diff --git a/.github/workflows/developer-tests.yml b/.github/workflows/developer-tests.yml
index 90f3956ae..eee844726 100644
--- a/.github/workflows/developer-tests.yml
+++ b/.github/workflows/developer-tests.yml
@@ -59,7 +59,7 @@ jobs:
- name: Install Node.js and basedpyright
run: |
- curl -fsSL https://deb.nodesource.com/setup_20.x | bash -
+ curl -fsSL https://deb.nodesource.com/setup_22.x | bash -
apt-get install -y nodejs
npm install -g basedpyright
diff --git a/config_library/pattern-2/bank-statement-sample/config.yaml b/config_library/pattern-2/bank-statement-sample/config.yaml
index cb770beb2..c4c6bd259 100644
--- a/config_library/pattern-2/bank-statement-sample/config.yaml
+++ b/config_library/pattern-2/bank-statement-sample/config.yaml
@@ -404,11 +404,7 @@ assessment:
image:
target_height: ""
target_width: ""
- granular:
- enabled: true
- max_workers: "20"
- simple_batch_size: "3"
- list_batch_size: "1"
+ max_workers: "20"
default_confidence_threshold: "0.8"
top_p: "0.1"
max_tokens: "10000"
@@ -455,107 +451,6 @@ assessment:
- Provide tight, accurate bounding boxes around the actual text
-
- For each field, provide bounding box coordinates:
- - bbox: [x1, y1, x2, y2] coordinates in normalized 0-1000 scale
- - page: Page number where the field appears (starting from 1)
-
- Coordinate system:
- - Use normalized scale 0-1000 for both x and y axes
- - x1, y1 = top-left corner of bounding box
- - x2, y2 = bottom-right corner of bounding box
- - Ensure x2 > x1 and y2 > y1
- - Make bounding boxes tight around the actual text content
- - If a field spans multiple lines, create a bounding box that encompasses all relevant text
-
-
-
- Analyze the extraction results against the source document and provide confidence assessments with spatial localization. Return a JSON object with the following structure based on the attribute type:
-
- For SIMPLE attributes:
- {
- "simple_attribute_name": {
- "confidence": 0.85,
- "bbox": [100, 200, 300, 250],
- "page": 1
- }
- }
-
- For GROUP attributes (nested object structure):
- {
- "group_attribute_name": {
- "sub_attribute_1": {
- "confidence": 0.90,
- "bbox": [150, 300, 250, 320],
- "page": 1
- },
- "sub_attribute_2": {
- "confidence": 0.75,
- "bbox": [150, 325, 280, 345],
- "page": 1
- }
- }
- }
-
- For LIST attributes (array of assessed items):
- {
- "list_attribute_name": [
- {
- "item_attribute_1": {
- "confidence": 0.95,
- "bbox": [100, 400, 200, 420],
- "page": 1
- },
- "item_attribute_2": {
- "confidence": 0.88,
- "bbox": [250, 400, 350, 420],
- "page": 1
- }
- },
- {
- "item_attribute_1": {
- "confidence": 0.92,
- "bbox": [100, 425, 200, 445],
- "page": 1
- },
- "item_attribute_2": {
- "confidence": 0.70,
- "bbox": [250, 425, 350, 445],
- "page": 1
- }
- }
- ]
- }
-
- IMPORTANT:
- - For LIST attributes like "Transactions", assess EACH individual item in the list separately with individual bounding boxes
- - Each transaction should be assessed as a separate object in the array with its own spatial coordinates
- - Do NOT provide aggregate assessments for list items - assess each one individually with precise locations
- - Include assessments AND bounding boxes for ALL attributes present in the extraction results
- - Match the exact structure of the extracted data
- - Provide page numbers for all bounding boxes (starting from 1)
-
-
- <>
-
-
- {DOCUMENT_IMAGE}
-
-
-
- {OCR_TEXT_CONFIDENCE}
-
-
- <>
-
-
- {ATTRIBUTE_NAMES_AND_DESCRIPTIONS}
-
-
-
- {EXTRACTION_RESULTS}
-
-
evaluation:
enabled: true
llm_method:
diff --git a/config_library/pattern-2/lending-package-sample/config.yaml b/config_library/pattern-2/lending-package-sample/config.yaml
index c68a30321..d5d5fae03 100644
--- a/config_library/pattern-2/lending-package-sample/config.yaml
+++ b/config_library/pattern-2/lending-package-sample/config.yaml
@@ -10,6 +10,7 @@ ocr:
- name: LAYOUT
- name: TABLES
- name: SIGNATURES
+ max_workers: "20"
image:
target_width: ""
target_height: ""
@@ -1188,6 +1189,7 @@ classes:
classification:
classificationMethod: multimodalPageLevelClassification
maxPagesForClassification: "ALL"
+ max_workers: "20"
image:
target_height: ""
target_width: ""
@@ -1252,6 +1254,7 @@ extraction:
agentic:
enabled: false
review_agent: false
+ max_workers: "20"
image:
target_width: ""
target_height: ""
@@ -1425,14 +1428,10 @@ summarization:
assessment:
enabled: true
validation_enabled: false
+ max_workers: "20"
image:
target_height: ""
target_width: ""
- granular:
- enabled: true
- max_workers: "20"
- simple_batch_size: "3"
- list_batch_size: "1"
default_confidence_threshold: "0.8"
top_p: "0.1"
max_tokens: "10000"
@@ -1479,106 +1478,6 @@ assessment:
- Provide tight, accurate bounding boxes around the actual text
-
- For each field, provide bounding box coordinates:
- - bbox: [x1, y1, x2, y2] coordinates in normalized 0-1000 scale
- - page: Page number where the field appears (starting from 1)
-
- Coordinate system:
- - Use normalized scale 0-1000 for both x and y axes
- - x1, y1 = top-left corner of bounding box
- - x2, y2 = bottom-right corner of bounding box
- - Ensure x2 > x1 and y2 > y1
- - Make bounding boxes tight around the actual text content
- - If a field spans multiple lines, create a bounding box that encompasses all relevant text
-
-
-
- Analyze the extraction results against the source document and provide confidence assessments with spatial localization. Return a JSON object with the following structure based on the attribute type:
-
- For SIMPLE attributes:
- {
- "simple_attribute_name": {
- "confidence": 0.85,
- "bbox": [100, 200, 300, 250],
- "page": 1
- }
- }
-
- For GROUP attributes (nested object structure):
- {
- "group_attribute_name": {
- "sub_attribute_1": {
- "confidence": 0.90,
- "bbox": [150, 300, 250, 320],
- "page": 1
- },
- "sub_attribute_2": {
- "confidence": 0.75,
- "bbox": [150, 325, 280, 345],
- "page": 1
- }
- }
- }
-
- For LIST attributes (array of assessed items):
- {
- "list_attribute_name": [
- {
- "item_attribute_1": {
- "confidence": 0.95,
- "bbox": [100, 400, 200, 420],
- "page": 1
- },
- "item_attribute_2": {
- "confidence": 0.88,
- "bbox": [250, 400, 350, 420],
- "page": 1
- }
- },
- {
- "item_attribute_1": {
- "confidence": 0.92,
- "bbox": [100, 425, 200, 445],
- "page": 1
- },
- "item_attribute_2": {
- "confidence": 0.70,
- "bbox": [250, 425, 350, 445],
- "page": 1
- }
- }
- ]
- }
-
- IMPORTANT:
- - For LIST attributes like "Transactions", assess EACH individual item in the list separately with individual bounding boxes
- - Each transaction should be assessed as a separate object in the array with its own spatial coordinates
- - Do NOT provide aggregate assessments for list items - assess each one individually with precise locations
- - Include assessments AND bounding boxes for ALL attributes present in the extraction results
- - Match the exact structure of the extracted data
- - Provide page numbers for all bounding boxes (starting from 1)
-
-
- <>
-
-
- {DOCUMENT_IMAGE}
-
-
-
- {OCR_TEXT_CONFIDENCE}
-
-
- <>
-
-
- {ATTRIBUTE_NAMES_AND_DESCRIPTIONS}
-
-
-
- {EXTRACTION_RESULTS}
-
evaluation:
enabled: true
llm_method:
@@ -1811,7 +1710,7 @@ agents:
parameters:
max_log_events: 5
time_range_hours_default: 24
-
+
chat_companion:
model_id: us.anthropic.claude-sonnet-4-20250514-v1:0
pricing:
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 14f2ba2d0..e0e75332e 100644
--- a/config_library/pattern-2/rvl-cdip-package-sample-with-few-shot-examples/config.yaml
+++ b/config_library/pattern-2/rvl-cdip-package-sample-with-few-shot-examples/config.yaml
@@ -11,6 +11,7 @@ ocr:
- name: LAYOUT
- name: TABLES
- name: SIGNATURES
+ max_workers: "20"
image:
dpi: "150"
target_width: ""
@@ -809,6 +810,7 @@ classes:
classification:
maxPagesForClassification: "ALL"
+ max_workers: "20"
image:
target_height: ""
target_width: ""
@@ -863,6 +865,7 @@ classification:
5. Outputting in the exact JSON format specified in
extraction:
+ max_workers: "20"
image:
target_height: ""
target_width: ""
@@ -963,14 +966,10 @@ extraction:
assessment:
enabled: true
validation_enabled: false
+ max_workers: "20"
image:
target_height: ""
target_width: ""
- granular:
- enabled: true
- max_workers: "20"
- simple_batch_size: "3"
- list_batch_size: "1"
default_confidence_threshold: "0.8"
top_p: "0.1"
max_tokens: "10000"
@@ -1017,106 +1016,6 @@ assessment:
- Provide tight, accurate bounding boxes around the actual text
-
- For each field, provide bounding box coordinates:
- - bbox: [x1, y1, x2, y2] coordinates in normalized 0-1000 scale
- - page: Page number where the field appears (starting from 1)
-
- Coordinate system:
- - Use normalized scale 0-1000 for both x and y axes
- - x1, y1 = top-left corner of bounding box
- - x2, y2 = bottom-right corner of bounding box
- - Ensure x2 > x1 and y2 > y1
- - Make bounding boxes tight around the actual text content
- - If a field spans multiple lines, create a bounding box that encompasses all relevant text
-
-
-
- Analyze the extraction results against the source document and provide confidence assessments with spatial localization. Return a JSON object with the following structure based on the attribute type:
-
- For SIMPLE attributes:
- {
- "simple_attribute_name": {
- "confidence": 0.85,
- "bbox": [100, 200, 300, 250],
- "page": 1
- }
- }
-
- For GROUP attributes (nested object structure):
- {
- "group_attribute_name": {
- "sub_attribute_1": {
- "confidence": 0.90,
- "bbox": [150, 300, 250, 320],
- "page": 1
- },
- "sub_attribute_2": {
- "confidence": 0.75,
- "bbox": [150, 325, 280, 345],
- "page": 1
- }
- }
- }
-
- For LIST attributes (array of assessed items):
- {
- "list_attribute_name": [
- {
- "item_attribute_1": {
- "confidence": 0.95,
- "bbox": [100, 400, 200, 420],
- "page": 1
- },
- "item_attribute_2": {
- "confidence": 0.88,
- "bbox": [250, 400, 350, 420],
- "page": 1
- }
- },
- {
- "item_attribute_1": {
- "confidence": 0.92,
- "bbox": [100, 425, 200, 445],
- "page": 1
- },
- "item_attribute_2": {
- "confidence": 0.70,
- "bbox": [250, 425, 350, 445],
- "page": 1
- }
- }
- ]
- }
-
- IMPORTANT:
- - For LIST attributes like "Transactions", assess EACH individual item in the list separately with individual bounding boxes
- - Each transaction should be assessed as a separate object in the array with its own spatial coordinates
- - Do NOT provide aggregate assessments for list items - assess each one individually with precise locations
- - Include assessments AND bounding boxes for ALL attributes present in the extraction results
- - Match the exact structure of the extracted data
- - Provide page numbers for all bounding boxes (starting from 1)
-
-
- <>
-
-
- {DOCUMENT_IMAGE}
-
-
-
- {OCR_TEXT_CONFIDENCE}
-
-
- <>
-
-
- {ATTRIBUTE_NAMES_AND_DESCRIPTIONS}
-
-
-
- {EXTRACTION_RESULTS}
-
evaluation:
enabled: true
llm_method:
@@ -1433,7 +1332,7 @@ agents:
parameters:
max_log_events: 5
time_range_hours_default: 24
-
+
chat_companion:
model_id: us.anthropic.claude-sonnet-4-20250514-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 2d869354a..d4365a96d 100644
--- a/config_library/pattern-2/rvl-cdip-package-sample/config.yaml
+++ b/config_library/pattern-2/rvl-cdip-package-sample/config.yaml
@@ -10,6 +10,7 @@ ocr:
- name: LAYOUT
- name: TABLES
- name: SIGNATURES
+ max_workers: "20"
image:
dpi: "150"
target_width: ""
@@ -764,6 +765,7 @@ classes:
labeled 'notes', 'remarks', or 'comments'.
classification:
maxPagesForClassification: "ALL"
+ max_workers: "20"
image:
target_height: ""
target_width: ""
@@ -906,6 +908,7 @@ classification:
You are a document classification expert who can analyze and classify multiple documents and their page boundaries within a document package from various domains. Your task is to determine the document type based on its content and structure, using the provided document type definitions. Your output must be valid JSON according to the requested format.
classificationMethod: textbasedHolisticClassification
extraction:
+ max_workers: "20"
image:
target_width: ""
target_height: ""
@@ -1067,14 +1070,10 @@ summarization:
assessment:
enabled: true
validation_enabled: false
+ max_workers: "20"
image:
target_height: ""
target_width: ""
- granular:
- enabled: true
- max_workers: "20"
- simple_batch_size: "3"
- list_batch_size: "1"
default_confidence_threshold: "0.8"
top_p: "0.1"
max_tokens: "10000"
@@ -1121,106 +1120,6 @@ assessment:
- Provide tight, accurate bounding boxes around the actual text
-
- For each field, provide bounding box coordinates:
- - bbox: [x1, y1, x2, y2] coordinates in normalized 0-1000 scale
- - page: Page number where the field appears (starting from 1)
-
- Coordinate system:
- - Use normalized scale 0-1000 for both x and y axes
- - x1, y1 = top-left corner of bounding box
- - x2, y2 = bottom-right corner of bounding box
- - Ensure x2 > x1 and y2 > y1
- - Make bounding boxes tight around the actual text content
- - If a field spans multiple lines, create a bounding box that encompasses all relevant text
-
-
-
- Analyze the extraction results against the source document and provide confidence assessments with spatial localization. Return a JSON object with the following structure based on the attribute type:
-
- For SIMPLE attributes:
- {
- "simple_attribute_name": {
- "confidence": 0.85,
- "bbox": [100, 200, 300, 250],
- "page": 1
- }
- }
-
- For GROUP attributes (nested object structure):
- {
- "group_attribute_name": {
- "sub_attribute_1": {
- "confidence": 0.90,
- "bbox": [150, 300, 250, 320],
- "page": 1
- },
- "sub_attribute_2": {
- "confidence": 0.75,
- "bbox": [150, 325, 280, 345],
- "page": 1
- }
- }
- }
-
- For LIST attributes (array of assessed items):
- {
- "list_attribute_name": [
- {
- "item_attribute_1": {
- "confidence": 0.95,
- "bbox": [100, 400, 200, 420],
- "page": 1
- },
- "item_attribute_2": {
- "confidence": 0.88,
- "bbox": [250, 400, 350, 420],
- "page": 1
- }
- },
- {
- "item_attribute_1": {
- "confidence": 0.92,
- "bbox": [100, 425, 200, 445],
- "page": 1
- },
- "item_attribute_2": {
- "confidence": 0.70,
- "bbox": [250, 425, 350, 445],
- "page": 1
- }
- }
- ]
- }
-
- IMPORTANT:
- - For LIST attributes like "Transactions", assess EACH individual item in the list separately with individual bounding boxes
- - Each transaction should be assessed as a separate object in the array with its own spatial coordinates
- - Do NOT provide aggregate assessments for list items - assess each one individually with precise locations
- - Include assessments AND bounding boxes for ALL attributes present in the extraction results
- - Match the exact structure of the extracted data
- - Provide page numbers for all bounding boxes (starting from 1)
-
-
- <>
-
-
- {DOCUMENT_IMAGE}
-
-
-
- {OCR_TEXT_CONFIDENCE}
-
-
- <>
-
-
- {ATTRIBUTE_NAMES_AND_DESCRIPTIONS}
-
-
-
- {EXTRACTION_RESULTS}
-
evaluation:
enabled: true
llm_method:
@@ -1453,7 +1352,7 @@ agents:
parameters:
max_log_events: 5
time_range_hours_default: 24
-
+
chat_companion:
model_id: us.anthropic.claude-sonnet-4-20250514-v1:0
pricing:
diff --git a/config_library/pattern-3/rvl-cdip-package-sample/config.yaml b/config_library/pattern-3/rvl-cdip-package-sample/config.yaml
index 89dcd6250..91a950870 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: ""
@@ -926,645 +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: ""
- granular:
- enabled: true
- max_workers: "20"
- simple_batch_size: "3"
- list_batch_size: "1"
- default_confidence_threshold: "0.8"
- top_p: "0.1"
- max_tokens: "10000"
- top_k: "5"
- temperature: "0.0"
- model: us.amazon.nova-lite-v1:0
- 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.
- When bounding boxes are requested, provide precise coordinate locations where information appears in the document.
- task_prompt: >-
-
- You are an expert document analysis assessment system. Your task is to evaluate the confidence of extraction results for a document of class {DOCUMENT_CLASS} and provide precise spatial localization for each field.
-
-
-
- Analyze the extraction results against the source document and provide confidence assessments AND bounding box coordinates for each extracted attribute. Consider factors such as:
- 1. Text clarity and OCR quality in the source regions
- 2. Alignment between extracted values and document content
- 3. Presence of clear evidence supporting the extraction
- 4. Potential ambiguity or uncertainty in the source material
- 5. Completeness and accuracy of the extracted information
- 6. Precise spatial location of each field in the document
-
-
-
- For each attribute, provide:
- - A confidence score between 0.0 and 1.0 where:
- - 1.0 = Very high confidence, clear and unambiguous evidence
- - 0.8-0.9 = High confidence, strong evidence with minor uncertainty
- - 0.6-0.7 = Medium confidence, reasonable evidence but some ambiguity
- - 0.4-0.5 = Low confidence, weak or unclear evidence
- - 0.0-0.3 = Very low confidence, little to no supporting evidence
- - A clear explanation of the confidence reasoning
- - Precise spatial coordinates where the field appears in the document
-
- Guidelines:
- - Base assessments on actual document content and OCR quality
- - Consider both text-based evidence and visual/layout clues
- - Account for OCR confidence scores when provided
- - Be objective and specific in reasoning
- - If an extraction appears incorrect, score accordingly with explanation
- - Provide tight, accurate bounding boxes around the actual text
-
-
-
- For each field, provide bounding box coordinates:
- - bbox: [x1, y1, x2, y2] coordinates in normalized 0-1000 scale
- - page: Page number where the field appears (starting from 1)
-
- Coordinate system:
- - Use normalized scale 0-1000 for both x and y axes
- - x1, y1 = top-left corner of bounding box
- - x2, y2 = bottom-right corner of bounding box
- - Ensure x2 > x1 and y2 > y1
- - Make bounding boxes tight around the actual text content
- - If a field spans multiple lines, create a bounding box that encompasses all relevant text
-
-
-
- Analyze the extraction results against the source document and provide confidence assessments with spatial localization. Return a JSON object with the following structure based on the attribute type:
-
- For SIMPLE attributes:
- {
- "simple_attribute_name": {
- "confidence": 0.85,
- "bbox": [100, 200, 300, 250],
- "page": 1
- }
- }
-
- For GROUP attributes (nested object structure):
- {
- "group_attribute_name": {
- "sub_attribute_1": {
- "confidence": 0.90,
- "bbox": [150, 300, 250, 320],
- "page": 1
- },
- "sub_attribute_2": {
- "confidence": 0.75,
- "bbox": [150, 325, 280, 345],
- "page": 1
- }
- }
- }
-
- For LIST attributes (array of assessed items):
- {
- "list_attribute_name": [
- {
- "item_attribute_1": {
- "confidence": 0.95,
- "bbox": [100, 400, 200, 420],
- "page": 1
- },
- "item_attribute_2": {
- "confidence": 0.88,
- "bbox": [250, 400, 350, 420],
- "page": 1
- }
- },
- {
- "item_attribute_1": {
- "confidence": 0.92,
- "bbox": [100, 425, 200, 445],
- "page": 1
- },
- "item_attribute_2": {
- "confidence": 0.70,
- "bbox": [250, 425, 350, 445],
- "page": 1
- }
- }
- ]
- }
-
- IMPORTANT:
- - For LIST attributes like "Transactions", assess EACH individual item in the list separately with individual bounding boxes
- - Each transaction should be assessed as a separate object in the array with its own spatial coordinates
- - Do NOT provide aggregate assessments for list items - assess each one individually with precise locations
- - Include assessments AND bounding boxes for ALL attributes present in the extraction results
- - Match the exact structure of the extracted data
- - Provide page numbers for all bounding boxes (starting from 1)
-
-
- <>
-
-
- {DOCUMENT_IMAGE}
-
-
-
- {OCR_TEXT_CONFIDENCE}
-
-
- <>
-
-
- {ATTRIBUTE_NAMES_AND_DESCRIPTIONS}
-
-
-
- {EXTRACTION_RESULTS}
-
-evaluation:
- enabled: true
- llm_method:
- top_p: "0.1"
- max_tokens: "4096"
- top_k: "5"
- task_prompt: >-
- I need to evaluate attribute extraction for a document of class: {DOCUMENT_CLASS}.
-
-
- For the attribute named "{ATTRIBUTE_NAME}" described as "{ATTRIBUTE_DESCRIPTION}":
-
- - Expected value: {EXPECTED_VALUE}
-
- - Actual value: {ACTUAL_VALUE}
-
-
- Do these values match in meaning, taking into account formatting differences, word order, abbreviations, and semantic equivalence?
