock:anthropic.claude-3-sonnet-20240229-v1:0')
...

Or initialize the model directly with just the model name:

from pydantic_ai import Agent
from pydantic_ai.models.bedrock import BedrockConverseModel

model = BedrockConverseModel('anthropic.claude-3-sonnet-20240229-v1:0')
agent = Agent(model)
...

Customizing Bedrock Runtime API

You can customize the Bedrock Runtime API calls by adding additional parameters, such as guardrail configurations and performance settings. For a complete list of configurable parameters, refer to the documentation for BedrockModelSettings.

customize_bedrock_model_settings.py

from pydantic_ai import Agent
from pydantic_ai.models.bedrock import BedrockConverseModel, BedrockModelSettings

# Define Bedrock model settings with guardrail and performance configurations
bedrock_model_settings = BedrockModelSettings(
    bedrock_guardrail_config={
        'guardrailIdentifier': 'v1',
        'guardrailVersion': 'v1',
        'trace': 'enabled'
    },
    bedrock_performance_configuration={
        'latency': 'optimized'
    }
)


model = BedrockConverseModel(model_name='us.amazon.nova-pro-v1:0')

agent = Agent(model=model, model_settings=bedrock_model_settings)

provider argument

You can provide a custom BedrockProvider via the provider argument. This is useful when you want to specify credentials directly or use a custom boto3 client:

from pydantic_ai import Agent
from pydantic_ai.models.bedrock import BedrockConverseModel
from pydantic_ai.providers.bedrock import BedrockProvider

# Using AWS credentials directly
model = BedrockConverseModel(
    'anthropic.claude-3-sonnet-20240229-v1:0',
    provider=BedrockProvider(
        region_name='us-east-1',
        aws_access_key_id='your-access-key',
        aws_secret_access_key='your-secret-key',
    ),
)
agent = Agent(model)
...

You can also pass a pre-configured boto3 client:

import boto3

from pydantic_ai import Agent
from pydantic_ai.models.bedrock import BedrockConverseModel
from pydantic_ai.providers.bedrock import BedrockProvider

# Using a pre-configured boto3 client
bedrock_client = boto3.client('bedrock-runtime', region_name='us-east-1')
model = BedrockConverseModel(
    'anthropic.claude-3-sonnet-20240229-v1:0',
    provider=BedrockProvider(bedrock_client=bedrock_client),
)
agent = Agent(model)
...