diff --git a/config_library/pattern-2/bank-statement-sample/config.yaml b/config_library/pattern-2/bank-statement-sample/config.yaml index f8422c6d..caf9fd4d 100644 --- a/config_library/pattern-2/bank-statement-sample/config.yaml +++ b/config_library/pattern-2/bank-statement-sample/config.yaml @@ -404,11 +404,7 @@ assessment: image: target_height: "" target_width: "" - granular: - enabled: true - max_workers: "20" - simple_batch_size: "3" - list_batch_size: "1" + max_workers: "20" default_confidence_threshold: "0.8" top_p: "0.1" max_tokens: "10000" @@ -455,107 +451,6 @@ assessment: - Provide tight, accurate bounding boxes around the actual text - - For each field, provide bounding box coordinates: - - bbox: [x1, y1, x2, y2] coordinates in normalized 0-1000 scale - - page: Page number where the field appears (starting from 1) - - Coordinate system: - - Use normalized scale 0-1000 for both x and y axes - - x1, y1 = top-left corner of bounding box - - x2, y2 = bottom-right corner of bounding box - - Ensure x2 > x1 and y2 > y1 - - Make bounding boxes tight around the actual text content - - If a field spans multiple lines, create a bounding box that encompasses all relevant text - - - - Analyze the extraction results against the source document and provide confidence assessments with spatial localization. Return a JSON object with the following structure based on the attribute type: - - For SIMPLE attributes: - { - "simple_attribute_name": { - "confidence": 0.85, - "bbox": [100, 200, 300, 250], - "page": 1 - } - } - - For GROUP attributes (nested object structure): - { - "group_attribute_name": { - "sub_attribute_1": { - "confidence": 0.90, - "bbox": [150, 300, 250, 320], - "page": 1 - }, - "sub_attribute_2": { - "confidence": 0.75, - "bbox": [150, 325, 280, 345], - "page": 1 - } - } - } - - For LIST attributes (array of assessed items): - { - "list_attribute_name": [ - { - "item_attribute_1": { - "confidence": 0.95, - "bbox": [100, 400, 200, 420], - "page": 1 - }, - "item_attribute_2": { - "confidence": 0.88, - "bbox": [250, 400, 350, 420], - "page": 1 - } - }, - { - "item_attribute_1": { - "confidence": 0.92, - "bbox": [100, 425, 200, 445], - "page": 1 - }, - "item_attribute_2": { - "confidence": 0.70, - "bbox": [250, 425, 350, 445], - "page": 1 - } - } - ] - } - - IMPORTANT: - - For LIST attributes like "Transactions", assess EACH individual item in the list separately with individual bounding boxes - - Each transaction should be assessed as a separate object in the array with its own spatial coordinates - - Do NOT provide aggregate assessments for list items - assess each one individually with precise locations - - Include assessments AND bounding boxes for ALL attributes present in the extraction results - - Match the exact structure of the extracted data - - Provide page numbers for all bounding boxes (starting from 1) - - - <> - - - {DOCUMENT_IMAGE} - - - - {OCR_TEXT_CONFIDENCE} - - - <> - - - {ATTRIBUTE_NAMES_AND_DESCRIPTIONS} - - - - {EXTRACTION_RESULTS} - - evaluation: enabled: true llm_method: diff --git a/config_library/pattern-2/lending-package-sample/config.yaml b/config_library/pattern-2/lending-package-sample/config.yaml index cbac09b4..cfacc0ce 100644 --- a/config_library/pattern-2/lending-package-sample/config.yaml +++ b/config_library/pattern-2/lending-package-sample/config.yaml @@ -10,6 +10,7 @@ ocr: - name: LAYOUT - name: TABLES - name: SIGNATURES + max_workers: "20" image: target_width: "" target_height: "" @@ -1188,6 +1189,7 @@ classes: classification: classificationMethod: multimodalPageLevelClassification maxPagesForClassification: "ALL" + max_workers: "20" image: target_height: "" target_width: "" @@ -1252,6 +1254,7 @@ extraction: agentic: enabled: false review_agent: false + max_workers: "20" image: target_width: "" target_height: "" @@ -1425,14 +1428,10 @@ summarization: assessment: enabled: true validation_enabled: false + max_workers: "20" image: target_height: "" target_width: "" - granular: - enabled: true - max_workers: "20" - simple_batch_size: "3" - list_batch_size: "1" default_confidence_threshold: "0.8" top_p: "0.1" max_tokens: "10000" @@ -1479,106 +1478,6 @@ assessment: - Provide tight, accurate bounding boxes around the actual text - - For each field, provide bounding box coordinates: - - bbox: [x1, y1, x2, y2] coordinates in normalized 0-1000 scale - - page: Page number where the field appears (starting from 1) - - Coordinate system: - - Use normalized scale 0-1000 for both x and y axes - - x1, y1 = top-left corner of bounding box - - x2, y2 = bottom-right corner of bounding box - - Ensure x2 > x1 and y2 > y1 - - Make bounding boxes tight around the actual text content - - If a field spans multiple lines, create a bounding box that encompasses all relevant text - - - - Analyze the extraction results against the source document and provide confidence assessments with spatial localization. Return a JSON object with the following structure based on the attribute type: - - For SIMPLE attributes: - { - "simple_attribute_name": { - "confidence": 0.85, - "bbox": [100, 200, 300, 250], - "page": 1 - } - } - - For GROUP attributes (nested object structure): - { - "group_attribute_name": { - "sub_attribute_1": { - "confidence": 0.90, - "bbox": [150, 300, 250, 320], - "page": 1 - }, - "sub_attribute_2": { - "confidence": 0.75, - "bbox": [150, 325, 280, 345], - "page": 1 - } - } - } - - For LIST attributes (array of assessed items): - { - "list_attribute_name": [ - { - "item_attribute_1": { - "confidence": 0.95, - "bbox": [100, 400, 200, 420], - "page": 1 - }, - "item_attribute_2": { - "confidence": 0.88, - "bbox": [250, 400, 350, 420], - "page": 1 - } - }, - { - "item_attribute_1": { - "confidence": 0.92, - "bbox": [100, 425, 200, 445], - "page": 1 - }, - "item_attribute_2": { - "confidence": 0.70, - "bbox": [250, 425, 350, 445], - "page": 1 - } - } - ] - } - - IMPORTANT: - - For LIST attributes like "Transactions", assess EACH individual item in the list separately with individual bounding boxes - - Each transaction should be assessed as a separate object in the array with its own spatial coordinates - - Do NOT provide aggregate assessments for list items - assess each one individually with precise locations - - Include assessments AND bounding boxes for ALL attributes present in the extraction results - - Match the exact structure of the extracted data - - Provide page numbers for all bounding boxes (starting from 1) - - - <> - - - {DOCUMENT_IMAGE} - - - - {OCR_TEXT_CONFIDENCE} - - - <> - - - {ATTRIBUTE_NAMES_AND_DESCRIPTIONS} - - - - {EXTRACTION_RESULTS} - evaluation: enabled: true llm_method: diff --git a/config_library/pattern-2/rvl-cdip-package-sample-with-few-shot-examples/config.yaml b/config_library/pattern-2/rvl-cdip-package-sample-with-few-shot-examples/config.yaml index 8ceab32c..fc3b0036 100644 --- a/config_library/pattern-2/rvl-cdip-package-sample-with-few-shot-examples/config.yaml +++ b/config_library/pattern-2/rvl-cdip-package-sample-with-few-shot-examples/config.yaml @@ -11,6 +11,7 @@ ocr: - name: LAYOUT - name: TABLES - name: SIGNATURES + max_workers: "20" image: dpi: "150" target_width: "" @@ -809,6 +810,7 @@ classes: classification: maxPagesForClassification: "ALL" + max_workers: "20" image: target_height: "" target_width: "" @@ -863,6 +865,7 @@ classification: 5. Outputting in the exact JSON format specified in extraction: + max_workers: "20" image: target_height: "" target_width: "" @@ -963,14 +966,10 @@ extraction: assessment: enabled: true validation_enabled: false + max_workers: "20" image: target_height: "" target_width: "" - granular: - enabled: true - max_workers: "20" - simple_batch_size: "3" - list_batch_size: "1" default_confidence_threshold: "0.8" top_p: "0.1" max_tokens: "10000" @@ -1017,106 +1016,6 @@ assessment: - Provide tight, accurate bounding boxes around the actual text - - For each field, provide bounding box coordinates: - - bbox: [x1, y1, x2, y2] coordinates in normalized 0-1000 scale - - page: Page number where the field appears (starting from 1) - - Coordinate system: - - Use normalized scale 0-1000 for both x and y axes - - x1, y1 = top-left corner of bounding box - - x2, y2 = bottom-right corner of bounding box - - Ensure x2 > x1 and y2 > y1 - - Make bounding boxes tight around the actual text content - - If a field spans multiple lines, create a bounding box that encompasses all relevant text - - - - Analyze the extraction results against the source document and provide confidence assessments with spatial localization. Return a JSON object with the following structure based on the attribute type: - - For SIMPLE attributes: - { - "simple_attribute_name": { - "confidence": 0.85, - "bbox": [100, 200, 300, 250], - "page": 1 - } - } - - For GROUP attributes (nested object structure): - { - "group_attribute_name": { - "sub_attribute_1": { - "confidence": 0.90, - "bbox": [150, 300, 250, 320], - "page": 1 - }, - "sub_attribute_2": { - "confidence": 0.75, - "bbox": [150, 325, 280, 345], - "page": 1 - } - } - } - - For LIST attributes (array of assessed items): - { - "list_attribute_name": [ - { - "item_attribute_1": { - "confidence": 0.95, - "bbox": [100, 400, 200, 420], - "page": 1 - }, - "item_attribute_2": { - "confidence": 0.88, - "bbox": [250, 400, 350, 420], - "page": 1 - } - }, - { - "item_attribute_1": { - "confidence": 0.92, - "bbox": [100, 425, 200, 445], - "page": 1 - }, - "item_attribute_2": { - "confidence": 0.70, - "bbox": [250, 425, 350, 445], - "page": 1 - } - } - ] - } - - IMPORTANT: - - For LIST attributes like "Transactions", assess EACH individual item in the list separately with individual bounding boxes - - Each transaction should be assessed as a separate object in the array with its own spatial coordinates - - Do NOT provide aggregate assessments for list items - assess each one individually with precise locations - - Include assessments AND bounding boxes for ALL attributes present in the extraction results - - Match the exact structure of the extracted data - - Provide page numbers for all bounding boxes (starting from 1) - - - <> - - - {DOCUMENT_IMAGE} - - - - {OCR_TEXT_CONFIDENCE} - - - <> - - - {ATTRIBUTE_NAMES_AND_DESCRIPTIONS} - - - - {EXTRACTION_RESULTS} - evaluation: enabled: true llm_method: @@ -1433,7 +1332,7 @@ agents: parameters: max_log_events: 5 time_range_hours_default: 24 - + chat_companion: model_id: us.anthropic.claude-haiku-4-5-20251001-v1:0 pricing: diff --git a/config_library/pattern-2/rvl-cdip-package-sample/config.yaml b/config_library/pattern-2/rvl-cdip-package-sample/config.yaml index 49ddd306..db04909f 100644 --- a/config_library/pattern-2/rvl-cdip-package-sample/config.yaml +++ b/config_library/pattern-2/rvl-cdip-package-sample/config.yaml @@ -10,6 +10,7 @@ ocr: - name: LAYOUT - name: TABLES - name: SIGNATURES + max_workers: "20" image: dpi: "150" target_width: "" @@ -764,6 +765,7 @@ classes: labeled 'notes', 'remarks', or 'comments'. classification: maxPagesForClassification: "ALL" + max_workers: "20" image: target_height: "" target_width: "" @@ -906,6 +908,7 @@ classification: You are a document classification expert who can analyze and classify multiple documents and their page boundaries within a document package from various domains. Your task is to determine the document type based on its content and structure, using the provided document type definitions. Your output must be valid JSON according to the requested format. classificationMethod: textbasedHolisticClassification extraction: + max_workers: "20" image: target_width: "" target_height: "" @@ -1067,14 +1070,10 @@ summarization: assessment: enabled: true validation_enabled: false + max_workers: "20" image: target_height: "" target_width: "" - granular: - enabled: true - max_workers: "20" - simple_batch_size: "3" - list_batch_size: "1" default_confidence_threshold: "0.8" top_p: "0.1" max_tokens: "10000" @@ -1121,106 +1120,6 @@ assessment: - Provide tight, accurate bounding boxes around the actual text - - For each field, provide bounding box coordinates: - - bbox: [x1, y1, x2, y2] coordinates in normalized 0-1000 scale - - page: Page number where the field appears (starting from 1) - - Coordinate system: - - Use normalized scale 0-1000 for both x and y axes - - x1, y1 = top-left corner of bounding box - - x2, y2 = bottom-right corner of bounding box - - Ensure x2 > x1 and y2 > y1 - - Make bounding boxes tight around the actual text content - - If a field spans multiple lines, create a bounding box that encompasses all relevant text - - - - Analyze the extraction results against the source document and provide confidence assessments with spatial localization. Return a JSON object with the following structure based on the attribute type: - - For SIMPLE attributes: - { - "simple_attribute_name": { - "confidence": 0.85, - "bbox": [100, 200, 300, 250], - "page": 1 - } - } - - For GROUP attributes (nested object structure): - { - "group_attribute_name": { - "sub_attribute_1": { - "confidence": 0.90, - "bbox": [150, 300, 250, 320], - "page": 1 - }, - "sub_attribute_2": { - "confidence": 0.75, - "bbox": [150, 325, 280, 345], - "page": 1 - } - } - } - - For LIST attributes (array of assessed items): - { - "list_attribute_name": [ - { - "item_attribute_1": { - "confidence": 0.95, - "bbox": [100, 400, 200, 420], - "page": 1 - }, - "item_attribute_2": { - "confidence": 0.88, - "bbox": [250, 400, 350, 420], - "page": 1 - } - }, - { - "item_attribute_1": { - "confidence": 0.92, - "bbox": [100, 425, 200, 445], - "page": 1 - }, - "item_attribute_2": { - "confidence": 0.70, - "bbox": [250, 425, 350, 445], - "page": 1 - } - } - ] - } - - IMPORTANT: - - For LIST attributes like "Transactions", assess EACH individual item in the list separately with individual bounding boxes - - Each transaction should be assessed as a separate object in the array with its own spatial coordinates - - Do NOT provide aggregate assessments for list items - assess each one individually with precise locations - - Include assessments AND bounding boxes for ALL attributes present in the extraction results - - Match the exact structure of the extracted data - - Provide page numbers for all bounding boxes (starting from 1) - - - <> - - - {DOCUMENT_IMAGE} - - - - {OCR_TEXT_CONFIDENCE} - - - <> - - - {ATTRIBUTE_NAMES_AND_DESCRIPTIONS} - - - - {EXTRACTION_RESULTS} - evaluation: enabled: true llm_method: @@ -1453,7 +1352,7 @@ agents: parameters: max_log_events: 5 time_range_hours_default: 24 - + chat_companion: model_id: us.anthropic.claude-haiku-4-5-20251001-v1:0 pricing: diff --git a/config_library/pattern-3/rvl-cdip-package-sample/config.yaml b/config_library/pattern-3/rvl-cdip-package-sample/config.yaml index dce11ff1..90678e4f 100644 --- a/config_library/pattern-3/rvl-cdip-package-sample/config.yaml +++ b/config_library/pattern-3/rvl-cdip-package-sample/config.yaml @@ -11,6 +11,7 @@ ocr: - name: LAYOUT - name: TABLES - name: SIGNATURES + max_workers: "20" image: dpi: "150" target_width: "" @@ -766,6 +767,7 @@ classes: classification: model: Custom fine tuned UDOP model extraction: + max_workers: "20" image: target_width: "" target_height: "" @@ -926,14 +928,10 @@ summarization: You are a document summarization expert who can analyze and summarize documents from various domains including medical, financial, legal, and general business documents. Your task is to create a summary that captures the key information, main points, and important details from the document. Your output must be in valid JSON format. \nSummarization Style: Balanced\\nCreate a balanced summary that provides a moderate level of detail. Include the main points and key supporting information, while maintaining the document's overall structure. Aim for a comprehensive yet concise summary.\n Your output MUST be in valid JSON format with markdown content. You MUST strictly adhere to the output format specified in the instructions. assessment: enabled: true + max_workers: "20" image: target_height: "" target_width: "" - granular: - enabled: true - max_workers: "20" - simple_batch_size: "3" - list_batch_size: "1" default_confidence_threshold: "0.8" top_p: "0.1" max_tokens: "10000" diff --git a/idp_cli/pyproject.toml b/idp_cli/pyproject.toml index 03836a16..9e2130fd 100644 --- a/idp_cli/pyproject.toml +++ b/idp_cli/pyproject.toml @@ -9,32 +9,28 @@ build-backend = "setuptools.build_meta" name = "idp-cli" version = "0.4.5" description = "Command-line interface for IDP Accelerator batch document processing" -authors = [{name = "AWS"}] -license = {text = "MIT-0"} -requires-python = ">=3.10" +authors = [{ name = "AWS" }] +license = { text = "MIT-0" } +requires-python = ">=3.12" classifiers = [ - "Development Status :: 4 - Beta", - "Intended Audience :: Developers", - "License :: OSI Approved :: MIT License", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Programming Language :: Python :: 3.12", - "Programming Language :: Python :: 3.13", + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", ] dependencies = [ - "click>=8.1.0", - "rich>=13.0.0", - "boto3>=1.28.0", - "botocore>=1.31.0", + "click>=8.1.0", + "rich>=13.0.0", + "boto3>=1.28.0", + "botocore>=1.31.0", ] [project.optional-dependencies] -test = [ - "pytest>=7.4.0", - "pytest-mock>=3.11.0", - "moto>=4.2.0", -] +test = ["pytest>=7.4.0", "pytest-mock>=3.11.0", "moto>=4.2.0"] [project.scripts] idp-cli = "idp_cli.cli:main" diff --git a/lib/idp_common_pkg/idp_common/assessment/__init__.py b/lib/idp_common_pkg/idp_common/assessment/__init__.py index 17281d46..39d2330f 100644 --- a/lib/idp_common_pkg/idp_common/assessment/__init__.py +++ b/lib/idp_common_pkg/idp_common/assessment/__init__.py @@ -7,9 +7,8 @@ This module provides services for assessing the confidence and accuracy of extraction results by analyzing them against source documents using LLMs. -The module supports both: -1. Original approach: Single inference for all attributes in a section -2. Granular approach: Multiple focused inferences with caching and parallelization +All assessment now uses the granular approach with Strands agents for +multiple focused inferences with caching and parallelization. """ import logging @@ -19,33 +18,32 @@ from .granular_service import GranularAssessmentService from .models import AssessmentResult, AttributeAssessment -from .service import AssessmentService as OriginalAssessmentService logger = logging.getLogger(__name__) class AssessmentService: """ - Backward-compatible AssessmentService that automatically selects the appropriate implementation. + Assessment service for evaluating document extraction confidence. - This class maintains the same interface as the original AssessmentService but automatically - chooses between the original and granular implementations based on configuration. + This class uses the granular Strands-based assessment approach for all assessments. + It provides backward compatibility by maintaining the same interface. """ def __init__(self, region: str | None = None, config: IDPConfig | None = None): """ - Initialize the assessment service with automatic implementation selection. + Initialize the assessment service. Args: region: AWS region for Bedrock - config: Configuration dictionary + config: Configuration dictionary or IDPConfig model """ if config is None: config = IDPConfig() elif isinstance(config, dict): config = IDPConfig(**config) - self._service = create_assessment_service(region=region, config=config) + self._service = GranularAssessmentService(region=region, config=config) def process_document_section(self, document, section_id: str): """Process a single section from a Document object to assess extraction confidence.""" @@ -60,40 +58,25 @@ def create_assessment_service( region: Optional[str] = None, config: Optional[IDPConfig] = None ): """ - Factory function to create the appropriate assessment service based on configuration. + Factory function to create the assessment service. Args: region: AWS region for Bedrock - config: Configuration dictionary + config: Configuration dictionary or IDPConfig model Returns: - OriginalAssessmentService or GranularAssessmentService based on configuration + GranularAssessmentService instance """ if not config: config = IDPConfig() - logger.info("No config provided, using original AssessmentService") - return OriginalAssessmentService(region=region, config=config) - # Check if granular assessment is enabled (default: False for backward compatibility) - - # Normalize the enabled value to handle both boolean and string values - - logger.info( - f"Granular assessment enabled check: raw_value={config.assessment.granular.enabled} (type: {type(config.assessment.granular.enabled)})" - ) - - if config.assessment.granular.enabled: - logger.info("Granular assessment enabled, using GranularAssessmentService") - return GranularAssessmentService(region=region, config=config) - else: - logger.info("Using original AssessmentService") - return OriginalAssessmentService(region=region, config=config) + logger.info("Creating GranularAssessmentService (Strands-based assessment)") + return GranularAssessmentService(region=region, config=config) __all__ = [ "AssessmentService", "GranularAssessmentService", - "OriginalAssessmentService", "AssessmentResult", "AttributeAssessment", "create_assessment_service", diff --git a/lib/idp_common_pkg/idp_common/assessment/example_usage.py b/lib/idp_common_pkg/idp_common/assessment/example_usage.py index 90ecde7f..ebc8c0d0 100644 --- a/lib/idp_common_pkg/idp_common/assessment/example_usage.py +++ b/lib/idp_common_pkg/idp_common/assessment/example_usage.py @@ -3,10 +3,11 @@ # SPDX-License-Identifier: MIT-0 """ -Example usage of the Granular Assessment Service. +Example usage of the Strands-Based Assessment Service. -This script demonstrates how to use the granular assessment approach +This script demonstrates how to use the Strands-based assessment approach for improved accuracy and scalability when assessing document extraction confidence. +All assessment now uses this unified approach with tool-based agent interactions. """ import json @@ -30,13 +31,14 @@ def load_config(config_path: str) -> Dict[str, Any]: def example_granular_assessment(): - """Example of using granular assessment service.""" + """Example of using Strands-based assessment service.""" - # Load configuration with granular assessment enabled + # Load configuration for assessment (always uses Strands-based approach) config = { "assessment": { + "enabled": True, "default_confidence_threshold": 0.9, - "model": "us.anthropic.claude-3-7-sonnet-20250219-v1:0", + "model": "us.anthropic.claude-sonnet-4-20250514-v1:0", "system_prompt": "You are a document analysis assessment expert...", "task_prompt": """ @@ -53,13 +55,8 @@ def example_granular_assessment(): Analyze and provide confidence assessments... """, - # Granular assessment configuration - "granular": { - "enabled": True, - "max_workers": 20, - "simple_batch_size": 3, - "list_batch_size": 1, - }, + # Strands-based assessment settings + "max_workers": 20, }, "classes": [ { @@ -145,99 +142,75 @@ def example_granular_assessment(): # Demonstrate task creation (this would normally be done internally) if hasattr(assessment_service, "_create_assessment_tasks"): - attributes = assessment_service._get_class_attributes("Bank Statement") - tasks = assessment_service._create_assessment_tasks( - example_extraction_results, attributes, 0.9 + class_schema = assessment_service._get_class_schema("Bank Statement") + properties = class_schema.get("properties", {}) + tasks, assessment_structure = assessment_service._create_assessment_tasks( + example_extraction_results, properties, 0.9 ) logger.info(f"\nCreated {len(tasks)} assessment tasks:") for task in tasks: - logger.info(f" - {task.task_id}: {task.task_type} for {task.attributes}") + logger.info(f" - {task.task_id}: {task.task_type} for {task.field_name}") return assessment_service, config def compare_approaches(): - """Compare original vs granular assessment approaches.""" - - logger.info("=== Comparison: Original vs Granular Assessment ===") + """Demonstrate the Strands-based assessment approach.""" - # Configuration for original approach - original_config = {"assessment": {"granular": {"enabled": False}}} + logger.info("=== Strands-Based Assessment Approach ===") - # Configuration for granular approach - granular_config = { + # Configuration for Strands-based assessment (always used) + assessment_config = { "assessment": { - "granular": { - "enabled": True, - "max_workers": 4, - "simple_batch_size": 3, - "list_batch_size": 1, - } + "enabled": True, + "max_workers": 4, } } from idp_common.assessment import create_assessment_service - # Create both services - original_service = create_assessment_service(config=original_config) - granular_service = create_assessment_service(config=granular_config) - - logger.info(f"Original service: {type(original_service).__name__}") - logger.info(f"Granular service: {type(granular_service).__name__}") + # Create assessment service + assessment_service = create_assessment_service(config=assessment_config) - # Show the differences - logger.info("\nKey Differences:") - logger.info("Original Approach:") - logger.info(" - Single inference for all attributes") - logger.info(" - Simple implementation") - logger.info(" - May struggle with complex documents") + logger.info(f"Assessment service: {type(assessment_service).__name__}") - logger.info("\nGranular Approach:") - logger.info(" - Multiple focused inferences") + # Show the features + logger.info("\nStrands-Based Assessment Features:") + logger.info(" - Multiple focused inferences per field") + logger.info(" - Tool-based interaction with Strands agents") logger.info(" - Prompt caching for cost optimization") logger.info(" - Parallel processing for speed") logger.info(" - Better handling of complex documents") + logger.info(" - Consistent assessment structure") def demonstrate_configuration_options(): - """Demonstrate different configuration options for granular assessment.""" + """Demonstrate different configuration options for Strands-based assessment.""" logger.info("=== Configuration Options ===") # Conservative configuration (good for starting) conservative_config = { "assessment": { - "granular": { - "enabled": True, - "max_workers": 2, - "simple_batch_size": 2, - "list_batch_size": 1, - } + "enabled": True, + "max_workers": 2, } } # Aggressive configuration (for high-throughput) aggressive_config = { "assessment": { - "granular": { - "enabled": True, - "max_workers": 8, - "simple_batch_size": 5, - "list_batch_size": 2, - } + "enabled": True, + "max_workers": 50, } } # Balanced configuration (recommended) balanced_config = { "assessment": { - "granular": { - "enabled": True, - "max_workers": 4, - "simple_batch_size": 3, - "list_batch_size": 1, - } + "enabled": True, + "max_workers": 20, } } @@ -249,23 +222,23 @@ def demonstrate_configuration_options(): for name, config in configs.items(): logger.info(f"\n{name} Configuration:") - granular_settings = config["assessment"]["granular"] - for key, value in granular_settings.items(): + assessment_settings = config["assessment"] + for key, value in assessment_settings.items(): logger.info(f" {key}: {value}") def main(): """Main example function.""" - logger.info("=== Granular Assessment Service Examples ===") + logger.info("=== Strands-Based Assessment Service Examples ===") try: # Example 1: Basic usage logger.info("\n1. Basic Usage Example") service, config = example_granular_assessment() - # Example 2: Compare approaches - logger.info("\n2. Approach Comparison") + # Example 2: Demonstrate the approach + logger.info("\n2. Assessment Approach") compare_approaches() # Example 3: Configuration options @@ -273,11 +246,12 @@ def main(): demonstrate_configuration_options() logger.info("\n=== Examples Complete ===") - logger.info("To use granular assessment in your application:") - logger.info("1. Add granular configuration to your config file") - logger.info("2. Use create_assessment_service() factory function") - logger.info("3. Process documents with the same interface") - logger.info("4. Monitor performance and tune parameters") + logger.info("To use Strands-based assessment in your application:") + logger.info("1. Set assessment.enabled to true in your config") + logger.info("2. Configure max_workers based on your throughput needs") + logger.info("3. Use create_assessment_service() factory function") + logger.info("4. Process documents with the same interface") + logger.info("5. Monitor performance and tune max_workers parameter") except ImportError as e: logger.error(f"Import error: {e}") diff --git a/lib/idp_common_pkg/idp_common/assessment/geometry_utils.py b/lib/idp_common_pkg/idp_common/assessment/geometry_utils.py new file mode 100644 index 00000000..36cd09fa --- /dev/null +++ b/lib/idp_common_pkg/idp_common/assessment/geometry_utils.py @@ -0,0 +1,116 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: MIT-0 + +""" +Shared utilities for geometry data conversion. + +Consolidates duplicate geometry conversion logic from service.py and granular_service.py. +""" + +import os +from typing import Any + +from aws_lambda_powertools import Logger + +from idp_common.assessment.models import Geometry + +logger = Logger(service="assessment", level=os.getenv("LOG_LEVEL", "INFO")) + + +def process_assessment_geometry( + attr_assessment: dict[str, Any], attr_name: str = "" +) -> dict[str, Any]: + """ + Process and standardize geometry data in assessment response. + + Args: + attr_assessment: Assessment dict with potential bbox/page fields + attr_name: Field name for logging + + Returns: + Enhanced assessment with standardized geometry + """ + enhanced = attr_assessment.copy() + + # Check for bbox data + if "bbox" in attr_assessment and "page" in attr_assessment: + try: + bbox_coords = attr_assessment["bbox"] + page_num = attr_assessment["page"] + + if isinstance(bbox_coords, list) and len(bbox_coords) == 4: + # Create Geometry object and convert to UI format + geometry = Geometry.from_bbox_list(bbox_coords, page_num) + enhanced["geometry"] = [geometry.to_ui_format()] + + logger.debug( + f"Converted bbox for {attr_name}: {bbox_coords} -> geometry" + ) + else: + logger.warning(f"Invalid bbox format for {attr_name}: {bbox_coords}") + except Exception as e: + logger.warning(f"Failed to process bbox for {attr_name}: {e}") + elif "bbox" in attr_assessment and "page" not in attr_assessment: + logger.warning( + f"Found bbox without page for {attr_name} - removing incomplete bbox data" + ) + elif "page" in attr_assessment and "bbox" not in attr_assessment: + logger.warning( + f"Found page without bbox for {attr_name} - removing incomplete page data" + ) + + # Remove raw bbox/page data + enhanced.pop("bbox", None) + enhanced.pop("page", None) + + return enhanced + + +def extract_geometry_from_nested_dict( + data: dict[str, Any], path: list[str] | None = None +) -> dict[str, Any]: + """ + Recursively process geometry data in nested assessment structures. + + Args: + data: Assessment data dictionary (may contain nested dicts/lists) + path: Current path for logging + + Returns: + Enhanced data with processed geometry + """ + if path is None: + path = [] + + if not isinstance(data, dict): + return data + + result = {} + + for key, value in data.items(): + current_path = path + [key] + + if isinstance(value, dict): + # Check if this looks like an assessment entry + if "confidence" in value or "bbox" in value: + # Process this assessment + result[key] = process_assessment_geometry(value, ".".join(current_path)) + else: + # Recurse into nested dict + result[key] = extract_geometry_from_nested_dict(value, current_path) + + elif isinstance(value, list): + # Process each item in list + processed_list = [] + for i, item in enumerate(value): + if isinstance(item, dict): + processed_list.append( + extract_geometry_from_nested_dict(item, current_path + [str(i)]) + ) + else: + processed_list.append(item) + result[key] = processed_list + else: + result[key] = value + + return result diff --git a/lib/idp_common_pkg/idp_common/assessment/granular_service.py b/lib/idp_common_pkg/idp_common/assessment/granular_service.py index b2a4fc67..008ac058 100644 --- a/lib/idp_common_pkg/idp_common/assessment/granular_service.py +++ b/lib/idp_common_pkg/idp_common/assessment/granular_service.py @@ -2,27 +2,29 @@ # SPDX-License-Identifier: MIT-0 """ -Granular assessment service for evaluating document extraction confidence using LLMs. +Granular assessment service for evaluating document extraction confidence using Strands agents. -This module provides a more scalable approach to assessment by: -1. Breaking down assessments into smaller, focused inferences -2. Leveraging prompt caching to reduce costs -3. Using multi-threading for parallel processing -4. Adapting batch sizes based on attribute complexity +This module provides a scalable approach to assessment by: +1. Breaking down assessments into single-field focused tasks +2. Leveraging Strands agents with tool-based interaction +3. Using parallel async execution for performance +4. Maintaining assessment structure that mirrors extraction results """ import json -import logging import os import time -from concurrent.futures import ThreadPoolExecutor, as_completed -from dataclasses import dataclass -from typing import Any, Dict, Generator, List, Optional, Tuple +from typing import Any -from idp_common import bedrock, image, metrics, s3, utils +from aws_lambda_powertools import Logger +from mypy_boto3_dynamodb.service_resource import DynamoDBServiceResource + +from idp_common import image, metrics, s3, utils +from idp_common.assessment.models import AssessmentResult, AssessmentTask +from idp_common.assessment.strands_executor import execute_assessment_tasks_parallel +from idp_common.assessment.strands_service import _convert_field_path_to_string from idp_common.config.models import IDPConfig from idp_common.config.schema_constants import ( - SCHEMA_DESCRIPTION, SCHEMA_ITEMS, SCHEMA_PROPERTIES, SCHEMA_TYPE, @@ -30,37 +32,12 @@ TYPE_OBJECT, X_AWS_IDP_CONFIDENCE_THRESHOLD, X_AWS_IDP_DOCUMENT_TYPE, - X_AWS_IDP_LIST_ITEM_DESCRIPTION, ) +from idp_common.extraction.models import ExtractionData from idp_common.models import Document, Status -from idp_common.utils import check_token_limit, extract_json_from_text - -logger = logging.getLogger(__name__) - - -@dataclass -class AssessmentTask: - """Represents a single assessment task to be processed.""" - - task_id: str - task_type: str # 'simple_batch', 'group', 'list_item' - attributes: List[str] # Attribute names to assess - extraction_data: Dict[str, Any] # Relevant extraction data - confidence_thresholds: Dict[str, float] # Attribute -> threshold mapping - list_item_index: Optional[int] = None # For list items +from idp_common.utils import check_token_limit - -@dataclass -class AssessmentResult: - """Result of a single assessment task.""" - - task_id: str - success: bool - assessment_data: Dict[str, Any] - confidence_alerts: List[Dict[str, Any]] - error_message: Optional[str] = None - processing_time: float = 0.0 - metering: Optional[Dict[str, Any]] = None +logger = Logger(service="assessment", level=os.getenv("LOG_LEVEL", "INFO")) def _safe_float_conversion(value: Any, default: float = 0.0) -> float: @@ -108,7 +85,7 @@ class GranularAssessmentService: def __init__( self, region: str | None = None, - config: Dict[str, Any] | IDPConfig | None = None, + config: dict[str, Any] | IDPConfig | None = None, cache_table: str | None = None, ): """ @@ -130,18 +107,13 @@ def __init__( self.config = config_model self.region = region or os.environ.get("AWS_REGION") - # Granular processing configuration (type-safe access, Pydantic handles conversions) - self.max_workers = self.config.assessment.granular.max_workers - self.simple_batch_size = self.config.assessment.granular.simple_batch_size - self.list_batch_size = self.config.assessment.granular.list_batch_size + # Assessment configuration (granular is now always enabled) + self.max_workers = self.config.assessment.max_workers - # Ensure safe minimum values + # Ensure safe minimum value self.max_workers = max(1, self.max_workers) - self.simple_batch_size = max(1, self.simple_batch_size) - self.list_batch_size = max(1, self.list_batch_size) - # Auto-determine caching and parallel processing - # Caching is automatically handled by the bedrock client based on model support + # Auto-determine parallel processing # Parallel processing is enabled when max_workers > 1 self.enable_parallel = self.max_workers > 1 @@ -151,7 +123,9 @@ def __init__( if self.cache_table_name: import boto3 - dynamodb = boto3.resource("dynamodb", region_name=self.region) + dynamodb: DynamoDBServiceResource = boto3.resource( + "dynamodb", region_name=self.region + ) # pyright: ignore[reportAssignmentType] self.cache_table = dynamodb.Table(self.cache_table_name) logger.info( f"Granular assessment caching enabled using table: {self.cache_table_name}" @@ -172,14 +146,12 @@ def __init__( model_id = self.config.assessment.model logger.info(f"Initialized granular assessment service with model {model_id}") logger.info( - f"Granular config: max_workers={self.max_workers}, " - f"simple_batch_size={self.simple_batch_size}, " - f"list_batch_size={self.list_batch_size}, " + f"Assessment config: max_workers={self.max_workers}, " f"parallel={self.enable_parallel}, " f"caching={'enabled' if self.cache_table else 'disabled'}" ) - def _get_class_schema(self, class_label: str) -> Dict[str, Any]: + def _get_class_schema(self, class_label: str) -> dict[str, Any]: """ Get JSON Schema for a specific document class. @@ -196,83 +168,8 @@ def _get_class_schema(self, class_label: str) -> Dict[str, Any]: return schema return {} - def _walk_properties_for_assessment( - self, properties: Dict[str, Any], parent_path: str = "" - ) -> Generator[Dict[str, Any], None, None]: - """ - Walk JSON Schema properties and yield assessment property information. - Generator pattern for efficient schema traversal. - - Args: - properties: JSON Schema properties dict - parent_path: Parent path for nested properties (e.g., "CompanyAddress") - - Yields: - Dict containing property information: - { - 'path': 'CompanyAddress.Street', # Full path - 'name': 'Street', # Property name - 'parent_path': 'CompanyAddress', # Parent path (empty string for top-level) - 'type': 'string', # JSON Schema type - 'description': 'Street address', - 'confidence_threshold': 0.9, # From x-aws-idp-confidence-threshold - 'prop_schema': {...} # Full property schema for reference - } - """ - for prop_name, prop_schema in properties.items(): - prop_type = prop_schema.get(SCHEMA_TYPE) - full_path = f"{parent_path}.{prop_name}" if parent_path else prop_name - - # Get confidence threshold for this property - threshold = prop_schema.get(X_AWS_IDP_CONFIDENCE_THRESHOLD) - - if prop_type == TYPE_OBJECT: - # Yield info for the group itself - yield { - "path": full_path, - "name": prop_name, - "parent_path": parent_path, - "type": TYPE_OBJECT, - "description": prop_schema.get(SCHEMA_DESCRIPTION, ""), - "confidence_threshold": threshold, - "prop_schema": prop_schema, - } - # Recurse into nested object properties - yield from self._walk_properties_for_assessment( - prop_schema.get(SCHEMA_PROPERTIES, {}), full_path - ) - - elif prop_type == TYPE_ARRAY: - # Yield info for the list itself - yield { - "path": full_path, - "name": prop_name, - "parent_path": parent_path, - "type": TYPE_ARRAY, - "description": prop_schema.get(SCHEMA_DESCRIPTION, ""), - "confidence_threshold": threshold, - "list_item_description": prop_schema.get( - X_AWS_IDP_LIST_ITEM_DESCRIPTION, "" - ), - "prop_schema": prop_schema, - } - # Note: We don't recurse into array items here because list items - # are handled specially in task creation (one task per item) - - else: - # Leaf property (simple type: string, number, boolean, etc.) - yield { - "path": full_path, - "name": prop_name, - "parent_path": parent_path, - "type": prop_type or "string", - "description": prop_schema.get(SCHEMA_DESCRIPTION, ""), - "confidence_threshold": threshold, - "prop_schema": prop_schema, - } - def _get_confidence_threshold_by_path( - self, properties: Dict[str, Any], path: str, default: float = 0.9 + self, properties: dict[str, Any], path: str, default: float = 0.9 ) -> float: """ Get confidence threshold for a property path (e.g., 'CompanyAddress.Street'). @@ -315,296 +212,19 @@ def _get_confidence_threshold_by_path( return default - def _format_property_descriptions( - self, properties: Dict[str, Any], filter_names: Optional[List[str]] = None - ) -> str: - """ - Format property descriptions from JSON Schema properties for the prompt. - Can optionally filter to specific property names. - - Args: - properties: JSON Schema properties dict - filter_names: Optional list of property names to include (None = all) - - Returns: - Formatted property descriptions as a string - """ - formatted_lines = [] - - for prop_name, prop_schema in properties.items(): - # Skip if filtering and this property is not in the filter list - if filter_names is not None and prop_name not in filter_names: - continue - - prop_type = prop_schema.get(SCHEMA_TYPE) - description = prop_schema.get(SCHEMA_DESCRIPTION, "") - - if prop_type == TYPE_OBJECT: - formatted_lines.append(f"{prop_name} \t[ {description} ]") - nested_props = prop_schema.get(SCHEMA_PROPERTIES, {}) - for nested_name, nested_schema in nested_props.items(): - nested_desc = nested_schema.get(SCHEMA_DESCRIPTION, "") - formatted_lines.append(f" - {nested_name} \t[ {nested_desc} ]") - - elif prop_type == TYPE_ARRAY: - formatted_lines.append(f"{prop_name} \t[ {description} ]") - items_schema = prop_schema.get(SCHEMA_ITEMS, {}) - - item_desc = prop_schema.get(X_AWS_IDP_LIST_ITEM_DESCRIPTION, "") - if item_desc: - formatted_lines.append(f" Each item: {item_desc}") - - if items_schema.get(SCHEMA_TYPE) == TYPE_OBJECT: - item_props = items_schema.get(SCHEMA_PROPERTIES, {}) - for item_name, item_schema in item_props.items(): - item_prop_desc = item_schema.get(SCHEMA_DESCRIPTION, "") - formatted_lines.append( - f" - {item_name} \t[ {item_prop_desc} ]" - ) - else: - formatted_lines.append(f"{prop_name} \t[ {description} ]") - - return "\n".join(formatted_lines) - - def _get_attribute_confidence_threshold( - self, attr_name: str, attributes: List[Dict[str, Any]], default_threshold: float - ) -> float: - """ - Get confidence threshold (legacy format, for internal granular service use). - - Args: - attr_name: Name of the attribute - attributes: List of attribute dicts in legacy format - default_threshold: Default threshold if not found - - Returns: - Confidence threshold for the attribute - """ - for attr in attributes: - if attr.get("name") == attr_name: - return _safe_float_conversion( - attr.get("confidence_threshold", default_threshold), - default_threshold, - ) - - if attr.get("attributeType") == "group": - group_attributes = attr.get("groupAttributes", []) - for group_attr in group_attributes: - if group_attr.get("name") == attr_name: - return _safe_float_conversion( - group_attr.get("confidence_threshold", default_threshold), - default_threshold, - ) - - if attr.get("attributeType") == "list": - list_template = attr.get("listItemTemplate", {}) - item_attributes = list_template.get("itemAttributes", []) - for item_attr in item_attributes: - if item_attr.get("name") == attr_name: - return _safe_float_conversion( - item_attr.get("confidence_threshold", default_threshold), - default_threshold, - ) - - return default_threshold - - def _build_cached_prompt_base( - self, - document_text: str, - class_label: str, - attribute_descriptions: str, - ocr_text_confidence: str, - page_images: List[Any], - ) -> List[Dict[str, Any]]: - """ - Build the cacheable base portion of the assessment prompt using the configured task_prompt template. - This will be the same for all tasks and can be cached. - - Args: - document_text: The document text content - class_label: The document class label - attribute_descriptions: Formatted attribute names and descriptions (will be replaced per task) - ocr_text_confidence: Raw OCR results with confidence scores - page_images: List of page images - - Returns: - List of content items for the cacheable portion - """ - # Get the base task prompt template (type-safe access) - task_prompt_template = self.config.assessment.task_prompt - - if not task_prompt_template: - raise ValueError( - "Assessment task_prompt is required in configuration but not found" - ) - - # For granular assessment, we need to build the base content that will be cached - # and leave placeholders for task-specific content - - # Replace common placeholders but leave task-specific ones - base_substitutions = { - "DOCUMENT_TEXT": document_text, - "DOCUMENT_CLASS": class_label, - "OCR_TEXT_CONFIDENCE": ocr_text_confidence, - } - - # Replace placeholders in the template - base_prompt = task_prompt_template - for placeholder, value in base_substitutions.items(): - base_prompt = base_prompt.replace(f"{{{placeholder}}}", value) - - # Handle {DOCUMENT_IMAGE} placeholder if present - if "{DOCUMENT_IMAGE}" in base_prompt: - # Split the prompt at the DOCUMENT_IMAGE placeholder - parts = base_prompt.split("{DOCUMENT_IMAGE}") - if len(parts) != 2: - raise ValueError( - f"Invalid DOCUMENT_IMAGE placeholder usage: found {len(parts) - 1} occurrences, " - f"but exactly 1 is required." - ) - - content = [] - - # Add the part before the image - if parts[0].strip(): - content.append({"text": parts[0]}) - - # Add the images if available - if page_images: - if isinstance(page_images, list): - # Multiple images (limit to 20 as per Bedrock constraints) - if len(page_images) > 20: - logger.warning( - f"Found {len(page_images)} images, truncating to 20 due to Bedrock constraints. " - f"{len(page_images) - 20} images will be dropped." - ) - for img in page_images[:20]: - content.append(image.prepare_bedrock_image_attachment(img)) - else: - # Single image - content.append(image.prepare_bedrock_image_attachment(page_images)) - - # Add the part after the image - if parts[1].strip(): - content.append({"text": parts[1]}) - - else: - # No DOCUMENT_IMAGE placeholder - just add the base prompt - content = [] - if base_prompt.strip(): - content.append({"text": base_prompt}) - - return content - - def _get_task_specific_attribute_descriptions( - self, task: AssessmentTask, properties: Dict[str, Any] - ) -> str: - """ - Get attribute descriptions specific to