-
- Provide your assessment as a JSON with three fields:
-
- - "match": boolean (true if they match, false if not)
-
- - "score": number between 0 and 1 representing the confidence/similarity score
-
- - "reason": brief explanation of your decision
-
-
- Respond ONLY with the JSON and nothing else. Here's the exact format:
-
- {
- "match": true or false,
- "score": 0.0 to 1.0,
- "reason": "Your explanation here"
- }
- temperature: "0.0"
- model: us.anthropic.claude-3-haiku-20240307-v1:0
- system_prompt: >-
- You are an evaluator that helps determine if the predicted and expected values match for document attribute extraction. You will consider the context and meaning rather than just exact string matching.
-discovery:
- output_format:
- sample_json: |-
- {
- "document_class" : "Form-1040",
- "document_description" : "Brief summary of the document",
- "groups" : [
- {
- "name" : "PersonalInformation",
- "description" : "Personal information of Tax payer",
- "attributeType" : "group",
- "groupAttributes" : [
- {
- "name": "FirstName",
- "dataType" : "string",
- "description" : "First Name of Taxpayer"
- },
- {
- "name": "Age",
- "dataType" : "number",
- "description" : "Age of Taxpayer"
- }
- ]
- },
- {
- "name" : "Dependents",
- "description" : "Dependents of taxpayer",
- "attributeType" : "list",
- "listItemTemplate": {
- "itemAttributes" : [
- {
- "name": "FirstName",
- "dataType" : "string",
- "description" : "Dependent first name"
- },
- {
- "name": "Age",
- "dataType" : "number",
- "description" : "Dependent Age"
- }
- ]
- }
- }
- ]
- }
- with_ground_truth:
- top_p: "0.1"
- temperature: "1.0"
- user_prompt: >-
- This image contains unstructured data. Analyze the data line by line using the provided ground truth as reference.
-
- {ground_truth_json}
-
- Ground truth reference JSON has the fields we are interested in extracting from the document/image. Use the ground truth to optimize field extraction. Match field names, data types, and groupings from the reference.
- Image may contain multiple pages, process all pages.
- Extract all field names including those without values.
- Do not change the group name and field name from ground truth in the extracted data json.
- Add field_description field for every field which will contain instruction to LLM to extract the field data from the image/document. Add data_type field for every field.
- Add two fields document_class and document_description.
- For document_class generate a short name based on the document content like W4, I-9, Paystub.
- For document_description generate a description about the document in less than 50 words.
- If the group repeats and follows table format, update the attributeType as "list".
- Do not extract the values.
- Format the extracted data using the below JSON format:
- Format the extracted groups and fields using the below JSON format:
-
- model_id: us.amazon.nova-pro-v1:0
- system_prompt: >-
- You are an expert in processing forms. Extracting data from images and
- documents. Use provided ground truth data as reference to optimize field
- extraction and ensure consistency with expected document structure and
- field definitions.
- max_tokens: "10000"
- without_ground_truth:
- top_p: "0.1"
- temperature: "1.0"
- user_prompt: >-
- This image contains forms data. Analyze the form line by line.
- Image may contains multiple pages, process all the pages.
- Form may contain multiple name value pair in one line.
- Extract all the names in the form including the name value pair which doesn't have value.
- Organize them into groups, extract field_name, data_type and field description
- Field_name should be less than 60 characters, should not have space use '-' instead of space.
- field_description is a brief description of the field and the location of the field like box number or line number in the form and section of the form.
- Field_name should be unique within the group.
- Add two fields document_class and document_description.
- For document_class generate a short name based on the document content like W4, I-9, Paystub.
- For document_description generate a description about the document in less than 50 words.
-
- Group the fields based on the section they are grouped in the form. Group should have attributeType as "group".
- If the group repeats and follows table format, update the attributeType as "list".
- Do not extract the values.
- Return the extracted data in JSON format.
- Format the extracted data using the below JSON format:
- Format the extracted groups and fields using the below JSON format:
- model_id: us.amazon.nova-pro-v1:0
- system_prompt: >-
- You are an expert in processing forms. Extracting data from images and
- documents. Analyze forms line by line to identify field names, data types,
- and organizational structure. Focus on creating comprehensive blueprints
- for document processing without extracting actual values.
- max_tokens: "10000"
-agents:
- error_analyzer:
- model_id: us.anthropic.claude-sonnet-4-20250514-v1:0
- system_prompt: |-
- You are an intelligent error analysis agent for the GenAI IDP system with access to specialized diagnostic tools.
-
- GENERAL TROUBLESHOOTING WORKFLOW:
- 1. Identify document status from DynamoDB
- 2. Find any errors reported during Step Function execution
- 3. Collect relevant logs from CloudWatch
- 4. Identify any performance issues from X-Ray traces
- 5. Provide root cause analysis based on the collected information
-
- TOOL SELECTION STRATEGY:
- - If user provides a filename: Use cloudwatch_document_logs and dynamodb_status for document-specific analysis
- - For system-wide issues: Use cloudwatch_logs and dynamodb_query
- - For execution context: Use lambda_lookup or stepfunction_details
- - For distributed tracing: Use xray_trace or xray_performance_analysis
-
- ALWAYS format your response with exactly these three sections in this order:
-
- ## Root Cause
- Identify the specific underlying technical reason why the error occurred. Focus on the primary cause, not symptoms.
-
- ## Recommendations
- Provide specific, actionable steps to resolve the issue. Limit to top three recommendations only.
-
-
- Evidence
-
- Format evidence with source information. Include relevant data from tool responses:
-
- **For CloudWatch logs:**
- **Log Group:** [full log_group name]
- **Log Stream:** [full log_stream name]
- ```
- [ERROR] timestamp message
- ```
-
- **For other sources (DynamoDB, Step Functions, X-Ray):**
- **Source:** [service name and resource]
- ```
- Relevant data from tool response
- ```
-
-
-
- FORMATTING RULES:
- - Use the exact three-section structure above
- - Make Evidence section collapsible using HTML details tags
- - Include relevant data from all tool responses (CloudWatch, DynamoDB, Step Functions, X-Ray)
- - For CloudWatch: Show complete log group and log stream names without truncation
- - Present evidence data in code blocks with appropriate source labels
-
- ANALYSIS GUIDELINES:
- - Use multiple tools for comprehensive analysis when needed
- - Start with document-specific tools for targeted queries
- - Use system-wide tools for pattern analysis
- - Combine DynamoDB status with CloudWatch logs for complete picture
- - Leverage X-Ray for distributed system issues
-
- ROOT CAUSE DETERMINATION:
- 1. Document Status: Check dynamodb_status first
- 2. Execution Details: Use stepfunction_details for workflow failures
- 3. Log Analysis: Use cloudwatch_document_logs or cloudwatch_logs for error details
- 4. Distributed Tracing: Use xray_performance_analysis for service interaction issues
- 5. Context: Use lambda_lookup for execution environment
-
- RECOMMENDATION GUIDELINES:
- For code-related issues or system bugs:
- - Do not suggest code modifications
- - Include error details, timestamps, and context
-
- For configuration-related issues:
- - Direct users to UI configuration panel
- - Specify exact configuration section and parameter names
-
- For operational issues:
- - Provide immediate troubleshooting steps
- - Include preventive measures
-
- TIME RANGE PARSING:
- - recent: 1 hour
- - last week: 168 hours
- - last day: 24 hours
- - No time specified: 24 hours (default)
-
- IMPORTANT: Do not include any search quality reflections, search quality scores, or meta-analysis sections in your response. Only provide the three required sections: Root Cause, Recommendations, and Evidence.
- parameters:
- max_log_events: 5
- time_range_hours_default: 24
-
- chat_companion:
- model_id: us.anthropic.claude-sonnet-4-20250514-v1:0
-pricing:
- - name: textract/detect_document_text
- units:
- - name: pages
- price: "0.0015"
- - name: textract/analyze_document-Layout
- units:
- - name: pages
- price: "0.004"
- - name: textract/analyze_document-Signatures
- units:
- - name: pages
- price: "0.0035"
- - name: textract/analyze_document-Forms
- units:
- - name: pages
- price: "0.05"
- - name: textract/analyze_document-Tables
- units:
- - name: pages
- price: "0.015"
- - name: textract/analyze_document-Tables+Forms
- units:
- - name: pages
- price: "0.065"
- - name: bedrock/us.amazon.nova-lite-v1:0
- units:
- - name: inputTokens
- price: "6.0E-8"
- - name: outputTokens
- price: "2.4E-7"
- - name: cacheReadInputTokens
- price: "1.5E-8"
- - name: cacheWriteInputTokens
- price: "6.0E-8"
- - name: bedrock/us.amazon.nova-pro-v1:0
- units:
- - name: inputTokens
- price: "8.0E-7"
- - name: outputTokens
- price: "3.2E-6"
- - name: cacheReadInputTokens
- price: "2.0E-7"
- - name: cacheWriteInputTokens
- price: "8.0E-7"
- - name: bedrock/us.amazon.nova-premier-v1:0
- units:
- - name: inputTokens
- price: "2.5E-6"
- - name: outputTokens
- price: "1.25E-5"
- - name: bedrock/us.anthropic.claude-3-haiku-20240307-v1:0
- units:
- - name: inputTokens
- price: "2.5E-7"
- - name: outputTokens
- price: "1.25E-6"
- - name: bedrock/us.anthropic.claude-3-5-haiku-20241022-v1:0
- units:
- - name: inputTokens
- price: "8.0E-7"
- - name: outputTokens
- price: "4.0E-6"
- - name: cacheReadInputTokens
- price: "8.0E-8"
- - name: cacheWriteInputTokens
- price: "1.0E-6"
- - name: bedrock/us.anthropic.claude-haiku-4-5-20251001-v1:0
- units:
- - name: inputTokens
- price: "1.1E-06"
- - name: outputTokens
- price: "5.5E-06"
- - name: cacheReadInputTokens
- price: "1.1E-07"
- - name: cacheWriteInputTokens
- price: "1.4E-06"
- - name: bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0
- units:
- - name: inputTokens
- price: "3.0E-6"
- - name: outputTokens
- price: "1.5E-5"
- - name: cacheReadInputTokens
- price: "3.0E-7"
- - name: cacheWriteInputTokens
- price: "3.75E-6"
- - name: bedrock/us.anthropic.claude-3-7-sonnet-20250219-v1:0
- units:
- - name: inputTokens
- price: "3.0E-6"
- - name: outputTokens
- price: "1.5E-5"
- - name: cacheReadInputTokens
- price: "3.0E-7"
- - name: cacheWriteInputTokens
- price: "3.75E-6"
- - name: bedrock/us.anthropic.claude-sonnet-4-20250514-v1:0
- units:
- - name: inputTokens
- price: "3.0E-6"
- - name: outputTokens
- price: "1.5E-5"
- - name: cacheReadInputTokens
- price: "3.0E-7"
- - name: cacheWriteInputTokens
- price: "3.75E-6"
- - name: bedrock/us.anthropic.claude-sonnet-4-20250514-v1:0:1m
- units:
- - name: inputTokens
- price: "6.0E-6"
- - name: outputTokens
- price: "2.25E-5"
- - name: cacheReadInputTokens
- price: "6.0E-7"
- - name: cacheWriteInputTokens
- price: "7.5E-6"
- - name: bedrock/us.anthropic.claude-sonnet-4-5-20250929-v1:0
- units:
- - name: inputTokens
- price: "3.3E-6"
- - name: outputTokens
- price: "1.65E-5"
- - name: cacheReadInputTokens
- price: "3.3E-7"
- - name: cacheWriteInputTokens
- price: "4.125E-6"
- - name: bedrock/us.anthropic.claude-sonnet-4-5-20250929-v1:0:1m
- units:
- - name: inputTokens
- price: "6.6E-6"
- - name: outputTokens
- price: "2.475E-5"
- - name: cacheReadInputTokens
- price: "6.6E-7"
- - name: cacheWriteInputTokens
- price: "8.25E-6"
- - name: bedrock/us.anthropic.claude-opus-4-20250514-v1:0
- units:
- - name: inputTokens
- price: "1.5E-5"
- - name: outputTokens
- price: "7.5E-5"
- - name: cacheReadInputTokens
- price: "1.5E-6"
- - name: cacheWriteInputTokens
- price: "1.875E-5"
- - name: bedrock/us.anthropic.claude-opus-4-1-20250805-v1:0
- units:
- - name: inputTokens
- price: "1.5E-5"
- - name: outputTokens
- price: "7.5E-5"
- - name: cacheReadInputTokens
- price: "1.5E-6"
- - name: cacheWriteInputTokens
- price: "1.875E-5"
- # EU model pricing
- - name: bedrock/eu.amazon.nova-lite-v1:0
- units:
- - name: inputTokens
- price: "7.8E-8"
- - name: outputTokens
- price: "3.1E-7"
- - name: cacheReadInputTokens
- price: "1.9E-8"
- - name: cacheWriteInputTokens
- price: "7.8E-8"
- - name: bedrock/eu.amazon.nova-pro-v1:0
- units:
- - name: inputTokens
- price: "1.0E-6"
- - name: outputTokens
- price: "4.2E-6"
- - name: cacheReadInputTokens
- price: "2.6E-7"
- - name: cacheWriteInputTokens
- price: "1.0E-6"
- - name: bedrock/eu.anthropic.claude-3-haiku-20240307-v1:0
- units:
- - name: inputTokens
- price: "2.5E-7"
- - name: outputTokens
- price: "1.25E-6"
- - name: bedrock/eu.anthropic.claude-haiku-4-5-20251001-v1:0
- units:
- - name: inputTokens
- price: "1.1E-6"
- - name: outputTokens
- price: "5.5E-6"
- - name: cacheReadInputTokens
- price: "1.1E-7"
- - name: cacheWriteInputTokens
- price: "1.4E-6"
- - name: bedrock/eu.anthropic.claude-3-5-sonnet-20241022-v2:0
- units:
- - name: inputTokens
- price: "3.0E-6"
- - name: outputTokens
- price: "1.5E-5"
- - name: cacheReadInputTokens
- price: "3.0E-7"
- - name: cacheWriteInputTokens
- price: "3.75E-6"
- - name: bedrock/eu.anthropic.claude-3-7-sonnet-20250219-v1:0
- units:
- - name: inputTokens
- price: "3.0E-6"
- - name: outputTokens
- price: "1.5E-5"
- - name: cacheReadInputTokens
- price: "3.0E-7"
- - name: cacheWriteInputTokens
- price: "3.75E-6"
- - name: bedrock/eu.anthropic.claude-sonnet-4-20250514-v1:0
- units:
- - name: inputTokens
- price: "3.0E-6"
- - name: outputTokens
- price: "1.5E-5"
- - name: cacheReadInputTokens
- price: "3.0E-7"
- - name: cacheWriteInputTokens
- price: "3.75E-6"
- - name: bedrock/eu.anthropic.claude-sonnet-4-5-20250929-v1:0
- units:
- - name: inputTokens
- price: "3.3E-6"
- - name: outputTokens
- price: "1.65E-5"
- - name: cacheReadInputTokens
- price: "3.3E-7"
- - name: cacheWriteInputTokens
- price: "4.125E-6"
- - name: bedrock/eu.anthropic.claude-sonnet-4-5-20250929-v1:0:1m
- units:
- - name: inputTokens
- price: "6.6E-6"
- - name: outputTokens
- price: "2.475E-5"
- - name: cacheReadInputTokens
- price: "6.6E-7"
- - name: cacheWriteInputTokens
- price: "8.25E-6"
- # AWS Lambda pricing (US East - N. Virginia)
- - name: lambda/requests
- units:
- - name: invocations
- price: "2.0E-7" # $0.0000002 per request ($0.20 per 1M requests)
- - name: lambda/duration
- units:
- - name: gb_seconds
- price: "1.66667E-5" # $0.0000166667 per GB-second ($16.67 per 1M GB-seconds)
diff --git a/idp_cli/pyproject.toml b/idp_cli/pyproject.toml
index 344830d5e..6bf096007 100644
--- a/idp_cli/pyproject.toml
+++ b/idp_cli/pyproject.toml
@@ -9,32 +9,28 @@ build-backend = "setuptools.build_meta"
name = "idp-cli"
version = "0.4.4"
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 710104d02..bd25aa4e7 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 +2,28 @@
# SPDX-License-Identifier: MIT-0
"""
-Granular assessment service for evaluating document extraction confidence using LLMs.
+Granular assessment service for evaluating document extraction confidence using Strands agents.
-This module provides a more scalable approach to assessment by:
-1. Breaking down assessments into smaller, focused inferences
-2. Leveraging prompt caching to reduce costs
-3. Using multi-threading for parallel processing
-4. Adapting batch sizes based on attribute complexity
+This module provides a scalable approach to assessment by:
+1. Breaking down assessments into single-field focused tasks
+2. Leveraging Strands agents with tool-based interaction
+3. Using parallel async execution for performance
+4. Maintaining assessment structure that mirrors extraction results
"""
import json
-import logging
import os
import time
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from dataclasses import dataclass
-from typing import Any, Dict, Generator, List, Optional, Tuple
+from typing import Any
-from idp_common import bedrock, image, metrics, s3, utils
+from aws_lambda_powertools import Logger
+from mypy_boto3_dynamodb.service_resource import DynamoDBServiceResource
+
+from idp_common import image, metrics, s3, utils
+from idp_common.assessment.models import AssessmentResult, AssessmentTask
+from idp_common.assessment.strands_executor import execute_assessment_tasks_parallel
from idp_common.config.models import IDPConfig
from idp_common.config.schema_constants import (
- SCHEMA_DESCRIPTION,
SCHEMA_ITEMS,
SCHEMA_PROPERTIES,
SCHEMA_TYPE,
@@ -30,37 +31,13 @@
TYPE_OBJECT,
X_AWS_IDP_CONFIDENCE_THRESHOLD,
X_AWS_IDP_DOCUMENT_TYPE,
- X_AWS_IDP_LIST_ITEM_DESCRIPTION,
)
+from idp_common.extraction.models import ExtractionData
from idp_common.models import Document, Status
-from idp_common.utils import check_token_limit, extract_json_from_text
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class AssessmentTask:
- """Represents a single assessment task to be processed."""
+from idp_common.utils import check_token_limit
+from idp_common.utils.grid_overlay import add_ruler_edges
- 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
-
-
-@dataclass
-class AssessmentResult:
- """Result of a single assessment task."""
-
- task_id: str
- success: bool
- assessment_data: Dict[str, Any]
- confidence_alerts: List[Dict[str, Any]]
- error_message: Optional[str] = None
- processing_time: float = 0.0
- metering: Optional[Dict[str, Any]] = None
+logger = Logger(service="assessment", level=os.getenv("LOG_LEVEL", "INFO"))
def _safe_float_conversion(value: Any, default: float = 0.0) -> float:
@@ -108,7 +85,7 @@ class GranularAssessmentService:
def __init__(
self,
region: str | None = None,
- config: Dict[str, Any] | IDPConfig | None = None,
+ config: dict[str, Any] | IDPConfig | None = None,
cache_table: str | None = None,
):
"""
@@ -130,18 +107,13 @@ def __init__(
self.config = config_model
self.region = region or os.environ.get("AWS_REGION")
- # Granular processing configuration (type-safe access, Pydantic handles conversions)
- self.max_workers = self.config.assessment.granular.max_workers
- self.simple_batch_size = self.config.assessment.granular.simple_batch_size
- self.list_batch_size = self.config.assessment.granular.list_batch_size
+ # Assessment configuration (granular is now always enabled)
+ self.max_workers = self.config.assessment.max_workers
- # Ensure safe minimum values
+ # Ensure safe minimum value
self.max_workers = max(1, self.max_workers)
- self.simple_batch_size = max(1, self.simple_batch_size)
- self.list_batch_size = max(1, self.list_batch_size)
- # Auto-determine caching and parallel processing
- # Caching is automatically handled by the bedrock client based on model support
+ # Auto-determine parallel processing
# Parallel processing is enabled when max_workers > 1
self.enable_parallel = self.max_workers > 1
@@ -151,7 +123,9 @@ def __init__(
if self.cache_table_name:
import boto3
- dynamodb = boto3.resource("dynamodb", region_name=self.region)
+ dynamodb: DynamoDBServiceResource = boto3.resource(
+ "dynamodb", region_name=self.region
+ ) # pyright: ignore[reportAssignmentType]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}"
@@ -172,14 +146,12 @@ def __init__(
model_id = self.config.assessment.model
logger.info(f"Initialized granular assessment service with model {model_id}")
logger.info(
- f"Granular config: max_workers={self.max_workers}, "
- f"simple_batch_size={self.simple_batch_size}, "
- f"list_batch_size={self.list_batch_size}, "
+ f"Assessment config: max_workers={self.max_workers}, "
f"parallel={self.enable_parallel}, "
f"caching={'enabled' if self.cache_table else 'disabled'}"
)
- def _get_class_schema(self, class_label: str) -> Dict[str, Any]:
+ def _get_class_schema(self, class_label: str) -> dict[str, Any]:
"""
Get JSON Schema for a specific document class.
@@ -196,83 +168,8 @@ def _get_class_schema(self, class_label: str) -> Dict[str, Any]:
return schema
return {}
- def _walk_properties_for_assessment(
- self, properties: Dict[str, Any], parent_path: str = ""
- ) -> Generator[Dict[str, Any], None, None]:
- """
- Walk JSON Schema properties and yield assessment property information.
- Generator pattern for efficient schema traversal.
