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## [Unreleased]
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## [0.4.5]
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### Added
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-**Document Split Classification Metrics for Evaluating Page-Level Classification and Document Segmentation**
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- Added `DocSplitClassificationMetrics` class for comprehensive evaluation of document splitting and classification accuracy
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-**Three Accuracy Types**: Page-level classification accuracy, split accuracy without order consideration, and split accuracy with exact page order matching
-**Automatic Integration**: Integrates with evaluation service when ground truth and predicted sections are available
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-**Documentation**: Guide in `lib/idp_common_pkg/idp_common/evaluation/README.md` with usage examples, metric explanations, and best practices
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-**Caching improvements to Agentic Extraction Service**
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- Optimized prompt caching by caching document context (text/images) on first LLM call, reducing token costs and quota consumption
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-**Enhanced Bedrock Retry Logic for Agentic Extraction**
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- New `bedrock_utils.py` module with exponential backoff and comprehensive error handling
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- Improves agentic extraction reliability for transient failures and rate limiting
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-**Review Agent Model Configuration**
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- Added `review_agent_model` parameter to enable separate model for reviewing extraction work
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- Defaults to main extraction model if not specified
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- Configurable through Web UI extraction settings
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### Fixed
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-**Evaluation Output URI Fields Lost Across All Patterns - causing (a) missing Page Text Confidence content in UI, (2) failed Assessment step when reprocessing document after editing classes (No module named 'fitz')**
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- Fixed bug where `text_confidence_uri` was being set to null in evaluation output for all three patterns
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- Root cause: AppSync service `_appsync_to_document()` method incorrectly mapped page URIs, and evaluation functions overwrote correct documents with corrupted AppSync responses
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-**UI: Metering Data Not Displayed During Document Processing**
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**Prefer AWS CDK?** This solution is also available as [GenAI IDP Accelerator for AWS CDK](https://github.com/cdklabs/genai-idp), providing the same functional capabilities through AWS CDK constructs for customers who prefer Infrastructure-as-Code with CDK.
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**Prefer Terraform?** This solution is also available as [GenAI IDP Terraform](https://github.com/awslabs/genai-idp-terraform), providing the same functional capabilities as a Terraform module that integrates with existing infrastructure and supports customization through module variables.
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## Key Features
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-**Serverless Architecture**: Built entirely on AWS serverless technologies including Lambda, Step Functions, SQS, and DynamoDB
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