this task using JSON Schema properties. - - Args: - task: The assessment task - properties: JSON Schema properties dict - - Returns: - Formatted attribute descriptions for this specific task - """ - if task.task_type == "simple_batch": - # For simple batches, filter to only the attributes in this batch - return self._format_property_descriptions( - properties, filter_names=task.attributes - ) - - elif task.task_type == "group": - # For groups, filter to just the group attribute (which includes nested props) - group_attr_name = task.attributes[0] - return self._format_property_descriptions( - properties, filter_names=[group_attr_name] - ) - - elif task.task_type == "list_item": - # For list items, show the item schema properties - list_attr_name = task.attributes[0] - if list_attr_name in properties: - list_prop_schema = properties[list_attr_name] - items_schema = list_prop_schema.get(SCHEMA_ITEMS, {}) - if items_schema.get(SCHEMA_TYPE) == TYPE_OBJECT: - item_properties = items_schema.get(SCHEMA_PROPERTIES, {}) - return self._format_property_descriptions(item_properties) - return "" - - return "" - - def _build_specific_assessment_prompt( - self, - task: AssessmentTask, - base_content: List[Dict[str, Any]], - properties: Dict[str, Any], - ) -> List[Dict[str, Any]]: - """ - Build the specific assessment prompt for a task by replacing the {EXTRACTION_RESULTS} placeholder - in the base content with task-specific extraction data. - - Args: - task: The assessment task - base_content: The cached base content (which has empty {EXTRACTION_RESULTS}) - properties: JSON Schema properties dict for task-specific filtering - - Returns: - Complete content list for the assessment - """ - # Build extraction results for this specific task - task_extraction_data = {} - for attr_name in task.attributes: - if attr_name in task.extraction_data: - task_extraction_data[attr_name] = task.extraction_data[attr_name] - - # For list items, we need to handle the data differently - if task.task_type == "list_item": - extraction_results_str = json.dumps(task.extraction_data, indent=2) - item_index = task.list_item_index if task.list_item_index is not None else 0 - extraction_results_str = f"Item #{item_index + 1}: {extraction_results_str}" - else: - extraction_results_str = json.dumps(task_extraction_data, indent=2) - - # Get task-specific attribute descriptions - task_specific_attributes = self._get_task_specific_attribute_descriptions( - task, properties - ) - - # Create a new content list by replacing placeholders in the base content - content = [] - for item in base_content: - if "text" in item: - # Replace any remaining placeholders in the text - text = item["text"] - - # Replace EXTRACTION_RESULTS placeholder with task-specific data - text = text.replace("{EXTRACTION_RESULTS}", extraction_results_str) - - # Replace ATTRIBUTE_NAMES_AND_DESCRIPTIONS with task-specific attributes if needed - if "{ATTRIBUTE_NAMES_AND_DESCRIPTIONS}" in text: - text = text.replace( - "{ATTRIBUTE_NAMES_AND_DESCRIPTIONS}", task_specific_attributes - ) - - # Only add non-empty text content (must have actual content, not just whitespace) - if text.strip(): - content.append({"text": text}) - else: - # Non-text content (like images, cache points) - pass through unchanged - content.append(item) - - return content - def _create_assessment_tasks( self, - extraction_results: Dict[str, Any], - properties: Dict[str, Any], + extraction_results: dict[str, Any], + properties: dict[str, Any], default_confidence_threshold: float, - ) -> List[AssessmentTask]: + ) -> tuple[list[AssessmentTask], dict[str, Any]]: """ - Create assessment tasks based on JSON Schema property types and extraction results. + Create assessment tasks and pre-build assessment structure. + + This function recursively traverses the schema and extraction results to: + 1. Build an assessment structure that mirrors the extraction results + 2. Create one task per leaf field (no batching - one field at a time) + 3. Store direct parent dict references in tasks for O(1) insertion Args: extraction_results: The extraction results to assess @@ -612,349 +232,117 @@ def _create_assessment_tasks( default_confidence_threshold: Default confidence threshold Returns: - List of assessment tasks + Tuple of (tasks, assessment_structure) + - tasks: list of AssessmentTask objects + - assessment_structure: dict mirroring extraction_results shape """ - tasks = [] - task_counter = 0 + tasks: list[AssessmentTask] = [] + assessment_structure: dict[str, Any] = {} + task_counter = [0] # Use list for mutable counter in nested function + + def _traverse( + schema_props: dict[str, Any], + extraction_data: dict[str, Any], + current_path: tuple[str | int, ...], + parent_dict: dict[str, Any], + ) -> None: + """ + Recursively traverse schema and extraction data to build tasks and structure. + + Args: + schema_props: Current level schema properties + extraction_data: Current level extraction data + current_path: Tuple path to current location + parent_dict: Parent dict in assessment structure for insertion + """ + for prop_name, prop_schema in schema_props.items(): + if prop_name not in extraction_data: + continue # Skip properties not in extraction results - # Group properties by type for efficient processing - simple_props = [] - group_props = [] - list_props = [] + prop_type = prop_schema.get(SCHEMA_TYPE) + prop_value = extraction_data[prop_name] + field_path = current_path + (prop_name,) - for prop_name, prop_schema in properties.items(): - if prop_name not in extraction_results: - continue # Skip properties not in extraction results + if prop_type == TYPE_OBJECT and isinstance(prop_value, dict): + # Create nested dict in assessment structure + nested_dict: dict[str, Any] = {} + parent_dict[prop_name] = nested_dict - prop_type = prop_schema.get(SCHEMA_TYPE) + # Recurse into nested object + nested_props = prop_schema.get(SCHEMA_PROPERTIES, {}) + _traverse(nested_props, prop_value, field_path, nested_dict) - if prop_type == TYPE_OBJECT: - group_props.append((prop_name, prop_schema)) - elif prop_type == TYPE_ARRAY: - list_props.append((prop_name, prop_schema)) - else: - # Simple types: string, number, boolean, etc. - simple_props.append((prop_name, prop_schema)) - - # Create tasks for simple properties (batch them) - for i in range(0, len(simple_props), self.simple_batch_size): - batch = simple_props[i : i + self.simple_batch_size] - prop_names = [name for name, _ in batch] - - # Build confidence thresholds for this batch - confidence_thresholds = {} - for prop_name, prop_schema in batch: - threshold = self._get_confidence_threshold_by_path( - properties, prop_name, default_confidence_threshold - ) - confidence_thresholds[prop_name] = threshold + elif prop_type == TYPE_ARRAY and isinstance(prop_value, list): + # Create list in assessment structure + assessment_list: list[Any] = [] + parent_dict[prop_name] = assessment_list - # Extract relevant data for this batch - batch_extraction_data = { - name: extraction_results[name] - for name in prop_names - if name in extraction_results - } + # Process each array item + items_schema = prop_schema.get(SCHEMA_ITEMS, {}) + item_type = items_schema.get(SCHEMA_TYPE) - task = AssessmentTask( - task_id=f"simple_batch_{task_counter}", - task_type="simple_batch", - attributes=prop_names, - extraction_data=batch_extraction_data, - confidence_thresholds=confidence_thresholds, - ) - tasks.append(task) - task_counter += 1 - - # Create tasks for group properties (one per group) - for prop_name, prop_schema in group_props: - # Build confidence thresholds for nested properties - confidence_thresholds = {} - nested_props = prop_schema.get(SCHEMA_PROPERTIES, {}) - for nested_name in nested_props.keys(): - nested_path = f"{prop_name}.{nested_name}" - threshold = self._get_confidence_threshold_by_path( - properties, nested_path, default_confidence_threshold - ) - confidence_thresholds[nested_name] = threshold - - task = AssessmentTask( - task_id=f"group_{task_counter}", - task_type="group", - attributes=[prop_name], - extraction_data={prop_name: extraction_results[prop_name]}, - confidence_thresholds=confidence_thresholds, - ) - tasks.append(task) - task_counter += 1 + for idx, item_value in enumerate(prop_value): + item_path = field_path + (idx,) - # Create tasks for list properties (one per list item) - for prop_name, prop_schema in list_props: - list_data = extraction_results.get(prop_name, []) + if item_type == TYPE_OBJECT and isinstance(item_value, dict): + # Create dict for this array item + item_dict: dict[str, Any] = {} + assessment_list.append(item_dict) - if not isinstance(list_data, list): - logger.warning(f"List property {prop_name} is not a list, skipping") - continue + # Recurse into array item properties + item_props = items_schema.get(SCHEMA_PROPERTIES, {}) + _traverse(item_props, item_value, item_path, item_dict) - # Build confidence thresholds for list item properties - confidence_thresholds = {} - items_schema = prop_schema.get(SCHEMA_ITEMS, {}) - if items_schema.get(SCHEMA_TYPE) == TYPE_OBJECT: - item_props = items_schema.get(SCHEMA_PROPERTIES, {}) - for item_prop_name in item_props.keys(): - # For list items, the path includes the list name - item_path = f"{prop_name}.{item_prop_name}" - threshold = self._get_confidence_threshold_by_path( - properties, item_path, default_confidence_threshold - ) - confidence_thresholds[item_prop_name] = threshold + else: + # Leaf field in array - create task + threshold = self._get_confidence_threshold_by_path( + properties, + ".".join(str(p) for p in field_path), + default_confidence_threshold, + ) - # Create tasks for list items (batch them if configured) - for i in range(0, len(list_data), self.list_batch_size): - batch_end = min(i + self.list_batch_size, len(list_data)) + task = AssessmentTask( + task_id=f"task_{task_counter[0]}", + task_type="attribute", + field_path=item_path, + field_name=_convert_field_path_to_string(item_path), + field_schema=items_schema, + confidence_threshold=threshold, + ) + tasks.append(task) + task_counter[0] += 1 - for j in range(i, batch_end): - item_data = list_data[j] + # Pre-allocate slot in list (will be replaced by assessment obj) + assessment_list.append(None) + + else: + # Leaf field - create task + threshold = self._get_confidence_threshold_by_path( + properties, + ".".join(str(p) for p in field_path), + default_confidence_threshold, + ) task = AssessmentTask( - task_id=f"list_{prop_name}_item_{j}", - task_type="list_item", - attributes=[prop_name], - extraction_data=item_data, - confidence_thresholds=confidence_thresholds, - list_item_index=j, + task_id=f"task_{task_counter[0]}", + task_type="attribute", + field_path=field_path, + field_name=_convert_field_path_to_string(field_path), + field_schema=prop_schema, + confidence_threshold=threshold, ) tasks.append(task) - task_counter += 1 - - logger.info( - f"Created {len(tasks)} assessment tasks: " - f"{len([t for t in tasks if t.task_type == 'simple_batch'])} simple batches, " - f"{len([t for t in tasks if t.task_type == 'group'])} groups, " - f"{len([t for t in tasks if t.task_type == 'list_item'])} list items" - ) - - return tasks - - def _process_assessment_task( - self, - task: AssessmentTask, - base_content: List[Dict[str, Any]], - properties: Dict[str, Any], - model_id: str, - system_prompt: str, - temperature: float, - top_k: float, - top_p: float, - max_tokens: Optional[int], - ) -> AssessmentResult: - """ - Process a single assessment task. - - Args: - task: The assessment task to process - base_content: The cached base content - properties: JSON Schema properties dict - model_id: Bedrock model ID - system_prompt: System prompt - temperature: Temperature parameter - top_k: Top-k parameter - top_p: Top-p parameter - max_tokens: Max tokens parameter - - Returns: - Assessment result - """ - start_time = time.time() - - try: - # Build the complete prompt - content = self._build_specific_assessment_prompt( - task, base_content, properties - ) - - logger.debug( - f"Processing assessment task {task.task_id} with {len(task.attributes)} attributes" - ) - - # Invoke Bedrock - response_with_metering = bedrock.invoke_model( - model_id=model_id, - system_prompt=system_prompt, - content=content, - temperature=temperature, - top_k=top_k, - top_p=top_p, - max_tokens=max_tokens, - context="GranularAssessment", - ) - - # Extract text from response - assessment_text = bedrock.extract_text_from_response(response_with_metering) - metering = response_with_metering.get("metering", {}) + task_counter[0] += 1 - # Parse response into JSON - assessment_data = {} - task_failed = False - error_messages = [] - try: - assessment_data = json.loads(extract_json_from_text(assessment_text)) - except Exception as e: - logger.error( - f"Error parsing assessment LLM output for task {task.task_id}: {e}" - ) - task_failed = True - error_messages.append( - f"Error parsing assessment LLM output for task {task.task_id}" - ) - # Create default assessments - for attr_name in task.attributes: - if task.task_type == "list_item": - # For list items, create assessments for each sub-attribute - assessment_data = {} - for ( - sub_attr_name, - threshold, - ) in task.confidence_thresholds.items(): - assessment_data[sub_attr_name] = { - "confidence": 0.5, - "confidence_reason": f"Unable to parse assessment response for {sub_attr_name} - default score assigned", - } - else: - assessment_data[attr_name] = { - "confidence": 0.5, - "confidence_reason": f"Unable to parse assessment response for {attr_name} - default score assigned", - } + # Pre-allocate placeholder in dict (will be replaced by assessment obj) + parent_dict[prop_name] = None - # Process bounding boxes automatically if bbox data is present - try: - logger.debug( - f"Checking for bounding box data in granular assessment task {task.task_id}" - ) - assessment_data = self._extract_geometry_from_assessment( - assessment_data - ) - except Exception as e: - logger.warning( - f"Failed to extract geometry data for task {task.task_id}: {str(e)}" - ) - # Continue with assessment even if geometry extraction fails + # Start recursive traversal from root + _traverse(properties, extraction_results, (), assessment_structure) - # Check for confidence threshold alerts - confidence_alerts = [] - self._check_confidence_alerts_for_task( - task, assessment_data, confidence_alerts - ) - - processing_time = time.time() - start_time - if task_failed: - return AssessmentResult( - task_id=task.task_id, - success=False, - assessment_data=assessment_data, - confidence_alerts=confidence_alerts, - error_message=self._convert_error_list_to_string(error_messages), - processing_time=processing_time, - ) - else: - return AssessmentResult( - task_id=task.task_id, - success=True, - assessment_data=assessment_data, - confidence_alerts=confidence_alerts, - processing_time=processing_time, - metering=metering, - ) + logger.info(f"Created {len(tasks)} assessment tasks (one per leaf field)") - except Exception as e: - processing_time = time.time() - start_time - logger.error(f"Error processing assessment task {task.task_id}: {str(e)}") - - return AssessmentResult( - task_id=task.task_id, - success=False, - assessment_data={}, - confidence_alerts=[], - error_message=str(e), - processing_time=processing_time, - ) - - def _check_confidence_alerts_for_task( - self, - task: AssessmentTask, - assessment_data: Dict[str, Any], - alerts_list: List[Dict[str, Any]], - ) -> None: - """ - Check assessment data for confidence threshold violations for a specific task. - - Args: - task: The assessment task - assessment_data: Dictionary containing assessment data - alerts_list: List to append alerts to (modified in place) - """ - if task.task_type == "simple_batch": - for attr_name in task.attributes: - if attr_name in assessment_data and isinstance( - assessment_data[attr_name], dict - ): - confidence = _safe_float_conversion( - assessment_data[attr_name].get("confidence", 0.0), 0.0 - ) - threshold = task.confidence_thresholds.get(attr_name, 0.9) - if confidence < threshold: - alerts_list.append( - { - "attribute_name": attr_name, - "confidence": confidence, - "confidence_threshold": threshold, - } - ) - - elif task.task_type == "group": - attr_name = task.attributes[0] # Group tasks have one attribute - if attr_name in assessment_data and isinstance( - assessment_data[attr_name], dict - ): - for sub_attr_name, sub_assessment in assessment_data[attr_name].items(): - if ( - isinstance(sub_assessment, dict) - and "confidence" in sub_assessment - ): - confidence = _safe_float_conversion( - sub_assessment.get("confidence", 0.0), 0.0 - ) - threshold = task.confidence_thresholds.get(sub_attr_name, 0.9) - if confidence < threshold: - alerts_list.append( - { - "attribute_name": f"{attr_name}.{sub_attr_name}", - "confidence": confidence, - "confidence_threshold": threshold, - } - ) - - elif task.task_type == "list_item": - attr_name = task.attributes[0] # List item tasks have one attribute - item_index = task.list_item_index if task.list_item_index is not None else 0 - - for item_attr_name, item_assessment in assessment_data.items(): - if ( - isinstance(item_assessment, dict) - and "confidence" in item_assessment - ): - confidence = _safe_float_conversion( - item_assessment.get("confidence", 0.0), 0.0 - ) - threshold = task.confidence_thresholds.get(item_attr_name, 0.9) - if confidence < threshold: - alerts_list.append( - { - "attribute_name": f"{attr_name}[{item_index}].{item_attr_name}", - "confidence": confidence, - "confidence_threshold": threshold, - } - ) + return tasks, assessment_structure def _get_cache_key( self, document_id: str, workflow_execution_arn: str, section_id: str @@ -979,7 +367,7 @@ def _get_cache_key( def _get_cached_assessment_tasks( self, document_id: str, workflow_execution_arn: str, section_id: str - ) -> Dict[str, AssessmentResult]: + ) -> dict[str, AssessmentResult]: """ Retrieve cached assessment task results for a document section. @@ -989,7 +377,7 @@ def _get_cached_assessment_tasks( section_id: Section ID Returns: - Dictionary mapping task_id to cached AssessmentResult, empty dict if no cache + dictionary mapping task_id to cached AssessmentResult, empty dict if no cache """ logger.info( f"Attempting to retrieve cached assessment tasks for document {document_id} section {section_id}" @@ -1017,8 +405,6 @@ def _get_cached_assessment_tasks( # Extract task results from JSON attribute if "task_results" in cached_data: try: - import json - task_data_list = json.loads(cached_data["task_results"]) for task_data in task_data_list: @@ -1056,7 +442,7 @@ def _cache_successful_assessment_tasks( document_id: str, workflow_execution_arn: str, section_id: str, - task_results: List[AssessmentResult], + task_results: list[AssessmentResult], ) -> None: """ Cache successful assessment task results to DynamoDB as a JSON-serialized list. @@ -1065,7 +451,7 @@ def _cache_successful_assessment_tasks( document_id: Document ID workflow_execution_arn: Workflow execution ARN section_id: Section ID - task_results: List of successful assessment task results + task_results: list of successful assessment task results """ if not self.cache_table or not task_results: return @@ -1134,8 +520,8 @@ def _is_throttling_exception(self, exception: Exception) -> bool: Returns: True if exception indicates throttling, False otherwise """ - if hasattr(exception, "response") and "Error" in exception.response: - error_code = exception.response["Error"]["Code"] + if hasattr(exception, "response") and "Error" in exception.response: # pyright: ignore[reportAttributeAccessIssue] + error_code = exception.response["Error"]["Code"] # pyright: ignore[reportAttributeAccessIssue] return error_code in self.throttling_exceptions # Check exception class name and message for throttling indicators @@ -1147,38 +533,62 @@ def _is_throttling_exception(self, exception: Exception) -> bool: for throttle_term in self.throttling_exceptions ) + def _insert_at_field_path( + self, + structure: dict[str, Any], + field_path: tuple[str | int, ...], + value: Any, + ) -> None: + """ + Navigate through structure using field_path and insert value at the end. + + Args: + structure: The assessment structure to navigate + field_path: Tuple path like ("Account Holder Address", "City") or ("Transactions", 0, "Amount") + value: The assessment data to insert + + Example: + field_path = ("Account Holder Address", "City") + -> structure["Account Holder Address"]["City"] = value + + field_path = ("Transactions", 0, "Amount") + -> structure["Transactions"][0]["Amount"] = value + """ + parent = structure + for key in field_path[:-1]: + parent = parent[key] + parent[field_path[-1]] = value + def _aggregate_assessment_results( self, - tasks: List[AssessmentTask], - results: List[AssessmentResult], - extraction_results: Dict[str, Any], - ) -> Tuple[Dict[str, Any], List[Dict[str, Any]], Dict[str, Any]]: + tasks: list[AssessmentTask], + results: list[AssessmentResult], + assessment_structure: dict[str, Any], + ) -> tuple[dict[str, Any], list[dict[str, Any]], dict[str, Any]]: """ - Aggregate individual task results into the final assessment structure. + Aggregate individual task results into assessment structure using field_path navigation. Args: - tasks: List of assessment tasks - results: List of assessment results - extraction_results: Original extraction results + tasks: list of assessment tasks + results: list of assessment results + assessment_structure: Pre-built assessment structure from _create_assessment_tasks Returns: - Tuple of (enhanced_assessment_data, confidence_alerts, aggregated_metering) + Tuple of (assessment_structure, confidence_alerts, aggregated_metering) """ - enhanced_assessment_data = {} - all_confidence_alerts = [] - aggregated_metering = {} + all_confidence_alerts: list[dict[str, Any]] = [] + aggregated_metering: dict[str, Any] = {} # Create a mapping from task_id to result result_map = {result.task_id: result for result in results} - # Process results by task type for task in tasks: result = result_map.get(task.task_id) if not result or not result.success: logger.warning(f"Task {task.task_id} failed or missing result") continue - # Aggregate metering data using the same pattern as classification service + # Aggregate metering data if result.metering: aggregated_metering = utils.merge_metering_data( aggregated_metering, result.metering @@ -1187,85 +597,34 @@ def _aggregate_assessment_results( # Add confidence alerts all_confidence_alerts.extend(result.confidence_alerts) - # Process assessment data based on task type - if task.task_type == "simple_batch": - for attr_name in task.attributes: - if attr_name in result.assessment_data: - # Add confidence threshold to the assessment - assessment_value = result.assessment_data[attr_name] - if isinstance(assessment_value, dict): - assessment = assessment_value.copy() - threshold = task.confidence_thresholds.get(attr_name, 0.9) - assessment["confidence_threshold"] = threshold - enhanced_assessment_data[attr_name] = assessment - else: - logger.warning( - f"Unexpected assessment data type for {attr_name}: {type(assessment_value)}" - ) - - elif task.task_type == "group": - attr_name = task.attributes[0] - if attr_name in result.assessment_data: - assessment_value = result.assessment_data[attr_name] - if isinstance(assessment_value, dict): - group_assessment = {} - for sub_attr_name, sub_assessment in assessment_value.items(): - if isinstance(sub_assessment, dict): - enhanced_sub_assessment = sub_assessment.copy() - threshold = task.confidence_thresholds.get( - sub_attr_name, 0.9 - ) - enhanced_sub_assessment["confidence_threshold"] = ( - threshold - ) - group_assessment[sub_attr_name] = ( - enhanced_sub_assessment - ) - else: - logger.warning( - f"Unexpected sub-assessment data type for {attr_name}.{sub_attr_name}: {type(sub_assessment)}" - ) - group_assessment[sub_attr_name] = sub_assessment - enhanced_assessment_data[attr_name] = group_assessment - else: - logger.warning( - f"Unexpected group assessment data type for {attr_name}: {type(assessment_value)}" - ) + # Get assessment data directly from result + # strands_service returns flat assessment dict: {confidence, value, reasoning, ...} + field_assessment = result.assessment_data - elif task.task_type == "list_item": - attr_name = task.attributes[0] - item_index = ( - task.list_item_index if task.list_item_index is not None else 0 + if not isinstance(field_assessment, dict): + logger.warning( + f"Task {task.task_id}: expected dict assessment, got {type(field_assessment)}" ) + continue - # Initialize list structure if not exists - if attr_name not in enhanced_assessment_data: - enhanced_assessment_data[attr_name] = [] - - # Ensure the list is long enough for this item - while len(enhanced_assessment_data[attr_name]) <= item_index: - enhanced_assessment_data[attr_name].append({}) - - # Add assessments for this list item - item_assessment = {} - for ( - item_attr_name, - item_assessment_data, - ) in result.assessment_data.items(): - if isinstance(item_assessment_data, dict): - enhanced_item_assessment = item_assessment_data.copy() - threshold = task.confidence_thresholds.get(item_attr_name, 0.9) - enhanced_item_assessment["confidence_threshold"] = threshold - item_assessment[item_attr_name] = enhanced_item_assessment - else: - logger.warning( - f"Unexpected list item assessment data type for {attr_name}[{item_index}].{item_attr_name}: {type(item_assessment_data)}" - ) - item_assessment[item_attr_name] = item_assessment_data + # Add confidence_threshold if not already present + if "confidence_threshold" not in field_assessment: + field_assessment["confidence_threshold"] = task.confidence_threshold - enhanced_assessment_data[attr_name][item_index] = item_assessment + # Insert directly at field_path - no unwrapping needed + try: + self._insert_at_field_path( + assessment_structure, task.field_path, field_assessment + ) + logger.debug( + f"Task {task.task_id}: Inserted assessment at {task.field_path}" + ) + except (KeyError, IndexError, TypeError) as e: + logger.error( + f"Task {task.task_id}: Failed to insert at path {task.field_path}: {e}" + ) - return enhanced_assessment_data, all_confidence_alerts, aggregated_metering + return assessment_structure, all_confidence_alerts, aggregated_metering def _get_text_confidence_data(self, page) -> str: """ @@ -1295,148 +654,7 @@ def _get_text_confidence_data(self, page) -> str: ) return "Text Confidence Data Unavailable" - def _convert_bbox_to_geometry( - self, bbox_coords: List[float], page_num: int - ) -> Dict[str, Any]: - """ - Convert [x1,y1,x2,y2] coordinates to geometry format. - - Args: - bbox_coords: List of 4 coordinates [x1, y1, x2, y2] in 0-1000 scale - page_num: Page number where the bounding box appears - - Returns: - Dictionary in geometry format compatible with pattern-1 UI - """ - if len(bbox_coords) != 4: - raise ValueError(f"Expected 4 coordinates, got {len(bbox_coords)}") - - x1, y1, x2, y2 = bbox_coords - - # Ensure coordinates are in correct order - x1, x2 = min(x1, x2), max(x1, x2) - y1, y2 = min(y1, y2), max(y1, y2) - - # Convert from normalized 0-1000 scale to 0-1 - left = x1 / 1000.0 - top = y1 / 1000.0 - width = (x2 - x1) / 1000.0 - height = (y2 - y1) / 1000.0 - - return { - "boundingBox": {"top": top, "left": left, "width": width, "height": height}, - "page": page_num, - } - - def _process_single_assessment_geometry( - self, attr_assessment: Dict[str, Any], attr_name: str = "" - ) -> Dict[str, Any]: - """ - Process geometry data for a single assessment (with confidence key). - - Args: - attr_assessment: Single assessment dictionary with confidence data - attr_name: Name of attribute for logging - - Returns: - Enhanced assessment with geometry converted to proper format - """ - enhanced_attr = attr_assessment.copy() - - # Check if this assessment includes bbox data - if "bbox" in attr_assessment or "page" in attr_assessment: - # Both bbox and page are required for valid geometry - if "bbox" in attr_assessment and "page" in attr_assessment: - try: - bbox_coords = attr_assessment["bbox"] - page_num = attr_assessment["page"] - - # Validate bbox coordinates - if isinstance(bbox_coords, list) and len(bbox_coords) == 4: - # Convert to geometry format - geometry = self._convert_bbox_to_geometry(bbox_coords, page_num) - enhanced_attr["geometry"] = [geometry] - - logger.debug( - f"Converted bounding box for {attr_name}: {bbox_coords} -> geometry format" - ) - else: - logger.warning( - f"Invalid bounding box format for {attr_name}: {bbox_coords}" - ) - except Exception as e: - logger.warning( - f"Failed to process bounding box for {attr_name}: {str(e)}" - ) - raise - else: - # If only one of bbox/page exists, log a warning about incomplete data - if "bbox" in attr_assessment and "page" not in attr_assessment: - logger.warning( - f"Found bbox without page for {attr_name} - removing incomplete bbox data" - ) - elif "page" in attr_assessment and "bbox" not in attr_assessment: - logger.warning( - f"Found page without bbox for {attr_name} - removing incomplete page data" - ) - - # Always remove raw bbox/page data from output (whether processed or incomplete) - enhanced_attr.pop("bbox", None) - enhanced_attr.pop("page", None) - - return enhanced_attr - - def _extract_geometry_from_assessment( - self, assessment_data: Dict[str, Any] - ) -> Dict[str, Any]: - """ - Extract geometry data from assessment response and convert to proper format. - Now supports recursive processing of nested group attributes. - - Args: - assessment_data: Dictionary containing assessment results from LLM - - Returns: - Enhanced assessment data with geometry information converted to proper format - """ - enhanced_assessment = {} - - for attr_name, attr_assessment in assessment_data.items(): - if isinstance(attr_assessment, dict): - # Check if this is a direct confidence assessment - if "confidence" in attr_assessment: - # This is a direct assessment - process its geometry - enhanced_assessment[attr_name] = ( - self._process_single_assessment_geometry( - attr_assessment, attr_name - ) - ) - else: - # This is a group attribute (no direct confidence) - recursively process nested attributes - logger.debug(f"Processing group attribute: {attr_name}") - enhanced_assessment[attr_name] = ( - self._extract_geometry_from_assessment(attr_assessment) - ) - - elif isinstance(attr_assessment, list): - # Handle list attributes - process each item recursively - enhanced_list = [] - for i, item_assessment in enumerate(attr_assessment): - if isinstance(item_assessment, dict): - # Recursively process each list item - enhanced_item = self._extract_geometry_from_assessment( - item_assessment - ) - enhanced_list.append(enhanced_item) - else: - # Non-dict items pass through unchanged - enhanced_list.append(item_assessment) - enhanced_assessment[attr_name] = enhanced_list - else: - # Other types pass through unchanged - enhanced_assessment[attr_name] = attr_assessment - - return enhanced_assessment + # Geometry processing uses shared utilities from geometry_utils module def process_document_section(self, document: Document, section_id: str) -> Document: """ @@ -1450,16 +668,10 @@ def process_document_section(self, document: Document, section_id: str) -> Docum Document: Updated Document object with assessment results appended to extraction results """ # Check if assessment is enabled in typed configuration - enabled = self.config.assessment.enabled - if not enabled: + if not self.config.assessment.enabled: logger.info("Assessment is disabled via configuration") return document - # Validate input document - if not document: - logger.error("No document provided") - return document - if not document.sections: logger.error("Document has no sections to process") document.errors.append("Document has no sections to process") @@ -1512,8 +724,9 @@ def process_document_section(self, document: Document, section_id: str) -> Docum try: # Read existing extraction results t0 = time.time() - extraction_data = s3.get_json_content(section.extraction_result_uri) - extraction_results = extraction_data.get("inference_result", {}) + extraction_data_dict = s3.get_json_content(section.extraction_result_uri) + extraction_data = ExtractionData.model_validate(extraction_data_dict) + extraction_results = extraction_data.inference_result # Skip assessment if no extraction results found if not extraction_results: @@ -1544,6 +757,9 @@ def process_document_section(self, document: Document, section_id: str) -> Docum # Read page images with configurable dimensions (type-safe access) target_width = self.config.assessment.image.target_width target_height = self.config.assessment.image.target_height + logger.info( + f"Image resize config: target_width={target_width}, target_height={target_height}" + ) page_images = [] for page_id in sorted_page_ids: @@ -1552,9 +768,12 @@ def process_document_section(self, document: Document, section_id: str) -> Docum page = document.pages[page_id] image_uri = page.image_uri - # Just pass the values directly - prepare_image handles empty strings/None + # For assessment, convert to PNG for better compression with rulers/overlays image_content = image.prepare_image( - image_uri, target_width, target_height + image_uri, target_width, target_height, output_format="PNG" + ) + logger.info( + f"Loaded page {page_id} image as PNG: {len(image_content):,} bytes" ) page_images.append(image_content) @@ -1578,14 +797,6 @@ def process_document_section(self, document: Document, section_id: str) -> Docum t4 = time.time() logger.info(f"Time taken to read raw OCR results: {t4 - t3:.2f} seconds") - # Get assessment configuration (type-safe, Pydantic handles conversions) - model_id = self.config.assessment.model - temperature = self.config.assessment.temperature - top_k = self.config.assessment.top_k - top_p = self.config.assessment.top_p - max_tokens = self.config.assessment.max_tokens - system_prompt = self.config.assessment.system_prompt - # Get schema for this document class class_schema = self._get_class_schema(class_label) if not class_schema: @@ -1599,17 +810,8 @@ def process_document_section(self, document: Document, section_id: str) -> Docum self.config.assessment.default_confidence_threshold ) - # Build the cached base prompt (without attribute descriptions - those are task-specific) - base_content = self._build_cached_prompt_base( - document_text, - class_label, - "", # Empty attribute descriptions - will be replaced per task - ocr_text_confidence, - page_images, - ) - - # Create assessment tasks - tasks = self._create_assessment_tasks( + # Create assessment tasks and pre-built assessment structure + tasks, assessment_structure = self._create_assessment_tasks( extraction_results, properties, default_confidence_threshold ) @@ -1624,12 +826,6 @@ def process_document_section(self, document: Document, section_id: str) -> Docum all_task_results = list(cached_task_results.values()) combined_metering = {} - # Use thread-safe error collection (similar to classification service) - import threading - - errors_lock = threading.Lock() - failed_task_exceptions = {} # Store original exceptions for failed tasks - # Determine which tasks need processing tasks_to_process = [] for task in tasks: @@ -1648,106 +844,49 @@ def process_document_section(self, document: Document, section_id: str) -> Docum f"Found {len(cached_task_results)} cached assessment task results, processing {len(tasks_to_process)} remaining tasks" ) - # Time the model invocations + # Execute tasks using Strands-based parallel executor + # Note: ruler overlay is added internally by strands_service/strands_tools + logger.info( + f"Processing {len(tasks_to_process)} assessment tasks with Strands executor (max_concurrent={self.max_workers})" + ) + request_start_time = time.time() - # Process tasks (parallel or sequential based on configuration) - if self.enable_parallel and len(tasks_to_process) > 1: - logger.info( - f"Processing {len(tasks_to_process)} assessment tasks in parallel with {self.max_workers} workers" + # Call Strands executor - handles both parallel and sequential based on max_concurrent + # Pass raw page_images - ruler overlay is added internally when needed + task_results, task_metering, processing_time = ( + execute_assessment_tasks_parallel( + tasks=tasks_to_process, + extraction_results=extraction_results, + page_images=page_images, + sorted_page_ids=sorted_page_ids, + model_id=self.config.assessment.model, + system_prompt=self.config.assessment.system_prompt, + temperature=self.config.assessment.temperature, + max_tokens=self.config.assessment.max_tokens, + document_schema=class_schema, + max_concurrent=self.max_workers, ) + ) - with ThreadPoolExecutor(max_workers=self.max_workers) as executor: - # Submit all uncached tasks - future_to_task = { - executor.submit( - self._process_assessment_task, - task, - base_content, - properties, - model_id, - system_prompt, - temperature, - top_k, - top_p, - max_tokens, - ): task - for task in tasks_to_process - } + # Merge results and metering + all_task_results.extend(task_results) + combined_metering = utils.merge_metering_data( + combined_metering, task_metering + ) - # Collect results with enhanced error handling - for future in as_completed(future_to_task): - task = future_to_task[future] - try: - result = future.result() - all_task_results.append(result) - - # Merge metering data - if result.metering: - combined_metering = utils.merge_metering_data( - combined_metering, result.metering - ) - except Exception as e: - # Capture exception details for later use - error_msg = f"Error processing assessment task {task.task_id}: {str(e)}" - logger.error(error_msg) - with errors_lock: - document.errors.append(error_msg) - # Store the original exception for later analysis - failed_task_exceptions[task.task_id] = e - - # Create failed result - failed_result = AssessmentResult( - task_id=task.task_id, - success=False, - assessment_data={}, - confidence_alerts=[], - error_message=str(e), - ) - all_task_results.append(failed_result) - else: - logger.info( - f"Processing {len(tasks_to_process)} assessment tasks sequentially" - ) - request_start_time = time.time() + logger.info( + f"Strands executor completed {len(task_results)} tasks in {processing_time:.2f}s" + ) - for task in tasks_to_process: - try: - result = self._process_assessment_task( - task, - base_content, - properties, - model_id, - system_prompt, - temperature, - top_k, - top_p, - max_tokens, - ) - all_task_results.append(result) - - # Merge metering data - if result.metering: - combined_metering = utils.merge_metering_data( - combined_metering, result.metering - ) - except Exception as e: - # Capture exception details for later use - error_msg = f"Error processing assessment task {task.task_id}: {str(e)}" - logger.error(error_msg) - document.errors.append(error_msg) - # Store the original exception for later analysis - failed_task_exceptions[task.task_id] = e - - # Create failed result - failed_result = AssessmentResult( - task_id=task.task_id, - success=False, - assessment_data={}, - confidence_alerts=[], - error_message=str(e), - ) - all_task_results.append(failed_result) + # Track failed tasks for metadata + failed_task_exceptions = {} + for result in task_results: + if not result.success and result.error_message: + # Create a simple exception object for compatibility + failed_task_exceptions[result.task_id] = Exception( + result.error_message + ) # Store failed task exceptions in document metadata for caller to access if failed_task_exceptions: @@ -1826,12 +965,12 @@ def process_document_section(self, document: Document, section_id: str) -> Docum f"Time taken for granular assessment: {total_duration:.2f} seconds" ) - # Aggregate results + # Aggregate results into pre-built assessment structure ( enhanced_assessment_data, confidence_threshold_alerts, aggregated_metering, - ) = self._aggregate_assessment_results(tasks, results, extraction_results) + ) = self._aggregate_assessment_results(tasks, results, assessment_structure) # Calculate success metrics successful_tasks = [r for r in results if r.success] @@ -1884,21 +1023,21 @@ def process_document_section(self, document: Document, section_id: str) -> Docum f"Document will be marked as failed without retry." ) - # Update the existing extraction result with enhanced assessment data - extraction_data["explainability_info"] = [enhanced_assessment_data] - extraction_data["metadata"] = extraction_data.get("metadata", {}) - extraction_data["metadata"]["assessment_time_seconds"] = total_duration - extraction_data["metadata"]["granular_assessment_used"] = True - extraction_data["metadata"]["assessment_tasks_total"] = len(tasks) - extraction_data["metadata"]["assessment_tasks_successful"] = len( - successful_tasks - ) - extraction_data["metadata"]["assessment_tasks_failed"] = len(failed_tasks) + # Update the existing extraction result with enhanced assessment data (typed) + extraction_data.explainability_info = [enhanced_assessment_data] + extraction_data.metadata.assessment_time_seconds = total_duration + extraction_data.metadata.granular_assessment_used = True + extraction_data.metadata.assessment_tasks_total = len(tasks) + extraction_data.metadata.assessment_tasks_successful = len(successful_tasks) + extraction_data.metadata.assessment_tasks_failed = len(failed_tasks) # Write the updated result back to S3 bucket, key = utils.parse_s3_uri(section.extraction_result_uri) s3.write_content( - extraction_data, bucket, key, content_type="application/json" + extraction_data.model_dump(mode="json"), + bucket, + key, + content_type="application/json", ) # Update the section in the document with confidence threshold alerts @@ -1947,9 +1086,10 @@ def process_document_section(self, document: Document, section_id: str) -> Docum if ( document.status == Status.FAILED and document.errors - and not hasattr(document, "metadata") - or not document.metadata - or "failed_assessment_tasks" not in document.metadata + and ( + not document.metadata + or "failed_assessment_tasks" not in document.metadata + ) ): # Check if any errors contain throttling keywords throttling_keywords = [ @@ -2015,10 +1155,10 @@ def assess_document(self, document: Document) -> Document: def _handle_parsing_errors( self, document: Document, - failed_tasks: List[str], + failed_tasks: list[AssessmentResult], document_text: str, - extraction_results: Dict, - ) -> Optional[str]: + extraction_results: dict, + ) -> str | None: """Handle multiple parsing errors with user-friendly messaging.""" # Check for token limit issues token_warning = check_token_limit( diff --git a/lib/idp_common_pkg/idp_common/assessment/models.py b/lib/idp_common_pkg/idp_common/assessment/models.py index 64f480db..b515a72a 100644 --- a/lib/idp_common_pkg/idp_common/assessment/models.py +++ b/lib/idp_common_pkg/idp_common/assessment/models.py @@ -8,12 +8,12 @@ the confidence and accuracy of extraction results. """ -from dataclasses import dataclass -from typing import Any, Dict, List, Optional +from typing import Any +from pydantic import BaseModel, Field, field_validator -@dataclass -class AttributeAssessment: + +class AttributeAssessment(BaseModel): """Assessment result for a single extracted attribute""" attribute_name: str @@ -22,30 +22,298 @@ class AttributeAssessment: extracted_value: Any = None -@dataclass -class AssessmentResult: - """Result of assessment for a document section""" +class LegacyAssessmentResult(BaseModel): + """Legacy result of assessment for a document section (for backwards compatibility)""" section_id: str document_class: str - attribute_assessments: List[AttributeAssessment] + attribute_assessments: list[AttributeAssessment] overall_confidence: float = 0.0 - raw_response: Optional[str] = None - metering: Optional[Dict[str, Any]] = None - metadata: Optional[Dict[str, Any]] = None - output_uri: Optional[str] = None + raw_response: str | None = None + metering: dict[str, Any] | None = None + metadata: dict[str, Any] | None = None + output_uri: str | None = None -@dataclass -class DocumentAssessmentResult: +class DocumentAssessmentResult(BaseModel): """Assessment result for an entire document""" document_id: str - section_assessments: List[AssessmentResult] + section_assessments: list[LegacyAssessmentResult] overall_document_confidence: float = 0.0 total_attributes_assessed: int = 0 high_confidence_attributes: int = 0 medium_confidence_attributes: int = 0 low_confidence_attributes: int = 0 - assessment_summary: Optional[str] = None - metadata: Optional[Dict[str, Any]] = None + assessment_summary: str | None = None + metadata: dict[str, Any] | None = None + + +# ============================================================================ +# Assessment Task Model (unified for all assessment services) +# ============================================================================ + + +class AssessmentTask(BaseModel): + """ + Single-field assessment task for granular assessment. + + Used by both granular_service.py (creation) and strands_service.py (execution). + """ + + model_config = {"arbitrary_types_allowed": True} + + task_id: str + task_type: str = Field(description="Always 'attribute' - single field assessment") + + # Path to field as tuple: ("address", "street") or ("items", 0, "price") + field_path: tuple[str | int, ...] + + # The field name being assessed (last element of path) + field_name: str + + # Schema for this specific field only + field_schema: dict[str, Any] + + # Confidence threshold for this field + confidence_threshold: float + + +class AssessmentResult(BaseModel): + """Result of a single assessment task (used by both granular and strands services).""" + + task_id: str + success: bool + assessment_data: dict[str, Any] + confidence_alerts: list[dict[str, Any]] + error_message: str | None = None + processing_time: float = 0.0 + metering: dict[str, Any] | None = None + + +# ============================================================================ +# Models for assessment service.py (data flow and processing) +# ============================================================================ + + +class BoundingBoxCoordinates(BaseModel): + """Normalized bounding box coordinates (0-1 scale).""" + + top: float = Field(..., ge=0.0, le=1.0, description="Top coordinate (normalized)") + left: float = Field(..., ge=0.0, le=1.0, description="Left coordinate (normalized)") + width: float = Field(..., ge=0.0, le=1.0, description="Width (normalized)") + height: float = Field(..., ge=0.0, le=1.0, description="Height (normalized)") + + @classmethod + def from_corners( + cls, + x1: float, + y1: float, + x2: float, + y2: float, + scale: float = 1000.0, + ) -> "BoundingBoxCoordinates": + """ + Create from corner coordinates. + + Args: + x1, y1: Top-left corner in 0-scale range + x2, y2: Bottom-right corner in 0-scale range + scale: Normalization scale (default 1000.0) + + Returns: + BoundingBoxCoordinates with normalized 0-1 values + """ + # Ensure coordinates are in correct order + x1, x2 = min(x1, x2), max(x1, x2) + y1, y2 = min(y1, y2), max(y1, y2) + + # Normalize to 0-1 scale + left = x1 / scale + top = y1 / scale + width = (x2 - x1) / scale + height = (y2 - y1) / scale + + # Clamp to valid range + left = min(max(left, 0.0), 1.0) + top = min(max(top, 0.0), 1.0) + width = min(width, 1.0 - left) + height = min(height, 1.0 - top) + + return cls(top=top, left=left, width=width, height=height) + + +class Geometry(BaseModel): + """ + Standard IDP geometry format compatible with UI expectations. + + This is the single source of truth for geometry data structure. + Frontend expects: geometry[0].boundingBox.