-
- Args:
- properties: JSON Schema properties dict
- parent_path: Parent path for nested properties (e.g., "CompanyAddress")
-
- Yields:
- Dict containing property information:
- {
- 'path': 'CompanyAddress.Street', # Full path
- 'name': 'Street', # Property name
- 'parent_path': 'CompanyAddress', # Parent path (empty string for top-level)
- 'type': 'string', # JSON Schema type
- 'description': 'Street address',
- 'confidence_threshold': 0.9, # From x-aws-idp-confidence-threshold
- 'prop_schema': {...} # Full property schema for reference
- }
- """
- for prop_name, prop_schema in properties.items():
- prop_type = prop_schema.get(SCHEMA_TYPE)
- full_path = f"{parent_path}.{prop_name}" if parent_path else prop_name
-
- # Get confidence threshold for this property
- threshold = prop_schema.get(X_AWS_IDP_CONFIDENCE_THRESHOLD)
-
- if prop_type == TYPE_OBJECT:
- # Yield info for the group itself
- yield {
- "path": full_path,
- "name": prop_name,
- "parent_path": parent_path,
- "type": TYPE_OBJECT,
- "description": prop_schema.get(SCHEMA_DESCRIPTION, ""),
- "confidence_threshold": threshold,
- "prop_schema": prop_schema,
- }
- # Recurse into nested object properties
- yield from self._walk_properties_for_assessment(
- prop_schema.get(SCHEMA_PROPERTIES, {}), full_path
- )
-
- elif prop_type == TYPE_ARRAY:
- # Yield info for the list itself
- yield {
- "path": full_path,
- "name": prop_name,
- "parent_path": parent_path,
- "type": TYPE_ARRAY,
- "description": prop_schema.get(SCHEMA_DESCRIPTION, ""),
- "confidence_threshold": threshold,
- "list_item_description": prop_schema.get(
- X_AWS_IDP_LIST_ITEM_DESCRIPTION, ""
- ),
- "prop_schema": prop_schema,
- }
- # Note: We don't recurse into array items here because list items
- # are handled specially in task creation (one task per item)
-
- else:
- # Leaf property (simple type: string, number, boolean, etc.)
- yield {
- "path": full_path,
- "name": prop_name,
- "parent_path": parent_path,
- "type": prop_type or "string",
- "description": prop_schema.get(SCHEMA_DESCRIPTION, ""),
- "confidence_threshold": threshold,
- "prop_schema": prop_schema,
- }
-
def _get_confidence_threshold_by_path(
- self, properties: Dict[str, Any], path: str, default: float = 0.9
+ self, properties: dict[str, Any], path: str, default: float = 0.9
) -> float:
"""
Get confidence threshold for a property path (e.g., 'CompanyAddress.Street').
@@ -315,296 +212,19 @@ def _get_confidence_threshold_by_path(
return default
- def _format_property_descriptions(
- self, properties: Dict[str, Any], filter_names: Optional[List[str]] = None
- ) -> str:
- """
- Format property descriptions from JSON Schema properties for the prompt.
- Can optionally filter to specific property names.
-
- Args:
- properties: JSON Schema properties dict
- filter_names: Optional list of property names to include (None = all)
-
- Returns:
- Formatted property descriptions as a string
- """
- formatted_lines = []
-
- for prop_name, prop_schema in properties.items():
- # Skip if filtering and this property is not in the filter list
- if filter_names is not None and prop_name not in filter_names:
- continue
-
- prop_type = prop_schema.get(SCHEMA_TYPE)
- description = prop_schema.get(SCHEMA_DESCRIPTION, "")
-
- if prop_type == TYPE_OBJECT:
- formatted_lines.append(f"{prop_name} \t[ {description} ]")
- nested_props = prop_schema.get(SCHEMA_PROPERTIES, {})
- for nested_name, nested_schema in nested_props.items():
- nested_desc = nested_schema.get(SCHEMA_DESCRIPTION, "")
- formatted_lines.append(f" - {nested_name} \t[ {nested_desc} ]")
-
- elif prop_type == TYPE_ARRAY:
- formatted_lines.append(f"{prop_name} \t[ {description} ]")
- items_schema = prop_schema.get(SCHEMA_ITEMS, {})
-
- item_desc = prop_schema.get(X_AWS_IDP_LIST_ITEM_DESCRIPTION, "")
- if item_desc:
- formatted_lines.append(f" Each item: {item_desc}")
-
- if items_schema.get(SCHEMA_TYPE) == TYPE_OBJECT:
- item_props = items_schema.get(SCHEMA_PROPERTIES, {})
- for item_name, item_schema in item_props.items():
- item_prop_desc = item_schema.get(SCHEMA_DESCRIPTION, "")
- formatted_lines.append(
- f" - {item_name} \t[ {item_prop_desc} ]"
- )
- else:
- formatted_lines.append(f"{prop_name} \t[ {description} ]")
-
- return "\n".join(formatted_lines)
-
- def _get_attribute_confidence_threshold(
- self, attr_name: str, attributes: List[Dict[str, Any]], default_threshold: float
- ) -> float:
- """
- Get confidence threshold (legacy format, for internal granular service use).
-
- Args:
- attr_name: Name of the attribute
- attributes: List of attribute dicts in legacy format
- default_threshold: Default threshold if not found
-
- Returns:
- Confidence threshold for the attribute
- """
- for attr in attributes:
- if attr.get("name") == attr_name:
- return _safe_float_conversion(
- attr.get("confidence_threshold", default_threshold),
- default_threshold,
- )
-
- if attr.get("attributeType") == "group":
- group_attributes = attr.get("groupAttributes", [])
- for group_attr in group_attributes:
- if group_attr.get("name") == attr_name:
- return _safe_float_conversion(
- group_attr.get("confidence_threshold", default_threshold),
- default_threshold,
- )
-
- if attr.get("attributeType") == "list":
- list_template = attr.get("listItemTemplate", {})
- item_attributes = list_template.get("itemAttributes", [])
- for item_attr in item_attributes:
- if item_attr.get("name") == attr_name:
- return _safe_float_conversion(
- item_attr.get("confidence_threshold", default_threshold),
- default_threshold,
- )
-
- return default_threshold
-
- def _build_cached_prompt_base(
- self,
- document_text: str,
- class_label: str,
- attribute_descriptions: str,
- ocr_text_confidence: str,
- page_images: List[Any],
- ) -> List[Dict[str, Any]]:
- """
- Build the cacheable base portion of the assessment prompt using the configured task_prompt template.
- This will be the same for all tasks and can be cached.
-
- Args:
- document_text: The document text content
- class_label: The document class label
- attribute_descriptions: Formatted attribute names and descriptions (will be replaced per task)
- ocr_text_confidence: Raw OCR results with confidence scores
- page_images: List of page images
-
- Returns:
- List of content items for the cacheable portion
- """
- # Get the base task prompt template (type-safe access)
- task_prompt_template = self.config.assessment.task_prompt
-
- if not task_prompt_template:
- raise ValueError(
- "Assessment task_prompt is required in configuration but not found"
- )
-
- # For granular assessment, we need to build the base content that will be cached
- # and leave placeholders for task-specific content
-
- # Replace common placeholders but leave task-specific ones
- base_substitutions = {
- "DOCUMENT_TEXT": document_text,
- "DOCUMENT_CLASS": class_label,
- "OCR_TEXT_CONFIDENCE": ocr_text_confidence,
- }
-
- # Replace placeholders in the template
- base_prompt = task_prompt_template
- for placeholder, value in base_substitutions.items():
- base_prompt = base_prompt.replace(f"{{{placeholder}}}", value)
-
- # Handle {DOCUMENT_IMAGE} placeholder if present
- if "{DOCUMENT_IMAGE}" in base_prompt:
- # Split the prompt at the DOCUMENT_IMAGE placeholder
- parts = base_prompt.split("{DOCUMENT_IMAGE}")
- if len(parts) != 2:
- raise ValueError(
- f"Invalid DOCUMENT_IMAGE placeholder usage: found {len(parts) - 1} occurrences, "
- f"but exactly 1 is required."
- )
-
- content = []
-
- # Add the part before the image
- if parts[0].strip():
- content.append({"text": parts[0]})
-
- # Add the images if available
- if page_images:
- if isinstance(page_images, list):
- # Multiple images (limit to 20 as per Bedrock constraints)
- if len(page_images) > 20:
- logger.warning(
- f"Found {len(page_images)} images, truncating to 20 due to Bedrock constraints. "
- f"{len(page_images) - 20} images will be dropped."
- )
- for img in page_images[:20]:
- content.append(image.prepare_bedrock_image_attachment(img))
- else:
- # Single image
- content.append(image.prepare_bedrock_image_attachment(page_images))
-
- # Add the part after the image
- if parts[1].strip():
- content.append({"text": parts[1]})
-
- else:
- # No DOCUMENT_IMAGE placeholder - just add the base prompt
- content = []
- if base_prompt.strip():
- content.append({"text": base_prompt})
-
- return content
-
- def _get_task_specific_attribute_descriptions(
- self, task: AssessmentTask, properties: Dict[str, Any]
- ) -> str:
- """
- Get attribute descriptions specific to this task using JSON Schema properties.
-
- Args:
- task: The assessment task
- properties: JSON Schema properties dict
-
- Returns:
- Formatted attribute descriptions for this specific task
- """
- if task.task_type == "simple_batch":
- # For simple batches, filter to only the attributes in this batch
- return self._format_property_descriptions(
- properties, filter_names=task.attributes
- )
-
- elif task.task_type == "group":
- # For groups, filter to just the group attribute (which includes nested props)
- group_attr_name = task.attributes[0]
- return self._format_property_descriptions(
- properties, filter_names=[group_attr_name]
- )
-
- elif task.task_type == "list_item":
- # For list items, show the item schema properties
- list_attr_name = task.attributes[0]
- if list_attr_name in properties:
- list_prop_schema = properties[list_attr_name]
- items_schema = list_prop_schema.get(SCHEMA_ITEMS, {})
- if items_schema.get(SCHEMA_TYPE) == TYPE_OBJECT:
- item_properties = items_schema.get(SCHEMA_PROPERTIES, {})
- return self._format_property_descriptions(item_properties)
- return ""
-
- return ""
-
- def _build_specific_assessment_prompt(
- self,
- task: AssessmentTask,
- base_content: List[Dict[str, Any]],
- properties: Dict[str, Any],
- ) -> List[Dict[str, Any]]:
- """
- Build the specific assessment prompt for a task by replacing the {EXTRACTION_RESULTS} placeholder
- in the base content with task-specific extraction data.
-
- Args:
- task: The assessment task
- base_content: The cached base content (which has empty {EXTRACTION_RESULTS})
- properties: JSON Schema properties dict for task-specific filtering
-
- Returns:
- Complete content list for the assessment
- """
- # Build extraction results for this specific task
- task_extraction_data = {}
- for attr_name in task.attributes:
- if attr_name in task.extraction_data:
- task_extraction_data[attr_name] = task.extraction_data[attr_name]
-
- # For list items, we need to handle the data differently
- if task.task_type == "list_item":
- extraction_results_str = json.dumps(task.extraction_data, indent=2)
- item_index = task.list_item_index if task.list_item_index is not None else 0
- extraction_results_str = f"Item #{item_index + 1}: {extraction_results_str}"
- else:
- extraction_results_str = json.dumps(task_extraction_data, indent=2)
-
- # Get task-specific attribute descriptions
- task_specific_attributes = self._get_task_specific_attribute_descriptions(
- task, properties
- )
-
- # Create a new content list by replacing placeholders in the base content
- content = []
- for item in base_content:
- if "text" in item:
- # Replace any remaining placeholders in the text
- text = item["text"]
-
- # Replace EXTRACTION_RESULTS placeholder with task-specific data
- text = text.replace("{EXTRACTION_RESULTS}", extraction_results_str)
-
- # Replace ATTRIBUTE_NAMES_AND_DESCRIPTIONS with task-specific attributes if needed
- if "{ATTRIBUTE_NAMES_AND_DESCRIPTIONS}" in text:
- text = text.replace(
- "{ATTRIBUTE_NAMES_AND_DESCRIPTIONS}", task_specific_attributes
- )
-
- # Only add non-empty text content (must have actual content, not just whitespace)
- if text.strip():
- content.append({"text": text})
- else:
- # Non-text content (like images, cache points) - pass through unchanged
- content.append(item)
-
- return content
-
def _create_assessment_tasks(
self,
- extraction_results: Dict[str, Any],
- properties: Dict[str, Any],
+ extraction_results: dict[str, Any],
+ properties: dict[str, Any],
default_confidence_threshold: float,
- ) -> List[AssessmentTask]:
+ ) -> tuple[list[AssessmentTask], dict[str, Any]]:
"""
- Create assessment tasks based on JSON Schema property types and extraction results.
+ Create assessment tasks and pre-build assessment structure.
+
+ This function recursively traverses the schema and extraction results to:
+ 1. Build an assessment structure that mirrors the extraction results
+ 2. Create one task per leaf field (no batching - one field at a time)
+ 3. Store direct parent dict references in tasks for O(1) insertion
Args:
extraction_results: The extraction results to assess
@@ -612,349 +232,119 @@ 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[str | int, ...],
+ parent_dict: dict[str, Any],
+ ) -> None:
+ """
+ Recursively traverse schema and extraction data to build tasks and structure.
+
+ Args:
+ schema_props: Current level schema properties
+ extraction_data: Current level extraction data
+ current_path: Tuple path to current location
+ parent_dict: Parent dict in assessment structure for insertion
+ """
+ for prop_name, prop_schema in schema_props.items():
+ if prop_name not in extraction_data:
+ continue # Skip properties not in extraction results
- # Group properties by type for efficient processing
- simple_props = []
- group_props = []
- list_props = []
+ prop_type = prop_schema.get(SCHEMA_TYPE)
+ prop_value = extraction_data[prop_name]
+ field_path = current_path + (prop_name,)
- for prop_name, prop_schema in properties.items():
- if prop_name not in extraction_results:
- continue # Skip properties not in extraction results
+ if prop_type == TYPE_OBJECT and isinstance(prop_value, dict):
+ # Create nested dict in assessment structure
+ nested_dict: dict[str, Any] = {}
+ parent_dict[prop_name] = nested_dict
- prop_type = prop_schema.get(SCHEMA_TYPE)
+ # Recurse into nested object
+ nested_props = prop_schema.get(SCHEMA_PROPERTIES, {})
+ _traverse(nested_props, prop_value, field_path, nested_dict)
- if prop_type == TYPE_OBJECT:
- group_props.append((prop_name, prop_schema))
- elif prop_type == TYPE_ARRAY:
- list_props.append((prop_name, prop_schema))
- else:
- # Simple types: string, number, boolean, etc.
- simple_props.append((prop_name, prop_schema))
-
- # Create tasks for simple properties (batch them)
- for i in range(0, len(simple_props), self.simple_batch_size):
- batch = simple_props[i : i + self.simple_batch_size]
- prop_names = [name for name, _ in batch]
-
- # Build confidence thresholds for this batch
- confidence_thresholds = {}
- for prop_name, prop_schema in batch:
- threshold = self._get_confidence_threshold_by_path(
- properties, prop_name, default_confidence_threshold
- )
- confidence_thresholds[prop_name] = threshold
+ elif prop_type == TYPE_ARRAY and isinstance(prop_value, list):
+ # Create list in assessment structure
+ assessment_list: list[Any] = []
+ parent_dict[prop_name] = assessment_list
- # Extract relevant data for this batch
- batch_extraction_data = {
- name: extraction_results[name]
- for name in prop_names
- if name in extraction_results
- }
+ # Process each array item
+ items_schema = prop_schema.get(SCHEMA_ITEMS, {})
+ item_type = items_schema.get(SCHEMA_TYPE)
- task = AssessmentTask(
- task_id=f"simple_batch_{task_counter}",
- task_type="simple_batch",
- attributes=prop_names,
- extraction_data=batch_extraction_data,
- confidence_thresholds=confidence_thresholds,
- )
- tasks.append(task)
- task_counter += 1
-
- # Create tasks for group properties (one per group)
- for prop_name, prop_schema in group_props:
- # Build confidence thresholds for nested properties
- confidence_thresholds = {}
- nested_props = prop_schema.get(SCHEMA_PROPERTIES, {})
- for nested_name in nested_props.keys():
- nested_path = f"{prop_name}.{nested_name}"
- threshold = self._get_confidence_threshold_by_path(
- properties, nested_path, default_confidence_threshold
- )
- confidence_thresholds[nested_name] = threshold
-
- task = AssessmentTask(
- task_id=f"group_{task_counter}",
- task_type="group",
- attributes=[prop_name],
- extraction_data={prop_name: extraction_results[prop_name]},
- confidence_thresholds=confidence_thresholds,
- )
- tasks.append(task)
- task_counter += 1
+ for idx, item_value in enumerate(prop_value):
+ item_path = field_path + (idx,)
- # Create tasks for list properties (one per list item)
- for prop_name, prop_schema in list_props:
- list_data = extraction_results.get(prop_name, [])
+ if item_type == TYPE_OBJECT and isinstance(item_value, dict):
+ # Create dict for this array item
+ item_dict: dict[str, Any] = {}
+ assessment_list.append(item_dict)
- if not isinstance(list_data, list):
- logger.warning(f"List property {prop_name} is not a list, skipping")
- continue
+ # Recurse into array item properties
+ item_props = items_schema.get(SCHEMA_PROPERTIES, {})
+ _traverse(item_props, item_value, item_path, item_dict)
- # Build confidence thresholds for list item properties
- confidence_thresholds = {}
- items_schema = prop_schema.get(SCHEMA_ITEMS, {})
- if items_schema.get(SCHEMA_TYPE) == TYPE_OBJECT:
- item_props = items_schema.get(SCHEMA_PROPERTIES, {})
- for item_prop_name in item_props.keys():
- # For list items, the path includes the list name
- item_path = f"{prop_name}.{item_prop_name}"
- threshold = self._get_confidence_threshold_by_path(
- properties, item_path, default_confidence_threshold
- )
- confidence_thresholds[item_prop_name] = threshold
+ else:
+ # Leaf field in array - create task
+ threshold = self._get_confidence_threshold_by_path(
+ properties,
+ ".".join(str(p) for p in field_path),
+ default_confidence_threshold,
+ )
- # Create tasks for list items (batch them if configured)
- for i in range(0, len(list_data), self.list_batch_size):
- batch_end = min(i + self.list_batch_size, len(list_data))
+ task = AssessmentTask(
+ task_id=f"task_{task_counter[0]}",
+ task_type="attribute",
+ field_path=item_path,
+ field_name=prop_name,
+ field_schema=items_schema,
+ confidence_threshold=threshold,
+ parent_assessment_dict=assessment_list, # type: ignore
+ )
+ tasks.append(task)
+ task_counter[0] += 1
- for j in range(i, batch_end):
- item_data = list_data[j]
+ # Pre-allocate slot in list (will be replaced by assessment obj)
+ assessment_list.append(None)
+
+ else:
+ # Leaf field - create task
+ threshold = self._get_confidence_threshold_by_path(
+ properties,
+ ".".join(str(p) for p in field_path),
+ default_confidence_threshold,
+ )
task = AssessmentTask(
- task_id=f"list_{prop_name}_item_{j}",
- task_type="list_item",
- attributes=[prop_name],
- extraction_data=item_data,
- confidence_thresholds=confidence_thresholds,
- list_item_index=j,
+ task_id=f"task_{task_counter[0]}",
+ task_type="attribute",
+ field_path=field_path,
+ field_name=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
+ task_counter[0] += 1
- # Check for confidence threshold alerts
- confidence_alerts = []
- self._check_confidence_alerts_for_task(
- task, assessment_data, confidence_alerts
- )
+ # Pre-allocate placeholder in dict (will be replaced by assessment obj)
+ parent_dict[prop_name] = None
- 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,
- )
+ # Start recursive traversal from root
+ _traverse(properties, extraction_results, (), assessment_structure)
- 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,
- )
+ logger.info(f"Created {len(tasks)} assessment tasks (one per leaf field)")
- def _check_confidence_alerts_for_task(
- self,
- task: AssessmentTask,
- assessment_data: Dict[str, Any],
- alerts_list: List[Dict[str, Any]],
- ) -> None:
- """
- Check assessment data for confidence threshold violations for a specific task.
-
- Args:
- task: The assessment task
- assessment_data: Dictionary containing assessment data
- alerts_list: List to append alerts to (modified in place)
- """
- if task.task_type == "simple_batch":
- for attr_name in task.attributes:
- if attr_name in assessment_data and isinstance(
- assessment_data[attr_name], dict
- ):
- confidence = _safe_float_conversion(
- assessment_data[attr_name].get("confidence", 0.0), 0.0
- )
- threshold = task.confidence_thresholds.get(attr_name, 0.9)
- if confidence < threshold:
- alerts_list.append(
- {
- "attribute_name": attr_name,
- "confidence": confidence,
- "confidence_threshold": threshold,
- }
- )
-
- elif task.task_type == "group":
- attr_name = task.attributes[0] # Group tasks have one attribute
- if attr_name in assessment_data and isinstance(
- assessment_data[attr_name], dict
- ):
- for sub_attr_name, sub_assessment in assessment_data[attr_name].items():
- if (
- isinstance(sub_assessment, dict)
- and "confidence" in sub_assessment
- ):
- confidence = _safe_float_conversion(
- sub_assessment.get("confidence", 0.0), 0.0
- )
- threshold = task.confidence_thresholds.get(sub_attr_name, 0.9)
- if confidence < threshold:
- alerts_list.append(
- {
- "attribute_name": f"{attr_name}.{sub_attr_name}",
- "confidence": confidence,
- "confidence_threshold": threshold,
- }
- )
-
- elif task.task_type == "list_item":
- attr_name = task.attributes[0] # List item tasks have one attribute
- item_index = task.list_item_index if task.list_item_index is not None else 0
-
- for item_attr_name, item_assessment in assessment_data.items():
- if (
- isinstance(item_assessment, dict)
- and "confidence" in item_assessment
- ):
- confidence = _safe_float_conversion(
- item_assessment.get("confidence", 0.0), 0.0
- )
- threshold = task.confidence_thresholds.get(item_attr_name, 0.9)
- if confidence < threshold:
- alerts_list.append(
- {
- "attribute_name": f"{attr_name}[{item_index}].{item_attr_name}",
- "confidence": confidence,
- "confidence_threshold": threshold,
- }
- )
+ return tasks, assessment_structure
def _get_cache_key(
self, document_id: str, workflow_execution_arn: str, section_id: str
@@ -979,7 +369,7 @@ def _get_cache_key(
def _get_cached_assessment_tasks(
self, document_id: str, workflow_execution_arn: str, section_id: str
- ) -> Dict[str, AssessmentResult]:
+ ) -> dict[str, AssessmentResult]:
"""
Retrieve cached assessment task results for a document section.
