From 491d8f06f42fd961c97d7962ad78e9921aa4bfad Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Wed, 19 Nov 2025 17:01:03 +0200 Subject: [PATCH 01/30] invoke extraction with long retries --- lib/idp_common_pkg/idp_common/extraction/agentic_idp.py | 3 +++ 1 file changed, 3 insertions(+) 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 f34ffc15..c563e03e 100644 --- a/lib/idp_common_pkg/idp_common/extraction/agentic_idp.py +++ b/lib/idp_common_pkg/idp_common/extraction/agentic_idp.py @@ -46,6 +46,9 @@ update_todo, view_todo_list, ) +from lib.idp_common_pkg.idp_common.utils.bedrock_utils import ( + async_exponential_backoff_retry, +) # Supported image formats for Bedrock API SUPPORTED_IMAGE_FORMATS = {"jpeg", "png", "gif", "webp"} From 3d38d1d9fa6fcca60dab7721aea9dd6c4b7d3fc5 Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Thu, 20 Nov 2025 11:31:34 +0200 Subject: [PATCH 02/30] add review agent model config --- lib/idp_common_pkg/idp_common/config/models.py | 9 +++++++++ lib/idp_common_pkg/idp_common/extraction/agentic_idp.py | 3 --- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/lib/idp_common_pkg/idp_common/config/models.py b/lib/idp_common_pkg/idp_common/config/models.py index 0c80fc62..209b880e 100644 --- a/lib/idp_common_pkg/idp_common/config/models.py +++ b/lib/idp_common_pkg/idp_common/config/models.py @@ -132,6 +132,15 @@ def parse_int(cls, v: Any) -> int: if isinstance(v, str): return int(v) if v else 0 return int(v) + + @model_validator(mode="after") + def model_validator(self) -> Self: + + if not self.agentic.review_agent_model: + self.agentic.review_agent_model = self.model + + return self + @model_validator(mode="after") def set_default_review_agent_model(self) -> Self: 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 c563e03e..f34ffc15 100644 --- a/lib/idp_common_pkg/idp_common/extraction/agentic_idp.py +++ b/lib/idp_common_pkg/idp_common/extraction/agentic_idp.py @@ -46,9 +46,6 @@ update_todo, view_todo_list, ) -from lib.idp_common_pkg.idp_common.utils.bedrock_utils import ( - async_exponential_backoff_retry, -) # Supported image formats for Bedrock API SUPPORTED_IMAGE_FORMATS = {"jpeg", "png", "gif", "webp"} From be4d16004de9d29893e0b9ae4f11bb1356e95dcf Mon Sep 17 00:00:00 2001 From: "Kazmer, Nagy-Betegh" Date: Wed, 26 Nov 2025 10:28:25 +0000 Subject: [PATCH 03/30] fixes --- lib/idp_common_pkg/idp_common/config/models.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/lib/idp_common_pkg/idp_common/config/models.py b/lib/idp_common_pkg/idp_common/config/models.py index 209b880e..0c657763 100644 --- a/lib/idp_common_pkg/idp_common/config/models.py +++ b/lib/idp_common_pkg/idp_common/config/models.py @@ -132,10 +132,10 @@ def parse_int(cls, v: Any) -> int: if isinstance(v, str): return int(v) if v else 0 return int(v) - - @model_validator(mode="after") - def model_validator(self) -> 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 From cbedb14a808698451b8056a245d4369ddfb275bc Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Thu, 20 Nov 2025 11:31:34 +0200 Subject: [PATCH 04/30] add review agent model config --- lib/idp_common_pkg/idp_common/config/models.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/lib/idp_common_pkg/idp_common/config/models.py b/lib/idp_common_pkg/idp_common/config/models.py index 0c657763..0d5f4bf8 100644 --- a/lib/idp_common_pkg/idp_common/config/models.py +++ b/lib/idp_common_pkg/idp_common/config/models.py @@ -133,6 +133,13 @@ def parse_int(cls, v: Any) -> int: return int(v) if v else 0 return int(v) + @model_validator(mode="after") + def model_validator(self) -> Self: + if not self.agentic.review_agent_model: + self.agentic.review_agent_model = self.model + + return self + @model_validator(mode="after") def set_default_review_agent_model(self) -> Self: """Set review_agent_model to extraction model if not specified.""" From 049c4fef2799ac4b3adce652fa5b2af345f518c0 Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Tue, 18 Nov 2025 16:16:11 +0200 Subject: [PATCH 05/30] new assesment --- .../bank-statement-sample/config.yaml | 22 +- .../lending-package-sample/config.yaml | 24 +- .../config.yaml | 25 +- .../rvl-cdip-package-sample/config.yaml | 25 +- .../rvl-cdip-package-sample/config.yaml | 3 + idp_cli/pyproject.toml | 36 +- .../idp_common/assessment/granular_service.py | 1162 +++--------- .../idp_common/assessment/strands_executor.py | 244 +++ .../idp_common/assessment/strands_models.py | 76 + .../idp_common/assessment/strands_service.py | 391 ++++ .../idp_common/assessment/strands_tools.py | 161 ++ .../idp_common/config/models.py | 26 +- .../idp_common/extraction/models.py | 77 + .../idp_common/image/__init__.py | 154 +- .../idp_common/utils/grid_overlay.py | 387 ++++ lib/idp_common_pkg/pyproject.toml | 24 +- lib/idp_common_pkg/tests/conftest.py | 7 +- .../tests/unit/assessment/conftest.py | 24 + .../unit/config/test_configuration_sync.py | 11 +- .../tests/unit/test_granular_assessment.py | 415 ++--- lib/idp_common_pkg/uv.lock | 1576 +++-------------- .../src/assessment_function/index.py | 256 ++- .../src/assessment_function/index.py | 125 +- scripts/test_grid_overlay.py | 325 ++++ 24 files changed, 2743 insertions(+), 2833 deletions(-) create mode 100644 lib/idp_common_pkg/idp_common/assessment/strands_executor.py create mode 100644 lib/idp_common_pkg/idp_common/assessment/strands_models.py create mode 100644 lib/idp_common_pkg/idp_common/assessment/strands_service.py create mode 100644 lib/idp_common_pkg/idp_common/assessment/strands_tools.py create mode 100644 lib/idp_common_pkg/idp_common/utils/grid_overlay.py create mode 100644 lib/idp_common_pkg/tests/unit/assessment/conftest.py create mode 100644 scripts/test_grid_overlay.py diff --git a/config_library/pattern-2/bank-statement-sample/config.yaml b/config_library/pattern-2/bank-statement-sample/config.yaml index 3a8a2086..d345dca4 100644 --- a/config_library/pattern-2/bank-statement-sample/config.yaml +++ b/config_library/pattern-2/bank-statement-sample/config.yaml @@ -405,11 +405,7 @@ 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: "0.0" max_tokens: "10000" @@ -462,12 +458,15 @@ assessment: - page: Page number where the field appears (starting from 1) Coordinate system: + - The document images have ruler markings along all edges showing the 0-1000 coordinate scale + - Use these ruler markings to determine precise coordinates for bounding boxes - 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 + - Reference the ruler markings on the image edges to provide accurate coordinates @@ -529,6 +528,9 @@ assessment: } IMPORTANT: + - The contains raw JSON schema for the attributes you should assess + - The contains the FULL extraction data (you have complete context for cross-referencing) + - The specifies which attributes to focus on - 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 @@ -549,9 +551,13 @@ assessment: <> - - {ATTRIBUTE_NAMES_AND_DESCRIPTIONS} - + + {TASK_SCHEMA} + + + + {TASK_INSTRUCTION} + {EXTRACTION_RESULTS} diff --git a/config_library/pattern-2/lending-package-sample/config.yaml b/config_library/pattern-2/lending-package-sample/config.yaml index 463f8814..92928e1b 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: "" @@ -1253,6 +1254,7 @@ extraction: agentic: enabled: false review_agent: false + max_workers: "20" image: target_width: "" target_height: "" @@ -1426,14 +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: "0.0" max_tokens: "10000" @@ -1486,12 +1484,15 @@ assessment: - page: Page number where the field appears (starting from 1) Coordinate system: + - The document images have ruler markings along all edges showing the 0-1000 coordinate scale + - Use these ruler markings to determine precise coordinates for bounding boxes - 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 + - Reference the ruler markings on the image edges to provide accurate coordinates @@ -1553,6 +1554,9 @@ assessment: } IMPORTANT: + - The contains raw JSON schema for the attributes you should assess + - The contains the FULL extraction data (you have complete context for cross-referencing) + - The specifies which attributes to focus on - 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 @@ -1573,9 +1577,13 @@ assessment: <> - - {ATTRIBUTE_NAMES_AND_DESCRIPTIONS} - + + {TASK_SCHEMA} + + + + {TASK_INSTRUCTION} + {EXTRACTION_RESULTS} 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 c725f83b..5009d75a 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: "" @@ -868,6 +870,7 @@ classification: 5. Outputting in the exact JSON format specified in extraction: + max_workers: "20" image: target_height: "" target_width: "" @@ -973,14 +976,10 @@ 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: "0.0" max_tokens: "10000" @@ -1033,12 +1032,15 @@ assessment: - page: Page number where the field appears (starting from 1) Coordinate system: + - The document images have ruler markings along all edges showing the 0-1000 coordinate scale + - Use these ruler markings to determine precise coordinates for bounding boxes - 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 + - Reference the ruler markings on the image edges to provide accurate coordinates @@ -1100,6 +1102,9 @@ assessment: } IMPORTANT: + - The contains raw JSON schema for the attributes you should assess + - The contains the FULL extraction data (you have complete context for cross-referencing) + - The specifies which attributes to focus on - 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 @@ -1120,9 +1125,13 @@ assessment: <> - - {ATTRIBUTE_NAMES_AND_DESCRIPTIONS} - + + {TASK_SCHEMA} + + + + {TASK_INSTRUCTION} + {EXTRACTION_RESULTS} 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 11b38ab0..f7720b03 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: "" @@ -907,6 +909,7 @@ classification: classificationMethod: textbasedHolisticClassification sectionSplitting: llm_determined extraction: + max_workers: "20" image: target_width: "" target_height: "" @@ -1068,14 +1071,10 @@ 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: "0.0" max_tokens: "10000" @@ -1128,12 +1127,15 @@ assessment: - page: Page number where the field appears (starting from 1) Coordinate system: + - The document images have ruler markings along all edges showing the 0-1000 coordinate scale + - Use these ruler markings to determine precise coordinates for bounding boxes - 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 + - Reference the ruler markings on the image edges to provide accurate coordinates @@ -1195,6 +1197,9 @@ assessment: } IMPORTANT: + - The contains raw JSON schema for the attributes you should assess + - The contains the FULL extraction data (you have complete context for cross-referencing) + - The specifies which attributes to focus on - 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 @@ -1215,9 +1220,13 @@ assessment: <> - - {ATTRIBUTE_NAMES_AND_DESCRIPTIONS} - + + {TASK_SCHEMA} + + + + {TASK_INSTRUCTION} + {EXTRACTION_RESULTS} 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 a6306e94..2ab91dba 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: "" @@ -926,6 +928,7 @@ 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: "" 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/granular_service.py b/lib/idp_common_pkg/idp_common/assessment/granular_service.py index bcf5440c..ccddbe67 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 +2,26 @@ # 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, Dict, List, Optional, Tuple, Union -from idp_common import bedrock, image, metrics, s3, utils +from idp_common import image, metrics, s3, utils +from idp_common.assessment.strands_executor import execute_assessment_tasks_parallel from idp_common.config.models import IDPConfig from idp_common.config.schema_constants import ( - SCHEMA_DESCRIPTION, SCHEMA_ITEMS, SCHEMA_PROPERTIES, SCHEMA_TYPE, @@ -30,24 +29,35 @@ TYPE_OBJECT, X_AWS_IDP_CONFIDENCE_THRESHOLD, X_AWS_IDP_DOCUMENT_TYPE, - X_AWS_IDP_LIST_ITEM_DESCRIPTION, ) from idp_common.models import Document, Status -from idp_common.utils import check_token_limit, extract_json_from_text +from idp_common.utils import check_token_limit, grid_overlay logger = logging.getLogger(__name__) @dataclass class AssessmentTask: - """Represents a single assessment task to be processed.""" + """Single-field assessment task for Strands executor.""" 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 + task_type: str # Always "attribute" - single field assessment + + # Path to field as tuple: ("address", "street") or ("items", 0, "price") + field_path: Tuple[Union[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 + + # Direct reference to parent container in assessment structure (for O(1) insertion) + # Can be Dict for regular fields or List for array items + parent_assessment_dict: Union[Dict[str, Any], List[Any]] @dataclass @@ -102,6 +112,42 @@ def _safe_float_conversion(value: Any, default: float = 0.0) -> float: return default +def _get_value_at_path(data: Dict[str, Any], path: Tuple[Union[str, int], ...]) -> Any: + """ + Navigate nested data structure using tuple path. + + Args: + data: Nested dictionary/list structure + path: Tuple of keys/indices like ("address", "street") or ("items", 0, "price") + + Returns: + Value at the specified path, or None if path doesn't exist + + Examples: + >>> data = {"address": {"street": "123 Main St"}} + >>> _get_value_at_path(data, ("address", "street")) + "123 Main St" + + >>> data = {"items": [{"price": 10.99}, {"price": 20.99}]} + >>> _get_value_at_path(data, ("items", 0, "price")) + 10.99 + """ + current = data + for key in path: + if current is None: + return None + if isinstance(current, dict): + current = current.get(key) + elif isinstance(current, list): + if isinstance(key, int) and 0 <= key < len(current): + current = current[key] + else: + return None + else: + return None + return current + + class GranularAssessmentService: """Enhanced assessment service with granular, cached, and parallel processing.""" @@ -130,18 +176,13 @@ 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 @@ -172,9 +213,7 @@ 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'}" ) @@ -196,81 +235,6 @@ 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 ) -> float: @@ -315,294 +279,19 @@ 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 - no limit with latest Bedrock API - logger.info( - f"Attaching {len(page_images)} images to granular assessment prompt" - ) - for img in page_images: - 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], 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 @@ -610,349 +299,119 @@ 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[Union[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, + ) + + task = AssessmentTask( + task_id=f"task_{task_counter[0]}", + task_type="attribute", + field_path=item_path, + field_name=prop_name, + field_schema=items_schema, + confidence_threshold=threshold, + parent_assessment_dict=assessment_list, # type: ignore + ) + tasks.append(task) + task_counter[0] += 1 - # 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)) + # Pre-allocate slot in list (will be replaced by assessment obj) + assessment_list.append(None) - for j in range(i, batch_end): - item_data = list_data[j] + 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=prop_name, + field_schema=prop_schema, + confidence_threshold=threshold, + parent_assessment_dict=parent_dict, ) 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", {}) - - # 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", - } - - # 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 - - # Check for confidence threshold alerts - confidence_alerts = [] - self._check_confidence_alerts_for_task( - task, assessment_data, confidence_alerts - ) + task_counter[0] += 1 - 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, - ) - - 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, - } - ) + # Pre-allocate placeholder in dict (will be replaced by assessment obj) + parent_dict[prop_name] = None - 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, - } - ) + # Start recursive traversal from root + _traverse(properties, extraction_results, (), assessment_structure) - 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 + logger.info(f"Created {len(tasks)} assessment tasks (one per leaf field)") - 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 @@ -1132,8 +591,8 @@ 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: # type: ignore[attr-defined] - error_code = exception.response["Error"]["Code"] # type: ignore[attr-defined] + 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 @@ -1149,34 +608,33 @@ def _aggregate_assessment_results( self, tasks: List[AssessmentTask], results: List[AssessmentResult], - extraction_results: Dict[str, Any], + 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 direct parent insertion. Args: tasks: List of assessment tasks results: List of assessment results - extraction_results: Original extraction 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 = {} # Create a mapping from task_id to result result_map = {result.task_id: result for result in results} - # Process results by task type + # Process each task result - direct O(1) insertion using parent reference 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 @@ -1185,85 +643,48 @@ 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)}" - ) + # Get assessment data from result - should be a single assessment object + # The Strands agent returns the assessment in result.assessment_data + assessment_obj = result.assessment_data - 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)}" - ) - - 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(assessment_obj, dict): + logger.warning( + f"Task {task.task_id}: expected dict assessment, got {type(assessment_obj)}" ) + 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 + # Add confidence_threshold to the assessment object + assessment_obj["confidence_threshold"] = task.confidence_threshold + + # Direct insertion using parent reference - O(1) operation! + parent = task.parent_assessment_dict + field_name = task.field_name + + if isinstance(parent, dict): + # Regular field - insert into parent dict + parent[field_name] = assessment_obj + elif isinstance(parent, list): + # Array item - get index from field_path + # field_path is like ("items", 0, "price") - second-to-last is the index + if len(task.field_path) >= 2 and isinstance(task.field_path[-2], int): + idx = task.field_path[-2] + # Replace the None placeholder we created during structure building + if idx < len(parent): + parent[idx] = assessment_obj else: logger.warning( - f"Unexpected list item assessment data type for {attr_name}[{item_index}].