{left, top, width, height} + """ + + boundingBox: BoundingBoxCoordinates = Field( + ..., + description="Normalized bounding box coordinates", + serialization_alias="boundingBox", # Ensure lowercase in JSON + ) + page: int = Field(..., ge=1, description="Page number (1-indexed)") + vertices: list[dict[str, float]] | None = Field( + None, description="Optional polygon vertices for complex shapes" + ) + + @classmethod + def from_bbox_list(cls, bbox_coords: list[float], page_num: int) -> "Geometry": + """ + Create from LLM bbox response format [x1, y1, x2, y2]. + + Args: + bbox_coords: List of 4 coordinates in 0-1000 scale + page_num: Page number (1-indexed) + + Returns: + Geometry object + + Raises: + ValueError: If bbox_coords is not exactly 4 values + """ + if len(bbox_coords) != 4: + raise ValueError(f"Expected 4 coordinates, got {len(bbox_coords)}") + + x1, y1, x2, y2 = bbox_coords + bbox = BoundingBoxCoordinates.from_corners(x1, y1, x2, y2, scale=1000.0) + + return cls(boundingBox=bbox, page=page_num, vertices=None) + + def to_ui_format(self) -> dict[str, Any]: + """ + Convert to UI-compatible format. + + Returns: + Dict with geometry data: {"boundingBox": {...}, "page": 1} + """ + result: dict[str, Any] = { + "boundingBox": { + "top": self.boundingBox.top, + "left": self.boundingBox.left, + "width": self.boundingBox.width, + "height": self.boundingBox.height, + }, + "page": self.page, + } + if self.vertices is not None: + result["vertices"] = self.vertices + return result + + +class FieldAssessmentData(BaseModel): + """ + Standard assessment data for a single field. + Ensures consistent structure across all assessment services. + """ + + confidence: float = Field(..., ge=0.0, le=1.0) + reasoning: str = Field(..., description="Confidence reasoning") + confidence_threshold: float = Field(..., ge=0.0, le=1.0) + geometry: list[Geometry] | None = Field( + None, + description="Bounding box locations (always wrapped in list for UI compatibility)", + ) + + @classmethod + def from_llm_response( + cls, + confidence: float, + reasoning: str, + confidence_threshold: float, + bbox_coords: list[float] | None = None, + page_num: int | None = None, + ) -> "FieldAssessmentData": + """ + Create from LLM response data. + + Args: + confidence: Confidence score (0-1) + reasoning: Explanation for confidence + confidence_threshold: Threshold for confidence alerts + bbox_coords: Optional bounding box coordinates [x1, y1, x2, y2] + page_num: Optional page number (1-indexed) + """ + geometry = None + if bbox_coords is not None and page_num is not None: + geom = Geometry.from_bbox_list(bbox_coords, page_num) + geometry = [geom] # Always wrap in list + + return cls( + confidence=confidence, + reasoning=reasoning, + confidence_threshold=confidence_threshold, + geometry=geometry, + ) + + def to_explainability_format(self) -> dict[str, Any]: + """Convert to explainability_info format for frontend.""" + result: dict[str, Any] = { + "confidence": self.confidence, + "confidence_reason": self.reasoning, + "confidence_threshold": self.confidence_threshold, + } + + if self.geometry: + result["geometry"] = [g.to_ui_format() for g in self.geometry] + + return result + + +class ConfidenceAlert(BaseModel): + """Alert for confidence threshold violation.""" + + attribute_name: str = Field(serialization_alias="attributeName") + confidence: float + confidence_threshold: float = Field(serialization_alias="confidenceThreshold") + + @field_validator("confidence", "confidence_threshold", mode="before") + @classmethod + def parse_float(cls, v: Any) -> float: + """Parse float from string or number, handle None.""" + if v is None: + return 0.0 + if isinstance(v, (int, float)): + return float(v) + if isinstance(v, str): + if not v.strip(): + return 0.0 + try: + return float(v) + except (ValueError, TypeError): + return 0.0 + # Fallback for other types + try: + return float(v) + except (ValueError, TypeError): + return 0.0 + + +class DocumentContent(BaseModel): + """Loaded content from document pages.""" + + document_text: str + page_images: list[Any] + ocr_text_confidence: str + + +class ExtractionData(BaseModel): + """Loaded extraction data from S3.""" + + extraction_results: dict[str, Any] # The inference_result dict + full_data: dict[str, Any] # Complete data including metadata + + +class AssessmentProcessingResult(BaseModel): + """Result of processing assessment data.""" + + enhanced_assessment_data: dict[str, Any] + confidence_alerts: list[ConfidenceAlert] + metering: dict[str, Any] + processing_metadata: dict[ + str, Any + ] # Contains assessment_time_seconds, parsing_succeeded, etc. diff --git a/lib/idp_common_pkg/idp_common/assessment/service.py b/lib/idp_common_pkg/idp_common/assessment/service.py index bbe8e467..7521143a 100644 --- a/lib/idp_common_pkg/idp_common/assessment/service.py +++ b/lib/idp_common_pkg/idp_common/assessment/service.py @@ -17,9 +17,16 @@ import logging import os import time -from typing import Any, Dict, List, Union +from typing import Any from idp_common import bedrock, image, metrics, s3, utils +from idp_common.assessment.geometry_utils import extract_geometry_from_nested_dict +from idp_common.assessment.models import ( + ConfidenceAlert, + DocumentContent, + ExtractionData, +) +from idp_common.bedrock import format_prompt from idp_common.config.models import IDPConfig from idp_common.config.schema_constants import ( SCHEMA_DESCRIPTION, @@ -34,57 +41,37 @@ X_AWS_IDP_LIST_ITEM_DESCRIPTION, ) from idp_common.models import Document +from idp_common.ocr.service import OcrService from idp_common.utils import extract_json_from_text logger = logging.getLogger(__name__) -def _safe_float_conversion(value: Any, default: float = 0.0) -> float: +class AssessmentService: """ - Safely convert a value to float, handling strings and None values. - - Args: - value: Value to convert to float - default: Default value if conversion fails - - Returns: - Float value or default if conversion fails + Service for assessing extraction result confidence using LLMs. + + This service evaluates extraction results by analyzing them against source documents, + providing confidence scores and optional bounding box information for each extracted field. + + The class is organized into the following sections: + 1. INITIALIZATION - Setup and configuration + 2. SCHEMA & CONFIGURATION - Schema lookup and property formatting + 3. CONFIDENCE THRESHOLD HANDLING - Threshold validation and alert generation + 4. PROMPT BUILDING - Template processing and content preparation + 5. DATA LOADING - Loading document content and OCR data + 6. GEOMETRY PROCESSING - Bounding box conversion and validation + 7. PUBLIC API - Main entry points for assessment """ - if value is None: - return default - - if isinstance(value, (int, float)): - return float(value) - - if isinstance(value, str): - # Handle empty strings - if not value.strip(): - return default - try: - return float(value) - except (ValueError, TypeError): - logger.warning( - f"Could not convert string '{value}' to float, using default {default}" - ) - return default - - # Handle other types by attempting conversion - try: - return float(value) - except (ValueError, TypeError): - logger.warning( - f"Could not convert {type(value)} '{value}' to float, using default {default}" - ) - return default - -class AssessmentService: - """Service for assessing extraction result confidence using LLMs.""" + # ============================================================================ + # INITIALIZATION + # ============================================================================ def __init__( self, region: str | None = None, - config: Union[Dict[str, Any], IDPConfig, None] = None, + config: dict[str, Any] | IDPConfig | None = None, ): """ Initialize the assessment service. @@ -117,7 +104,11 @@ def __init__( model_id = self.config.assessment.model logger.info(f"Initialized assessment service with model {model_id}") - def _get_class_schema(self, class_label: str) -> Dict[str, Any]: + # ============================================================================ + # SCHEMA & CONFIGURATION + # ============================================================================ + + def _get_class_schema(self, class_label: str) -> dict[str, Any]: """ Get JSON Schema for a specific document class. @@ -134,7 +125,7 @@ def _get_class_schema(self, class_label: str) -> Dict[str, Any]: return schema return {} - def _format_property_descriptions(self, schema: Dict[str, Any]) -> str: + def _format_property_descriptions(self, schema: dict[str, Any]) -> str: """ Format property descriptions from JSON Schema for the prompt. @@ -178,9 +169,13 @@ def _format_property_descriptions(self, schema: Dict[str, Any]) -> str: return "\n".join(formatted_lines) + # ============================================================================ + # CONFIDENCE THRESHOLD HANDLING + # ============================================================================ + def _enhance_dict_assessment( - self, assessment_dict: Dict[str, Any], threshold: float - ) -> Dict[str, Any]: + self, assessment_dict: dict[str, Any], threshold: float + ) -> dict[str, Any]: """ Enhance an assessment dictionary by adding confidence thresholds to confidence assessments. @@ -228,12 +223,31 @@ def _enhance_dict_assessment( enhanced[key] = value return enhanced + def _get_confidence_threshold( + self, prop_schema: dict[str, Any], default: float + ) -> float: + """ + Get confidence threshold from property schema with validation. + + Args: + prop_schema: Property schema dictionary + default: Default threshold if not specified in schema + + Returns: + Validated float threshold value + """ + value = prop_schema.get(X_AWS_IDP_CONFIDENCE_THRESHOLD, default) + # Use ConfidenceAlert's validator to parse the float safely + return ConfidenceAlert( + attribute_name="", confidence=0.0, confidence_threshold=value + ).confidence_threshold + def _check_confidence_alerts( self, - assessment_data: Dict[str, Any], + assessment_data: dict[str, Any], attr_name: str, threshold: float, - alerts_list: List[Dict[str, Any]], + alerts_list: list[ConfidenceAlert], ) -> None: """ Check assessment data for confidence threshold violations and add alerts. @@ -252,48 +266,41 @@ def _check_confidence_alerts( ) return - # Safety check: ensure threshold is a valid float - safe_threshold = _safe_float_conversion(threshold, 0.9) - # First check if this assessment_data itself is a direct confidence assessment if "confidence" in assessment_data: - confidence = _safe_float_conversion( - assessment_data.get("confidence", 0.0), 0.0 + alert = ConfidenceAlert( + attribute_name=attr_name, + confidence=assessment_data.get("confidence", 0.0), + confidence_threshold=threshold, ) - if confidence < safe_threshold: - alerts_list.append( - { - "attribute_name": attr_name, - "confidence": confidence, - "confidence_threshold": safe_threshold, - } - ) + if alert.confidence < alert.confidence_threshold: + alerts_list.append(alert) # Then check for nested sub-attributes (for group/complex attributes) for sub_attr_name, sub_assessment in assessment_data.items(): if isinstance(sub_assessment, dict) and "confidence" in sub_assessment: - confidence = _safe_float_conversion( - sub_assessment.get("confidence", 0.0), 0.0 + full_attr_name = ( + f"{attr_name}.{sub_attr_name}" + if "." not in attr_name + else f"{attr_name}.{sub_attr_name}" ) - if confidence < safe_threshold: - full_attr_name = ( - f"{attr_name}.{sub_attr_name}" - if "." not in attr_name - else f"{attr_name}.{sub_attr_name}" - ) - alerts_list.append( - { - "attribute_name": full_attr_name, - "confidence": confidence, - "confidence_threshold": safe_threshold, - } - ) + alert = ConfidenceAlert( + attribute_name=full_attr_name, + confidence=sub_assessment.get("confidence", 0.0), + confidence_threshold=threshold, + ) + if alert.confidence < alert.confidence_threshold: + alerts_list.append(alert) + + # ============================================================================ + # PROMPT BUILDING + # ============================================================================ def _prepare_prompt_from_template( self, prompt_template: str, - substitutions: Dict[str, str], - required_placeholders: List[str] = None, + substitutions: dict[str, str], + required_placeholders: list[str] | None = None, ) -> str: """ Prepare prompt from template by replacing placeholders with values. @@ -309,8 +316,6 @@ def _prepare_prompt_from_template( Raises: ValueError: If a required placeholder is missing from the template """ - from idp_common.bedrock import format_prompt - return format_prompt(prompt_template, substitutions, required_placeholders) def _build_content_with_or_without_image_placeholder( @@ -322,7 +327,7 @@ def _build_content_with_or_without_image_placeholder( extraction_results: str, ocr_text_confidence: str = "", image_content: Any = None, - ) -> List[Dict[str, Any]]: + ) -> list[dict[str, Any]]: """ Build content array, automatically deciding whether to use image placeholder processing. @@ -368,7 +373,7 @@ def _build_content_with_image_placeholder( extraction_results: str, ocr_text_confidence: str, image_content: Any = None, - ) -> List[Dict[str, Any]]: + ) -> list[dict[str, Any]]: """ Build content array with image inserted at DOCUMENT_IMAGE placeholder if present. @@ -455,7 +460,7 @@ def _build_content_without_image_placeholder( extraction_results: str, ocr_text_confidence: str, image_content: Any = None, - ) -> List[Dict[str, Any]]: + ) -> list[dict[str, Any]]: """ Build content array without DOCUMENT_IMAGE placeholder (text-only processing). @@ -487,6 +492,101 @@ def _build_content_without_image_placeholder( # Return text content only - no images unless DOCUMENT_IMAGE placeholder is used return [{"text": task_prompt}] + # ============================================================================ + # DATA LOADING + # ============================================================================ + + def _load_extraction_data(self, section) -> ExtractionData: + """ + Load extraction results from S3. + + Args: + section: Section object containing extraction_result_uri + + Returns: + ExtractionData with extraction_results and full_data + + Raises: + ValueError: If no extraction results found + """ + extraction_data = s3.get_json_content(section.extraction_result_uri) + extraction_results = extraction_data.get("inference_result", {}) + + if not extraction_results: + logger.warning( + f"No extraction results found for section {section.section_id}, skipping assessment" + ) + + return ExtractionData( + extraction_results=extraction_results, full_data=extraction_data + ) + + def _load_document_content(self, document: Document, section) -> DocumentContent: + """ + Load document text, images, and OCR confidence for all pages in section. + + Args: + document: Document object containing pages + section: Section object with page_ids + + Returns: + DocumentContent with document_text, page_images, and ocr_text_confidence + """ + # Sort pages by page number + sorted_page_ids = sorted(section.page_ids, key=int) + + # Read document text from all pages in order + document_texts = [] + for page_id in sorted_page_ids: + if page_id not in document.pages: + logger.warning(f"Page {page_id} not found in document") + continue + + page = document.pages[page_id] + text_path = page.parsed_text_uri + if text_path: + page_text = s3.get_text_content(text_path) + document_texts.append(page_text) + + document_text = "\n".join(document_texts) + + # Read page images with configurable dimensions + target_width = self.config.assessment.image.target_width + target_height = self.config.assessment.image.target_height + + page_images = [] + for page_id in sorted_page_ids: + if page_id not in document.pages: + continue + + page = document.pages[page_id] + image_uri = page.image_uri + if image_uri: + image_content = image.prepare_image( + image_uri, target_width, target_height + ) + page_images.append(image_content) + + # Read text confidence data for confidence information + ocr_text_confidence = "" + for page_id in sorted_page_ids: + if page_id not in document.pages: + continue + + page = document.pages[page_id] + text_confidence_data_str = self._get_text_confidence_data(page) + if text_confidence_data_str: + ocr_text_confidence += ( + f"\n--- Page {page_id} Text Confidence Data ---\n" + ) + ocr_text_confidence += text_confidence_data_str + + return DocumentContent( + document_text=document_text, + page_images=page_images, + ocr_text_confidence=ocr_text_confidence, + ) + def _get_text_confidence_data(self, page) -> str: """ Get text confidence data for a page from pre-generated text confidence files. @@ -510,8 +610,6 @@ def _get_text_confidence_data(self, page) -> str: # Fallback: use raw OCR data if text confidence is not available (for backward compatibility) if page.raw_text_uri: try: - from idp_common.ocr.service import OcrService - ocr_service = OcrService() raw_ocr_data = s3.get_json_content(page.raw_text_uri) text_confidence_data = ocr_service._generate_text_confidence_data( @@ -525,175 +623,229 @@ def _get_text_confidence_data(self, page) -> str: return "" - def _convert_bbox_to_geometry( - self, bbox_coords: List[float], page_num: int - ) -> Dict[str, Any]: + # ============================================================================ + # GEOMETRY PROCESSING (uses shared utilities from geometry_utils) + # ============================================================================ + + # ============================================================================ + # RESULT PROCESSING + # ============================================================================ + + def _process_assessment_response( + self, + assessment_text: str, + extraction_results: dict[str, Any], + class_schema: dict[str, Any], + ) -> tuple[dict[str, Any], list[ConfidenceAlert], bool]: """ - Convert [x1,y1,x2,y2] coordinates to geometry format. + Process raw assessment response from LLM. Args: - bbox_coords: List of 4 coordinates [x1, y1, x2, y2] in 0-1000 scale - page_num: Page number where the bounding box appears + assessment_text: Raw text response from LLM + extraction_results: Original extraction results + class_schema: JSON Schema for the document class Returns: - Dictionary in geometry format compatible with pattern-1 UI + Tuple of (enhanced_assessment_data, confidence_alerts, parsing_succeeded) """ - if len(bbox_coords) != 4: - raise ValueError(f"Expected 4 coordinates, got {len(bbox_coords)}") + # Parse response into JSON + assessment_data = {} + parsing_succeeded = True - x1, y1, x2, y2 = bbox_coords + try: + assessment_data = json.loads(extract_json_from_text(assessment_text)) + except Exception as e: + logger.error( + f"Error parsing assessment LLM output - invalid JSON?: {assessment_text} - {e}" + ) + logger.info("Using default confidence scores.") + # Create default assessments for all extracted attributes + assessment_data = {} + for attr_name in extraction_results.keys(): + assessment_data[attr_name] = { + "confidence": 0.5, + "confidence_reason": "Unable to parse assessment response - default score assigned", + } + parsing_succeeded = False - # Ensure coordinates are in correct order - x1, x2 = min(x1, x2), max(x1, x2) - y1, y2 = min(y1, y2), max(y1, y2) + # Process bounding boxes automatically if bbox data is present + try: + logger.debug("Checking for bounding box data in assessment response") + assessment_data = extract_geometry_from_nested_dict(assessment_data) + except Exception as e: + logger.warning(f"Failed to extract geometry data: {str(e)}") - # Convert from normalized 0-1000 scale to 0-1 - left = x1 / 1000.0 - top = y1 / 1000.0 - width = (x2 - x1) / 1000.0 - height = (y2 - y1) / 1000.0 + # Enhance assessment data with confidence thresholds and create alerts + enhanced_assessment_data, confidence_alerts = ( + self._enhance_and_check_thresholds(assessment_data, class_schema) + ) - return { - "boundingBox": {"top": top, "left": left, "width": width, "height": height}, - "page": page_num, - } + return enhanced_assessment_data, confidence_alerts, parsing_succeeded - def _process_single_assessment_geometry( - self, attr_assessment: Dict[str, Any], attr_name: str = "" - ) -> Dict[str, Any]: + def _enhance_and_check_thresholds( + self, assessment_data: dict[str, Any], class_schema: dict[str, Any] + ) -> tuple[dict[str, Any], list[ConfidenceAlert]]: """ - Process geometry data for a single assessment (with confidence key). + Enhance assessment data with thresholds and generate alerts. Args: - attr_assessment: Single assessment dictionary with confidence data - attr_name: Name of attribute for logging + assessment_data: Raw assessment data from LLM + class_schema: JSON Schema for the document class Returns: - Enhanced assessment with geometry converted to proper format + Tuple of (enhanced_assessment_data, confidence_alerts) """ - enhanced_attr = attr_assessment.copy() + default_confidence_threshold = ( + self.config.assessment.default_confidence_threshold + ) - # Check if this assessment includes bbox data - if "bbox" in attr_assessment or "page" in attr_assessment: - # Both bbox and page are required for valid geometry - if "bbox" in attr_assessment and "page" in attr_assessment: - try: - bbox_coords = attr_assessment["bbox"] - page_num = attr_assessment["page"] + enhanced_assessment_data = {} + confidence_threshold_alerts: list[ConfidenceAlert] = [] + properties = class_schema.get(SCHEMA_PROPERTIES, {}) + + for attr_name, attr_assessment in assessment_data.items(): + prop_schema = properties.get(attr_name, {}) + attr_threshold = self._get_confidence_threshold( + prop_schema, default_confidence_threshold + ) + + # Get property type + prop_type_json = prop_schema.get(SCHEMA_TYPE, TYPE_STRING) + if prop_type_json == TYPE_OBJECT: + attr_type = "group" + elif prop_type_json == TYPE_ARRAY: + attr_type = "list" + else: + attr_type = "simple" - # Validate bbox coordinates - if isinstance(bbox_coords, list) and len(bbox_coords) == 4: - # Convert to geometry format - geometry = self._convert_bbox_to_geometry(bbox_coords, page_num) - enhanced_attr["geometry"] = [geometry] + # Process based on type + if isinstance(attr_assessment, dict): + enhanced_assessment_data[attr_name] = self._enhance_dict_assessment( + attr_assessment, attr_threshold + ) + self._check_confidence_alerts( + attr_assessment, + attr_name, + attr_threshold, + confidence_threshold_alerts, + ) - logger.debug( - f"Converted bounding box for {attr_name}: {bbox_coords} -> geometry format" + elif isinstance(attr_assessment, list) and attr_type == "list": + enhanced_list = [] + for i, item_assessment in enumerate(attr_assessment): + if isinstance(item_assessment, dict): + enhanced_item = self._enhance_dict_assessment( + item_assessment, attr_threshold + ) + enhanced_list.append(enhanced_item) + self._check_confidence_alerts( + item_assessment, + f"{attr_name}[{i}]", + attr_threshold, + confidence_threshold_alerts, ) else: + # Unexpected format within list logger.warning( - f"Invalid bounding box format for {attr_name}: {bbox_coords}" + f"List item {i} in attribute '{attr_name}' is not a dictionary. Using default confidence." ) + default_item = { + "confidence": 0.5, + "confidence_reason": f"List item {i} in '{attr_name}' has unexpected format.", + "confidence_threshold": attr_threshold, + } + enhanced_list.append(default_item) + + if 0.5 < attr_threshold: + alert = ConfidenceAlert( + attribute_name=f"{attr_name}[{i}]", + confidence=0.5, + confidence_threshold=attr_threshold, + ) + confidence_threshold_alerts.append(alert) + + enhanced_assessment_data[attr_name] = enhanced_list - except Exception as e: - logger.warning( - f"Failed to process bounding box for {attr_name}: {str(e)}" - ) else: - # If only one of bbox/page exists, log a warning about incomplete data - if "bbox" in attr_assessment and "page" not in attr_assessment: - logger.warning( - f"Found bbox without page for {attr_name} - removing incomplete bbox data" - ) - elif "page" in attr_assessment and "bbox" not in attr_assessment: - logger.warning( - f"Found page without bbox for {attr_name} - removing incomplete page data" - ) + # Unexpected type - use default + logger.warning( + f"Attribute '{attr_name}' has unexpected assessment format. Using default confidence." + ) + default_assessment = { + "confidence": 0.5, + "confidence_reason": f"LLM returned unexpected format for '{attr_name}'.", + "confidence_threshold": attr_threshold, + } + enhanced_assessment_data[attr_name] = default_assessment - # Always remove raw bbox/page data from output (whether processed or incomplete) - enhanced_attr.pop("bbox", None) - enhanced_attr.pop("page", None) + return enhanced_assessment_data, confidence_threshold_alerts - return enhanced_attr + # ============================================================================ + # ASSESSMENT EXECUTION + # ============================================================================ - def _extract_geometry_from_assessment( - self, assessment_data: Dict[str, Any] - ) -> Dict[str, Any]: + def _execute_bedrock_assessment( + self, content: list[dict[str, Any]] + ) -> tuple[str, dict[str, Any], float]: """ - Extract geometry data from assessment response and convert to proper format. - Now supports recursive processing of nested group attributes. + Execute Bedrock model invocation for assessment. Args: - assessment_data: Dictionary containing assessment results from LLM + content: Formatted content for the model Returns: - Enhanced assessment data with geometry information converted to proper format + Tuple of (assessment_text, metering_data, duration_seconds) """ - enhanced_assessment = {} + # Get assessment configuration + model_id = self.config.assessment.model + if not model_id: + raise ValueError("Assessment model_id is required but not configured") + + request_start_time = time.time() + + # Invoke Bedrock + response_with_metering = bedrock.invoke_model( + model_id=model_id, + system_prompt=self.config.assessment.system_prompt, + content=content, + temperature=self.config.assessment.temperature, + top_k=self.config.assessment.top_k, + top_p=self.config.assessment.top_p, + max_tokens=self.config.assessment.max_tokens, + context="Assessment", + ) - for attr_name, attr_assessment in assessment_data.items(): - if isinstance(attr_assessment, dict): - # Check if this is a direct confidence assessment - if "confidence" in attr_assessment: - # This is a direct assessment - process its geometry - enhanced_assessment[attr_name] = ( - self._process_single_assessment_geometry( - attr_assessment, attr_name - ) - ) - else: - # This is a group attribute (no direct confidence) - recursively process nested attributes - logger.debug(f"Processing group attribute: {attr_name}") - enhanced_assessment[attr_name] = ( - self._extract_geometry_from_assessment(attr_assessment) - ) + total_duration = time.time() - request_start_time - elif isinstance(attr_assessment, list): - # Handle list attributes - process each item recursively - enhanced_list = [] - for i, item_assessment in enumerate(attr_assessment): - if isinstance(item_assessment, dict): - # Recursively process each list item - enhanced_item = self._extract_geometry_from_assessment( - item_assessment - ) - enhanced_list.append(enhanced_item) - else: - # Non-dict items pass through unchanged - enhanced_list.append(item_assessment) - enhanced_assessment[attr_name] = enhanced_list - else: - # Other types pass through unchanged - enhanced_assessment[attr_name] = attr_assessment + # Extract text from response + assessment_text = bedrock.extract_text_from_response(response_with_metering) + metering = response_with_metering.get("metering", {}) - return enhanced_assessment + return assessment_text, metering, total_duration - def process_document_section(self, document: Document, section_id: str) -> Document: + # ============================================================================ + # VALIDATION & HELPERS + # ============================================================================ + + def _validate_and_get_section(self, document: Document, section_id: str): """ - Process a single section from a Document object to assess extraction confidence. + Validate document and return the section to process. Args: - document: Document object containing section to process - section_id: ID of the section to process + document: Document object to validate + section_id: ID of section to retrieve Returns: - Document: Updated Document object with assessment results appended to extraction results - """ - # Check if assessment is enabled in typed configuration - enabled = self.config.assessment.enabled - if not enabled: - logger.info("Assessment is disabled via configuration") - return document + Section object - # Validate input document + Raises: + ValueError: If validation fails + """ if not document: - logger.error("No document provided") - return document + raise ValueError("No document provided") if not document.sections: - logger.error("Document has no sections to process") - document.errors.append("Document has no sections to process") - return document + raise ValueError("Document has no sections to process") # Find the section with the given ID section = None @@ -703,28 +855,48 @@ def process_document_section(self, document: Document, section_id: str) -> Docum break if not section: - error_msg = f"Section {section_id} not found in document" - logger.error(error_msg) - document.errors.append(error_msg) - return document + raise ValueError(f"Section {section_id} not found in document") - # Check if section has extraction results to assess if not section.extraction_result_uri: - error_msg = f"Section {section_id} has no extraction results to assess" - logger.error(error_msg) - document.errors.append(error_msg) - return document - - # Extract information about the section - class_label = section.classification + raise ValueError( + f"Section {section_id} has no extraction results to assess" + ) - # Check if the section has required pages if not section.page_ids: - error_msg = f"Section {section_id} has no page IDs" - logger.error(error_msg) - document.errors.append(error_msg) + raise ValueError(f"Section {section_id} has no page IDs") + + return section + + # ============================================================================ + # PUBLIC API + # ============================================================================ + + def process_document_section(self, document: Document, section_id: str) -> Document: + """ + Process a single section from a Document object to assess extraction confidence. + + Args: + document: Document object containing section to process + section_id: ID of the section to process + + Returns: + Document: Updated Document object with assessment results appended to extraction results + """ + # Check if assessment is enabled + if not self.config.assessment.enabled: + logger.info("Assessment is disabled via configuration") return document + # Validate and get section + try: + section = self._validate_and_get_section(document, section_id) + except ValueError as e: + logger.error(str(e)) + document.errors.append(str(e)) + return document + + class_label = section.classification + # Sort pages by page number sorted_page_ids = sorted(section.page_ids, key=int) start_page = int(sorted_page_ids[0]) @@ -738,81 +910,24 @@ def process_document_section(self, document: Document, section_id: str) -> Docum metrics.put_metric("InputDocumentPagesForAssessment", len(section.page_ids)) try: - # Read existing extraction results + # Load extraction data t0 = time.time() - extraction_data = s3.get_json_content(section.extraction_result_uri) - extraction_results = extraction_data.get("inference_result", {}) + extraction_data_model = self._load_extraction_data(section) + extraction_results = extraction_data_model.extraction_results + t1 = time.time() + logger.info(f"Time taken to load extraction data: {t1 - t0:.2f} seconds") - # Skip assessment if no extraction results found + # Handle empty extraction results gracefully if not extraction_results: - logger.warning(f"No extraction results found for section {section_id}") + logger.warning( + f"No extraction results for section {section_id}, skipping assessment" + ) return document - t1 = time.time() - logger.info(f"Time taken to read extraction results: {t1 - t0:.2f} seconds") - - # Read document text from all pages in order - document_texts = [] - for page_id in sorted_page_ids: - if page_id not in document.pages: - error_msg = f"Page {page_id} not found in document" - logger.error(error_msg) - document.errors.append(error_msg) - continue - - page = document.pages[page_id] - text_path = page.parsed_text_uri - page_text = s3.get_text_content(text_path) - document_texts.append(page_text) - - document_text = "\n".join(document_texts) + # Load document content (text, images, OCR confidence) + document_content = self._load_document_content(document, section) t2 = time.time() - logger.info(f"Time taken to read text content: {t2 - t1:.2f} seconds") - - # Read page images with configurable dimensions (type-safe access) - target_width = self.config.assessment.image.target_width - target_height = self.config.assessment.image.target_height - - page_images = [] - for page_id in sorted_page_ids: - if page_id not in document.pages: - continue - - page = document.pages[page_id] - image_uri = page.image_uri - # Just pass the values directly - prepare_image handles empty strings/None - image_content = image.prepare_image( - image_uri, target_width, target_height - ) - page_images.append(image_content) - - t3 = time.time() - logger.info(f"Time taken to read images: {t3 - t2:.2f} seconds") - - # Read text confidence data for confidence information - ocr_text_confidence = "" - for page_id in sorted_page_ids: - if page_id not in document.pages: - continue - - page = document.pages[page_id] - text_confidence_data_str = self._get_text_confidence_data(page) - if text_confidence_data_str: - ocr_text_confidence += ( - f"\n--- Page {page_id} Text Confidence Data ---\n" - ) - ocr_text_confidence += text_confidence_data_str - - t4 = time.time() - logger.info(f"Time taken to read raw OCR results: {t4 - t3:.2f} seconds") - - # Get assessment configuration (type-safe access, Pydantic handles conversions) - model_id = self.config.assessment.model - temperature = self.config.assessment.temperature - top_k = self.config.assessment.top_k - top_p = self.config.assessment.top_p - max_tokens = self.config.assessment.max_tokens - system_prompt = self.config.assessment.system_prompt + logger.info(f"Time taken to load document content: {t2 - t1:.2f} seconds") # Get schema for this document class class_schema = self._get_class_schema(class_label) @@ -834,12 +949,12 @@ def process_document_section(self, document: Document, section_id: str) -> Docum try: content = self._build_content_with_or_without_image_placeholder( prompt_template, - document_text, + document_content.document_text, class_label, property_descriptions, extraction_results_str, - ocr_text_confidence, - page_images, # Pass images to the content builder + document_content.ocr_text_confidence, + document_content.page_images, ) except ValueError as e: logger.error(f"Error formatting prompt template: {str(e)}") @@ -851,203 +966,54 @@ def process_document_section(self, document: Document, section_id: str) -> Docum f"Assessing extraction confidence for {class_label} document, section {section_id}" ) - # Time the model invocation - request_start_time = time.time() - - # Invoke Bedrock with the common library - response_with_metering = bedrock.invoke_model( - model_id=model_id, - system_prompt=system_prompt, - content=content, - temperature=temperature, - top_k=top_k, - top_p=top_p, - max_tokens=max_tokens, - context="Assessment", + # Execute Bedrock assessment + assessment_text, metering, total_duration = ( + self._execute_bedrock_assessment(content) ) - - total_duration = time.time() - request_start_time logger.info(f"Time taken for assessment: {total_duration:.2f} seconds") - # Extract text from response - assessment_text = bedrock.extract_text_from_response(response_with_metering) - metering = response_with_metering.get("metering", {}) - - # Parse response into JSON - assessment_data = {} - parsing_succeeded = True # Flag to track if parsing was successful - - try: - # Try to parse the assessment text as JSON - assessment_data = json.loads(extract_json_from_text(assessment_text)) - except Exception as e: - # Handle parsing error - logger.error( - f"Error parsing assessment LLM output - invalid JSON?: {assessment_text} - {e}" - ) - logger.info("Using default confidence scores.") - # Create default assessments for all extracted attributes - assessment_data = {} - for attr_name in extraction_results.keys(): - assessment_data[attr_name] = { - "confidence": 0.5, - "confidence_reason": "Unable to parse assessment response - default score assigned", - } - parsing_succeeded = False # Mark that parsing failed - - # Process bounding boxes automatically if bbox data is present - try: - logger.debug("Checking for bounding box data in assessment response") - assessment_data = self._extract_geometry_from_assessment( - assessment_data - ) - except Exception as e: - logger.warning(f"Failed to extract geometry data: {str(e)}") - # Continue with assessment even if geometry extraction fails - - # Get confidence thresholds (type-safe, already float from Pydantic) - default_confidence_threshold = ( - self.config.assessment.default_confidence_threshold + # Process assessment response + ( + enhanced_assessment_data, + confidence_threshold_alerts, + parsing_succeeded, + ) = self._process_assessment_response( + assessment_text, extraction_results, class_schema ) - # Enhance assessment data with confidence thresholds and create confidence threshold alerts - enhanced_assessment_data = {} - confidence_threshold_alerts = [] - - # Get properties dict once for efficient access - properties = class_schema.get(SCHEMA_PROPERTIES, {}) - - for attr_name, attr_assessment in assessment_data.items(): - # Get property schema (if it exists in schema) - prop_schema = properties.get(attr_name, {}) - - # Get threshold for this property - attr_threshold = _safe_float_conversion( - prop_schema.get( - X_AWS_IDP_CONFIDENCE_THRESHOLD, default_confidence_threshold - ), - default_confidence_threshold, - ) - - # Get property type - prop_type_json = prop_schema.get(SCHEMA_TYPE, TYPE_STRING) - - # Map JSON Schema type to legacy attribute type for existing logic - if prop_type_json == TYPE_OBJECT: - attr_type = "group" - elif prop_type_json == TYPE_ARRAY: - attr_type = "list" - else: - attr_type = "simple" - - # Check if attr_assessment is a dictionary (expected format for simple/group attributes) - if isinstance(attr_assessment, dict): - # For simple attributes or group attributes - add confidence_threshold to each confidence assessment - enhanced_assessment_data[attr_name] = self._enhance_dict_assessment( - attr_assessment, attr_threshold - ) - - # Check for confidence threshold alerts in the assessment - self._check_confidence_alerts( - attr_assessment, - attr_name, - attr_threshold, - confidence_threshold_alerts, - ) - - elif isinstance(attr_assessment, list): - # Handle list attributes (expected format for LIST attributes like transactions) - if attr_type == "list": - # This is expected for list attributes - process each item in the list - enhanced_list = [] - for i, item_assessment in enumerate(attr_assessment): - if isinstance(item_assessment, dict): - enhanced_item = self._enhance_dict_assessment( - item_assessment, attr_threshold - ) - enhanced_list.append(enhanced_item) - - # Check for confidence threshold alerts in list items - self._check_confidence_alerts( - item_assessment, - f"{attr_name}[{i}]", - attr_threshold, - confidence_threshold_alerts, - ) - else: - # Handle unexpected format within list - logger.warning( - f"List item {i} in attribute '{attr_name}' is not a dictionary. " - f"Expected dict, got {type(item_assessment)}. Using default confidence." - ) - default_item = { - "confidence": 0.5, - "confidence_reason": f"List item {i} in '{attr_name}' has unexpected format. Using default confidence.", - "confidence_threshold": attr_threshold, - } - enhanced_list.append(default_item) - - # Add alert for default confidence - if 0.5 < attr_threshold: - confidence_threshold_alerts.append( - { - "attribute_name": f"{attr_name}[{i}]", - "confidence": 0.5, - "confidence_threshold": attr_threshold, - } - ) - - enhanced_assessment_data[attr_name] = enhanced_list - else: - # List format for non-list attribute is unexpected - logger.warning( - f"Attribute '{attr_name}' (type: {attr_type}) assessment is a list but attribute is not configured as list type. " - f"Using default confidence." - ) - - # Create a default assessment structure - default_assessment = { - "confidence": 0.5, - "confidence_reason": f"LLM returned list format for non-list attribute '{attr_name}'. Using default confidence (0.5) and threshold ({attr_threshold}).", - "confidence_threshold": attr_threshold, - } - enhanced_assessment_data[attr_name] = default_assessment - - else: - # Handle other unexpected types - logger.warning( - f"Attribute '{attr_name}' assessment is of unexpected type {type(attr_assessment)}. " - f"Expected dictionary or list (for list attributes). Using default confidence." - ) - - # Create a default assessment structure - default_assessment = { - "confidence": 0.5, - "confidence_reason": f"LLM returned unexpected type {type(attr_assessment)} for attribute '{attr_name}'. Using default confidence (0.5) and threshold ({attr_threshold}).", - "confidence_threshold": attr_threshold, - } - enhanced_assessment_data[attr_name] = default_assessment - # Update the existing extraction result with enhanced assessment data - extraction_data["explainability_info"] = [enhanced_assessment_data] - extraction_data["metadata"] = extraction_data.get("metadata", {}) - extraction_data["metadata"]["assessment_time_seconds"] = total_duration - extraction_data["metadata"]["assessment_parsing_succeeded"] = ( - parsing_succeeded + extraction_data_model.full_data["explainability_info"] = [ + enhanced_assessment_data + ] + extraction_data_model.full_data["metadata"] = ( + extraction_data_model.full_data.get("metadata", {}) + ) + extraction_data_model.full_data["metadata"]["assessment_time_seconds"] = ( + total_duration ) + extraction_data_model.full_data["metadata"][ + "assessment_parsing_succeeded" + ] = parsing_succeeded # Write the updated result back to S3 + # extraction_result_uri is guaranteed to exist by _validate_and_get_section + assert section.extraction_result_uri is not None bucket, key = utils.parse_s3_uri(section.extraction_result_uri) s3.write_content( - extraction_data, bucket, key, content_type="application/json" + extraction_data_model.full_data, + bucket, + key, + content_type="application/json", ) # Update the section in the document with confidence threshold alerts for doc_section in document.sections: if doc_section.section_id == section_id: - doc_section.confidence_threshold_alerts = ( - confidence_threshold_alerts - ) + # Convert ConfidenceAlert objects to dicts with camelCase keys for UI + doc_section.confidence_threshold_alerts = [ + alert.model_dump(by_alias=True) + for alert in confidence_threshold_alerts + ] break # Update document with metering data diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_executor.py b/lib/idp_common_pkg/idp_common/assessment/strands_executor.py new file mode 100644 index 00000000..a3330304 --- /dev/null +++ b/lib/idp_common_pkg/idp_common/assessment/strands_executor.py @@ -0,0 +1,246 @@ +""" +Parallel executor for Strands-based assessment tasks. + +This module provides asyncio-based parallel execution of assessment tasks +with concurrency control via semaphores. +""" + +import asyncio +import os +import time +from typing import Any, cast + +from aws_lambda_powertools import Logger + +from idp_common.assessment.models import AssessmentResult, AssessmentTask +from idp_common.assessment.strands_service import assess_attribute_with_strands +from idp_common.utils import merge_metering_data + +logger = Logger(service="assessment", level=os.getenv("LOG_LEVEL", "INFO")) + + +async def execute_tasks_async( + tasks: list[AssessmentTask], + extraction_results: dict[str, Any], + page_images: list[bytes], + sorted_page_ids: list[str], + model_id: str, + system_prompt: str, + temperature: float, + max_tokens: int, + document_schema: dict[str, Any], + max_concurrent: int = 5, + max_retries: int = 7, + connect_timeout: float = 10.0, + read_timeout: float = 300.0, +) -> tuple[list[AssessmentResult], dict[str, Any]]: + """ + Execute assessment tasks in parallel using asyncio. + + Args: + tasks: List of assessment tasks to execute + extraction_results: Full extraction results + page_images: List of raw page images (ruler overlay added internally) + sorted_page_ids: List of page IDs + model_id: Model to use + system_prompt: System prompt + temperature: Model temperature + max_tokens: Max tokens + document_schema: Full document JSON schema + max_concurrent: Maximum concurrent tasks (default 5) + max_retries: Maximum retry attempts + connect_timeout: Connection timeout in seconds + read_timeout: Read timeout in seconds + + Returns: + Tuple of (results, combined_metering) + """ + logger.info( + f"Starting parallel execution of {len(tasks)} assessment tasks with max_concurrent={max_concurrent}" + ) + + # Create semaphore to limit concurrency + semaphore = asyncio.Semaphore(max_concurrent) + + async def execute_with_semaphore(task: AssessmentTask) -> AssessmentResult: + """Execute task with semaphore to limit concurrency.""" + async with semaphore: + logger.debug( + f"Executing task {task.task_id} (type: {task.task_type})", + extra={"task_id": task.task_id, "task_type": task.task_type}, + ) + return await assess_attribute_with_strands( + task=task, + extraction_results=extraction_results, + page_images=page_images, + sorted_page_ids=sorted_page_ids, + model_id=model_id, + system_prompt=system_prompt, + temperature=temperature, + max_tokens=max_tokens, + document_schema=document_schema, + max_retries=max_retries, + connect_timeout=connect_timeout, + read_timeout=read_timeout, + ) + + # Execute all tasks concurrently (with semaphore limit) + # Use return_exceptions=True to capture failures without stopping others + results = await asyncio.gather( + *[execute_with_semaphore(task) for task in tasks], + return_exceptions=True, + ) + + # Process results and handle exceptions + processed_results: list[AssessmentResult] = [] + combined_metering: dict[str, Any] = {} + + for i, result in enumerate(results): + if isinstance(result, Exception): + # Convert exception to failed AssessmentResult + logger.error( + f"Task {tasks[i].task_id} failed with exception", + extra={ + "task_id": tasks[i].task_id, + "exception": str(result), + "exception_type": type(result).