@@ -989,7 +379,7 @@ def _get_cached_assessment_tasks(
section_id: Section ID
Returns:
- Dictionary mapping task_id to cached AssessmentResult, empty dict if no cache
+ dictionary mapping task_id to cached AssessmentResult, empty dict if no cache
"""
logger.info(
f"Attempting to retrieve cached assessment tasks for document {document_id} section {section_id}"
@@ -1017,8 +407,6 @@ def _get_cached_assessment_tasks(
# Extract task results from JSON attribute
if "task_results" in cached_data:
try:
- import json
-
task_data_list = json.loads(cached_data["task_results"])
for task_data in task_data_list:
@@ -1056,7 +444,7 @@ def _cache_successful_assessment_tasks(
document_id: str,
workflow_execution_arn: str,
section_id: str,
- task_results: List[AssessmentResult],
+ task_results: list[AssessmentResult],
) -> None:
"""
Cache successful assessment task results to DynamoDB as a JSON-serialized list.
@@ -1065,7 +453,7 @@ def _cache_successful_assessment_tasks(
document_id: Document ID
workflow_execution_arn: Workflow execution ARN
section_id: Section ID
- task_results: List of successful assessment task results
+ task_results: list of successful assessment task results
"""
if not self.cache_table or not task_results:
return
@@ -1134,8 +522,8 @@ def _is_throttling_exception(self, exception: Exception) -> bool:
Returns:
True if exception indicates throttling, False otherwise
"""
- if hasattr(exception, "response") and "Error" in exception.response:
- error_code = exception.response["Error"]["Code"]
+ if hasattr(exception, "response") and "Error" in exception.response: # pyright: ignore[reportAttributeAccessIssue]
+ error_code = exception.response["Error"]["Code"] # pyright: ignore[reportAttributeAccessIssue]
return error_code in self.throttling_exceptions
# Check exception class name and message for throttling indicators
@@ -1149,36 +537,35 @@ def _is_throttling_exception(self, exception: Exception) -> bool:
def _aggregate_assessment_results(
self,
- tasks: List[AssessmentTask],
- results: List[AssessmentResult],
- extraction_results: Dict[str, Any],
- ) -> Tuple[Dict[str, Any], List[Dict[str, Any]], Dict[str, Any]]:
+ tasks: list[AssessmentTask],
+ results: list[AssessmentResult],
+ assessment_structure: dict[str, Any],
+ ) -> tuple[dict[str, Any], list[dict[str, Any]], dict[str, Any]]:
"""
- Aggregate individual task results into the final assessment structure.
+ Aggregate individual task results into assessment structure using direct parent insertion.
Args:
- tasks: List of assessment tasks
- results: List of assessment results
- extraction_results: Original extraction results
+ tasks: list of assessment tasks
+ results: list of assessment results
+ assessment_structure: Pre-built assessment structure from _create_assessment_tasks
Returns:
- Tuple of (enhanced_assessment_data, confidence_alerts, aggregated_metering)
+ Tuple of (assessment_structure, confidence_alerts, aggregated_metering)
"""
- enhanced_assessment_data = {}
all_confidence_alerts = []
aggregated_metering = {}
# 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
@@ -1187,85 +574,48 @@ 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)}"
- )
-
- elif task.task_type == "group":
- attr_name = task.attributes[0]
- if attr_name in result.assessment_data:
- assessment_value = result.assessment_data[attr_name]
- if isinstance(assessment_value, dict):
- group_assessment = {}
- for sub_attr_name, sub_assessment in assessment_value.items():
- if isinstance(sub_assessment, dict):
- enhanced_sub_assessment = sub_assessment.copy()
- threshold = task.confidence_thresholds.get(
- sub_attr_name, 0.9
- )
- enhanced_sub_assessment["confidence_threshold"] = (
- threshold
- )
- group_assessment[sub_attr_name] = (
- enhanced_sub_assessment
- )
- else:
- logger.warning(
- f"Unexpected sub-assessment data type for {attr_name}.{sub_attr_name}: {type(sub_assessment)}"
- )
- group_assessment[sub_attr_name] = sub_assessment
- enhanced_assessment_data[attr_name] = group_assessment
- else:
- logger.warning(
- f"Unexpected group assessment data type for {attr_name}: {type(assessment_value)}"
- )
+ # Get assessment data 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 == "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:
"""
@@ -1307,17 +657,17 @@ def _get_text_confidence_data(self, page) -> str:
return ""
def _convert_bbox_to_geometry(
- self, bbox_coords: List[float], page_num: int
- ) -> Dict[str, Any]:
+ 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)}")
@@ -1340,8 +690,8 @@ 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).
@@ -1398,8 +748,8 @@ 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.
@@ -1461,16 +811,10 @@ 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")
@@ -1523,8 +867,9 @@ 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:
@@ -1555,6 +900,9 @@ 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:
@@ -1563,9 +911,12 @@ 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)
@@ -1589,14 +940,6 @@ def process_document_section(self, document: Document, section_id: str) -> Docum
t4 = time.time()
logger.info(f"Time taken to read raw OCR results: {t4 - t3:.2f} seconds")
- # Get assessment configuration (type-safe, Pydantic handles conversions)
- model_id = self.config.assessment.model
- temperature = self.config.assessment.temperature
- top_k = self.config.assessment.top_k
- top_p = self.config.assessment.top_p
- max_tokens = self.config.assessment.max_tokens
- system_prompt = self.config.assessment.system_prompt
-
# Get schema for this document class
class_schema = self._get_class_schema(class_label)
if not class_schema:
@@ -1610,17 +953,8 @@ 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
)
@@ -1635,12 +969,6 @@ 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:
@@ -1659,106 +987,56 @@ 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
- request_start_time = time.time()
-
- # Process tasks (parallel or sequential based on configuration)
- if self.enable_parallel and len(tasks_to_process) > 1:
+ # 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"Processing {len(tasks_to_process)} assessment tasks in parallel with {self.max_workers} workers"
+ f"Added ruler overlay to page {idx}: {len(page_img):,} bytes -> {len(grid_img):,} bytes"
)
+ grid_page_images.append(grid_img)
- 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
- }
+ # 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})"
+ )
- # 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()
+
+ # 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=self.config.assessment.model,
+ system_prompt=self.config.assessment.system_prompt,
+ temperature=self.config.assessment.temperature,
+ max_tokens=self.config.assessment.max_tokens,
+ document_schema=class_schema,
+ max_concurrent=self.max_workers,
)
- request_start_time = time.time()
+ )
- 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)
+ # Merge results and metering
+ all_task_results.extend(task_results)
+ combined_metering = utils.merge_metering_data(
+ combined_metering, task_metering
+ )
+
+ logger.info(
+ f"Strands executor completed {len(task_results)} tasks in {processing_time:.2f}s"
+ )
+
+ # 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:
@@ -1837,12 +1115,12 @@ 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]
@@ -1895,21 +1173,21 @@ 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
@@ -2026,10 +1304,10 @@ 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 64f480dbb..07c8d2cd7 100644
--- a/lib/idp_common_pkg/idp_common/assessment/models.py
+++ b/lib/idp_common_pkg/idp_common/assessment/models.py
@@ -8,12 +8,12 @@
the confidence and accuracy of extraction results.
"""
-from dataclasses import dataclass
-from typing import Any, Dict, List, Optional
+from typing import Any
+from pydantic import BaseModel, Field, field_validator
-@dataclass
-class AttributeAssessment:
+
+class AttributeAssessment(BaseModel):
"""Assessment result for a single extracted attribute"""
attribute_name: str
@@ -22,30 +22,141 @@ 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
+
+
+# ============================================================================
+# 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 = Field(serialization_alias="attributeName")
+ confidence: float
+ confidence_threshold: float = Field(serialization_alias="confidenceThreshold")
+
+ @field_validator("confidence", "confidence_threshold", mode="before")
+ @classmethod
+ def parse_float(cls, v: Any) -> float:
+ """Parse float from string or number, handle None."""
+ if v is None:
+ return 0.0
+ if isinstance(v, (int, float)):
+ return float(v)
+ if isinstance(v, str):
+ if not v.strip():
+ return 0.0
+ try:
+ return float(v)
+ except (ValueError, TypeError):
+ return 0.0
+ # Fallback for other types
+ try:
+ return float(v)
+ except (ValueError, TypeError):
+ return 0.0
+
+
+class DocumentContent(BaseModel):
+ """Loaded content from document pages."""
+
+ document_text: str
+ page_images: list[Any]
+ ocr_text_confidence: str
+
+
+class ExtractionData(BaseModel):
+ """Loaded extraction data from S3."""
+
+ extraction_results: dict[str, Any] # The inference_result dict
+ full_data: dict[str, Any] # Complete data including metadata
+
+
+class AssessmentProcessingResult(BaseModel):
+ """Result of processing assessment data."""
+
+ enhanced_assessment_data: dict[str, Any]
+ confidence_alerts: list[ConfidenceAlert]
+ metering: dict[str, Any]
+ processing_metadata: dict[
+ str, Any
+ ] # Contains assessment_time_seconds, parsing_succeeded, etc.
diff --git a/lib/idp_common_pkg/idp_common/assessment/service.py b/lib/idp_common_pkg/idp_common/assessment/service.py
index bbe8e467f..8fadf1afa 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.
@@ -455,7 +459,7 @@ def _build_content_without_image_placeholder(
extraction_results: str,
ocr_text_confidence: str,
image_content: Any = None,
- ) -> List[Dict[str, Any]]:
+ ) -> list[dict[str, Any]]:
"""
Build content array without DOCUMENT_IMAGE placeholder (text-only processing).
@@ -487,6 +491,101 @@ 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.
@@ -510,8 +609,6 @@ def _get_text_confidence_data(self, page) -> str:
# Fallback: use raw OCR data if text confidence is not available (for backward compatibility)
if page.raw_text_uri:
try:
- from idp_common.ocr.service import OcrService
-
ocr_service = OcrService()
raw_ocr_data = s3.get_json_content(page.raw_text_uri)
text_confidence_data = ocr_service._generate_text_confidence_data(
@@ -525,9 +622,13 @@ 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.
@@ -559,8 +660,8 @@ 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).
@@ -617,8 +718,8 @@ 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.
@@ -668,32 +769,225 @@ 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
@@ -703,28 +997,48 @@ def process_document_section(self, document: Document, section_id: str) -> Docum
break
if not section:
- error_msg = f"Section {section_id} not found in document"
- logger.error(error_msg)
- document.errors.append(error_msg)
- return document
+ raise ValueError(f"Section {section_id} not found in document")
- # Check if section has extraction results to assess
if not section.extraction_result_uri:
- error_msg = f"Section {section_id} has no extraction results to assess"
- logger.error(error_msg)
- document.errors.append(error_msg)
- return document
-
- # Extract information about the section
- class_label = section.classification
+ raise ValueError(
+ f"Section {section_id} has no extraction results to assess"
+ )
- # Check if the section has required pages
if not section.page_ids:
- error_msg = f"Section {section_id} has no page IDs"
- logger.error(error_msg)
- document.errors.append(error_msg)
+ raise ValueError(f"Section {section_id} has no page IDs")
+
+ return section
+
+ # ============================================================================
+ # PUBLIC API
+ # ============================================================================
+
+ def process_document_section(self, document: Document, section_id: str) -> Document:
+ """
+ Process a single section from a Document object to assess extraction confidence.
+
+ Args:
+ document: Document object containing section to process
+ section_id: ID of the section to process
+
+ Returns:
+ Document: Updated Document object with assessment results appended to extraction results
+ """
+ # Check if assessment is enabled
+ if not self.config.assessment.enabled:
+ logger.info("Assessment is disabled via configuration")
return document
+ # Validate and get section
+ try:
+ section = self._validate_and_get_section(document, section_id)
+ except ValueError as e:
+ logger.error(str(e))
+ document.errors.append(str(e))
+ return document
+
+ class_label = section.classification
+
# Sort pages by page number
sorted_page_ids = sorted(section.page_ids, key=int)
start_page = int(sorted_page_ids[0])
@@ -738,81 +1052,17 @@ 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)
@@ -834,12 +1084,12 @@ def process_document_section(self, document: Document, section_id: str) -> Docum
try:
content = self._build_content_with_or_without_image_placeholder(
prompt_template,
- document_text,
+ document_content.document_text,
class_label,
property_descriptions,
extraction_results_str,
- ocr_text_confidence,
- page_images, # Pass images to the content builder
+ document_content.ocr_text_confidence,
+ document_content.page_images,
)
except ValueError as e:
logger.error(f"Error formatting prompt template: {str(e)}")
@@ -851,203 +1101,54 @@ def process_document_section(self, document: Document, section_id: str) -> Docum
f"Assessing extraction confidence for {class_label} document, section {section_id}"
)
- # Time the model invocation
- request_start_time = time.time()
-
- # Invoke Bedrock with the common library
- response_with_metering = bedrock.invoke_model(
- model_id=model_id,
- system_prompt=system_prompt,
- content=content,
- temperature=temperature,
- top_k=top_k,
- top_p=top_p,
- max_tokens=max_tokens,
- context="Assessment",
+ # Execute Bedrock assessment
+ assessment_text, metering, total_duration = (
+ self._execute_bedrock_assessment(content)
)
-
- total_duration = time.time() - request_start_time
logger.info(f"Time taken for assessment: {total_duration:.2f} seconds")
- # Extract text from response
- assessment_text = bedrock.extract_text_from_response(response_with_metering)
- metering = response_with_metering.get("metering", {})
-
- # Parse response into JSON
- assessment_data = {}
- parsing_succeeded = True # Flag to track if parsing was successful
-
- try:
- # Try to parse the assessment text as JSON
- assessment_data = json.loads(extract_json_from_text(assessment_text))
- except Exception as e:
- # Handle parsing error
- logger.error(
- f"Error parsing assessment LLM output - invalid JSON?: {assessment_text} - {e}"
- )
- logger.info("Using default confidence scores.")
- # Create default assessments for all extracted attributes
- assessment_data = {}
- for attr_name in extraction_results.keys():
- assessment_data[attr_name] = {
- "confidence": 0.5,
- "confidence_reason": "Unable to parse assessment response - default score assigned",
- }
- parsing_succeeded = False # Mark that parsing failed
-
- # Process bounding boxes automatically if bbox data is present
- try:
- logger.debug("Checking for bounding box data in assessment response")
- assessment_data = self._extract_geometry_from_assessment(
- assessment_data
- )
- except Exception as e:
- logger.warning(f"Failed to extract geometry data: {str(e)}")
- # Continue with assessment even if geometry extraction fails
-
- # Get confidence thresholds (type-safe, already float from Pydantic)
- default_confidence_threshold = (
- self.config.assessment.default_confidence_threshold
+ # Process assessment response
+ (
+ enhanced_assessment_data,
+ confidence_threshold_alerts,
+ parsing_succeeded,
+ ) = self._process_assessment_response(
+ assessment_text, extraction_results, class_schema
)
- # Enhance assessment data with confidence thresholds and create confidence threshold alerts
- enhanced_assessment_data = {}
- confidence_threshold_alerts = []
-
- # Get properties dict once for efficient access
- properties = class_schema.get(SCHEMA_PROPERTIES, {})
-
- for attr_name, attr_assessment in assessment_data.items():
- # Get property schema (if it exists in schema)
- prop_schema = properties.get(attr_name, {})
-
- # Get threshold for this property
- attr_threshold = _safe_float_conversion(
- prop_schema.get(
- X_AWS_IDP_CONFIDENCE_THRESHOLD, default_confidence_threshold
- ),
- default_confidence_threshold,
- )
-
- # Get property type
- prop_type_json = prop_schema.get(SCHEMA_TYPE, TYPE_STRING)
-
- # Map JSON Schema type to legacy attribute type for existing logic
- if prop_type_json == TYPE_OBJECT:
- attr_type = "group"
- elif prop_type_json == TYPE_ARRAY:
- attr_type = "list"
- else:
- attr_type = "simple"
-
- # Check if attr_assessment is a dictionary (expected format for simple/group attributes)
- if isinstance(attr_assessment, dict):
- # For simple attributes or group attributes - add confidence_threshold to each confidence assessment
- enhanced_assessment_data[attr_name] = self._enhance_dict_assessment(
- attr_assessment, attr_threshold
- )
-
- # Check for confidence threshold alerts in the assessment
- self._check_confidence_alerts(
- attr_assessment,
- attr_name,
- attr_threshold,
- confidence_threshold_alerts,
- )
-
- elif isinstance(attr_assessment, list):
- # Handle list attributes (expected format for LIST attributes like transactions)
- if attr_type == "list":
- # This is expected for list attributes - process each item in the list
- enhanced_list = []
- for i, item_assessment in enumerate(attr_assessment):
- if isinstance(item_assessment, dict):
- enhanced_item = self._enhance_dict_assessment(
- item_assessment, attr_threshold
- )
- enhanced_list.append(enhanced_item)
-
- # Check for confidence threshold alerts in list items
- self._check_confidence_alerts(
- item_assessment,
- f"{attr_name}[{i}]",
- attr_threshold,
- confidence_threshold_alerts,
- )
- else:
- # Handle unexpected format within list
- logger.warning(
- f"List item {i} in attribute '{attr_name}' is not a dictionary. "
- f"Expected dict, got {type(item_assessment)}. Using default confidence."
- )
- default_item = {
- "confidence": 0.5,
- "confidence_reason": f"List item {i} in '{attr_name}' has unexpected format. Using default confidence.",
- "confidence_threshold": attr_threshold,
- }
- enhanced_list.append(default_item)
-
- # Add alert for default confidence
- if 0.5 < attr_threshold:
- confidence_threshold_alerts.append(
- {
- "attribute_name": f"{attr_name}[{i}]",
- "confidence": 0.5,
- "confidence_threshold": attr_threshold,
- }
- )
-
- enhanced_assessment_data[attr_name] = enhanced_list
- else:
- # List format for non-list attribute is unexpected
- logger.warning(
- f"Attribute '{attr_name}' (type: {attr_type}) assessment is a list but attribute is not configured as list type. "
- f"Using default confidence."
- )
-
- # Create a default assessment structure
- default_assessment = {
- "confidence": 0.5,
- "confidence_reason": f"LLM returned list format for non-list attribute '{attr_name}'. Using default confidence (0.5) and threshold ({attr_threshold}).",
- "confidence_threshold": attr_threshold,
- }
- enhanced_assessment_data[attr_name] = default_assessment
-
- else:
- # Handle other unexpected types
- logger.warning(
- f"Attribute '{attr_name}' assessment is of unexpected type {type(attr_assessment)}. "
- f"Expected dictionary or list (for list attributes). Using default confidence."
- )
-
- # Create a default assessment structure
- default_assessment = {
- "confidence": 0.5,
- "confidence_reason": f"LLM returned unexpected type {type(attr_assessment)} for attribute '{attr_name}'. Using default confidence (0.5) and threshold ({attr_threshold}).",
- "confidence_threshold": attr_threshold,
- }
- enhanced_assessment_data[attr_name] = default_assessment
-
# Update the existing extraction result with enhanced assessment data
- extraction_data["explainability_info"] = [enhanced_assessment_data]
- extraction_data["metadata"] = extraction_data.get("metadata", {})
- extraction_data["metadata"]["assessment_time_seconds"] = total_duration
- extraction_data["metadata"]["assessment_parsing_succeeded"] = (
- parsing_succeeded
+ extraction_data_model.full_data["explainability_info"] = [
+ enhanced_assessment_data
+ ]
+ extraction_data_model.full_data["metadata"] = (
+ extraction_data_model.full_data.get("metadata", {})
+ )
+ extraction_data_model.full_data["metadata"]["assessment_time_seconds"] = (
+ total_duration
)
+ extraction_data_model.full_data["metadata"][
+ "assessment_parsing_succeeded"
+ ] = parsing_succeeded
# Write the updated result back to S3
+ # extraction_result_uri is guaranteed to exist by _validate_and_get_section
+ assert section.extraction_result_uri is not None
bucket, key = utils.parse_s3_uri(section.extraction_result_uri)
s3.write_content(
- extraction_data, bucket, key, content_type="application/json"
+ extraction_data_model.full_data,
+ bucket,
+ key,
+ content_type="application/json",
)
# Update the section in the document with confidence threshold alerts
for doc_section in document.sections:
if doc_section.section_id == section_id:
- doc_section.confidence_threshold_alerts = (
- confidence_threshold_alerts
- )
+ # Convert ConfidenceAlert objects to dicts with camelCase keys for UI
+ doc_section.confidence_threshold_alerts = [
+ alert.model_dump(by_alias=True)
+ for alert in confidence_threshold_alerts
+ ]
break
# Update document with metering data
diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_executor.py b/lib/idp_common_pkg/idp_common/assessment/strands_executor.py
new file mode 100644
index 000000000..7b0d3015a
--- /dev/null
+++ b/lib/idp_common_pkg/idp_common/assessment/strands_executor.py
@@ -0,0 +1,248 @@
+"""
+Parallel executor for Strands-based assessment tasks.
+
+This module provides asyncio-based parallel execution of assessment tasks
+with concurrency control via semaphores.
+"""
+
+import asyncio
+import os
+import time
+from typing import Any, cast
+
+from aws_lambda_powertools import Logger
+
+from idp_common.assessment.models import AssessmentResult, AssessmentTask
+from idp_common.assessment.strands_service import assess_attribute_with_strands
+from idp_common.utils import merge_metering_data
+
+logger = Logger(service="assessment", level=os.getenv("LOG_LEVEL", "INFO"))
+
+
+async def execute_tasks_async(
+ tasks: list[AssessmentTask],
+ extraction_results: dict[str, Any],
+ page_images: list[bytes],
+ sorted_page_ids: list[str],
+ model_id: str,
+ system_prompt: str,
+ temperature: float,
+ max_tokens: int,
+ document_schema: dict[str, Any],
+ max_concurrent: int = 5,
+ max_retries: int = 7,
+ connect_timeout: float = 10.0,
+ read_timeout: float = 300.0,
+) -> tuple[list[AssessmentResult], dict[str, Any]]:
+ """
+ Execute assessment tasks in parallel using asyncio.