{item_attr_name}: {type(item_assessment_data)}" + f"Task {task.task_id}: index {idx} out of range for list of length {len(parent)}" ) - item_assessment[item_attr_name] = item_assessment_data - - enhanced_assessment_data[attr_name][item_index] = item_assessment + else: + logger.warning( + f"Task {task.task_id}: cannot determine array index from path {task.field_path}" + ) + else: + logger.warning( + f"Task {task.task_id}: unexpected parent type {type(parent)}" + ) - 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: """ @@ -1579,8 +1000,6 @@ def process_document_section(self, document: Document, section_id: str) -> Docum # 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 @@ -1597,17 +1016,8 @@ 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 ) @@ -1622,12 +1032,6 @@ 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: @@ -1646,106 +1050,52 @@ 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 + # Apply grid overlay to page images for assessment + grid_page_images = [] + for page_img in page_images: + grid_img = grid_overlay.add_grid_overlay(page_img) + grid_page_images.append(grid_img) + + # Execute tasks using Strands-based parallel executor + 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 + task_results, task_metering, processing_time = ( + execute_assessment_tasks_parallel( + tasks=tasks_to_process, + extraction_results=extraction_results, + page_images=grid_page_images, + sorted_page_ids=sorted_page_ids, + model_id=model_id, + system_prompt=system_prompt, + temperature=temperature, + max_tokens=max_tokens, + 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: @@ -1824,12 +1174,12 @@ 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] 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..ba91fc5b --- /dev/null +++ b/lib/idp_common_pkg/idp_common/assessment/strands_executor.py @@ -0,0 +1,244 @@ +""" +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.strands_service import ( + AssessmentResult, + AssessmentTask, + 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, + 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 + base_content: Base prompt content + extraction_results: Full extraction results + page_images: List of page images (with grid overlay) + sorted_page_ids: List of page IDs + model_id: Model to use + system_prompt: System prompt + temperature: Model temperature + max_tokens: Max tokens + 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, + 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, + 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 + base_content: Base prompt content + extraction_results: Full extraction results + page_images: List of page images (with grid overlay already applied) + sorted_page_ids: List of page IDs in sorted order + model_id: Model ID + system_prompt: System prompt + temperature: Temperature + max_tokens: Max tokens + 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, + 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, + 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..80cc261e --- /dev/null +++ b/lib/idp_common_pkg/idp_common/assessment/strands_models.py @@ -0,0 +1,76 @@ +""" +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 + + +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. + + Returns: + Dictionary with BoundingBox and Page in IDP format + """ + return { + "BoundingBox": { + "Width": (self.x2 - self.x1) / 1000.0, + "Height": (self.y2 - self.y1) / 1000.0, + "Left": self.x1 / 1000.0, + "Top": self.y1 / 1000.0, + }, + "Page": self.page, + } + + +class ConfidenceAssessment(BaseModel): + """Confidence assessment for an attribute value.""" + + value: Any = Field(..., description="The extracted value") + confidence: float = Field(..., ge=0.0, le=1.0, description="Confidence score 0-1") + reasoning: str = Field(..., description="Explanation for the confidence score") + threshold: float = Field( + ..., ge=0.0, le=1.0, description="Required confidence threshold" + ) + bounding_box: BoundingBox | None = Field( + None, description="Location of value in document" + ) + + @property + def meets_threshold(self) -> bool: + """Computed field: whether confidence meets threshold.""" + return self.confidence >= self.threshold + + +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" + ) + alerts: list[str] = Field( + default_factory=list, description="Any confidence threshold alerts or issues" + ) 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..cb665e98 --- /dev/null +++ b/lib/idp_common_pkg/idp_common/assessment/strands_service.py @@ -0,0 +1,391 @@ +""" +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 botocore.config import Config +from pydantic import BaseModel +from strands import Agent, tool +from strands.agent.conversation_manager import SummarizingConversationManager +from strands.models.bedrock import BedrockModel +from strands.types.content import ContentBlock, Message + +from idp_common.assessment.strands_models import AssessmentOutput +from idp_common.assessment.strands_tools import create_strands_tools + +logger = Logger(service="assessment", level=os.getenv("LOG_LEVEL", "INFO")) + + +# Pydantic versions of AssessmentTask/Result for Strands compatibility +# Note: granular_service has dataclass versions - these are separate for Strands +class AssessmentTask(BaseModel): + """Assessment task definition.""" + + task_id: str + task_type: str + attributes: list[str] + task_schema: dict[str, Any] + confidence_thresholds: dict[str, float] + + +class AssessmentResult(BaseModel): + """Assessment result.""" + + 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 + + +def create_submit_assessment_tool(): + """ + Create a tool for submitting assessment results. + + Returns: + A Strands tool function for submitting assessments + """ + + @tool + def submit_assessment(assessment: AssessmentOutput, agent: Agent) -> str: + """ + Submit your final confidence assessment. + + Use this tool when you have: + 1. Located the values in the document images + 2. Determined precise bounding box coordinates using ruler markings + 3. Assessed the confidence based on clarity and accuracy + + Args: + assessment: Dictionary with: + - assessments: dict mapping attribute names to ConfidenceAssessment + - alerts: list of any threshold alerts (optional) + + Returns: + Success confirmation message or validation error details + """ + # Validate assessment structure and return helpful errors + validated_assessment = AssessmentOutput(**assessment) # pyright: ignore[reportCallIssue] + + # Store in agent state + agent.state.set("assessment_output", validated_assessment.model_dump()) + + logger.info( + "Assessment submitted successfully", + extra={"assessment": validated_assessment.model_dump()}, + ) + + return "Assessment submitted successfully. You can now finish the task." + + return submit_assessment + + +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, + 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 + base_content: Base prompt content (includes images) + extraction_results: Full extraction results + page_images: List of page images (with grid overlay already applied) + 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() + + try: + # 1. Create tools (image viewer + todo list + submit assessment) + base_tools = create_strands_tools(page_images, sorted_page_ids) + submit_tool = create_submit_assessment_tool() + tools = base_tools + [submit_tool] + + # 2. Build task-specific prompt + task_prompt = _build_task_prompt(task, extraction_results, len(page_images)) + + # 3. Create Bedrock model config (following agentic_idp.py pattern) + boto_config = Config( + retries={ + "max_attempts": max_retries, + "mode": "adaptive", + }, + connect_timeout=connect_timeout, + read_timeout=read_timeout, + ) + + model_config = { + "model_id": model_id, + "temperature": temperature, + "max_tokens": max_tokens, + "boto_client_config": boto_config, + } + + # 4. Initialize Strands agent + agent = Agent( + model=BedrockModel(**model_config), + tools=tools, + system_prompt=system_prompt, + state={ + "task": task.model_dump(), + "extraction_results": extraction_results, + "assessment_output": None, + }, + conversation_manager=SummarizingConversationManager( + summary_ratio=0.8, preserve_recent_messages=2 + ), + ) + + # 5. Create user message with task prompt + user_message = Message(role="user", content=[ContentBlock(text=task_prompt)]) + + # 6. Run agent + logger.info( + "Starting Strands assessment", + extra={ + "task_id": task.task_id, + "task_type": task.task_type, + "attributes": task.attributes, + }, + ) + + response = await agent.invoke_async([user_message]) + + logger.debug("Agent response received", extra={"task_id": task.task_id}) + + # 7. Extract assessment from agent state + assessment_dict = agent.state.get("assessment_output") + if not assessment_dict: + raise ValueError( + "Agent did not produce assessment output. Agent may not have called submit_assessment tool." + ) + + # Validate to Pydantic model + assessment_output = AssessmentOutput(**assessment_dict) + + # Validate that agent assessed exactly the expected field + expected_field = task.attributes[0] # Task assesses exactly one field + if assessment_output.field_name != expected_field: + raise ValueError( + f"Agent assessed wrong field: expected '{expected_field}', " + f"got '{assessment_output.field_name}'" + ) + + # 8. Extract metering from response + metering = {} + if response.metrics and response.metrics.accumulated_usage: + token_usage = { + "inputTokens": response.metrics.accumulated_usage.get("inputTokens", 0), + "outputTokens": response.metrics.accumulated_usage.get( + "outputTokens", 0 + ), + "totalTokens": response.metrics.accumulated_usage.get("totalTokens", 0), + "cacheReadInputTokens": response.metrics.accumulated_usage.get( + "cacheReadInputTokens", 0 + ), + "cacheWriteInputTokens": response.metrics.accumulated_usage.get( + "cacheWriteInputTokens", 0 + ), + } + metering[f"assessment/bedrock/{model_id}"] = token_usage + + # 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 + + except Exception as e: + # Return failed result + logger.error( + "Assessment failed", + extra={ + "task_id": task.task_id, + "error": str(e), + "processing_time": time.time() - start_time, + }, + ) + + return AssessmentResult( + task_id=task.task_id, + success=False, + assessment_data={}, + confidence_alerts=[], + error_message=str(e), + processing_time=time.time() - start_time, + ) + + +def _build_task_prompt( + task: AssessmentTask, + extraction_results: dict[str, Any], + num_images: int, +) -> str: + """ + Build prompt for assessing a single field. + + Includes: + - Clear field path (e.g., "address.street" or "items[2].price") + - Full extraction results for context + - Schema and threshold for the specific field + - Instructions for using images and tools + + Args: + task: Assessment task for one specific field + extraction_results: Complete extraction results (arbitrarily nested) + num_images: Number of available page images + + Returns: + Formatted prompt string + """ + # Get the single field being assessed + field_path = task.attributes[ + 0 + ] # e.g., "name" or "address.street" or "items[0].price" + threshold = list(task.confidence_thresholds.values())[0] + + prompt = f"""# Confidence Assessment Task + +You are assessing the confidence of a SINGLE extracted field from a document. + +## Field to Assess +**Field Path**: `{field_path}` +**Confidence Threshold**: {threshold} + +## Complete Extraction Results +(Full document context - locate the value for `{field_path}`) +{json.dumps(extraction_results, indent=2)} + +## Field Schema +{json.dumps(task.task_schema, indent=2)} + +## Your Task +Assess ONLY the field `{field_path}`. Do not assess any other fields. + +## 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}} + +## Output Schema + +Your assessment must match the {task.task_type} schema. +Use the `submit_assessment` tool when ready with a complete assessment dict. + +**Important**: You MUST call `submit_assessment` to complete this task. +""" + return prompt + + +def _convert_to_assessment_result( + task: AssessmentTask, + output: AssessmentOutput, + metering: dict[str, Any], + processing_time: float, +) -> AssessmentResult: + """Convert Strands AssessmentOutput to AssessmentResult.""" + + # Single field assessment + field_name = output.field_name + assessment = output.assessment + + # Build assessment data with confidence score + assessment_data = { + field_name: { + "confidence": assessment.confidence, + "value": assessment.value, + "reasoning": assessment.reasoning, + } + } + + # Add geometry if bounding box provided + if assessment.bounding_box: + assessment_data[field_name]["Geometry"] = assessment.bounding_box.to_geometry() + + # Check for confidence threshold violations + confidence_alerts = [] + if not assessment.meets_threshold: + confidence_alerts.append( + { + "attribute_name": field_name, + "confidence": assessment.confidence, + "confidence_threshold": assessment.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..6ab9fd33 --- /dev/null +++ b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py @@ -0,0 +1,161 @@ +""" +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 BoundingBox +from idp_common.utils.grid_overlay import draw_bounding_boxes +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") + + +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 page image bytes (with grid overlay already applied) + sorted_page_ids: List of page IDs in sorted order + + Returns: + A Strands tool function for viewing images + """ + + @tool + def view_image(input_data: dict[str, Any], agent: Agent) -> str: + """ + 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: + Success message with image details + + 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(**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 base image (already has grid overlay) + img_bytes = page_images[view_input.image_index] + page_id = sorted_page_ids[view_input.image_index] + + # If bounding box is specified, draw it on the image + if view_input.bounding_box: + # Convert BoundingBox to dict format for draw_bounding_boxes + 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", + } + + # Draw the bounding box on the image (which already has ruler) + # Let drawing errors propagate - if we can't draw, something is wrong + img_bytes = draw_bounding_boxes( + img_bytes, + [bbox_dict], + has_ruler=True, + ruler_width=30, + ) + + logger.debug( + "Drew bounding box on image", + extra={ + "image_index": view_input.image_index, + "bbox": bbox_dict["bbox"], + }, + ) + + # Store the image in agent state using standardized key + image_key = f"page_{view_input.image_index}_{page_id}" + agent.state.set(image_key, img_bytes) + + logger.info( + "Stored image in agent state", + extra={ + "image_index": view_input.image_index, + "page_id": page_id, + "has_bbox": view_input.bounding_box is not None, + }, + ) + + bbox_info = "" + if view_input.bounding_box: + bbox_info = f"\nHighlighted region: [{view_input.bounding_box.x1}, {view_input.bounding_box.y1}, {view_input.bounding_box.x2}, {view_input.bounding_box.y2}] on page {view_input.bounding_box.page}" + if view_input.label: + bbox_info += f'\nLabel: "{view_input.label}"' + + return f"Showing page {view_input.image_index} (Page ID: {page_id}){bbox_info}\nUse the coordinate grid (0-1000 scale) to specify bounding boxes." + + 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, + ] diff --git a/lib/idp_common_pkg/idp_common/config/models.py b/lib/idp_common_pkg/idp_common/config/models.py index 0d5f4bf8..e0fa885f 100644 --- a/lib/idp_common_pkg/idp_common/config/models.py +++ b/lib/idp_common_pkg/idp_common/config/models.py @@ -233,25 +233,6 @@ def validate_section_splitting(cls, v: Any) -> str: return 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""" @@ -334,7 +315,10 @@ 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=20, gt=0, description="Max concurrent workers for parallel assessment" + ) @field_validator( "temperature", "top_p", "top_k", "default_confidence_threshold", mode="before" @@ -346,7 +330,7 @@ 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/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..65cbdd33 100644 --- a/lib/idp_common_pkg/idp_common/image/__init__.py +++ b/lib/idp_common_pkg/idp_common/image/__init__.py @@ -1,30 +1,33 @@ # 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 +from typing import Tuple, Optional, Dict, Any, Union, List from ..s3 import get_binary_content from ..utils import parse_s3_uri logger = logging.getLogger(__name__) -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, +) -> 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 - + Returns: Resized image bytes in original format (or JPEG if format cannot be preserved) """ @@ -33,153 +36,176 @@ def resize_image(image_data: bytes, 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 - + # 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})") + 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) - + # 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']: + if 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 - + # 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() 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"Image {current_width}x{current_height} already fits within {target_width}x{target_height}, returning original" + ) 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, +) -> 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 - + Returns: Processed image bytes ready for model input (preserves format when possible) """ # 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) + 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 +216,18 @@ 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/grid_overlay.py b/lib/idp_common_pkg/idp_common/utils/grid_overlay.py new file mode 100644 index 00000000..9430e471 --- /dev/null +++ b/lib/idp_common_pkg/idp_common/utils/grid_overlay.py @@ -0,0 +1,387 @@ +# 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() + canvas.save(img_byte_array, format="JPEG", quality=95) + + 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], + has_ruler: bool = False, + ruler_width: int = 30, + 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 + - 'label': Optional label text for the box + - 'color': Optional color override for this box + - 'page': Optional page number (for multi-page docs) + has_ruler: If True, account for ruler margins in coordinate calculation + ruler_width: Width of ruler margin (only used if has_ruler=True) + 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 + + # If image has ruler edges, calculate the actual document area + if has_ruler: + doc_width = width - ruler_width + doc_height = height - ruler_width + offset_x = ruler_width + offset_y = ruler_width + else: + doc_width = width + doc_height = height + offset_x = 0 + offset_y = 0 + + # 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() + result.save(img_byte_array, format="JPEG", quality=95) + + 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 ruler offset) + result = draw_bounding_boxes( + image_with_ruler, + bboxes, + has_ruler=True, + ruler_width=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/pyproject.toml b/lib/idp_common_pkg/pyproject.toml index fe787afe..14e933fd 100644 --- a/lib/idp_common_pkg/pyproject.toml +++ b/lib/idp_common_pkg/pyproject.toml @@ -19,7 +19,7 @@ name = "idp_common" version = "0.4.6" 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==1.42.0", # Core dependency for AWS services "jsonschema>=4.25.1", @@ -66,7 +66,7 @@ classification = [ # Extraction module dependencies extraction = [ - "Pillow==11.2.1", # For image handling + "Pillow==11.2.1", # For image handling ] # Assessment module dependencies @@ -100,24 +100,24 @@ appsync = ["requests==2.32.4"] agents = [ "strands-agents==1.14.0; 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==1.14.0", + "strands-agents-tools==0.2.13", + "bedrock-agentcore>=0.1.1", + "PyYAML>=6.0.0", + "pathspec>=0.11.0", + "chardet>=5.0.0", ] # 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 ] @@ -173,7 +173,7 @@ agentic-extraction = [ "jsonpatch==1.33", "strands-agents==1.14.0 ; 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/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/unit/assessment/conftest.py b/lib/idp_common_pkg/tests/unit/assessment/conftest.py new file mode 100644 index 00000000..a1b17cd9 --- /dev/null +++ b/lib/idp_common_pkg/tests/unit/assessment/conftest.py @@ -0,0 +1,24 @@ +# 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.hooks"] = MagicMock() +sys.modules["strands.hooks.events"] = MagicMock() 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..96db5c1f 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 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..5df0b926 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 parent_assessment_dict reference + assert all(t.parent_assessment_dict is not None 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 +232,112 @@ 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 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, + parent_assessment_dict=assessment_structure, ) 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, + parent_assessment_dict=assessment_structure, ) + task3 = AssessmentTask( + task_id="task_2", + task_type="attribute", + field_path=("date",), + field_name="date", + field_schema={"type": "string"}, + confidence_threshold=0.9, + parent_assessment_dict=assessment_structure, + ) + + # 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 +346,11 @@ 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 66681778..2be2cafe 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 = 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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 +147,32 @@ 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 = ( + "GranularAssessment" if config.assessment.granular.enabled else "Assessment" + ) + logger.info( + f"Assessment mode: {'Granular' if config.assessment.granular.enabled else 'Regular'} (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 +191,57 @@ 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, @@ -215,76 +252,89 @@ 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') - + 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") + + # Use 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 granular 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 + 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: @@ -294,33 +344,43 @@ def handler(event, context): 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-3/src/assessment_function/index.py b/patterns/pattern-3/src/assessment_function/index.py index 38cbcb4a..ad29b551 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 @@ -52,7 +59,7 @@ 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") @@ -60,19 +67,26 @@ def handler(event, 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})") + logger.info( + f"Assessment mode: {'Granular' if granular_enabled else 'Regular'} (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 +105,57 @@ 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 +165,45 @@ 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 extraction 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..1cfeb691 --- /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.