AWS Adds Claude Tool Use to Bedrock for Custom Entity Extraction

Amazon Web Services has published a production-ready implementation guide for using Anthropic's Claude model with tool use (function calling) capabilities in Amazon Bedrock to extract structured data from unstructured documents.

What's New

The solution demonstrates how to build a serverless document processing pipeline that extracts custom fields from documents like driver's licenses without requiring model training or fine-tuning. Claude evaluates prompts and automatically determines whether to invoke predefined tools to complete extraction tasks.

Technical Implementation

The architecture combines AWS services into an automated workflow:

1. Document Upload: Users upload documents to Amazon S3
2. Event Triggering: S3 PUT event automatically triggers an AWS Lambda function
3. Model Invocation: Lambda sends the document to Claude via Amazon Bedrock's invoke-model API
4. Entity Extraction: Claude processes the image and extracts fields using defined tool schemas
5. Monitoring: Results are logged in Amazon CloudWatch

The implementation uses Claude 3.5 Sonnet (global.anthropic.claude-sonnet-4-5-20250929-v1:0) with the tool_choice: auto parameter, allowing Claude to decide whether tool invocation is necessary. For deterministic behavior in production, developers can hardcode specific tool selection.

Key Technical Details

Tool Definition: Developers define tools with JSON schemas specifying field names, types, and requirements. The example extracts driver's license fields including first name, last name, issue date, license number, and address components.

Lambda Configuration: The guide recommends setting Lambda timeout to 30 seconds minimum (1-2 minutes for high-resolution images) to accommodate Claude's processing latency. Default 3-second timeouts will cause failures.

Image Processing: Documents are base64-encoded and sent with text prompts to Claude. The model returns structured JSON matching the defined tool schema.

IAM Permissions: Required permissions include bedrock:InvokeModel for Claude access and s3:GetObject for document retrieval.

Capabilities

The tool use approach supports:

• Dynamic field extraction without retraining
• Multiple document types (driver's licenses, insurance cards, ID badges, business forms)
• Scalable, serverless processing
• Real-time extraction with no batch processing required
• Flexible schema definition based on use case

What This Means

This integration reduces barrier to entry for document automation. Previously, extracting data from images required either rule-based OCR systems, fine-tuned models, or manual setup. Claude's tool use with vision capabilities removes the training requirement—developers can define extraction schemas in minutes and deploy production pipelines immediately. The serverless architecture eliminates infrastructure management while AWS Lambda's automatic scaling handles variable document volumes. Organizations processing hundreds or thousands of documents can now automate extraction without building custom ML pipelines.