Introducing Structured Output for Custom Model Import in Amazon Bedrock

Streamlining AI Integration with Predictable, Schema-Aligned Outputs

Understanding Structured Output

Using Structured Output with Custom Model Import in Amazon Bedrock

Implementing Structured Output

Conclusion

About the Authors

Unlocking the Power of Consistency: Introducing Structured Output in Amazon Bedrock’s Custom Model Import

In the fast-evolving landscape of artificial intelligence, precision and reliability are key differentiators for businesses looking to harness the full potential of their models. Amazon Bedrock’s Custom Model Import feature has taken a significant step forward by introducing structured output, allowing you to deploy and scale fine-tuned or proprietary foundation models in a fully managed, serverless environment while maintaining rigorous output standards.

What Is Structured Output?

Structured output, or constrained decoding, directs the model’s generation process to conform to a predefined schema—like valid JSON. This real-time validation ensures that every token produced adheres to the defined structure. Traditional prompt-engineering methods or brittle post-processing scripts are now replaced by a robust solution that guarantees your model’s outputs remain consistent and machine-readable.

Consider the difference in applications: a customer service chatbot may thrive on creative variability, whereas an order processing system needs rigidly structured data. Structured output bridges this gap, allowing the intelligent capabilities of foundation models to work seamlessly within stringent formatting requirements.

Why Does It Matter?

The shift from free-form text generation to machine-readable outputs significantly impacts how businesses operate. Here are a few reasons why structured output is critical for production applications:

1. Precision Over Ambiguity: In automated systems, variability can lead to misunderstandings or errors. For instance, a simplistic classification model might produce inconsistent outputs like "Category = BILLING" or "I’d classify this as: Billing," making it tough for downstream processes to interpret results. Structured output eliminates this inconsistency by enforcing predictable, schema-aligned responses.

   Example:

   {
     "category": "billing",
     "priority": "high",
     "sentiment": "negative"
   }

2. Performance and Cost Efficiency: By constraining outputs to a defined schema, structured output reduces token usage and increases response speed, which enhances overall performance and lowers costs.

3. Enhanced Security: Structured output limits the model’s expression space, making it harder for malicious users to exploit it. Each generated token must conform to the designated format, mitigating risks associated with prompt injections.

4. Safety and Compliance: You can design schemas to inherently prevent harmful or policy-violating content, aligning model outputs with regulatory requirements seamlessly.

Implementing Structured Output in Amazon Bedrock

Ready to tap into the power of structured output? Here’s a step-by-step guide to implement it using Amazon Bedrock.

Prerequisites

• An active AWS account with Amazon Bedrock access.
• A custom model already imported using the Custom Model Import feature.
• Necessary AWS Identity and Access Management (IAM) permissions.

Step-by-Step Instructions

Step 1: Define Your Data Structure

Utilize Pydantic to define the expected output schema, creating a structured contract for your data.

from pydantic import BaseModel, Field

class Address(BaseModel):
    street_number: str = Field(description="Street number")
    street_name: str = Field(description="Street name")
    city: str = Field(description="City name")
    state: str = Field(description="Two-letter state abbreviation")
    zip_code: str = Field(description="5-digit ZIP code")

Step 2: Generate the JSON Schema

Create your schema that will guide the model during output generation.

schema = Address.model_json_schema()
address_schema = {
    "name": "Address",
    "schema": schema
}

Step 3: Prepare Your Input Messages

Format the input to align with your model’s requirements.

messages = [{
    "role": "user",
    "content": "Extract the address: 456 Tech Boulevard, San Francisco, CA 94105"
}]

Step 4: Apply the Chat Template

Generate the prompt necessary for consistent and reliable inference.

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

Step 5: Build the Request Payload

Include the JSON schema in your request.

request_body = {
    'prompt': prompt,
    'temperature': 0.1,
    'max_gen_len': 1000,
    'top_p': 0.9,
    'response_format': {
        "type": "json_schema",
        "json_schema": address_schema
    }
}

Step 6: Invoke the Model

Use the Bedrock client to invoke your model.

response = bedrock_runtime.invoke_model(
    modelId=model_arn,
    body=json.dumps(request_body),
    accept="application/json",
    contentType="application/json"
)

Step 7: Parse the Response

Extract and validate your output seamlessly.

result = json.loads(response['body'].read().decode('utf-8'))
raw_output = result['choices'][0]['text']
print(raw_output)

Conclusion

The integration of structured output in Amazon Bedrock’s Custom Model Import streamlines the generation of predictable, schema-aligned outputs. It allows businesses to utilize AI solutions that are reliable and efficient, paving the way for advancements in automation, compliance, and system integration.

Start exploring structured output with your Custom Model Import today and witness how it can transform your AI applications into consistent, production-ready solutions.

About the Authors

Manoj Selvakumar is a Generative AI Specialist Solutions Architect at AWS, focusing on designing scalable AI solutions. With extensive experience in deep learning, he is passionate about enabling responsible innovation in AI.

Yanyan Zhang is a Senior Generative AI Data Scientist at Amazon AWS, working on cutting-edge technologies to help customers achieve their AI goals.

Lokeshwaran Ravi is a Senior Deep Learning Compiler Engineer at AWS, specializing in ML optimization and AI security.

Revendra Kumar is a Senior Software Development Engineer at AWS, with experience in MLOps for both quantum computing and traditional cloud systems.

Muzart Tuman is a software engineer focused on impactful AI-driven applications that aim to advance technological capabilities for real-world problems.