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) + 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() From 1371f29163e732bc2fb59b3e580c12888149ce95 Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Tue, 18 Nov 2025 17:33:01 +0200 Subject: [PATCH 06/30] typed metadata model --- .../idp_common/assessment/granular_service.py | 55 ++++++++----------- 1 file changed, 23 insertions(+), 32 deletions(-) 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 ccddbe67..aa9c1481 100644 --- a/lib/idp_common_pkg/idp_common/assessment/granular_service.py +++ b/lib/idp_common_pkg/idp_common/assessment/granular_service.py @@ -30,8 +30,10 @@ X_AWS_IDP_CONFIDENCE_THRESHOLD, X_AWS_IDP_DOCUMENT_TYPE, ) +from idp_common.extraction.models import ExtractionData, ExtractionMetadata from idp_common.models import Document, Status -from idp_common.utils import check_token_limit, grid_overlay +from idp_common.utils import check_token_limit +from idp_common.utils.grid_overlay import add_ruler_edges logger = logging.getLogger(__name__) @@ -869,16 +871,10 @@ 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") @@ -931,8 +927,9 @@ 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: @@ -997,12 +994,6 @@ 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 - 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: @@ -1053,7 +1044,7 @@ def process_document_section(self, document: Document, section_id: str) -> Docum # Apply grid overlay to page images for assessment grid_page_images = [] for page_img in page_images: - grid_img = grid_overlay.add_grid_overlay(page_img) + grid_img = add_ruler_edges(page_img) grid_page_images.append(grid_img) # Execute tasks using Strands-based parallel executor @@ -1070,10 +1061,10 @@ def process_document_section(self, document: Document, section_id: str) -> Docum extraction_results=extraction_results, page_images=grid_page_images, sorted_page_ids=sorted_page_ids, - model_id=model_id, - system_prompt=system_prompt, - temperature=temperature, - max_tokens=max_tokens, + 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, max_concurrent=self.max_workers, ) ) @@ -1232,21 +1223,21 @@ 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 From fe56e98d071e437f07715cb3c00b19c8ffe32879 Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Wed, 19 Nov 2025 11:58:14 +0200 Subject: [PATCH 07/30] fixes --- .../idp_common/assessment/granular_service.py | 180 +++++-------- .../idp_common/assessment/models.py | 78 ++++-- .../idp_common/assessment/strands_executor.py | 14 +- .../idp_common/assessment/strands_service.py | 159 +++++++----- .../idp_common/assessment/strands_tools.py | 30 ++- .../idp_common/config/models.py | 4 +- .../idp_common/image/__init__.py | 44 +++- .../idp_common/utils/grid_overlay.py | 6 +- .../idp_common/utils/pdf_helpers.py | 242 ++++++++++++++++++ lib/idp_common_pkg/uv.lock | 29 ++- lib/idp_common_pkg/verify_stickler.py | 6 +- 11 files changed, 545 insertions(+), 247 deletions(-) create mode 100644 lib/idp_common_pkg/idp_common/utils/pdf_helpers.py 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 aa9c1481..83d6455e 100644 --- a/lib/idp_common_pkg/idp_common/assessment/granular_service.py +++ b/lib/idp_common_pkg/idp_common/assessment/granular_service.py @@ -12,13 +12,14 @@ """ import json -import logging import os import time -from dataclasses import dataclass from typing import Any, Dict, List, Optional, Tuple, Union +from aws_lambda_powertools import Logger + 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.config.models import IDPConfig from idp_common.config.schema_constants import ( @@ -35,44 +36,7 @@ from idp_common.utils import check_token_limit from idp_common.utils.grid_overlay import add_ruler_edges -logger = logging.getLogger(__name__) - - -@dataclass -class AssessmentTask: - """Single-field assessment task for Strands executor.""" - - task_id: str - task_type: str # Always "attribute" - single field assessment - - # Path to field as tuple: ("address", "street") or ("items", 0, "price") - field_path: Tuple[Union[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 - - # Direct reference to parent container in assessment structure (for O(1) insertion) - # Can be Dict for regular fields or List for array items - parent_assessment_dict: Union[Dict[str, Any], List[Any]] - - -@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: @@ -114,49 +78,13 @@ def _safe_float_conversion(value: Any, default: float = 0.0) -> float: return default -def _get_value_at_path(data: Dict[str, Any], path: Tuple[Union[str, int], ...]) -> Any: - """ - Navigate nested data structure using tuple path. - - Args: - data: Nested dictionary/list structure - path: Tuple of keys/indices like ("address", "street") or ("items", 0, "price") - - Returns: - Value at the specified path, or None if path doesn't exist - - Examples: - >>> data = {"address": {"street": "123 Main St"}} - >>> _get_value_at_path(data, ("address", "street")) - "123 Main St" - - >>> data = {"items": [{"price": 10.99}, {"price": 20.99}]} - >>> _get_value_at_path(data, ("items", 0, "price")) - 10.99 - """ - current = data - for key in path: - if current is None: - return None - if isinstance(current, dict): - current = current.get(key) - elif isinstance(current, list): - if isinstance(key, int) and 0 <= key < len(current): - current = current[key] - else: - return None - else: - return None - return current - - class GranularAssessmentService: """Enhanced assessment service with granular, cached, and parallel processing.""" 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, ): """ @@ -194,8 +122,10 @@ def __init__( if self.cache_table_name: import boto3 - dynamodb = boto3.resource("dynamodb", region_name=self.region) - self.cache_table = dynamodb.Table(self.cache_table_name) # type: ignore[attr-defined] + dynamodb: DynamoDBServiceResource = boto3.resource( + "dynamodb", region_name=self.region + ) # pyright: ignore[reportAssignmentType]modb", region_name=self.region) + self.cache_table = dynamodb.Table(self.cache_table_name) logger.info( f"Granular assessment caching enabled using table: {self.cache_table_name}" ) @@ -220,7 +150,7 @@ def __init__( 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. @@ -238,7 +168,7 @@ def _get_class_schema(self, class_label: str) -> Dict[str, Any]: return {} 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'). @@ -283,10 +213,10 @@ def _get_confidence_threshold_by_path( 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, - ) -> Tuple[List[AssessmentTask], Dict[str, Any]]: + ) -> tuple[list[AssessmentTask], dict[str, Any]]: """ Create assessment tasks and pre-build assessment structure. @@ -302,18 +232,18 @@ def _create_assessment_tasks( Returns: Tuple of (tasks, assessment_structure) - - tasks: List of AssessmentTask objects - - assessment_structure: Dict mirroring extraction_results shape + - tasks: list of AssessmentTask objects + - assessment_structure: dict mirroring extraction_results shape """ - tasks: List[AssessmentTask] = [] - assessment_structure: Dict[str, Any] = {} + 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[Union[str, int], ...], - parent_dict: Dict[str, Any], + 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. @@ -334,7 +264,7 @@ def _traverse( if prop_type == TYPE_OBJECT and isinstance(prop_value, dict): # Create nested dict in assessment structure - nested_dict: Dict[str, Any] = {} + nested_dict: dict[str, Any] = {} parent_dict[prop_name] = nested_dict # Recurse into nested object @@ -343,7 +273,7 @@ def _traverse( elif prop_type == TYPE_ARRAY and isinstance(prop_value, list): # Create list in assessment structure - assessment_list: List[Any] = [] + assessment_list: list[Any] = [] parent_dict[prop_name] = assessment_list # Process each array item @@ -355,7 +285,7 @@ def _traverse( if item_type == TYPE_OBJECT and isinstance(item_value, dict): # Create dict for this array item - item_dict: Dict[str, Any] = {} + item_dict: dict[str, Any] = {} assessment_list.append(item_dict) # Recurse into array item properties @@ -438,7 +368,7 @@ 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. @@ -448,7 +378,7 @@ 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}" @@ -476,8 +406,6 @@ 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: @@ -515,7 +443,7 @@ 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. @@ -524,7 +452,7 @@ 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 @@ -608,16 +536,16 @@ def _is_throttling_exception(self, exception: Exception) -> bool: def _aggregate_assessment_results( self, - tasks: List[AssessmentTask], - results: List[AssessmentResult], - assessment_structure: 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 assessment structure using direct parent insertion. Args: - tasks: List of assessment tasks - results: List of assessment results + tasks: list of assessment tasks + results: list of assessment results assessment_structure: Pre-built assessment structure from _create_assessment_tasks Returns: @@ -717,17 +645,17 @@ 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]: + 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 + 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 + dictionary in geometry format compatible with pattern-1 UI """ if len(bbox_coords) != 4: raise ValueError(f"Expected 4 coordinates, got {len(bbox_coords)}") @@ -750,8 +678,8 @@ def _convert_bbox_to_geometry( } def _process_single_assessment_geometry( - self, attr_assessment: Dict[str, Any], attr_name: str = "" - ) -> Dict[str, Any]: + self, attr_assessment: dict[str, Any], attr_name: str = "" + ) -> dict[str, Any]: """ Process geometry data for a single assessment (with confidence key). @@ -808,8 +736,8 @@ def _process_single_assessment_geometry( return enhanced_attr def _extract_geometry_from_assessment( - self, assessment_data: Dict[str, Any] - ) -> Dict[str, Any]: + 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. @@ -960,6 +888,9 @@ 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: @@ -968,9 +899,12 @@ 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) @@ -1043,8 +977,11 @@ def process_document_section(self, document: Document, section_id: str) -> Docum # Apply grid overlay to page images for assessment grid_page_images = [] - for page_img in page_images: + for idx, page_img in enumerate(page_images): grid_img = add_ruler_edges(page_img) + logger.info( + f"Added ruler overlay to page {idx}: {len(page_img):,} bytes -> {len(grid_img):,} bytes" + ) grid_page_images.append(grid_img) # Execute tasks using Strands-based parallel executor @@ -1065,6 +1002,7 @@ def process_document_section(self, document: Document, section_id: str) -> Docum 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, ) ) @@ -1354,10 +1292,10 @@ def assess_document(self, document: Document) -> Document: def _handle_parsing_errors( self, document: Document, - failed_tasks: List[str], + failed_tasks: list[str], 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..f76361c3 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 -@dataclass -class AttributeAssessment: + +class AttributeAssessment(BaseModel): """Assessment result for a single extracted attribute""" attribute_name: str @@ -22,30 +22,74 @@ 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 + + # Direct reference to parent container in assessment structure (for O(1) insertion) + # Can be Dict for regular fields or list for array items + parent_assessment_dict: dict[str, Any] | list[Any] + + +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 diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_executor.py b/lib/idp_common_pkg/idp_common/assessment/strands_executor.py index ba91fc5b..7b0d3015 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_executor.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_executor.py @@ -12,11 +12,8 @@ from aws_lambda_powertools import Logger -from idp_common.assessment.strands_service import ( - AssessmentResult, - AssessmentTask, - assess_attribute_with_strands, -) +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")) @@ -31,6 +28,7 @@ async def execute_tasks_async( 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, @@ -49,6 +47,7 @@ async def execute_tasks_async( 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 @@ -80,6 +79,7 @@ async def execute_with_semaphore(task: AssessmentTask) -> AssessmentResult: 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, @@ -152,6 +152,7 @@ def execute_assessment_tasks_parallel( 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, @@ -173,6 +174,7 @@ def execute_assessment_tasks_parallel( 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 @@ -201,6 +203,7 @@ def execute_assessment_tasks_parallel( 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, @@ -225,6 +228,7 @@ def execute_assessment_tasks_parallel( 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, diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_service.py b/lib/idp_common_pkg/idp_common/assessment/strands_service.py index cb665e98..b3df37ca 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_service.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_service.py @@ -12,42 +12,18 @@ from aws_lambda_powertools import Logger from botocore.config import Config -from pydantic import BaseModel from strands import Agent, tool from strands.agent.conversation_manager import SummarizingConversationManager from strands.models.bedrock import BedrockModel from strands.types.content import ContentBlock, Message +from idp_common.assessment.models import AssessmentResult, AssessmentTask from idp_common.assessment.strands_models import AssessmentOutput from idp_common.assessment.strands_tools import create_strands_tools logger = Logger(service="assessment", level=os.getenv("LOG_LEVEL", "INFO")) -# Pydantic versions of AssessmentTask/Result for Strands compatibility -# Note: granular_service has dataclass versions - these are separate for Strands -class AssessmentTask(BaseModel): - """Assessment task definition.""" - - task_id: str - task_type: str - attributes: list[str] - task_schema: dict[str, Any] - confidence_thresholds: dict[str, float] - - -class AssessmentResult(BaseModel): - """Assessment result.""" - - 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 - - def create_submit_assessment_tool(): """ Create a tool for submitting assessment results. @@ -99,6 +75,7 @@ async def assess_attribute_with_strands( 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, @@ -131,10 +108,15 @@ async def assess_attribute_with_strands( submit_tool = create_submit_assessment_tool() tools = base_tools + [submit_tool] - # 2. Build task-specific prompt - task_prompt = _build_task_prompt(task, extraction_results, len(page_images)) + # 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. Create Bedrock model config (following agentic_idp.py pattern) + # 3. Build minimal task-specific prompt (just field path and threshold) + task_prompt = _build_task_prompt(task) + + # 4. Create Bedrock model config (following agentic_idp.py pattern) boto_config = Config( retries={ "max_attempts": max_retries, @@ -151,18 +133,18 @@ async def assess_attribute_with_strands( "boto_client_config": boto_config, } - # 4. Initialize Strands agent + # 5. Initialize Strands agent agent = Agent( model=BedrockModel(**model_config), tools=tools, - system_prompt=system_prompt, + system_prompt=enhanced_system_prompt, state={ "task": task.model_dump(), "extraction_results": extraction_results, "assessment_output": None, }, conversation_manager=SummarizingConversationManager( - summary_ratio=0.8, preserve_recent_messages=2 + summary_ratio=0.8, preserve_recent_messages=1 ), ) @@ -175,7 +157,7 @@ async def assess_attribute_with_strands( extra={ "task_id": task.task_id, "task_type": task.task_type, - "attributes": task.attributes, + "field_name": task.field_name, }, ) @@ -193,12 +175,29 @@ async def assess_attribute_with_strands( # Validate to Pydantic model assessment_output = AssessmentOutput(**assessment_dict) - # Validate that agent assessed exactly the expected field - expected_field = task.attributes[0] # Task assesses exactly one field - if assessment_output.field_name != expected_field: + # Validate that agent assessed the expected field + # The agent may return: + # - Just the field name: "Street" + # - Full path with dots: "VendorAddress.Street" + # - Full path with array indices: "Items[0].Description" + # We accept any of these as long as the expected field_name appears + expected_field = task.field_name + assessed_field = assessment_output.field_name + + # Check if fields match: + # 1. Exact match + # 2. Expected field is at the end after a dot: "VendorAddress.Street" ends with ".Street" + # 3. Expected field is at the end after bracket: "Items[0].Description" ends with ".Description" + if not ( + assessed_field == expected_field + or assessed_field.endswith(f".{expected_field}") + or assessed_field.endswith(f"]{expected_field}") + or f".{expected_field}" in assessed_field + or f"]{expected_field}" in assessed_field + ): raise ValueError( f"Agent assessed wrong field: expected '{expected_field}', " - f"got '{assessment_output.field_name}'" + f"got '{assessed_field}'" ) # 8. Extract metering from response @@ -259,51 +258,44 @@ async def assess_attribute_with_strands( ) -def _build_task_prompt( - task: AssessmentTask, +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 prompt for assessing a single field. + Build system prompt with full schema and extraction results for prompt caching. - Includes: - - Clear field path (e.g., "address.street" or "items[2].price") - - Full extraction results for context - - Schema and threshold for the specific field - - Instructions for using images and tools + This puts the static/cacheable content (schema, extraction, general instructions) + in the system prompt, which benefits from prompt caching. Args: - task: Assessment task for one specific field - extraction_results: Complete extraction results (arbitrarily nested) + 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: - Formatted prompt string + Enhanced system prompt with schema and extraction context """ - # Get the single field being assessed - field_path = task.attributes[ - 0 - ] # e.g., "name" or "address.street" or "items[0].price" - threshold = list(task.confidence_thresholds.values())[0] + return f"""{base_system_prompt} - prompt = f"""# Confidence Assessment Task +## Document Schema -You are assessing the confidence of a SINGLE extracted field from a document. +Below is the full JSON schema for this document type. This defines all fields, their types, and confidence thresholds. -## Field to Assess -**Field Path**: `{field_path}` -**Confidence Threshold**: {threshold} +```json +{json.dumps(document_schema, indent=2)} +``` ## Complete Extraction Results -(Full document context - locate the value for `{field_path}`) -{json.dumps(extraction_results, indent=2)} -## Field Schema -{json.dumps(task.task_schema, indent=2)} +Below are the complete extraction results for the document being assessed. When assessing a specific field, locate its value in this structure. -## Your Task -Assess ONLY the field `{field_path}`. Do not assess any other fields. +```json +{json.dumps(extraction_results, indent=2)} +``` ## Available Document Images @@ -335,14 +327,45 @@ def _build_task_prompt( Example: {{"x1": 150, "y1": 220, "x2": 380, "y2": 245, "page": 1}} -## Output Schema +**Important**: You MUST call `submit_assessment` to complete each task. +""" + + +def _build_task_prompt(task: AssessmentTask) -> str: + """ + 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 + + Returns: + Minimal task prompt string + """ + # Convert field_path tuple to string representation + # e.g., ("address", "street") -> "address.street" + # e.g., ("items", 0, "price") -> "items[0].price" + path_parts = [] + for part in task.field_path: + if isinstance(part, int): + path_parts[-1] = f"{path_parts[-1]}[{part}]" + else: + path_parts.append(str(part)) + field_path_str = ".".join(path_parts) + + return f"""# Assessment Task + +Assess the confidence of this field: + +**Field Path**: `{field_path_str}` +**Confidence Threshold**: {task.confidence_threshold} -Your assessment must match the {task.task_type} schema. -Use the `submit_assessment` tool when ready with a complete assessment dict. +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. -**Important**: You MUST call `submit_assessment` to complete this task. +You MUST assess ONLY this field - do not assess any other fields. """ - return prompt def _convert_to_assessment_result( diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py index 6ab9fd33..a7b18a6f 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py @@ -11,6 +11,7 @@ from aws_lambda_powertools import Logger from pydantic import BaseModel, Field from strands import Agent, tool +from strands.types.content import ContentBlock, ImageContent from idp_common.assessment.strands_models import BoundingBox from idp_common.utils.grid_overlay import draw_bounding_boxes @@ -48,7 +49,7 @@ def create_view_image_tool(page_images: list[bytes], sorted_page_ids: list[str]) """ @tool - def view_image(input_data: dict[str, Any], agent: Agent) -> str: + def view_image(input_data: dict[str, Any], agent: Agent) -> ImageContent: """ View a specific page image, optionally highlighting a bounding box area. @@ -62,7 +63,7 @@ def view_image(input_data: dict[str, Any], agent: Agent) -> str: - label (str, optional): Label for the bounding box Returns: - Success message with image details + ImageContent object that the LLM can see Example: view_image({ @@ -116,26 +117,29 @@ def view_image(input_data: dict[str, Any], agent: Agent) -> str: }, ) - # Store the image in agent state using standardized key - image_key = f"page_{view_input.image_index}_{page_id}" - agent.state.set(image_key, img_bytes) - logger.info( - "Stored image in agent state", + "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), }, ) - bbox_info = "" - if view_input.bounding_box: - bbox_info = f"\nHighlighted region: [{view_input.bounding_box.x1}, {view_input.bounding_box.y1}, {view_input.bounding_box.x2}, {view_input.bounding_box.y2}] on page {view_input.bounding_box.page}" - if view_input.label: - bbox_info += f'\nLabel: "{view_input.label}"' + # Return the image as ImageContent so the LLM can actually see it + import base64 + + image_b64 = base64.b64encode(img_bytes).decode("utf-8") - return f"Showing page {view_input.image_index} (Page ID: {page_id}){bbox_info}\nUse the coordinate grid (0-1000 scale) to specify bounding boxes." + return ImageContent( + type="image", + source={ + "type": "base64", + "media_type": "image/png", + "data": image_b64, + }, + ) return view_image diff --git a/lib/idp_common_pkg/idp_common/config/models.py b/lib/idp_common_pkg/idp_common/config/models.py index e0fa885f..ae558ce4 100644 --- a/lib/idp_common_pkg/idp_common/config/models.py +++ b/lib/idp_common_pkg/idp_common/config/models.py @@ -35,10 +35,10 @@ 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( diff --git a/lib/idp_common_pkg/idp_common/image/__init__.py b/lib/idp_common_pkg/idp_common/image/__init__.py index 65cbdd33..eb43f726 100644 --- a/lib/idp_common_pkg/idp_common/image/__init__.py +++ b/lib/idp_common_pkg/idp_common/image/__init__.py @@ -3,12 +3,13 @@ from PIL import Image, ImageFilter, ImageChops, ImageOps, ImageDraw, ImageFont import