Building Intelligent Document Processing Solutions with Amazon Bedrock and Strands SDK

Introduction to Intelligent Document Processing

Prerequisites for Implementation

Solution Architecture Overview

Step-by-Step Implementation Guide

Configuring the AWS CLI

Cloning the GitHub Repository

Bedrock Data Automation and AgentCore Notebook Instructions

Security Considerations for Implementation

Benefits and Use Cases of the IDP Solution

Conclusion and Key Takeaways

Additional Resources

About the Authors

Transforming Unstructured Document Data with Intelligent Document Processing (IDP)

In today’s data-driven world, organizations are inundated with unstructured documents—think invoices, contracts, and reports. Intelligent Document Processing (IDP) is revolutionizing how these entities manage this valuable information by automating data extraction, yielding meaningful insights from chaos. In this post, we will explore how to programmatically create an IDP solution utilizing Strands SDK, Amazon Bedrock AgentCore, Amazon Bedrock Knowledge Base, and Bedrock Data Automation (BDA). Our approach employs a Jupyter notebook, enabling users to upload multi-modal business documents and extract relevant insights, particularly from the U.S. Department of Education’s Nation’s Report Card for public school districts.

The Power of Bedrock Data Automation (BDA)

Amazon Bedrock Data Automation can serve as a standalone feature or as an integral parser when establishing a knowledge base for Retrieval-Augmented Generation (RAG) workflows. It excels in generating insights from various unstructured, multi-modal content, including documents, images, videos, and audio. BDA allows you to build automated IDP and RAG workflows, offering a quick and cost-effective solution. Using Amazon OpenSearch Service, you can efficiently store the vector embeddings of necessary documents. Here, Bedrock AgentCore taps into the potential of BDA to conduct multi-modal RAG for our IDP solution.

Build Autonomous Agents with Amazon Bedrock AgentCore

Amazon Bedrock AgentCore is a fully managed service that empowers developers to construct and configure autonomous agents without the hassle of managing underlying infrastructure or writing extensive custom code. You can deploy agents using popular frameworks and leverage a suite of models, including those from Amazon Bedrock, Anthropic, Google, and OpenAI, enhancing flexibility and scalability.

Harnessing Strands SDK for Intelligent Document Processing

The Strands Agents SDK is a cutting-edge open-source toolkit designed to simplify AI agent development through a model-driven approach. Developers can create a Strands Agent by defining an agent’s behavior using prompts and a suite of tools. This approach allows a large language model (LLM) to perform reasoning, autonomously determining optimal actions based on context and tasks—significantly minimizing the code needed for multi-agent collaboration.

Getting Started: Prerequisites and Architecture

To follow this guide, ensure you have the following prerequisites set up in your AWS environment:

Architecture Overview

The solution leverages several AWS services:

• Amazon S3 for document storage and upload capabilities
• Bedrock Knowledge Bases for converting objects in S3 into a RAG-ready workflow
• Amazon OpenSearch for vector embeddings
• Amazon Bedrock AgentCore for the IDP workflow
• Strands Agent SDK for defining tools to perform IDP
• Bedrock Data Automation (BDA) for extracting structured insights from documents

Step-by-Step Implementation

1. Upload relevant documents to Amazon S3.
2. Create an Amazon Bedrock Knowledge Base and parse the S3 data source using BDA.
3. Store document chunks as vector embeddings in Amazon OpenSearch.
4. Deploy a Strands Agent on Amazon Bedrock AgentCore Runtime to conduct RAG for user inquiries.
5. The end user receives a response.

Configuring AWS CLI

To set up the AWS Command Line Interface (CLI), run the following command with your AWS credentials and region:

aws configure

Before starting, verify the latest region availability and pricing for AWS Bedrock Data Automation.

Clone and Build the GitHub Repository Locally

To get started, clone the repository:

git clone https://github.com/aws-samples/sample-for-amazon-bda-agents
cd sample-for-amazon-bda-agents

Open the Jupyter notebook named:

bedrock-data-automation-with-agents.ipynb

Notebook Implementation

This notebook showcases how to craft an IDP solution using BDA within the Amazon Bedrock AgentCore Runtime. Instead of utilizing traditional Bedrock Agents, we will deploy a Strands Agent through AgentCore, offering enterprise-grade capabilities while maintaining framework flexibility. Specific instructions are outlined in the notebook, but here’s a brief overview:

1. Import necessary libraries and set up AgentCore capabilities.
2. Create the Knowledge Base for Amazon Bedrock using BDA.
3. Upload your dataset of academic reports to Amazon S3.
4. Deploy the Strands Agent through AgentCore Runtime.
5. Test the agent hosted on AgentCore.
6. Clean up your resources post-testing.

Security Considerations

Incorporating robust security practices is crucial for any deployment. This solution uses security features such as:

• Secure file upload handling
• Role-based access control via Identity and Access Management (IAM)
• Input validation and error handling

Note: This implementation is for demonstration purposes, and further security controls must be reviewed before going live in a production environment.

Benefits and Use Cases

This IDP solution is especially beneficial for:

• Automating document processing workflows
• Conducting intelligent document analysis on extensive datasets
• Implementing question-answering systems based on document content
• Processing multi-modal content effectively

Conclusion

By utilizing Amazon Bedrock AgentCore’s capabilities in conjunction with Strands Agents and BDA, organizations can develop powerful IDP applications. This solution empowers businesses to comprehend and interact with multi-modal document content effectively. With the advantages of BDA, we can enhance the RAG experience and cater to more complex data formats including visually rich documents, images, audio, and video.

Additional Resources

For more information, visit Amazon Bedrock.

About the Authors

Raian Osman is a Technical Account Manager at AWS, focusing on education technology. With over three years at AWS, he helps organizations optimize their workloads and explore innovative generative AI use cases.

Andy Orlosky is a Strategic Pursuit Solutions Architect at AWS, specializing in generative AI solutions and holding seven AWS certifications.

Spencer Harrison is a partner solutions architect at AWS, dedicated to helping public sector organizations leverage cloud technology to achieve business outcomes.

Dive into the future of document processing and let IDP transform your organization’s data management strategies!