+
+ Args:
+ tasks: List of assessment tasks to execute
+ 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
+ document_schema: Full document JSON schema
+ max_concurrent: Maximum concurrent tasks (default 5)
+ max_retries: Maximum retry attempts
+ connect_timeout: Connection timeout in seconds
+ read_timeout: Read timeout in seconds
+
+ Returns:
+ Tuple of (results, combined_metering)
+ """
+ logger.info(
+ f"Starting parallel execution of {len(tasks)} assessment tasks with max_concurrent={max_concurrent}"
+ )
+
+ # Create semaphore to limit concurrency
+ semaphore = asyncio.Semaphore(max_concurrent)
+
+ async def execute_with_semaphore(task: AssessmentTask) -> AssessmentResult:
+ """Execute task with semaphore to limit concurrency."""
+ async with semaphore:
+ logger.debug(
+ f"Executing task {task.task_id} (type: {task.task_type})",
+ extra={"task_id": task.task_id, "task_type": task.task_type},
+ )
+ return await assess_attribute_with_strands(
+ task=task,
+ extraction_results=extraction_results,
+ page_images=page_images,
+ sorted_page_ids=sorted_page_ids,
+ model_id=model_id,
+ system_prompt=system_prompt,
+ temperature=temperature,
+ max_tokens=max_tokens,
+ document_schema=document_schema,
+ max_retries=max_retries,
+ connect_timeout=connect_timeout,
+ read_timeout=read_timeout,
+ )
+
+ # Execute all tasks concurrently (with semaphore limit)
+ # Use return_exceptions=True to capture failures without stopping others
+ results = await asyncio.gather(
+ *[execute_with_semaphore(task) for task in tasks],
+ return_exceptions=True,
+ )
+
+ # Process results and handle exceptions
+ processed_results: list[AssessmentResult] = []
+ combined_metering: dict[str, Any] = {}
+
+ for i, result in enumerate(results):
+ if isinstance(result, Exception):
+ # Convert exception to failed AssessmentResult
+ logger.error(
+ f"Task {tasks[i].task_id} failed with exception",
+ extra={
+ "task_id": tasks[i].task_id,
+ "exception": str(result),
+ "exception_type": type(result).__name__,
+ },
+ )
+ processed_results.append(
+ AssessmentResult(
+ task_id=tasks[i].task_id,
+ success=False,
+ assessment_data={},
+ confidence_alerts=[],
+ error_message=str(result),
+ processing_time=0.0,
+ )
+ )
+ else:
+ # result is AssessmentResult here (not Exception)
+ assessment_result = cast(AssessmentResult, result)
+ processed_results.append(assessment_result)
+ # Merge metering data
+ if assessment_result.metering:
+ combined_metering = merge_metering_data(
+ combined_metering, assessment_result.metering
+ )
+
+ # Log summary
+ successful_tasks = sum(1 for r in processed_results if r.success)
+ failed_tasks = len(processed_results) - successful_tasks
+
+ logger.info(
+ f"Completed {len(processed_results)} tasks: {successful_tasks} successful, {failed_tasks} failed",
+ extra={
+ "total_tasks": len(processed_results),
+ "successful": successful_tasks,
+ "failed": failed_tasks,
+ },
+ )
+
+ return processed_results, combined_metering
+
+
+def execute_assessment_tasks_parallel(
+ tasks: list[AssessmentTask],
+ extraction_results: dict[str, Any],
+ page_images: list[bytes],
+ sorted_page_ids: list[str],
+ model_id: str,
+ system_prompt: str,
+ temperature: float,
+ max_tokens: int,
+ document_schema: dict[str, Any],
+ max_concurrent: int = 5,
+ max_retries: int = 7,
+ connect_timeout: float = 10.0,
+ read_timeout: float = 300.0,
+) -> tuple[list[AssessmentResult], dict[str, Any], float]:
+ """
+ Execute assessment tasks in parallel (synchronous wrapper).
+
+ This is the main entry point called from process_document_section.
+ It wraps the async executor and provides synchronous interface.
+
+ Args:
+ tasks: List of assessment tasks
+ 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
+ document_schema: Full document JSON schema
+ max_concurrent: Max concurrent tasks (default 5)
+ max_retries: Maximum retry attempts
+ connect_timeout: Connection timeout in seconds
+ read_timeout: Read timeout in seconds
+
+ Returns:
+ Tuple of (results, metering, duration)
+ """
+ logger.info(
+ f"Starting parallel assessment execution for {len(tasks)} tasks",
+ extra={"num_tasks": len(tasks), "max_concurrent": max_concurrent},
+ )
+
+ start_time = time.time()
+
+ # Run async executor
+ # Use asyncio.run() for clean event loop management
+ try:
+ results, metering = asyncio.run(
+ execute_tasks_async(
+ tasks=tasks,
+ extraction_results=extraction_results,
+ page_images=page_images,
+ sorted_page_ids=sorted_page_ids,
+ model_id=model_id,
+ system_prompt=system_prompt,
+ temperature=temperature,
+ max_tokens=max_tokens,
+ document_schema=document_schema,
+ max_concurrent=max_concurrent,
+ max_retries=max_retries,
+ connect_timeout=connect_timeout,
+ read_timeout=read_timeout,
+ )
+ )
+ except RuntimeError as e:
+ # Handle case where event loop already exists (shouldn't happen in Lambda)
+ if "There is no current event loop" in str(e) or "asyncio.run()" in str(e):
+ logger.warning(
+ "Event loop already exists, using get_event_loop",
+ extra={"error": str(e)},
+ )
+ loop = asyncio.get_event_loop()
+ results, metering = loop.run_until_complete(
+ execute_tasks_async(
+ tasks=tasks,
+ extraction_results=extraction_results,
+ page_images=page_images,
+ sorted_page_ids=sorted_page_ids,
+ model_id=model_id,
+ system_prompt=system_prompt,
+ temperature=temperature,
+ max_tokens=max_tokens,
+ document_schema=document_schema,
+ max_concurrent=max_concurrent,
+ max_retries=max_retries,
+ connect_timeout=connect_timeout,
+ read_timeout=read_timeout,
+ )
+ )
+ else:
+ raise
+
+ duration = time.time() - start_time
+
+ logger.info(
+ f"Parallel assessment execution completed in {duration:.2f}s",
+ extra={"duration_seconds": duration, "num_tasks": len(tasks)},
+ )
+
+ return results, metering, duration
diff --git a/lib/idp_common_pkg/idp_common/assessment/strands_models.py b/lib/idp_common_pkg/idp_common/assessment/strands_models.py
new file mode 100644
index 000000000..80cc261e7
--- /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 000000000..f49632618
--- /dev/null
+++ b/lib/idp_common_pkg/idp_common/assessment/strands_service.py
@@ -0,0 +1,441 @@
+"""
+Core assessment service using Strands agents with interactive tools.
+
+This module provides the main assessment functions that use Strands agents
+to assess extraction confidence with bounding boxes and interactive image viewing.
+"""
+
+import json
+import os
+import time
+from typing import Any
+
+from aws_lambda_powertools import Logger
+from strands import Agent
+from strands.agent.conversation_manager import SummarizingConversationManager
+from strands.models.bedrock import BedrockModel
+from strands.types.content import CachePoint, ContentBlock, Message
+from strands.types.media import ImageContent, ImageSource
+
+from idp_common.assessment.models import AssessmentResult, AssessmentTask
+from idp_common.assessment.strands_models import AssessmentOutput
+from idp_common.assessment.strands_tools import create_strands_tools
+from idp_common.bedrock import build_model_config
+from idp_common.utils.bedrock_utils import async_exponential_backoff_retry
+
+logger = Logger(service="assessment", level=os.getenv("LOG_LEVEL", "INFO"))
+
+
+async def assess_attribute_with_strands(
+ task: AssessmentTask,
+ extraction_results: dict[str, Any],
+ page_images: list[bytes],
+ sorted_page_ids: list[str],
+ model_id: str,
+ system_prompt: str,
+ temperature: float,
+ max_tokens: int,
+ document_schema: dict[str, Any],
+ max_retries: int = 7,
+ connect_timeout: float = 10.0,
+ read_timeout: float = 300.0,
+) -> AssessmentResult:
+ """
+ Assess attributes using Strands agent with interactive tools.
+
+ Args:
+ task: Assessment task to process
+ 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()
+
+ # 1. Create tools (image viewer + todo list + submit assessment)
+ tools = create_strands_tools(page_images, sorted_page_ids)
+
+ # 2. Build enhanced system prompt with schema and extraction (for caching)
+ enhanced_system_prompt = _build_system_prompt_with_context(
+ system_prompt, document_schema, extraction_results, len(page_images)
+ )
+
+ # 3. Build minimal task-specific prompt (just field path and threshold)
+ task_prompt = _build_task_prompt(task, page_images)
+
+ # 4. Create Bedrock model config using shared utility
+ model_config = build_model_config(
+ model_id=model_id,
+ max_tokens=max_tokens,
+ max_retries=max_retries,
+ connect_timeout=connect_timeout,
+ read_timeout=read_timeout,
+ )
+ # Add temperature (not handled by build_model_config)
+ model_config["temperature"] = temperature
+
+ # 5. Initialize Strands agent
+ agent = Agent(
+ model=BedrockModel(**model_config),
+ tools=tools,
+ system_prompt=enhanced_system_prompt,
+ state={
+ "task": task.model_dump(),
+ "extraction_results": extraction_results,
+ "assessment_output": None,
+ },
+ conversation_manager=SummarizingConversationManager(
+ summary_ratio=0.8, preserve_recent_messages=1
+ ),
+ )
+
+ # 6. Create user message and run agent with retry
+ user_message = Message(role="user", content=task_prompt)
+
+ logger.info(
+ "Starting Strands assessment",
+ extra={
+ "task_id": task.task_id,
+ "task_type": task.task_type,
+ "field_name": task.field_name,
+ },
+ )
+
+ @async_exponential_backoff_retry(
+ max_retries=30,
+ initial_delay=5,
+ exponential_base=2,
+ jitter=0.5,
+ max_delay=900,
+ )
+ async def invoke_agent_with_retry():
+ return await agent.invoke_async([user_message])
+
+ try:
+ response = await invoke_agent_with_retry()
+ logger.debug("Agent response received", extra={"task_id": task.task_id})
+ except Exception as e:
+ logger.error(
+ "Agent invocation failed",
+ extra={"task_id": task.task_id, "error": str(e)},
+ )
+ return AssessmentResult(
+ task_id=task.task_id,
+ success=False,
+ assessment_data={},
+ confidence_alerts=[],
+ error_message=f"Agent invocation failed: {str(e)}",
+ processing_time=time.time() - start_time,
+ )
+
+ # 7. Extract and validate assessment from agent state
+ assessment_dict = agent.state.get("assessment_output")
+ if not assessment_dict:
+ return AssessmentResult(
+ task_id=task.task_id,
+ success=False,
+ assessment_data={},
+ confidence_alerts=[],
+ error_message="Agent did not produce assessment output. Agent may not have called submit_assessment tool.",
+ processing_time=time.time() - start_time,
+ )
+
+ try:
+ assessment_output = AssessmentOutput(**assessment_dict)
+ except Exception as e:
+ return AssessmentResult(
+ task_id=task.task_id,
+ success=False,
+ assessment_data={},
+ confidence_alerts=[],
+ error_message=f"Invalid assessment output format: {str(e)}",
+ processing_time=time.time() - start_time,
+ )
+
+ # Validate that agent assessed the expected field
+ if not _field_names_match(task.field_name, assessment_output.field_name):
+ return AssessmentResult(
+ task_id=task.task_id,
+ success=False,
+ assessment_data={},
+ confidence_alerts=[],
+ error_message=f"Agent assessed wrong field: expected '{task.field_name}', got '{assessment_output.field_name}'",
+ processing_time=time.time() - start_time,
+ )
+
+ # 8. Extract metering from response
+ metering = _extract_metering(response, model_id)
+
+ # 9. Convert to AssessmentResult format
+ result = _convert_to_assessment_result(
+ task,
+ assessment_output,
+ metering,
+ time.time() - start_time,
+ )
+
+ logger.info(
+ "Assessment completed successfully",
+ extra={
+ "task_id": task.task_id,
+ "processing_time": result.processing_time,
+ "success": result.success,
+ },
+ )
+
+ return result
+
+
+def _field_names_match(expected: str, actual: str) -> bool:
+ """
+ Check if field names match, handling nested paths with dots and array indices.
+
+ Examples:
+ - "address.street" matches "address.street"
+ - "items[0].price" matches "items[0].price"
+ - "address" matches "address"
+
+ Args:
+ expected: Expected field name/path
+ actual: Actual field name/path from agent
+
+ Returns:
+ True if field names match
+ """
+ return expected == actual
+
+
+def _extract_metering(response: Any, model_id: str) -> dict[str, Any]:
+ """
+ Extract metering data from Strands AgentResult response.
+
+ Args:
+ response: AgentResult from agent.invoke_async() (has metrics attribute)
+ model_id: Model ID for metering key
+
+ Returns:
+ Metering dict with token usage, or empty dict if no metrics
+ """
+ metering = {}
+ # AgentResult has metrics attribute at runtime (from Strands)
+ if (
+ hasattr(response, "metrics")
+ and response.metrics
+ and hasattr(response.metrics, "accumulated_usage")
+ and response.metrics.accumulated_usage
+ ): # type: ignore[attr-defined]
+ token_usage = {
+ "inputTokens": response.metrics.accumulated_usage.get("inputTokens", 0), # type: ignore[attr-defined]
+ "outputTokens": response.metrics.accumulated_usage.get("outputTokens", 0), # type: ignore[attr-defined]
+ "totalTokens": response.metrics.accumulated_usage.get("totalTokens", 0), # type: ignore[attr-defined]
+ "cacheReadInputTokens": response.metrics.accumulated_usage.get( # type: ignore[attr-defined]
+ "cacheReadInputTokens", 0
+ ),
+ "cacheWriteInputTokens": response.metrics.accumulated_usage.get( # type: ignore[attr-defined]
+ "cacheWriteInputTokens", 0
+ ),
+ }
+ metering[f"assessment/bedrock/{model_id}"] = token_usage
+
+ return metering
+
+
+def _build_system_prompt_with_context(
+ base_system_prompt: str,
+ document_schema: dict[str, Any],
+ extraction_results: dict[str, Any],
+ num_images: int,
+) -> str:
+ """
+ Build system prompt with full schema and extraction results for prompt caching.
+
+ This puts the static/cacheable content (schema, extraction, general instructions)
+ in the system prompt, which benefits from prompt caching.
+
+ Args:
+ base_system_prompt: Base assessment system prompt
+ document_schema: Full JSON schema for the document class
+ extraction_results: Complete extraction results
+ num_images: Number of available page images
+
+ Returns:
+ Enhanced system prompt with schema and extraction context
+ """
+ return f"""{base_system_prompt}
+
+## Document Schema
+
+Below is the full JSON schema for this document type. This defines all fields, their types, and confidence thresholds.
+
+```json
+{json.dumps(document_schema, indent=2)}
+```
+
+## Complete Extraction Results
+
+Below are the complete extraction results for the document being assessed. When assessing a specific field, locate its value in this structure.
+
+```json
+{json.dumps(extraction_results, indent=2)}
+```
+
+## Available Document Images
+
+You have access to {num_images} document page images (indices 0-{num_images - 1}).
+Each image has ruler markings along the edges showing the 0-1000 coordinate scale.
+
+Use the `view_image` tool to:
+1. View images to locate the extracted values
+2. Draw bounding boxes to verify coordinates
+3. Check if values are clearly visible and readable
+
+## Assessment Process
+
+1. **Plan**: Use `create_todo_list` to organize your assessment steps
+2. **Locate**: Use `view_image` to find each value in the document
+3. **Coordinate**: Determine precise bounding box coordinates using ruler markings (0-1000 scale)
+4. **Assess**: Evaluate confidence based on:
+ - Text clarity and OCR quality
+ - Value correctness compared to what you see in the image
+ - Bounding box accuracy
+5. **Submit**: Use `submit_assessment` tool with your final assessment
+
+## Bounding Box Format
+
+Bounding boxes use normalized 0-1000 coordinates:
+- x1, y1: Top-left corner
+- x2, y2: Bottom-right corner
+- page: Page number (1-indexed)
+
+Example: {{"x1": 150, "y1": 220, "x2": 380, "y2": 245, "page": 1}}
+
+**Important**: You MUST call `submit_assessment` to complete each task.
+"""
+
+
+def _convert_field_path_to_string(field_path: tuple[str | int, ...]) -> str:
+ """
+ Convert field path tuple to dot notation string.
+
+ Examples:
+ ("address", "street") → "address.street"
+ ("items", 0, "price") → "items[0].price"
+ ("orders", 2, "line_items", 1, "quantity") → "orders[2].line_items[1].quantity"
+
+ Args:
+ field_path: Tuple of field names (str) and array indices (int)
+
+ Returns:
+ Dot notation path string with array indices in brackets
+ """
+ path_parts = []
+ for part in field_path:
+ if isinstance(part, int):
+ # Append array index to previous part: "items" → "items[0]"
+ path_parts[-1] = f"{path_parts[-1]}[{part}]"
+ else:
+ # Add new field name
+ path_parts.append(str(part))
+
+ return ".".join(path_parts)
+
+
+def _build_task_prompt(
+ task: AssessmentTask, page_images: list[bytes]
+) -> list[ContentBlock]:
+ """
+ Build minimal task-specific prompt for assessing a single field.
+
+ This is minimal (just field path and threshold) to maximize the benefit
+ of caching the system prompt which contains the schema and extraction.
+
+ Args:
+ task: Assessment task for one specific field
+ page_images: List of page images to include in the prompt
+
+ 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(
+ 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 000000000..44379aa5e
--- /dev/null
+++ b/lib/idp_common_pkg/idp_common/assessment/strands_tools.py
@@ -0,0 +1,197 @@
+"""
+Strands tools for confidence assessment with image viewing and grid overlay.
+
+This module provides tools for Strands agents to view document pages and
+mark bounding boxes during confidence assessment tasks.
+"""
+
+import os
+from typing import Any
+
+from aws_lambda_powertools import Logger
+from pydantic import BaseModel, Field
+from strands import Agent, tool
+
+from idp_common.assessment.strands_models import AssessmentOutput, BoundingBox
+from idp_common.utils.grid_overlay import 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")
+
+
+@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.
+
+ 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) -> dict:
+ """
+ View a specific page image, optionally highlighting a bounding box area.
+
+ Use this tool to examine document pages when assessing confidence.
+ You can specify a bounding box to highlight a specific region.
+
+ Args:
+ input_data: Dictionary with:
+ - image_index (int): Index of page to view (0-based)
+ - bounding_box (dict, optional): Bounding box with x1, y1, x2, y2, page
+ - label (str, optional): Label for the bounding box
+
+ Returns:
+ ImageContent object that the LLM can see
+
+ Example:
+ view_image({
+ "image_index": 0,
+ "bounding_box": {"x1": 100, "y1": 200, "x2": 300, "y2": 250, "page": 1},
+ "label": "Account Number"
+ }, agent)
+ """
+ # Validate input - let ValidationError propagate
+ view_input = ViewImageInput(**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"],
+ },
+ )
+
+ logger.info(
+ "Returning image to agent",
+ extra={
+ "image_index": view_input.image_index,
+ "page_id": page_id,
+ "has_bbox": view_input.bounding_box is not None,
+ "image_size_bytes": len(img_bytes),
+ },
+ )
+
+ return {
+ "status": "success",
+ "content": [
+ {
+ "image": {
+ "format": "png",
+ "source": {
+ "bytes": img_bytes,
+ },
+ }
+ }
+ ],
+ }
+
+ return view_image
+
+
+def create_strands_tools(
+ page_images: list[bytes], sorted_page_ids: list[str]
+) -> list[Any]:
+ """
+ Create all tools needed for Strands-based assessment.
+
+ Args:
+ page_images: List of page image bytes (with grid overlay already applied)
+ sorted_page_ids: List of page IDs in sorted order
+
+ Returns:
+ List of Strands tool functions
+ """
+ return [
+ create_view_image_tool(page_images, sorted_page_ids),
+ create_todo_list,
+ update_todo,
+ view_todo_list,
+ submit_assessment,
+ ]
diff --git a/lib/idp_common_pkg/idp_common/bedrock/__init__.py b/lib/idp_common_pkg/idp_common/bedrock/__init__.py
index a26729732..c93d637a4 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 000000000..0df2da004
--- /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 604d84fc0..c8e0e070f 100644
--- a/lib/idp_common_pkg/idp_common/config/models.py
+++ b/lib/idp_common_pkg/idp_common/config/models.py
@@ -19,17 +19,18 @@
"""
from typing import Any, Dict, List, Optional, Union, Literal, Annotated
-from pydantic import BaseModel, ConfigDict, Field, field_validator, Discriminator
+from typing_extensions import Self
+from pydantic import BaseModel, ConfigDict, Field, field_validator, Discriminator, model_validator
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(
@@ -78,6 +79,7 @@ class AgenticConfig(BaseModel):
enabled: bool = Field(default=False, description="Enable agentic extraction")
review_agent: bool = Field(default=False, description="Enable review agent")
+ review_agent_model: str | None= Field(default=None, description="Model used for reviewing and correcting extraction work")
class ExtractionConfig(BaseModel):
@@ -119,6 +121,15 @@ 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
+
class ClassificationConfig(BaseModel):
@@ -172,25 +183,6 @@ def parse_max_pages(cls, v: Any) -> int:
return int(v)
-class GranularAssessmentConfig(BaseModel):
- """Granular assessment configuration"""
-
- enabled: bool = Field(default=False, description="Enable granular assessment")
- list_batch_size: int = Field(default=1, gt=0)
- simple_batch_size: int = Field(default=3, gt=0)
- max_workers: int = Field(default=20, gt=0)
-
- @field_validator(
- "list_batch_size", "simple_batch_size", "max_workers", mode="before"
- )
- @classmethod
- def parse_int(cls, v: Any) -> int:
- """Parse int from string or number"""
- if isinstance(v, str):
- return int(v) if v else 0
- return int(v)
-
-
class AssessmentConfig(BaseModel):
"""Document assessment configuration"""
@@ -236,34 +228,7 @@ 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)
@@ -273,7 +238,10 @@ class AssessmentConfig(BaseModel):
default_confidence_threshold: float = Field(default=0.8, ge=0.0, le=1.0)
validation_enabled: bool = Field(default=False, description="Enable validation")
image: ImageConfig = Field(default_factory=ImageConfig)
- granular: GranularAssessmentConfig = Field(default_factory=GranularAssessmentConfig)
+ # Granular assessment settings (always enabled, no longer nested)
+ max_workers: int = Field(
+ default=50, gt=0, description="Max concurrent workers for parallel assessment"
+ )
@field_validator(
"temperature", "top_p", "top_k", "default_confidence_threshold", mode="before"
@@ -285,7 +253,7 @@ 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"""
@@ -423,7 +391,7 @@ class ErrorAnalyzerConfig(BaseModel):
"AccessDenied",
"ThrottlingException",
],
- description="Error patterns to search for in logs"
+ description="Error patterns to search for in logs",
)
system_prompt: str = Field(
default="""
@@ -511,11 +479,10 @@ class ErrorAnalyzerConfig(BaseModel):
- No time specified: 24 hours (default)
IMPORTANT: Do not include any search quality reflections, search quality scores, or meta-analysis sections in your response. Only provide the three required sections: Root Cause, Recommendations, and Evidence.""",
- description="System prompt for error analyzer"
+ description="System prompt for error analyzer",
)
parameters: ErrorAnalyzerParameters = Field(
- default_factory=ErrorAnalyzerParameters,
- description="Error analyzer parameters"
+ default_factory=ErrorAnalyzerParameters, description="Error analyzer parameters"
)
@@ -635,12 +602,10 @@ class AgentsConfig(BaseModel):
"""Agents configuration"""
error_analyzer: Optional[ErrorAnalyzerConfig] = Field(
- default_factory=ErrorAnalyzerConfig,
- description="Error analyzer configuration"
+ default_factory=ErrorAnalyzerConfig, description="Error analyzer configuration"
)
chat_companion: Optional[ChatCompanionConfig] = Field(
- default_factory=ChatCompanionConfig,
- description="Chat companion configuration"
+ default_factory=ChatCompanionConfig, description="Chat companion 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 7d8a24157..b7d0352ae 100644
--- a/lib/idp_common_pkg/idp_common/extraction/agentic_idp.py
+++ b/lib/idp_common_pkg/idp_common/extraction/agentic_idp.py
@@ -9,10 +9,9 @@
import asyncio
import io
import json
+import logging
import os
-import re
import threading
-import traceback
from pathlib import Path
from typing import (
Any,
@@ -22,21 +21,26 @@
import jsonpatch
from aws_lambda_powertools import Logger
-from botocore.config import Config
-from botocore.exceptions import ClientError
from PIL import Image
from pydantic import BaseModel, Field
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 strands.models import BedrockModel
+from strands.types.agent import AgentInput
+from strands.types.content import CachePoint, ContentBlock, Message
from strands.types.media import (
DocumentContent,
ImageContent,
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,
+)
from idp_common.utils.strands_agent_tools.todo_list import (
create_todo_list,
update_todo,
@@ -48,39 +52,10 @@
# In Lambda: Full JSON structured logs
# Outside Lambda: Human-readable format for local development
logger = Logger(service="agentic_idp", level=os.getenv("LOG_LEVEL", "INFO"))
-
+logging.getLogger("strands.models.bedrock").setLevel(logging.DEBUG)
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."""
@@ -134,14 +109,6 @@ class BedrockInvokeModelResponse(TypedDict):
metering: dict[str, BedrockUsage] # Key format: "{context}/bedrock/{model_id}"
-# Data Models for structured extraction
-class BoolResponseModel(BaseModel):
- """Model for boolean validation responses."""
-
- valid_result: bool
- description: str = Field(..., description="explanation of the decision")
-
-
class JsonPatchModel(BaseModel):
"""Model for JSON patch operations."""
@@ -178,6 +145,59 @@ def apply_patches_to_data(
return patched_dict
+def create_view_image_tool(page_images: list[bytes]) -> 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(image_index: int, agent: Agent) -> dict:
+ """
+ View a specific page image. Use this tool when the doc has more images than what you already see.
+ """
+
+ # Validate image index exists
+ if image_index >= len(page_images):
+ raise ValueError(
+ f"Invalid image_index {image_index}. "
+ f"Valid range: 0-{len(page_images) - 1}"
+ )
+
+ # Get the base image (already has grid overlay)
+ img_bytes = page_images[image_index]
+
+ logger.info(
+ "Returning image to agent",
+ extra={
+ "image_index": image_index,
+ "image_size_bytes": len(img_bytes),
+ },
+ )
+
+ return {
+ "status": "success",
+ "content": [
+ {
+ "image": {
+ "format": "png",
+ "source": {
+ "bytes": img_bytes,
+ },
+ }
+ }
+ ],
+ }
+
+ return view_image
+
+
def create_dynamic_extraction_tool_and_patch_tool(model_class: type[TargetModel]):
"""
Create a dynamic tool function that extracts data according to a Pydantic model.
@@ -458,6 +478,231 @@ def patch_buffer_data(patches: list[dict[str, Any]], agent: Agent) -> str:
"""
+@async_exponential_backoff_retry(
+ max_retries=50,
+ initial_delay=5,
+ max_delay=1800,
+ jitter=0.5,
+)
+async def invoke_agent_with_retry(input: AgentInput, agent: Agent):
+ return await agent.invoke_async(input)
+
+
+def _initialize_token_usage() -> dict[str, int]:
+ """Initialize token usage tracking dictionary."""
+ return {
+ "inputTokens": 0,
+ "outputTokens": 0,
+ "totalTokens": 0,
+ "cacheReadInputTokens": 0,
+ "cacheWriteInputTokens": 0,
+ }
+
+
+def _accumulate_token_usage(response: Any, token_usage: dict[str, int]) -> None:
+ """
+ Accumulate token usage from response into usage dict.
+
+ Args:
+ response: Agent response object with metrics
+ token_usage: Dictionary to accumulate usage into (modified in place)
+ """
+ if response and response.metrics and response.metrics.accumulated_usage:
+ for key in token_usage.keys():
+ token_usage[key] += response.metrics.accumulated_usage.get(key, 0)
+
+
+def _build_system_prompt(
+ base_prompt: str, custom_instruction: str | None, data_format: type[BaseModel]
+) -> tuple[str, str]:
+ """
+ Build complete system prompt with custom instructions and schema.
+
+ Args:
+ base_prompt: The base system prompt (typically SYSTEM_PROMPT constant)
+ custom_instruction: Optional custom instructions to append
+ data_format: Pydantic model class to extract schema from
+
+ Returns:
+ Tuple of (complete system prompt with schema, schema_json for state storage)
+ """
+ # Generate and clean schema
+ schema_json = json.dumps(data_format.model_json_schema(), indent=2)
+
+ # Build final prompt
+ final_prompt = base_prompt
+ if custom_instruction:
+ final_prompt = f"{final_prompt}\n\nCustom Instructions for this specific task: {custom_instruction}"
+
+ complete_prompt = f"{final_prompt}\n\nExpected Schema:\n{schema_json}"
+
+ return complete_prompt, schema_json
+
+
+def _prepare_prompt_content(
+ prompt: str | Message | Image.Image,
+ page_images: list[bytes] | None,
+ existing_data: BaseModel | None,
+) -> list[ContentBlock]:
+ """
+ Prepare prompt content from various input types.
+
+ Converts different prompt types (text, PIL Image, Message dict) into
+ a list of ContentBlocks, adds page images, and appends existing data context.
+
+ Args:
+ prompt: Input content (text string, PIL Image, or Message dict)
+ page_images: Optional list of page image bytes to include
+ existing_data: Optional existing extraction data to update
+
+ Returns:
+ List of ContentBlock objects ready for agent invocation
+ """
+ prompt_content: list[ContentBlock] = []
+
+ # Process prompt based on type
+ if isinstance(prompt, Image.Image):
+ # Convert PIL Image to binary string
+ img_buffer = io.BytesIO()
+ prompt.save(img_buffer, format="PNG")
+ img_bytes = img_buffer.getvalue()
+
+ logger.debug(
+ "Processing PIL Image",
+ extra={"size": prompt.size, "mode": prompt.mode},
+ )
+
+ prompt_content = [
+ ContentBlock(text="Extract structured data from this image:"),
+ ContentBlock(
+ image=ImageContent(format="png", source=ImageSource(bytes=img_bytes))
+ ),
+ ]
+ elif isinstance(prompt, dict) and "content" in prompt:
+ prompt_content = prompt["content"] # type: ignore
+ else:
+ prompt_content = [ContentBlock(text=str(prompt))]
+
+ # Add page images if provided
+ if page_images:
+ if len(page_images) > 20:
+ prompt_content.append(
+ ContentBlock(
+ text=f"There are {len(page_images)} images, initially you'll see 20 of them, use the tools to see the rest."
+ )
+ )
+
+ prompt_content += [
+ ContentBlock(
+ image=ImageContent(format="png", source=ImageSource(bytes=img_bytes))
+ )
+ for img_bytes in page_images
+ ]
+
+ # Add existing data context if provided
+ if existing_data:
+ prompt_content.append(
+ ContentBlock(
+ text=f"Please update the existing data using the extraction tool or patches. Existing data: {existing_data.model_dump()}"
+ )
+ )
+
+ prompt_content += [
+ ContentBlock(text="end of your main task description"),
+ ContentBlock(cachePoint=CachePoint(type="default")),
+ ]
+ return prompt_content
+
+
+async def _invoke_agent_for_extraction(
+ agent: Agent,
+ prompt_content: list[ContentBlock],
+ data_format: type[TargetModel],
+ max_extraction_retries: int = 3,
+) -> tuple[Any, TargetModel | None]:
+ """
+ Invoke agent and retry if extraction fails.
+
+ Unlike network retries (handled by invoke_agent_with_retry), this retries when
+ the agent completes successfully but fails to produce a valid extraction.
+
+ Args:
+ agent: The Strands agent to invoke
+ prompt_content: List of ContentBlocks to send to the agent
+ data_format: Pydantic model class for validation
+ max_extraction_retries: Maximum retry attempts for failed extractions (default: 3)
+
+ Returns:
+ Tuple of (response, validated_result or None)
+ """
+ response = None
+
+ for attempt in range(max_extraction_retries):
+ # invoke_agent_with_retry already handles network errors and throttling
+ response = await invoke_agent_with_retry(agent=agent, input=prompt_content)
+ logger.debug("Agent response received")
+
+ # Try to get extraction from state
+ current_extraction = agent.state.get("current_extraction")
+
+ if current_extraction:
+ try:
+ result = data_format(**current_extraction)
+ logger.debug(
+ "Successfully validated extraction",
+ extra={"data_format": data_format.__name__, "attempt": attempt + 1},
+ )
+ return response, result
+ except Exception as e:
+ logger.warning(
+ "Extraction validation failed, retrying",
+ extra={
+ "attempt": attempt + 1,
+ "max_retries": max_extraction_retries,
+ "error": str(e),
+ "data_format": data_format.__name__,
+ },
+ )
+ if attempt < max_extraction_retries - 1:
+ # Ask agent to fix the extraction
+ prompt_content = [
+ ContentBlock(
+ text=f"The extraction failed validation with error: {str(e)}. Please fix the extraction using the tools."
+ )
+ ]
+ continue
+ else:
+ # Last attempt failed
+ logger.error(
+ "Failed to validate extraction after all retries",
+ extra={
+ "data_format": data_format.__name__,
+ "error": str(e),
+ "extraction_data": current_extraction,
+ },
+ )
+ return response, None
+ else:
+ logger.warning(
+ "No extraction found in agent state",
+ extra={"attempt": attempt + 1, "max_retries": max_extraction_retries},
+ )
+ if attempt < max_extraction_retries - 1:
+ # Ask agent to provide extraction
+ prompt_content = [
+ ContentBlock(
+ text="No extraction was found. Please use the extraction_tool to provide the extracted data."
+ )
+ ]
+ continue
+
+ # Should never reach here, but handle it gracefully
+ if response is None:
+ raise ValueError("No response from agent after retries")
+
+ return response, None
+
+
async def structured_output_async(
model_id: str,
data_format: type[TargetModel],
@@ -465,7 +710,8 @@ async def structured_output_async(
existing_data: BaseModel | None = None,
system_prompt: str | None = None,
custom_instruction: str | None = None,
- review_agent: bool = False,
+ config: IDPConfig = IDPConfig(),
+ page_images: list[bytes] | None = None,
context: str = "Extraction",
max_retries: int = 7,
connect_timeout: float = 10.0,
@@ -552,10 +798,14 @@ async def structured_output_async(
dynamic_extraction_tools = create_dynamic_extraction_tool_and_patch_tool(
data_format
)
+ image_tools = []
+ if page_images:
+ image_tools.append(create_view_image_tool(page_images))
# Prepare tools list
tools = [
*dynamic_extraction_tools,
+ *image_tools,
view_existing_extraction,
patch_buffer_data,
view_buffer_data,
@@ -568,8 +818,13 @@ async def structured_output_async(
view_todo_list,
]
- # Create agent with system prompt and tools
- schema_json = json.dumps(data_format.model_json_schema(), indent=2)
+ # Build system prompt with schema
+ final_system_prompt, schema_json = _build_system_prompt(
+ base_prompt=system_prompt or SYSTEM_PROMPT,
+ custom_instruction=custom_instruction,
+ data_format=data_format,
+ )
+
tool_names = [getattr(tool, "__name__", str(tool)) for tool in tools]
logger.debug(
"Created agent with tools",
@@ -580,103 +835,26 @@ async def structured_output_async(
},
)
- # Build final system prompt without modifying the original
- final_system_prompt = system_prompt
-
- # 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
- },
+ # Build model configuration with token limits and caching
+ 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,
)
- model_config = dict(model_id=model_id, boto_client_config=boto_config)
- # Set max_tokens based on actual model limits
- # Reference: https://docs.aws.amazon.com/bedrock/latest/userguide/
-
- # Determine model's maximum
- # Use regex for more flexible matching (e.g., claude-sonnet-4-5 should match claude-sonnet-4)
-
- model_max = 4_096 # Default fallback
- model_id_lower = model_id.lower()
- # Check Claude 4 patterns first (more specific)
- if re.search(r"claude-(opus|sonnet|haiku)-4", model_id_lower):
- model_max = 64_000
- # Check Nova models
- elif any(
- nova in model_id_lower
- for nova in ["nova-premier", "nova-pro", "nova-lite", "nova-micro"]
- ):
- model_max = 10_000
- # Check Claude 3 models
- elif "claude-3" in model_id_lower:
- model_max = 8_192
-
- # Use config value if provided, but cap at model's maximum
- if max_tokens is not None:
- if max_tokens > model_max:
- logger.warning(
- "Config max_tokens exceeds model limit, capping at model maximum",
- extra={
- "config_max_tokens": max_tokens,
- "model_max_tokens": model_max,
- "model_id": model_id,
- },
- )
- max_output_tokens = model_max
- else:
- max_output_tokens = max_tokens
- else:
- # No config value - use model maximum for agentic extraction
- max_output_tokens = model_max
-
- 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,
- },
+ # Prepare prompt content
+ prompt_content = _prepare_prompt_content(
+ prompt=prompt, page_images=page_images, existing_data=existing_data
)
- # 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})
-
- final_system_prompt = SYSTEM_PROMPT
-
- if custom_instruction:
- final_system_prompt = f"{final_system_prompt}\n\nCustom Instructions for this specific task: {custom_instruction}"
-
+ # Track token usage
+ token_usage = _initialize_token_usage()
agent = Agent(
model=BedrockModel(**model_config), # pyright: ignore[reportArgumentType]
tools=tools,
- system_prompt=f"{final_system_prompt}\n\nExpected Schema:\n{schema_json}",
+ system_prompt=final_system_prompt,
state={
"current_extraction": None,
"images": {},
@@ -687,188 +865,40 @@ async def structured_output_async(
summary_ratio=0.8, preserve_recent_messages=2
),
)
-
- # Process prompt based on type
- if isinstance(prompt, Image.Image):
- # Convert PIL Image to binary string for state storage
- img_buffer = io.BytesIO()
- prompt.save(img_buffer, format="PNG")
- img_bytes = img_buffer.getvalue()
-
- logger.debug(
- "Processing PIL Image",
- extra={"size": prompt.size, "mode": prompt.mode},
- )
-
- # Store image as binary string in state
-
- prompt_content = [
- Message(
- role="user",
- content=[
- ContentBlock(text="Extract structured data from this image:"),
- ContentBlock(
- image=ImageContent(
- format="png", source=ImageSource(bytes=img_bytes)
- )
- ),
- ],
- )
- ]
- elif isinstance(prompt, dict) and "content" in prompt:
- prompt_content = [prompt]
- # Extract and store images as binary strings
- else:
- prompt_content = [
- Message(role="user", content=[ContentBlock(text=str(prompt))])
- ]
-
- # Track token usage
- token_usage = {
- "inputTokens": 0,
- "outputTokens": 0,
- "totalTokens": 0,
- "cacheReadInputTokens": 0,
- "cacheWriteInputTokens": 0,
- }
-
- # Main extraction loop
- result = None
- response = None
- # Prepare prompt for this cycle
if existing_data:
- prompt_content.append(
- Message(
- role="user",
- content=[
- ContentBlock(
- text=f"Please update the existing data using the extraction tool or patches. Existing data: {existing_data.model_dump()}"
- ),
- ],
- )
- )
agent.state.set("current_extraction", existing_data.model_dump())
- # Retry logic for network errors (ProtocolError, etc.)
- max_retries = 3
- retry_delay = 2 # seconds
-
- for attempt in range(max_retries):
- try:
- response = await agent.invoke_async(prompt_content)
- logger.debug("Agent response received")
- break # Success, exit retry loop
- except Exception as e:
- error_type = type(e).__name__
- error_msg = str(e)
- is_last_attempt = attempt == max_retries - 1
-
- # Check if this is a retryable network error
- is_retryable = (
- error_type
- in [
- "ProtocolError",
- "ConnectionError",
- "ReadTimeoutError",
- "IncompleteRead",
- ]
- or "Response ended prematurely" in error_msg
- or "Connection" in error_msg
- )
-
- if is_retryable and not is_last_attempt:
- logger.warning(
- "Network error during agent invocation, retrying",
- extra={
- "attempt": attempt + 1,
- "max_retries": max_retries,
- "error_type": error_type,
- "error_message": error_msg,
- "retry_delay_seconds": retry_delay,
- },
- )
- await asyncio.sleep(retry_delay)
- retry_delay *= 2 # Exponential backoff
- continue
-
- # Log the error
-
- logger.error(
- "Agent invocation failed",
- extra={
- "error_type": error_type,
- "error_message": error_msg,
- "traceback": traceback.format_exc(),
- },
- )
-
- # Re-raise ClientError (including ThrottlingException) directly for Step Functions retry handling
- if isinstance(e, ClientError):
- logger.error(
- "Bedrock ClientError detected",
- extra={
- "error_code": e.response["Error"]["Code"],
- "error_message": e.response["Error"].get("Message", ""),
- },
- )
- raise
-
- # Wrap other exceptions
- raise ValueError(f"Agent invocation failed: {error_msg}")
+ response, result = await _invoke_agent_for_extraction(
+ agent=agent,
+ prompt_content=prompt_content,
+ data_format=data_format,
+ max_extraction_retries=3,
+ )
# Accumulate token usage
- if response and response.metrics and response.metrics.accumulated_usage:
- for key in token_usage.keys():
- token_usage[key] += response.metrics.accumulated_usage.get(key, 0)
+ _accumulate_token_usage(response, token_usage)
- # Check for extraction in state
- current_extraction = agent.state.get("current_extraction")
- logger.debug(
- "Current extraction from state",
- extra={"extraction": current_extraction},
- )
+ # Add explicit review step (Option 2)
+ if (
+ config.extraction.agentic.enabled
+ and config.extraction.agentic.review_agent
+ and config.extraction.agentic.review_agent_model
+ ):
+ # result is guaranteed to be non-None here (we raised an error earlier if it was None)
+ assert result is not None
- if current_extraction:
- try:
- result = data_format(**current_extraction)
- logger.debug(
- "Successfully created extraction instance",
- extra={"data_format": data_format.__name__},
- )
- except Exception as e:
- logger.error(
- "Failed to validate extraction against schema",
- extra={
- "data_format": data_format.__name__,
- "error": str(e),
- "extraction_data": current_extraction,
- },
- )
- raise ValueError(f"Failed to validate extraction against schema: {str(e)}")
- else:
- logger.error(
- "No extraction found in agent state",
- extra={"agent_state_keys": list(agent.state._state.keys())},
- )
- logger.error(
- "Full agent state dump",
- extra={"agent_state": agent.state._state},
+ logger.debug(
+ "Initiating final review of extracted data",
+ extra={"review_enabled": True},
)
-
- # Add explicit review step (Option 2)
- if review_agent:
- logger.debug(
- "Initiating final review of extracted data",
- extra={"review_enabled": True},
- )
- review_prompt = prompt_content.append(
- Message(
- role="user",
- content=[
- ContentBlock(
- text=f"""
+ review_prompt = Message(
+ role="user",
+ content=[
+ *prompt_content,
+ ContentBlock(
+ text=f"""
You have successfully extracted the following data:
- {json.dumps(current_extraction, indent=2)}
+ {json.dumps(result.model_dump(), indent=2)}
Please take one final careful look at this extraction:
1. Check each field against the source document
@@ -880,36 +910,56 @@ async def structured_output_async(
If everything is correct, respond with "Data verified and accurate."
If corrections are needed, use the apply_json_patches tool to fix any issues you find.