io -import logging +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( @@ -16,6 +17,7 @@ def resize_image( 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. @@ -27,9 +29,10 @@ def resize_image( 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(): @@ -57,6 +60,11 @@ def resize_image( 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 @@ -76,8 +84,14 @@ def resize_image( # 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 [ + # 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", @@ -97,6 +111,8 @@ def resize_image( 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"]: @@ -105,11 +121,17 @@ def resize_image( 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" + 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 @@ -119,6 +141,7 @@ def prepare_image( 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 @@ -128,9 +151,10 @@ def prepare_image( 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://"): @@ -143,7 +167,9 @@ def prepare_image( ) # 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: diff --git a/lib/idp_common_pkg/idp_common/utils/grid_overlay.py b/lib/idp_common_pkg/idp_common/utils/grid_overlay.py index 9430e471..51c1c802 100644 --- a/lib/idp_common_pkg/idp_common/utils/grid_overlay.py +++ b/lib/idp_common_pkg/idp_common/utils/grid_overlay.py @@ -137,7 +137,8 @@ def add_ruler_edges( canvas = canvas.convert("RGB") img_byte_array = io.BytesIO() - canvas.save(img_byte_array, format="JPEG", quality=95) + # 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() @@ -291,7 +292,8 @@ def draw_bounding_boxes( result = result.convert("RGB") img_byte_array = io.BytesIO() - result.save(img_byte_array, format="JPEG", quality=95) + # 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() 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..c102ee52 --- /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) + + # 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) + + # 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/uv.lock b/lib/idp_common_pkg/uv.lock index 2be2cafe..77d3ab47 100644 --- a/lib/idp_common_pkg/uv.lock +++ b/lib/idp_common_pkg/uv.lock @@ -964,7 +964,10 @@ appsync = [ { name = "requests" }, ] assessment = [ + { name = "aws-lambda-powertools" }, + { name = "mypy-boto3-dynamodb" }, { name = "pillow" }, + { name = "strands-agents" }, ] classification = [ { name = "pillow" }, @@ -1045,6 +1048,7 @@ requires-dist = [ { name = "amazon-textract-textractor", extras = ["pandas"], marker = "extra == 'all'", specifier = "==1.9.2" }, { name = "amazon-textract-textractor", extras = ["pandas"], marker = "extra == 'ocr'", specifier = "==1.9.2" }, { name = "aws-lambda-powertools", marker = "extra == 'agentic-extraction'", specifier = ">=3.2.0" }, + { name = "aws-lambda-powertools", marker = "extra == 'assessment'", specifier = ">=3.2.0" }, { name = "aws-xray-sdk", marker = "extra == 'docs-service'", specifier = ">=2.14.0" }, { name = "bedrock-agentcore", marker = "python_full_version >= '3.10' and extra == 'agents'", specifier = ">=0.1.1" }, { name = "bedrock-agentcore", marker = "python_full_version >= '3.10' and extra == 'all'", specifier = ">=0.1.1" }, @@ -1067,6 +1071,7 @@ requires-dist = [ { name = "moto", extras = ["s3"], marker = "extra == 'test'", specifier = "==5.1.8" }, { name = "munkres", marker = "extra == 'all'", specifier = ">=1.1.4" }, { name = "munkres", marker = "extra == 'evaluation'", specifier = ">=1.1.4" }, + { name = "mypy-boto3-dynamodb", marker = "extra == 'assessment'", specifier = ">=1.40.56" }, { name = "numpy", marker = "extra == 'all'", specifier = "==1.26.4" }, { name = "numpy", marker = "extra == 'evaluation'", specifier = "==1.26.4" }, { name = "numpy", marker = "extra == 'ocr'", specifier = "==1.26.4" }, @@ -1111,10 +1116,11 @@ requires-dist = [ { name = "s3fs", marker = "extra == 'criteria-validation'", specifier = "==2023.12.2" }, { name = "stickler-eval", marker = "extra == 'all'", specifier = "==0.1.2" }, { name = "stickler-eval", marker = "extra == 'evaluation'", specifier = "==0.1.2" }, - { name = "strands-agents", marker = "python_full_version >= '3.10' and extra == 'agentic-extraction'", specifier = "==1.14.0" }, - { name = "strands-agents", marker = "python_full_version >= '3.10' and extra == 'agents'", specifier = "==1.14.0" }, - { name = "strands-agents", marker = "python_full_version >= '3.10' and extra == 'all'", specifier = "==1.14.0" }, - { name = "strands-agents", marker = "extra == 'code-intel'", specifier = "==1.14.0" }, + { name = "strands-agents", marker = "python_full_version >= '3.10' and extra == 'agentic-extraction'", specifier = "==1.17.0" }, + { name = "strands-agents", marker = "python_full_version >= '3.10' and extra == 'agents'", specifier = "==1.17.0" }, + { name = "strands-agents", marker = "python_full_version >= '3.10' and extra == 'all'", specifier = "==1.17.0" }, + { name = "strands-agents", marker = "extra == 'assessment'", specifier = "==1.17.0" }, + { name = "strands-agents", marker = "extra == 'code-intel'", specifier = "==1.17.0" }, { name = "strands-agents-tools", marker = "python_full_version >= '3.10' and extra == 'agents'", specifier = "==0.2.13" }, { name = "strands-agents-tools", marker = "python_full_version >= '3.10' and extra == 'all'", specifier = "==0.2.13" }, { name = "strands-agents-tools", marker = "extra == 'code-intel'", specifier = "==0.2.13" }, @@ -1852,6 +1858,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/90/ab/0301c945a704218bc9435f0e3c88884f6b19ef234d8899fb47ce1ccfd0c9/munkres-1.1.4-py2.py3-none-any.whl", hash = "sha256:6b01867d4a8480d865aea2326e4b8f7c46431e9e55b4a2e32d989307d7bced2a", size = 7015, upload-time = "2020-09-15T15:12:19.627Z" }, ] +[[package]] +name = "mypy-boto3-dynamodb" +version = "1.40.56" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/8a/20/b543f76de1e5744b32d9051716ce464e595765b5aadac7ac36e24363a986/mypy_boto3_dynamodb-1.40.56.tar.gz", hash = "sha256:576dd12fe1125754066e7fa480f92c123220970a9d69f7663a56d701f2978ac5", size = 47972, upload-time = "2025-10-21T20:35:02.447Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/32/93/2bb83680398a28cbc729e120c4ae745adf76d7a20f31499189cce2a46485/mypy_boto3_dynamodb-1.40.56-py3-none-any.whl", hash = "sha256:3bf3f541a0d21c249109dd65f18c61b3e6a0fe7124b3afe989877d5cca42b65a", size = 56996, upload-time = "2025-10-21T20:34:54.187Z" }, +] + [[package]] name = "mypy-extensions" version = "1.1.0" @@ -3089,7 +3104,7 @@ wheels = [ [[package]] name = "strands-agents" -version = "1.14.0" +version = "1.17.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "boto3" }, @@ -3104,9 +3119,9 @@ dependencies = [ { name = "typing-extensions" }, { name = "watchdog" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/26/dd/a2dc96614bb1dd7c1623cbdf6df268eb307038b2fe27bc5a6148f4223f59/strands_agents-1.14.0.tar.gz", hash = "sha256:f86dd2b92d50196acd0c5ff5404fcd1b6c3715ae56fcceb2a78210ab47860585", size = 471216, upload-time = "2025-10-29T14:20:27.721Z" } +sdist = { url = "https://files.pythonhosted.org/packages/89/c4/925da2e52279e53067c1306ce1b116bd0cee334ddee751a207b05ad0e668/strands_agents-1.17.0.tar.gz", hash = "sha256:60b0006f2d60534761a468aa15916f0ec7670952ce32c829d4f91bc3a243dfec", size = 514561, upload-time = "2025-11-18T19:13:33.746Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/8f/08/848c26d917d8f75bc10edeea63578af218df5c6efeb69e3cf91eb8fd396e/strands_agents-1.14.0-py3-none-any.whl", hash = "sha256:d2ebc1b991c37e891cfe79cf5dea1bfa021c3e6d93e4d2f238042ea688019bf3", size = 238974, upload-time = "2025-10-29T14:20:24.841Z" }, + { url = "https://files.pythonhosted.org/packages/07/fb/2d9f15af01dbd4854fbaf68bf8c41b23359878182639f3686c555189964b/strands_agents-1.17.0-py3-none-any.whl", hash = "sha256:8b8412048a3ca3568a9f7253de0d2c931e36149cee2b9ca7b823cce51d64e36d", size = 253993, upload-time = "2025-11-18T19:13:31.964Z" }, ] [[package]] diff --git a/lib/idp_common_pkg/verify_stickler.py b/lib/idp_common_pkg/verify_stickler.py index 16d29410..42d82917 100644 --- a/lib/idp_common_pkg/verify_stickler.py +++ b/lib/idp_common_pkg/verify_stickler.py @@ -10,7 +10,7 @@ """ import sys -from typing import Any, Dict +from typing import Any, dict def verify_stickler_import(): @@ -65,7 +65,7 @@ def verify_dynamic_model_creation(): from stickler import StructuredModel # 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": { From 760135492c512734db49e92949bd3c1f0c879df8 Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Wed, 19 Nov 2025 13:40:42 +0200 Subject: [PATCH 08/30] fix tool --- .../idp_common/assessment/strands_tools.py | 29 ++++++++++--------- 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py index a7b18a6f..2b6c1f30 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py @@ -12,6 +12,7 @@ from pydantic import BaseModel, Field from strands import Agent, tool from strands.types.content import ContentBlock, ImageContent +from strands.types.tools import ToolResult, ToolResultContent, ToolResultStatus from idp_common.assessment.strands_models import BoundingBox from idp_common.utils.grid_overlay import draw_bounding_boxes @@ -49,7 +50,7 @@ def create_view_image_tool(page_images: list[bytes], sorted_page_ids: list[str]) """ @tool - def view_image(input_data: dict[str, Any], agent: Agent) -> ImageContent: + def view_image(input_data: dict[str, Any], agent: Agent) -> dict: """ View a specific page image, optionally highlighting a bounding box area. @@ -127,19 +128,19 @@ def view_image(input_data: dict[str, Any], agent: Agent) -> ImageContent: }, ) - # Return the image as ImageContent so the LLM can actually see it - import base64 - - image_b64 = base64.b64encode(img_bytes).decode("utf-8") - - return ImageContent( - type="image", - source={ - "type": "base64", - "media_type": "image/png", - "data": image_b64, - }, - ) + return { + "status": "success", + "content": [ + { + "image": { + "format": "png", + "source": { + "bytes": img_bytes, + }, + } + } + ], + } return view_image From eedd6a8ec6bb3e8144a96e083aad99cf93eb92ae Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Wed, 19 Nov 2025 16:54:08 +0200 Subject: [PATCH 09/30] assesment update --- .../idp_common/assessment/granular_service.py | 5 +- .../idp_common/assessment/strands_service.py | 62 +++++-------------- .../idp_common/assessment/strands_tools.py | 37 ++++++++++- .../idp_common/config/models.py | 2 +- lib/idp_common_pkg/pyproject.toml | 11 ++-- lib/idp_common_pkg/uv.lock | 11 ++++ 6 files changed, 71 insertions(+), 57 deletions(-) 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 83d6455e..482885d6 100644 --- a/lib/idp_common_pkg/idp_common/assessment/granular_service.py +++ b/lib/idp_common_pkg/idp_common/assessment/granular_service.py @@ -14,9 +14,10 @@ import json import os import time -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any 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 @@ -31,7 +32,7 @@ X_AWS_IDP_CONFIDENCE_THRESHOLD, X_AWS_IDP_DOCUMENT_TYPE, ) -from idp_common.extraction.models import ExtractionData, ExtractionMetadata +from idp_common.extraction.models import ExtractionData from idp_common.models import Document, Status from idp_common.utils import check_token_limit from idp_common.utils.grid_overlay import add_ruler_edges diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_service.py b/lib/idp_common_pkg/idp_common/assessment/strands_service.py index b3df37ca..04da4fc2 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_service.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_service.py @@ -12,7 +12,7 @@ from aws_lambda_powertools import Logger from botocore.config import Config -from strands import Agent, tool +from strands import Agent from strands.agent.conversation_manager import SummarizingConversationManager from strands.models.bedrock import BedrockModel from strands.types.content import ContentBlock, Message @@ -20,52 +20,13 @@ from idp_common.assessment.models import AssessmentResult, AssessmentTask from idp_common.assessment.strands_models import AssessmentOutput from idp_common.assessment.strands_tools import create_strands_tools +from idp_common.utils.bedrock_utils import ( + async_exponential_backoff_retry, +) logger = Logger(service="assessment", level=os.getenv("LOG_LEVEL", "INFO")) -def create_submit_assessment_tool(): - """ - Create a tool for submitting assessment results. - - Returns: - A Strands tool function for submitting assessments - """ - - @tool - def submit_assessment(assessment: AssessmentOutput, agent: Agent) -> str: - """ - Submit your final confidence assessment. - - Use this tool when you have: - 1. Located the values in the document images - 2. Determined precise bounding box coordinates using ruler markings - 3. Assessed the confidence based on clarity and accuracy - - Args: - assessment: Dictionary with: - - assessments: dict mapping attribute names to ConfidenceAssessment - - alerts: list of any threshold alerts (optional) - - Returns: - Success confirmation message or validation error details - """ - # Validate assessment structure and return helpful errors - validated_assessment = AssessmentOutput(**assessment) # pyright: ignore[reportCallIssue] - - # Store in agent state - agent.state.set("assessment_output", validated_assessment.model_dump()) - - logger.info( - "Assessment submitted successfully", - extra={"assessment": validated_assessment.model_dump()}, - ) - - return "Assessment submitted successfully. You can now finish the task." - - return submit_assessment - - async def assess_attribute_with_strands( task: AssessmentTask, extraction_results: dict[str, Any], @@ -105,9 +66,7 @@ async def assess_attribute_with_strands( try: # 1. Create tools (image viewer + todo list + submit assessment) base_tools = create_strands_tools(page_images, sorted_page_ids) - submit_tool = create_submit_assessment_tool() - tools = base_tools + [submit_tool] - + tools = base_tools # 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) @@ -161,8 +120,17 @@ async def assess_attribute_with_strands( }, ) - response = await agent.invoke_async([user_message]) + @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]) + response = await invoke_agent_with_retry() logger.debug("Agent response received", extra={"task_id": task.task_id}) # 7. Extract assessment from agent state diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py index 2b6c1f30..44379aa5 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py @@ -11,10 +11,8 @@ from aws_lambda_powertools import Logger from pydantic import BaseModel, Field from strands import Agent, tool -from strands.types.content import ContentBlock, ImageContent -from strands.types.tools import ToolResult, ToolResultContent, ToolResultStatus -from idp_common.assessment.strands_models import BoundingBox +from idp_common.assessment.strands_models import AssessmentOutput, BoundingBox from idp_common.utils.grid_overlay import draw_bounding_boxes from idp_common.utils.strands_agent_tools.todo_list import ( create_todo_list, @@ -37,6 +35,38 @@ class ViewImageInput(BaseModel): label: str | None = Field(None, description="Optional label for the bounding box") +@tool +def submit_assessment(assessment: AssessmentOutput, agent: Agent) -> str: + """ + Submit your final confidence assessment. + + Use this tool when you have: + 1. Located the values in the document images + 2. Determined precise bounding box coordinates using ruler markings + 3. Assessed the confidence based on clarity and accuracy + + Args: + assessment: Dictionary with: + - assessments: dict mapping attribute names to ConfidenceAssessment + - alerts: list of any threshold alerts (optional) + + Returns: + Success confirmation message or validation error details + """ + # Validate assessment structure and return helpful errors + validated_assessment = AssessmentOutput(**assessment) # pyright: ignore[reportCallIssue] + + # Store in agent state + agent.state.set("assessment_output", validated_assessment.model_dump()) + + logger.info( + "Assessment submitted successfully", + extra={"assessment": validated_assessment.model_dump()}, + ) + + 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. @@ -163,4 +193,5 @@ def create_strands_tools( create_todo_list, update_todo, view_todo_list, + submit_assessment, ] diff --git a/lib/idp_common_pkg/idp_common/config/models.py b/lib/idp_common_pkg/idp_common/config/models.py index ae558ce4..04f97632 100644 --- a/lib/idp_common_pkg/idp_common/config/models.py +++ b/lib/idp_common_pkg/idp_common/config/models.py @@ -317,7 +317,7 @@ class AssessmentConfig(BaseModel): image: ImageConfig = Field(default_factory=ImageConfig) # Granular assessment settings (always enabled, no longer nested) max_workers: int = Field( - default=20, gt=0, description="Max concurrent workers for parallel assessment" + default=50, gt=0, description="Max concurrent workers for parallel assessment" ) @field_validator( diff --git a/lib/idp_common_pkg/pyproject.toml b/lib/idp_common_pkg/pyproject.toml index 14e933fd..5d2dbd2a 100644 --- a/lib/idp_common_pkg/pyproject.toml +++ b/lib/idp_common_pkg/pyproject.toml @@ -25,6 +25,7 @@ dependencies = [ "jsonschema>=4.25.1", "pydantic>=2.12.0", "deepdiff>=6.0.0", + "mypy-boto3-bedrock-runtime>=1.40.76", ] readme = "README.md" classifiers = [ @@ -73,6 +74,8 @@ extraction = [ assessment = [ "Pillow==11.2.1", # For image handling "aws-lambda-powertools>=3.2.0", # Structured logging and observability + "mypy-boto3-dynamodb>=1.40.56", + "strands-agents==1.17.0", ] # Evaluation module dependencies @@ -98,7 +101,7 @@ 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 ] @@ -106,7 +109,7 @@ agents = [ # Code intelligence module dependencies code_intel = [ "requests==2.32.4", - "strands-agents==1.14.0", + "strands-agents==1.17.0", "strands-agents-tools==0.2.13", "bedrock-agentcore>=0.1.1", "PyYAML>=6.0.0", @@ -164,14 +167,14 @@ 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 "email-validator>=2.3.0", diff --git a/lib/idp_common_pkg/uv.lock b/lib/idp_common_pkg/uv.lock index 77d3ab47..62e1f082 100644 --- a/lib/idp_common_pkg/uv.lock +++ b/lib/idp_common_pkg/uv.lock @@ -924,6 +924,7 @@ dependencies = [ { name = "boto3" }, { name = "deepdiff" }, { name = "jsonschema" }, + { name = "mypy-boto3-bedrock-runtime" }, { name = "pydantic" }, ] @@ -1071,6 +1072,7 @@ requires-dist = [ { name = "moto", extras = ["s3"], marker = "extra == 'test'", specifier = "==5.1.8" }, { name = "munkres", marker = "extra == 'all'", specifier = ">=1.1.4" }, { name = "munkres", marker = "extra == 'evaluation'", specifier = ">=1.1.4" }, + { name = "mypy-boto3-bedrock-runtime", specifier = ">=1.40.76" }, { name = "mypy-boto3-dynamodb", marker = "extra == 'assessment'", specifier = ">=1.40.56" }, { name = "numpy", marker = "extra == 'all'", specifier = "==1.26.4" }, { name = "numpy", marker = "extra == 'evaluation'", specifier = "==1.26.4" }, @@ -1858,6 +1860,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/90/ab/0301c945a704218bc9435f0e3c88884f6b19ef234d8899fb47ce1ccfd0c9/munkres-1.1.4-py2.py3-none-any.whl", hash = "sha256:6b01867d4a8480d865aea2326e4b8f7c46431e9e55b4a2e32d989307d7bced2a", size = 7015, upload-time = "2020-09-15T15:12:19.627Z" }, ] +[[package]] +name = "mypy-boto3-bedrock-runtime" +version = "1.40.76" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/e7/db/cc668a48a27973df31c7044a6785bd0e8691b1a0419dae001c4c29f1c98f/mypy_boto3_bedrock_runtime-1.40.76.tar.gz", hash = "sha256:52f2a2b3955eb9f4f0d075398f2d430abcc6bf56ff00815b94e3371e66030059", size = 28428, upload-time = "2025-11-18T21:42:43.41Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/05/6f/8b04729224a76952e08406eccbbbebfa75ee7df91313279d76428f13fdc2/mypy_boto3_bedrock_runtime-1.40.76-py3-none-any.whl", hash = "sha256:0347f6d78e342d640da74bbd6158b276c5cb39ef73405084a65fe490766b6dab", size = 34454, upload-time = "2025-11-18T21:42:42.156Z" }, +] + [[package]] name = "mypy-boto3-dynamodb" version = "1.40.56" From 6a6d610c383a1b854510372f3b489f3506407e75 Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Fri, 21 Nov 2025 15:55:53 +0000 Subject: [PATCH 10/30] further streamlining --- .../bank-statement-sample/config.yaml | 111 --- .../lending-package-sample/config.yaml | 110 --- .../config.yaml | 112 +-- .../rvl-cdip-package-sample/config.yaml | 112 +-- .../idp_common/assessment/models.py | 69 +- .../idp_common/assessment/service.py | 852 ++++++++++-------- .../idp_common/assessment/strands_service.py | 383 ++++---- .../idp_common/bedrock/__init__.py | 16 +- .../idp_common/bedrock/model_config.py | 178 ++++ .../idp_common/config/models.py | 29 +- .../idp_common/extraction/agentic_idp.py | 39 +- lib/idp_common_pkg/uv.lock | 10 +- 12 files changed, 969 insertions(+), 1052 deletions(-) create mode 100644 lib/idp_common_pkg/idp_common/bedrock/model_config.py diff --git a/config_library/pattern-2/bank-statement-sample/config.yaml b/config_library/pattern-2/bank-statement-sample/config.yaml index d345dca4..9a9ef807 100644 --- a/config_library/pattern-2/bank-statement-sample/config.yaml +++ b/config_library/pattern-2/bank-statement-sample/config.yaml @@ -452,117 +452,6 @@ 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: - - The document images have ruler markings along all edges showing the 0-1000 coordinate scale - - Use these ruler markings to determine precise coordinates for bounding boxes - - 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 - - Reference the ruler markings on the image edges to provide accurate coordinates - - - - 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: - - The contains raw JSON schema for the attributes you should assess - - The contains the FULL extraction data (you have complete context for cross-referencing) - - The specifies which attributes to focus on - - 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} - - - <> - - - {TASK_SCHEMA} - - - - {TASK_INSTRUCTION} - - - - {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 