Accelerating custom entity recognition with Claude tool use in Amazon Bedrock
New serverless pipeline extracts data from documents like driver's licenses without model training.
Anthropic, in partnership with AWS, has released a detailed implementation guide for using Claude's Tool Use feature within Amazon Bedrock to perform dynamic custom entity recognition. The solution creates a serverless pipeline where documents uploaded to Amazon S3 automatically trigger an AWS Lambda function. This function sends the document content to Claude in Bedrock, which uses pre-defined 'tools'—essentially function schemas described in natural language—to extract structured data like names, dates, and addresses. This approach eliminates the traditional need for training or fine-tuning a dedicated machine learning model for each new document type or entity.
The architecture is designed for production, leveraging AWS services for scalability and monitoring. After Claude processes the prompt and extracts the entities, the results are logged to Amazon CloudWatch. The guide walks through setting up the entire environment, including IAM permissions and a cross-region inference profile for Claude models, in about 10 minutes. This method is particularly effective for processing semi-structured documents such as driver's licenses, invoices, or forms, where the data schema is known but the format can vary. It represents a significant shift from static, trained models to a flexible, prompt-driven extraction system powered by a large language model's reasoning and tool-calling capabilities.
- Uses Claude's Tool Use (function calling) to define extraction schemas with natural language prompts, not code.
- Leverages a serverless AWS pipeline (S3, Lambda, Bedrock) for automatic, scalable document processing.
- Extracts custom entities like names and dates from documents without any model training or fine-tuning.
Why It Matters
Dramatically reduces the time and ML expertise needed to build automated document processing systems for businesses.