__name__, + }, + ) + processed_results.append( + AssessmentResult( + task_id=tasks[i].task_id, + success=False, + assessment_data={}, + confidence_alerts=[], + error_message=str(result), + processing_time=0.0, + ) + ) + else: + # result is AssessmentResult here (not Exception) + assessment_result = cast(AssessmentResult, result) + processed_results.append(assessment_result) + # Merge metering data + if assessment_result.metering: + combined_metering = merge_metering_data( + combined_metering, assessment_result.metering + ) + + # Log summary + successful_tasks = sum(1 for r in processed_results if r.success) + failed_tasks = len(processed_results) - successful_tasks + + logger.info( + f"Completed {len(processed_results)} tasks: {successful_tasks} successful, {failed_tasks} failed", + extra={ + "total_tasks": len(processed_results), + "successful": successful_tasks, + "failed": failed_tasks, + }, + ) + + return processed_results, combined_metering + + +def execute_assessment_tasks_parallel( + tasks: list[AssessmentTask], + extraction_results: dict[str, Any], + page_images: list[bytes], + sorted_page_ids: list[str], + model_id: str, + system_prompt: str, + temperature: float, + max_tokens: int, + document_schema: dict[str, Any], + max_concurrent: int = 5, + max_retries: int = 7, + connect_timeout: float = 10.0, + read_timeout: float = 300.0, +) -> tuple[list[AssessmentResult], dict[str, Any], float]: + """ + Execute assessment tasks in parallel (synchronous wrapper). + + This is the main entry point called from process_document_section. + It wraps the async executor and provides synchronous interface. + + Args: + tasks: List of assessment tasks + extraction_results: Full extraction results + page_images: List of raw page images (ruler overlay added internally) + sorted_page_ids: List of page IDs in sorted order + model_id: Model ID + system_prompt: System prompt + temperature: Temperature + max_tokens: Max tokens + document_schema: Full document JSON schema + max_concurrent: Max concurrent tasks (default 5) + max_retries: Maximum retry attempts + connect_timeout: Connection timeout in seconds + read_timeout: Read timeout in seconds + + Returns: + Tuple of (results, metering, duration) + """ + logger.info( + f"Starting parallel assessment execution for {len(tasks)} tasks", + extra={"num_tasks": len(tasks), "max_concurrent": max_concurrent}, + ) + + start_time = time.time() + + # Run async executor + # Use asyncio.run() for clean event loop management + try: + results, metering = asyncio.run( + execute_tasks_async( + tasks=tasks, + extraction_results=extraction_results, + page_images=page_images, + sorted_page_ids=sorted_page_ids, + model_id=model_id, + system_prompt=system_prompt, + temperature=temperature, + max_tokens=max_tokens, + document_schema=document_schema, + max_concurrent=max_concurrent, + max_retries=max_retries, + connect_timeout=connect_timeout, + read_timeout=read_timeout, + ) + ) + except RuntimeError as e: + # Handle case where event loop already exists (shouldn't happen in Lambda) + if "There is no current event loop" in str(e) or "asyncio.run()" in str(e): + logger.warning( + "Event loop already exists, using get_event_loop", + extra={"error": str(e)}, + ) + loop = asyncio.get_event_loop() + results, metering = loop.run_until_complete( + execute_tasks_async( + tasks=tasks, + extraction_results=extraction_results, + page_images=page_images, + sorted_page_ids=sorted_page_ids, + model_id=model_id, + system_prompt=system_prompt, + temperature=temperature, + max_tokens=max_tokens, + document_schema=document_schema, + max_concurrent=max_concurrent, + max_retries=max_retries, + connect_timeout=connect_timeout, + read_timeout=read_timeout, + ) + ) + else: + raise + + duration = time.time() - start_time + + logger.info( + f"Parallel assessment execution completed in {duration:.2f}s", + extra={"duration_seconds": duration, "num_tasks": len(tasks)}, + ) + + return results, metering, duration diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_models.py b/lib/idp_common_pkg/idp_common/assessment/strands_models.py new file mode 100644 index 00000000..a971de03 --- /dev/null +++ b/lib/idp_common_pkg/idp_common/assessment/strands_models.py @@ -0,0 +1,64 @@ +""" +Pydantic models for Strands-based assessment structured output. + +These models define the structured data format that Strands agents return +when assessing document extraction confidence with bounding boxes. +""" + +from typing import Any + +from pydantic import BaseModel, Field + +from idp_common.assessment.models import Geometry + + +class BoundingBox(BaseModel): + """Bounding box coordinates in normalized 0-1000 scale.""" + + x1: int = Field(..., ge=0, le=1000, description="Top-left X coordinate") + y1: int = Field(..., ge=0, le=1000, description="Top-left Y coordinate") + x2: int = Field(..., ge=0, le=1000, description="Bottom-right X coordinate") + y2: int = Field(..., ge=0, le=1000, description="Bottom-right Y coordinate") + page: int = Field(..., ge=1, description="Page number (1-indexed)") + + def to_geometry(self) -> dict[str, Any]: + """ + Convert to IDP geometry format compatible with UI. + + Returns: + Dictionary in UI-compatible format (lowercase, no array wrapper here) + """ + # Create proper Geometry object + geometry = Geometry.from_bbox_list( + [self.x1, self.y1, self.x2, self.y2], self.page + ) + + # Return UI format (will be wrapped in array by caller) + return geometry.to_ui_format() + + +class ConfidenceAssessment(BaseModel): + """Confidence assessment for an attribute value.""" + + confidence: float = Field(..., ge=0.0, le=1.0, description="Confidence score 0-1") + reasoning: str = Field(..., description="Explanation for the confidence score") + bounding_box: BoundingBox | None = Field( + None, description="Location of value in document" + ) + + +class AssessmentOutput(BaseModel): + """ + Structured output for confidence assessment of a single field. + + Each task assesses exactly ONE field (e.g., "name" or "address.street"). + The assessment is directly the ConfidenceAssessment for that field. + """ + + field_name: str = Field( + ..., + description="The name/path of the field being assessed (e.g., 'name' or 'address.street')", + ) + assessment: ConfidenceAssessment = Field( + ..., description="Confidence assessment for this specific field" + ) diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_service.py b/lib/idp_common_pkg/idp_common/assessment/strands_service.py new file mode 100644 index 00000000..4fb26a53 --- /dev/null +++ b/lib/idp_common_pkg/idp_common/assessment/strands_service.py @@ -0,0 +1,463 @@ +""" +Core assessment service using Strands agents with interactive tools. + +This module provides the main assessment functions that use Strands agents +to assess extraction confidence with bounding boxes and interactive image viewing. +""" + +import json +import os +import time +from typing import Any + +from aws_lambda_powertools import Logger +from strands import Agent +from strands.agent.conversation_manager import SummarizingConversationManager +from strands.models.bedrock import BedrockModel +from strands.types.content import CachePoint, ContentBlock, Message +from strands.types.media import ImageContent, ImageSource + +from idp_common.assessment.models import ( + AssessmentResult, + AssessmentTask, + FieldAssessmentData, +) +from idp_common.assessment.strands_models import AssessmentOutput +from idp_common.assessment.strands_tools import create_strands_tools +from idp_common.bedrock import build_model_config +from idp_common.utils.bedrock_utils import async_exponential_backoff_retry +from idp_common.utils.grid_overlay import add_ruler_edges + +logger = Logger(service="assessment", level=os.getenv("LOG_LEVEL", "INFO")) + + +async def assess_attribute_with_strands( + task: AssessmentTask, + extraction_results: dict[str, Any], + page_images: list[bytes], + sorted_page_ids: list[str], + model_id: str, + system_prompt: str, + temperature: float, + max_tokens: int, + document_schema: dict[str, Any], + max_retries: int = 7, + connect_timeout: float = 10.0, + read_timeout: float = 300.0, +) -> AssessmentResult: + """ + Assess attributes using Strands agent with interactive tools. + + Args: + task: Assessment task to process + extraction_results: Full extraction results + page_images: List of raw page images (ruler overlay added internally) + sorted_page_ids: List of page IDs in sorted order + model_id: Bedrock model ID + system_prompt: System prompt for assessment + temperature: Model temperature + max_tokens: Max tokens for response + max_retries: Maximum retry attempts for API calls + connect_timeout: Connection timeout in seconds + read_timeout: Read timeout in seconds + + Returns: + AssessmentResult with structured assessment data + """ + start_time = time.time() + + # 1. Create tools (image viewer + todo list + submit assessment) + tools = create_strands_tools(page_images, sorted_page_ids) + + # 2. Build enhanced system prompt with schema and extraction (for caching) + enhanced_system_prompt = _build_system_prompt_with_context( + system_prompt, document_schema, extraction_results, len(page_images) + ) + + # 3. Build minimal task-specific prompt (just field path and threshold) + task_prompt = _build_task_prompt(task, page_images) + + # 4. Create Bedrock model config using shared utility + model_config = build_model_config( + model_id=model_id, + max_tokens=max_tokens, + max_retries=max_retries, + connect_timeout=connect_timeout, + read_timeout=read_timeout, + ) + # Add temperature (not handled by build_model_config) + model_config["temperature"] = temperature + + # 5. Initialize Strands agent + agent = Agent( + model=BedrockModel(**model_config), + tools=tools, + system_prompt=enhanced_system_prompt, + state={ + "task": task.model_dump(), + "assessment_output": None, # Will be populated by submit_assessment tool + }, + conversation_manager=SummarizingConversationManager( + summary_ratio=0.8, preserve_recent_messages=1 + ), + ) + + # 6. Create user message and run agent with retry + user_message = Message(role="user", content=task_prompt) + + logger.info( + "Starting Strands assessment", + extra={ + "task_id": task.task_id, + "task_type": task.task_type, + "field_name": task.field_name, + }, + ) + + @async_exponential_backoff_retry( + max_retries=30, + initial_delay=5, + exponential_base=2, + jitter=0.5, + max_delay=900, + ) + async def invoke_agent_with_retry(): + return await agent.invoke_async([user_message]) + + try: + response = await invoke_agent_with_retry() + logger.debug("Agent response received", extra={"task_id": task.task_id}) + except Exception as e: + logger.error( + "Agent invocation failed", + extra={"task_id": task.task_id, "error": str(e)}, + ) + return AssessmentResult( + task_id=task.task_id, + success=False, + assessment_data={}, + confidence_alerts=[], + error_message=f"Agent invocation failed: {str(e)}", + processing_time=time.time() - start_time, + ) + + # 7. Extract and validate assessment from agent state + assessment_dict = agent.state.get("assessment_output") + if not assessment_dict: + return AssessmentResult( + task_id=task.task_id, + success=False, + assessment_data={}, + confidence_alerts=[], + error_message="Agent did not produce assessment output. Agent may not have called submit_assessment tool.", + processing_time=time.time() - start_time, + ) + + try: + assessment_output = AssessmentOutput(**assessment_dict) + except Exception as e: + return AssessmentResult( + task_id=task.task_id, + success=False, + assessment_data={}, + confidence_alerts=[], + error_message=f"Invalid assessment output format: {str(e)}", + processing_time=time.time() - start_time, + ) + + # Validate that agent assessed the expected field + if not _field_names_match(task.field_name, assessment_output.field_name): + return AssessmentResult( + task_id=task.task_id, + success=False, + assessment_data={}, + confidence_alerts=[], + error_message=f"Agent assessed wrong field: expected '{task.field_name}', got '{assessment_output.field_name}'", + processing_time=time.time() - start_time, + ) + + # 8. Extract metering from response + metering = _extract_metering(response, model_id) + + # 9. Convert to AssessmentResult format + result = _convert_to_assessment_result( + task, + assessment_output, + metering, + time.time() - start_time, + ) + + logger.info( + "Assessment completed successfully", + extra={ + "task_id": task.task_id, + "processing_time": result.processing_time, + "success": result.success, + }, + ) + + return result + + +def _field_names_match(expected: str, actual: str) -> bool: + """ + Check if field names match, handling nested paths with dots and array indices. + + Examples: + - "address.street" matches "address.street" + - "items[0].price" matches "items[0].price" + - "address" matches "address" + + Args: + expected: Expected field name/path + actual: Actual field name/path from agent + + Returns: + True if field names match + """ + return expected == actual + + +def _extract_metering(response: Any, model_id: str) -> dict[str, Any]: + """ + Extract metering data from Strands AgentResult response. + + Args: + response: AgentResult from agent.invoke_async() (has metrics attribute) + model_id: Model ID for metering key + + Returns: + Metering dict with token usage, or empty dict if no metrics + """ + metering = {} + # AgentResult has metrics attribute at runtime (from Strands) + if ( + hasattr(response, "metrics") + and response.metrics + and hasattr(response.metrics, "accumulated_usage") + and response.metrics.accumulated_usage + ): # type: ignore[attr-defined] + token_usage = { + "inputTokens": response.metrics.accumulated_usage.get("inputTokens", 0), # type: ignore[attr-defined] + "outputTokens": response.metrics.accumulated_usage.get("outputTokens", 0), # type: ignore[attr-defined] + "totalTokens": response.metrics.accumulated_usage.get("totalTokens", 0), # type: ignore[attr-defined] + "cacheReadInputTokens": response.metrics.accumulated_usage.get( # type: ignore[attr-defined] + "cacheReadInputTokens", 0 + ), + "cacheWriteInputTokens": response.metrics.accumulated_usage.get( # type: ignore[attr-defined] + "cacheWriteInputTokens", 0 + ), + } + metering[f"assessment/bedrock/{model_id}"] = token_usage + + return metering + + +def _build_system_prompt_with_context( + base_system_prompt: str, + document_schema: dict[str, Any], + extraction_results: dict[str, Any], + num_images: int, +) -> str: + """ + Build system prompt with full schema and extraction results for prompt caching. + + This puts the static/cacheable content (schema, extraction, general instructions) + in the system prompt, which benefits from prompt caching. + + Args: + base_system_prompt: Base assessment system prompt + document_schema: Full JSON schema for the document class + extraction_results: Complete extraction results + num_images: Number of available page images + + Returns: + Enhanced system prompt with schema and extraction context + """ + return f"""{base_system_prompt} + +## Document Schema + +Below is the full JSON schema for this document type. This defines all fields, their types, and confidence thresholds. + +```json +{json.dumps(document_schema, indent=2)} +``` + +## Complete Extraction Results + +Below are the complete extraction results for the document being assessed. When assessing a specific field, locate its value in this structure. + +```json +{json.dumps(extraction_results, indent=2)} +``` + +## Available Document Images + +You have access to {num_images} document page images (indices 0-{num_images - 1}). +Each image has ruler markings along the edges showing the 0-1000 coordinate scale. + +Use the `view_image` tool to: +1. View images to locate the extracted values +2. Draw bounding boxes to verify coordinates +3. Check if values are clearly visible and readable + +## Assessment Process + +1. **Plan**: Use `create_todo_list` to organize your assessment steps +2. **Locate**: Use `view_image` to find each value in the document +3. **Coordinate**: Determine precise bounding box coordinates using ruler markings (0-1000 scale) +4. **Assess**: Evaluate confidence based on: + - Text clarity and OCR quality + - Value correctness compared to what you see in the image + - Bounding box accuracy +5. **Submit**: Use `submit_assessment` tool with your final assessment + +## Bounding Box Format + +Bounding boxes use normalized 0-1000 coordinates: +- x1, y1: Top-left corner +- x2, y2: Bottom-right corner +- page: Page number (1-indexed) + +Example: {{"x1": 150, "y1": 220, "x2": 380, "y2": 245, "page": 1}} + +**Important**: You MUST call `submit_assessment` to complete each task. +""" + + +def _convert_field_path_to_string(field_path: tuple[str | int, ...]) -> str: + """ + Convert field path tuple to dot notation string. + + Examples: + ("address", "street") → "address.street" + ("items", 0, "price") → "items[0].price" + ("orders", 2, "line_items", 1, "quantity") → "orders[2].line_items[1].quantity" + + Args: + field_path: Tuple of field names (str) and array indices (int) + + Returns: + Dot notation path string with array indices in brackets + """ + path_parts = [] + for part in field_path: + if isinstance(part, int): + # Append array index to previous part: "items" → "items[0]" + path_parts[-1] = f"{path_parts[-1]}[{part}]" + else: + # Add new field name + path_parts.append(str(part)) + + return ".".join(path_parts) + + +def _build_task_prompt( + task: AssessmentTask, page_images: list[bytes] +) -> list[ContentBlock]: + """ + Build minimal task-specific prompt for assessing a single field. + + This is minimal (just field path and threshold) to maximize the benefit + of caching the system prompt which contains the schema and extraction. + + Args: + task: Assessment task for one specific field + page_images: List of raw page images (ruler will be added here) + + Returns: + List of content blocks with images and task text + """ + field_path_str = _convert_field_path_to_string(task.field_path) + + # Create image content blocks with ruler overlay + # Rulers are added here so the LLM can see coordinate reference marks + image_blocks = [ + ContentBlock( + image=ImageContent( + format="png", source=ImageSource(bytes=add_ruler_edges(img)) + ) + ) + for img in page_images + ] + + # Create task instruction block + task_block = ContentBlock( + text=f"""# Assessment Task + + Assess the confidence of this field: + + **Field Path**: `{field_path_str}` + **Confidence Threshold**: {task.confidence_threshold} + + Locate the value for `{field_path_str}` in the extraction results provided in the system context, verify it against the document images, and submit your assessment. + + You MUST assess ONLY this field - do not assess any other fields. + """ + ) + + # Add cache point after task instructions + cache_block = ContentBlock(cachePoint=CachePoint(type="default")) + + return [*image_blocks, task_block, cache_block] + + +def _convert_to_assessment_result( + task: AssessmentTask, + output: AssessmentOutput, + metering: dict[str, Any], + processing_time: float, +) -> AssessmentResult: + """ + Convert Strands AssessmentOutput to AssessmentResult with standardized geometry format. + + The assessment_data is returned as a flat dict (not wrapped by field name) because + the aggregation step uses task.field_path for insertion into the final structure. + """ + field_name = output.field_name + assessment = output.assessment + + # Create standardized field assessment data + # Note: bounding box coordinates are already adjusted for ruler offset + # by the submit_assessment tool in strands_tools.py + field_data = FieldAssessmentData.from_llm_response( + confidence=assessment.confidence, + reasoning=assessment.reasoning, + confidence_threshold=task.confidence_threshold, + bbox_coords=( + [ + assessment.bounding_box.x1, + assessment.bounding_box.y1, + assessment.bounding_box.x2, + assessment.bounding_box.y2, + ] + if assessment.bounding_box + else None + ), + page_num=assessment.bounding_box.page if assessment.bounding_box else None, + ) + + # Return assessment data directly (not wrapped by field name) + # The aggregation step uses task.field_path for proper insertion + assessment_data = field_data.to_explainability_format() + + # Check for confidence threshold violations + confidence_alerts = [] + if assessment.confidence < task.confidence_threshold: + confidence_alerts.append( + { + "attribute_name": field_name, + "confidence": assessment.confidence, + "confidence_threshold": task.confidence_threshold, + } + ) + + return AssessmentResult( + task_id=task.task_id, + success=True, + assessment_data=assessment_data, + confidence_alerts=confidence_alerts, + processing_time=processing_time, + metering=metering, + ) diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py new file mode 100644 index 00000000..57f5b220 --- /dev/null +++ b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py @@ -0,0 +1,187 @@ +""" +Strands tools for confidence assessment with image viewing and grid overlay. + +This module provides tools for Strands agents to view document pages and +mark bounding boxes during confidence assessment tasks. +""" + +import os +from typing import Any + +from aws_lambda_powertools import Logger +from pydantic import BaseModel, Field +from strands import Agent, tool + +from idp_common.assessment.strands_models import AssessmentOutput, BoundingBox +from idp_common.utils.grid_overlay import add_ruler_and_draw_boxes, add_ruler_edges +from idp_common.utils.strands_agent_tools.todo_list import ( + create_todo_list, + update_todo, + view_todo_list, +) + +logger = Logger(service="assessment", level=os.getenv("LOG_LEVEL", "INFO")) + + +class ViewImageInput(BaseModel): + """Input model for view_image tool.""" + + image_index: int = Field( + ..., ge=0, description="Index of the page image to view (0-based)" + ) + bounding_box: BoundingBox | None = Field( + None, description="Optional bounding box to highlight on the image" + ) + label: str | None = Field(None, description="Optional label for the bounding box") + + +@tool +def submit_assessment(assessment: AssessmentOutput, agent: Agent) -> str: + """Submit the final assessment for a field.""" + # Validate assessment structure and return helpful errors + validated_assessment = AssessmentOutput.model_validate(assessment) + + # Store in agent state - coordinates are in 0-1000 document space + # The ruler shows 0-1000 scale mapping to the document, so LLM coordinates + # are already in document space and need no adjustment + agent.state.set("assessment_output", validated_assessment.model_dump(mode="json")) + + logger.info( + "Assessment submitted successfully", + extra={"assessment": validated_assessment.model_dump(mode="json")}, + ) + + return "Assessment submitted successfully. You can now finish the task." + + +def create_view_image_tool(page_images: list[bytes], sorted_page_ids: list[str]) -> Any: + """ + Create a view_image tool that has access to page images. + + Args: + page_images: List of raw page image bytes (without ruler overlay) + sorted_page_ids: List of page IDs in sorted order + + Returns: + A Strands tool function for viewing images + """ + + @tool + def view_image(input_data: ViewImageInput, agent: Agent) -> dict[str, Any]: + """ + View a specific page image, optionally highlighting a bounding box area. + + Use this tool to examine document pages when assessing confidence. + You can specify a bounding box to highlight a specific region. + + Args: + input_data: Dictionary with: + - image_index (int): Index of page to view (0-based) + - bounding_box (dict, optional): Bounding box with x1, y1, x2, y2, page + - label (str, optional): Label for the bounding box + + Returns: + ImageContent object that the LLM can see + + Example: + view_image({ + "image_index": 0, + "bounding_box": {"x1": 100, "y1": 200, "x2": 300, "y2": 250, "page": 1}, + "label": "Account Number" + }, agent) + """ + # Validate input - let ValidationError propagate + view_input = ViewImageInput.model_validate(input_data) + + # Validate image index exists + if view_input.image_index >= len(page_images): + raise ValueError( + f"Invalid image_index {view_input.image_index}. " + f"Valid range: 0-{len(page_images) - 1}" + ) + + # Get the raw image (no ruler overlay yet) + raw_img_bytes = page_images[view_input.image_index] + page_id = sorted_page_ids[view_input.image_index] + + # Add ruler and optionally draw bounding box + if view_input.bounding_box: + # Convert BoundingBox to dict format + bbox_dict = { + "bbox": [ + view_input.bounding_box.x1, + view_input.bounding_box.y1, + view_input.bounding_box.x2, + view_input.bounding_box.y2, + ], + "label": view_input.label or "Highlighted Region", + "color": "red", + } + + # Add ruler overlay and draw bounding box in one step + img_bytes = add_ruler_and_draw_boxes(raw_img_bytes, [bbox_dict]) + + logger.debug( + "Added ruler and drew bounding box on image", + extra={ + "image_index": view_input.image_index, + "page_id": page_id, + "x1": view_input.bounding_box.x1, + "y1": view_input.bounding_box.y1, + "x2": view_input.bounding_box.x2, + "y2": view_input.bounding_box.y2, + "page": view_input.bounding_box.page, + "label": view_input.label, + }, + ) + else: + # Just add ruler overlay (no bounding box) + img_bytes = add_ruler_edges(raw_img_bytes) + + logger.info( + "Returning image to agent", + extra={ + "image_index": view_input.image_index, + "page_id": page_id, + "has_bbox": view_input.bounding_box is not None, + "image_size_bytes": len(img_bytes), + }, + ) + + return { + "status": "success", + "content": [ + { + "image": { + "format": "png", + "source": { + "bytes": img_bytes, + }, + } + } + ], + } + + return view_image + + +def create_strands_tools( + page_images: list[bytes], sorted_page_ids: list[str] +) -> list[Any]: + """ + Create all tools needed for Strands-based assessment. + + Args: + page_images: List of page image bytes (with grid overlay already applied) + sorted_page_ids: List of page IDs in sorted order + + Returns: + List of Strands tool functions + """ + return [ + create_view_image_tool(page_images, sorted_page_ids), + create_todo_list, + update_todo, + view_todo_list, + submit_assessment, + ] diff --git a/lib/idp_common_pkg/idp_common/bedrock/__init__.py b/lib/idp_common_pkg/idp_common/bedrock/__init__.py index a2672973..c93d637a 100644 --- a/lib/idp_common_pkg/idp_common/bedrock/__init__.py +++ b/lib/idp_common_pkg/idp_common/bedrock/__init__.py @@ -3,7 +3,13 @@ """Bedrock integration module for IDP Common package.""" -from .client import BedrockClient, invoke_model, default_client +from .client import BedrockClient, default_client, invoke_model +from .model_config import ( + build_model_config, + get_model_max_tokens, + supports_prompt_caching, + supports_tool_caching, +) # Add version info __version__ = "0.1.0" @@ -12,10 +18,14 @@ __all__ = [ "BedrockClient", "invoke_model", - "default_client" + "default_client", + "build_model_config", + "get_model_max_tokens", + "supports_prompt_caching", + "supports_tool_caching", ] # Re-export key functions from the default client for backward compatibility extract_text_from_response = default_client.extract_text_from_response generate_embedding = default_client.generate_embedding -format_prompt = default_client.format_prompt \ No newline at end of file +format_prompt = default_client.format_prompt diff --git a/lib/idp_common_pkg/idp_common/bedrock/model_config.py b/lib/idp_common_pkg/idp_common/bedrock/model_config.py new file mode 100644 index 00000000..0df2da00 --- /dev/null +++ b/lib/idp_common_pkg/idp_common/bedrock/model_config.py @@ -0,0 +1,178 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: MIT-0 + +""" +Bedrock model configuration utilities. + +This module provides utilities for building model configurations with +proper token limits, caching settings, and retry behavior. +""" + +import logging +import re +from typing import Any + +from botocore.config import Config + +from idp_common.bedrock.client import CACHEPOINT_SUPPORTED_MODELS + +logger = logging.getLogger(__name__) + + +def supports_prompt_caching(model_id: str) -> bool: + """ + Check if a model supports prompt caching (cachePoint in system prompt). + + Args: + model_id: The Bedrock model identifier + + Returns: + True if the model supports prompt caching, False otherwise + """ + return model_id in CACHEPOINT_SUPPORTED_MODELS + + +def supports_tool_caching(model_id: str) -> bool: + """ + Check if a model supports tool caching (cachePoint in toolConfig). + + Note: Only Claude models support tool caching. Nova models support + prompt caching but NOT tool caching. + + Args: + model_id: The Bedrock model identifier + + Returns: + True if the model supports tool caching, False otherwise + """ + return "anthropic.claude" in model_id or "us.anthropic.claude" in model_id + + +def get_model_max_tokens(model_id: str) -> int: + """ + Get the maximum output tokens supported by a model. + + Args: + model_id: The Bedrock model identifier + + Returns: + Maximum output tokens for the model + """ + model_id_lower = model_id.lower() + + # Check Claude 4 patterns first (more specific) + if re.search(r"claude-(opus|sonnet|haiku)-4", model_id_lower): + return 64_000 + + # Check Nova models + if any( + nova in model_id_lower + for nova in ["nova-premier", "nova-pro", "nova-lite", "nova-micro"] + ): + return 10_000 + + # Check Claude 3 models + if "claude-3" in model_id_lower: + return 8_192 + + # Default fallback + return 4_096 + + +def build_model_config( + model_id: str, + max_tokens: int | None = None, + max_retries: int = 3, + connect_timeout: float = 60.0, + read_timeout: float = 300.0, +) -> dict[str, Any]: + """ + Build model configuration with token limits and caching settings. + + This function: + 1. Creates boto3 Config with retry and timeout settings + 2. Determines model-specific max token limits + 3. Validates and caps max_tokens if needed + 4. Auto-detects and enables caching support (prompt and tool caching) + + Args: + model_id: Bedrock model identifier (supports us.*, eu.*, and global.anthropic.*) + max_tokens: Optional max tokens override (will be capped at model max) + max_retries: Maximum retry attempts for API calls (default: 3) + connect_timeout: Connection timeout in seconds (default: 60.0) + read_timeout: Read timeout in seconds (default: 300.0) + + Returns: + Dictionary of model configuration parameters. + Automatically uses BedrockModel for regional models (us.*, eu.*) and + AnthropicModel with AnthropicBedrock for cross-region models (global.anthropic.*). + """ + # Configure retry behavior and timeouts using boto3 Config + boto_config = Config( + retries={ + "max_attempts": max_retries, + "mode": "adaptive", # Uses exponential backoff with adaptive retry mode + }, + connect_timeout=connect_timeout, + read_timeout=read_timeout, + ) + + # Get model-specific maximum token limits + model_max = get_model_max_tokens(model_id) + + # Use config value if provided, but cap at model's maximum + if max_tokens is not None: + if max_tokens > model_max: + logger.warning( + "Config max_tokens exceeds model limit, capping at model maximum", + extra={ + "config_max_tokens": max_tokens, + "model_max_tokens": model_max, + "model_id": model_id, + }, + ) + max_output_tokens = model_max + else: + max_output_tokens = max_tokens + else: + # No config value - use model maximum + max_output_tokens = model_max + + # Build base model config + model_config = dict( + model_id=model_id, boto_client_config=boto_config, max_tokens=max_output_tokens + ) + + logger.info( + "Setting max_tokens for model", + extra={ + "max_tokens": max_output_tokens, + "model_id": model_id, + "model_max_tokens": model_max, + }, + ) + + # Auto-detect caching support based on model capabilities + if supports_prompt_caching(model_id): + model_config["cache_prompt"] = "default" + logger.info( + "Prompt caching enabled for model", + extra={"model_id": model_id, "auto_detected": True}, + ) + + # Only enable tool caching if the model supports it (Claude only, not Nova) + if supports_tool_caching(model_id): + model_config["cache_tools"] = "default" + logger.info( + "Tool caching enabled for model", + extra={"model_id": model_id, "auto_detected": True}, + ) + else: + logger.info( + "Tool caching not supported for model", + extra={"model_id": model_id, "reason": "prompt_caching_only"}, + ) + else: + logger.debug("Caching not supported for model", extra={"model_id": model_id}) + + return model_config diff --git a/lib/idp_common_pkg/idp_common/config/models.py b/lib/idp_common_pkg/idp_common/config/models.py index b06f14f3..98cfba4c 100644 --- a/lib/idp_common_pkg/idp_common/config/models.py +++ b/lib/idp_common_pkg/idp_common/config/models.py @@ -34,10 +34,10 @@ class ImageConfig(BaseModel): """Image processing configuration""" target_width: Optional[int] = Field( - default=None, description="Target width for images" + default=1200, description="Target width for images (default: 1200px for ~1MP)" ) target_height: Optional[int] = Field( - default=None, description="Target height for images" + default=1200, description="Target height for images (default: 1200px for ~1MP)" ) dpi: Optional[int] = Field(default=None, description="DPI for image rendering") preprocessing: Optional[bool] = Field( @@ -140,6 +140,14 @@ def set_default_review_agent_model(self) -> Self: return self + @model_validator(mode="after") + def set_default_review_agent_model(self) -> Self: + """Set review_agent_model to extraction model if not specified.""" + if not self.agentic.review_agent_model: + self.agentic.review_agent_model = self.model + + return self + class ClassificationConfig(BaseModel): """Document classification configuration""" @@ -192,31 +200,13 @@ def parse_max_pages(cls, v: Any) -> int: return int(v) -class GranularAssessmentConfig(BaseModel): - """Granular assessment configuration""" - - enabled: bool = Field(default=False, description="Enable granular assessment") - list_batch_size: int = Field(default=1, gt=0) - simple_batch_size: int = Field(default=3, gt=0) - max_workers: int = Field(default=20, gt=0) - - @field_validator( - "list_batch_size", "simple_batch_size", "max_workers", mode="before" - ) - @classmethod - def parse_int(cls, v: Any) -> int: - """Parse int from string or number""" - if isinstance(v, str): - return int(v) if v else 0 - return int(v) - - class AssessmentConfig(BaseModel): """Document assessment configuration""" enabled: bool = Field(default=True, description="Enable assessment") model: Optional[str] = Field( - default=None, description="Bedrock model ID for assessment" + default="us.anthropic.claude-haiku-4-5-20251001-v1:0", + description="Bedrock model ID for assessment", ) system_prompt: str = Field( default="You are a document analysis assessment expert. Your role is to evaluate the confidence and accuracy of data extraction results by analyzing them against source documents.\n\nProvide accurate confidence scores for each assessment.", @@ -256,34 +246,7 @@ class AssessmentConfig(BaseModel): - For bounding boxes, provide normalized coordinates (0.0 to 1.0) in the format: {"left": x1, "top": y1, "width": w, "height": h} - -{ATTRIBUTE_NAMES_AND_DESCRIPTIONS} - - -<> - - -{DOCUMENT_IMAGE} - - - -{OCR_TEXT_CONFIDENCE} - - -<> - - -{EXTRACTION_RESULTS} - - -Provide your assessment as a JSON object with this exact structure: -{ - "attribute_name": { - "confidence": 0.0 to 1.0, - "confidence_reason": "explanation", - "bounding_box": {"left": 0.0, "top": 0.0, "width": 0.0, "height": 0.0} - } -}""", +""", description="Task prompt template for assessment", ) temperature: float = Field(default=0.0, ge=0.0, le=1.0) @@ -293,7 +256,10 @@ class AssessmentConfig(BaseModel): default_confidence_threshold: float = Field(default=0.8, ge=0.0, le=1.0) validation_enabled: bool = Field(default=False, description="Enable validation") image: ImageConfig = Field(default_factory=ImageConfig) - granular: GranularAssessmentConfig = Field(default_factory=GranularAssessmentConfig) + # Granular assessment settings (always enabled, no longer nested) + max_workers: int = Field( + default=50, gt=0, description="Max concurrent workers for parallel assessment" + ) @field_validator( "temperature", "top_p", "top_k", "default_confidence_threshold", mode="before" @@ -305,7 +271,7 @@ def parse_float(cls, v: Any) -> float: return float(v) if v else 0.0 return float(v) - @field_validator("max_tokens", mode="before") + @field_validator("max_tokens", "max_workers", mode="before") @classmethod def parse_int(cls, v: Any) -> int: """Parse int from string or number""" diff --git a/lib/idp_common_pkg/idp_common/extraction/agentic_idp.py b/lib/idp_common_pkg/idp_common/extraction/agentic_idp.py index cdd7b3e0..a4f9eb58 100644 --- a/lib/idp_common_pkg/idp_common/extraction/agentic_idp.py +++ b/lib/idp_common_pkg/idp_common/extraction/agentic_idp.py @@ -11,7 +11,6 @@ import json import logging import os -import re import threading from pathlib import Path from typing import ( @@ -22,7 +21,6 @@ import jsonpatch from aws_lambda_powertools import Logger -from botocore.config import Config from PIL import Image from pydantic import BaseModel, Field from strands import Agent, tool @@ -36,7 +34,9 @@ ImageSource, ) -from idp_common.bedrock.client import CACHEPOINT_SUPPORTED_MODELS +from idp_common.bedrock import ( + build_model_config, +) from idp_common.config.models import IDPConfig from idp_common.utils.bedrock_utils import ( async_exponential_backoff_retry, @@ -59,35 +59,6 @@ TargetModel = TypeVar("TargetModel", bound=BaseModel) -def supports_tool_caching(model_id: str) -> bool: - """ - Check if a model supports tool caching (cachePoint in toolConfig). - - Note: Only Claude models support tool caching. Nova models support - prompt caching but NOT tool caching. - - Args: - model_id: The Bedrock model identifier - - Returns: - True if the model supports tool caching, False otherwise - """ - return "anthropic.claude" in model_id or "us.anthropic.claude" in model_id - - -def supports_prompt_caching(model_id: str) -> bool: - """ - Check if a model supports prompt caching (cachePoint in system prompt). - - Args: - model_id: The Bedrock model identifier - - Returns: - True if the model supports prompt caching, False otherwise - """ - return model_id in CACHEPOINT_SUPPORTED_MODELS - - class BedrockUsage(TypedDict, total=False): """Token usage information from Bedrock response.""" @@ -465,7 +436,7 @@ def patch_buffer_data(patches: list[dict[str, Any]], agent: Agent) -> str: logger.info(f"Current length of buffer data {len(patched_data)} ") - return f"Successfully patched {str(patched_data)[100:]}...." + return f"Successfully patched {str(patched_data)[:100]}...." SYSTEM_PROMPT = """ @@ -578,119 +549,6 @@ def _build_system_prompt( return complete_prompt, schema_json -def _build_model_config( - model_id: str, - max_tokens: int | None, - max_retries: int, - connect_timeout: float, - read_timeout: float, -) -> dict[str, Any]: - """ - Build model configuration with token limits and caching settings. - - This function: - 1. Creates boto3 Config with retry and timeout settings - 2. Determines model-specific max token limits - 3. Validates and caps max_tokens if needed - 4. Auto-detects and enables caching support (prompt and tool caching) - - Args: - model_id: Bedrock model identifier (supports us.*, eu.*, and global.anthropic.*) - max_tokens: Optional max tokens override (will be capped at model max) - max_retries: Maximum retry attempts for API calls - connect_timeout: Connection timeout in seconds - read_timeout: Read timeout in seconds - - Returns: - Dictionary of model configuration parameters for create_strands_bedrock_model. - Automatically uses BedrockModel for regional models (us.*, eu.*) and - AnthropicModel with AnthropicBedrock for cross-region models (global.anthropic.*). - """ - # Configure retry behavior and timeouts using boto3 Config - boto_config = Config( - retries={ - "max_attempts": max_retries, - "mode": "adaptive", # Uses exponential backoff with adaptive retry mode - }, - connect_timeout=connect_timeout, - read_timeout=read_timeout, - ) - - # Determine model-specific maximum token limits - model_max = 4_096 # Default fallback - model_id_lower = model_id.lower() - - # Check Claude 4 patterns first (more specific) - if re.search(r"claude-(opus|sonnet|haiku)-4", model_id_lower): - model_max = 64_000 - # Check Nova models - elif any( - nova in model_id_lower - for nova in ["nova-premier", "nova-pro", "nova-lite", "nova-micro"] - ): - model_max = 10_000 - # Check Claude 3 models - elif "claude-3" in model_id_lower: - model_max = 8_192 - - # Use config value if provided, but cap at model's maximum - if max_tokens is not None: - if max_tokens > model_max: - logger.warning( - "Config max_tokens exceeds model limit, capping at model maximum", - extra={ - "config_max_tokens": max_tokens, - "model_max_tokens": model_max, - "model_id": model_id, - }, - ) - max_output_tokens = model_max - else: - max_output_tokens = max_tokens - else: - # No config value - use model maximum for agentic extraction - max_output_tokens = model_max - - # Build base model config - model_config = dict( - model_id=model_id, boto_client_config=boto_config, max_tokens=max_output_tokens - ) - - logger.info( - "Setting max_tokens for model", - extra={ - "max_tokens": max_output_tokens, - "model_id": model_id, - "model_max_tokens": model_max, - }, - ) - - # Auto-detect caching support based on model capabilities - if supports_prompt_caching(model_id): - model_config["cache_prompt"] = "default" - logger.info( - "Prompt caching enabled for model", - extra={"model_id": model_id, "auto_detected": True}, - ) - - # Only enable tool caching if the model supports it (Claude only, not Nova) - if supports_tool_caching(model_id): - model_config["cache_tools"] = "default" - logger.info( - "Tool caching enabled for model", - extra={"model_id": model_id, "auto_detected": True}, - ) - else: - logger.info( - "Tool caching not supported for model", - extra={"model_id": model_id, "reason": "prompt_caching_only"}, - ) - else: - logger.debug("Caching not supported for model", extra={"model_id": model_id}) - - return model_config - - def _prepare_prompt_content( prompt: str | Message | Image.Image, page_images: list[bytes] | None, @@ -988,7 +846,7 @@ async def structured_output_async( ) # Build model configuration with token limits and caching - model_config = _build_model_config( + model_config = build_model_config( model_id=model_id, max_tokens=max_tokens, max_retries=max_retries, @@ -1069,7 +927,7 @@ async def structured_output_async( ], ) # Build config for review agent - review_model_config = _build_model_config( + review_model_config = build_model_config( model_id=config.extraction.agentic.review_agent_model, max_tokens=max_tokens, max_retries=max_retries, diff --git a/lib/idp_common_pkg/idp_common/extraction/models.py b/lib/idp_common_pkg/idp_common/extraction/models.py index a207adbb..2347c712 100644 --- a/lib/idp_common_pkg/idp_common/extraction/models.py +++ b/lib/idp_common_pkg/idp_common/extraction/models.py @@ -10,6 +10,8 @@ from dataclasses import dataclass from typing import Any, Dict, List, Optional +from pydantic import BaseModel, Field + @dataclass class ExtractedAttribute: @@ -41,3 +43,78 @@ class PageInfo: text_uri: Optional[str] = None image_uri: Optional[str] = None raw_text_uri: Optional[str] = None + + +class DocumentClassInfo(BaseModel): + """Document classification information""" + + type: str = Field(description="Document class/type") + + +class SplitDocumentInfo(BaseModel): + """Information about document splitting""" + + page_indices: List[int] = Field( + default_factory=list, description="Page indices in the split document" + ) + + +class ExtractionMetadata(BaseModel): + """Metadata about the extraction process""" + + parsing_succeeded: bool = Field( + default=True, description="Whether parsing succeeded" + ) + extraction_time_seconds: Optional[float] = Field( + default=None, description="Time taken for extraction" + ) + skipped_due_to_empty_attributes: Optional[bool] = Field( + default=None, + description="Whether extraction was skipped due to empty attributes", + ) + assessment_time_seconds: Optional[float] = Field( + default=None, description="Time taken for assessment" + ) + granular_assessment_used: Optional[bool] = Field( + default=None, description="Whether granular assessment was used" + ) + assessment_tasks_total: Optional[int] = Field( + default=None, description="Total number of assessment tasks" + ) + assessment_tasks_successful: Optional[int] = Field( + default=None, description="Number of successful assessment tasks" + ) + assessment_tasks_failed: Optional[int] = Field( + default=None, description="Number of failed assessment tasks" + ) + + +class ExtractionData(BaseModel): + """ + Complete extraction data structure stored in S3. + + This model represents the JSON structure written to S3 containing + extraction results, assessment information, and metadata. + """ + + document_class: DocumentClassInfo = Field( + description="Document classification information" + ) + split_document: SplitDocumentInfo = Field( + default_factory=SplitDocumentInfo, + description="Information about document splitting", + ) + inference_result: Dict[str, Any] = Field( + default_factory=dict, description="Extracted data from the document" + ) + explainability_info: Optional[List[Dict[str, Any]]] = Field( + default=None, description="Assessment/explainability information" + ) + metadata: ExtractionMetadata = Field( + default_factory=ExtractionMetadata, + description="Extraction and assessment metadata", + ) + + class Config: + # Allow extra fields for forward compatibility + extra = "allow" diff --git a/lib/idp_common_pkg/idp_common/image/__init__.py b/lib/idp_common_pkg/idp_common/image/__init__.py index da867890..09fb21f4 100644 --- a/lib/idp_common_pkg/idp_common/image/__init__.py +++ b/lib/idp_common_pkg/idp_common/image/__init__.py @@ -1,185 +1,237 @@ # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # SPDX-License-Identifier: MIT-0 -from PIL import Image, ImageFilter, ImageChops, ImageOps +from PIL import Image, ImageFilter, ImageChops, ImageOps, ImageDraw, ImageFont import io -import logging -from typing import Tuple, Optional, Dict, Any, Union +import os +from typing import Tuple, Optional, Dict, Any, Union, List +from aws_lambda_powertools import Logger from ..s3 import get_binary_content from ..utils import parse_s3_uri -logger = logging.getLogger(__name__) +logger = Logger(service="image", level=os.getenv("LOG_LEVEL", "INFO")) -def resize_image(image_data: bytes, - target_width: Optional[int] = None, - target_height: Optional[int] = None, - allow_upscale: bool = False) -> bytes: + +def resize_image( + image_data: bytes, + target_width: Optional[int] = None, + target_height: Optional[int] = None, + allow_upscale: bool = False, + output_format: Optional[str] = None, +) -> bytes: """ Resize an image to fit within target dimensions while preserving aspect ratio. No padding, no distortion - pure proportional scaling. Preserves original format when possible. - + Args: image_data: Raw image bytes target_width: Target width in pixels (None or empty string = no resize) target_height: Target height in pixels (None or empty string = no resize) allow_upscale: Whether to allow making the image larger than original - + output_format: Force output format (e.g. 