92928e1b..cd0646ac 100644 --- a/config_library/pattern-2/lending-package-sample/config.yaml +++ b/config_library/pattern-2/lending-package-sample/config.yaml @@ -1478,116 +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: - - The document images have ruler markings along all edges showing the 0-1000 coordinate scale - - Use these ruler markings to determine precise coordinates for bounding boxes - - 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 - - Reference the ruler markings on the image edges to provide accurate coordinates - - - - 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: - - The contains raw JSON schema for the attributes you should assess - - The contains the FULL extraction data (you have complete context for cross-referencing) - - The specifies which attributes to focus on - - 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} - - - <> - - - {TASK_SCHEMA} - - - - {TASK_INSTRUCTION} - - - - {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 5009d75a..31ed72d7 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 @@ -1026,116 +1026,6 @@ 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: - - The document images have ruler markings along all edges showing the 0-1000 coordinate scale - - Use these ruler markings to determine precise coordinates for bounding boxes - - 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 - - Reference the ruler markings on the image edges to provide accurate coordinates - - - - 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: - - The contains raw JSON schema for the attributes you should assess - - The contains the FULL extraction data (you have complete context for cross-referencing) - - The specifies which attributes to focus on - - 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} - - - <> - - - {TASK_SCHEMA} - - - - {TASK_INSTRUCTION} - - - - {EXTRACTION_RESULTS} - evaluation: enabled: true llm_method: @@ -1452,7 +1342,7 @@ 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 f7720b03..05bde818 100644 --- a/config_library/pattern-2/rvl-cdip-package-sample/config.yaml +++ b/config_library/pattern-2/rvl-cdip-package-sample/config.yaml @@ -1121,116 +1121,6 @@ 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: - - The document images have ruler markings along all edges showing the 0-1000 coordinate scale - - Use these ruler markings to determine precise coordinates for bounding boxes - - 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 - - Reference the ruler markings on the image edges to provide accurate coordinates - - - - 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: - - The contains raw JSON schema for the attributes you should assess - - The contains the FULL extraction data (you have complete context for cross-referencing) - - The specifies which attributes to focus on - - 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} - - - <> - - - {TASK_SCHEMA} - - - - {TASK_INSTRUCTION} - - - - {EXTRACTION_RESULTS} - evaluation: enabled: true llm_method: @@ -1463,7 +1353,7 @@ 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/lib/idp_common_pkg/idp_common/assessment/models.py b/lib/idp_common_pkg/idp_common/assessment/models.py index f76361c3..909a6ae3 100644 --- a/lib/idp_common_pkg/idp_common/assessment/models.py +++ b/lib/idp_common_pkg/idp_common/assessment/models.py @@ -10,7 +10,7 @@ from typing import Any -from pydantic import BaseModel, Field +from pydantic import BaseModel, Field, field_validator class AttributeAssessment(BaseModel): @@ -93,3 +93,70 @@ class AssessmentResult(BaseModel): 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 Geometry(BaseModel): + """Geometry in IDP format (converted from BoundingBox).""" + + boundingBox: dict[str, float] # {top, left, width, height} + page: int + + +class ConfidenceAlert(BaseModel): + """Alert for confidence threshold violation.""" + + attribute_name: str + confidence: float + confidence_threshold: float + + @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 b8949c78..4ffe976a 100644 --- a/lib/idp_common_pkg/idp_common/assessment/service.py +++ b/lib/idp_common_pkg/idp_common/assessment/service.py @@ -17,9 +17,15 @@ 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.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 +40,37 @@ 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 +103,11 @@ 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 +124,7 @@ 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 +168,13 @@ 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 +222,31 @@ 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 +265,41 @@ 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 +315,6 @@ 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 +326,7 @@ 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 +372,7 @@ 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. @@ -453,7 +457,7 @@ 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). @@ -485,6 +489,101 @@ 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: + raise ValueError( + f"No extraction results found for section {section.section_id}" + ) + + 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. @@ -508,8 +607,6 @@ 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( @@ -523,9 +620,13 @@ def _get_text_confidence_data(self, page) -> str: return "" + # ============================================================================ + # GEOMETRY PROCESSING + # ============================================================================ + def _convert_bbox_to_geometry( - self, bbox_coords: List[float], page_num: int - ) -> Dict[str, Any]: + self, bbox_coords: list[float], page_num: int + ) -> dict[str, Any]: """ Convert [x1,y1,x2,y2] coordinates to geometry format. @@ -557,8 +658,8 @@ def _convert_bbox_to_geometry( } def _process_single_assessment_geometry( - self, attr_assessment: Dict[str, Any], attr_name: str = "" - ) -> Dict[str, Any]: + self, attr_assessment: dict[str, Any], attr_name: str = "" + ) -> dict[str, Any]: """ Process geometry data for a single assessment (with confidence key). @@ -615,8 +716,8 @@ def _process_single_assessment_geometry( return enhanced_attr def _extract_geometry_from_assessment( - self, assessment_data: Dict[str, Any] - ) -> Dict[str, Any]: + 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. @@ -666,32 +767,225 @@ def _extract_geometry_from_assessment( return enhanced_assessment - def process_document_section(self, document: Document, section_id: str) -> Document: + # ============================================================================ + # 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]: """ - Process a single section from a Document object to assess extraction confidence. + Process raw assessment response from LLM. Args: - document: Document object containing section to process - section_id: ID of the section to process + assessment_text: Raw text response from LLM + extraction_results: Original extraction results + class_schema: JSON Schema for the document class Returns: - Document: Updated Document object with assessment results appended to extraction results + Tuple of (enhanced_assessment_data, confidence_alerts, parsing_succeeded) """ - # 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 + # Parse response into JSON + assessment_data = {} + parsing_succeeded = True + + 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 + + # 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)}") + + # Enhance assessment data with confidence thresholds and create alerts + enhanced_assessment_data, confidence_alerts = ( + self._enhance_and_check_thresholds(assessment_data, class_schema) + ) + + return enhanced_assessment_data, confidence_alerts, parsing_succeeded + + def _enhance_and_check_thresholds( + self, assessment_data: dict[str, Any], class_schema: dict[str, Any] + ) -> tuple[dict[str, Any], list[ConfidenceAlert]]: + """ + Enhance assessment data with thresholds and generate alerts. + + Args: + assessment_data: Raw assessment data from LLM + class_schema: JSON Schema for the document class + + Returns: + Tuple of (enhanced_assessment_data, confidence_alerts) + """ + default_confidence_threshold = ( + self.config.assessment.default_confidence_threshold + ) + + 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" + + # 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, + ) + + 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"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 + + else: + # 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 + + return enhanced_assessment_data, confidence_threshold_alerts + + # ============================================================================ + # ASSESSMENT EXECUTION + # ============================================================================ + + def _execute_bedrock_assessment( + self, content: list[dict[str, Any]] + ) -> tuple[str, dict[str, Any], float]: + """ + Execute Bedrock model invocation for assessment. + + Args: + content: Formatted content for the model - # Validate input document + Returns: + Tuple of (assessment_text, metering_data, duration_seconds) + """ + # 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", + ) + + total_duration = time.time() - request_start_time + + # Extract text from response + assessment_text = bedrock.extract_text_from_response(response_with_metering) + metering = response_with_metering.get("metering", {}) + + return assessment_text, metering, total_duration + + # ============================================================================ + # VALIDATION & HELPERS + # ============================================================================ + + def _validate_and_get_section(self, document: Document, section_id: str): + """ + Validate document and return the section to process. + + Args: + document: Document object to validate + section_id: ID of section to retrieve + + Returns: + Section object + + 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 @@ -701,28 +995,48 @@ 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]) @@ -736,81 +1050,17 @@ 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", {}) - - # Skip assessment if no extraction results found - if not extraction_results: - logger.warning(f"No extraction results found for section {section_id}") - return document - + extraction_data_model = self._load_extraction_data(section) + extraction_results = extraction_data_model.extraction_results 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) + logger.info(f"Time taken to load extraction data: {t1 - t0:.2f} seconds") - 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) @@ -832,12 +1082,12 @@ 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)}") @@ -849,203 +1099,53 @@ 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 + doc_section.confidence_threshold_alerts = [ + alert.model_dump() for alert in confidence_threshold_alerts + ] break # Update document with metering data diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_service.py b/lib/idp_common_pkg/idp_common/assessment/strands_service.py index 04da4fc2..f4963261 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_service.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_service.py @@ -11,18 +11,17 @@ from typing import Any from aws_lambda_powertools import Logger -from botocore.config import Config from strands import Agent from strands.agent.conversation_manager import SummarizingConversationManager from strands.models.bedrock import BedrockModel -from strands.types.content import ContentBlock, Message +from strands.types.content import CachePoint, ContentBlock, Message +from strands.types.media import ImageContent, ImageSource from idp_common.assessment.models import AssessmentResult, AssessmentTask from idp_common.assessment.strands_models import AssessmentOutput from idp_common.assessment.strands_tools import create_strands_tools -from idp_common.utils.bedrock_utils import ( - async_exponential_backoff_retry, -) +from idp_common.bedrock import build_model_config +from idp_common.utils.bedrock_utils import async_exponential_backoff_retry logger = Logger(service="assessment", level=os.getenv("LOG_LEVEL", "INFO")) @@ -63,168 +62,193 @@ async def assess_attribute_with_strands( """ start_time = time.time() - try: - # 1. Create tools (image viewer + todo list + submit assessment) - base_tools = create_strands_tools(page_images, sorted_page_ids) - tools = base_tools - # 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) - ) + # 1. Create tools (image viewer + todo list + submit assessment) + tools = create_strands_tools(page_images, sorted_page_ids) - # 3. Build minimal task-specific prompt (just field path and threshold) - task_prompt = _build_task_prompt(task) - - # 4. Create Bedrock model config (following agentic_idp.py pattern) - boto_config = Config( - retries={ - "max_attempts": max_retries, - "mode": "adaptive", - }, - connect_timeout=connect_timeout, - read_timeout=read_timeout, - ) + # 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) + ) - model_config = { - "model_id": model_id, - "temperature": temperature, - "max_tokens": max_tokens, - "boto_client_config": boto_config, - } + # 3. Build minimal task-specific prompt (just field path and threshold) + task_prompt = _build_task_prompt(task, page_images) - # 5. Initialize Strands agent - agent = Agent( - model=BedrockModel(**model_config), - tools=tools, - system_prompt=enhanced_system_prompt, - state={ - "task": task.model_dump(), - "extraction_results": extraction_results, - "assessment_output": None, - }, - conversation_manager=SummarizingConversationManager( - summary_ratio=0.8, preserve_recent_messages=1 - ), - ) + # 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(), + "extraction_results": extraction_results, + "assessment_output": None, + }, + conversation_manager=SummarizingConversationManager( + summary_ratio=0.8, preserve_recent_messages=1 + ), + ) - # 5. Create user message with task prompt - user_message = Message(role="user", content=[ContentBlock(text=task_prompt)]) - - # 6. Run agent - logger.info( - "Starting Strands assessment", - extra={ - "task_id": task.task_id, - "task_type": task.task_type, - "field_name": task.field_name, - }, - ) + # 6. Create user message and run agent with retry + user_message = Message(role="user", content=task_prompt) - @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]) + 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}) - - # 7. Extract assessment from agent state - assessment_dict = agent.state.get("assessment_output") - if not assessment_dict: - raise ValueError( - "Agent did not produce assessment output. Agent may not have called submit_assessment tool." - ) - - # Validate to Pydantic model - assessment_output = AssessmentOutput(**assessment_dict) - - # Validate that agent assessed the expected field - # The agent may return: - # - Just the field name: "Street" - # - Full path with dots: "VendorAddress.Street" - # - Full path with array indices: "Items[0].Description" - # We accept any of these as long as the expected field_name appears - expected_field = task.field_name - assessed_field = assessment_output.field_name - - # Check if fields match: - # 1. Exact match - # 2. Expected field is at the end after a dot: "VendorAddress.Street" ends with ".Street" - # 3. Expected field is at the end after bracket: "Items[0].Description" ends with ".Description" - if not ( - assessed_field == expected_field - or assessed_field.endswith(f".{expected_field}") - or assessed_field.endswith(f"]{expected_field}") - or f".{expected_field}" in assessed_field - or f"]{expected_field}" in assessed_field - ): - raise ValueError( - f"Agent assessed wrong field: expected '{expected_field}', " - f"got '{assessed_field}'" - ) - - # 8. Extract metering from response - metering = {} - if response.metrics and response.metrics.accumulated_usage: - token_usage = { - "inputTokens": response.metrics.accumulated_usage.get("inputTokens", 0), - "outputTokens": response.metrics.accumulated_usage.get( - "outputTokens", 0 - ), - "totalTokens": response.metrics.accumulated_usage.get("totalTokens", 0), - "cacheReadInputTokens": response.metrics.accumulated_usage.get( - "cacheReadInputTokens", 0 - ), - "cacheWriteInputTokens": response.metrics.accumulated_usage.get( - "cacheWriteInputTokens", 0 - ), - } - metering[f"assessment/bedrock/{model_id}"] = token_usage - - # 9. Convert to AssessmentResult format - result = _convert_to_assessment_result( - task, - assessment_output, - metering, - time.time() - start_time, + except Exception as e: + logger.error( + "Agent invocation failed", + extra={"task_id": task.task_id, "error": str(e)}, ) - - logger.info( - "Assessment completed successfully", - extra={ - "task_id": task.task_id, - "processing_time": result.processing_time, - "success": result.success, - }, + 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, ) - return result + # 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 failed result - logger.error( - "Assessment failed", - extra={ - "task_id": task.task_id, - "error": str(e), - "processing_time": time.time() - start_time, - }, + 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=str(e), + 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, @@ -299,41 +323,76 @@ def _build_system_prompt_with_context( """ -def _build_task_prompt(task: AssessmentTask) -> str: +def _convert_field_path_to_string(field_path: tuple[str | int, ...]) -> str: """ - Build minimal task-specific prompt for assessing a single field. + Convert field path tuple to dot notation string. - This is minimal (just field path and threshold) to maximize the benefit - of caching the system prompt which contains the schema and extraction. + Examples: + ("address", "street") ā†’ "address.street" + ("items", 0, "price") ā†’ "items[0].price" + ("orders", 2, "line_items", 1, "quantity") ā†’ "orders[2].line_items[1].quantity" Args: - task: Assessment task for one specific field + field_path: Tuple of field names (str) and array indices (int) Returns: - Minimal task prompt string + Dot notation path string with array indices in brackets """ - # Convert field_path tuple to string representation - # e.g., ("address", "street") -> "address.street" - # e.g., ("items", 0, "price") -> "items[0].price" path_parts = [] - for part in task.field_path: + for part in field_path: if isinstance(part, int): + # Append array index to previous part: "items" ā†’ "items[0]" path_parts[-1] = f"{path_parts[-1]}[{part}]" else: + # Add new field name path_parts.append(str(part)) - field_path_str = ".".join(path_parts) - return f"""# Assessment Task + return ".".join(path_parts) + -Assess the confidence of this field: +def _build_task_prompt( + task: AssessmentTask, page_images: list[bytes] +) -> list[ContentBlock]: + """ + Build minimal task-specific prompt for assessing a single field. -**Field Path**: `{field_path_str}` -**Confidence Threshold**: {task.confidence_threshold} + This is minimal (just field path and threshold) to maximize the benefit + of caching the system prompt which contains the schema and extraction. -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. + Args: + task: Assessment task for one specific field + page_images: List of page images to include in the prompt -You MUST assess ONLY this field - do not assess any other fields. -""" + 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 + image_blocks = [ + ContentBlock(image=ImageContent(format="png", source=ImageSource(bytes=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( 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 04f97632..1e1c3126 100644 --- a/lib/idp_common_pkg/idp_common/config/models.py +++ b/lib/idp_common_pkg/idp_common/config/models.py @@ -278,34 +278,7 @@ 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) 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 f34ffc15..6154f185 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, @@ -86,35 +86,6 @@ def detect_image_format(image_bytes: bytes) -> str: 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.""" @@ -1055,7 +1026,7 @@ 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, @@ -1145,7 +1116,7 @@ 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/uv.lock b/lib/idp_common_pkg/uv.lock index 62e1f082..eb12c58e 100644 --- a/lib/idp_common_pkg/uv.lock +++ b/lib/idp_common_pkg/uv.lock @@ -1116,8 +1116,8 @@ requires-dist = [ { name = "rich", marker = "extra == 'test'", specifier = ">=13.0.0" }, { name = "ruff", marker = "extra == 'test'", specifier = ">=0.14.0" }, { name = "s3fs", marker = "extra == 'criteria-validation'", specifier = "==2023.12.2" }, - { name = "stickler-eval", marker = "extra == 'all'", specifier = "==0.1.2" }, - { name = "stickler-eval", marker = "extra == 'evaluation'", specifier = "==0.1.2" }, + { name = "stickler-eval", marker = "extra == 'all'", specifier = "==0.1.3" }, + { name = "stickler-eval", marker = "extra == 'evaluation'", specifier = "==0.1.3" }, { name = "strands-agents", marker = "python_full_version >= '3.10' and extra == 'agentic-extraction'", specifier = "==1.17.0" }, { name = "strands-agents", marker = "python_full_version >= '3.10' and extra == 'agents'", specifier = "==1.17.0" }, { name = "strands-agents", marker = "python_full_version >= '3.10' and extra == 'all'", specifier = "==1.17.0" }, @@ -3096,7 +3096,7 @@ wheels = [ [[package]] name = "stickler-eval" -version = "0.1.2" +version = "0.1.3" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "jsonschema" }, @@ -3108,9 +3108,9 @@ dependencies = [ { name = "rapidfuzz" }, { name = "scipy" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/a1/7d/001bffe60e417315c470e77d7d0832f3fce9565f9f491450a3d3497df2df/stickler_eval-0.1.2.tar.gz", hash = "sha256:a9bded773c0c4d8ee08ba03b422ee4655c6a7292f572264f2cd74a565fdbc3f8", size = 132677, upload-time = "2025-11-06T20:07:41.704Z" } +sdist = { url = "https://files.pythonhosted.org/packages/0f/0b/88054b339bc9c2f9147570710c41d2cf13e355df89894bb91ff3a93ff644/stickler_eval-0.1.3.tar.gz", hash = "sha256:d517a413f2e019a947e25bf0d87da6595ae908c0ff3b19e691af77589d1eb6a9", size = 141437, upload-time = "2025-11-18T19:49:29.495Z" } wheels = [ - { url = "https://files.pythonhosted.org/packages/4b/cf/e37a0731e2a1e4c48b5dbf4dd7b1f8d421367ed9a8fb09a1f78ccdebaaaf/stickler_eval-0.1.2-py3-none-any.whl", hash = "sha256:1631bb40cf625e473c22e92158e927412b59bb1f267c06ba47750bac71cc8483", size = 161224, upload-time = "2025-11-06T20:07:40.556Z" }, + { url = "https://files.pythonhosted.org/packages/28/42/502852c1951f1297f4b751c68f4eec1d103468e6b3fc20c75d3120eeb8b7/stickler_eval-0.1.3-py3-none-any.whl", hash = "sha256:b1256f6fd4aa167e67bff14133fe7149e5e48f0f2f526d9174075df3a4333f57", size = 173279, upload-time = "2025-11-18T19:49:28.093Z" }, ] [[package]] From 076092fe96d649ebd48cb38ff081205e9a672d6a Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Fri, 21 Nov 2025 16:14:59 +0000 Subject: [PATCH 11/30] fix template --- lib/idp_common_pkg/idp_common/assessment/models.py | 4 ++-- lib/idp_common_pkg/idp_common/assessment/service.py | 5 +++-- lib/idp_common_pkg/pyproject.toml | 1 + lib/idp_common_pkg/uv.lock | 2 ++ 4 files changed, 8 insertions(+), 4 deletions(-) diff --git a/lib/idp_common_pkg/idp_common/assessment/models.py b/lib/idp_common_pkg/idp_common/assessment/models.py index 909a6ae3..07c8d2cd 100644 --- a/lib/idp_common_pkg/idp_common/assessment/models.py +++ b/lib/idp_common_pkg/idp_common/assessment/models.py @@ -110,9 +110,9 @@ class Geometry(BaseModel): class ConfidenceAlert(BaseModel): """Alert for confidence threshold violation.""" - attribute_name: str + attribute_name: str = Field(serialization_alias="attributeName") confidence: float - confidence_threshold: float + confidence_threshold: float = Field(serialization_alias="confidenceThreshold") @field_validator("confidence", "confidence_threshold", mode="before") @classmethod diff --git a/lib/idp_common_pkg/idp_common/assessment/service.py b/lib/idp_common_pkg/idp_common/assessment/service.py index 4ffe976a..5e7f0973 100644 --- a/lib/idp_common_pkg/idp_common/assessment/service.py +++ b/lib/idp_common_pkg/idp_common/assessment/service.py @@ -1142,9 +1142,10 @@ def process_document_section(self, document: Document, section_id: str) -> Docum # Update the section in the document with confidence threshold alerts for doc_section in document.sections: if doc_section.section_id == section_id: - # Convert ConfidenceAlert objects to dicts + # Convert ConfidenceAlert objects to dicts with camelCase keys for UI doc_section.confidence_threshold_alerts = [ - alert.model_dump() for alert in confidence_threshold_alerts + alert.model_dump(by_alias=True) + for alert in confidence_threshold_alerts ] break diff --git a/lib/idp_common_pkg/pyproject.toml b/lib/idp_common_pkg/pyproject.toml index 5d2dbd2a..34ff7683 100644 --- a/lib/idp_common_pkg/pyproject.toml +++ b/lib/idp_common_pkg/pyproject.toml @@ -26,6 +26,7 @@ dependencies = [ "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 = [ diff --git a/lib/idp_common_pkg/uv.lock b/lib/idp_common_pkg/uv.lock index eb12c58e..26dfb208 100644 --- a/lib/idp_common_pkg/uv.lock +++ b/lib/idp_common_pkg/uv.lock @@ -921,6 +921,7 @@ name = "idp-common" version = "0.4.2" source = { editable = "." } dependencies = [ + { name = "aws-lambda-powertools" }, { name = "boto3" }, { name = "deepdiff" }, { name = "jsonschema" }, @@ -1048,6 +1049,7 @@ test = [ requires-dist = [ { name = "amazon-textract-textractor", extras = ["pandas"], marker = "extra == 'all'", specifier = "==1.9.2" }, { name = "amazon-textract-textractor", extras = ["pandas"], marker = "extra == 'ocr'", specifier = "==1.9.2" }, + { name = "aws-lambda-powertools", specifier = ">=3.21.0" }, { name = "aws-lambda-powertools", marker = "extra == 'agentic-extraction'", specifier = ">=3.2.0" }, { name = "aws-lambda-powertools", marker = "extra == 'assessment'", specifier = ">=3.2.0" }, { name = "aws-xray-sdk", marker = "extra == 'docs-service'", specifier = ">=2.14.0" }, From 839e5206295b7b117ad00a2f6587753b58edc2e8 Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Sat, 22 Nov 2025 18:27:56 +0000 Subject: [PATCH 12/30] missing dep --- lib/idp_common_pkg/pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/lib/idp_common_pkg/pyproject.toml b/lib/idp_common_pkg/pyproject.toml index 34ff7683..ba459bba 100644 --- a/lib/idp_common_pkg/pyproject.toml +++ b/lib/idp_common_pkg/pyproject.toml @@ -74,6 +74,7 @@ extraction = [ # 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", From 79fe3e763466b43007c6865a753b34b456f50f7c Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Sun, 23 Nov 2025 12:01:12 +0000 Subject: [PATCH 13/30] update config model usage --- .../idp_common/assessment/__init__.py | 43 ++++++------------- .../tests/unit/config/test_config_models.py | 19 +++----- .../config/test_config_models_integration.py | 14 +++--- .../src/assessment_function/index.py | 24 ++++------- .../src/assessment_function/index.py | 10 ++--- 5 files changed, 38 insertions(+), 72 deletions(-) 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/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..af18086e 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""" @@ -284,8 +282,8 @@ 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/patterns/pattern-2/src/assessment_function/index.py b/patterns/pattern-2/src/assessment_function/index.py index e1ea12bd..18d3b281 100644 --- a/patterns/pattern-2/src/assessment_function/index.py +++ b/patterns/pattern-2/src/assessment_function/index.py @@ -151,13 +151,9 @@ def handler(event, context): 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(): @@ -254,15 +250,13 @@ def handler(event, context): # Initialize assessment service with cache table for enhanced retry handling cache_table = os.environ.get("TRACKING_TABLE") - # Check if granular assessment is enabled - - # Use granular assessment service (always enabled) + # 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 granular assessment service") + logger.info("Using Strands-based assessment service") # Process the document section for assessment t0 = time.time() @@ -333,11 +327,11 @@ def handler(event, context): # Assessment validation validation_enabled = ( - config.assessment.granular.enabled and config.assessment.validation_enabled + config.assessment.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: + 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.") diff --git a/patterns/pattern-3/src/assessment_function/index.py b/patterns/pattern-3/src/assessment_function/index.py index ad29b551..9411d028 100644 --- a/patterns/pattern-3/src/assessment_function/index.py +++ b/patterns/pattern-3/src/assessment_function/index.py @@ -63,13 +63,9 @@ def handler(event, context): 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(): From 2e93ad3fbd5f8cc63445b5f364c1a2fc52e9c86c Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Mon, 24 Nov 2025 10:44:23 +0000 Subject: [PATCH 14/30] strands argument passing update --- lib/idp_common_pkg/idp_common/assessment/strands_tools.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py index 44379aa5..8c319096 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py @@ -54,7 +54,7 @@ def submit_assessment(assessment: AssessmentOutput, agent: Agent) -> str: Success confirmation message or validation error details """ # Validate assessment structure and return helpful errors - validated_assessment = AssessmentOutput(**assessment) # pyright: ignore[reportCallIssue] + validated_assessment = AssessmentOutput.model_validate(assessment) # Store in agent state agent.state.set("assessment_output", validated_assessment.model_dump()) @@ -80,7 +80,7 @@ def create_view_image_tool(page_images: list[bytes], sorted_page_ids: list[str]) """ @tool - def view_image(input_data: dict[str, Any], agent: Agent) -> dict: + def view_image(input_data: ViewImageInput, agent: Agent) -> dict: """ View a specific page image, optionally highlighting a bounding box area. @@ -104,7 +104,7 @@ def view_image(input_data: dict[str, Any], agent: Agent) -> dict: }, agent) """ # Validate input - let ValidationError propagate - view_input = ViewImageInput(**input_data) + view_input = ViewImageInput.model_validate(input_data) # Validate image index exists if view_input.image_index >= len(page_images): From 338c87fbe86f0160303ac9a9b488a622e0ac9a62 Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Mon, 24 Nov 2025 10:46:54 +0000 Subject: [PATCH 15/30] update the tests for the config --- .../idp_common/assessment/example_usage.py | 109 ++- .../test_assessment_backward_compatibility.py | 650 ------------------ .../config/test_config_models_integration.py | 8 +- lib/idp_common_pkg/uv.lock | 2 + 4 files changed, 45 insertions(+), 724 deletions(-) delete mode 100644 lib/idp_common_pkg/tests/test_assessment_backward_compatibility.py 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..4cbe36f0 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": [ { @@ -158,86 +155,61 @@ def example_granular_assessment(): 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 +221,23 @@ 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 +245,12 @@ 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/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/config/test_config_models_integration.py b/lib/idp_common_pkg/tests/unit/config/test_config_models_integration.py index af18086e..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 @@ -261,12 +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": [], } diff --git a/lib/idp_common_pkg/uv.lock b/lib/idp_common_pkg/uv.lock index 26dfb208..db0a0b14 100644 --- a/lib/idp_common_pkg/uv.lock +++ b/lib/idp_common_pkg/uv.lock @@ -969,6 +969,7 @@ assessment = [ { name = "aws-lambda-powertools" }, { name = "mypy-boto3-dynamodb" }, { name = "pillow" }, + { name = "pymupdf" }, { name = "strands-agents" }, ] classification = [ @@ -1099,6 +1100,7 @@ requires-dist = [ { name = "pydantic", specifier = ">=2.12.0" }, { name = "pymupdf", marker = "extra == 'agentic-extraction'", specifier = "==1.25.5" }, { name = "pymupdf", marker = "extra == 'all'", specifier = "==1.25.5" }, + { name = "pymupdf", marker = "extra == 'assessment'", specifier = "==1.25.5" }, { name = "pymupdf", marker = "extra == 'ocr'", specifier = "==1.25.5" }, { name = "pytest", marker = "extra == 'test'", specifier = ">=7.4.0" }, { name = "pytest-asyncio", marker = "extra == 'test'", specifier = ">=1.1.0" }, From 16480da3323dd8b79f94790b4ff3539f27ac3c01 Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Mon, 24 Nov 2025 11:26:27 +0000 Subject: [PATCH 16/30] bug fixes --- lib/idp_common_pkg/idp_common/assessment/granular_service.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) 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 482885d6..730ac76b 100644 --- a/lib/idp_common_pkg/idp_common/assessment/granular_service.py +++ b/lib/idp_common_pkg/idp_common/assessment/granular_service.py @@ -22,6 +22,7 @@ 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_ITEMS, @@ -305,7 +306,7 @@ def _traverse( task_id=f"task_{task_counter[0]}", task_type="attribute", field_path=item_path, - field_name=prop_name, + field_name=_convert_field_path_to_string(item_path), field_schema=items_schema, confidence_threshold=threshold, parent_assessment_dict=assessment_list, # type: ignore @@ -328,7 +329,7 @@ def _traverse( task_id=f"task_{task_counter[0]}", task_type="attribute", field_path=field_path, - field_name=prop_name, + field_name=_convert_field_path_to_string(field_path), field_schema=prop_schema, confidence_threshold=threshold, parent_assessment_dict=parent_dict, From 984ac36cd80d341e5484c56975091a412c1b931e Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Mon, 24 Nov 2025 13:30:08 +0000 Subject: [PATCH 17/30] make sure model dump uses json mode --- lib/idp_common_pkg/idp_common/assessment/strands_tools.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py index 8c319096..ca5fcdeb 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py @@ -57,11 +57,11 @@ def submit_assessment(assessment: AssessmentOutput, agent: Agent) -> str: validated_assessment = AssessmentOutput.model_validate(assessment) # Store in agent state - agent.state.set("assessment_output", validated_assessment.model_dump()) + agent.state.set("assessment_output", validated_assessment.model_dump(mode="json")) logger.info( "Assessment submitted successfully", - extra={"assessment": validated_assessment.model_dump()}, + extra={"assessment": validated_assessment.model_dump(mode="json")}, ) return "Assessment submitted successfully. You can now finish the task." From af89f4721946a092d48e0c7fa430c3c5ef78a212 Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Mon, 24 Nov 2025 15:13:39 +0000 Subject: [PATCH 18/30] bbox update --- .../idp_common/assessment/geometry_utils.py | 116 ++++++++++++++ .../idp_common/assessment/granular_service.py | 143 +---------------- .../idp_common/assessment/models.py | 151 +++++++++++++++++- .../idp_common/assessment/service.py | 148 +---------------- .../idp_common/assessment/strands_models.py | 33 ++-- .../idp_common/assessment/strands_service.py | 44 +++-- 6 files changed, 307 insertions(+), 328 deletions(-) create mode 100644 lib/idp_common_pkg/idp_common/assessment/geometry_utils.py 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 730ac76b..9a5fd70c 100644 --- a/lib/idp_common_pkg/idp_common/assessment/granular_service.py +++ b/lib/idp_common_pkg/idp_common/assessment/granular_service.py @@ -646,148 +646,7 @@ 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: """ diff --git a/lib/idp_common_pkg/idp_common/assessment/models.py b/lib/idp_common_pkg/idp_common/assessment/models.py index 07c8d2cd..f700e2df 100644 --- a/lib/idp_common_pkg/idp_common/assessment/models.py +++ b/lib/idp_common_pkg/idp_common/assessment/models.py @@ -100,11 +100,156 @@ class AssessmentResult(BaseModel): # ============================================================================ +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 + + return cls(top=top, left=left, width=width, height=height) + + class Geometry(BaseModel): - """Geometry in IDP format (converted from BoundingBox).""" + """ + Standard IDP geometry format compatible with UI expectations. - boundingBox: dict[str, float] # {top, left, width, height} - page: int + 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) + value: Any = Field(None, description="The extracted value") + 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, + value: Any, + reasoning: str, + confidence_threshold: float, + bbox_coords: list[float] | None = None, + page_num: int | None = None, + ) -> "FieldAssessmentData": + """Create from LLM response data.""" + 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, + value=value, + reasoning=reasoning, + confidence_threshold=confidence_threshold, + geometry=geometry, + ) + + def to_explainability_format(self) -> dict[str, Any]: + """Convert to explainability_info format for frontend.""" + result = { + "confidence": self.confidence, + "value": self.value, + "reasoning": 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): diff --git a/lib/idp_common_pkg/idp_common/assessment/service.py b/lib/idp_common_pkg/idp_common/assessment/service.py index 5e7f0973..de010bdb 100644 --- a/lib/idp_common_pkg/idp_common/assessment/service.py +++ b/lib/idp_common_pkg/idp_common/assessment/service.py @@ -20,6 +20,7 @@ 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, @@ -621,152 +622,9 @@ def _get_text_confidence_data(self, page) -> str: return "" # ============================================================================ - # GEOMETRY PROCESSING + # GEOMETRY PROCESSING (uses shared utilities from geometry_utils) # ============================================================================ - 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)}" - ) - 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 - # ============================================================================ # RESULT PROCESSING # ============================================================================ @@ -811,7 +669,7 @@ def _process_assessment_response( # 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) + assessment_data = extract_geometry_from_nested_dict(assessment_data) except Exception as e: logger.warning(f"Failed to extract geometry data: {str(e)}") diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_models.py b/lib/idp_common_pkg/idp_common/assessment/strands_models.py index 80cc261e..ba04ac65 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_models.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_models.py @@ -9,6 +9,8 @@ from pydantic import BaseModel, Field +from idp_common.assessment.models import Geometry + class BoundingBox(BaseModel): """Bounding box coordinates in normalized 0-1000 scale.""" @@ -21,20 +23,18 @@ class BoundingBox(BaseModel): def to_geometry(self) -> dict[str, Any]: """ - Convert to IDP geometry format. + Convert to IDP geometry format compatible with UI. Returns: - Dictionary with BoundingBox and Page in IDP format + Dictionary in UI-compatible format (lowercase, no array wrapper here) """ - return { - "BoundingBox": { - "Width": (self.x2 - self.x1) / 1000.0, - "Height": (self.y2 - self.y1) / 1000.0, - "Left": self.x1 / 1000.0, - "Top": self.y1 / 1000.0, - }, - "Page": self.page, - } + # 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): @@ -43,18 +43,10 @@ class ConfidenceAssessment(BaseModel): value: Any = Field(..., description="The extracted value") confidence: float = Field(..., ge=0.0, le=1.0, description="Confidence score 0-1") reasoning: str = Field(..., description="Explanation for the confidence score") - threshold: float = Field( - ..., ge=0.0, le=1.0, description="Required confidence threshold" - ) bounding_box: BoundingBox | None = Field( None, description="Location of value in document" ) - @property - def meets_threshold(self) -> bool: - """Computed field: whether confidence meets threshold.""" - return self.confidence >= self.threshold - class AssessmentOutput(BaseModel): """ @@ -71,6 +63,3 @@ class AssessmentOutput(BaseModel): assessment: ConfidenceAssessment = Field( ..., description="Confidence assessment for this specific field" ) - alerts: list[str] = Field( - default_factory=list, description="Any confidence threshold alerts or issues" - ) diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_service.py b/lib/idp_common_pkg/idp_common/assessment/strands_service.py index f4963261..f05aae12 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_service.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_service.py @@ -17,7 +17,11 @@ from strands.types.content import CachePoint, ContentBlock, Message from strands.types.media import ImageContent, ImageSource -from idp_common.assessment.models import AssessmentResult, AssessmentTask +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 @@ -401,33 +405,41 @@ def _convert_to_assessment_result( metering: dict[str, Any], processing_time: float, ) -> AssessmentResult: - """Convert Strands AssessmentOutput to AssessmentResult.""" - + """Convert Strands AssessmentOutput to AssessmentResult with standardized geometry format.""" # Single field assessment field_name = output.field_name assessment = output.assessment - # Build assessment data with confidence score - assessment_data = { - field_name: { - "confidence": assessment.confidence, - "value": assessment.value, - "reasoning": assessment.reasoning, - } - } + # Create standardized field assessment data + field_data = FieldAssessmentData.from_llm_response( + confidence=assessment.confidence, + value=assessment.value, + 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, + ) - # Add geometry if bounding box provided - if assessment.bounding_box: - assessment_data[field_name]["Geometry"] = assessment.bounding_box.to_geometry() + # Convert to explainability format + assessment_data = {field_name: field_data.to_explainability_format()} # Check for confidence threshold violations confidence_alerts = [] - if not assessment.meets_threshold: + if assessment.confidence < task.confidence_threshold: confidence_alerts.append( { "attribute_name": field_name, "confidence": assessment.confidence, - "confidence_threshold": assessment.threshold, + "confidence_threshold": task.confidence_threshold, } ) From cedf899550e494f73683cfa2e69e5822f84fef6b Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Mon, 24 Nov 2025 17:22:02 +0000 Subject: [PATCH 19/30] memory update --- patterns/pattern-2/template.