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Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.SPDX-License-Identifier: MIT-0

• Introduction
• Key Features
• Architecture Overview
• Quick Start
  • Processing Your First Document
• Updating an Existing Deployment
• Detailed Documentation
  • Core Documentation
  • Processing Patterns
  • Python Development
  • Planning & Operations
• Contributing
• License

A scalable, serverless solution for automated document processing and information extraction using AWS services. This system combines OCR capabilities with generative AI to convert unstructured documents into structured data at scale.

• Serverless Architecture: Built entirely on AWS serverless technologies including Lambda, Step Functions, SQS, and DynamoDB
• Modular, pluggable patterns: Pre-built processing patterns using state-of-the-art models and AWS services
• Advanced Classification: Support for page-level and holistic document packet classification
• Few Shot Example Support: Improve accuracy through example-based prompting
• High Throughput Processing: Handles large volumes of documents through intelligent queuing
• Built-in Resilience: Comprehensive error handling, retries, and throttling management
• Cost Optimization: Pay-per-use pricing model with built-in controls
• Comprehensive Monitoring: Rich CloudWatch dashboard with detailed metrics and logs
• Web User Interface: Modern UI for inspecting document workflow status and results
• AI-Powered Evaluation: Framework to assess accuracy against baseline data
• Extraction Confidence Assessment: LLM-powered assessment of extraction confidence with multimodal document analysis
• Document Knowledge Base Query: Ask questions about your processed documents

The solution uses a modular architecture with nested CloudFormation stacks to support multiple document processing patterns while maintaining common infrastructure for queueing, tracking, and monitoring.

Current patterns include:

• Pattern 1: Packet or Media processing with Bedrock Data Automation (BDA)
• Pattern 2: OCR → Bedrock Classification (page-level or holistic) → Bedrock Extraction
• Pattern 3: OCR → UDOP Classification (SageMaker) → Bedrock Extraction

To quickly deploy the GenAI-IDP solution in your AWS account:

1. Log into theAWS console
2. Choose theLaunch Stack button below for your desired region:

Region name	Region code	Launch
US West (Oregon)	us-west-2
US East (N.Virginia)	us-east-1

3. When the stack deploys for the first time, you'll receive an email with a temporary password to access the web UI
4. Use this temporary password for your first login to set up a permanent password

After deployment, you can quickly process a document and view results:

1. Upload a Document:

   • Via Web UI: Open the Web UI URL from the CloudFormation stack's Outputs tab, log in, and click "Upload Document"
   • Via S3: Upload directly to the S3 input bucket (find the bucket URL in CloudFormation stack Outputs)

2. Use Sample Documents:

   • For Pattern 1 (BDA): Usesamples/lending_package.pdf
   • For Patterns 2 and 3: Usesamples/rvl_cdip_package.pdf

3. Monitor Processing:

   • Via Web UI: Track document status on the dashboard
   • Via Step Functions: Open the StateMachine URL from CloudFormation stack Outputs to observe workflow execution

4. View Results:

   • Via Web UI: Access processing results through the document details page
   • Via S3: Check the output bucket for structured JSON files with extracted data

See theDeployment Guide for more detailed testing instructions.

IMPORTANT: If you have not previously done so, you mustrequest access to the following Amazon Bedrock models:

• Amazon: All Nova models, plus Titan Text Embeddings V2
• Anthropic: Claude 3.x models, Claude 4.x models

To update an existing GenAIIDP stack to a new version:

1. Navigate to CloudFormation in the AWS Management Console
2. Select your existing stack
3. Click "Update"
4. Select "Replace current template"
5. Enter the template URL:
   • us-west-2:https://s3.us-west-2.amazonaws.com/aws-ml-blog-us-west-2/artifacts/genai-idp/idp-main.yaml
   • us-east-1:https://s3.us-east-1.amazonaws.com/aws-ml-blog-us-east-1/artifacts/genai-idp/idp-main.yaml
6. Follow the prompts to update your stack, reviewing any parameter changes
7. For detailed instructions, see theDeployment Guide

For testing, use these sample files:

• Pattern-1 BDA default project:samples/lending_package.pdf
• Patterns 2 and 3 default configurations:samples/rvl_cdip_package.pdf

For detailed deployment and testing instructions, see theDeployment Guide.

• Architecture - Detailed component architecture and data flow
• Deployment - Build, publish, deploy, and test instructions
• Web UI - Web interface features and usage
• Configuration - Configuration and customization options
• Classification - Customizing document classification
• Extraction - Customizing information extraction
• Assessment - Extraction confidence evaluation using LLMs
• Evaluation Framework - Accuracy assessment system with analytics database and reporting
• Knowledge Base - Document knowledge base query feature
• Monitoring - Monitoring and logging capabilities
• Reporting Database - Analytics database for evaluation metrics and metering data
• Troubleshooting - Troubleshooting and performance guides

• Pattern 1: BDA - Packet or Media processing with Bedrock Data Automation (BDA)
• Pattern 2: Textract + Bedrock - OCR with Textract and generative AI with Bedrock
• Pattern 3: Textract + UDOP + Bedrock - OCR with Textract, UDOP Classification, and Bedrock extraction
• Few-Shot Examples - Implementing few-shot examples for improved accuracy

• Using Notebooks with IDP Common Library - Guide for using and creating Jupyter notebooks to experiment with the IDP Common Library
• IDP Common Package - Documentation for the core library powering the accelerator

• Well-Architected Framework Assessment - Analysis based on AWS Well-Architected Framework
• AWS Services & IAM Roles - AWS services used and IAM role requirements
• Cost Calculator - Framework for estimating solution costs

We welcome contributions to the GenAI Intelligent Document Processing accelerator! Whether you're fixing bugs, improving documentation, or proposing new features, your contributions are appreciated.

Please refer to ourContributing Guide for detailed information on:

• Setting up your development environment
• Project structure
• Making and testing changes
• Pull request process
• Coding standards
  • Python code usesruff for linting
  • UI code uses ESLint (npm run lint to verify)
• Documentation requirements
• Issue reporting guidelines

Thank you to everyone who has contributed to making this project better!

This project is licensed under the terms specified in the LICENSE file.