'PNG', 'JPEG'). If None, preserves original format. + Returns: - Resized image bytes in original format (or JPEG if format cannot be preserved) + Resized image bytes in specified format (or original format if output_format is None) """ # Handle empty strings - convert to None if isinstance(target_width, str) and not target_width.strip(): target_width = None if isinstance(target_height, str) and not target_height.strip(): target_height = None - + # If either dimension is None, return original image unchanged if target_width is None or target_height is None: - logger.info("No resize requested (width or height is None/empty), returning original image") + logger.info( + "No resize requested (width or height is None/empty), returning original image" + ) return image_data - + # Convert to int if needed try: target_width = int(target_width) target_height = int(target_height) except (ValueError, TypeError): - logger.warning(f"Invalid resize dimensions: width={target_width}, height={target_height}, returning original image") + logger.warning( + f"Invalid resize dimensions: width={target_width}, height={target_height}, returning original image" + ) return image_data image = Image.open(io.BytesIO(image_data)) current_width, current_height = image.size original_format = image.format # Store original format - + + logger.info( + f"resize_image: original={current_width}x{current_height} ({original_format}), " + f"target={target_width}x{target_height}, output_format={output_format}" + ) + # Calculate scaling factor to fit within bounds while preserving aspect ratio width_ratio = target_width / current_width height_ratio = target_height / current_height scale_factor = min(width_ratio, height_ratio) # Fit within bounds - + # Determine if resizing is needed needs_resize = (scale_factor < 1.0) or (allow_upscale and scale_factor > 1.0) - + if needs_resize: new_width = int(current_width * scale_factor) new_height = int(current_height * scale_factor) - logger.info(f"Resizing image from {current_width}x{current_height} to {new_width}x{new_height} (scale: {scale_factor:.3f})") - image = image.resize((new_width, new_height), Image.LANCZOS) - + logger.info( + f"Resizing image from {current_width}x{current_height} to {new_width}x{new_height} (scale: {scale_factor:.3f})" + ) + image = image.resize((new_width, new_height), Image.Resampling.LANCZOS) + # Save in original format if possible img_byte_array = io.BytesIO() - - # Determine save format - use original if available, otherwise JPEG - if original_format and original_format in ['JPEG', 'PNG', 'GIF', 'BMP', 'TIFF', 'WEBP']: + + # Determine save format + if output_format: + # Use explicitly requested format + save_format = output_format.upper() + logger.info( + f"Converting from {original_format or 'unknown'} to {save_format}" + ) + elif original_format and original_format in [ + "JPEG", + "PNG", + "GIF", + "BMP", + "TIFF", + "WEBP", + ]: save_format = original_format else: - save_format = 'JPEG' + save_format = "JPEG" logger.info(f"Converting from {original_format or 'unknown'} to JPEG") - + # Prepare save parameters save_kwargs = {"format": save_format} - + # Add quality parameters for JPEG - if save_format in ['JPEG', 'JPG']: + if save_format in ["JPEG", "JPG"]: save_kwargs["quality"] = 95 # High quality save_kwargs["optimize"] = True - + elif save_format == "PNG": + save_kwargs["optimize"] = True + # Handle format-specific requirements - if save_format == 'PNG' and image.mode not in ['RGBA', 'LA', 'L', 'P']: + if save_format == "PNG" and image.mode not in ["RGBA", "LA", "L", "P"]: # PNG requires specific modes - if image.mode == 'CMYK': - image = image.convert('RGB') - + if image.mode == "CMYK": + image = image.convert("RGB") + image.save(img_byte_array, **save_kwargs) - return img_byte_array.getvalue() + result_bytes = img_byte_array.getvalue() + logger.info( + f"resize_image: resized to {image.width}x{image.height}, " + f"saved as {save_format}, size={len(result_bytes):,} bytes" + ) + return result_bytes else: # No resizing needed - return original data unchanged - logger.info(f"Image {current_width}x{current_height} already fits within {target_width}x{target_height}, returning original") + logger.info( + f"resize_image: image {current_width}x{current_height} already fits within " + f"{target_width}x{target_height}, returning original ({len(image_data):,} bytes)" + ) return image_data -def prepare_image(image_source: Union[str, bytes], - target_width: Optional[int] = None, - target_height: Optional[int] = None, - allow_upscale: bool = False) -> bytes: + +def prepare_image( + image_source: Union[str, bytes], + target_width: Optional[int] = None, + target_height: Optional[int] = None, + allow_upscale: bool = False, + output_format: Optional[str] = None, +) -> bytes: """ Prepare an image for model input from either S3 URI or raw bytes - + Args: image_source: Either an S3 URI (s3://bucket/key) or raw image bytes target_width: Target width in pixels (None or empty string = no resize) target_height: Target height in pixels (None or empty string = no resize) allow_upscale: Whether to allow making the image larger than original - + output_format: Force output format (e.g. 'PNG', 'JPEG'). If None, preserves original format. + Returns: - Processed image bytes ready for model input (preserves format when possible) + Processed image bytes ready for model input """ # Get the image data - if isinstance(image_source, str) and image_source.startswith('s3://'): + if isinstance(image_source, str) and image_source.startswith("s3://"): image_data = get_binary_content(image_source) elif isinstance(image_source, bytes): image_data = image_source else: - raise ValueError(f"Invalid image source: {type(image_source)}. Must be S3 URI or bytes.") - + raise ValueError( + f"Invalid image source: {type(image_source)}. Must be S3 URI or bytes." + ) + # Resize and process - return resize_image(image_data, target_width, target_height, allow_upscale) + return resize_image( + image_data, target_width, target_height, allow_upscale, output_format + ) + def apply_adaptive_binarization(image_data: bytes) -> bytes: """ Apply adaptive binarization using Pillow-only implementation. - + This preprocessing step can significantly improve OCR accuracy on documents with: - Uneven lighting or shadows - Low contrast text - Background noise or gradients - + Implements adaptive mean thresholding similar to OpenCV's ADAPTIVE_THRESH_MEAN_C with block_size=15 and C=10. - + Args: image_data: Raw image bytes - + Returns: Processed image as JPEG bytes with adaptive binarization applied """ try: # Convert bytes to PIL Image pil_image = Image.open(io.BytesIO(image_data)) - + # Convert to grayscale if not already - if pil_image.mode != 'L': - pil_image = pil_image.convert('L') - + if pil_image.mode != "L": + pil_image = pil_image.convert("L") + # Apply adaptive thresholding using Pillow operations block_size = 15 C = 10 - + # Create a blurred version for local mean calculation # Use BoxBlur with radius = block_size // 2 to approximate local mean radius = block_size // 2 blurred = pil_image.filter(ImageFilter.BoxBlur(radius)) - + # Apply adaptive threshold: original > (blurred - C) ? 255 : 0 # Load pixel data for efficient access width, height = pil_image.size original_pixels = list(pil_image.getdata()) blurred_pixels = list(blurred.getdata()) - + binary_pixels = [] # Apply thresholding pixel by pixel for orig, blur in zip(original_pixels, blurred_pixels): threshold = blur - C binary_pixels.append(255 if orig > threshold else 0) - + # Create binary image - binary_image = Image.new('L', (width, height)) + binary_image = Image.new("L", (width, height)) binary_image.putdata(binary_pixels) - + # Convert to JPEG bytes img_byte_array = io.BytesIO() binary_image.save(img_byte_array, format="JPEG") - - logger.debug("Applied adaptive binarization preprocessing (Pillow implementation)") + + logger.debug( + "Applied adaptive binarization preprocessing (Pillow implementation)" + ) return img_byte_array.getvalue() - + except Exception as e: logger.error(f"Error applying adaptive binarization: {str(e)}") # Return original image if preprocessing fails @@ -190,28 +242,18 @@ def apply_adaptive_binarization(image_data: bytes) -> bytes: def prepare_bedrock_image_attachment(image_data: bytes) -> Dict[str, Any]: """ Format an image for Bedrock API attachment - + Args: image_data: Raw image bytes - + Returns: Formatted image attachment for Bedrock API """ # Detect image format from image data image = Image.open(io.BytesIO(image_data)) - format_mapping = { - 'JPEG': 'jpeg', - 'PNG': 'png', - 'GIF': 'gif', - 'WEBP': 'webp' - } + format_mapping = {"JPEG": "jpeg", "PNG": "png", "GIF": "gif", "WEBP": "webp"} detected_format = format_mapping.get(image.format) if not detected_format: raise ValueError(f"Unsupported image format: {image.format}") logger.info(f"Detected image format: {detected_format}") - return { - "image": { - "format": detected_format, - "source": {"bytes": image_data} - } - } + return {"image": {"format": detected_format, "source": {"bytes": image_data}}} diff --git a/lib/idp_common_pkg/idp_common/utils/bedrock_utils.py b/lib/idp_common_pkg/idp_common/utils/bedrock_utils.py index 3f901b20..bee846c3 100644 --- a/lib/idp_common_pkg/idp_common/utils/bedrock_utils.py +++ b/lib/idp_common_pkg/idp_common/utils/bedrock_utils.py @@ -19,11 +19,33 @@ InvokeModelRequestTypeDef, InvokeModelResponseTypeDef, ) +from strands.models.bedrock import ModelThrottledException # Configure logger logger = logging.getLogger(__name__) logger.setLevel(os.environ.get("LOG_LEVEL", "INFO")) +# Default retryable error codes (matched against ClientError codes and exception messages) +DEFAULT_RETRYABLE_ERRORS = { + "ThrottlingException", + "throttlingException", + "ModelThrottledException", # Strands wrapper for throttling + "ModelErrorException", + "ValidationException", + "ServiceQuotaExceededException", + "RequestLimitExceeded", + "TooManyRequestsException", + "ServiceUnavailableException", + "serviceUnavailableException", # lowercase variant from EventStreamError + "RequestTimeout", + "RequestTimeoutException", +} + +# Default retryable exception types (caught by isinstance check) +DEFAULT_RETRYABLE_EXCEPTION_TYPES: tuple[type[Exception], ...] = ( + ModelThrottledException, +) + def async_exponential_backoff_retry[T, **P]( max_retries: int = 5, @@ -32,23 +54,13 @@ def async_exponential_backoff_retry[T, **P]( exponential_base: float = 2.0, jitter: float = 0.1, retryable_errors: set[str] | None = None, + retryable_exception_types: tuple[type[Exception], ...] | None = None, ) -> Callable[[Callable[P, Awaitable[T]]], Callable[P, Awaitable[T]]]: - if not retryable_errors: - retryable_errors = set( - [ - "ThrottlingException", - "throttlingException", - "ModelErrorException", - "ValidationException", - "ServiceQuotaExceededException", - "RequestLimitExceeded", - "TooManyRequestsException", - "ServiceUnavailableException", - "serviceUnavailableException", # lowercase variant from EventStreamError - "RequestTimeout", - "RequestTimeoutException", - ] - ) + # Use defaults if not provided + if retryable_errors is None: + retryable_errors = DEFAULT_RETRYABLE_ERRORS + if retryable_exception_types is None: + retryable_exception_types = DEFAULT_RETRYABLE_EXCEPTION_TYPES def decorator(func: Callable[P, Awaitable[T]]) -> Callable[P, Awaitable[T]]: @wraps(func) @@ -104,7 +116,34 @@ def log_bedrock_invocation_error(error: Exception, attempt_num: int): await asyncio.sleep(sleep_time) delay = min(delay * exponential_base, max_delay) except Exception as e: - # Log bedrock invocation details for non-ClientError exceptions too + # Check if this is a retryable exception type (e.g., Strands ModelThrottledException) + is_retryable_type = retryable_exception_types and isinstance( + e, retryable_exception_types + ) + + # Also check if exception name or message contains retryable error patterns + exception_name = type(e).__name__ + exception_str = str(e) + is_retryable_name = exception_name in retryable_errors or any( + err in exception_str for err in retryable_errors + ) + + if ( + is_retryable_type or is_retryable_name + ) and attempt < max_retries - 1: + # Log and retry + log_bedrock_invocation_error(e, attempt + 1) + jitter_value = random.uniform(-jitter, jitter) + sleep_time = max(0.1, delay * (1 + jitter_value)) + logger.warning( + f"{exception_name}: {exception_str} encountered in {func.__name__}. " + f"Retrying in {sleep_time:.2f} seconds. Attempt {attempt + 1}/{max_retries}" + ) + await asyncio.sleep(sleep_time) + delay = min(delay * exponential_base, max_delay) + continue + + # Log bedrock invocation details for non-retryable exceptions log_bedrock_invocation_error(e, attempt + 1) raise diff --git a/lib/idp_common_pkg/idp_common/utils/grid_overlay.py b/lib/idp_common_pkg/idp_common/utils/grid_overlay.py new file mode 100644 index 00000000..c2fd05e1 --- /dev/null +++ b/lib/idp_common_pkg/idp_common/utils/grid_overlay.py @@ -0,0 +1,380 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: MIT-0 + +""" +Grid overlay module for adding coordinate references to document images. + +This module provides functions to add ruler-style coordinate grids to images, +enabling LLMs to provide precise bounding box coordinates for extracted fields. +""" + +import io +import logging + +from PIL import Image, ImageDraw, ImageFont + +logger = logging.getLogger(__name__) + + +def add_ruler_edges( + image_data: bytes, + ruler_width: int = 30, + tick_interval: int = 50, + label_interval: int = 100, + ruler_color: tuple[int, int, int, int] = (240, 240, 240, 255), + tick_color: str = "black", + label_color: str = "black", + font_size: int = 12, +) -> bytes: + """ + Add ruler-style edges to the image (like graph paper margins). + Document content remains completely unobscured. + + Args: + image_data: Raw image bytes (JPEG, PNG, etc.) + ruler_width: Width of the ruler margin in pixels + tick_interval: Spacing between minor tick marks (in 0-1000 scale) + label_interval: Spacing between labeled major tick marks (in 0-1000 scale) + ruler_color: Background color of ruler (RGBA tuple) + tick_color: Color of tick marks + label_color: Color of coordinate labels + font_size: Font size for labels + + Returns: + Image bytes with ruler edges added (JPEG format) + """ + image = Image.open(io.BytesIO(image_data)).convert("RGBA") + orig_width, orig_height = image.size + + logger.info(f"Adding ruler edges to image {orig_width}x{orig_height}") + + # Create canvas with ruler margins on top and left + new_width = orig_width + ruler_width + new_height = orig_height + ruler_width + + canvas = Image.new("RGBA", (new_width, new_height), (255, 255, 255, 255)) + + draw = ImageDraw.Draw(canvas) + + # Create ruler backgrounds + # Top ruler (horizontal) - for X coordinates + draw.rectangle([(ruler_width, 0), (new_width, ruler_width)], fill=ruler_color) + # Left ruler (vertical) - for Y coordinates + draw.rectangle([(0, ruler_width), (ruler_width, new_height)], fill=ruler_color) + # Corner square + draw.rectangle([(0, 0), (ruler_width, ruler_width)], fill=ruler_color) + + # Paste original image offset by ruler width + canvas.paste(image, (ruler_width, ruler_width)) + + # Load font + font = _load_font(font_size) + small_font = _load_font(max(font_size - 2, 8)) + + # Draw tick marks and labels on TOP ruler (X-axis) + for i in range(0, 1001, tick_interval): + pixel_x = ruler_width + int((i / 1000.0) * orig_width) + + if i % label_interval == 0: + # Major tick with label + draw.line( + [(pixel_x, ruler_width - 12), (pixel_x, ruler_width)], + fill=tick_color, + width=2, + ) + # Center the label above the tick + label = str(i) + bbox = draw.textbbox((0, 0), label, font=font) + label_width = bbox[2] - bbox[0] + draw.text( + (pixel_x - label_width // 2, 2), + label, + fill=label_color, + font=font, + ) + else: + # Minor tick (no label) + draw.line( + [(pixel_x, ruler_width - 6), (pixel_x, ruler_width)], + fill=tick_color, + width=1, + ) + + # Draw tick marks and labels on LEFT ruler (Y-axis) + for i in range(0, 1001, tick_interval): + pixel_y = ruler_width + int((i / 1000.0) * orig_height) + + if i % label_interval == 0: + # Major tick with label + draw.line( + [(ruler_width - 12, pixel_y), (ruler_width, pixel_y)], + fill=tick_color, + width=2, + ) + # Right-align the label + label = str(i) + bbox = draw.textbbox((0, 0), label, font=font) + label_width = bbox[2] - bbox[0] + draw.text( + (ruler_width - label_width - 14, pixel_y - 6), + label, + fill=label_color, + font=font, + ) + else: + # Minor tick + draw.line( + [(ruler_width - 6, pixel_y), (ruler_width, pixel_y)], + fill=tick_color, + width=1, + ) + + # Add origin marker in corner (skip if using default font that doesn't support sizing) + try: + draw.text((2, 2), "0", fill=label_color, font=small_font) + except (OSError, AttributeError): + logger.debug("Skipping origin label - font rendering issue") + + canvas = canvas.convert("RGB") + img_byte_array = io.BytesIO() + # Use PNG with optimization for better compression while preserving quality + canvas.save(img_byte_array, format="PNG", optimize=True) + + logger.info(f"Ruler edges added. New size: {new_width}x{new_height}") + return img_byte_array.getvalue() + + +def draw_bounding_boxes( + image_data: bytes, + bboxes: list[dict], + margin_offset: int = 0, + box_color: str = "red", + box_width: int = 3, + label_font_size: int = 12, + show_labels: bool = True, +) -> bytes: + """ + Draw bounding boxes on an image using normalized 0-1000 coordinates. + + Args: + image_data: Raw image bytes + bboxes: List of bounding box dictionaries, each containing: + - 'bbox': [x1, y1, x2, y2] in 0-1000 normalized scale (document space) + - 'label': Optional label text for the box + - 'color': Optional color override for this box + - 'page': Optional page number (for multi-page docs) + margin_offset: Pixel offset for top-left margin (e.g., if image has decorative margins) + box_color: Default color for bounding boxes + box_width: Line width for bounding boxes + label_font_size: Font size for box labels + show_labels: Whether to show labels on boxes + + Returns: + Image bytes with bounding boxes drawn + + Example: + bboxes = [ + { + 'bbox': [150, 220, 380, 245], + 'label': 'Account Number', + 'color': 'green' + }, + { + 'bbox': [100, 300, 500, 330], + 'label': 'Balance' + } + ] + result = draw_bounding_boxes(image_data, bboxes) + """ + image = Image.open(io.BytesIO(image_data)).convert("RGBA") + width, height = image.size + + # Calculate document area (excluding any margin offset) + doc_width = width - margin_offset + doc_height = height - margin_offset + offset_x = margin_offset + offset_y = margin_offset + + # Create overlay for semi-transparent boxes + overlay = Image.new("RGBA", (width, height), (0, 0, 0, 0)) + draw = ImageDraw.Draw(overlay) + + font = _load_font(label_font_size) + + for i, bbox_info in enumerate(bboxes): + bbox = bbox_info.get("bbox", []) + if len(bbox) != 4: + logger.warning(f"Invalid bbox format at index {i}: {bbox}") + continue + + x1_norm, y1_norm, x2_norm, y2_norm = bbox + + # Convert from 0-1000 scale to pixel coordinates + x1_pixel = offset_x + int((x1_norm / 1000.0) * doc_width) + y1_pixel = offset_y + int((y1_norm / 1000.0) * doc_height) + x2_pixel = offset_x + int((x2_norm / 1000.0) * doc_width) + y2_pixel = offset_y + int((y2_norm / 1000.0) * doc_height) + + # Get color for this box + color = bbox_info.get("color", box_color) + + # Draw rectangle outline + draw.rectangle( + [(x1_pixel, y1_pixel), (x2_pixel, y2_pixel)], + outline=color, + width=box_width, + ) + + # Add semi-transparent fill + fill_color = _get_rgba_color(color, alpha=50) + draw.rectangle( + [ + (x1_pixel + box_width, y1_pixel + box_width), + (x2_pixel - box_width, y2_pixel - box_width), + ], + fill=fill_color, + ) + + # Add label if provided + if show_labels and "label" in bbox_info: + label = bbox_info["label"] + + # Draw label background + label_bbox = draw.textbbox((0, 0), label, font=font) + label_width = label_bbox[2] - label_bbox[0] + label_height = label_bbox[3] - label_bbox[1] + + # Position label above the box + label_x = x1_pixel + label_y = y1_pixel - label_height - 4 + + # If label would go off top of image, put it below the box + if label_y < offset_y: + label_y = y2_pixel + 2 + + # Draw label background + draw.rectangle( + [ + (label_x - 2, label_y - 2), + (label_x + label_width + 2, label_y + label_height + 2), + ], + fill=(255, 255, 255, 220), + ) + + # Draw label text + draw.text((label_x, label_y), label, fill=color, font=font) + + # Add coordinate annotation + coord_text = f"[{x1_norm},{y1_norm},{x2_norm},{y2_norm}]" + coord_bbox = draw.textbbox((0, 0), coord_text, font=_load_font(8)) + coord_width = coord_bbox[2] - coord_bbox[0] + + # Position coordinates at bottom-right of box + coord_x = x2_pixel - coord_width - 2 + coord_y = y2_pixel + 2 + + draw.rectangle( + [(coord_x - 1, coord_y - 1), (coord_x + coord_width + 1, coord_y + 10)], + fill=(255, 255, 255, 200), + ) + draw.text((coord_x, coord_y), coord_text, fill="gray", font=_load_font(8)) + + # Composite overlay onto original image + result = Image.alpha_composite(image, overlay) + result = result.convert("RGB") + + img_byte_array = io.BytesIO() + # Use PNG with optimization for better compression while preserving quality + result.save(img_byte_array, format="PNG", optimize=True) + + logger.info(f"Drew {len(bboxes)} bounding boxes on image") + return img_byte_array.getvalue() + + +def add_ruler_and_draw_boxes( + image_data: bytes, + bboxes: list[dict], + ruler_width: int = 30, + tick_interval: int = 50, + label_interval: int = 100, + box_color: str = "red", + box_width: int = 3, +) -> bytes: + """ + Convenience function to add ruler edges and draw bounding boxes in one step. + + Args: + image_data: Raw image bytes + bboxes: List of bounding box dictionaries + ruler_width: Width of ruler margin + tick_interval: Spacing between minor ticks + label_interval: Spacing between major ticks + box_color: Default color for boxes + box_width: Line width for boxes + + Returns: + Image bytes with ruler and bounding boxes + """ + # First add ruler edges + image_with_ruler = add_ruler_edges( + image_data, + ruler_width=ruler_width, + tick_interval=tick_interval, + label_interval=label_interval, + ) + + # Then draw bounding boxes (accounting for margin offset from ruler) + result = draw_bounding_boxes( + image_with_ruler, + bboxes, + margin_offset=ruler_width, + box_color=box_color, + box_width=box_width, + ) + + return result + + +def _load_font(size: int): + """Load a font, falling back to default if not available.""" + font_paths = [ + "/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", + "/usr/share/fonts/truetype/liberation/LiberationSans-Regular.ttf", + "/System/Library/Fonts/Helvetica.ttc", + "/Library/Fonts/Arial.ttf", + "/Library/Fonts/Arial Unicode.ttf", + "C:\\Windows\\Fonts\\arial.ttf", + ] + + for font_path in font_paths: + try: + return ImageFont.truetype(font_path, size) + except (OSError, IOError): + continue + + # Fall back to default font without size (it's fixed size) + logger.warning( + "Could not load TrueType font from standard paths, using PIL default" + ) + return ImageFont.load_default() + + +def _get_rgba_color(color_name: str, alpha: int = 255) -> tuple[int, int, int, int]: + """Convert color name to RGBA tuple.""" + color_map = { + "red": (255, 0, 0, alpha), + "green": (0, 255, 0, alpha), + "blue": (0, 0, 255, alpha), + "yellow": (255, 255, 0, alpha), + "orange": (255, 165, 0, alpha), + "purple": (128, 0, 128, alpha), + "cyan": (0, 255, 255, alpha), + "magenta": (255, 0, 255, alpha), + "lime": (0, 255, 0, alpha), + "pink": (255, 192, 203, alpha), + "black": (0, 0, 0, alpha), + "white": (255, 255, 255, alpha), + "gray": (128, 128, 128, alpha), + } + + return color_map.get(color_name.lower(), (255, 0, 0, alpha)) diff --git a/lib/idp_common_pkg/idp_common/utils/pdf_helpers.py b/lib/idp_common_pkg/idp_common/utils/pdf_helpers.py new file mode 100644 index 00000000..440a1b2c --- /dev/null +++ b/lib/idp_common_pkg/idp_common/utils/pdf_helpers.py @@ -0,0 +1,242 @@ +""" +PDF utility functions for loading and converting PDF pages to images. + +This module provides utilities for working with PDF documents in assessment +and other workflows that need to display PDF pages as images. +""" + +import io +from pathlib import Path +from typing import Optional + +try: + import fitz # PyMuPDF + + HAS_PYMUPDF = True +except ImportError: + HAS_PYMUPDF = False + +try: + from PIL import Image + + HAS_PIL = True +except ImportError: + HAS_PIL = False + + +def pdf_page_to_image( + pdf_path: str | Path, + page_number: int = 0, + max_width: int = 1200, + max_height: int = 1200, + dpi_scale: float = 1.0, +) -> bytes: + """ + Convert a PDF page to a PNG image with optional resizing. + + Args: + pdf_path: Path to the PDF file + page_number: Page number (0-based index) + max_width: Maximum width in pixels (default: 1200 for ~1MP) + max_height: Maximum height in pixels (default: 1200 for ~1MP) + dpi_scale: DPI scaling factor (default: 1.0 = 72 DPI, 2.0 = 144 DPI) + + Returns: + PNG image as bytes + + Raises: + ImportError: If PyMuPDF is not installed + FileNotFoundError: If PDF file doesn't exist + ValueError: If page number is invalid + """ + if not HAS_PYMUPDF: + raise ImportError( + "PyMuPDF (fitz) is required for PDF handling. " + "Install with: pip install PyMuPDF" + ) + + pdf_path = Path(pdf_path) + if not pdf_path.exists(): + raise FileNotFoundError(f"PDF file not found: {pdf_path}") + + # Open PDF + pdf_doc = fitz.open(str(pdf_path)) + + try: + # Validate page number + if page_number < 0 or page_number >= len(pdf_doc): + raise ValueError( + f"Invalid page number {page_number}. " + f"PDF has {len(pdf_doc)} pages (0-{len(pdf_doc) - 1})" + ) + + # Get the page + page = pdf_doc[page_number] + + # Render page to pixmap + mat = fitz.Matrix(dpi_scale, dpi_scale) + pix = page.get_pixmap(matrix=mat) # pyright: ignore[reportAttributeAccessIssue] + + # Convert to PNG bytes + png_bytes = pix.tobytes("png") + + # Always resize to ensure we stay within limits + if HAS_PIL: + png_bytes = _resize_image(png_bytes, max_width, max_height) + + return png_bytes + + finally: + pdf_doc.close() + + +def pdf_to_images( + pdf_path: str | Path, + max_width: int = 1200, + max_height: int = 1200, + dpi_scale: float = 1.0, + page_numbers: Optional[list[int]] = None, +) -> list[bytes]: + """ + Convert multiple PDF pages to PNG images. + + Args: + pdf_path: Path to the PDF file + max_width: Maximum width in pixels (default: 1200 for ~1MP) + max_height: Maximum height in pixels (default: 1200 for ~1MP) + dpi_scale: DPI scaling factor (default: 1.0 = 72 DPI) + page_numbers: List of page numbers to convert (0-based). If None, converts all pages. + + Returns: + List of PNG images as bytes, one per page + + Raises: + ImportError: If PyMuPDF is not installed + FileNotFoundError: If PDF file doesn't exist + """ + if not HAS_PYMUPDF: + raise ImportError( + "PyMuPDF (fitz) is required for PDF handling. " + "Install with: pip install PyMuPDF" + ) + + pdf_path = Path(pdf_path) + if not pdf_path.exists(): + raise FileNotFoundError(f"PDF file not found: {pdf_path}") + + pdf_doc = fitz.open(str(pdf_path)) + + try: + # Determine which pages to convert + if page_numbers is None: + page_numbers = list(range(len(pdf_doc))) + + images = [] + for page_num in page_numbers: + if page_num < 0 or page_num >= len(pdf_doc): + raise ValueError( + f"Invalid page number {page_num}. " + f"PDF has {len(pdf_doc)} pages (0-{len(pdf_doc) - 1})" + ) + + # Get the page + page = pdf_doc[page_num] + + # Render page to pixmap + mat = fitz.Matrix(dpi_scale, dpi_scale) + pix = page.get_pixmap(matrix=mat) # pyright: ignore[reportAttributeAccessIssue] + + # Convert to PNG bytes + png_bytes = pix.tobytes("png") + + # Always resize to ensure we stay within limits + if HAS_PIL: + png_bytes = _resize_image(png_bytes, max_width, max_height) + + images.append(png_bytes) + + return images + + finally: + pdf_doc.close() + + +def get_pdf_page_count(pdf_path: str | Path) -> int: + """ + Get the number of pages in a PDF. + + Args: + pdf_path: Path to the PDF file + + Returns: + Number of pages in the PDF + + Raises: + ImportError: If PyMuPDF is not installed + FileNotFoundError: If PDF file doesn't exist + """ + if not HAS_PYMUPDF: + raise ImportError( + "PyMuPDF (fitz) is required for PDF handling. " + "Install with: pip install PyMuPDF" + ) + + pdf_path = Path(pdf_path) + if not pdf_path.exists(): + raise FileNotFoundError(f"PDF file not found: {pdf_path}") + + pdf_doc = fitz.open(str(pdf_path)) + page_count = len(pdf_doc) + pdf_doc.close() + + return page_count + + +def _resize_image( + png_bytes: bytes, + max_width: int, + max_height: int, +) -> bytes: + """ + Resize a PNG image while maintaining aspect ratio. + + Args: + png_bytes: PNG image as bytes + max_width: Maximum width in pixels + max_height: Maximum height in pixels + + Returns: + Resized PNG image as bytes + """ + if not HAS_PIL: + # If PIL not available, return original + return png_bytes + + img = Image.open(io.BytesIO(png_bytes)) + + # Resize to max dimensions while maintaining aspect ratio + img.thumbnail((max_width, max_height), Image.Resampling.LANCZOS) + + # Save as optimized PNG + buffer = io.BytesIO() + img.save(buffer, format="PNG", optimize=True) + + return buffer.getvalue() + + +def create_minimal_png() -> bytes: + """ + Create a minimal 1x1 pixel white PNG image. + + Useful as a fallback when PDF loading fails or for testing. + + Returns: + Minimal PNG image as bytes (1x1 white pixel) + """ + return ( + b"\x89PNG\r\n\x1a\n" + b"\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00\x00\x01" + b"\x08\x02\x00\x00\x00\x90wS\xde" + b"\x00\x00\x00\x0cIDATx\x9cc\x00\x01\x00\x00\x05\x00\x01\r\n-\xb4" + b"\x00\x00\x00\x00IEND\xaeB`\x82" + ) diff --git a/lib/idp_common_pkg/pyproject.toml b/lib/idp_common_pkg/pyproject.toml index a4407e38..2dbee311 100644 --- a/lib/idp_common_pkg/pyproject.toml +++ b/lib/idp_common_pkg/pyproject.toml @@ -19,12 +19,14 @@ name = "idp_common" version = "0.4.2" description = "Common utilities for GenAI IDP Accelerator patterns" authors = [{ name = "AWS", email = "noreply@amazon.com" }] -requires-python = ">=3.10,<3.14" +requires-python = ">=3.12,<3.14" dependencies = [ - "boto3==1.39.7", # Core dependency for AWS services + "boto3==1.39.7", # Core dependency for AWS services "jsonschema>=4.25.1", "pydantic>=2.12.0", "deepdiff>=6.0.0", + "mypy-boto3-bedrock-runtime>=1.40.76", + "aws-lambda-powertools>=3.21.0", ] readme = "README.md" classifiers = [ @@ -66,13 +68,16 @@ classification = [ # Extraction module dependencies extraction = [ - "Pillow==11.2.1", # For image handling + "Pillow==11.2.1", # For image handling ] # Assessment module dependencies assessment = [ "Pillow==11.2.1", # For image handling + "PyMuPDF==1.25.5", # Required by OcrService for PDF processing "aws-lambda-powertools>=3.2.0", # Structured logging and observability + "mypy-boto3-dynamodb>=1.40.56", + "strands-agents==1.17.0", ] # Evaluation module dependencies @@ -98,26 +103,26 @@ appsync = ["requests==2.32.4"] # Agents module dependencies agents = [ - "strands-agents==1.14.0; python_version>='3.10'", + "strands-agents==1.17.0; python_version>='3.10'", "strands-agents-tools==0.2.13; python_version>='3.10'", - "bedrock-agentcore>=0.1.1; python_version>='3.10'", # Specifically for the code interpreter tool + "bedrock-agentcore>=0.1.1; python_version>='3.10'", # Specifically for the code interpreter tool ] # Code intelligence module dependencies code_intel = [ - "requests==2.32.4", - "strands-agents==1.14.0", - "strands-agents-tools==0.2.13", - "bedrock-agentcore>=0.1.1", - "PyYAML>=6.0.0", - "pathspec>=0.11.0", - "chardet>=5.0.0" + "requests==2.32.4", + "strands-agents==1.17.0", + "strands-agents-tools==0.2.13", + "bedrock-agentcore>=0.1.1", + "PyYAML>=6.0.0", + "pathspec>=0.11.0", + "chardet>=5.0.0", ] # Document service factory dependencies (includes both appsync and dynamodb support) # This includes all dependencies needed for both backends docs_service = [ - "requests==2.32.4", # Required for appsync module (dynamodb only needs boto3 which is core) + "requests==2.32.4", # Required for appsync module (dynamodb only needs boto3 which is core) "aws-xray-sdk>=2.14.0", # Required for X-Ray tracing ] @@ -164,16 +169,16 @@ all = [ "pyarrow==20.0.0", "openpyxl==3.1.5", "python-docx==1.2.0", - "strands-agents==1.14.0; python_version>='3.10'", + "strands-agents==1.17.0; python_version>='3.10'", "strands-agents-tools==0.2.13; python_version>='3.10'", "bedrock-agentcore>=0.1.1; python_version>='3.10'", # "s3fs==2023.12.2" - - disabled till we fix package dependencies ] agentic-extraction = [ "jsonpatch==1.33", - "strands-agents==1.14.0 ; python_full_version >= '3.10'", + "strands-agents==1.17.0 ; python_full_version >= '3.10'", "pandas>=2.2.3", - "pymupdf==1.25.5", # Pinned to 1.25.5 - has pre-built ARM64 wheels, 1.26.x requires compilation + "pymupdf==1.25.5", # Pinned to 1.25.5 - has pre-built ARM64 wheels, 1.26.x requires compilation "email-validator>=2.3.0", "tabulate>=0.9.0", "aws-lambda-powertools>=3.2.0", # Structured logging and observability diff --git a/lib/idp_common_pkg/test_granular_assessment_full.py b/lib/idp_common_pkg/test_granular_assessment_full.py new file mode 100755 index 00000000..d3a3c8ce --- /dev/null +++ b/lib/idp_common_pkg/test_granular_assessment_full.py @@ -0,0 +1,684 @@ +#!/usr/bin/env python3 +# pyright: reportAttributeAccessIssue=false +# ruff: noqa +""" +Full integration test for granular assessment service with mocked AWS services. + +This script: +1. Uses moto's mock_aws() to mock S3 and other AWS services +2. Configures passthrough for Bedrock API calls (to use real AWS Bedrock) +3. Creates a complete test document with pages and images +4. Uploads test data to mocked S3 +5. Runs the full assessment pipeline with REAL Bedrock calls +6. Validates the results + +Usage: + python test_granular_assessment_full.py [--max-workers N] + +Requirements: + - Valid AWS credentials for Bedrock access (calls go to real Bedrock!) + - Bedrock model access: us.anthropic.claude-3-5-sonnet-20241022-v2:0 + - pip install moto[all] boto3 PyMuPDF pillow + +Note: + This test uses moto's passthrough feature to allow Bedrock calls + while still mocking S3/DynamoDB. You will incur real Bedrock costs! + + Uses real sample document from samples/bank-statement-multipage.pdf +""" + +import argparse +import json +from pathlib import Path +from typing import Any + +import boto3 +from idp_common.assessment.granular_service import GranularAssessmentService +from idp_common.config.models import AssessmentConfig, IDPConfig +from idp_common.models import Document, Page, Section +from idp_common.utils.pdf_helpers import create_minimal_png, pdf_page_to_image +from moto import mock_aws + +# Test configuration +TEST_BUCKET = "test-idp-bucket" +TEST_REGION = "us-east-1" + + +def load_sample_document_image() -> bytes: + """Load a real sample document from the samples folder and convert first page to PNG.""" + # Use the bank statement sample - it's a real invoice-like document + sample_path = ( + Path(__file__).parent.parent.parent / "samples" / "bank-statement-multipage.pdf" + ) + + if not sample_path.exists(): + print(f"Warning: Sample document not found at {sample_path}") + print("Falling back to minimal test image") + return create_minimal_png() + + try: + # Convert first page of PDF to image with size limits + # Max 1200x1200 pixels for ~1MP (~100-200KB depending on content) + png_bytes = pdf_page_to_image( + pdf_path=sample_path, + page_number=0, # First page + max_width=1200, + max_height=1200, + dpi_scale=1.0, # Standard DPI + ) + + print( + f"✓ Loaded real document image from {sample_path.name} ({len(png_bytes):,} bytes)" + ) + return png_bytes + + except Exception as e: + print(f"Warning: Failed to load sample document: {e}") + print("Falling back to minimal test image") + return create_minimal_png() + + +def create_sample_extraction_result() -> dict[str, Any]: + """Create sample extraction results for a bank statement.""" + return { + "Account Number": "1234567890", + "Statement Period": "January 2024", + "Account Holder Address": { + "Street Number": "123", + "Street Name": "Main St", + "City": "San Francisco", + "State": "CA", + "ZIP Code": "94102", + }, + "Transactions": [ + { + "Date": "01/05/2024", + "Description": "Direct Deposit - Acme Corp", + "Amount": 2500.00, + }, + { + "Date": "01/10/2024", + "Description": "ATM Withdrawal", + "Amount": -200.00, + }, + { + "Date": "01/15/2024", + "Description": "Online Payment - Electric Co", + "Amount": -150.00, + }, + ], + } + + +def create_sample_schema() -> dict[str, Any]: + """Create a sample JSON schema for bank statement documents.""" + return { + "type": "object", + "x-aws-idp-document-type": "Bank Statement", + "properties": { + "Account Number": { + "type": "string", + "description": "Primary account identifier", + "x-aws-idp-confidence-threshold": 0.95, + }, + "Statement Period": { + "type": "string", + "description": "Statement period (e.g., January 2024)", + "x-aws-idp-confidence-threshold": 0.90, + }, + "Account Holder Address": { + "type": "object", + "description": "Complete address information for the account holder", + "x-aws-idp-confidence-threshold": 0.85, + "properties": { + "Street Number": { + "type": "string", + "description": "House or building number", + "x-aws-idp-confidence-threshold": 0.90, + }, + "Street Name": { + "type": "string", + "description": "Name of the street", + "x-aws-idp-confidence-threshold": 0.80, + }, + "City": { + "type": "string", + "description": "City name", + "x-aws-idp-confidence-threshold": 0.90, + }, + "State": { + "type": "string", + "description": "State abbreviation (e.g., CA, NY)", + "x-aws-idp-confidence-threshold": 0.90, + }, + "ZIP Code": { + "type": "string", + "description": "5 or 9 digit postal code", + "x-aws-idp-confidence-threshold": 0.90, + }, + }, + }, + "Transactions": { + "type": "array", + "description": "List of all transactions in the statement period", + "items": { + "type": "object", + "properties": { + "Date": { + "type": "string", + "format": "date", + "description": "Transaction date (MM/DD/YYYY)", + "x-aws-idp-confidence-threshold": 0.90, + }, + "Description": { + "type": "string", + "description": "Transaction description or merchant name", + "x-aws-idp-confidence-threshold": 0.70, + }, + "Amount": { + "type": "number", + "description": "Transaction amount (positive for deposits, negative for withdrawals)", + "x-aws-idp-confidence-threshold": 0.95, + }, + }, + }, + }, + }, + } + + +def create_parsed_text() -> str: + """Create sample parsed text.""" + return """BANK STATEMENT + +Account Number: 1234567890 +Statement Period: January 2024 + +Account Holder Address: +123 Main St +San Francisco, CA 94102 + +Transaction History: +Date Description Amount +01/05/2024 Direct Deposit - Acme Corp $2,500.00 +01/10/2024 ATM Withdrawal -$200.00 +01/15/2024 Online Payment - Electric Co -$150.00 + +Ending Balance: $2,150.00 +""" + + +def create_raw_ocr_data() -> dict[str, Any]: + """Create sample raw OCR data.""" + return { + "Blocks": [ + { + "BlockType": "LINE", + "Text": "BANK STATEMENT", + "Confidence": 99.5, + "Geometry": { + "BoundingBox": { + "Left": 0.4, + "Top": 0.1, + "Width": 0.2, + "Height": 0.05, + } + }, + }, + { + "BlockType": "LINE", + "Text": "Account Number: 1234567890", + "Confidence": 98.9, + "Geometry": { + "BoundingBox": { + "Left": 0.35, + "Top": 0.15, + "Width": 0.3, + "Height": 0.04, + } + }, + }, + { + "BlockType": "LINE", + "Text": "123 Main St", + "Confidence": 98.2, + "Geometry": { + "BoundingBox": { + "Left": 0.3, + "Top": 0.25, + "Width": 0.2, + "Height": 0.03, + } + }, + }, + ] + } + + +def create_text_confidence_data() -> dict[str, Any]: + """Create sample text confidence data.""" + return { + "text_blocks": [ + { + "text": "BANK STATEMENT", + "confidence": 0.995, + "bbox": [400, 100, 600, 150], + "page": 1, + }, + { + "text": "Account Number: 1234567890", + "confidence": 0.989, + "bbox": [350, 150, 650, 190], + "page": 1, + }, + { + "text": "123 Main St", + "confidence": 0.985, + "bbox": [300, 250, 500, 280], + "page": 1, + }, + { + "text": "$2,500.00", + "confidence": 0.975, + "bbox": [600, 400, 750, 430], + "page": 1, + }, + ] + } + + +def setup_s3_test_data( + s3_client, bucket: str, doc_id: str +) -> tuple[Document, dict[str, Any]]: + """ + Set up test data in mocked S3 bucket. + + Returns: + Tuple of (Document, extraction_result) + """ + print("📦 Setting up S3 test data...") + + # Create bucket + s3_client.create_bucket(Bucket=bucket) + print(f" Created bucket: {bucket}") + + # Upload page image + image_key = f"documents/{doc_id}/pages/page-1.png" + image_bytes = load_sample_document_image() + s3_client.put_object( + Bucket=bucket, Key=image_key, Body=image_bytes, ContentType="image/png" + ) + print( + f" ✓ Uploaded image: s3://{bucket}/{image_key} ({len(image_bytes):,} bytes)" + ) + + # Upload parsed text + text_key = f"documents/{doc_id}/pages/page-1.txt" + s3_client.put_object( + Bucket=bucket, + Key=text_key, + Body=create_parsed_text().encode("utf-8"), + ContentType="text/plain", + ) + print(f" ✓ Uploaded text: s3://{bucket}/{text_key}") + + # Upload raw OCR data + # raw_ocr_key = f"documents/{doc_id}/pages/page-1-raw.json" + # s3_client.put_object( + # Bucket=bucket, + # Key=raw_ocr_key, + # Body=json.dumps(create_raw_ocr_data()).encode("utf-8"), + # ContentType="application/json", + # ) + # print(f" ✓ Uploaded raw OCR: s3://{bucket}/{raw_ocr_key}") + + # Upload text confidence data + confidence_key = f"documents/{doc_id}/pages/page-1-confidence.json" + s3_client.put_object( + Bucket=bucket, + Key=confidence_key, + Body=json.dumps(create_text_confidence_data()).encode("utf-8"), + ContentType="application/json", + ) + print(f" ✓ Uploaded confidence: s3://{bucket}/{confidence_key}") + + # Create and upload extraction results + extraction_result = create_sample_extraction_result() + extraction_data = { + "document_class": {"type": "Bank Statement"}, + "split_document": {"page_indices": [1]}, + "inference_result": extraction_result, + "metadata": {"extraction_time_seconds": 2.5}, + "explainability_info": [], + } + + extraction_key = f"documents/{doc_id}/extraction/result.json" + s3_client.put_object( + Bucket=bucket, + Key=extraction_key, + Body=json.dumps(extraction_data).encode("utf-8"), + ContentType="application/json", + ) + print(f" ✓ Uploaded extraction: s3://{bucket}/{extraction_key}") + + # Create Document object + doc = Document( + id=doc_id, + workflow_execution_arn=f"arn:aws:states:{TEST_REGION}:123456789012:execution:test-workflow:test-exec-001", + pages={}, + sections=[], + ) + + # Add page + page = Page( + page_id="1", + image_uri=f"s3://{bucket}/{image_key}", + parsed_text_uri=f"s3://{bucket}/{text_key}", + # raw_text_uri=f"s3://{bucket}/{raw_ocr_key}", + text_confidence_uri=f"s3://{bucket}/{confidence_key}", + ) + doc.pages["1"] = page + + # Add section + section = Section( + section_id="section-001", + classification="Bank Statement", + page_ids=["1"], + extraction_result_uri=f"s3://{bucket}/{extraction_key}", + ) + doc.sections.append(section) + + print(" ✓ Created Document object with 1 page, 1 section") + print() + + return doc, extraction_result + + +def create_test_config(schema: dict[str, Any], max_workers: int = 2) -> IDPConfig: + """Create test configuration.""" + return IDPConfig( + classes=[schema], + assessment=AssessmentConfig( + enabled=True, + model="us.anthropic.claude-sonnet-4-20250514-v1:0", # Use inference profile + system_prompt="You are a document analysis assessment expert. Your role is to evaluate the confidence and accuracy of data extraction results by analyzing them against source documents. Provide accurate confidence scores for each assessment.", + temperature=0.0, + max_tokens=4096, + default_confidence_threshold=0.90, + max_workers=max_workers, + # Use default ImageConfig (1200x1200 for ~1MP images) + ), + ) + + +def print_assessment_results(doc: Document, section_id: str, s3_client): + """Print detailed assessment results.""" + print("=" * 80) + print("📊 Assessment Results") + print("=" * 80) + print() + + print(f"Document Status: {doc.status}") + print(f"Document ID: {doc.id}") + print() + + # Find assessed section + section = next((s for s in doc.sections if s.section_id == section_id), None) + if not section: + print("❌ Section not found") + return + + print(f"Section ID: {section.section_id}") + print(f"Classification: {section.classification}") + + # Check for confidence alerts + if section.confidence_threshold_alerts: + print(f"\n⚠️ Confidence Alerts: {len(section.confidence_threshold_alerts)}") + for i, alert in enumerate(section.confidence_threshold_alerts[:5]): + print(f" {i + 1}. {alert}") + if len(section.confidence_threshold_alerts) > 5: + print(f" ... and {len(section.confidence_threshold_alerts) - 5} more") + else: + print("\n✅ No confidence threshold alerts") + + # Read assessment data from S3 + if section.extraction_result_uri: + print("\n📄 Assessment Data:") + + # Parse S3 URI + uri_parts = section.extraction_result_uri.replace("s3://", "").split("/", 1) + bucket = uri_parts[0] + key = uri_parts[1] + + try: + response = s3_client.get_object(Bucket=bucket, Key=key) + extraction_with_assessment = json.loads(response["Body"].read()) + + if "explainability_info" in extraction_with_assessment: + explainability = extraction_with_assessment["explainability_info"] + if explainability and len(explainability) > 0: + assessment_data = explainability[0] + + print(f" Assessed {len(assessment_data)} fields\n") + + # Show sample assessments + sample_fields = list(assessment_data.keys())[:5] + for field in sample_fields: + field_assessment = assessment_data[field] + if ( + isinstance(field_assessment, dict) + and "confidence" in field_assessment + ): + conf = field_assessment["confidence"] + value = field_assessment.get("value", "N/A") + threshold = field_assessment.get( + "confidence_threshold", 0.90 + ) + status_icon = "✅" if conf >= threshold else "⚠️" + print(f" {status_icon} {field}:") + print(f" Value: {value}") + print( + f" Confidence: {conf:.2f} (threshold: {threshold:.2f})" + ) + + # Show bounding box if present + if "bounding_box" in field_assessment: + bbox = field_assessment["bounding_box"] + if isinstance(bbox, dict): + print( + f" Bounding Box: [x1={bbox.get('x1', 'N/A')}, y1={bbox.get('y1', 'N/A')}, " + f"x2={bbox.get('x2', 'N/A')}, y2={bbox.get('y2', 'N/A')}, page={bbox.get('page', 'N/A')}]" + ) + elif isinstance(bbox, list) and len(bbox) > 0: + # If multiple bounding boxes, show first one + first_bbox = bbox[0] + print( + f" Bounding Box: [x1={first_bbox.get('x1', 'N/A')}, y1={first_bbox.get('y1', 'N/A')}, " + f"x2={first_bbox.get('x2', 'N/A')}, y2={first_bbox.get('y2', 'N/A')}, page={first_bbox.get('page', 'N/A')}]" + ) + if len(bbox) > 1: + print( + f" (+ {len(bbox) - 1} more bounding boxes)" + ) + + if len(assessment_data) > 5: + print(f"\n ... and {len(assessment_data) - 5} more fields") + + # Show metadata + if "metadata" in extraction_with_assessment: + metadata = extraction_with_assessment["metadata"] + print("\n⏱️ Timing:") + if "assessment_time_seconds" in metadata: + print( + f" Assessment time: {metadata['assessment_time_seconds']:.2f}s" + ) + if "assessment_tasks_total" in metadata: + print(f" Total tasks: {metadata['assessment_tasks_total']}") + print( + f" Successful: {metadata.get('assessment_tasks_successful', 0)}" + ) + print( + f" Failed: {metadata.get('assessment_tasks_failed', 0)}" + ) + except Exception as e: + print(f" Could not read assessment data: {e}") + + # Show metering + if doc.metering: + print("\n💰 Token Usage:") + for key, value in doc.metering.items(): + if isinstance(value, dict): + total_tokens = value.get("totalTokens", 0) + input_tokens = value.get("inputTokens", 0) + output_tokens = value.get("outputTokens", 0) + cache_read = value.get("cacheReadInputTokens", 0) + cache_write = value.get("cacheWriteInputTokens", 0) + + print(f" {key}:") + print(f" Total: {total_tokens:,} tokens") + print(f" Input: {input_tokens:,} | Output: {output_tokens:,}") + if cache_read > 0: + print(f" Cache read: {cache_read:,}") + if cache_write > 0: + print(f" Cache write: {cache_write:,}") + + +def main(): + """Run the full assessment test.""" + parser = argparse.ArgumentParser( + description="Test granular assessment with mocked AWS" + ) + parser.add_argument( + "--max-workers", + type=int, + default=2, + help="Number of parallel workers (default: 2)", + ) + args = parser.parse_args() + + print("=" * 80) + print("Granular Assessment Full Integration Test") + print(f"(With mocked S3, real Bedrock calls, {args.max_workers} workers)") + print("=" * 80) + print() + + doc_id = "test-bank-statement-001" + + # Use moto to mock AWS services (S3, DynamoDB, etc.) + # But allow Bedrock calls to pass through to real AWS + with mock_aws( + config={ + "core": { + "mock_credentials": False, + "passthrough": { + "urls": [ + r".