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/patterns/pattern-2/template.yaml b/patterns/pattern-2/template.yaml index 6605c8af..43bd97e6 100644 --- a/patterns/pattern-2/template.yaml +++ b/patterns/pattern-2/template.yaml @@ -1820,7 +1820,7 @@ Resources: Command: - "index.handler" Timeout: 900 - MemorySize: 512 + MemorySize: 4096 Tracing: Active Environment: Variables: @@ -1946,7 +1946,7 @@ Resources: Command: - "index.handler" Timeout: 900 - MemorySize: 512 + MemorySize: 4096 Tracing: Active Environment: Variables: From a92f70ea5d1941cb8c791b751c66b0c227273ff2 Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Mon, 24 Nov 2025 22:40:02 +0000 Subject: [PATCH 20/30] cleanup --- .../idp_common/assessment/example_usage.py | 9 +- .../idp_common/assessment/granular_service.py | 89 ++- .../idp_common/assessment/models.py | 4 - .../idp_common/assessment/strands_service.py | 13 +- .../idp_common/assessment/strands_tools.py | 16 - .../idp_common/image/__init__.py | 2 +- .../idp_common/utils/pdf_helpers.py | 4 +- .../test_granular_assessment_full.py | 684 ++++++++++++++++++ .../tests/unit/assessment/conftest.py | 1 + .../test_aggregate_assessment_results.py | 430 +++++++++++ .../unit/config/test_configuration_sync.py | 4 +- .../tests/unit/test_granular_assessment.py | 9 +- lib/idp_common_pkg/verify_stickler.py | 2 +- 13 files changed, 1187 insertions(+), 80 deletions(-) create mode 100755 lib/idp_common_pkg/test_granular_assessment_full.py create mode 100644 lib/idp_common_pkg/tests/unit/assessment/test_aggregate_assessment_results.py 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 4cbe36f0..ebc8c0d0 100644 --- a/lib/idp_common_pkg/idp_common/assessment/example_usage.py +++ b/lib/idp_common_pkg/idp_common/assessment/example_usage.py @@ -142,14 +142,15 @@ 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 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 9a5fd70c..acb338a4 100644 --- a/lib/idp_common_pkg/idp_common/assessment/granular_service.py +++ b/lib/idp_common_pkg/idp_common/assessment/granular_service.py @@ -309,7 +309,6 @@ def _traverse( field_name=_convert_field_path_to_string(item_path), field_schema=items_schema, confidence_threshold=threshold, - parent_assessment_dict=assessment_list, # type: ignore ) tasks.append(task) task_counter[0] += 1 @@ -332,7 +331,6 @@ def _traverse( field_name=_convert_field_path_to_string(field_path), field_schema=prop_schema, confidence_threshold=threshold, - parent_assessment_dict=parent_dict, ) tasks.append(task) task_counter[0] += 1 @@ -536,6 +534,32 @@ 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], @@ -543,7 +567,7 @@ def _aggregate_assessment_results( assessment_structure: dict[str, Any], ) -> tuple[dict[str, Any], list[dict[str, Any]], dict[str, Any]]: """ - Aggregate individual task results into assessment structure using direct parent insertion. + Aggregate individual task results into assessment structure using field_path navigation. Args: tasks: list of assessment tasks @@ -553,13 +577,12 @@ def _aggregate_assessment_results( Returns: Tuple of (assessment_structure, confidence_alerts, aggregated_metering) """ - 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 each task result - direct O(1) insertion using parent reference for task in tasks: result = result_map.get(task.task_id) if not result or not result.success: @@ -575,45 +598,31 @@ def _aggregate_assessment_results( # Add confidence alerts all_confidence_alerts.extend(result.confidence_alerts) - # Get assessment data from result - should be a single assessment object - # The Strands agent returns the assessment in result.assessment_data - assessment_obj = result.assessment_data + # Get assessment data directly from result + # strands_service returns flat assessment dict: {confidence, value, reasoning, ...} + field_assessment = result.assessment_data - if not isinstance(assessment_obj, dict): + if not isinstance(field_assessment, dict): logger.warning( - f"Task {task.task_id}: expected dict assessment, got {type(assessment_obj)}" + f"Task {task.task_id}: expected dict assessment, got {type(field_assessment)}" ) continue - # Add confidence_threshold to the assessment object - assessment_obj["confidence_threshold"] = task.confidence_threshold - - # Direct insertion using parent reference - O(1) operation! - parent = task.parent_assessment_dict - field_name = task.field_name - - if isinstance(parent, dict): - # Regular field - insert into parent dict - parent[field_name] = assessment_obj - elif isinstance(parent, list): - # Array item - get index from field_path - # field_path is like ("items", 0, "price") - second-to-last is the index - if len(task.field_path) >= 2 and isinstance(task.field_path[-2], int): - idx = task.field_path[-2] - # Replace the None placeholder we created during structure building - if idx < len(parent): - parent[idx] = assessment_obj - else: - logger.warning( - f"Task {task.task_id}: index {idx} out of range for list of length {len(parent)}" - ) - else: - logger.warning( - f"Task {task.task_id}: cannot determine array index from path {task.field_path}" - ) - else: - logger.warning( - f"Task {task.task_id}: unexpected parent type {type(parent)}" + # Add confidence_threshold if not already present + if "confidence_threshold" not in field_assessment: + field_assessment["confidence_threshold"] = task.confidence_threshold + + # 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 assessment_structure, all_confidence_alerts, aggregated_metering diff --git a/lib/idp_common_pkg/idp_common/assessment/models.py b/lib/idp_common_pkg/idp_common/assessment/models.py index f700e2df..11b0adb8 100644 --- a/lib/idp_common_pkg/idp_common/assessment/models.py +++ b/lib/idp_common_pkg/idp_common/assessment/models.py @@ -78,10 +78,6 @@ class AssessmentTask(BaseModel): # Confidence threshold for this field confidence_threshold: float - # Direct reference to parent container in assessment structure (for O(1) insertion) - # Can be Dict for regular fields or list for array items - parent_assessment_dict: dict[str, Any] | list[Any] - class AssessmentResult(BaseModel): """Result of a single assessment task (used by both granular and strands services).""" diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_service.py b/lib/idp_common_pkg/idp_common/assessment/strands_service.py index f05aae12..e6f997ee 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_service.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_service.py @@ -405,8 +405,12 @@ def _convert_to_assessment_result( metering: dict[str, Any], processing_time: float, ) -> AssessmentResult: - """Convert Strands AssessmentOutput to AssessmentResult with standardized geometry format.""" - # Single field assessment + """ + 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 @@ -429,8 +433,9 @@ def _convert_to_assessment_result( page_num=assessment.bounding_box.page if assessment.bounding_box else None, ) - # Convert to explainability format - assessment_data = {field_name: field_data.to_explainability_format()} + # 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 = [] diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py index ca5fcdeb..de79e59b 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py @@ -37,22 +37,6 @@ class ViewImageInput(BaseModel): @tool def submit_assessment(assessment: AssessmentOutput, agent: Agent) -> str: - """ - Submit your final confidence assessment. - - Use this tool when you have: - 1. Located the values in the document images - 2. Determined precise bounding box coordinates using ruler markings - 3. Assessed the confidence based on clarity and accuracy - - Args: - assessment: Dictionary with: - - assessments: dict mapping attribute names to ConfidenceAssessment - - alerts: list of any threshold alerts (optional) - - Returns: - Success confirmation message or validation error details - """ # Validate assessment structure and return helpful errors validated_assessment = AssessmentOutput.model_validate(assessment) diff --git a/lib/idp_common_pkg/idp_common/image/__init__.py b/lib/idp_common_pkg/idp_common/image/__init__.py index eb43f726..09fb21f4 100644 --- a/lib/idp_common_pkg/idp_common/image/__init__.py +++ b/lib/idp_common_pkg/idp_common/image/__init__.py @@ -79,7 +79,7 @@ def resize_image( 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) + image = image.resize((new_width, new_height), Image.Resampling.LANCZOS) # Save in original format if possible img_byte_array = io.BytesIO() diff --git a/lib/idp_common_pkg/idp_common/utils/pdf_helpers.py b/lib/idp_common_pkg/idp_common/utils/pdf_helpers.py index c102ee52..440a1b2c 100644 --- a/lib/idp_common_pkg/idp_common/utils/pdf_helpers.py +++ b/lib/idp_common_pkg/idp_common/utils/pdf_helpers.py @@ -75,7 +75,7 @@ def pdf_page_to_image( # Render page to pixmap mat = fitz.Matrix(dpi_scale, dpi_scale) - pix = page.get_pixmap(matrix=mat) + pix = page.get_pixmap(matrix=mat) # pyright: ignore[reportAttributeAccessIssue] # Convert to PNG bytes png_bytes = pix.tobytes("png") @@ -144,7 +144,7 @@ def pdf_to_images( # Render page to pixmap mat = fitz.Matrix(dpi_scale, dpi_scale) - pix = page.get_pixmap(matrix=mat) + pix = page.get_pixmap(matrix=mat) # pyright: ignore[reportAttributeAccessIssue] # Convert to PNG bytes png_bytes = pix.tobytes("png") 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"āœ“ 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("šŸ“¦ 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" āœ“ 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" āœ“ 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" āœ“ 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" āœ“ 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" āœ“ 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(" āœ“ 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("šŸ“Š 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("āŒ 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"\nāš ļø 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("\nāœ… No confidence threshold alerts") + + # Read assessment data from S3 + if section.extraction_result_uri: + print("\nšŸ“„ 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 = "āœ…" if conf >= threshold else "āš ļø" + 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("\nā±ļø 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("\nšŸ’° 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("šŸ“‹ 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("āš™ļø Initializing GranularAssessmentService...") + service = GranularAssessmentService( + region=TEST_REGION, config=config, cache_table=None + ) + print(" āœ“ 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("šŸ” 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 Ɨ 3 fields each)") + print() + + print("=" * 80) + print("šŸš€ Running Full Assessment Pipeline") + print("=" * 80) + print() + print("āš ļø 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("ā³ Calling process_document_section()...") + print() + + updated_doc = service.process_document_section(doc, section.section_id) + + print() + print("āœ… Assessment completed successfully!") + print() + + # Print results + print_assessment_results(updated_doc, section.section_id, s3_client) + + print() + print("=" * 80) + print("āœ… Full Integration Test PASSED!") + print("=" * 80) + + except Exception as e: + print() + print(f"āŒ Assessment failed: {e}") + print() + + import traceback + + print("Full error traceback:") + traceback.print_exc() + print() + + print("=" * 80) + print("āŒ 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( + "\nšŸ’” 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/unit/assessment/conftest.py b/lib/idp_common_pkg/tests/unit/assessment/conftest.py index a1b17cd9..352d58b2 100644 --- a/lib/idp_common_pkg/tests/unit/assessment/conftest.py +++ b/lib/idp_common_pkg/tests/unit/assessment/conftest.py @@ -20,5 +20,6 @@ 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/config/test_configuration_sync.py b/lib/idp_common_pkg/tests/unit/config/test_configuration_sync.py index 96db5c1f..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 @@ -257,7 +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"}], @@ -298,7 +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/test_granular_assessment.py b/lib/idp_common_pkg/tests/unit/test_granular_assessment.py index 5df0b926..c9520334 100644 --- a/lib/idp_common_pkg/tests/unit/test_granular_assessment.py +++ b/lib/idp_common_pkg/tests/unit/test_granular_assessment.py @@ -155,8 +155,8 @@ def test_create_assessment_tasks_simple_attributes( # All tasks should have field_path as tuple assert all(isinstance(t.field_path, tuple) for t in tasks) - # All tasks should have parent_assessment_dict reference - assert all(t.parent_assessment_dict is not None for t in tasks) + # All tasks should have confidence_threshold set + assert all(t.confidence_threshold > 0 for t in tasks) # Check that assessment_structure mirrors extraction_results assert isinstance(assessment_structure, dict) @@ -268,7 +268,7 @@ def test_aggregate_assessment_results_new_approach(self, sample_config): "date": None, } - # Create tasks with new structure + # Create tasks with new structure (field_path used for navigation) task1 = AssessmentTask( task_id="task_0", task_type="attribute", @@ -276,7 +276,6 @@ def test_aggregate_assessment_results_new_approach(self, sample_config): field_name="sender_name", field_schema={"type": "string"}, confidence_threshold=0.9, - parent_assessment_dict=assessment_structure, ) task2 = AssessmentTask( @@ -286,7 +285,6 @@ def test_aggregate_assessment_results_new_approach(self, sample_config): field_name="recipient_name", field_schema={"type": "string"}, confidence_threshold=0.9, - parent_assessment_dict=assessment_structure, ) task3 = AssessmentTask( @@ -296,7 +294,6 @@ def test_aggregate_assessment_results_new_approach(self, sample_config): field_name="date", field_schema={"type": "string"}, confidence_threshold=0.9, - parent_assessment_dict=assessment_structure, ) # Create results diff --git a/lib/idp_common_pkg/verify_stickler.py b/lib/idp_common_pkg/verify_stickler.py index 42d82917..3323644f 100644 --- a/lib/idp_common_pkg/verify_stickler.py +++ b/lib/idp_common_pkg/verify_stickler.py @@ -10,7 +10,7 @@ """ import sys -from typing import Any, dict +from typing import Any def verify_stickler_import(): From 5a066fc493695b8ea43d5521be0d3e0ea45a3c86 Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Mon, 24 Nov 2025 22:40:47 +0000 Subject: [PATCH 21/30] fix failing test --- lib/idp_common_pkg/idp_common/assessment/service.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/lib/idp_common_pkg/idp_common/assessment/service.py b/lib/idp_common_pkg/idp_common/assessment/service.py index de010bdb..0e64dc7f 100644 --- a/lib/idp_common_pkg/idp_common/assessment/service.py +++ b/lib/idp_common_pkg/idp_common/assessment/service.py @@ -511,8 +511,8 @@ def _load_extraction_data(self, section) -> ExtractionData: extraction_results = extraction_data.get("inference_result", {}) if not extraction_results: - raise ValueError( - f"No extraction results found for section {section.section_id}" + logger.warning( + f"No extraction results found for section {section.section_id}, skipping assessment" ) return ExtractionData( @@ -915,6 +915,13 @@ def process_document_section(self, document: Document, section_id: str) -> Docum t1 = time.time() logger.info(f"Time taken to load extraction data: {t1 - t0:.2f} seconds") + # Handle empty extraction results gracefully + if not extraction_results: + logger.warning( + f"No extraction results for section {section_id}, skipping assessment" + ) + return document + # Load document content (text, images, OCR confidence) document_content = self._load_document_content(document, section) t2 = time.time() From f3ab5981d601db96fbbc36281600efc2a37457e6 Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Mon, 24 Nov 2025 22:42:14 +0000 Subject: [PATCH 22/30] import fix --- scripts/test_grid_overlay.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/scripts/test_grid_overlay.py b/scripts/test_grid_overlay.py index 1cfeb691..3b5ab726 100644 --- a/scripts/test_grid_overlay.py +++ b/scripts/test_grid_overlay.py @@ -28,7 +28,7 @@ # Add the library to path sys.path.insert(0, str(Path(__file__).parent.parent / "lib" / "idp_common_pkg")) -from idp_common.grid_overlay import ( +from idp_common.utils.grid_overlay import ( add_ruler_edges, draw_bounding_boxes, add_ruler_and_draw_boxes, @@ -44,7 +44,7 @@ def convert_pdf_to_image(pdf_path: str) -> bytes: page = doc.load_page(0) # First page # Render at 150 DPI for good quality - pix = page.get_pixmap(dpi=150) + pix = page.get_pixmap(dpi=150) # pyright: ignore[reportAttributeAccessIssue] return pix.tobytes("jpeg") except ImportError: print("ERROR: PyMuPDF (fitz) is required for PDF conversion.") From 29b477313b74c3374984c30abc7d8199c9dc7a1e Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Mon, 24 Nov 2025 22:56:33 +0000 Subject: [PATCH 23/30] cleanup: remove artifacts and redundant code from PR review --- lib/idp_common_pkg/idp_common/assessment/granular_service.py | 2 +- lib/idp_common_pkg/idp_common/assessment/strands_executor.py | 2 -- lib/idp_common_pkg/idp_common/assessment/strands_service.py | 3 +-- 3 files changed, 2 insertions(+), 5 deletions(-) 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 acb338a4..6cef8a2f 100644 --- a/lib/idp_common_pkg/idp_common/assessment/granular_service.py +++ b/lib/idp_common_pkg/idp_common/assessment/granular_service.py @@ -126,7 +126,7 @@ def __init__( dynamodb: DynamoDBServiceResource = boto3.resource( "dynamodb", region_name=self.region - ) # pyright: ignore[reportAssignmentType]modb", 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}" diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_executor.py b/lib/idp_common_pkg/idp_common/assessment/strands_executor.py index 7b0d3015..8e478e48 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_executor.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_executor.py @@ -39,7 +39,6 @@ async def execute_tasks_async( Args: tasks: List of assessment tasks to execute - base_content: Base prompt content extraction_results: Full extraction results page_images: List of page images (with grid overlay) sorted_page_ids: List of page IDs @@ -166,7 +165,6 @@ def execute_assessment_tasks_parallel( Args: tasks: List of assessment tasks - base_content: Base prompt content extraction_results: Full extraction results page_images: List of page images (with grid overlay already applied) sorted_page_ids: List of page IDs in sorted order diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_service.py b/lib/idp_common_pkg/idp_common/assessment/strands_service.py index e6f997ee..1b0d13c7 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_service.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_service.py @@ -95,8 +95,7 @@ async def assess_attribute_with_strands( system_prompt=enhanced_system_prompt, state={ "task": task.model_dump(), - "extraction_results": extraction_results, - "assessment_output": None, + "assessment_output": None, # Will be populated by submit_assessment tool }, conversation_manager=SummarizingConversationManager( summary_ratio=0.8, preserve_recent_messages=1 From 958d775df64e2876f08d014a9c05b515bc8b3e76 Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Wed, 26 Nov 2025 14:18:11 +0000 Subject: [PATCH 24/30] update tests to pass --- .../idp_common/config/models.py | 7 ----- .../test_classification_service.py | 3 ++- .../tests/unit/ocr/test_ocr_service.py | 26 +++++++++++-------- 3 files changed, 17 insertions(+), 19 deletions(-) diff --git a/lib/idp_common_pkg/idp_common/config/models.py b/lib/idp_common_pkg/idp_common/config/models.py index 1e1c3126..60dcefce 100644 --- a/lib/idp_common_pkg/idp_common/config/models.py +++ b/lib/idp_common_pkg/idp_common/config/models.py @@ -133,13 +133,6 @@ def parse_int(cls, v: Any) -> int: return int(v) if v else 0 return int(v) - @model_validator(mode="after") - def model_validator(self) -> Self: - if not self.agentic.review_agent_model: - self.agentic.review_agent_model = self.model - - return self - @model_validator(mode="after") def set_default_review_agent_model(self) -> Self: """Set review_agent_model to extraction model if not specified.""" 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 3a294bbd..e7100539 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/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): From 049fb7449c0b2011956b5ede883f79979ba17410 Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Wed, 26 Nov 2025 14:36:26 +0000 Subject: [PATCH 25/30] fixes --- .../pattern-3/rvl-cdip-package-sample/config.yaml | 5 ----- .../idp_common/assessment/granular_service.py | 9 +++++---- lib/idp_common_pkg/idp_common/extraction/agentic_idp.py | 2 +- patterns/pattern-2/src/assessment_function/index.py | 2 +- patterns/pattern-3/src/assessment_function/index.py | 2 +- 5 files changed, 8 insertions(+), 12 deletions(-) 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 2ab91dba..913899c5 100644 --- a/config_library/pattern-3/rvl-cdip-package-sample/config.yaml +++ b/config_library/pattern-3/rvl-cdip-package-sample/config.yaml @@ -932,11 +932,6 @@ assessment: 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: "0.0" max_tokens: "10000" 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 6cef8a2f..d2b42e0b 100644 --- a/lib/idp_common_pkg/idp_common/assessment/granular_service.py +++ b/lib/idp_common_pkg/idp_common/assessment/granular_service.py @@ -1094,9 +1094,10 @@ 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 = [ @@ -1162,7 +1163,7 @@ 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, ) -> str | None: 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 6154f185..c24769f5 100644 --- a/lib/idp_common_pkg/idp_common/extraction/agentic_idp.py +++ b/lib/idp_common_pkg/idp_common/extraction/agentic_idp.py @@ -467,7 +467,7 @@ 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 = """ diff --git a/patterns/pattern-2/src/assessment_function/index.py b/patterns/pattern-2/src/assessment_function/index.py index 18d3b281..dec12139 100644 --- a/patterns/pattern-2/src/assessment_function/index.py +++ b/patterns/pattern-2/src/assessment_function/index.py @@ -6,7 +6,7 @@ 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 diff --git a/patterns/pattern-3/src/assessment_function/index.py b/patterns/pattern-3/src/assessment_function/index.py index 9411d028..432385a1 100644 --- a/patterns/pattern-3/src/assessment_function/index.py +++ b/patterns/pattern-3/src/assessment_function/index.py @@ -174,7 +174,7 @@ def handler(event, context): 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: From 0777e5a3682e65872ab7652ac6bf7a7dac1ecfd8 Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Thu, 27 Nov 2025 14:16:51 +0000 Subject: [PATCH 26/30] fix the ruler offset --- .../idp_common/assessment/models.py | 23 +- .../idp_common/assessment/strands_models.py | 1 - .../idp_common/assessment/strands_service.py | 1 - .../idp_common/assessment/strands_tools.py | 5 +- .../idp_common/config/models.py | 4 +- .../idp_common/utils/grid_overlay.py | 29 +- .../document-viewer/VisualEditorModal.jsx | 336 +++++++++--------- 7 files changed, 194 insertions(+), 205 deletions(-) diff --git a/lib/idp_common_pkg/idp_common/assessment/models.py b/lib/idp_common_pkg/idp_common/assessment/models.py index 11b0adb8..3678e068 100644 --- a/lib/idp_common_pkg/idp_common/assessment/models.py +++ b/lib/idp_common_pkg/idp_common/assessment/models.py @@ -106,10 +106,15 @@ class BoundingBoxCoordinates(BaseModel): @classmethod def from_corners( - cls, x1: float, y1: float, x2: float, y2: float, scale: float = 1000.0 + cls, + x1: float, + y1: float, + x2: float, + y2: float, + scale: float = 1000.0, ) -> "BoundingBoxCoordinates": """ - Create from corner coordinates. + Create from corner coordinates in document space. Args: x1, y1: Top-left corner in 0-scale range @@ -129,6 +134,12 @@ def from_corners( 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) @@ -201,7 +212,6 @@ class FieldAssessmentData(BaseModel): """ confidence: float = Field(..., ge=0.0, le=1.0) - value: Any = Field(None, description="The extracted value") reasoning: str = Field(..., description="Confidence reasoning") confidence_threshold: float = Field(..., ge=0.0, le=1.0) geometry: list[Geometry] | None = Field( @@ -213,7 +223,6 @@ class FieldAssessmentData(BaseModel): def from_llm_response( cls, confidence: float, - value: Any, reasoning: str, confidence_threshold: float, bbox_coords: list[float] | None = None, @@ -227,7 +236,6 @@ def from_llm_response( return cls( confidence=confidence, - value=value, reasoning=reasoning, confidence_threshold=confidence_threshold, geometry=geometry, @@ -235,10 +243,9 @@ def from_llm_response( def to_explainability_format(self) -> dict[str, Any]: """Convert to explainability_info format for frontend.""" - result = { + result: dict[str, Any] = { "confidence": self.confidence, - "value": self.value, - "reasoning": self.reasoning, + "confidence_reason": self.reasoning, "confidence_threshold": self.confidence_threshold, } diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_models.py b/lib/idp_common_pkg/idp_common/assessment/strands_models.py index ba04ac65..a971de03 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_models.