"""
- )
- ],
- )
- )
+ ),
+ ContentBlock(cachePoint=CachePoint(type="default")),
+ ],
+ )
+ # Build config for review agent
+ review_model_config = build_model_config(
+ model_id=config.extraction.agentic.review_agent_model,
+ max_tokens=max_tokens,
+ max_retries=max_retries,
+ connect_timeout=connect_timeout,
+ read_timeout=read_timeout,
+ )
+ agent = Agent(
+ model=BedrockModel(**review_model_config), # pyright: ignore[reportArgumentType]
+ tools=tools,
+ system_prompt=f"{final_system_prompt}",
+ state={
+ "current_extraction": None,
+ "images": {},
+ "existing_data": existing_data.model_dump() if existing_data else None,
+ "extraction_schema_json": schema_json, # Store for schema reminder tool
+ },
+ conversation_manager=SummarizingConversationManager(
+ summary_ratio=0.8, preserve_recent_messages=2
+ ),
+ )
- review_response = await agent.invoke_async(review_prompt)
- logger.debug("Review response received", extra={"review_completed": True})
+ review_response = await invoke_agent_with_retry(
+ agent=agent, input=review_prompt
+ )
+ logger.debug("Review response received", extra={"review_completed": True})
- # Accumulate token usage from review
- if review_response.metrics and review_response.metrics.accumulated_usage:
- for key in token_usage.keys():
- token_usage[key] += review_response.metrics.accumulated_usage.get(
- key, 0
- )
+ # Accumulate token usage from review
+ _accumulate_token_usage(review_response, token_usage)
- # Check if patches were applied during review
- updated_extraction = agent.state.get("current_extraction")
- if updated_extraction != current_extraction:
- # Patches were applied, validate the new extraction
- try:
- result = data_format(**updated_extraction)
- logger.debug(
- "Applied corrections after final review",
- extra={"corrections_applied": True},
- )
- except Exception as e:
- logger.debug(
- "Post-review validation failed",
- extra={"error": str(e)},
- )
+ # Check if patches were applied during review
+ updated_extraction = agent.state.get("current_extraction")
+ if updated_extraction != result.model_dump():
+ # Patches were applied, validate the new extraction
+ try:
+ result = data_format(**updated_extraction)
+ logger.debug(
+ "Applied corrections after final review",
+ extra={"corrections_applied": True},
+ )
+ except Exception as e:
+ logger.debug(
+ "Post-review validation failed",
+ extra={"error": str(e)},
+ )
# Return best effort result
if result and response:
@@ -939,8 +989,9 @@ def structured_output(
existing_data: BaseModel | None = None,
system_prompt: str | None = None,
custom_instruction: str | None = None,
- review_agent: bool = False,
+ page_images: list[bytes] | None = None,
context: str = "Extraction",
+ config: IDPConfig = IDPConfig(),
max_retries: int = 7,
connect_timeout: float = 10.0,
read_timeout: float = 300.0,
@@ -1024,11 +1075,12 @@ def run_in_new_loop():
existing_data=existing_data,
system_prompt=system_prompt,
custom_instruction=custom_instruction,
- review_agent=review_agent,
+ config=config,
context=context,
max_retries=max_retries,
connect_timeout=connect_timeout,
read_timeout=read_timeout,
+ page_images=page_images,
)
)
except Exception as e:
@@ -1055,11 +1107,12 @@ def run_in_new_loop():
existing_data=existing_data,
system_prompt=system_prompt,
custom_instruction=custom_instruction,
- review_agent=review_agent,
+ config=config,
context=context,
max_retries=max_retries,
connect_timeout=connect_timeout,
read_timeout=read_timeout,
+ page_images=page_images,
)
)
diff --git a/lib/idp_common_pkg/idp_common/extraction/models.py b/lib/idp_common_pkg/idp_common/extraction/models.py
index a207adbb4..2347c712b 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/extraction/service.py b/lib/idp_common_pkg/idp_common/extraction/service.py
index 65e1336c5..49b027c14 100644
--- a/lib/idp_common_pkg/idp_common/extraction/service.py
+++ b/lib/idp_common_pkg/idp_common/extraction/service.py
@@ -14,9 +14,10 @@
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.bedrock import format_prompt
from idp_common.config.models import IDPConfig
from idp_common.config.schema_constants import (
ID_FIELD,
@@ -36,18 +37,54 @@
AGENTIC_AVAILABLE = True
except ImportError:
AGENTIC_AVAILABLE = False
+from pydantic import BaseModel
+
from idp_common.utils import extract_json_from_text
logger = logging.getLogger(__name__)
+# Pydantic models for internal data transfer
+class SectionInfo(BaseModel):
+ """Metadata about a document section being processed."""
+
+ class_label: str
+ sorted_page_ids: list[str]
+ page_indices: list[int]
+ output_bucket: str
+ output_key: str
+ output_uri: str
+ start_page: int
+ end_page: int
+
+
+class ExtractionConfig(BaseModel):
+ """Configuration for model invocation."""
+
+ model_id: str
+ temperature: float
+ top_k: float
+ top_p: float
+ max_tokens: int | None
+ system_prompt: str
+
+
+class ExtractionResult(BaseModel):
+ """Result from model extraction."""
+
+ extracted_fields: dict[str, Any]
+ metering: dict[str, Any]
+ parsing_succeeded: bool
+ total_duration: float
+
+
class ExtractionService:
"""Service for extracting fields from documents using LLMs."""
def __init__(
self,
- region: str = None,
- config: Union[Dict[str, Any], "IDPConfig"] = None,
+ region: str | None = None,
+ config: dict[str, Any] | IDPConfig | None = None,
):
"""
Initialize the extraction service.
@@ -67,13 +104,62 @@ def __init__(
self.config = config_model
self.region = region or os.environ.get("AWS_REGION")
+ # Instance variables for prompt context
+ # These are initialized here and populated during each process_document_section call
+ # This allows methods to access context without passing multiple parameters
+ self._document_text: str = ""
+ self._class_label: str = ""
+ self._attribute_descriptions: str = ""
+ self._class_schema: dict[str, Any] = {}
+ self._page_images: list[Any] = []
+ self._image_uris: list[str] = []
+
# Get model_id from config for logging (type-safe access with fallback)
model_id = (
self.config.extraction.model if self.config.extraction else "not configured"
)
logger.info(f"Initialized extraction service with model {model_id}")
- def _get_class_schema(self, class_label: str) -> Dict[str, Any]:
+ @property
+ def _substitutions(self) -> dict[str, str]:
+ """Get prompt placeholder substitutions from stored context."""
+ return {
+ "DOCUMENT_TEXT": self._document_text,
+ "DOCUMENT_CLASS": self._class_label,
+ "ATTRIBUTE_NAMES_AND_DESCRIPTIONS": self._attribute_descriptions,
+ }
+
+ def _get_default_prompt_content(self) -> list[dict[str, Any]]:
+ """
+ Build default fallback prompt content when no template is provided.
+
+ Returns:
+ List of content items with default prompt text and images
+ """
+ task_prompt = f"""
+ Extract the following fields from this {self._class_label} document:
+
+ {self._attribute_descriptions}
+
+ Document text:
+ {self._document_text}
+
+ Respond with a JSON object containing each field name and its extracted value.
+ """
+ content = [{"text": task_prompt}]
+
+ # Add image attachments to the content (limit to 20 images as per Bedrock constraints)
+ if self._page_images:
+ logger.info(
+ f"Attaching images to default prompt, for {len(self._page_images)} pages."
+ )
+ # Limit to 20 images as per Bedrock constraints
+ for img in self._page_images[:20]:
+ content.append(image.prepare_bedrock_image_attachment(img))
+
+ return content
+
+ def _get_class_schema(self, class_label: str) -> dict[str, Any]:
"""
Get JSON Schema for a specific document class from configuration.
@@ -96,7 +182,7 @@ def _get_class_schema(self, class_label: str) -> Dict[str, Any]:
return {}
- def _clean_schema_for_prompt(self, schema: Dict[str, Any]) -> Dict[str, Any]:
+ def _clean_schema_for_prompt(self, schema: dict[str, Any]) -> dict[str, Any]:
"""
Clean JSON Schema by removing IDP custom fields (x-aws-idp-*) for the prompt.
Keeps all standard JSON Schema fields including descriptions.
@@ -129,7 +215,7 @@ def _clean_schema_for_prompt(self, schema: Dict[str, Any]) -> Dict[str, Any]:
return cleaned
- def _format_schema_for_prompt(self, schema: Dict[str, Any]) -> str:
+ def _format_schema_for_prompt(self, schema: dict[str, Any]) -> str:
"""
Format JSON Schema for inclusion in the extraction prompt.
@@ -148,8 +234,8 @@ def _format_schema_for_prompt(self, schema: Dict[str, Any]) -> str:
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.
@@ -165,338 +251,156 @@ 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(
+ def _build_prompt_content(
self,
prompt_template: str,
- document_text: str,
- class_label: str,
- attribute_descriptions: str,
image_content: Any = None,
- ) -> List[Dict[str, Any]]:
+ ) -> list[dict[str, Any]]:
"""
- Build content array, automatically deciding whether to use image placeholder processing.
+ Build prompt content array handling FEW_SHOT_EXAMPLES and DOCUMENT_IMAGE placeholders.
- If the prompt contains {DOCUMENT_IMAGE}, the image will be inserted at that location.
- If the prompt does NOT contain {DOCUMENT_IMAGE}, the image will NOT be included at all.
+ This consolidated method handles all placeholder types and combinations:
+ - {FEW_SHOT_EXAMPLES}: Inserts few-shot examples from config
+ - {DOCUMENT_IMAGE}: Inserts images at specific location
+ - Regular text placeholders: DOCUMENT_TEXT, DOCUMENT_CLASS, etc.
Args:
- prompt_template: The prompt template that may contain {DOCUMENT_IMAGE}
- document_text: The document text content
- class_label: The document class label
- attribute_descriptions: Formatted attribute names and descriptions
- image_content: Optional image content to insert (only used when {DOCUMENT_IMAGE} is present)
+ prompt_template: The prompt template with optional placeholders
+ image_content: Optional image content to insert (only used with {DOCUMENT_IMAGE})
Returns:
- List of content items with text and image content properly ordered based on presence of placeholder
+ List of content items with text and image content properly ordered
"""
- if "{DOCUMENT_IMAGE}" in prompt_template:
- return self._build_content_with_image_placeholder(
- prompt_template,
- document_text,
- class_label,
- attribute_descriptions,
- image_content,
- )
- else:
- return self._build_content_without_image_placeholder(
- prompt_template,
- document_text,
- class_label,
- attribute_descriptions,
- image_content,
- )
+ content: list[dict[str, Any]] = []
+
+ # Handle FEW_SHOT_EXAMPLES placeholder first
+ if "{FEW_SHOT_EXAMPLES}" in prompt_template:
+ parts = prompt_template.split("{FEW_SHOT_EXAMPLES}")
+ if len(parts) == 2:
+ # Process before examples
+ content.extend(
+ self._build_text_and_image_content(parts[0], image_content)
+ )
+
+ # Add few-shot examples
+ content.extend(self._build_few_shot_examples_content())
+
+ # Process after examples (only pass images if not already used)
+ image_for_after = (
+ None if "{DOCUMENT_IMAGE}" in parts[0] else image_content
+ )
+ content.extend(
+ self._build_text_and_image_content(parts[1], image_for_after)
+ )
+
+ return content
+
+ # No FEW_SHOT_EXAMPLES, just handle text and images
+ return self._build_text_and_image_content(prompt_template, image_content)
- def _build_content_with_image_placeholder(
+ def _build_text_and_image_content(
self,
prompt_template: str,
- document_text: str,
- class_label: str,
- attribute_descriptions: 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.
+ Build content array with text and optionally images based on DOCUMENT_IMAGE placeholder.
Args:
- prompt_template: The prompt template that may contain {DOCUMENT_IMAGE}
- document_text: The document text content
- class_label: The document class label
- attribute_descriptions: Formatted attribute names and descriptions
- image_content: Optional image content to insert
+ prompt_template: Template that may contain {DOCUMENT_IMAGE}
+ image_content: Optional image content
Returns:
- List of content items with text and image content properly ordered
+ List of content items
"""
- # Split the prompt at the DOCUMENT_IMAGE placeholder
- parts = prompt_template.split("{DOCUMENT_IMAGE}")
-
- if len(parts) != 2:
- logger.warning(
- "Invalid DOCUMENT_IMAGE placeholder usage, falling back to standard processing"
- )
- # Fallback to standard processing
- return self._build_content_without_image_placeholder(
- prompt_template,
- document_text,
- class_label,
- attribute_descriptions,
- image_content,
- )
-
- # Process the parts before and after the image placeholder
- before_image = self._prepare_prompt_from_template(
- parts[0],
- {
- "DOCUMENT_TEXT": document_text,
- "DOCUMENT_CLASS": class_label,
- "ATTRIBUTE_NAMES_AND_DESCRIPTIONS": attribute_descriptions,
- },
- required_placeholders=[], # Don't enforce required placeholders for partial templates
- )
+ content: list[dict[str, Any]] = []
- after_image = self._prepare_prompt_from_template(
- parts[1],
- {
- "DOCUMENT_TEXT": document_text,
- "DOCUMENT_CLASS": class_label,
- "ATTRIBUTE_NAMES_AND_DESCRIPTIONS": attribute_descriptions,
- },
- required_placeholders=[], # Don't enforce required placeholders for partial templates
- )
+ # Handle DOCUMENT_IMAGE placeholder
+ if "{DOCUMENT_IMAGE}" in prompt_template:
+ parts = prompt_template.split("{DOCUMENT_IMAGE}")
+ if len(parts) == 2:
+ # Add text before image
+ before_text = self._prepare_prompt_from_template(
+ parts[0], self._substitutions, required_placeholders=[]
+ )
+ if before_text.strip():
+ content.append({"text": before_text})
- # Build content array with image in the middle
- content = []
+ # Add images
+ if image_content:
+ content.extend(self._prepare_image_attachments(image_content))
- # Add the part before the image
- if before_image.strip():
- content.append({"text": before_image})
+ # Add text after image
+ after_text = self._prepare_prompt_from_template(
+ parts[1], self._substitutions, required_placeholders=[]
+ )
+ if after_text.strip():
+ content.append({"text": after_text})
- # Add the image if available
- if image_content:
- if isinstance(image_content, list):
- # Multiple images (limit to 20 as per Bedrock constraints)
- if len(image_content) > 20:
- logger.warning(
- f"Found {len(image_content)} images, truncating to 20 due to Bedrock constraints. "
- f"{len(image_content) - 20} images will be dropped."
- )
- for img in image_content[:20]:
- content.append(image.prepare_bedrock_image_attachment(img))
+ return content
else:
- # Single image
- content.append(image.prepare_bedrock_image_attachment(image_content))
-
- # Add the part after the image
- if after_image.strip():
- content.append({"text": after_image})
-
- return content
-
- def _build_content_without_image_placeholder(
- self,
- prompt_template: str,
- document_text: str,
- class_label: str,
- attribute_descriptions: str,
- image_content: Any = None,
- ) -> List[Dict[str, Any]]:
- """
- Build content array without DOCUMENT_IMAGE placeholder (standard processing).
-
- Note: This method does NOT attach the image content when no placeholder is present.
-
- Args:
- prompt_template: The prompt template
- document_text: The document text content
- class_label: The document class label
- attribute_descriptions: Formatted attribute names and descriptions
- image_content: Optional image content (not used when no placeholder is present)
+ logger.warning("Invalid DOCUMENT_IMAGE placeholder usage")
- Returns:
- List of content items with text content only (no image)
- """
- # Prepare the full prompt
+ # No image placeholder, just text
task_prompt = self._prepare_prompt_from_template(
- prompt_template,
- {
- "DOCUMENT_TEXT": document_text,
- "DOCUMENT_CLASS": class_label,
- "ATTRIBUTE_NAMES_AND_DESCRIPTIONS": attribute_descriptions,
- },
- required_placeholders=[],
+ prompt_template, self._substitutions, required_placeholders=[]
)
-
- content = [{"text": task_prompt}]
-
- # No longer adding image content when no placeholder is present
+ content.append({"text": task_prompt})
return content
- def _build_content_with_few_shot_examples(
- self,
- task_prompt_template: str,
- document_text: str,
- class_label: str,
- attribute_descriptions: str,
- image_content: Any = None,
- ) -> List[Dict[str, Any]]:
+ def _prepare_image_attachments(self, image_content: Any) -> list[dict[str, Any]]:
"""
- Build content array with few-shot examples inserted at the FEW_SHOT_EXAMPLES placeholder.
- Also supports DOCUMENT_IMAGE placeholder for image positioning.
+ Prepare image attachments for Bedrock, limiting to 20 images.
Args:
- task_prompt_template: The task prompt template containing {FEW_SHOT_EXAMPLES}
- document_text: The document text content
- class_label: The document class label
- attribute_descriptions: Formatted attribute names and descriptions
- image_content: Optional image content to insert
+ image_content: Single image or list of images
Returns:
- List of content items with text and image content properly ordered
+ List of image attachment dicts
"""
- # Split the task prompt at the FEW_SHOT_EXAMPLES placeholder
- parts = task_prompt_template.split("{FEW_SHOT_EXAMPLES}")
-
- if len(parts) != 2:
- # Fallback to regular prompt processing if placeholder not found or malformed
- return self._build_content_with_or_without_image_placeholder(
- task_prompt_template,
- document_text,
- class_label,
- attribute_descriptions,
- image_content,
- )
-
- # Process each part using the unified function
- before_examples_content = self._build_content_with_or_without_image_placeholder(
- parts[0], document_text, class_label, attribute_descriptions, image_content
- )
-
- # Only pass image_content if it wasn't already used in the first part
- image_for_second_part = (
- None if "{DOCUMENT_IMAGE}" in parts[0] else image_content
- )
- after_examples_content = self._build_content_with_or_without_image_placeholder(
- parts[1],
- document_text,
- class_label,
- attribute_descriptions,
- image_for_second_part,
- )
-
- # Build content array
- content = []
-
- # Add the part before examples (may include image if DOCUMENT_IMAGE was in the first part)
- content.extend(before_examples_content)
-
- # Add few-shot examples from config for this specific class
- examples_content = self._build_few_shot_examples_content(class_label)
- content.extend(examples_content)
-
- # Add the part after examples (may include image if DOCUMENT_IMAGE was in the second part)
- content.extend(after_examples_content)
-
- # No longer appending image content when no placeholder is found
+ attachments: list[dict[str, Any]] = []
+
+ if isinstance(image_content, list):
+ # Multiple images (limit to 20 as per Bedrock constraints)
+ if len(image_content) > 20:
+ logger.warning(
+ f"Found {len(image_content)} images, truncating to 20 due to Bedrock constraints. "
+ f"{len(image_content) - 20} images will be dropped."
+ )
+ for img in image_content[:20]:
+ attachments.append(image.prepare_bedrock_image_attachment(img))
+ else:
+ # Single image
+ attachments.append(image.prepare_bedrock_image_attachment(image_content))
- return content
+ return attachments
- def _build_few_shot_examples_content(
- self, class_label: str
- ) -> List[Dict[str, Any]]:
+ def _build_few_shot_examples_content(self) -> list[dict[str, Any]]:
"""
Build content items for few-shot examples from the configuration for a specific class.
- Args:
- class_label: The document class label to get examples for
-
Returns:
List of content items containing text and image content for examples
"""
- content = []
+ content: list[dict[str, Any]] = []
- # Find the specific class that matches the class_label (now in JSON Schema format)
- target_class = self._get_class_schema(class_label)
-
- if not target_class:
+ # Use the stored class schema
+ if not self._class_schema:
logger.warning(
- f"No class found matching '{class_label}' for few-shot examples"
+ f"No class schema found for '{self._class_label}' for few-shot examples"
)
return content
# Get examples from the JSON Schema for this specific class
- content = build_few_shot_extraction_examples_content(target_class)
+ content = build_few_shot_extraction_examples_content(self._class_schema)
return content
- def _get_image_files_from_path(self, image_path: str) -> List[str]:
- """
- Get list of image files from a path that could be a single file, directory, or S3 prefix.
-
- Args:
- image_path: Path to image file, directory, or S3 prefix
-
- Returns:
- List of image file paths/URIs sorted by filename
- """
- import os
-
- from idp_common import s3
-
- # Handle S3 URIs
- if image_path.startswith("s3://"):
- # Check if it's a direct file or a prefix
- if image_path.endswith(
- (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif", ".webp")
- ):
- # Direct S3 file
- return [image_path]
- else:
- # S3 prefix - list all images
- return s3.list_images_from_path(image_path)
- else:
- # Handle local paths
- config_bucket = os.environ.get("CONFIGURATION_BUCKET")
- root_dir = os.environ.get("ROOT_DIR")
-
- if config_bucket:
- # Use environment bucket with imagePath as key
- s3_uri = f"s3://{config_bucket}/{image_path}"
-
- # Check if it's a direct file or a prefix
- if image_path.endswith(
- (".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".tif", ".webp")
- ):
- # Direct S3 file
- return [s3_uri]
- else:
- # S3 prefix - list all images
- return s3.list_images_from_path(s3_uri)
- elif root_dir:
- # Use relative path from ROOT_DIR
- full_path = os.path.join(root_dir, image_path)
- full_path = os.path.normpath(full_path)
-
- if os.path.isfile(full_path):
- # Single local file
- return [full_path]
- elif os.path.isdir(full_path):
- # Local directory - list all images
- return s3.list_images_from_path(full_path)
- else:
- # Path doesn't exist
- logger.warning(f"Image path does not exist: {full_path}")
- return []
- else:
- raise ValueError(
- "No CONFIGURATION_BUCKET or ROOT_DIR set. Cannot read example images from local filesystem."
- )
-
- def _make_json_serializable(self, obj):
+ def _make_json_serializable(self, obj: Any) -> Any:
"""
Recursively convert any object to a JSON-serializable format.
@@ -534,96 +438,18 @@ def _make_json_serializable(self, obj):
# Convert non-serializable objects to string representation
return str(obj)
- def _convert_image_bytes_to_uris_in_content(
- self, content: List[Dict[str, Any]]
- ) -> List[Dict[str, Any]]:
- """
- Convert image bytes to URIs in content array for JSON serialization.
-
- Args:
- content: Content array that may contain image objects with bytes
-
- Returns:
- Content array with image URIs instead of bytes
- """
- converted_content = []
-
- for item in content:
- if "image" in item and isinstance(item["image"], dict):
- # Extract image URI if it exists, or use placeholder
- if "source" in item["image"] and "bytes" in item["image"]["source"]:
- # This is a bytes-based image - replace with URI reference
- # In practice, we need to store these bytes somewhere accessible
- # For now, we'll use a placeholder that indicates bytes were present
- converted_item = {
- "image_uri": f""
- }
- else:
- # Keep other image formats as-is
- converted_item = item.copy()
- else:
- # Keep non-image items as-is
- converted_item = item.copy()
-
- converted_content.append(converted_item)
-
- return converted_content
-
- def _convert_image_uris_to_bytes_in_content(
- self, content: List[Dict[str, Any]], original_images: List[Any]
- ) -> List[Dict[str, Any]]:
- """
- Convert image URIs back to bytes in content array after Lambda processing.