*bedrock.*\.amazonaws\.com.*", + r".*bedrock-runtime.*\.amazonaws\.com.*", + ] + }, + } + } + ): + # Create S3 client (will use mocked S3) + s3_client = boto3.client("s3", region_name=TEST_REGION) + + # Set up test data + doc, extraction_result = setup_s3_test_data(s3_client, TEST_BUCKET, doc_id) + + print("📋 Test Document:") + print(f" ID: {doc.id}") + print(f" Pages: {len(doc.pages)}") + print(f" Sections: {len(doc.sections)}") + print(f" Fields in extraction: {len(extraction_result)} top-level") + print() + + # Create schema and config + schema = create_sample_schema() + config = create_test_config(schema, max_workers=args.max_workers) + + print("⚙️ Initializing GranularAssessmentService...") + service = GranularAssessmentService( + region=TEST_REGION, config=config, cache_table=None + ) + print(" ✓ Service initialized") + print(f" Max workers: {service.max_workers}") + print(f" Parallel: {service.enable_parallel}") + print() + + # Get task count + section = doc.sections[0] + properties = schema["properties"] + + tasks, _ = service._create_assessment_tasks( + extraction_results=extraction_result, + properties=properties, + default_confidence_threshold=config.assessment.default_confidence_threshold, + ) + + print("🔍 Assessment Plan:") + print(f" Total tasks to execute: {len(tasks)}") + print(" Expected: 17 tasks") + print(" - 2 top-level scalars (Account Number, Statement Period)") + print( + " - 5 address fields (Street Number, Street Name, City, State, ZIP Code)" + ) + print(" - 9 transaction fields (3 transactions × 3 fields each)") + print() + + print("=" * 80) + print("🚀 Running Full Assessment Pipeline") + print("=" * 80) + print() + print("⚠️ This will make REAL calls to AWS Bedrock!") + print( + f" Assessing {len(tasks)} fields with {service.max_workers} parallel workers" + ) + print(f" Model: {config.assessment.model}") + print() + + try: + # Run the assessment + print("⏳ Calling process_document_section()...") + print() + + updated_doc = service.process_document_section(doc, section.section_id) + + print() + print("✅ Assessment completed successfully!") + print() + + # Print results + print_assessment_results(updated_doc, section.section_id, s3_client) + + print() + print("=" * 80) + print("✅ Full Integration Test PASSED!") + print("=" * 80) + + except Exception as e: + print() + print(f"❌ Assessment failed: {e}") + print() + + import traceback + + print("Full error traceback:") + traceback.print_exc() + print() + + print("=" * 80) + print("❌ Full Integration Test FAILED") + print("=" * 80) + + # Still return success code if it was just a Bedrock auth issue + if "credentials" in str(e).lower() or "bedrock" in str(e).lower(): + print( + "\n💡 This appears to be an AWS credentials/Bedrock access issue." + ) + print( + " The test infrastructure (S3 mocking, task creation) is working correctly." + ) + + +if __name__ == "__main__": + main() diff --git a/lib/idp_common_pkg/tests/conftest.py b/lib/idp_common_pkg/tests/conftest.py index a83d71d2..f17c0da1 100644 --- a/lib/idp_common_pkg/tests/conftest.py +++ b/lib/idp_common_pkg/tests/conftest.py @@ -14,11 +14,8 @@ # Mock external dependencies that may not be available in test environments # These mocks need to be set up before any imports that might use these packages -# Mock strands modules for agent functionality -sys.modules["strands"] = MagicMock() -sys.modules["strands.models"] = MagicMock() -sys.modules["strands.hooks"] = MagicMock() -sys.modules["strands.hooks.events"] = MagicMock() +# NOTE: strands mocking has been moved to individual test files that need it +# to avoid conflicts with tests that use the real strands package # Mock bedrock_agentcore modules for secure code execution sys.modules["bedrock_agentcore"] = MagicMock() diff --git a/lib/idp_common_pkg/tests/test_assessment_backward_compatibility.py b/lib/idp_common_pkg/tests/test_assessment_backward_compatibility.py deleted file mode 100644 index a8e17c88..00000000 --- a/lib/idp_common_pkg/tests/test_assessment_backward_compatibility.py +++ /dev/null @@ -1,650 +0,0 @@ -# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. -# SPDX-License-Identifier: MIT-0 - -""" -Test backward compatibility of assessment service with granular assessment enabled/disabled. -This test emulates the Pattern-2 AssessmentFunction to ensure both standard and granular -assessment services work correctly. -""" - -import json -import os -import unittest -from typing import Any, Dict -from unittest.mock import patch - -from idp_common import assessment -from idp_common.config.models import IDPConfig -from idp_common.models import Document, Page, Section, Status - - -class TestAssessmentBackwardCompatibility(unittest.TestCase): - """Test assessment service backward compatibility.""" - - def setUp(self): - """Set up test fixtures.""" - # Create a mock document with extraction results - self.document = self._create_test_document() - self.section_id = "section_1" - - # Base configuration for assessment (JSON Schema format) - self.base_config = { - "classes": [ - { - "$schema": "https://json-schema.org/draft/2020-12/schema", - "$id": "invoice", - "x-aws-idp-document-type": "invoice", - "type": "object", - "description": "A billing document", - "properties": { - "invoice_number": { - "type": "string", - "description": "The unique identifier for the invoice", - "x-aws-idp-confidence-threshold": 0.85, - }, - "total_amount": { - "type": "string", - "description": "The final amount to be paid", - "x-aws-idp-confidence-threshold": 0.9, - }, - "vendor_info": { - "type": "object", - "description": "Vendor information", - "properties": { - "vendor_name": { - "type": "string", - "description": "Name of the vendor", - "x-aws-idp-confidence-threshold": 0.8, - }, - "vendor_address": { - "type": "string", - "description": "Address of the vendor", - "x-aws-idp-confidence-threshold": 0.75, - }, - }, - }, - "line_items": { - "type": "array", - "description": "List of invoice line items", - "x-aws-idp-list-item-description": "Individual line item", - "items": { - "type": "object", - "properties": { - "item_description": { - "type": "string", - "description": "Description of the item", - "x-aws-idp-confidence-threshold": 0.7, - }, - "item_amount": { - "type": "string", - "description": "Amount for this item", - "x-aws-idp-confidence-threshold": 0.8, - }, - }, - }, - }, - }, - } - ], - "assessment": { - "model": "us.anthropic.claude-3-haiku-20240307-v1:0", - "system_prompt": "You are an assessment expert.", - "task_prompt": "Assess the confidence of extraction results for this {DOCUMENT_CLASS} document.\n\n\n{ATTRIBUTE_NAMES_AND_DESCRIPTIONS}\n\n\n<>\n\n\n{DOCUMENT_IMAGE}\n\n\n\n{OCR_TEXT_CONFIDENCE}\n\n\n<>\n\n\n{EXTRACTION_RESULTS}\n", - "temperature": "0.0", - "top_p": "0.1", - "top_k": "5", - "max_tokens": "4096", - "default_confidence_threshold": "0.9", - }, - } - - def _create_test_document(self) -> Document: - """Create a test document with extraction results.""" - # Create test pages - pages = {} - for i in range(1, 3): # 2 pages - page = Page( - page_id=str(i), - image_uri=f"s3://test-bucket/images/page_{i}.jpg", - parsed_text_uri=f"s3://test-bucket/text/page_{i}.txt", - raw_text_uri=f"s3://test-bucket/raw/page_{i}.json", - ) - pages[str(i)] = page - - # Create test section with extraction results - section = Section( - section_id="section_1", - classification="invoice", - page_ids=["1", "2"], - extraction_result_uri="s3://test-bucket/extraction/section_1.json", - ) - - # Create document - document = Document( - id="test_doc_123", - input_key="test_document.pdf", - status=Status.EXTRACTING, - pages=pages, - sections=[section], - ) - - return document - - def _create_test_extraction_results(self) -> Dict[str, Any]: - """Create test extraction results.""" - return { - "inference_result": { - "invoice_number": "INV-2024-001", - "total_amount": "$1,234.56", - "vendor_info": { - "vendor_name": "ACME Corp", - "vendor_address": "123 Main St, City, State", - }, - "line_items": [ - {"item_description": "Widget A", "item_amount": "$500.00"}, - {"item_description": "Widget B", "item_amount": "$734.56"}, - ], - }, - "metadata": {"extraction_time": 5.2}, - } - - def _create_mock_image_data(self) -> bytes: - """Create a valid mock image data for testing.""" - import io - - from PIL import Image - - # Create a simple 100x100 white image - img = Image.new("RGB", (100, 100), color="white") - img_bytes = io.BytesIO() - img.save(img_bytes, format="JPEG") - return img_bytes.getvalue() - - def _create_mock_assessment_response(self, task_type: str = "standard") -> str: - """Create mock assessment response based on task type.""" - if task_type == "simple_batch": - return json.dumps( - { - "invoice_number": { - "confidence": 0.95, - "confidence_reason": "Clear text, high OCR confidence", - }, - "total_amount": { - "confidence": 0.88, - "confidence_reason": "Clearly visible amount", - }, - } - ) - elif task_type == "group": - return json.dumps( - { - "vendor_info": { - "vendor_name": { - "confidence": 0.92, - "confidence_reason": "Company name clearly visible", - }, - "vendor_address": { - "confidence": 0.78, - "confidence_reason": "Address partially obscured", - }, - } - } - ) - elif task_type == "list_item": - return json.dumps( - { - "item_description": { - "confidence": 0.85, - "confidence_reason": "Item description clear", - }, - "item_amount": { - "confidence": 0.90, - "confidence_reason": "Amount clearly visible", - }, - } - ) - else: # standard assessment - return json.dumps( - { - "invoice_number": { - "confidence": 0.95, - "confidence_reason": "Clear text, high OCR confidence", - }, - "total_amount": { - "confidence": 0.88, - "confidence_reason": "Clearly visible amount", - }, - "vendor_info": { - "vendor_name": { - "confidence": 0.92, - "confidence_reason": "Company name clearly visible", - }, - "vendor_address": { - "confidence": 0.78, - "confidence_reason": "Address partially obscured", - }, - }, - "line_items": [ - { - "item_description": { - "confidence": 0.85, - "confidence_reason": "Item description clear", - }, - "item_amount": { - "confidence": 0.90, - "confidence_reason": "Amount clearly visible", - }, - }, - { - "item_description": { - "confidence": 0.82, - "confidence_reason": "Item description mostly clear", - }, - "item_amount": { - "confidence": 0.87, - "confidence_reason": "Amount visible", - }, - }, - ], - } - ) - - @patch("idp_common.s3.get_json_content") - @patch("idp_common.s3.get_text_content") - @patch("idp_common.s3.write_content") - @patch("idp_common.image.prepare_image") - @patch("idp_common.image.prepare_bedrock_image_attachment") - @patch("idp_common.bedrock.invoke_model") - def test_standard_assessment_service( - self, - mock_invoke_model, - mock_prepare_bedrock_image, - mock_prepare_image, - mock_write_content, - mock_get_text_content, - mock_get_json_content, - ): - """Test standard assessment service (granular disabled).""" - # Configure mocks - mock_get_json_content.return_value = self._create_test_extraction_results() - mock_get_text_content.return_value = "Sample document text content" - mock_prepare_image.return_value = self._create_mock_image_data() - mock_prepare_bedrock_image.return_value = { - "type": "image", - "source": { - "type": "base64", - "media_type": "image/jpeg", - "data": "mock_data", - }, - } - - # Mock Bedrock response for standard assessment - mock_response = { - "output": { - "message": { - "content": [ - {"text": self._create_mock_assessment_response("standard")} - ] - } - }, - "metering": {"inputTokens": 1000, "outputTokens": 200, "totalTokens": 1200}, - } - mock_invoke_model.return_value = mock_response - - # Create config with granular assessment disabled - config = self.base_config.copy() - config["assessment"]["granular"] = { - "enabled": False, - "max_workers": 4, - "simple_batch_size": 3, - "list_batch_size": 1, - } - - # Initialize assessment service with IDPConfig model - idp_config = IDPConfig.model_validate(config) - assessment_service = assessment.AssessmentService(config=idp_config) - - # Process document section - result_document = assessment_service.process_document_section( - self.document, self.section_id - ) - - # Verify the service used standard assessment - self.assertIsNotNone(result_document) - self.assertEqual(result_document.id, self.document.id) - - # Verify Bedrock was called once (standard assessment) - self.assertEqual(mock_invoke_model.call_count, 1) - - # Verify the call was made with expected parameters - call_args = mock_invoke_model.call_args - self.assertIn("system_prompt", call_args[1]) - self.assertIn("content", call_args[1]) - - # Verify extraction results were written back - mock_write_content.assert_called_once() - written_data = mock_write_content.call_args[0][0] - self.assertIn("explainability_info", written_data) - self.assertIsInstance(written_data["explainability_info"], list) - - @patch("idp_common.s3.get_json_content") - @patch("idp_common.s3.get_text_content") - @patch("idp_common.s3.write_content") - @patch("idp_common.image.prepare_image") - @patch("idp_common.image.prepare_bedrock_image_attachment") - @patch("idp_common.bedrock.invoke_model") - def test_granular_assessment_service( - self, - mock_invoke_model, - mock_prepare_bedrock_image, - mock_prepare_image, - mock_write_content, - mock_get_text_content, - mock_get_json_content, - ): - """Test granular assessment service (granular enabled).""" - # Configure mocks - mock_get_json_content.return_value = self._create_test_extraction_results() - mock_get_text_content.return_value = "Sample document text content" - mock_prepare_image.return_value = self._create_mock_image_data() - mock_prepare_bedrock_image.return_value = { - "type": "image", - "source": { - "type": "base64", - "media_type": "image/jpeg", - "data": "mock_data", - }, - } - - # Mock Bedrock responses for granular assessment (multiple calls) - def mock_bedrock_side_effect(*args, **kwargs): - # Determine response based on content - content = kwargs.get("content", []) - content_text = "" - for item in content: - if isinstance(item, dict) and "text" in item: - content_text += item["text"] - - # Return appropriate response based on content - if "invoice_number" in content_text and "total_amount" in content_text: - response_text = self._create_mock_assessment_response("simple_batch") - elif "vendor_info" in content_text: - response_text = self._create_mock_assessment_response("group") - elif "item_description" in content_text: - response_text = self._create_mock_assessment_response("list_item") - else: - response_text = self._create_mock_assessment_response("standard") - - return { - "content": [{"text": response_text}], - "metering": { - "inputTokens": 500, - "outputTokens": 100, - "totalTokens": 600, - }, - } - - mock_invoke_model.side_effect = mock_bedrock_side_effect - - # Create config with granular assessment enabled - config = self.base_config.copy() - config["assessment"]["granular"] = { - "enabled": True, - "max_workers": 4, - "simple_batch_size": 2, # Will batch invoice_number and total_amount - "list_batch_size": 1, # Will process each list item separately - } - - # Initialize assessment service with IDPConfig model - idp_config = IDPConfig.model_validate(config) - assessment_service = assessment.AssessmentService(config=idp_config) - - # Process document section - result_document = assessment_service.process_document_section( - self.document, self.section_id - ) - - # Verify the service used granular assessment - self.assertIsNotNone(result_document) - self.assertEqual(result_document.id, self.document.id) - - # Verify Bedrock was called multiple times (granular assessment) - # Expected: 1 simple batch + 1 group + 2 list items = 4 calls - self.assertGreater(mock_invoke_model.call_count, 1) - - # Verify extraction results were written back - mock_write_content.assert_called_once() - written_data = mock_write_content.call_args[0][0] - self.assertIn("explainability_info", written_data) - self.assertIsInstance(written_data["explainability_info"], list) - - # Verify granular assessment metadata - metadata = written_data.get("metadata", {}) - self.assertTrue(metadata.get("granular_assessment_used", False)) - self.assertIn("assessment_tasks_total", metadata) - self.assertIn("assessment_tasks_successful", metadata) - - @patch("idp_common.s3.get_json_content") - @patch("idp_common.s3.get_text_content") - @patch("idp_common.s3.write_content") - @patch("idp_common.image.prepare_image") - @patch("idp_common.bedrock.invoke_model") - def test_assessment_factory_selection( - self, - mock_invoke_model, - mock_prepare_image, - mock_write_content, - mock_get_text_content, - mock_get_json_content, - ): - """Test that the assessment factory correctly selects standard vs granular service.""" - # Configure mocks - mock_get_json_content.return_value = self._create_test_extraction_results() - mock_get_text_content.return_value = "Sample document text content" - mock_prepare_image.return_value = b"mock_image_data" - mock_invoke_model.return_value = { - "content": [{"text": self._create_mock_assessment_response("standard")}], - "metering": {"inputTokens": 1000, "outputTokens": 200, "totalTokens": 1200}, - } - - # Test 1: Granular disabled - should use standard service - config_standard = self.base_config.copy() - config_standard["assessment"]["granular"] = {"enabled": False} - - idp_config_standard = IDPConfig.model_validate(config_standard) - service_standard = assessment.AssessmentService(config=idp_config_standard) - # The main service is AssessmentService, but it should use the standard service internally - self.assertEqual(type(service_standard).__name__, "AssessmentService") - # Check that granular is disabled in config - granular_config = config_standard.get("assessment", {}).get("granular", {}) - self.assertFalse(granular_config.get("enabled", False)) - - # Test 2: Granular enabled - should use granular service - config_granular = self.base_config.copy() - config_granular["assessment"]["granular"] = {"enabled": True} - - idp_config_granular = IDPConfig.model_validate(config_granular) - service_granular = assessment.AssessmentService(config=idp_config_granular) - self.assertEqual(type(service_granular).__name__, "AssessmentService") - # Check that granular is enabled in config - granular_config = config_granular.get("assessment", {}).get("granular", {}) - self.assertTrue(granular_config.get("enabled", False)) - - # Test 3: No granular config - should default to standard - config_default = self.base_config.copy() - # Remove granular config entirely - if "granular" in config_default["assessment"]: - del config_default["assessment"]["granular"] - - idp_config_default = IDPConfig.model_validate(config_default) - service_default = assessment.AssessmentService(config=idp_config_default) - self.assertEqual(type(service_default).__name__, "AssessmentService") - # Check that granular is not configured (defaults to disabled) - granular_config = config_default.get("assessment", {}).get("granular", {}) - self.assertFalse(granular_config.get("enabled", False)) - - def test_confidence_threshold_handling(self): - """Test that confidence thresholds are handled correctly in both services.""" - # Test with various threshold formats (float, int, None) - test_cases = [ - (0.85, 0.85), - (0.9, 0.9), - (1, 1.0), - (None, 0.9), # Should use default - ] - - for threshold_input, expected_output in test_cases: - config = self.base_config.copy() - if threshold_input is not None: - config["classes"][0]["properties"]["invoice_number"][ - "x-aws-idp-confidence-threshold" - ] = threshold_input - else: - # Remove confidence_threshold to test None case - if ( - "x-aws-idp-confidence-threshold" - in config["classes"][0]["properties"]["invoice_number"] - ): - del config["classes"][0]["properties"]["invoice_number"][ - "x-aws-idp-confidence-threshold" - ] - - # Test with both standard and granular services - for granular_enabled in [False, True]: - config["assessment"]["granular"] = {"enabled": granular_enabled} - - idp_config = IDPConfig.model_validate(config) - service = assessment.AssessmentService(config=idp_config) - - # The service should initialize without errors - self.assertIsNotNone(service) - - @patch.dict(os.environ, {"WORKING_BUCKET": "test-bucket"}) - def test_assessment_function_emulation(self): - """Test that emulates the actual AssessmentFunction handler.""" - # This test emulates the patterns/pattern-2/src/assessment_function/index.py handler - - # Mock event data (similar to what Lambda receives) - event = { - "document": { - "id": "test_doc_123", - "input_key": "test_document.pdf", - "status": "EXTRACTING", - "pages": { - "1": { - "page_id": "1", - "image_uri": "s3://test-bucket/images/page_1.jpg", - "parsed_text_uri": "s3://test-bucket/text/page_1.txt", - "raw_text_uri": "s3://test-bucket/raw/page_1.json", - } - }, - "sections": [ - { - "section_id": "section_1", - "classification": "invoice", - "page_ids": ["1"], - "extraction_result_uri": "s3://test-bucket/extraction/section_1.json", - } - ], - }, - "section_id": "section_1", - } - - # Test with both granular enabled and disabled - for granular_enabled in [False, True]: - with self.subTest(granular_enabled=granular_enabled): - config = self.base_config.copy() - config["assessment"]["granular"] = {"enabled": granular_enabled} - - # Emulate the handler logic - with ( - patch("idp_common.get_config", return_value=config), - patch( - "idp_common.s3.get_json_content", - return_value=self._create_test_extraction_results(), - ), - patch("idp_common.s3.get_text_content", return_value="Sample text"), - patch("idp_common.s3.write_content"), - patch( - "idp_common.image.prepare_image", - return_value=self._create_mock_image_data(), - ), - patch( - "idp_common.image.prepare_bedrock_image_attachment", - return_value={ - "type": "image", - "source": { - "type": "base64", - "media_type": "image/jpeg", - "data": "mock_data", - }, - }, - ), - patch("idp_common.bedrock.invoke_model") as mock_invoke, - ): - # Configure mock response - mock_invoke.return_value = { - "output": { - "message": { - "content": [ - { - "text": self._create_mock_assessment_response( - "standard" - ) - } - ] - } - }, - "metering": { - "inputTokens": 1000, - "outputTokens": 200, - "totalTokens": 1200, - }, - } - - # Extract inputs (emulating handler) - document_data = event.get("document", {}) - section_id = event.get("section_id") - - # Validate inputs - self.assertIsNotNone(document_data) - self.assertIsNotNone(section_id) - - # Convert to Document object (emulating Document.load_document) - document = Document.from_dict(document_data) - document.status = Status.ASSESSING - - # Initialize assessment service - idp_config = IDPConfig.model_validate(config) - assessment_service = assessment.AssessmentService(config=idp_config) - - # Process the document section - updated_document = assessment_service.process_document_section( - document, str(section_id) - ) - - # Verify processing succeeded - self.assertIsNotNone(updated_document) - self.assertEqual(updated_document.id, document.id) - self.assertNotEqual(updated_document.status, Status.FAILED) - - # Verify appropriate service was used - if granular_enabled: - self.assertEqual( - type(assessment_service._service).__name__, - "GranularAssessmentService", - ) - # Granular assessment may make multiple calls - self.assertGreaterEqual(mock_invoke.call_count, 1) - else: - # The original service is called "AssessmentService" in service.py - self.assertEqual( - type(assessment_service._service).__name__, - "AssessmentService", - ) - # Standard assessment makes exactly one call - self.assertEqual(mock_invoke.call_count, 1) - - -if __name__ == "__main__": - unittest.main() diff --git a/lib/idp_common_pkg/tests/unit/assessment/conftest.py b/lib/idp_common_pkg/tests/unit/assessment/conftest.py new file mode 100644 index 00000000..352d58b2 --- /dev/null +++ b/lib/idp_common_pkg/tests/unit/assessment/conftest.py @@ -0,0 +1,25 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: MIT-0 + +""" +Pytest configuration for assessment tests. + +These tests need strands modules mocked since they test the assessment service +which imports strands modules but the tests don't actually execute strands code. +""" + +import sys +from unittest.mock import MagicMock + +# Mock strands modules before any assessment imports +# This allows tests to import assessment code without requiring strands package +sys.modules["strands"] = MagicMock() +sys.modules["strands.agent"] = MagicMock() +sys.modules["strands.agent.conversation_manager"] = MagicMock() +sys.modules["strands.models"] = MagicMock() +sys.modules["strands.models.bedrock"] = MagicMock() +sys.modules["strands.types"] = MagicMock() +sys.modules["strands.types.content"] = MagicMock() +sys.modules["strands.types.media"] = MagicMock() +sys.modules["strands.hooks"] = MagicMock() +sys.modules["strands.hooks.events"] = MagicMock() diff --git a/lib/idp_common_pkg/tests/unit/assessment/test_aggregate_assessment_results.py b/lib/idp_common_pkg/tests/unit/assessment/test_aggregate_assessment_results.py new file mode 100644 index 00000000..cc546efa --- /dev/null +++ b/lib/idp_common_pkg/tests/unit/assessment/test_aggregate_assessment_results.py @@ -0,0 +1,430 @@ +# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. +# SPDX-License-Identifier: MIT-0 + +""" +Unit tests for the _aggregate_assessment_results function in GranularAssessmentService. +Tests the aggregation logic with real-world data to ensure assessment data is properly +inserted into the assessment structure. +""" + +import pytest +from idp_common.assessment.granular_service import GranularAssessmentService +from idp_common.assessment.models import AssessmentResult, AssessmentTask + + +@pytest.mark.unit +class TestAggregateAssessmentResults: + """Tests for _aggregate_assessment_results function.""" + + def test_aggregate_simple_fields(self): + """Test aggregation with simple top-level fields using real data.""" + # Create service instance + service = GranularAssessmentService() + + # Create initial assessment structure (mimics what _create_assessment_tasks returns) + assessment_structure = { + "Account Number": None, + "Statement Period": None, + } + + # Create tasks using field_path (no parent references needed) + tasks = [ + AssessmentTask( + task_id="task_0", + task_type="attribute", + field_path=("Account Number",), + field_name="Account Number", + field_schema={"type": "string"}, + confidence_threshold=0.95, + ), + AssessmentTask( + task_id="task_1", + task_type="attribute", + field_path=("Statement Period",), + field_name="Statement Period", + field_schema={"type": "string"}, + confidence_threshold=0.9, + ), + ] + + # Create results with simplified data structure (flat assessment dict, not wrapped by field name) + results = [ + AssessmentResult( + task_id="task_0", + success=True, + assessment_data={ + "confidence": 0.0, + "value": "1234567890", + "reasoning": "The extracted value '1234567890' is completely incorrect.", + "confidence_threshold": 0.95, + "geometry": [ + { + "boundingBox": { + "top": 0.458, + "left": 0.215, + "width": 0.07, + "height": 0.014, + }, + "page": 1, + } + ], + }, + confidence_alerts=[ + { + "attribute_name": "Account Number", + "confidence": 0.0, + "confidence_threshold": 0.95, + } + ], + error_message=None, + processing_time=195.07, + metering={ + "assessment/bedrock/us.anthropic.claude-sonnet-4-20250514-v1:0": { + "inputTokens": 17457, + "outputTokens": 1320, + } + }, + ), + AssessmentResult( + task_id="task_1", + success=True, + assessment_data={ + "confidence": 0.0, + "value": "January 2024", + "reasoning": "The extracted value 'January 2024' is completely incorrect.", + "confidence_threshold": 0.9, + "geometry": [ + { + "boundingBox": { + "top": 0.27, + "left": 0.058, + "width": 0.092, + "height": 0.015, + }, + "page": 1, + } + ], + }, + confidence_alerts=[ + { + "attribute_name": "Statement Period", + "confidence": 0.0, + "confidence_threshold": 0.9, + } + ], + error_message=None, + processing_time=148.42, + metering={ + "assessment/bedrock/us.anthropic.claude-sonnet-4-20250514-v1:0": { + "inputTokens": 14097, + "outputTokens": 1104, + } + }, + ), + ] + + # Call the function under test + ( + aggregated_structure, + alerts, + metering, + ) = service._aggregate_assessment_results(tasks, results, assessment_structure) + + # Assertions + assert aggregated_structure is not None + assert "Account Number" in aggregated_structure + assert "Statement Period" in aggregated_structure + + # Check Account Number was properly inserted (not None!) + assert aggregated_structure["Account Number"] is not None + assert aggregated_structure["Account Number"]["confidence"] == 0.0 + assert aggregated_structure["Account Number"]["value"] == "1234567890" + assert "reasoning" in aggregated_structure["Account Number"] + assert "geometry" in aggregated_structure["Account Number"] + + # Check Statement Period was properly inserted + assert aggregated_structure["Statement Period"] is not None + assert aggregated_structure["Statement Period"]["confidence"] == 0.0 + assert aggregated_structure["Statement Period"]["value"] == "January 2024" + + # Check alerts + assert len(alerts) == 2 + + # Check metering + assert metering is not None + assert ( + "assessment/bedrock/us.anthropic.claude-sonnet-4-20250514-v1:0" in metering + ) + + def test_aggregate_nested_fields(self): + """Test aggregation with nested object fields using real data.""" + service = GranularAssessmentService() + + # Create nested assessment structure + nested_address = { + "Street Number": None, + "Street Name": None, + "City": None, + } + assessment_structure = {"Account Holder Address": nested_address} + + # Create tasks using field_path for navigation + tasks = [ + AssessmentTask( + task_id="task_0", + task_type="attribute", + field_path=("Account Holder Address", "Street Number"), + field_name="Street Number", + field_schema={"type": "string"}, + confidence_threshold=0.85, + ), + AssessmentTask( + task_id="task_1", + task_type="attribute", + field_path=("Account Holder Address", "Street Name"), + field_name="Street Name", + field_schema={"type": "string"}, + confidence_threshold=0.85, + ), + AssessmentTask( + task_id="task_2", + task_type="attribute", + field_path=("Account Holder Address", "City"), + field_name="City", + field_schema={"type": "string"}, + confidence_threshold=0.85, + ), + ] + + # Create results with flat assessment data (not wrapped by field name) + results = [ + AssessmentResult( + task_id="task_0", + success=True, + assessment_data={ + "confidence": 0.0, + "value": "123", + "reasoning": "The extracted value '123' is completely incorrect.", + "confidence_threshold": 0.85, + "geometry": [ + { + "boundingBox": { + "top": 0.17, + "left": 0.114, + "width": 0.021, + "height": 0.012, + }, + "page": 1, + } + ], + }, + confidence_alerts=[], + error_message=None, + processing_time=216.45, + ), + AssessmentResult( + task_id="task_1", + success=True, + assessment_data={ + "confidence": 0.0, + "value": "Main St", + "reasoning": "The extracted value 'Main St' is completely incorrect.", + "confidence_threshold": 0.85, + "geometry": [ + { + "boundingBox": { + "top": 0.17, + "left": 0.13, + "width": 0.09, + "height": 0.02, + }, + "page": 1, + } + ], + }, + confidence_alerts=[], + error_message=None, + processing_time=217.03, + ), + AssessmentResult( + task_id="task_2", + success=True, + assessment_data={ + "confidence": 0.1, + "value": "San Francisco", + "reasoning": "The extracted value 'San Francisco' does not match.", + "confidence_threshold": 0.85, + "geometry": [ + { + "boundingBox": { + "top": 0.17, + "left": 0.2, + "width": 0.09, + "height": 0.015, + }, + "page": 1, + } + ], + }, + confidence_alerts=[], + error_message=None, + processing_time=217.32, + ), + ] + + # Call the function under test + ( + aggregated_structure, + alerts, + metering, + ) = service._aggregate_assessment_results(tasks, results, assessment_structure) + + # Assertions - check nested structure + assert "Account Holder Address" in aggregated_structure + nested = aggregated_structure["Account Holder Address"] + + # CRITICAL: These should NOT be None! + assert nested["Street Number"] is not None + assert nested["Street Number"]["confidence"] == 0.0 + assert nested["Street Number"]["value"] == "123" + + assert nested["Street Name"] is not None + assert nested["Street Name"]["confidence"] == 0.0 + assert nested["Street Name"]["value"] == "Main St" + + assert nested["City"] is not None + assert nested["City"]["confidence"] == 0.1 + assert nested["City"]["value"] == "San Francisco" + + def test_aggregate_array_fields(self): + """Test aggregation with array fields using real data.""" + service = GranularAssessmentService() + + # Create array structure - each array item is a dict + transaction_0 = {"Date": None, "Amount": None} + transaction_1 = {"Date": None, "Amount": None} + transactions_array = [transaction_0, transaction_1] + assessment_structure = {"Transactions": transactions_array} + + # Create tasks using field_path for navigation + tasks = [ + AssessmentTask( + task_id="task_0", + task_type="attribute", + field_path=("Transactions", 0, "Date"), + field_name="Date", + field_schema={"type": "string"}, + confidence_threshold=0.9, + ), + AssessmentTask( + task_id="task_1", + task_type="attribute", + field_path=("Transactions", 0, "Amount"), + field_name="Amount", + field_schema={"type": "number"}, + confidence_threshold=0.9, + ), + AssessmentTask( + task_id="task_2", + task_type="attribute", + field_path=("Transactions", 1, "Date"), + field_name="Date", + field_schema={"type": "string"}, + confidence_threshold=0.9, + ), + AssessmentTask( + task_id="task_3", + task_type="attribute", + field_path=("Transactions", 1, "Amount"), + field_name="Amount", + field_schema={"type": "number"}, + confidence_threshold=0.9, + ), + ] + + # Create results with flat assessment data (not wrapped by field name) + results = [ + AssessmentResult( + task_id="task_0", + success=True, + assessment_data={ + "confidence": 0.2, + "value": "01/05/2024", + "reasoning": "Date mismatch", + "confidence_threshold": 0.9, + "geometry": [], + }, + confidence_alerts=[], + error_message=None, + processing_time=230.94, + ), + AssessmentResult( + task_id="task_1", + success=True, + assessment_data={ + "confidence": 0.1, + "value": 2500.0, + "reasoning": "Cannot verify amount", + "confidence_threshold": 0.9, + }, + confidence_alerts=[], + error_message=None, + processing_time=216.57, + ), + AssessmentResult( + task_id="task_2", + success=True, + assessment_data={ + "confidence": 0.0, + "value": "01/10/2024", + "reasoning": "Completely fabricated", + "confidence_threshold": 0.9, + }, + confidence_alerts=[], + error_message=None, + processing_time=207.28, + ), + AssessmentResult( + task_id="task_3", + success=True, + assessment_data={ + "confidence": 0.1, + "value": -200.0, + "reasoning": "Cannot verify", + "confidence_threshold": 0.9, + }, + confidence_alerts=[], + error_message=None, + processing_time=225.86, + ), + ] + + # Call the function under test + ( + aggregated_structure, + alerts, + metering, + ) = service._aggregate_assessment_results(tasks, results, assessment_structure) + + # Assertions - check array structure + assert "Transactions" in aggregated_structure + transactions = aggregated_structure["Transactions"] + assert len(transactions) == 2 + + # Check first transaction + assert transactions[0]["Date"] is not None + assert transactions[0]["Date"]["confidence"] == 0.2 + assert transactions[0]["Date"]["value"] == "01/05/2024" + + assert transactions[0]["Amount"] is not None + assert transactions[0]["Amount"]["confidence"] == 0.1 + assert transactions[0]["Amount"]["value"] == 2500.0 + + # Check second transaction + assert transactions[1]["Date"] is not None + assert transactions[1]["Date"]["confidence"] == 0.0 + assert transactions[1]["Date"]["value"] == "01/10/2024" + + assert transactions[1]["Amount"] is not None + assert transactions[1]["Amount"]["confidence"] == 0.1 + assert transactions[1]["Amount"]["value"] == -200.0 diff --git a/lib/idp_common_pkg/tests/unit/assessment/test_bounding_box_integration.py b/lib/idp_common_pkg/tests/unit/assessment/test_bounding_box_integration.py index 51e0b079..0d4b3a8f 100644 --- a/lib/idp_common_pkg/tests/unit/assessment/test_bounding_box_integration.py +++ b/lib/idp_common_pkg/tests/unit/assessment/test_bounding_box_integration.py @@ -2,203 +2,193 @@ # SPDX-License-Identifier: MIT-0 """ -Unit tests for bounding box integration in AssessmentService. -""" - -import pytest -from idp_common.assessment.service import AssessmentService - - -class TestBoundingBoxIntegration: - """Test bounding box functionality in AssessmentService.""" - - def test_convert_bbox_to_geometry_valid_coordinates(self): - """Test conversion from bbox coordinates to geometry format.""" - service = AssessmentService() - - # Test normal coordinates - bbox_coords = [100, 200, 300, 400] # x1, y1, x2, y2 in 0-1000 scale - page_num = 1 +Unit tests for bounding box conversion models. - result = service._convert_bbox_to_geometry(bbox_coords, page_num) +These tests validate the coordinate conversion from LLM bounding box responses +(in 0-1000 scale) to normalized 0-1 scale for the UI. - expected = { - "boundingBox": { - "top": 0.2, # y1/1000 - "left": 0.1, # x1/1000 - "width": 0.2, # (x2-x1)/1000 - "height": 0.2, # (y2-y1)/1000 - }, - "page": 1, - } - - assert result == expected +Note: Ruler offset adjustment is handled separately in strands_tools.py when +the LLM submits assessments. The models here work with document-space coordinates. +""" - def test_convert_bbox_to_geometry_reversed_coordinates(self): +import pytest +from idp_common.assessment.models import ( + BoundingBoxCoordinates, + FieldAssessmentData, + Geometry, +) + + +class TestBoundingBoxCoordinates: + """Test BoundingBoxCoordinates model.""" + + def test_from_corners_basic_conversion(self): + """Test basic conversion from corner coordinates to normalized bbox.""" + bbox = BoundingBoxCoordinates.from_corners( + x1=100, + y1=200, + x2=300, + y2=400, + scale=1000.0, + ) + + assert bbox.left == 0.1 + assert bbox.top == 0.2 + assert bbox.width == 0.2 + assert bbox.height == 0.2 + + def test_from_corners_handles_reversed_coordinates(self): """Test that coordinates are corrected when reversed.""" - service = AssessmentService() - - # Test with reversed coordinates (x2 < x1, y2 < y1) - bbox_coords = [300, 400, 100, 200] # Reversed - page_num = 2 - - result = service._convert_bbox_to_geometry(bbox_coords, page_num) + bbox = BoundingBoxCoordinates.from_corners( + x1=300, + y1=400, + x2=100, + y2=200, # Reversed + scale=1000.0, + ) # Should be corrected to proper order - expected = { - "boundingBox": { - "top": 0.2, # min(y1,y2)/1000 - "left": 0.1, # min(x1,x2)/1000 - "width": 0.2, # (max(x)-min(x))/1000 - "height": 0.2, # (max(y)-min(y))/1000 - }, - "page": 2, - } - - assert result == expected - - def test_convert_bbox_to_geometry_invalid_coordinates(self): + assert bbox.left == 0.1 + assert bbox.top == 0.2 + assert bbox.width == 0.2 + assert bbox.height == 0.2 + + def test_from_corners_clamps_to_valid_range(self): + """Test that values are clamped to 0-1 range.""" + # Coordinates that would exceed bounds + bbox = BoundingBoxCoordinates.from_corners( + x1=-50, + y1=-50, + x2=1100, + y2=1100, + scale=1000.0, + ) + + # Should clamp to valid range + assert bbox.left >= 0.0 + assert bbox.top >= 0.0 + assert bbox.left + bbox.width <= 1.0 + assert bbox.top + bbox.height <= 1.0 + + def test_from_corners_edge_coordinates(self): + """Test with coordinates at edges of document.""" + # Full document bbox + bbox = BoundingBoxCoordinates.from_corners( + x1=0, + y1=0, + x2=1000, + y2=1000, + scale=1000.0, + ) + + assert bbox.left == 0.0 + assert bbox.top == 0.0 + assert bbox.width == 1.0 + assert bbox.height == 1.0 + + def test_from_corners_small_region(self): + """Test with a small region.""" + bbox = BoundingBoxCoordinates.from_corners( + x1=500, + y1=500, + x2=510, + y2=510, + scale=1000.0, + ) + + assert bbox.left == 0.5 + assert bbox.top == 0.5 + assert bbox.width == 0.01 + assert bbox.height == 0.01 + + +class TestGeometry: + """Test Geometry model.""" + + def test_from_bbox_list_valid(self): + """Test creation from bbox list format.""" + # LLM response format: [x1, y1, x2, y2] in 0-1000 scale + geom = Geometry.from_bbox_list([100, 200, 300, 400], page_num=1) + + assert geom.page == 1 + assert geom.boundingBox is not None + assert geom.boundingBox.left == 0.1 # 100/1000 + assert geom.boundingBox.top == 0.2 # 200/1000 + + def test_from_bbox_list_different_page(self): + """Test creation with different page number.""" + geom = Geometry.from_bbox_list([100, 200, 300, 400], page_num=3) + + assert geom.page == 3 + assert geom.boundingBox is not None + + def test_from_bbox_list_invalid_length(self): """Test error handling for invalid coordinate count.""" - service = AssessmentService() - - # Test with wrong number of coordinates - bbox_coords = [100, 200, 300] # Only 3 coordinates - page_num = 1 - with pytest.raises(ValueError, match="Expected 4 coordinates"): - service._convert_bbox_to_geometry(bbox_coords, page_num) - - def test_extract_geometry_from_assessment_with_bbox_data(self): - """Test extraction of geometry data from assessment response.""" - service = AssessmentService() - - assessment_data = { - "account_number": { - "confidence": 0.95, - "confidence_reason": "Clear text with high OCR confidence", - "bbox": [100, 200, 300, 400], - "page": 1, - }, - "account_balance": { - "confidence": 0.88, - "confidence_reason": "Good text quality", - "bbox": [400, 500, 600, 550], - "page": 1, - }, - } - - result = service._extract_geometry_from_assessment(assessment_data) - - # Check that bbox/page data was converted to geometry format - assert "geometry" in result["account_number"] - assert "bbox" not in result["account_number"] - assert "page" not in result["account_number"] - - # Check geometry structure - geometry = result["account_number"]["geometry"][0] - assert "boundingBox" in geometry - assert "page" in geometry - assert geometry["page"] == 1 - - # Check bounding box values - bbox = geometry["boundingBox"] - assert bbox["top"] == 0.2 # 200/1000 - assert bbox["left"] == 0.1 # 100/1000 - assert bbox["width"] == 0.2 # (300-100)/1000 - assert bbox["height"] == 0.2 # (400-200)/1000 - - def test_extract_geometry_from_assessment_without_bbox_data(self): - """Test that assessment data without bbox passes through unchanged.""" - service = AssessmentService() - - assessment_data = { - "account_number": { - "confidence": 0.95, - "confidence_reason": "Clear text with high OCR confidence", - # No bbox or page data - } - } - - result = service._extract_geometry_from_assessment(assessment_data) - - # Should pass through unchanged - assert result == assessment_data - assert "geometry" not in result["account_number"] - - def test_extract_geometry_from_assessment_invalid_bbox_format(self): - """Test handling of invalid bbox format.""" - service = AssessmentService() - - assessment_data = { - "account_number": { - "confidence": 0.95, - "confidence_reason": "Clear text", - "bbox": "invalid_format", # Invalid format - "page": 1, - } - } - - result = service._extract_geometry_from_assessment(assessment_data) - - # Should remove invalid bbox data but keep confidence info - assert "geometry" not in result["account_number"] - assert "bbox" not in result["account_number"] - assert "page" not in result["account_number"] - assert result["account_number"]["confidence"] == 0.95 - - def test_extract_geometry_from_assessment_missing_page(self): - """Test handling when bbox exists but page is missing.""" - service = AssessmentService() - - assessment_data = { - "account_number": { - "confidence": 0.95, - "confidence_reason": "Clear text", - "bbox": [100, 200, 300, 400], - # Missing page - } - } - - result = service._extract_geometry_from_assessment(assessment_data) - - # Should not create geometry without page info - assert "geometry" not in result["account_number"] - assert "bbox" not in result["account_number"] - assert result["account_number"]["confidence"] == 0.95 - - def test_extract_geometry_from_assessment_edge_coordinates(self): - """Test conversion with edge case coordinates.""" - service = AssessmentService() - - assessment_data = { - "top_left_field": { - "confidence": 0.9, - "confidence_reason": "Located at top-left", - "bbox": [0, 0, 100, 100], # Top-left corner - "page": 1, - }, - "bottom_right_field": { - "confidence": 0.85, - "confidence_reason": "Located at bottom-right", - "bbox": [900, 900, 1000, 1000], # Bottom-right corner - "page": 2, - }, - } - - result = service._extract_geometry_from_assessment(assessment_data) - - # Check top-left field - top_left_geometry = result["top_left_field"]["geometry"][0] - assert top_left_geometry["boundingBox"]["top"] == 0.0 - assert top_left_geometry["boundingBox"]["left"] == 0.0 - assert top_left_geometry["boundingBox"]["width"] == 0.1 - assert top_left_geometry["boundingBox"]["height"] == 0.1 - assert top_left_geometry["page"] == 1 - - # Check bottom-right field - bottom_right_geometry = result["bottom_right_field"]["geometry"][0] - assert bottom_right_geometry["boundingBox"]["top"] == 0.9 - assert bottom_right_geometry["boundingBox"]["left"] == 0.9 - assert bottom_right_geometry["boundingBox"]["width"] == 0.1 - assert bottom_right_geometry["boundingBox"]["height"] == 0.1 - assert bottom_right_geometry["page"] == 2 + Geometry.from_bbox_list([100, 200, 300], page_num=1) + + def test_to_ui_format(self): + """Test conversion to UI-compatible format.""" + geom = Geometry.from_bbox_list([100, 200, 300, 400], page_num=2) + ui_format = geom.to_ui_format() + + assert "boundingBox" in ui_format + assert "page" in ui_format + assert ui_format["page"] == 2 + assert "top" in ui_format["boundingBox"] + assert "left" in ui_format["boundingBox"] + assert "width" in ui_format["boundingBox"] + assert "height" in ui_format["boundingBox"] + + +class TestFieldAssessmentData: + """Test FieldAssessmentData model.""" + + def test_from_llm_response_with_bbox(self): + """Test creation from LLM response with bounding box.""" + assessment = FieldAssessmentData.from_llm_response( + confidence=0.95, + reasoning="Clear text with high OCR confidence", + confidence_threshold=0.8, + bbox_coords=[100, 200, 300, 400], + page_num=1, + ) + + assert assessment.confidence == 0.95 + assert assessment.reasoning == "Clear text with high OCR confidence" + assert assessment.geometry is not None + assert len(assessment.geometry) == 1 + assert assessment.geometry[0].page == 1 + + def test_from_llm_response_without_bbox(self): + """Test creation from LLM response without bounding box.""" + assessment = FieldAssessmentData.from_llm_response( + confidence=0.85, + reasoning="Good text quality", + confidence_threshold=0.8, + bbox_coords=None, + page_num=None, + ) + + assert assessment.confidence == 0.85 + assert assessment.geometry is None + + def test_to_explainability_format(self): + """Test conversion to explainability format for frontend.""" + assessment = FieldAssessmentData.from_llm_response( + confidence=0.95, + reasoning="Clear text", + confidence_threshold=0.8, + bbox_coords=[100, 200, 300, 400], + page_num=1, + ) + + result = assessment.to_explainability_format() + + assert result["confidence"] == 0.95 + assert result["confidence_reason"] == "Clear text" + assert result["confidence_threshold"] == 0.8 + assert "geometry" in result + assert len(result["geometry"]) == 1 + assert "boundingBox" in result["geometry"][0] + assert result["geometry"][0]["page"] == 1 diff --git a/lib/idp_common_pkg/tests/unit/assessment/test_company_address_fix.py b/lib/idp_common_pkg/tests/unit/assessment/test_company_address_fix.py index 84bc3249..81e9b61d 100644 --- a/lib/idp_common_pkg/tests/unit/assessment/test_company_address_fix.py +++ b/lib/idp_common_pkg/tests/unit/assessment/test_company_address_fix.py @@ -7,13 +7,11 @@ import json -from idp_common.assessment.service import AssessmentService +from idp_common.assessment.geometry_utils import extract_geometry_from_nested_dict def test_company_address_geometry_conversion(): """Test the exact CompanyAddress case provided by the user.""" - service = AssessmentService() - # This is the exact data structure from the user's example assessment_data = { "YTDCityTax": { @@ -49,7 +47,7 @@ def test_company_address_geometry_conversion(): } # Process the assessment data - result = service._extract_geometry_from_assessment(assessment_data) + result = extract_geometry_from_nested_dict(assessment_data) print("=== Before/After Comparison ===") print("\nBEFORE (with bbox):") diff --git a/lib/idp_common_pkg/tests/unit/assessment/test_granular_bbox_conversion.py b/lib/idp_common_pkg/tests/unit/assessment/test_granular_bbox_conversion.py index 9908f73e..cb483a8b 100644 --- a/lib/idp_common_pkg/tests/unit/assessment/test_granular_bbox_conversion.py +++ b/lib/idp_common_pkg/tests/unit/assessment/test_granular_bbox_conversion.py @@ -2,16 +2,15 @@ # SPDX-License-Identifier: MIT-0 """ -Test to verify that both regular and granular assessment services -handle bounding box conversion correctly. +Test to verify that the geometry_utils module correctly handles +bounding box conversion for assessment data. """ -from idp_common.assessment.granular_service import GranularAssessmentService -from idp_common.assessment.service import AssessmentService +from idp_common.assessment.geometry_utils import extract_geometry_from_nested_dict def test_both_services_convert_bbox_to_geometry(): - """Test that both regular and granular services convert bbox to geometry.""" + """Test that geometry_utils converts bbox to geometry correctly.""" # Test data with bbox coordinates mock_assessment_data = { @@ -37,105 +36,56 @@ def test_both_services_convert_bbox_to_geometry(): }, } - print("=== Testing Bounding Box Conversion in Both Services ===") + print("=== Testing Bounding Box Conversion in geometry_utils ===") - # Test regular assessment service - print("\n📝 Testing Regular AssessmentService") - regular_service = AssessmentService() - regular_result = regular_service._extract_geometry_from_assessment( - mock_assessment_data - ) + # Test geometry conversion using geometry_utils + print("\n📝 Testing extract_geometry_from_nested_dict") + result = extract_geometry_from_nested_dict(mock_assessment_data) # Check YTDNetPay conversion - regular_ytd = regular_result["YTDNetPay"] - regular_ytd_has_geometry = "geometry" in regular_ytd - regular_ytd_has_bbox = "bbox" in regular_ytd + ytd = result["YTDNetPay"] + ytd_has_geometry = "geometry" in ytd + ytd_has_bbox = "bbox" in ytd - print( - f"Regular Service - YTDNetPay: geometry={regular_ytd_has_geometry}, bbox={regular_ytd_has_bbox}" - ) + print(f"YTDNetPay: geometry={ytd_has_geometry}, bbox={ytd_has_bbox}") # Check CompanyAddress.State conversion - regular_state = regular_result["CompanyAddress"]["State"] - regular_state_has_geometry = "geometry" in regular_state - regular_state_has_bbox = "bbox" in regular_state + state = result["CompanyAddress"]["State"] + state_has_geometry = "geometry" in state + state_has_bbox = "bbox" in state - print( - f"Regular Service - CompanyAddress.State: geometry={regular_state_has_geometry}, bbox={regular_state_has_bbox}" - ) + print(f"CompanyAddress.State: geometry={state_has_geometry}, bbox={state_has_bbox}") - # Test granular assessment service - print("\n📝 Testing GranularAssessmentService") - granular_service = GranularAssessmentService() - granular_result = granular_service._extract_geometry_from_assessment( - mock_assessment_data - ) - - # Check YTDNetPay conversion - granular_ytd = granular_result["YTDNetPay"] - granular_ytd_has_geometry = "geometry" in granular_ytd - granular_ytd_has_bbox = "bbox" in granular_ytd - - print( - f"Granular Service - YTDNetPay: geometry={granular_ytd_has_geometry}, bbox={granular_ytd_has_bbox}" - ) - - # Check CompanyAddress.State conversion - granular_state = granular_result["CompanyAddress"]["State"] - granular_state_has_geometry = "geometry" in granular_state - granular_state_has_bbox = "bbox" in granular_state - - print( - f"Granular Service - CompanyAddress.State: geometry={granular_state_has_geometry}, bbox={granular_state_has_bbox}" - ) - - # Verify both services work identically + # Verify conversion print("\n🔍 Verification:") - # Both should convert bbox to geometry - assert regular_ytd_has_geometry, ( - "Regular service should convert YTDNetPay bbox to geometry" - ) - assert not regular_ytd_has_bbox, ( - "Regular service should remove YTDNetPay bbox after conversion" - ) - assert granular_ytd_has_geometry, ( - "Granular service should convert YTDNetPay bbox to geometry" - ) - assert not granular_ytd_has_bbox, ( - "Granular service should remove YTDNetPay bbox after conversion" - ) - - # Both should handle nested attributes - assert regular_state_has_geometry, ( - "Regular service should convert nested State bbox to geometry" - ) - assert not regular_state_has_bbox, ( - "Regular service should remove nested State bbox after conversion" - ) - assert granular_state_has_geometry, ( - "Granular service should convert nested State bbox to geometry" - ) - assert not granular_state_has_bbox, ( - "Granular service should remove nested State bbox after conversion" - ) - - # Check geometry values are equivalent - regular_ytd_geometry = regular_ytd["geometry"][0]["boundingBox"] - granular_ytd_geometry = granular_ytd["geometry"][0]["boundingBox"] - - assert regular_ytd_geometry == granular_ytd_geometry, ( - "Both services should produce identical geometry" - ) - - print("✅ Regular AssessmentService: Converts bbox → geometry correctly") - print("✅ GranularAssessmentService: Converts bbox → geometry correctly") - print("✅ Both services handle nested attributes (CompanyAddress.State)") - print("✅ Both services produce identical geometry output") - print("✅ Both services remove raw bbox data after conversion") - - print("\n🎉 Both services now support automatic bounding box conversion!") - print("Your deployed stack with granular assessment will now work correctly!") + # Should convert bbox to geometry + assert ytd_has_geometry, "Should convert YTDNetPay bbox to geometry" + assert not ytd_has_bbox, "Should remove YTDNetPay bbox after conversion" + + # Should handle nested attributes + assert state_has_geometry, "Should convert nested State bbox to geometry" + assert not state_has_bbox, "Should remove nested State bbox after conversion" + + # Check geometry values are correct + ytd_geometry = ytd["geometry"][0]["boundingBox"] + assert ytd_geometry["top"] == 0.333 # 333/1000 + assert ytd_geometry["left"] == 0.443 # 443/1000 + assert ytd_geometry["width"] == 0.064 # (507-443)/1000 + assert ytd_geometry["height"] == 0.012 # (345-333)/1000 + + state_geometry = state["geometry"][0]["boundingBox"] + assert state_geometry["top"] == 0.116 # 116/1000 + assert state_geometry["left"] == 0.23 # 230/1000 + assert state_geometry["width"] == 0.029 # (259-230)/1000 + assert state_geometry["height"] == 0.01 # (126-116)/1000 + + print("✅ Converts bbox → geometry correctly") + print("✅ Handles nested attributes (CompanyAddress.State)") + print("✅ Removes raw bbox data after conversion") + print("✅ Produces correct normalized geometry values") + + print("\n🎉 geometry_utils correctly supports automatic bounding box conversion!") return True diff --git a/lib/idp_common_pkg/tests/unit/assessment/test_nested_geometry_conversion.py b/lib/idp_common_pkg/tests/unit/assessment/test_nested_geometry_conversion.py index a739fd2e..bf51621c 100644 --- a/lib/idp_common_pkg/tests/unit/assessment/test_nested_geometry_conversion.py +++ b/lib/idp_common_pkg/tests/unit/assessment/test_nested_geometry_conversion.py @@ -2,11 +2,14 @@ # SPDX-License-Identifier: MIT-0 """ -Unit tests for nested geometry conversion in AssessmentService. +Unit tests for nested geometry conversion in geometry_utils. Tests the recursive processing of group attributes with bounding boxes. """ -from idp_common.assessment.service import AssessmentService +from idp_common.assessment.geometry_utils import ( + extract_geometry_from_nested_dict, + process_assessment_geometry, +) class TestNestedGeometryConversion: @@ -14,8 +17,6 @@ class TestNestedGeometryConversion: def test_simple_attribute_geometry_conversion(self): """Test that simple attributes still work correctly.""" - service = AssessmentService() - assessment_data = { "YTDCityTax": { "confidence": 1.0, @@ -25,7 +26,7 @@ def test_simple_attribute_geometry_conversion(self): } } - result = service._extract_geometry_from_assessment(assessment_data) + result = extract_geometry_from_nested_dict(assessment_data) # Check conversion worked assert "geometry" in result["YTDCityTax"] @@ -45,8 +46,6 @@ def test_simple_attribute_geometry_conversion(self): def test_nested_group_attribute_geometry_conversion(self): """Test that nested group attributes are processed recursively.""" - service = AssessmentService() - assessment_data = { "CompanyAddress": { "State": { @@ -64,7 +63,7 @@ def test_nested_group_attribute_geometry_conversion(self): } } - result = service._extract_geometry_from_assessment(assessment_data) + result = extract_geometry_from_nested_dict(assessment_data) # Check that nested attributes were processed company_address = result["CompanyAddress"] @@ -90,8 +89,6 @@ def test_nested_group_attribute_geometry_conversion(self): def test_mixed_attributes_with_and_without_geometry(self): """Test processing of mixed attributes - some with geometry, some without.""" - service = AssessmentService() - assessment_data = { "currency": { "confidence": 0.0, @@ -119,7 +116,7 @@ def test_mixed_attributes_with_and_without_geometry(self): }, } - result = service._extract_geometry_from_assessment(assessment_data) + result = extract_geometry_from_nested_dict(assessment_data) # currency should pass through unchanged (no geometry) assert "geometry" not in result["currency"] @@ -141,8 +138,6 @@ def test_mixed_attributes_with_and_without_geometry(self): def test_list_attributes_with_nested_geometry(self): """Test processing of list attributes where each item may have geometry.""" - service = AssessmentService() - assessment_data = { "Transactions": [ { @@ -175,7 +170,7 @@ def test_list_attributes_with_nested_geometry(self): ] } - result = service._extract_geometry_from_assessment(assessment_data) + result = extract_geometry_from_nested_dict(assessment_data) transactions = result["Transactions"] assert len(transactions) == 2 @@ -196,8 +191,6 @@ def test_list_attributes_with_nested_geometry(self): def test_deeply_nested_group_attributes(self): """Test deeply nested group attributes.""" - service = AssessmentService() - assessment_data = { "EmployeeInfo": { "PersonalDetails": { @@ -224,7 +217,7 @@ def test_deeply_nested_group_attributes(self): } } - result = service._extract_geometry_from_assessment(assessment_data) + result = extract_geometry_from_nested_dict(assessment_data) employee_info = result["EmployeeInfo"] @@ -247,8 +240,6 @@ def test_deeply_nested_group_attributes(self): def test_invalid_nested_bbox_data(self): """Test handling of invalid bbox data in nested attributes.""" - service = AssessmentService() - assessment_data = { "CompanyAddress": { "State": { @@ -266,7 +257,7 @@ def test_invalid_nested_bbox_data(self): } } - result = service._extract_geometry_from_assessment(assessment_data) + result = extract_geometry_from_nested_dict(assessment_data) # Both should have invalid bbox data removed but confidence preserved state = result["CompanyAddress"]["State"] @@ -281,9 +272,7 @@ def test_invalid_nested_bbox_data(self): assert zip_code["confidence"] == 0.85 def test_process_single_assessment_geometry_method(self): - """Test the helper method for processing single assessments.""" - service = AssessmentService() - + """Test the helper function for processing single assessments.""" # Test valid geometry conversion assessment = { "confidence": 0.95, @@ -292,7 +281,7 @@ def test_process_single_assessment_geometry_method(self): "page": 1, } - result = service._process_single_assessment_geometry(assessment, "test_field") + result = process_assessment_geometry(assessment, "test_field") assert "geometry" in result assert "bbox" not in result diff --git a/lib/idp_common_pkg/tests/unit/classification/test_classification_service.py b/lib/idp_common_pkg/tests/unit/classification/test_classification_service.py index ec48ba0e..afcce081 100644 --- a/lib/idp_common_pkg/tests/unit/classification/test_classification_service.py +++ b/lib/idp_common_pkg/tests/unit/classification/test_classification_service.py @@ -291,7 +291,8 @@ def test_classify_page_bedrock_success( # Verify calls mock_get_text.assert_called_once_with("s3://bucket/text.txt") - mock_prepare_image.assert_called_once_with("s3://bucket/image.jpg", None, None) + # ImageConfig defaults to 1200x1200 when not explicitly configured + mock_prepare_image.assert_called_once_with("s3://bucket/image.jpg", 1200, 1200) mock_prepare_bedrock_image.assert_called_once_with(b"image_data") mock_invoke.assert_called_once() diff --git a/lib/idp_common_pkg/tests/unit/config/test_config_models.py b/lib/idp_common_pkg/tests/unit/config/test_config_models.py index 63b91ad0..9cd18788 100644 --- a/lib/idp_common_pkg/tests/unit/config/test_config_models.py +++ b/lib/idp_common_pkg/tests/unit/config/test_config_models.py @@ -142,23 +142,18 @@ def process_config(config: ExtractionConfig) -> bool: result = process_config(config) assert result is True - def test_assessment_granular_config(self): - """Test granular assessment configuration""" + def test_assessment_config(self): + """Test assessment configuration with new flat structure""" config_dict = { "model": "us.amazon.nova-lite-v1:0", - "granular": { - "enabled": True, - "list_batch_size": "5", - "simple_batch_size": "10", - "max_workers": "20", - }, + "enabled": True, + "max_workers": "20", } config = AssessmentConfig.model_validate(config_dict) - assert config.granular.enabled is True - assert config.granular.list_batch_size == 5 - assert config.granular.simple_batch_size == 10 - assert config.granular.max_workers == 20 + assert config.enabled is True + assert config.max_workers == 20 + assert isinstance(config.max_workers, int) def test_config_validation_range_checks(self): """Test that validation enforces ranges""" diff --git a/lib/idp_common_pkg/tests/unit/config/test_config_models_integration.py b/lib/idp_common_pkg/tests/unit/config/test_config_models_integration.py index bb433598..ad1e854d 100644 --- a/lib/idp_common_pkg/tests/unit/config/test_config_models_integration.py +++ b/lib/idp_common_pkg/tests/unit/config/test_config_models_integration.py @@ -217,12 +217,10 @@ def test_criteria_validation_config(self, config_root): assert config.assessment is not None assert isinstance(config.assessment.enabled, bool) - # Validate granular assessment settings - if hasattr(config.assessment, "granular"): - assert isinstance(config.assessment.granular.enabled, bool) - if config.assessment.granular.enabled: - assert config.assessment.granular.list_batch_size > 0 - assert config.assessment.granular.simple_batch_size > 0 + # Validate assessment settings + assert isinstance(config.assessment.enabled, bool) + if config.assessment.enabled: + assert config.assessment.max_workers > 0 def test_config_with_all_optional_fields(self, config_root): """Test that configs work even if optional fields are missing""" @@ -263,12 +261,8 @@ def test_config_type_coercion(self): }, "assessment": { "model": "test", - "granular": { - "enabled": True, - "list_batch_size": "5", # String - "simple_batch_size": 10, # Int - "max_workers": "20", # String - }, + "enabled": True, + "max_workers": "20", # String }, "classes": [], } @@ -284,8 +278,8 @@ def test_config_type_coercion(self): assert config.extraction.top_p == 0.2 assert isinstance(config.extraction.top_p, float) - assert config.assessment.granular.list_batch_size == 5 - assert isinstance(config.assessment.granular.list_batch_size, int) + assert config.assessment.max_workers == 20 + assert isinstance(config.assessment.max_workers, int) def test_boolean_variations(self): """Test various boolean representations""" diff --git a/lib/idp_common_pkg/tests/unit/config/test_configuration_sync.py b/lib/idp_common_pkg/tests/unit/config/test_configuration_sync.py index fc8d6d1c..3fc0ecba 100644 --- a/lib/idp_common_pkg/tests/unit/config/test_configuration_sync.py +++ b/lib/idp_common_pkg/tests/unit/config/test_configuration_sync.py @@ -16,7 +16,6 @@ from idp_common.config.models import ( AssessmentConfig, ExtractionConfig, - GranularAssessmentConfig, IDPConfig, ImageConfig, ) @@ -185,9 +184,7 @@ def test_complex_real_world_scenario(self): top_p=0.1, max_tokens=10000, ), - assessment=AssessmentConfig( - enabled=True, temperature=0.0, granular={"enabled": False} - ), + assessment=AssessmentConfig(enabled=True, temperature=0.0, max_workers=10), classes=[], ) @@ -205,7 +202,7 @@ def test_complex_real_world_scenario(self): assessment=AssessmentConfig( enabled=False, # CUSTOM temperature=0.0, - granular=GranularAssessmentConfig(enabled=False), + max_workers=10, ), classes=[{"$id": "Invoice", "properties": {}}], # CUSTOM ) @@ -225,7 +222,7 @@ def test_complex_real_world_scenario(self): assessment=AssessmentConfig( enabled=True, temperature=0.5, # NEW - granular=GranularAssessmentConfig(enabled=True), # NEW + max_workers=20, # NEW ), classes=[], ) @@ -246,7 +243,7 @@ def test_complex_real_world_scenario(self): assert new_custom.extraction.top_p == 0.2 assert new_custom.extraction.max_tokens == 15000 assert new_custom.assessment.temperature == 0.5 - assert new_custom.assessment.granular.enabled + assert new_custom.assessment.max_workers == 20 @pytest.mark.unit @@ -260,7 +257,7 @@ def test_save_default_triggers_sync(self): dynamodb = boto3.resource("dynamodb", region_name="us-east-1") table_name = "test-config-table" - dynamodb.create_table( + dynamodb.create_table( # pyright: ignore[reportAttributeAccessIssue] TableName=table_name, KeySchema=[{"AttributeName": "id", "KeyType": "HASH"}], AttributeDefinitions=[{"AttributeName": "id", "AttributeType": "S"}], @@ -301,7 +298,7 @@ def test_save_custom_does_not_trigger_sync(self): dynamodb = boto3.resource("dynamodb", region_name="us-east-1") table_name = "test-config-table" - dynamodb.create_table( + dynamodb.create_table( # pyright: ignore[reportAttributeAccessIssue] TableName=table_name, KeySchema=[{"AttributeName": "id", "KeyType": "HASH"}], AttributeDefinitions=[{"AttributeName": "id", "AttributeType": "S"}], diff --git a/lib/idp_common_pkg/tests/unit/ocr/test_ocr_service.py b/lib/idp_common_pkg/tests/unit/ocr/test_ocr_service.py index b9a9d748..da26492f 100644 --- a/lib/idp_common_pkg/tests/unit/ocr/test_ocr_service.py +++ b/lib/idp_common_pkg/tests/unit/ocr/test_ocr_service.py @@ -106,10 +106,10 @@ def test_init_textract_backend_default(self): assert service.max_workers == 20 assert service.dpi is None # Default is None assert service.enhanced_features is False - # Default image sizing + # Default image sizing (from ImageConfig defaults) assert service.resize_config == { - "target_width": 951, - "target_height": 1268, + "target_width": 1200, + "target_height": 1200, } assert service.preprocessing_config is None @@ -198,10 +198,10 @@ def test_init_config_pattern_default_sizing(self): with patch("boto3.client"): service = OcrService(config=config) - # Verify defaults are applied + # Verify defaults are applied (from ImageConfig model defaults) assert service.resize_config == { - "target_width": 951, - "target_height": 1268, + "target_width": 1200, + "target_height": 1200, } assert service.dpi == 200 @@ -242,7 +242,8 @@ def test_init_config_pattern_empty_strings_apply_defaults(self): with patch("boto3.client"): service = OcrService(config=config) - # Verify defaults are applied (empty strings treated same as None) + # Verify OCR service defaults are applied (empty strings treated same as None) + # Note: Empty strings bypass Pydantic defaults, triggering OCR service defaults assert service.resize_config == { "target_width": 951, "target_height": 1268, @@ -250,13 +251,13 @@ def test_init_config_pattern_empty_strings_apply_defaults(self): assert service.dpi == 150 def test_init_config_pattern_partial_sizing(self): - """Test initialization with partial sizing configuration preserves existing behavior.""" + """Test initialization with partial sizing configuration uses explicit width and default height.""" config = { "ocr": { "image": { "dpi": 150, "target_width": 800, - # target_height missing - should disable defaults + # target_height missing - Pydantic model provides default of 1200 } } } @@ -264,8 +265,11 @@ def test_init_config_pattern_partial_sizing(self): with patch("boto3.client"): service = OcrService(config=config) - # Verify partial config disables defaults - assert service.resize_config is None + # Verify explicit width is used with default height from ImageConfig model + assert service.resize_config == { + "target_width": 800, + "target_height": 1200, + } assert service.dpi == 150 def test_init_config_pattern_invalid_sizing_fallback(self): diff --git a/lib/idp_common_pkg/tests/unit/test_granular_assessment.py b/lib/idp_common_pkg/tests/unit/test_granular_assessment.py index 4c4904ef..c9520334 100644 --- a/lib/idp_common_pkg/tests/unit/test_granular_assessment.py +++ b/lib/idp_common_pkg/tests/unit/test_granular_assessment.py @@ -5,8 +5,6 @@ Unit tests for the granular assessment service. """ -from unittest.mock import patch - import pytest from idp_common.assessment.granular_service import ( AssessmentResult, @@ -46,18 +44,13 @@ def sample_config(self): """Sample configuration for testing.""" return { "assessment": { - "granular": { - "max_workers": 4, - "simple_batch_size": 3, - "list_batch_size": 1, - }, + "max_workers": 4, "model": "us.anthropic.claude-3-7-sonnet-20250219-v1:0", "temperature": 0.0, "top_k": 5, "top_p": 0.1, "max_tokens": 4096, "system_prompt": "You are an assessment expert.", - "task_prompt": "Assess {DOCUMENT_CLASS} with {ATTRIBUTE_NAMES_AND_DESCRIPTIONS}. Results: {EXTRACTION_RESULTS}", "default_confidence_threshold": 0.9, }, "classes": [ @@ -110,13 +103,11 @@ def test_initialization(self, sample_config): service = GranularAssessmentService(config=idp_config) assert service.max_workers == 4 - assert service.simple_batch_size == 3 - assert service.list_batch_size == 1 assert service.enable_parallel # max_workers > 1 def test_initialization_single_worker(self, sample_config): """Test service initialization with single worker.""" - sample_config["assessment"]["granular"]["max_workers"] = 1 + sample_config["assessment"]["max_workers"] = 1 idp_config = IDPConfig.model_validate(sample_config) service = GranularAssessmentService(config=idp_config) @@ -143,43 +134,37 @@ def test_get_class_schema_not_found(self, sample_config): assert schema == {} - def test_format_property_descriptions(self, sample_config): - """Test formatting property descriptions from JSON Schema.""" - idp_config = IDPConfig.model_validate(sample_config) - service = GranularAssessmentService(config=idp_config) - properties = service._get_class_schema("letter").get("properties", {}) - descriptions = service._format_property_descriptions(properties) - - assert "sender_name" in descriptions - assert "Name of the sender" in descriptions - assert "recipient_name" in descriptions - - def test_create_assessment_tasks_simple_batching( + def test_create_assessment_tasks_simple_attributes( self, sample_config, sample_extraction_results ): - """Test creating assessment tasks with simple attribute batching.""" + """Test creating assessment tasks with simple attributes - new Strands approach.""" idp_config = IDPConfig.model_validate(sample_config) service = GranularAssessmentService(config=idp_config) properties = service._get_class_schema("letter").get("properties", {}) - tasks = service._create_assessment_tasks( + tasks, assessment_structure = service._create_assessment_tasks( sample_extraction_results, properties, 0.9 ) - # With 5 simple attributes and batch_size=3, we should get 2 batches - assert len(tasks) == 2 - assert tasks[0].task_type == "simple_batch" - assert tasks[0].task_id == "simple_batch_0" - assert len(tasks[0].attributes) == 3 # First batch: 3 attributes - assert len(tasks[1].attributes) == 2 # Second batch: 2 attributes + # With new approach: one task per leaf field = 5 tasks + assert len(tasks) == 5 + + # All tasks should be "attribute" type (single field assessment) + assert all(t.task_type == "attribute" for t in tasks) + + # All tasks should have field_path as tuple + assert all(isinstance(t.field_path, tuple) for t in tasks) - # Check that extraction data is properly included - assert "sender_name" in tasks[0].extraction_data - assert "recipient_name" in tasks[0].extraction_data + # All tasks should have confidence_threshold set + assert all(t.confidence_threshold > 0 for t in tasks) - def test_create_assessment_tasks_with_group_attributes(self, sample_config): - """Test creating assessment tasks with group attributes.""" - # Add a group property to the config + # Check that assessment_structure mirrors extraction_results + assert isinstance(assessment_structure, dict) + assert set(assessment_structure.keys()) == set(sample_extraction_results.keys()) + + def test_create_assessment_tasks_with_nested_object(self, sample_config): + """Test creating assessment tasks with nested object attributes.""" + # Add a nested object property to the config sample_config["classes"][0]["properties"]["address_info"] = { "type": "object", "description": "Address information", @@ -198,21 +183,31 @@ def test_create_assessment_tasks_with_group_attributes(self, sample_config): service = GranularAssessmentService(config=idp_config) properties = service._get_class_schema("letter").get("properties", {}) - tasks = service._create_assessment_tasks(extraction_results, properties, 0.9) + tasks, assessment_structure = service._create_assessment_tasks( + extraction_results, properties, 0.9 + ) - # Should have simple batches + 1 group task - group_tasks = [t for t in tasks if t.task_type == "group"] - assert len(group_tasks) == 1 - assert group_tasks[0].attributes == ["address_info"] - assert "address_info" in group_tasks[0].extraction_data + # Should have 3 tasks: sender_name, address_info.street, address_info.city + assert len(tasks) == 3 - def test_create_assessment_tasks_with_list_attributes(self, sample_config): - """Test creating assessment tasks with list attributes.""" - # Add a list property to the config + # Find nested tasks + nested_tasks = [t for t in tasks if len(t.field_path) > 1] + assert len(nested_tasks) == 2 + + # Check nested paths are tuples + assert any(t.field_path == ("address_info", "street") for t in nested_tasks) + assert any(t.field_path == ("address_info", "city") for t in nested_tasks) + + # Check assessment structure has nested dict + assert "address_info" in assessment_structure + assert isinstance(assessment_structure["address_info"], dict) + + def test_create_assessment_tasks_with_array(self, sample_config): + """Test creating assessment tasks with array attributes.""" + # Add an array property to the config sample_config["classes"][0]["properties"]["transactions"] = { "type": "array", "description": "List of transactions", - "x-aws-idp-list-item-description": "A single transaction", "items": { "type": "object", "properties": { @@ -237,284 +232,109 @@ def test_create_assessment_tasks_with_list_attributes(self, sample_config): service = GranularAssessmentService(config=idp_config) properties = service._get_class_schema("letter").get("properties", {}) - tasks = service._create_assessment_tasks(extraction_results, properties, 0.9) - - # Should have simple batches + 2 list item tasks - list_tasks = [t for t in tasks if t.task_type == "list_item"] - assert len(list_tasks) == 2 - assert list_tasks[0].task_id == "list_transactions_item_0" - assert list_tasks[1].task_id == "list_transactions_item_1" - assert list_tasks[0].list_item_index == 0 - assert list_tasks[1].list_item_index == 1 - - def test_get_task_specific_attribute_descriptions(self, sample_config): - """Test getting task-specific attribute descriptions.""" - idp_config = IDPConfig.model_validate(sample_config) - service = GranularAssessmentService(config=idp_config) - properties = service._get_class_schema("letter").get("properties", {}) - - # Create a simple batch task - task = AssessmentTask( - task_id="test_batch", - task_type="simple_batch", - attributes=["sender_name", "recipient_name"], - extraction_data={"sender_name": "John", "recipient_name": "Jane"}, - confidence_thresholds={"sender_name": 0.9, "recipient_name": 0.9}, + tasks, assessment_structure = service._create_assessment_tasks( + extraction_results, properties, 0.9 ) - descriptions = service._get_task_specific_attribute_descriptions( - task, properties - ) + # Should have 5 tasks: sender_name + 2 items * 2 fields each = 1 + 4 = 5 + assert len(tasks) == 5 - assert "sender_name" in descriptions - assert "recipient_name" in descriptions - assert "date" not in descriptions # Should only include task attributes + # Find array item tasks + array_tasks = [ + t for t in tasks if len(t.field_path) == 3 + ] # ("transactions", 0, "amount") + assert len(array_tasks) == 4 - def test_build_specific_assessment_prompt(self, sample_config): - """Test building specific assessment prompt.""" - idp_config = IDPConfig.model_validate(sample_config) - service = GranularAssessmentService(config=idp_config) - properties = service._get_class_schema("letter").get("properties", {}) - - # Mock base content with placeholders (like what would come from the real base content) - base_content = [ - {"text": "Base prompt content with {EXTRACTION_RESULTS} placeholder"}, - {"text": "<>"}, - ] - - # Create a simple batch task - task = AssessmentTask( - task_id="test_batch", - task_type="simple_batch", - attributes=["sender_name", "recipient_name"], - extraction_data={"sender_name": "John", "recipient_name": "Jane"}, - confidence_thresholds={"sender_name": 0.9, "recipient_name": 0.9}, - ) - - content = service._build_specific_assessment_prompt( - task, base_content, properties - ) - - # Should have same number of content items as base content - assert len(content) == 2 - - # First item should have placeholder replaced with extraction results - first_content = content[0]["text"] - assert "Base prompt content with" in first_content - assert ( - "{EXTRACTION_RESULTS}" not in first_content - ) # Placeholder should be replaced - assert "sender_name" in first_content - assert "recipient_name" in first_content - assert "John" in first_content - assert "Jane" in first_content - - # Cache point should be preserved - assert content[1]["text"] == "<>" - - def test_build_cached_prompt_base(self, sample_config): - """Test building cached prompt base.""" - idp_config = IDPConfig.model_validate(sample_config) - service = GranularAssessmentService(config=idp_config) - - content = service._build_cached_prompt_base( - document_text="Sample document text", - class_label="letter", - attribute_descriptions="", # Empty for base content - will be task-specific - ocr_text_confidence="OCR confidence data", - page_images=[], - ) - - # Should have at least one text content item - assert len(content) >= 1 - # Check that the task prompt template is used and placeholders are replaced + # Check array paths include indices + assert any(t.field_path == ("transactions", 0, "amount") for t in array_tasks) assert any( - "letter" in item.get("text", "") for item in content - ) # DOCUMENT_CLASS - # Attribute descriptions should NOT be in base content (they're task-specific) - assert not any( - "sender_name: Name of sender" in item.get("text", "") for item in content - ) # ATTRIBUTE_NAMES_AND_DESCRIPTIONS should be placeholder - # Should contain placeholders for task-specific content - assert any( - "{ATTRIBUTE_NAMES_AND_DESCRIPTIONS}" in item.get("text", "") - or "{EXTRACTION_RESULTS}" in item.get("text", "") - for item in content + t.field_path == ("transactions", 1, "description") for t in array_tasks ) - @patch("idp_common.bedrock.invoke_model") - def test_process_assessment_task_success(self, mock_bedrock, sample_config): - """Test successful processing of an assessment task.""" - # Mock Bedrock response - mock_response = { - "metering": { - "us.anthropic.claude-3-7-sonnet-20250219-v1:0": { - "input_tokens": 100, - "output_tokens": 50, - } - }, - "response": { - "output": { - "message": { - "content": [ - { - "text": '{"sender_name": {"confidence": 0.95, "confidence_reason": "Clear evidence"}}' - } - ] - } - } - }, - } - mock_bedrock.return_value = mock_response + # Check assessment structure has array + assert "transactions" in assessment_structure + assert isinstance(assessment_structure["transactions"], list) + assert len(assessment_structure["transactions"]) == 2 + def test_aggregate_assessment_results_new_approach(self, sample_config): + """Test aggregating assessment results with new Strands approach.""" idp_config = IDPConfig.model_validate(sample_config) service = GranularAssessmentService(config=idp_config) - properties = service._get_class_schema("letter").get("properties", {}) - # Create a task - task = AssessmentTask( - task_id="test_batch", - task_type="simple_batch", - attributes=["sender_name"], - extraction_data={"sender_name": "John"}, - confidence_thresholds={"sender_name": 0.9}, - ) - - base_content = [{"text": "Base prompt"}] - - result = service._process_assessment_task( - task, - base_content, - properties, - "test-model", - "system prompt", - 0.0, - 5, - 0.1, - 4096, - ) - - assert result.success - assert result.task_id == "test_batch" - assert "sender_name" in result.assessment_data - assert result.assessment_data["sender_name"]["confidence"] == 0.95 - - @patch("idp_common.bedrock.invoke_model") - def test_process_assessment_task_bedrock_error(self, mock_bedrock, sample_config): - """Test processing assessment task with Bedrock error.""" - # Mock Bedrock to raise an exception - mock_bedrock.side_effect = Exception("Bedrock error") - - idp_config = IDPConfig.model_validate(sample_config) - service = GranularAssessmentService(config=idp_config) - properties = service._get_class_schema("letter").get("properties", {}) - - task = AssessmentTask( - task_id="test_batch", - task_type="simple_batch", - attributes=["sender_name"], - extraction_data={"sender_name": "John"}, - confidence_thresholds={"sender_name": 0.9}, - ) - - base_content = [{"text": "Base prompt"}] - - result = service._process_assessment_task( - task, - base_content, - properties, - "test-model", - "system prompt", - 0.0, - 5, - 0.1, - 4096, - ) - - assert not result.success - assert result.error_message == "Bedrock error" - - def test_check_confidence_alerts_simple_batch(self, sample_config): - """Test confidence alert checking for simple batch tasks.""" - idp_config = IDPConfig.model_validate(sample_config) - service = GranularAssessmentService(config=idp_config) - - task = AssessmentTask( - task_id="test_batch", - task_type="simple_batch", - attributes=["sender_name", "recipient_name"], - extraction_data={}, - confidence_thresholds={"sender_name": 0.9, "recipient_name": 0.8}, - ) - - assessment_data = { - "sender_name": {"confidence": 0.95}, # Above threshold - "recipient_name": {"confidence": 0.7}, # Below threshold + # Create pre-built assessment structure + assessment_structure = { + "sender_name": None, + "recipient_name": None, + "date": None, } - alerts = [] - service._check_confidence_alerts_for_task(task, assessment_data, alerts) - - assert len(alerts) == 1 - assert alerts[0]["attribute_name"] == "recipient_name" - assert alerts[0]["confidence"] == 0.7 - assert alerts[0]["confidence_threshold"] == 0.8 - - def test_aggregate_assessment_results(self, sample_config): - """Test aggregating assessment results.""" - idp_config = IDPConfig.model_validate(sample_config) - service = GranularAssessmentService(config=idp_config) - - # Create tasks and results + # Create tasks with new structure (field_path used for navigation) task1 = AssessmentTask( - task_id="batch_0", - task_type="simple_batch", - attributes=["sender_name", "recipient_name"], - extraction_data={}, - confidence_thresholds={"sender_name": 0.9, "recipient_name": 0.9}, + task_id="task_0", + task_type="attribute", + field_path=("sender_name",), + field_name="sender_name", + field_schema={"type": "string"}, + confidence_threshold=0.9, ) task2 = AssessmentTask( - task_id="batch_1", - task_type="simple_batch", - attributes=["date"], - extraction_data={}, - confidence_thresholds={"date": 0.9}, + task_id="task_1", + task_type="attribute", + field_path=("recipient_name",), + field_name="recipient_name", + field_schema={"type": "string"}, + confidence_threshold=0.9, ) + task3 = AssessmentTask( + task_id="task_2", + task_type="attribute", + field_path=("date",), + field_name="date", + field_schema={"type": "string"}, + confidence_threshold=0.9, + ) + + # Create results result1 = AssessmentResult( - task_id="batch_0", + task_id="task_0", success=True, - assessment_data={ - "sender_name": {"confidence": 0.95, "confidence_reason": "Clear"}, - "recipient_name": {"confidence": 0.85, "confidence_reason": "Good"}, - }, + assessment_data={"confidence": 0.95, "confidence_reason": "Clear"}, confidence_alerts=[], metering={"model": {"input_tokens": 100}}, ) result2 = AssessmentResult( - task_id="batch_1", + task_id="task_1", success=True, - assessment_data={ - "date": {"confidence": 0.90, "confidence_reason": "Clear date"} - }, + assessment_data={"confidence": 0.85, "confidence_reason": "Good"}, confidence_alerts=[], metering={"model": {"input_tokens": 50}}, ) + result3 = AssessmentResult( + task_id="task_2", + success=True, + assessment_data={"confidence": 0.90, "confidence_reason": "Clear date"}, + confidence_alerts=[], + metering={"model": {"input_tokens": 25}}, + ) + + # Aggregate results using new signature enhanced_data, alerts, metering = service._aggregate_assessment_results( - [task1, task2], [result1, result2], {} + [task1, task2, task3], [result1, result2, result3], assessment_structure ) - # Check enhanced data + # Check enhanced data (should be the assessment_structure with values filled in) assert "sender_name" in enhanced_data assert "recipient_name" in enhanced_data assert "date" in enhanced_data assert enhanced_data["sender_name"]["confidence_threshold"] == 0.9 + assert enhanced_data["sender_name"]["confidence"] == 0.95 - # Check metering aggregation (using utils.merge_metering_data) - assert metering["model"]["input_tokens"] == 150 + # Check metering aggregation + assert metering["model"]["input_tokens"] == 175 # 100 + 50 + 25 def test_empty_extraction_results_handling(self, sample_config): """Test handling of empty extraction results.""" @@ -523,23 +343,11 @@ def test_empty_extraction_results_handling(self, sample_config): properties = service._get_class_schema("letter").get("properties", {}) # Empty extraction results should create no tasks - tasks = service._create_assessment_tasks({}, properties, 0.9) + tasks, assessment_structure = service._create_assessment_tasks( + {}, properties, 0.9 + ) assert len(tasks) == 0 - - def test_missing_task_prompt_uses_default(self, sample_config): - """Test that default task_prompt is used when not in config.""" - # Remove task_prompt from config - del sample_config["assessment"]["task_prompt"] - - idp_config = IDPConfig.model_validate(sample_config) - service = GranularAssessmentService(config=idp_config) - - # Should not raise an error, should use default task_prompt from IDPConfig - prompt = service._build_cached_prompt_base("text", "letter", "attrs", "ocr", []) - - # Verify a prompt was generated (not empty) - assert prompt is not None - assert len(prompt) > 0 + assert assessment_structure == {} def test_confidence_threshold_inheritance(self, sample_config): """Test that confidence thresholds are properly inherited.""" diff --git a/lib/idp_common_pkg/uv.lock b/lib/idp_common_pkg/uv.lock index 1a133d21..b7e3855b 100644 --- a/lib/idp_common_pkg/uv.lock +++ b/lib/idp_common_pkg/uv.lock @@ -1,12 +1,6 @@ version = 1 revision = 3 -requires-python = ">=3.9, <3.14" -resolution-markers = [ - "python_full_version >= '3.12'", - "python_full_version == '3.11.*'", - "python_full_version == '3.10.*'", - "python_full_version < '3.10'", -] +requires-python = ">=3.12, <3.14" [[package]] name = "aiobotocore" @@ -42,7 +36,6 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohappyeyeballs" }, { name = "aiosignal" }, - { name = "async-timeout", marker = "python_full_version < '3.11'" }, { name = "attrs" }, { name = "frozenlist" }, { name = "multidict" }, @@ -51,40 +44,6 @@ dependencies = [ ] sdist = { url = "https://files.pythonhosted.org/packages/62/f1/8515650ac3121a9e55c7b217c60e7fae3e0134b5acfe65691781b5356929/aiohttp-3.13.0.tar.gz", hash 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Test configuration - config: Dict[str, Any] = { + config: dict[str, Any] = { "model_name": "TestModel", "match_threshold": 0.8, "fields": { @@ -105,7 +105,7 @@ def verify_list_matching(): try: from stickler import StructuredModel - config: Dict[str, Any] = { + config: dict[str, Any] = { "model_name": "ListTestModel", "fields": { "items": { diff --git a/patterns/pattern-2/src/assessment_function/index.py b/patterns/pattern-2/src/assessment_function/index.py index 59ec9004..dec12139 100644 --- a/patterns/pattern-2/src/assessment_function/index.py +++ b/patterns/pattern-2/src/assessment_function/index.py @@ -6,92 +6,103 @@ import time import logging -from idp_common import get_config, assessment +from idp_common import get_config from idp_common.models import Document, Status from idp_common.docs_service import create_document_service from idp_common import s3 -from idp_common.utils import normalize_boolean_value, calculate_lambda_metering, merge_metering_data +from idp_common.utils import ( + normalize_boolean_value, + calculate_lambda_metering, + merge_metering_data, +) from assessment_validator import AssessmentValidator from aws_xray_sdk.core import xray_recorder, patch_all patch_all() + # Custom exception for throttling scenarios class ThrottlingException(Exception): """Exception raised when throttling is detected in document processing results""" + pass + # Throttling detection constants THROTTLING_KEYWORDS = [ "throttlingexception", - "provisionedthroughputexceededexception", + "provisionedthroughputexceededexception", "servicequotaexceededexception", "toomanyrequestsexception", "requestlimitexceeded", "too many tokens", "please wait before trying again", - "reached max retries" + "reached max retries", ] THROTTLING_EXCEPTIONS = [ "ThrottlingException", "ProvisionedThroughputExceededException", - "ServiceQuotaExceededException", + "ServiceQuotaExceededException", "TooManyRequestsException", - "RequestLimitExceeded" + "RequestLimitExceeded", ] # Configuration will be loaded in handler function logger = logging.getLogger() logger.setLevel(os.environ.get("LOG_LEVEL", "INFO")) -logging.getLogger('idp_common.bedrock.client').setLevel(os.environ.get("BEDROCK_LOG_LEVEL", "INFO")) +logging.getLogger("idp_common.bedrock.client").setLevel( + os.environ.get("BEDROCK_LOG_LEVEL", "INFO") +) + def is_throttling_exception(exception): """ Check if an exception is related to throttling. - + Args: exception: The exception to check - + Returns: bool: True if the exception is throttling-related, False otherwise """ from botocore.exceptions import ClientError - + if isinstance(exception, ClientError): - error_code = exception.response.get('Error', {}).get('Code', '') + error_code = exception.response.get("Error", {}).get("Code", "") return error_code in THROTTLING_EXCEPTIONS - + exception_name = type(exception).__name__ exception_message = str(exception).lower() - - return ( - exception_name in THROTTLING_EXCEPTIONS or - any(keyword in exception_message for keyword in THROTTLING_KEYWORDS) + + return exception_name in THROTTLING_EXCEPTIONS or any( + keyword in exception_message for keyword in THROTTLING_KEYWORDS ) + def check_document_for_throttling_errors(document): """ Check if a document has throttling errors in its errors field. - + Args: document: The document object to check - + Returns: tuple: (has_throttling_errors: bool, first_throttling_error: str or None) """ if document.status != Status.FAILED or not document.errors: return False, None - + for error_msg in document.errors: error_lower = str(error_msg).lower() if any(keyword in error_lower for keyword in THROTTLING_KEYWORDS): return True, error_msg - + return False, None -@xray_recorder.capture('assessment_function') + +@xray_recorder.capture("assessment_function") def handler(event, context): """ Lambda handler for document assessment. @@ -99,32 +110,36 @@ def handler(event, context): using the Assessment service from the idp_common library. """ start_time = time.time() # Capture start time for Lambda metering - logger.info(f"Starting assessment processing for event: {json.dumps(event, default=str)}") + logger.info( + f"Starting assessment processing for event: {json.dumps(event, default=str)}" + ) # Load configuration config = get_config(as_model=True) # Use default=str to handle Decimal and other non-serializable types logger.info(f"Config: {json.dumps(config.model_dump(), default=str)}") - + # Extract input from event - handle both compressed and uncompressed - document_data = event.get('document', {}) - section_id = event.get('section_id') - + document_data = event.get("document", {}) + section_id = event.get("section_id") + # Validate inputs if not document_data: raise ValueError("No document provided in event") - + if not section_id: raise ValueError("No section_id provided in event") - + # Convert document data to Document object - handle compression - working_bucket = os.environ.get('WORKING_BUCKET') + working_bucket = os.environ.get("WORKING_BUCKET") document = Document.load_document(document_data, working_bucket, logger) - logger.info(f"Processing assessment for document {document.id}, section {section_id}") + logger.info( + f"Processing assessment for document {document.id}, section {section_id}" + ) # X-Ray annotations - xray_recorder.put_annotation('document_id', {document.id}) - xray_recorder.put_annotation('processing_stage', 'assessment') + xray_recorder.put_annotation("document_id", {document.id}) + xray_recorder.put_annotation("processing_stage", "assessment") # Find the section we're processing section = None @@ -132,24 +147,28 @@ def handler(event, context): if s.section_id == section_id: section = s break - + if not section: raise ValueError(f"Section {section_id} not found in document") - # Check if granular assessment is enabled (moved earlier for Lambda metering context) - assessment_context = "GranularAssessment" if config.assessment.granular.enabled else "Assessment" - logger.info(f"Assessment mode: {'Granular' if config.assessment.granular.enabled else 'Regular'} (context: {assessment_context})") + # Assessment context for Lambda metering + assessment_context = "Assessment" + logger.info(f"Assessment mode: Strands-based (context: {assessment_context})") # Intelligent Assessment Skip: Check if extraction results already contain explainability_info if section.extraction_result_uri and section.extraction_result_uri.strip(): try: - logger.info(f"Checking extraction results for existing assessment: {section.extraction_result_uri}") + logger.info( + f"Checking extraction results for existing assessment: {section.extraction_result_uri}" + ) extraction_data = s3.get_json_content(section.extraction_result_uri) - + # If explainability_info exists, assessment was already done - if extraction_data.get('explainability_info'): - logger.info(f"Skipping assessment for section {section_id} - extraction results already contain explainability_info") - + if extraction_data.get("explainability_info"): + logger.info( + f"Skipping assessment for section {section_id} - extraction results already contain explainability_info" + ) + # Create section-specific document (same as normal processing) to match output format section_document = Document( id=document.id, @@ -168,43 +187,57 @@ def handler(event, context): evaluation_report_uri=document.evaluation_report_uri, evaluation_results_uri=document.evaluation_results_uri, errors=document.errors, - metering={} # Empty metering for skipped processing + metering={}, # Empty metering for skipped processing ) - + # Add only the pages needed for this section for page_id in section.page_ids: if page_id in document.pages: section_document.pages[page_id] = document.pages[page_id] - + # Add only the section being processed (preserve existing data) section_document.sections = [section] - + # Add Lambda metering for assessment skip execution with dynamic context try: - lambda_metering = calculate_lambda_metering(assessment_context, context, start_time) - section_document.metering = merge_metering_data(section_document.metering, lambda_metering) + lambda_metering = calculate_lambda_metering( + assessment_context, context, start_time + ) + section_document.metering = merge_metering_data( + section_document.metering, lambda_metering + ) except Exception as e: - logger.warning(f"Failed to add Lambda metering for assessment skip: {str(e)}") - + logger.warning( + f"Failed to add Lambda metering for assessment skip: {str(e)}" + ) + # Return consistent format for Map state collation response = { - "section_id": section_id, - "document": section_document.serialize_document(working_bucket, f"assessment_skip_{section_id}", logger) + "section_id": section_id, + "document": section_document.serialize_document( + working_bucket, f"assessment_skip_{section_id}", logger + ), } - - logger.info(f"Assessment skipped - Response: {json.dumps(response, default=str)}") + + logger.info( + f"Assessment skipped - Response: {json.dumps(response, default=str)}" + ) return response else: - logger.info(f"Assessment needed for section {section_id} - no explainability_info found in extraction results") + logger.info( + f"Assessment needed for section {section_id} - no explainability_info found in extraction results" + ) except Exception as e: - logger.warning(f"Error checking extraction results for assessment skip: {e}") + logger.warning( + f"Error checking extraction results for assessment skip: {e}" + ) # Continue with normal assessment if check fails # Normal assessment processing document.status = Status.ASSESSING # Update document status to ASSESSING for UI only - # Create new 'shell' document since our input document has only 1 section. + # Create new 'shell' document since our input document has only 1 section. docStatus = Document( id=document.id, input_key=document.input_key, @@ -215,112 +248,133 @@ def handler(event, context): document_service.update_document(docStatus) # Initialize assessment service with cache table for enhanced retry handling - cache_table = os.environ.get('TRACKING_TABLE') - - # Check if granular assessment is enabled - - if config.assessment.granular.enabled: - # Use enhanced granular assessment service with caching and retry support - from idp_common.assessment.granular_service import GranularAssessmentService - assessment_service = GranularAssessmentService(config=config, cache_table=cache_table) - logger.info("Using granular assessment service with enhanced error handling and caching") - else: - # Use regular assessment service - assessment_service = assessment.AssessmentService(config=config) - logger.info("Using regular assessment service") + cache_table = os.environ.get("TRACKING_TABLE") + + # Use Strands-based granular assessment service (always enabled) + from idp_common.assessment.granular_service import GranularAssessmentService + + assessment_service = GranularAssessmentService( + config=config, cache_table=cache_table + ) + logger.info("Using Strands-based assessment service") # Process the document section for assessment t0 = time.time() logger.info(f"Starting assessment for section {section_id}") - + try: - updated_document = assessment_service.process_document_section(document, section_id) + updated_document = assessment_service.process_document_section( + document, section_id + ) t1 = time.time() - logger.info(f"Total assessment time: {t1-t0:.2f} seconds") - + logger.info(f"Total assessment time: {t1 - t0:.2f} seconds") + # Check for failed assessment tasks that might require retry (granular assessment) - if hasattr(updated_document, 'metadata') and updated_document.metadata: - failed_tasks = updated_document.metadata.get('failed_assessment_tasks', {}) + if hasattr(updated_document, "metadata") and updated_document.metadata: + failed_tasks = updated_document.metadata.get("failed_assessment_tasks", {}) if failed_tasks: throttling_tasks = { - task_id: task_info for task_id, task_info in failed_tasks.items() - if task_info.get('is_throttling', False) + task_id: task_info + for task_id, task_info in failed_tasks.items() + if task_info.get("is_throttling", False) } - + logger.warning( f"Assessment completed with {len(failed_tasks)} failed tasks, " f"{len(throttling_tasks)} due to throttling" ) - + if throttling_tasks: logger.info( f"Throttling detected in {len(throttling_tasks)} tasks. " f"Successful tasks have been cached for retry." ) - + # Check for throttling errors in document status and errors field - has_throttling, throttling_error = check_document_for_throttling_errors(updated_document) + has_throttling, throttling_error = check_document_for_throttling_errors( + updated_document + ) if has_throttling: - logger.error(f"Throttling error detected in document errors: {throttling_error}") + logger.error( + f"Throttling error detected in document errors: {throttling_error}" + ) logger.error("Raising ThrottlingException to trigger Step Functions retry") - raise ThrottlingException(f"Throttling detected in document processing: {throttling_error}") - + raise ThrottlingException( + f"Throttling detected in document processing: {throttling_error}" + ) + except Exception as e: t1 = time.time() - logger.error(f"Assessment failed after {t1-t0:.2f} seconds: {str(e)}") - + logger.error(f"Assessment failed after {t1 - t0:.2f} seconds: {str(e)}") + # Check if this is a throttling exception that should trigger retry if is_throttling_exception(e): - logger.error(f"Throttling exception detected: {type(e).__name__}. This will trigger state machine retry.") + logger.error( + f"Throttling exception detected: {type(e).__name__}. This will trigger state machine retry." + ) # Update document status before re-raising document_service.update_document(docStatus) # Re-raise to trigger state machine retry raise else: - logger.error(f"Non-throttling exception: {type(e).__name__}. Marking document as failed.") + logger.error( + f"Non-throttling exception: {type(e).__name__}. Marking document as failed." + ) # Set document status to failed for non-throttling exceptions updated_document = document updated_document.status = Status.FAILED updated_document.errors.append(str(e)) # Assessment validation - validation_enabled = config.assessment.granular.enabled and config.assessment.validation_enabled - logger.info(f"Assessment Enabled:{config.assessment.granular.enabled}") - logger.info(f"Validation Enabled:{validation_enabled}") - if not config.assessment.granular.enabled: + validation_enabled = ( + config.assessment.enabled and config.assessment.validation_enabled + ) + logger.info(f"Assessment Enabled: {config.assessment.enabled}") + logger.info(f"Validation Enabled: {validation_enabled}") + if not config.assessment.enabled: logger.info("Assessment is disabled.") elif not validation_enabled: logger.info("Assessment validation is disabled.") else: for section in updated_document.sections: if section.section_id == section_id and section.extraction_result_uri: - logger.info(f"Loading assessment results from: {section.extraction_result_uri}") + logger.info( + f"Loading assessment results from: {section.extraction_result_uri}" + ) # Load extraction data with assessment results extraction_data = s3.get_json_content(section.extraction_result_uri) - validator = AssessmentValidator(extraction_data, - assessment_config=config.assessment, - enable_missing_check=True, - enable_count_check=True) + validator = AssessmentValidator( + extraction_data, + assessment_config=config.assessment, + enable_missing_check=True, + enable_count_check=True, + ) validation_results = validator.validate_all() - if not validation_results['is_valid']: + if not validation_results["is_valid"]: # Handle validation failure updated_document.status = Status.FAILED - validation_errors = validation_results['validation_errors'] + validation_errors = validation_results["validation_errors"] updated_document.errors.extend(validation_errors) logger.error(f"Validation Error: {validation_errors}") # Add Lambda metering for successful assessment execution with dynamic context try: - lambda_metering = calculate_lambda_metering(assessment_context, context, start_time) - updated_document.metering = merge_metering_data(updated_document.metering, lambda_metering) + lambda_metering = calculate_lambda_metering( + assessment_context, context, start_time + ) + updated_document.metering = merge_metering_data( + updated_document.metering, lambda_metering + ) except Exception as e: logger.warning(f"Failed to add Lambda metering for assessment: {str(e)}") # Prepare output with automatic compression if needed result = { - 'document': updated_document.serialize_document(working_bucket, f"assessment_{section_id}", logger), - 'section_id': section_id + "document": updated_document.serialize_document( + working_bucket, f"assessment_{section_id}", logger + ), + "section_id": section_id, } - + logger.info("Assessment processing completed") return result diff --git a/patterns/pattern-2/template.yaml b/patterns/pattern-2/template.yaml index d987b160..6a9412c2 100644 --- a/patterns/pattern-2/template.yaml +++ b/patterns/pattern-2/template.yaml @@ -1443,7 +1443,7 @@ Resources: model_id: type: string description: Model ID for the chat companion agents - enum: ["us.amazon.nova-lite-v1:0", "us.amazon.nova-pro-v1:0", "us.amazon.nova-premier-v1:0", "us.anthropic.claude-3-haiku-20240307-v1:0", "us.anthropic.claude-haiku-4-5-20251001-v1:0", "us.anthropic.claude-3-5-sonnet-20241022-v2:0", "us.anthropic.claude-3-7-sonnet-20250219-v1:0", "us.anthropic.claude-sonnet-4-20250514-v1:0", "us.anthropic.claude-sonnet-4-5-20250929-v1:0", "us.anthropic.claude-opus-4-20250514-v1:0", "us.anthropic.claude-opus-4-1-20250805-v1:0", "eu.amazon.nova-lite-v1:0", "eu.amazon.nova-pro-v1:0", "eu.anthropic.claude-3-haiku-20240307-v1:0", "eu.anthropic.claude-haiku-4-5-20251001-v1:0", "eu.anthropic.claude-3-5-sonnet-20241022-v2:0", "eu.anthropic.claude-3-7-sonnet-20250219-v1:0", "eu.anthropic.claude-sonnet-4-20250514-v1:0", "eu.anthropic.claude-sonnet-4-5-20250929-v1:0"] + enum: ["us.amazon.nova-lite-v1:0", "us.amazon.nova-pro-v1:0", "us.amazon.nova-premier-v1:0", "us.anthropic.claude-3-haiku-20240307-v1:0", "us.anthropic.claude-haiku-4-5-20251001-v1:0", "us.anthropic.claude-3-5-sonnet-20241022-v2:0", "us.anthropic.claude-3-7-sonnet-20250219-v1:0", "us.anthropic.claude-sonnet-4-20250514-v1:0", "us.anthropic.claude-sonnet-4-5-20250929-v1:0", "us.anthropic.claude-opus-4-20250514-v1:0", "us.anthropic.claude-opus-4-1-20250805-v1:0", "eu.amazon.nova-lite-v1:0", "eu.amazon.nova-pro-v1:0", "eu.anthropic.claude-3-haiku-20240307-v1:0", "eu.anthropic.claude-haiku-4-5-20251001-v1:0", "eu.anthropic.claude-3-5-sonnet-20241022-v2:0", "eu.anthropic.claude-3-7-sonnet-20250219-v1:0", "eu.anthropic.claude-sonnet-4-20250514-v1:0", "eu.anthropic.claude-sonnet-4-5-20250929-v1:0"] default: "us.anthropic.claude-haiku-4-5-20251001-v1:0" order: 0 error_analyzer: @@ -1775,7 +1775,7 @@ Resources: Command: - "index.handler" Timeout: 900 - MemorySize: 512 + MemorySize: 4096 Tracing: Active Environment: Variables: @@ -1901,7 +1901,7 @@ Resources: Command: - "index.handler" Timeout: 900 - MemorySize: 512 + MemorySize: 4096 Tracing: Active Environment: Variables: diff --git a/patterns/pattern-3/src/assessment_function/index.py b/patterns/pattern-3/src/assessment_function/index.py index 38cbcb4a..432385a1 100644 --- a/patterns/pattern-3/src/assessment_function/index.py +++ b/patterns/pattern-3/src/assessment_function/index.py @@ -15,7 +15,10 @@ logger = logging.getLogger() logger.setLevel(os.environ.get("LOG_LEVEL", "INFO")) -logging.getLogger('idp_common.bedrock.client').setLevel(os.environ.get("BEDROCK_LOG_LEVEL", "INFO")) +logging.getLogger("idp_common.bedrock.client").setLevel( + os.environ.get("BEDROCK_LOG_LEVEL", "INFO") +) + def handler(event, context): """ @@ -24,27 +27,31 @@ def handler(event, context): using the Assessment service from the idp_common library. """ start_time = time.time() # Capture start time for Lambda metering - logger.info(f"Starting assessment processing for event: {json.dumps(event, default=str)}") + logger.info( + f"Starting assessment processing for event: {json.dumps(event, default=str)}" + ) # Load configuration - config = get_config(as_model = True) + config = get_config(as_model=True) logger.info(f"Config: {json.dumps(config.model_dump(), default=str)}") - + # Extract input from event - handle both compressed and uncompressed - document_data = event.get('document', {}) - section_id = event.get('section_id') - + document_data = event.get("document", {}) + section_id = event.get("section_id") + # Validate inputs if not document_data: raise ValueError("No document provided in event") - + if not section_id: raise ValueError("No section_id provided in event") - + # Convert document data to Document object - handle compression - working_bucket = os.environ.get('WORKING_BUCKET') + working_bucket = os.environ.get("WORKING_BUCKET") document = Document.load_document(document_data, working_bucket, logger) - logger.info(f"Processing assessment for document {document.id}, section {section_id}") + logger.info( + f"Processing assessment for document {document.id}, section {section_id}" + ) # Find the section we're processing section = None @@ -52,27 +59,30 @@ def handler(event, context): if s.section_id == section_id: section = s break - + if not section: raise ValueError(f"Section {section_id} not found in document") - # Check if granular assessment is enabled (for Lambda metering context) - granular_config = config.assessment.granular - granular_enabled = granular_config.enabled - assessment_context = "GranularAssessment" if granular_enabled else "Assessment" - logger.info(f"Assessment mode: {'Granular' if granular_enabled else 'Regular'} (context: {assessment_context})") + # Assessment context for Lambda metering + assessment_context = "Assessment" + logger.info(f"Assessment mode: Strands-based (context: {assessment_context})") # Intelligent Assessment Skip: Check if extraction results already contain explainability_info if section.extraction_result_uri and section.extraction_result_uri.strip(): try: from idp_common import s3 - logger.info(f"Checking extraction results for existing assessment: {section.extraction_result_uri}") + + logger.info( + f"Checking extraction results for existing assessment: {section.extraction_result_uri}" + ) extraction_data = s3.get_json_content(section.extraction_result_uri) - + # If explainability_info exists, assessment was already done - if extraction_data.get('explainability_info'): - logger.info(f"Skipping assessment for section {section_id} - extraction results already contain explainability_info") - + if extraction_data.get("explainability_info"): + logger.info( + f"Skipping assessment for section {section_id} - extraction results already contain explainability_info" + ) + # Create section-specific document (same as normal processing) to match output format section_document = Document( id=document.id, @@ -91,43 +101,57 @@ def handler(event, context): evaluation_report_uri=document.evaluation_report_uri, evaluation_results_uri=document.evaluation_results_uri, errors=document.errors, - metering={} # Empty metering for skipped processing + metering={}, # Empty metering for skipped processing ) - + # Add only the pages needed for this section for page_id in section.page_ids: if page_id in document.pages: section_document.pages[page_id] = document.pages[page_id] - + # Add only the section being processed (preserve existing data) section_document.sections = [section] - + # Add Lambda metering for assessment skip execution with dynamic context try: - lambda_metering = calculate_lambda_metering(assessment_context, context, start_time) - section_document.metering = merge_metering_data(section_document.metering, lambda_metering) + lambda_metering = calculate_lambda_metering( + assessment_context, context, start_time + ) + section_document.metering = merge_metering_data( + section_document.metering, lambda_metering + ) except Exception as e: - logger.warning(f"Failed to add Lambda metering for assessment skip: {str(e)}") - + logger.warning( + f"Failed to add Lambda metering for assessment skip: {str(e)}" + ) + # Return consistent format for Map state collation response = { - "section_id": section_id, - "document": section_document.serialize_document(working_bucket, f"assessment_skip_{section_id}", logger) + "section_id": section_id, + "document": section_document.serialize_document( + working_bucket, f"assessment_skip_{section_id}", logger + ), } - - logger.info(f"Assessment skipped - Response: {json.dumps(response, default=str)}") + + logger.info( + f"Assessment skipped - Response: {json.dumps(response, default=str)}" + ) return response else: - logger.info(f"Assessment needed for section {section_id} - no explainability_info found in extraction results") + logger.info( + f"Assessment needed for section {section_id} - no explainability_info found in extraction results" + ) except Exception as e: - logger.warning(f"Error checking extraction results for assessment skip: {e}") + logger.warning( + f"Error checking extraction results for assessment skip: {e}" + ) # Continue with normal assessment if check fails # Normal assessment processing document.status = Status.ASSESSING # Update document status to ASSESSING for UI only - # Create new 'shell' document since our input document has only 1 section. + # Create new 'shell' document since our input document has only 1 section. docStatus = Document( id=document.id, input_key=document.input_key, @@ -137,34 +161,45 @@ def handler(event, context): logger.info(f"Updating document status to {docStatus.status}") document_service.update_document(docStatus) - # Initialize assessment service - assessment_service = assessment.AssessmentService(config=config) + # Initialize granular assessment service + from idp_common.assessment.granular_service import GranularAssessmentService + + cache_table = os.environ.get("TRACKING_TABLE") + assessment_service = GranularAssessmentService( + config=config, cache_table=cache_table + ) # Process the document section for assessment t0 = time.time() logger.info(f"Starting assessment for section {section_id}") updated_document = assessment_service.process_document_section(document, section_id) t1 = time.time() - logger.info(f"Total extraction time: {t1-t0:.2f} seconds") + logger.info(f"Total assessment time: {t1 - t0:.2f} seconds") # Check if document processing failed if updated_document.status == Status.FAILED: error_message = f"Assessment failed for document {updated_document.id}, section {section_id}" logger.error(error_message) raise Exception(error_message) - + # Add Lambda metering for successful assessment execution with dynamic context try: - lambda_metering = calculate_lambda_metering(assessment_context, context, start_time) - updated_document.metering = merge_metering_data(updated_document.metering, lambda_metering) + lambda_metering = calculate_lambda_metering( + assessment_context, context, start_time + ) + updated_document.metering = merge_metering_data( + updated_document.metering, lambda_metering + ) except Exception as e: logger.warning(f"Failed to add Lambda metering for assessment: {str(e)}") - + # Prepare output with automatic compression if needed result = { - 'document': updated_document.serialize_document(working_bucket, f"assessment_{section_id}", logger), - 'section_id': section_id + "document": updated_document.serialize_document( + working_bucket, f"assessment_{section_id}", logger + ), + "section_id": section_id, } - + logger.info("Assessment processing completed") return result diff --git a/scripts/test_grid_overlay.py b/scripts/test_grid_overlay.py new file mode 100644 index 00000000..3b5ab726 --- /dev/null +++ b/scripts/test_grid_overlay.py @@ -0,0 +1,325 @@ +#!/usr/bin/env python3 +""" +Test script for grid overlay functionality. + +This script demonstrates how to: +1. Add ruler edges to a document image +2. Draw bounding boxes with normalized coordinates +3. Combine both features for LLM-assisted spatial localization + +Usage: + python scripts/test_grid_overlay.py [image_path] [output_dir] + +Examples: + # Use sample California license + python scripts/test_grid_overlay.py samples/old_cal_license.png + + # Use a PDF (first page will be converted) + python scripts/test_grid_overlay.py samples/lending_package.pdf + + # Specify output directory + python scripts/test_grid_overlay.py samples/old_cal_license.png /tmp/grid_test +""" + +import sys +import os +from pathlib import Path + +# Add the library to path +sys.path.insert(0, str(Path(__file__).parent.parent / "lib" / "idp_common_pkg")) + +from idp_common.utils.grid_overlay import ( + add_ruler_edges, + draw_bounding_boxes, + add_ruler_and_draw_boxes, +) + + +def convert_pdf_to_image(pdf_path: str) -> bytes: + """Convert first page of PDF to image bytes.""" + try: + import fitz # PyMuPDF + + doc = fitz.open(pdf_path) + page = doc.load_page(0) # First page + + # Render at 150 DPI for good quality + pix = page.get_pixmap(dpi=150) # pyright: ignore[reportAttributeAccessIssue] + return pix.tobytes("jpeg") + except ImportError: + print("ERROR: PyMuPDF (fitz) is required for PDF conversion.") + print("Install it with: pip install PyMuPDF") + sys.exit(1) + + +def load_image(image_path: str) -> bytes: + """Load image from file path.""" + ext = Path(image_path).suffix.lower() + + if ext == ".pdf": + print(f"Converting PDF first page to image...") + return convert_pdf_to_image(image_path) + else: + with open(image_path, "rb") as f: + return f.read() + + +def main(): + # Default paths + default_image = "samples/old_cal_license.png" + default_output_dir = "output/grid_overlay_test" + + # Parse arguments + if len(sys.argv) > 1: + image_path = sys.argv[1] + else: + image_path = default_image + + if len(sys.argv) > 2: + output_dir = sys.argv[2] + else: + output_dir = default_output_dir + + # Resolve paths + script_dir = Path(__file__).parent + project_root = script_dir.parent + + if not os.path.isabs(image_path): + image_path = str(project_root / image_path) + + if not os.path.isabs(output_dir): + output_dir = str(project_root / output_dir) + + # Check if input exists + if not os.path.exists(image_path): + print(f"ERROR: Image not found: {image_path}") + print(f"Available samples in {project_root / 'samples'}:") + for f in (project_root / "samples").iterdir(): + if f.is_file(): + print(f" - {f.name}") + sys.exit(1) + + # Create output directory + os.makedirs(output_dir, exist_ok=True) + + print(f"Input image: {image_path}") + print(f"Output directory: {output_dir}") + print() + + # Load the image + print("Loading image...") + image_data = load_image(image_path) + print(f"Image size: {len(image_data)} bytes") + print() + + # ======================================== + # Test 1: Add ruler edges only + # ======================================== + print("Test 1: Adding ruler edges...") + ruler_image = add_ruler_edges( + image_data, + ruler_width=30, + tick_interval=50, + label_interval=100, + ) + + output_path_1 = os.path.join(output_dir, "01_ruler_edges.jpg") + with open(output_path_1, "wb") as f: + f.write(ruler_image) + print(f" Saved: {output_path_1}") + print() + + # ======================================== + # Test 2: Draw bounding boxes on original + # ======================================== + print("Test 2: Drawing bounding boxes on original image...") + + # Example bounding boxes (adjust these based on your image) + # These are in normalized 0-1000 scale + sample_bboxes = [ + { + "bbox": [50, 100, 400, 180], + "label": "Header Area", + "color": "red", + }, + { + "bbox": [100, 300, 600, 380], + "label": "Name Field", + "color": "green", + }, + { + "bbox": [100, 450, 500, 530], + "label": "Address", + "color": "blue", + }, + { + "bbox": [600, 500, 900, 600], + "label": "Photo Area", + "color": "orange", + }, + ] + + bbox_image = draw_bounding_boxes( + image_data, + sample_bboxes, + has_ruler=False, + box_color="red", + box_width=3, + ) + + output_path_2 = os.path.join(output_dir, "02_bounding_boxes.jpg") + with open(output_path_2, "wb") as f: + f.write(bbox_image) + print(f" Saved: {output_path_2}") + print(f" Bounding boxes drawn:") + for bbox in sample_bboxes: + print(f" - {bbox['label']}: {bbox['bbox']}") + print() + + # ======================================== + # Test 3: Ruler + Bounding boxes combined + # ======================================== + print("Test 3: Combining ruler edges and bounding boxes...") + + combined_image = add_ruler_and_draw_boxes( + image_data, + sample_bboxes, + ruler_width=30, + tick_interval=50, + label_interval=100, + box_color="red", + box_width=3, + ) + + output_path_3 = os.path.join(output_dir, "03_ruler_with_boxes.jpg") + with open(output_path_3, "wb") as f: + f.write(combined_image) + print(f" Saved: {output_path_3}") + print() + + # ======================================== + # Test 4: Fine-grained grid (25 unit ticks) + # ======================================== + print("Test 4: Fine-grained ruler (25 unit minor ticks)...") + fine_ruler = add_ruler_edges( + image_data, + ruler_width=35, + tick_interval=25, # Finer ticks + label_interval=100, + font_size=9, + ) + + output_path_4 = os.path.join(output_dir, "04_fine_ruler.jpg") + with open(output_path_4, "wb") as f: + f.write(fine_ruler) + print(f" Saved: {output_path_4}") + print() + + # ======================================== + # Test 5: Different box colors + # ======================================== + print("Test 5: Multi-colored bounding boxes...") + + multi_color_bboxes = [ + {"bbox": [50, 50, 200, 150], "label": "Red", "color": "red"}, + {"bbox": [250, 50, 400, 150], "label": "Green", "color": "green"}, + {"bbox": [450, 50, 600, 150], "label": "Blue", "color": "blue"}, + {"bbox": [50, 200, 200, 300], "label": "Yellow", "color": "yellow"}, + {"bbox": [250, 200, 400, 300], "label": "Orange", "color": "orange"}, + {"bbox": [450, 200, 600, 300], "label": "Purple", "color": "purple"}, + ] + + multi_color_image = draw_bounding_boxes( + image_data, + multi_color_bboxes, + has_ruler=False, + ) + + output_path_5 = os.path.join(output_dir, "05_multi_color_boxes.jpg") + with open(output_path_5, "wb") as f: + f.write(multi_color_image) + print(f" Saved: {output_path_5}") + print() + + # ======================================== + # Summary + # ======================================== + print("=" * 60) + print("Grid Overlay Test Complete!") + print("=" * 60) + print() + print("Generated files:") + for i, path in enumerate( + [output_path_1, output_path_2, output_path_3, output_path_4, output_path_5], 1 + ): + print(f" {i}. {path}") + print() + print("Next steps:") + print(" 1. Open the generated images to see the grid overlays") + print(" 2. Note how the ruler edges provide coordinate references") + print(" 3. Observe how bounding boxes are labeled with their coordinates") + print() + print("To use in assessment:") + print(" - Add ruler edges to document images before sending to LLM") + print(" - Update prompt to instruct LLM to read coordinates from ruler") + print(" - LLM can now provide precise [x1, y1, x2, y2] coordinates") + print() + + # Interactive demo: Let user test their own coordinates + print("=" * 60) + print("Interactive Bounding Box Test") + print("=" * 60) + print("You can test drawing custom bounding boxes.") + print("Enter coordinates in format: x1,y1,x2,y2 (0-1000 scale)") + print("Example: 100,200,400,250") + print("Type 'quit' to exit") + print() + + custom_bboxes = [] + while True: + try: + user_input = input("Enter bbox coordinates (or 'quit'): ").strip() + if user_input.lower() == "quit": + break + + coords = [int(x.strip()) for x in user_input.split(",")] + if len(coords) != 4: + print(" Invalid format. Use: x1,y1,x2,y2") + continue + + label = input(" Label for this box (press Enter for default): ").strip() + if not label: + label = f"Box {len(custom_bboxes) + 1}" + + custom_bboxes.append( + { + "bbox": coords, + "label": label, + "color": ["red", "green", "blue", "orange", "purple"][ + len(custom_bboxes) % 5 + ], + } + ) + + print(f" Added: {label} at {coords}") + + except KeyboardInterrupt: + print("\nExiting...") + break + except Exception as e: + print(f" Error: {e}") + + if custom_bboxes: + print(f"\nDrawing {len(custom_bboxes)} custom bounding boxes...") + custom_image = add_ruler_and_draw_boxes(image_data, custom_bboxes) + + output_path_custom = os.path.join(output_dir, "06_custom_boxes.jpg") + with open(output_path_custom, "wb") as f: + f.write(custom_image) + print(f"Saved: {output_path_custom}") + + print("\nDone!") + + +if __name__ == "__main__": + main() diff --git a/src/ui/src/components/document-viewer/VisualEditorModal.jsx b/src/ui/src/components/document-viewer/VisualEditorModal.jsx index 879a78aa..5e8f4265 100644 --- a/src/ui/src/components/document-viewer/VisualEditorModal.jsx +++ b/src/ui/src/components/document-viewer/VisualEditorModal.jsx @@ -24,136 +24,136 @@ import { getFieldConfidenceInfo } from '../common/confidence-alerts-utils'; const logger = new ConsoleLogger('VisualEditorModal'); -// Memoized component to render a bounding box on an image -const BoundingBox = memo(({ box, page, currentPage, imageRef, zoomLevel = 1, panOffset = { x: 0, y: 0 } }) => { - const [dimensions, setDimensions] = useState({ width: 0, height: 0 }); +// Component that renders document image with bounding box overlay +// The bounding box is drawn on a canvas that sits directly on top of the image +// Both are wrapped in a container that handles transforms together +const DocumentImageWithOverlay = memo( + React.forwardRef( + ({ src, alt, geometry, maxHeight = 'min(70vh, 700px)', zoomLevel = 1, panOffset = { x: 0, y: 0 }, onError }, ref) => { + const containerRef = useRef(null); + const canvasRef = useRef(null); + const internalImageRef = useRef(null); + const [imageDimensions, setImageDimensions] = useState({ width: 0, height: 0 }); + + // Expose the image ref to parent component + React.useImperativeHandle(ref, () => internalImageRef.current); + + // Draw bounding box on canvas whenever geometry or dimensions change + useEffect(() => { + const canvas = canvasRef.current; + if (!canvas || !imageDimensions.width || !imageDimensions.height) { + logger.debug('DocumentImageWithOverlay - Canvas draw skipped:', { + hasCanvas: !!canvas, + imageDimensions, + geometry, + }); + return; + } - useEffect(() => { - if (imageRef.current && page === currentPage) { - const updateDimensions = () => { - const img = imageRef.current; - const rect = img.getBoundingClientRect(); - const containerRect = img.parentElement.getBoundingClientRect(); - - // Get the actual displayed dimensions and position after all transforms - const transformedWidth = rect.width; - const transformedHeight = rect.height; - const transformedOffsetX = rect.left - containerRect.left; - const transformedOffsetY = rect.top - containerRect.top; - - setDimensions({ - transformedWidth, - transformedHeight, - transformedOffsetX, - transformedOffsetY, - }); + // Set canvas internal resolution to match displayed size + // This is critical - canvas.width/height set the drawing resolution + canvas.width = imageDimensions.width; + canvas.height = imageDimensions.height; - logger.debug('VisualEditorModal - BoundingBox dimensions updated:', { - imageWidth: img.width, - imageHeight: img.height, - naturalWidth: img.naturalWidth, - naturalHeight: img.naturalHeight, - offsetX: rect.left - containerRect.left, - offsetY: rect.top - containerRect.top, - imageRect: rect, - containerRect, - }); - }; + const ctx = canvas.getContext('2d'); + // Clear previous drawings + ctx.clearRect(0, 0, canvas.width, canvas.height); - // Update dimensions when image loads - if (imageRef.current.complete && imageRef.current.naturalWidth > 0) { - updateDimensions(); - } else { - imageRef.current.addEventListener('load', updateDimensions); - } - - return () => { - if (imageRef.current) { - imageRef.current.removeEventListener('load', updateDimensions); + if (!geometry?.boundingBox) { + logger.debug('DocumentImageWithOverlay - No geometry to draw:', { geometry }); + return; } - }; - } - return undefined; - }, [imageRef, page, currentPage]); - // Update dimensions when zoom or pan changes - useEffect(() => { - if (imageRef.current && page === currentPage) { - const updateDimensions = () => { - const img = imageRef.current; - const rect = img.getBoundingClientRect(); - const containerRect = img.parentElement.getBoundingClientRect(); - - // Get the actual displayed dimensions and position after all transforms - const transformedWidth = rect.width; - const transformedHeight = rect.height; - const transformedOffsetX = rect.left - containerRect.left; - const transformedOffsetY = rect.top - containerRect.top; - - setDimensions({ - transformedWidth, - transformedHeight, - transformedOffsetX, - transformedOffsetY, + const bbox = geometry.boundingBox; + const padding = 3; + + // Calculate pixel coordinates from normalized values (0-1) + // Coordinates are in document space (0-1), directly mapped to image dimensions + const x = bbox.left * imageDimensions.width - padding; + const y = bbox.top * imageDimensions.height - padding; + const width = bbox.width * imageDimensions.width + padding * 2; + const height = bbox.height * imageDimensions.height + padding * 2; + + // Draw semi-transparent fill + ctx.fillStyle = 'rgba(255, 0, 0, 0.2)'; + ctx.fillRect(x, y, width, height); + + // Draw border + ctx.strokeStyle = 'red'; + ctx.lineWidth = 3; + ctx.strokeRect(x, y, width, height); + + logger.debug('DocumentImageWithOverlay - Drew bounding box:', { + bbox, + pixelCoords: { x, y, width, height }, + canvasDimensions: { width: canvas.width, height: canvas.height }, + imageDimensions, }); - }; - // Delay to allow transforms to complete - const timeoutId = setTimeout(updateDimensions, 150); - // Ensure accuracy after reset - const secondTimeoutId = setTimeout(updateDimensions, 300); - return () => { - clearTimeout(timeoutId); - clearTimeout(secondTimeoutId); - }; - } - return undefined; - }, [zoomLevel, panOffset, imageRef, page, currentPage]); - - if (page !== currentPage || !box || !dimensions.transformedWidth) { - return null; - } - - // Calculate position based on image dimensions with proper zoom and pan handling - if (!box.boundingBox) { - return null; - } - - const padding = 5; - const bbox = box.boundingBox; - - // Calculate position and size directly on the transformed image - const finalLeft = bbox.left * dimensions.transformedWidth + dimensions.transformedOffsetX - padding; - const finalTop = bbox.top * dimensions.transformedHeight + dimensions.transformedOffsetY - padding; - const finalWidth = bbox.width * dimensions.transformedWidth + padding * 2; - const finalHeight = bbox.height * dimensions.transformedHeight + padding * 2; - - // Position the bounding box directly without additional transforms - const style = { - position: 'absolute', - left: `${finalLeft}px`, - top: `${finalTop}px`, - width: `${finalWidth}px`, - height: `${finalHeight}px`, - border: '2px solid red', - pointerEvents: 'none', - zIndex: 10, - transition: 'all 0.1s ease-out', - }; + }, [geometry, imageDimensions]); - logger.debug('VisualEditorModal - BoundingBox style calculated:', { - bbox, - dimensions, - finalLeft, - finalTop, - finalWidth, - finalHeight, - style, - }); + const handleImageLoad = (e) => { + const img = e.target; + // Get the displayed dimensions of the image + const displayedWidth = img.offsetWidth; + const displayedHeight = img.offsetHeight; - return
; -}); + // Get bounding rect to check for any offset + setImageDimensions({ width: displayedWidth, height: displayedHeight }); + }; -BoundingBox.displayName = 'BoundingBox'; + return ( +
+ {alt} + 0 ? `${imageDimensions.width}px` : 'auto', + height: imageDimensions.height > 0 ? `${imageDimensions.height}px` : 'auto', + }} + /> +
+ ); + } + ) +); + +DocumentImageWithOverlay.displayName = 'DocumentImageWithOverlay'; // Memoized component to render a form field based on its type const FormFieldRenderer = memo( @@ -965,39 +965,43 @@ const VisualEditorModal = ({ visible, onDismiss, jsonData, onChange, isReadOnly, } }; + // Determine the geometry to display based on current page + const currentPageIndex = pageIds.indexOf(currentPage) + 1; // 1-based page number + const geometryForCurrentPage = + activeFieldGeometry && activeFieldGeometry.page === currentPageIndex ? activeFieldGeometry : null; + // Create carousel items from page images - const carouselItems = pageIds.map((pageId) => ({ - id: pageId, - content: ( -
1 ? 'grab' : 'default', - }} - onWheel={handleWheel} - > - {pageImages[pageId] ? ( - <> - { + const isCurrentPage = pageId === currentPage; + const pageIndex = pageIds.indexOf(pageId) + 1; + const geometryForPage = activeFieldGeometry && activeFieldGeometry.page === pageIndex ? activeFieldGeometry : null; + + return { + id: pageId, + content: ( +
1 ? 'grab' : 'default', + }} + onWheel={handleWheel} + > + {pageImages[pageId] ? ( + { logger.error(`Error loading image for page ${pageId}:`, e); // Fallback image for error state @@ -1009,26 +1013,16 @@ const VisualEditorModal = ({ visible, onDismiss, jsonData, onChange, isReadOnly, e.target.src = fallbackImage; }} /> - {activeFieldGeometry && ( - - )} - - ) : ( - - -
Loading image...
- - )} -
- ), - })); + ) : ( + + +
Loading image...
+ + )} +
+ ), + }; + }); return (