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_models.py @@ -40,7 +40,6 @@ def to_geometry(self) -> dict[str, Any]: class ConfidenceAssessment(BaseModel): """Confidence assessment for an attribute value.""" - value: Any = Field(..., description="The extracted 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( diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_service.py b/lib/idp_common_pkg/idp_common/assessment/strands_service.py index 1b0d13c7..460c32fd 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_service.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_service.py @@ -416,7 +416,6 @@ def _convert_to_assessment_result( # Create standardized field assessment data field_data = FieldAssessmentData.from_llm_response( confidence=assessment.confidence, - value=assessment.value, reasoning=assessment.reasoning, confidence_threshold=task.confidence_threshold, bbox_coords=( diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py index de79e59b..e0f74a15 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py @@ -115,13 +115,12 @@ def view_image(input_data: ViewImageInput, agent: Agent) -> dict: "color": "red", } - # Draw the bounding box on the image (which already has ruler) + # Draw the bounding box on the image (which has 30px margin for ruler) # Let drawing errors propagate - if we can't draw, something is wrong img_bytes = draw_bounding_boxes( img_bytes, [bbox_dict], - has_ruler=True, - ruler_width=30, + margin_offset=30, ) logger.debug( diff --git a/lib/idp_common_pkg/idp_common/config/models.py b/lib/idp_common_pkg/idp_common/config/models.py index 60dcefce..ef5c5b15 100644 --- a/lib/idp_common_pkg/idp_common/config/models.py +++ b/lib/idp_common_pkg/idp_common/config/models.py @@ -141,7 +141,6 @@ 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.""" @@ -231,7 +230,8 @@ class AssessmentConfig(BaseModel): 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.", diff --git a/lib/idp_common_pkg/idp_common/utils/grid_overlay.py b/lib/idp_common_pkg/idp_common/utils/grid_overlay.py index 51c1c802..c2fd05e1 100644 --- a/lib/idp_common_pkg/idp_common/utils/grid_overlay.py +++ b/lib/idp_common_pkg/idp_common/utils/grid_overlay.py @@ -147,8 +147,7 @@ def add_ruler_edges( def draw_bounding_boxes( image_data: bytes, bboxes: list[dict], - has_ruler: bool = False, - ruler_width: int = 30, + margin_offset: int = 0, box_color: str = "red", box_width: int = 3, label_font_size: int = 12, @@ -160,12 +159,11 @@ def draw_bounding_boxes( Args: image_data: Raw image bytes bboxes: List of bounding box dictionaries, each containing: - - 'bbox': [x1, y1, x2, y2] in 0-1000 normalized scale + - '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) - has_ruler: If True, account for ruler margins in coordinate calculation - ruler_width: Width of ruler margin (only used if has_ruler=True) + 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 @@ -191,17 +189,11 @@ def draw_bounding_boxes( image = Image.open(io.BytesIO(image_data)).convert("RGBA") width, height = image.size - # If image has ruler edges, calculate the actual document area - if has_ruler: - doc_width = width - ruler_width - doc_height = height - ruler_width - offset_x = ruler_width - offset_y = ruler_width - else: - doc_width = width - doc_height = height - offset_x = 0 - offset_y = 0 + # 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)) @@ -331,12 +323,11 @@ def add_ruler_and_draw_boxes( label_interval=label_interval, ) - # Then draw bounding boxes (accounting for ruler offset) + # Then draw bounding boxes (accounting for margin offset from ruler) result = draw_bounding_boxes( image_with_ruler, bboxes, - has_ruler=True, - ruler_width=ruler_width, + margin_offset=ruler_width, box_color=box_color, box_width=box_width, ) 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 ( Date: Mon, 1 Dec 2025 10:38:54 +0000 Subject: [PATCH 27/30] encapsulate ruler --- .../idp_common/assessment/granular_service.py | 14 +- .../idp_common/assessment/models.py | 13 +- .../idp_common/assessment/strands_executor.py | 4 +- .../idp_common/assessment/strands_service.py | 17 +- .../idp_common/assessment/strands_tools.py | 33 +- .../test_bounding_box_integration.py | 374 +++++++++--------- .../assessment/test_company_address_fix.py | 6 +- .../test_granular_bbox_conversion.py | 138 +++---- .../test_nested_geometry_conversion.py | 37 +- 9 files changed, 286 insertions(+), 350 deletions(-) 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 d2b42e0b..008ac058 100644 --- a/lib/idp_common_pkg/idp_common/assessment/granular_service.py +++ b/lib/idp_common_pkg/idp_common/assessment/granular_service.py @@ -36,7 +36,6 @@ from idp_common.extraction.models import ExtractionData from idp_common.models import Document, Status from idp_common.utils import check_token_limit -from idp_common.utils.grid_overlay import add_ruler_edges logger = Logger(service="assessment", level=os.getenv("LOG_LEVEL", "INFO")) @@ -845,16 +844,8 @@ 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" ) - # Apply grid overlay to page images for assessment - grid_page_images = [] - for idx, page_img in enumerate(page_images): - grid_img = add_ruler_edges(page_img) - logger.info( - f"Added ruler overlay to page {idx}: {len(page_img):,} bytes -> {len(grid_img):,} bytes" - ) - grid_page_images.append(grid_img) - # 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})" ) @@ -862,11 +853,12 @@ def process_document_section(self, document: Document, section_id: str) -> Docum request_start_time = time.time() # 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=grid_page_images, + page_images=page_images, sorted_page_ids=sorted_page_ids, model_id=self.config.assessment.model, system_prompt=self.config.assessment.system_prompt, diff --git a/lib/idp_common_pkg/idp_common/assessment/models.py b/lib/idp_common_pkg/idp_common/assessment/models.py index 3678e068..b515a72a 100644 --- a/lib/idp_common_pkg/idp_common/assessment/models.py +++ b/lib/idp_common_pkg/idp_common/assessment/models.py @@ -114,7 +114,7 @@ def from_corners( scale: float = 1000.0, ) -> "BoundingBoxCoordinates": """ - Create from corner coordinates in document space. + Create from corner coordinates. Args: x1, y1: Top-left corner in 0-scale range @@ -228,7 +228,16 @@ def from_llm_response( bbox_coords: list[float] | None = None, page_num: int | None = None, ) -> "FieldAssessmentData": - """Create from LLM response data.""" + """ + 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) diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_executor.py b/lib/idp_common_pkg/idp_common/assessment/strands_executor.py index 8e478e48..a3330304 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_executor.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_executor.py @@ -40,7 +40,7 @@ async def execute_tasks_async( Args: tasks: List of assessment tasks to execute extraction_results: Full extraction results - page_images: List of page images (with grid overlay) + 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 @@ -166,7 +166,7 @@ def execute_assessment_tasks_parallel( Args: tasks: List of assessment tasks extraction_results: Full extraction results - page_images: List of page images (with grid overlay already applied) + 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 diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_service.py b/lib/idp_common_pkg/idp_common/assessment/strands_service.py index 460c32fd..4fb26a53 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_service.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_service.py @@ -26,6 +26,7 @@ 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")) @@ -49,9 +50,8 @@ async def assess_attribute_with_strands( Args: task: Assessment task to process - base_content: Base prompt content (includes images) extraction_results: Full extraction results - page_images: List of page images (with grid overlay already applied) + 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 @@ -364,16 +364,21 @@ def _build_task_prompt( Args: task: Assessment task for one specific field - page_images: List of page images to include in the prompt + 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 + # 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=img))) + ContentBlock( + image=ImageContent( + format="png", source=ImageSource(bytes=add_ruler_edges(img)) + ) + ) for img in page_images ] @@ -414,6 +419,8 @@ def _convert_to_assessment_result( 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, diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py index e0f74a15..9c562c25 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py @@ -13,7 +13,7 @@ from strands import Agent, tool from idp_common.assessment.strands_models import AssessmentOutput, BoundingBox -from idp_common.utils.grid_overlay import draw_bounding_boxes +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, @@ -37,10 +37,13 @@ class ViewImageInput(BaseModel): @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 + # 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( @@ -56,7 +59,7 @@ def create_view_image_tool(page_images: list[bytes], sorted_page_ids: list[str]) Create a view_image tool that has access to page images. Args: - page_images: List of page image bytes (with grid overlay already applied) + page_images: List of raw page image bytes (without ruler overlay) sorted_page_ids: List of page IDs in sorted order Returns: @@ -64,7 +67,7 @@ def create_view_image_tool(page_images: list[bytes], sorted_page_ids: list[str]) """ @tool - def view_image(input_data: ViewImageInput, agent: Agent) -> dict: + def view_image(input_data: ViewImageInput, agent: Agent) -> dict[str, Any]: """ View a specific page image, optionally highlighting a bounding box area. @@ -97,13 +100,13 @@ def view_image(input_data: ViewImageInput, agent: Agent) -> dict: f"Valid range: 0-{len(page_images) - 1}" ) - # Get the base image (already has grid overlay) - img_bytes = page_images[view_input.image_index] + # 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] - # If bounding box is specified, draw it on the image + # Add ruler and optionally draw bounding box if view_input.bounding_box: - # Convert BoundingBox to dict format for draw_bounding_boxes + # Convert BoundingBox to dict format bbox_dict = { "bbox": [ view_input.bounding_box.x1, @@ -115,21 +118,19 @@ def view_image(input_data: ViewImageInput, agent: Agent) -> dict: "color": "red", } - # Draw the bounding box on the image (which has 30px margin for ruler) - # Let drawing errors propagate - if we can't draw, something is wrong - img_bytes = draw_bounding_boxes( - img_bytes, - [bbox_dict], - margin_offset=30, - ) + # Add ruler overlay and draw bounding box in one step + img_bytes = add_ruler_and_draw_boxes(raw_img_bytes, [bbox_dict]) logger.debug( - "Drew bounding box on image", + "Added ruler and drew bounding box on image", extra={ "image_index": view_input.image_index, "bbox": bbox_dict["bbox"], }, ) + else: + # Just add ruler overlay (no bounding box) + img_bytes = add_ruler_edges(raw_img_bytes) logger.info( "Returning image to agent", 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("\nšŸ“ Testing Regular AssessmentService") - regular_service = AssessmentService() - regular_result = regular_service._extract_geometry_from_assessment( - mock_assessment_data - ) + # Test geometry conversion using geometry_utils + print("\nšŸ“ 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("\nšŸ“ 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("\nšŸ” 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("āœ… Regular AssessmentService: Converts bbox ā†’ geometry correctly") - print("āœ… GranularAssessmentService: Converts bbox ā†’ geometry correctly") - print("āœ… Both services handle nested attributes (CompanyAddress.State)") - print("āœ… Both services produce identical geometry output") - print("āœ… Both services remove raw bbox data after conversion") - - print("\nšŸŽ‰ 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("āœ… Converts bbox ā†’ geometry correctly") + print("āœ… Handles nested attributes (CompanyAddress.State)") + print("āœ… Removes raw bbox data after conversion") + print("āœ… Produces correct normalized geometry values") + + print("\nšŸŽ‰ 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 From dd50777cd42e0461055f80292dc4da37e125cede Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Mon, 1 Dec 2025 13:37:08 +0000 Subject: [PATCH 28/30] add structured loggin --- lib/idp_common_pkg/idp_common/assessment/strands_tools.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py index 9c562c25..57f5b220 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py @@ -125,7 +125,13 @@ def view_image(input_data: ViewImageInput, agent: Agent) -> dict[str, Any]: "Added ruler and drew bounding box on image", extra={ "image_index": view_input.image_index, - "bbox": bbox_dict["bbox"], + "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: From e21efb890dada74cb7b9d04bc458813eb56b9c10 Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Mon, 1 Dec 2025 14:05:35 +0000 Subject: [PATCH 29/30] improve retry mechanism --- lib/idp_common_pkg/idp_common/utils/bedrock_utils.py | 1 + 1 file changed, 1 insertion(+) 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 09b61c45..987e2e10 100644 --- a/lib/idp_common_pkg/idp_common/utils/bedrock_utils.py +++ b/lib/idp_common_pkg/idp_common/utils/bedrock_utils.py @@ -19,6 +19,7 @@ InvokeModelRequestTypeDef, InvokeModelResponseTypeDef, ) +from strands.models.bedrock import ModelThrottledException # Optional import for strands-agents (may not be installed in all environments) try: From acd8967fe0019269f778eb0b41d97c5bb79ebb5d Mon Sep 17 00:00:00 2001 From: Kazmer Nagy-Betegh Date: Wed, 3 Dec 2025 12:00:43 +0000 Subject: [PATCH 30/30] small fixes --- .../idp_common/assessment/granular_service.py | 8 +- .../idp_common/assessment/strands_executor.py | 82 +++++++++---------- .../idp_common/assessment/strands_tools.py | 2 +- .../idp_common/config/models.py | 8 -- .../idp_common/extraction/agentic_idp.py | 4 + .../idp_common/utils/bedrock_utils.py | 25 +++--- 6 files changed, 62 insertions(+), 67 deletions(-) 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 008ac058..51b0da9a 100644 --- a/lib/idp_common_pkg/idp_common/assessment/granular_service.py +++ b/lib/idp_common_pkg/idp_common/assessment/granular_service.py @@ -11,15 +11,19 @@ 4. Maintaining assessment structure that mirrors extraction results """ +from __future__ import annotations + import json import os import time -from typing import Any +from typing import TYPE_CHECKING, Any from aws_lambda_powertools import Logger -from mypy_boto3_dynamodb.service_resource import DynamoDBServiceResource from idp_common import image, metrics, s3, utils + +if TYPE_CHECKING: + from mypy_boto3_dynamodb.service_resource import DynamoDBServiceResource 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 diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_executor.py b/lib/idp_common_pkg/idp_common/assessment/strands_executor.py index a3330304..0a95fade 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_executor.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_executor.py @@ -6,6 +6,7 @@ """ import asyncio +import concurrent.futures import os import time from typing import Any, cast @@ -188,53 +189,44 @@ def execute_assessment_tasks_parallel( start_time = time.time() - # Run async executor - # Use asyncio.run() for clean event loop management + # Define the async coroutine to run + async def _run() -> tuple[list[AssessmentResult], dict[str, Any]]: + return await 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, + ) + + # Check if there's already a running event loop + # This is more robust than catching exceptions with string matching 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, - ) + loop = asyncio.get_running_loop() + except RuntimeError: + loop = None + + if loop is not None and loop.is_running(): + # We're inside an async context (e.g., Jupyter, nested async call) + # Execute in a separate thread to avoid "cannot be called from a running event loop" + logger.warning( + "Event loop already running, executing in separate thread", + extra={"loop": str(loop)}, ) - 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 + with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor: + future = executor.submit(asyncio.run, _run()) + results, metering = future.result() + else: + # No running loop - safe to use asyncio.run() + results, metering = asyncio.run(_run()) duration = time.time() - start_time diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py index 57f5b220..d2a7ebbc 100644 --- a/lib/idp_common_pkg/idp_common/assessment/strands_tools.py +++ b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py @@ -172,7 +172,7 @@ def create_strands_tools( Create all tools needed for Strands-based assessment. Args: - page_images: List of page image bytes (with grid overlay already applied) + page_images: List of raw page image bytes (ruler overlay added on-demand by view_image tool) sorted_page_ids: List of page IDs in sorted order Returns: diff --git a/lib/idp_common_pkg/idp_common/config/models.py b/lib/idp_common_pkg/idp_common/config/models.py index ef5c5b15..3224beea 100644 --- a/lib/idp_common_pkg/idp_common/config/models.py +++ b/lib/idp_common_pkg/idp_common/config/models.py @@ -141,14 +141,6 @@ 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""" 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 c24769f5..e4f01ea3 100644 --- a/lib/idp_common_pkg/idp_common/extraction/agentic_idp.py +++ b/lib/idp_common_pkg/idp_common/extraction/agentic_idp.py @@ -11,6 +11,7 @@ import json import logging import os +import re import threading from pathlib import Path from typing import ( @@ -21,6 +22,7 @@ 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 @@ -36,6 +38,8 @@ from idp_common.bedrock import ( build_model_config, + supports_prompt_caching, + supports_tool_caching, ) from idp_common.config.models import IDPConfig from idp_common.utils.bedrock_utils import ( 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 987e2e10..674a2520 100644 --- a/lib/idp_common_pkg/idp_common/utils/bedrock_utils.py +++ b/lib/idp_common_pkg/idp_common/utils/bedrock_utils.py @@ -1,3 +1,5 @@ +from __future__ import annotations + import asyncio import json import logging @@ -7,19 +9,20 @@ import time from collections.abc import Awaitable, Callable from functools import wraps -from typing import Unpack +from typing import TYPE_CHECKING, Unpack import botocore.exceptions -from mypy_boto3_bedrock_runtime import BedrockRuntimeClient -from mypy_boto3_bedrock_runtime.type_defs import ( - ConverseRequestTypeDef, - ConverseResponseTypeDef, - ConverseStreamRequestTypeDef, - ConverseStreamResponseTypeDef, - InvokeModelRequestTypeDef, - InvokeModelResponseTypeDef, -) -from strands.models.bedrock import ModelThrottledException + +if TYPE_CHECKING: + from mypy_boto3_bedrock_runtime import BedrockRuntimeClient + from mypy_boto3_bedrock_runtime.type_defs import ( + ConverseRequestTypeDef, + ConverseResponseTypeDef, + ConverseStreamRequestTypeDef, + ConverseStreamResponseTypeDef, + InvokeModelRequestTypeDef, + InvokeModelResponseTypeDef, + ) # Optional import for strands-agents (may not be installed in all environments) try: