Enhancing Document Processing Capabilities with Anthropic Claude 3 Haiku Model on Amazon Bedrock

In today’s data-driven business landscape, the ability to efficiently extract and process information from a wide range of documents is crucial for informed decision-making and maintaining a competitive edge. However, traditional document processing workflows often involve complex and time-consuming manual tasks, hindering productivity and scalability.

In this post, we discuss an approach that uses the Anthropic Claude 3 Haiku model on Amazon Bedrock to enhance document processing capabilities. Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading artificial intelligence (AI) startups and Amazon available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case. With the Amazon Bedrock serverless experience, you can get started quickly, privately customize FMs with your own data, and integrate and deploy them into your applications using the AWS tools without having to manage any infrastructure.

At the heart of this solution lies the Anthropic Claude 3 Haiku model, the fastest and most affordable model in its intelligence class. With state-of-the-art vision capabilities and strong performance on industry benchmarks, Anthropic Claude 3 Haiku is a versatile solution for a wide range of enterprise applications. By using the advanced natural language processing (NLP) capabilities of Anthropic Claude 3 Haiku, our intelligent document processing (IDP) solution can extract valuable data directly from images, eliminating the need for complex postprocessing.

Scalable and efficient data extraction

Our solution overcomes the traditional limitations of document processing by addressing the following key challenges:

• Simple prompt-based extraction – This solution allows you to define the specific data you need to extract from the documents through intuitive prompts. The Anthropic Claude 3 Haiku model then processes the documents and returns the desired information, streamlining the entire workflow.
• Handling larger file sizes and multipage documents – To provide scalability and flexibility, this solution integrates additional AWS services to handle file sizes beyond the 5 MB limit of Anthropic Claude 3 Haiku. The solution can process both PDFs and image files, including multipage documents, providing comprehensive processing for unparalleled efficiency.

With the advanced NLP capabilities of the Anthropic Claude 3 Haiku model, our solution can directly extract the specific data you need without requiring complex postprocessing or parsing the output. This approach simplifies the workflow and enables more targeted and efficient document processing than traditional OCR-based solutions.

Confidence scores and human review

Maintaining data accuracy and quality is paramount in any document processing solution. This solution incorporates customizable rules, allowing you to define the criteria for invoking a human review. This provides a seamless collaboration between the automated extraction and human expertise, delivering high-quality results that meet your specific requirements.

In this post, we show how you can use Amazon Bedrock and Amazon Augmented AI (Amazon A2I) to build a workflow that enables multipage PDF document processing with a human reviewer loop.

Solution overview

The following architecture shows how you can have a serverless architecture to process multipage PDF documents or images with a human review. To implement this architecture, we take advantage of AWS Step Functions to build the overall workflow. As the workflow starts, it extracts individual pages from the multipage PDF document. It then uses the Map state to process multiple pages concurrently using the Amazon Bedrock API. After the data is extracted from the document, it validates against the business rules and sends the document to Amazon A2I for a human to review if any business rules fail. Reviewers use the Amazon A2I UI (a customizable website) to verify the extraction result. When the human review is complete, the callback task token is used to resume the state machine and store the output in an Amazon DynamoDB table.

You can deploy this solution following the steps in this post.

Prerequisites

For this walkthrough, you need the following:

Conclusion

In this post, we showed you how to use the Anthropic Claude 3 Haiku model on Amazon Bedrock and Amazon A2I to automatically extract data from multipage PDF documents and images. We also demonstrated how to conduct a human review of the pages for given business criteria. By eliminating the need for complex postprocessing, handling larger file sizes, and integrating a flexible human review process, this solution can help your business unlock the true value of your documents, drive informed decision-making, and gain a competitive edge in the market.

Overall, this post provides a roadmap for building a scalable document processing workflow using Anthropic Claude models on Amazon Bedrock.

As next steps, check out What is Amazon Bedrock to start using the service. Follow the Amazon Bedrock on the AWS Machine Learning Blog to keep up to date with new capabilities and use cases for Amazon Bedrock.

About the Authors

Venkata Kampana is a Senior Solutions Architect in the AWS Health and Human Services team and is based in Sacramento, CA. In that role, he helps public sector customers achieve their mission objectives with well-architected solutions on AWS.

Jim Daniel is the Public Health lead at Amazon Web Services. Previously, he held positions with the United States Department of Health and Human Services for nearly a decade, including Director of Public Health Innovation and Public Health Coordinator. Before his government service, Jim served as the Chief Information Officer for the Massachusetts Department of Public Health.