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Structured Outputs with AWS Bedrock¶

This guide demonstrates how to use AWS Bedrock with Instructor to generate structured outputs. You'll learn how to use AWS Bedrock's LLM models with Pydantic to create type-safe, validated responses.

Prerequisites¶

You'll need to have an AWS account with access to Bedrock and the appropriate permissions. You'll also need to set up your AWS credentials.

pip install "instructor[bedrock]"

AWS Bedrock¶

AWS Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single API.

Sync Example¶

import boto3
import instructor
from pydantic import BaseModel

# Initialize the Bedrock client
bedrock_client = boto3.client('bedrock-runtime')

# Enable instructor patches for Bedrock client
client = instructor.from_bedrock(bedrock_client)


class User(BaseModel):
    name: str
    age: int


# Create structured output
user = client.converse(
    modelId="anthropic.claude-3-sonnet-20240229-v1:0",
    messages=[
        {"role": "user", "content": "Extract: Jason is 25 years old"},
    ],
    response_model=User,
)

print(user)
# > User(name='Jason', age=25)

Supported Modes¶

AWS Bedrock supports the following modes with Instructor:

BEDROCK_TOOLS: Uses function calling for models that support it (like Claude models)
BEDROCK_JSON: Direct JSON response generation

import boto3
import instructor
from instructor import Mode
from pydantic import BaseModel

# Initialize the Bedrock client
bedrock_client = boto3.client('bedrock-runtime')

# Enable instructor patches for Bedrock client with specific mode
client = instructor.from_bedrock(
    bedrock_client, 
    mode=Mode.BEDROCK_TOOLS
)


class User(BaseModel):
    name: str
    age: int


# Create structured output
user = client.converse(
    modelId="anthropic.claude-3-sonnet-20240229-v1:0",
    messages=[
        {"role": "user", "content": "Extract: Jason is 25 years old"},
    ],
    response_model=User,
)

print(user)
# > User(name='Jason', age=25)

Nested Objects¶

import boto3
import instructor
from pydantic import BaseModel

# Initialize the Bedrock client
bedrock_client = boto3.client('bedrock-runtime')

# Enable instructor patches for Bedrock client
client = instructor.from_bedrock(bedrock_client)


class Address(BaseModel):
    street: str
    city: str
    country: str


class User(BaseModel):
    name: str
    age: int
    addresses: list[Address]


# Create structured output with nested objects
user = client.converse(
    modelId="anthropic.claude-3-sonnet-20240229-v1:0",
    messages=[
        {
            "role": "user",
            "content": """
            Extract: Jason is 25 years old.
            He lives at 123 Main St, New York, USA
            and has a summer house at 456 Beach Rd, Miami, USA
        """,
        },
    ],
    response_model=User,
)

print(user)
#> User(
#>     name='Jason',
#>     age=25,
#>     addresses=[
#>         Address(street='123 Main St', city='New York', country='USA'),
#>         Address(street='456 Beach Rd', city='Miami', country='USA')
#>     ]
#> )

Additional Resources¶