-
- Args:
- content: Content array from Lambda that may contain image URIs
- original_images: Original image data to restore
-
- Returns:
- Content array with image bytes restored
- """
- converted_content = []
- image_index = 0
-
- for item in content:
- if "image_uri" in item:
- # Convert image URI back to bytes format
- if image_index < len(original_images):
- # Restore original image bytes
- converted_item = image.prepare_bedrock_image_attachment(
- original_images[image_index]
- )
- image_index += 1
- else:
- # Skip if no original image data
- logger.warning(
- "No original image data available for URI conversion"
- )
- continue
- elif "image" in item:
- # Keep existing image objects as-is
- converted_item = item.copy()
- else:
- # Keep non-image items as-is
- converted_item = item.copy()
-
- converted_content.append(converted_item)
-
- return converted_content
-
def _invoke_custom_prompt_lambda(
- self, lambda_arn: str, payload: dict, original_images: List[Any] = None
- ) -> dict:
+ self, lambda_arn: str, payload: dict[str, Any]
+ ) -> dict[str, Any]:
"""
Invoke custom prompt generator Lambda function with JSON-serializable payload.
Args:
lambda_arn: ARN of the Lambda function to invoke
payload: Payload to send to Lambda function (must be JSON serializable)
- original_images: Original image data for restoration after Lambda processing
Returns:
- Dict containing system_prompt and task_prompt_content with images restored
+ Dict containing system_prompt and task_prompt_content
Raises:
Exception: If Lambda invocation fails or returns invalid response
@@ -665,14 +491,6 @@ def _invoke_custom_prompt_lambda(
logger.error(error_msg)
raise Exception(error_msg)
- # Convert image URIs back to bytes in the response
- if original_images:
- result["task_prompt_content"] = (
- self._convert_image_uris_to_bytes_in_content(
- result["task_prompt_content"], original_images
- )
- )
-
return result
except Exception as e:
@@ -680,41 +498,58 @@ def _invoke_custom_prompt_lambda(
logger.error(error_msg)
raise Exception(error_msg)
- def process_document_section(self, document: Document, section_id: str) -> Document:
+ def _reset_context(self) -> None:
+ """Reset instance variables for clean state before processing."""
+ self._document_text = ""
+ self._class_label = ""
+ self._attribute_descriptions = ""
+ self._class_schema = {}
+ self._page_images = []
+ self._image_uris = []
+
+ def _validate_and_find_section(
+ self, document: Document, section_id: str
+ ) -> Any | None:
"""
- Process a single section from a Document object.
+ Validate document and find section by ID.
Args:
- document: Document object containing section to process
- section_id: ID of the section to process
+ document: Document to validate
+ section_id: ID of section to find
Returns:
- Document: Updated Document object with extraction results for the section
+ Section if found, None otherwise (errors added to document)
"""
- # Validate input document
if not document:
logger.error("No document provided")
- return document
+ return None
if not document.sections:
logger.error("Document has no sections to process")
document.errors.append("Document has no sections to process")
- return document
+ return None
# Find the section with the given ID
- section = None
- for s in document.sections:
- if s.section_id == section_id:
- section = s
- break
+ for section in document.sections:
+ if section.section_id == section_id:
+ return section
- if not section:
- error_msg = f"Section {section_id} not found in document"
- logger.error(error_msg)
- document.errors.append(error_msg)
- return document
+ error_msg = f"Section {section_id} not found in document"
+ logger.error(error_msg)
+ document.errors.append(error_msg)
+ return None
- # Extract information about the section
+ def _prepare_section_info(self, document: Document, section: Any) -> SectionInfo:
+ """
+ Prepare section metadata and output paths.
+
+ Args:
+ document: Document being processed
+ section: Section being processed
+
+ Returns:
+ SectionInfo with all metadata
+ """
class_label = section.classification
output_bucket = document.output_bucket
output_prefix = document.input_key
@@ -723,22 +558,22 @@ def process_document_section(self, document: Document, section_id: str) -> Docum
# Check if the section has required pages
if not section.page_ids:
- error_msg = f"Section {section_id} has no page IDs"
+ error_msg = f"Section {section.section_id} has no page IDs"
logger.error(error_msg)
document.errors.append(error_msg)
- return document
+ raise ValueError(error_msg)
# Sort pages by page number
sorted_page_ids = sorted(section.page_ids, key=int)
start_page = int(sorted_page_ids[0])
end_page = int(sorted_page_ids[-1])
- # Find minimum page ID across all sections in the document to determine offset
+ # Find minimum page ID across all sections
min_page_id = min(
int(page_id) for sec in document.sections for page_id in sec.page_ids
)
- # Adjust page indices to be zero-based if document pages start at 1
+ # Adjust page indices to be zero-based
page_indices = [int(page_id) - min_page_id for page_id in sorted_page_ids]
logger.info(
@@ -749,442 +584,507 @@ def process_document_section(self, document: Document, section_id: str) -> Docum
metrics.put_metric("InputDocuments", 1)
metrics.put_metric("InputDocumentPages", len(section.page_ids))
- try:
- # Read document text from all pages in order
- t0 = time.time()
- 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
+ return SectionInfo(
+ class_label=class_label,
+ sorted_page_ids=sorted_page_ids,
+ page_indices=page_indices,
+ output_bucket=output_bucket,
+ output_key=output_key,
+ output_uri=output_uri,
+ start_page=start_page,
+ end_page=end_page,
+ )
- page = document.pages[page_id]
- text_path = page.parsed_text_uri
- page_text = s3.get_text_content(text_path)
- document_texts.append(page_text)
+ def _load_document_text(
+ self, document: Document, sorted_page_ids: list[str]
+ ) -> str:
+ """
+ Load and concatenate text from all pages.
+
+ Args:
+ document: Document containing pages
+ sorted_page_ids: Sorted list of page IDs
+
+ Returns:
+ Concatenated document text
+ """
+ t0 = time.time()
+ document_texts = []
+
+ for page_id in sorted_page_ids:
+ if page_id not in document.pages:
+ error_msg = f"Page {page_id} not found in document"
+ logger.error(error_msg)
+ document.errors.append(error_msg)
+ continue
+
+ page = document.pages[page_id]
+ text_path = page.parsed_text_uri
+ page_text = s3.get_text_content(text_path)
+ document_texts.append(page_text)
+
+ document_text = "\n".join(document_texts)
+ t1 = time.time()
+ logger.info(f"Time taken to read text content: {t1 - t0:.2f} seconds")
+
+ return document_text
+
+ def _load_document_images(
+ self, document: Document, sorted_page_ids: list[str]
+ ) -> list[Any]:
+ """
+ Load images from all pages.
+
+ Args:
+ document: Document containing pages
+ sorted_page_ids: Sorted list of page IDs
+
+ Returns:
+ List of prepared images
+ """
+ t0 = time.time()
+ target_width = self.config.extraction.image.target_width
+ target_height = self.config.extraction.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
+ image_content = image.prepare_image(image_uri, target_width, target_height)
+ page_images.append(image_content)
+
+ t1 = time.time()
+ logger.info(f"Time taken to read images: {t1 - t0:.2f} seconds")
- document_text = "\n".join(document_texts)
- t1 = time.time()
- logger.info(f"Time taken to read text content: {t1 - t0:.2f} seconds")
+ return page_images
- # Read page images with configurable dimensions (type-safe access)
- target_width = self.config.extraction.image.target_width
- target_height = self.config.extraction.image.target_height
+ def _initialize_extraction_context(
+ self,
+ class_label: str,
+ document_text: str,
+ page_images: list[Any],
+ sorted_page_ids: list[str],
+ document: Document,
+ ) -> tuple[dict[str, Any], str]:
+ """
+ Initialize extraction context and set instance variables.
- page_images = []
- for page_id in sorted_page_ids:
- if page_id not in document.pages:
- continue
+ Args:
+ class_label: Document class
+ document_text: Text content
+ page_images: Prepared images
+ sorted_page_ids: Sorted page IDs
+ document: Document being processed
+ Returns:
+ Tuple of (class_schema, attribute_descriptions)
+ """
+ # Get JSON Schema for this document class
+ class_schema = self._get_class_schema(class_label)
+ attribute_descriptions = self._format_schema_for_prompt(class_schema)
+
+ # Store context in instance variables
+ self._document_text = document_text
+ self._class_label = class_label
+ self._attribute_descriptions = attribute_descriptions
+ self._class_schema = class_schema
+ self._page_images = page_images
+
+ # Prepare image URIs for Lambda
+ image_uris = []
+ for page_id in sorted_page_ids:
+ if page_id in document.pages:
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)
-
- t2 = time.time()
- logger.info(f"Time taken to read images: {t2 - t1:.2f} seconds")
-
- # Get extraction configuration (type-safe access, automatic type conversion)
- model_id = self.config.extraction.model
- temperature = (
- self.config.extraction.temperature
- ) # Already float, no conversion needed!
- top_k = self.config.extraction.top_k # Already float!
- top_p = self.config.extraction.top_p # Already float!
- max_tokens = (
- self.config.extraction.max_tokens
- if self.config.extraction.max_tokens
- else None
- )
- system_prompt = self.config.extraction.system_prompt
+ if page.image_uri:
+ image_uris.append(page.image_uri)
+ self._image_uris = image_uris
- # Get JSON Schema for this document class
- class_schema = self._get_class_schema(class_label)
- attribute_descriptions = self._format_schema_for_prompt(class_schema)
+ return class_schema, attribute_descriptions
- # Check if schema has properties - if not, skip LLM invocation entirely
- if (
- not class_schema.get(SCHEMA_PROPERTIES)
- or not attribute_descriptions.strip()
- ):
- logger.info(
- f"No attributes defined for class {class_label}, skipping LLM extraction"
- )
+ def _handle_empty_schema(
+ self,
+ document: Document,
+ section: Any,
+ section_info: SectionInfo,
+ section_id: str,
+ t0: float,
+ ) -> Document:
+ """
+ Handle case when schema has no attributes - skip LLM and return empty result.
- # Create empty result structure without invoking LLM
- extracted_fields = {}
- metering = {
- "input_tokens": 0,
- "output_tokens": 0,
- "invocation_count": 0,
- "total_cost": 0.0,
- }
- total_duration = 0.0
- parsing_succeeded = True
-
- # Write to S3 with empty extraction result
- output = {
- "document_class": {"type": class_label},
- "split_document": {"page_indices": page_indices},
- "inference_result": extracted_fields,
- "metadata": {
- "parsing_succeeded": parsing_succeeded,
- "extraction_time_seconds": total_duration,
- "skipped_due_to_empty_attributes": True,
- },
- }
- s3.write_content(
- output, output_bucket, output_key, content_type="application/json"
- )
+ Args:
+ document: Document being processed
+ section: Section being processed
+ section_info: Section metadata
+ section_id: Section ID
+ t0: Start time
+
+ Returns:
+ Updated document
+ """
+ logger.info(
+ f"No attributes defined for class {section_info.class_label}, skipping LLM extraction"
+ )
- # Update the section with extraction result URI
- section.extraction_result_uri = output_uri
+ # Create empty result structure
+ extracted_fields = {}
+ metering = {
+ "input_tokens": 0,
+ "output_tokens": 0,
+ "invocation_count": 0,
+ "total_cost": 0.0,
+ }
+ total_duration = 0.0
+ parsing_succeeded = True
+
+ # Write to S3
+ output = {
+ "document_class": {"type": section_info.class_label},
+ "split_document": {"page_indices": section_info.page_indices},
+ "inference_result": extracted_fields,
+ "metadata": {
+ "parsing_succeeded": parsing_succeeded,
+ "extraction_time_seconds": total_duration,
+ "skipped_due_to_empty_attributes": True,
+ },
+ }
+ s3.write_content(
+ output,
+ section_info.output_bucket,
+ section_info.output_key,
+ content_type="application/json",
+ )
- # Update document with zero metering data
- document.metering = utils.merge_metering_data(
- document.metering, metering
- )
+ # Update section and document
+ section.extraction_result_uri = section_info.output_uri
+ document.metering = utils.merge_metering_data(document.metering, metering)
- t3 = time.time()
- logger.info(
- f"Skipped extraction for section {section_id} due to empty attributes: {t3 - t0:.2f} seconds"
- )
- return document
-
- # Check for custom prompt Lambda function (type-safe access)
- custom_lambda_arn = self.config.extraction.custom_prompt_lambda_arn
-
- if custom_lambda_arn and custom_lambda_arn.strip():
- logger.info(f"Using custom prompt Lambda: {custom_lambda_arn}")
-
- # Prepare prompt placeholders including image URIs
- image_uris = []
- for page_id in sorted_page_ids:
- if page_id in document.pages:
- page = document.pages[page_id]
- if page.image_uri:
- image_uris.append(page.image_uri)
-
- prompt_placeholders = {
- "DOCUMENT_TEXT": document_text,
- "DOCUMENT_CLASS": class_label,
- "ATTRIBUTE_NAMES_AND_DESCRIPTIONS": attribute_descriptions,
- "DOCUMENT_IMAGE": image_uris,
- }
-
- logger.info(
- f"Lambda will receive {len(image_uris)} image URIs in DOCUMENT_IMAGE placeholder"
+ t3 = time.time()
+ logger.info(
+ f"Skipped extraction for section {section_id} due to empty attributes: {t3 - t0:.2f} seconds"
+ )
+ return document
+
+ def _build_extraction_content(
+ self,
+ document: Document,
+ page_images: list[Any],
+ ) -> tuple[list[dict[str, Any]], str]:
+ """
+ Build prompt content (with or without custom Lambda).
+
+ Args:
+ document: Document being processed
+ page_images: Prepared page images
+
+ Returns:
+ Tuple of (content, system_prompt)
+ """
+ system_prompt = self.config.extraction.system_prompt
+ custom_lambda_arn = self.config.extraction.custom_prompt_lambda_arn
+
+ if custom_lambda_arn and custom_lambda_arn.strip():
+ logger.info(f"Using custom prompt Lambda: {custom_lambda_arn}")
+
+ prompt_placeholders = {
+ "DOCUMENT_TEXT": self._document_text,
+ "DOCUMENT_CLASS": self._class_label,
+ "ATTRIBUTE_NAMES_AND_DESCRIPTIONS": self._attribute_descriptions,
+ "DOCUMENT_IMAGE": self._image_uris,
+ }
+
+ logger.info(
+ f"Lambda will receive {len(self._image_uris)} image URIs in DOCUMENT_IMAGE placeholder"
+ )
+
+ # Build default content for Lambda input
+ prompt_template = self.config.extraction.task_prompt
+ if prompt_template:
+ default_content = self._build_prompt_content(
+ prompt_template, page_images
)
+ else:
+ default_content = self._get_default_prompt_content()
+
+ # Prepare Lambda payload
+ try:
+ document_dict = document.to_dict()
+ except Exception as e:
+ logger.warning(f"Error serializing document for Lambda payload: {e}")
+ document_dict = {"id": getattr(document, "id", "unknown")}
+
+ payload = {
+ "config": self._make_json_serializable(self.config),
+ "prompt_placeholders": prompt_placeholders,
+ "default_task_prompt_content": self._make_json_serializable(
+ default_content
+ ),
+ "serialized_document": document_dict,
+ }
+
+ # Invoke custom Lambda
+ lambda_result = self._invoke_custom_prompt_lambda(
+ custom_lambda_arn, payload
+ )
+
+ # Use Lambda results
+ system_prompt = lambda_result.get("system_prompt", system_prompt)
+ content = lambda_result.get("task_prompt_content", default_content)
- # Build default content for Lambda input
- prompt_template = self.config.extraction.task_prompt
- if prompt_template:
- # Check if task prompt contains FEW_SHOT_EXAMPLES placeholder
- if "{FEW_SHOT_EXAMPLES}" in prompt_template:
- default_content = self._build_content_with_few_shot_examples(
- prompt_template,
- document_text,
- class_label,
- attribute_descriptions,
- page_images,
- )
- else:
- # Use the unified content builder for DOCUMENT_IMAGE placeholder support
- default_content = (
- self._build_content_with_or_without_image_placeholder(
- prompt_template,
- document_text,
- class_label,
- attribute_descriptions,
- page_images,
- )
- )
- else:
- # Default content if no template
- task_prompt = f"""
- Extract the following fields from this {class_label} document:
-
- {attribute_descriptions}
-
- Document text:
- {document_text}
-
- Respond with a JSON object containing each field name and its extracted value.
- """
- default_content = [{"text": task_prompt}]
- if page_images:
- for img in page_images[:20]:
- default_content.append(
- image.prepare_bedrock_image_attachment(img)
- )
-
- # Prepare Lambda payload with JSON-serializable content
+ logger.info("Successfully applied custom prompt from Lambda function")
+ else:
+ # Use default prompt logic
+ logger.info(
+ "No custom prompt Lambda configured - using default prompt generation"
+ )
+ prompt_template = self.config.extraction.task_prompt
+
+ if not prompt_template:
+ content = self._get_default_prompt_content()
+ else:
try:
- # Use Document's built-in to_dict() method which properly handles Status enum conversion
- document_dict = document.to_dict()
- except Exception as e:
+ content = self._build_prompt_content(prompt_template, page_images)
+ except ValueError as e:
logger.warning(
- f"Error serializing document for Lambda payload: {e}"
+ f"Error formatting prompt template: {str(e)}. Using default prompt."
)
- document_dict = {"id": getattr(document, "id", "unknown")}
+ content = self._get_default_prompt_content()
- # Convert image bytes to URIs in default content for JSON serialization
- serializable_default_content = (
- self._convert_image_bytes_to_uris_in_content(default_content)
- )
+ return content, system_prompt
- # Create fully serializable payload using comprehensive helper
- payload = {
- "config": self._make_json_serializable(self.config),
- "prompt_placeholders": prompt_placeholders,
- "default_task_prompt_content": serializable_default_content,
- "serialized_document": document_dict,
- }
+ def _invoke_extraction_model(
+ self,
+ content: list[dict[str, Any]],
+ system_prompt: str,
+ section_info: SectionInfo,
+ ) -> ExtractionResult:
+ """
+ Invoke Bedrock model (agentic or standard) and parse response.
- # Test JSON serialization before sending to Lambda to catch any remaining issues
- try:
- json.dumps(payload)
- logger.info("Lambda payload successfully serialized")
- except (TypeError, ValueError) as e:
- logger.error(
- f"Lambda payload still contains non-serializable data: {e}"
- )
- logger.info("Using comprehensive serialization as fallback")
- # Apply comprehensive serialization to entire payload
- payload = self._make_json_serializable(payload)
- try:
- json.dumps(payload)
- logger.info("Comprehensive serialization successful")
- except (TypeError, ValueError) as e2:
- logger.error(f"Even comprehensive serialization failed: {e2}")
- # Ultimate fallback to minimal payload
- payload = {
- "config": {
- "extraction": {"model": self.config.extraction.model}
- },
- "prompt_placeholders": prompt_placeholders,
- "default_task_prompt_content": [
- {"text": "Fallback content"}
- ],
- "serialized_document": {
- "id": str(document.id),
- "status": "PROCESSING",
- },
- }
-
- # Invoke custom Lambda and get result (pass original images for restoration)
- lambda_result = self._invoke_custom_prompt_lambda(
- custom_lambda_arn, payload, page_images
+ Args:
+ content: Prompt content
+ system_prompt: System prompt
+ section_info: Section metadata
+
+ Returns:
+ ExtractionResult with extracted fields and metering
+ """
+ logger.info(
+ f"Extracting fields for {section_info.class_label} document, section"
+ )
+
+ # Get extraction config
+ model_id = self.config.extraction.model
+ temperature = self.config.extraction.temperature
+ top_k = self.config.extraction.top_k
+ top_p = self.config.extraction.top_p
+ max_tokens = (
+ self.config.extraction.max_tokens
+ if self.config.extraction.max_tokens
+ else None
+ )
+
+ # Time the model invocation
+ request_start_time = time.time()
+
+ if self.config.extraction.agentic.enabled:
+ if not AGENTIC_AVAILABLE:
+ raise ImportError(
+ "Agentic extraction requires Python 3.10+ and strands-agents dependencies. "
+ "Install with: pip install 'idp_common[agents]' or use agentic=False"
)
- # Use Lambda results
- system_prompt = lambda_result.get("system_prompt", system_prompt)
- content = lambda_result.get("task_prompt_content", default_content)
+ # Create dynamic Pydantic model from JSON Schema
+ dynamic_model = create_pydantic_model_from_json_schema(
+ schema=self._class_schema,
+ class_label=section_info.class_label,
+ clean_schema=False, # Already cleaned
+ )
- logger.info("Successfully applied custom prompt from Lambda function")
+ # Log schema for debugging
+ model_schema = dynamic_model.model_json_schema()
+ logger.debug(f"Pydantic model schema for {section_info.class_label}:")
+ logger.debug(json.dumps(model_schema, indent=2))
+ # Use agentic extraction
+ if isinstance(content, list):
+ message_prompt = {"role": "user", "content": content}
else:
- # Use default prompt logic when no custom Lambda is configured
- logger.info(
- "No custom prompt Lambda configured - using default prompt generation"
- )
- prompt_template = self.config.extraction.task_prompt
-
- if not prompt_template:
- # Default prompt if template not found
- task_prompt = f"""
- Extract the following fields from this {class_label} document:
-
- {attribute_descriptions}
-
- Document text:
- {document_text}
-
- Respond with a JSON object containing each field name and its extracted value.
- """
- content = [{"text": task_prompt}]
-
- # Add image attachments to the content (limit to 20 images as per Bedrock constraints)
- if page_images:
- logger.info(
- f"Attaching images to prompt, for {len(page_images)} pages."
- )
- # Limit to 20 images as per Bedrock constraints
- for img in page_images[:20]:
- content.append(image.prepare_bedrock_image_attachment(img))
- else:
- # Check if task prompt contains FEW_SHOT_EXAMPLES placeholder
- if "{FEW_SHOT_EXAMPLES}" in prompt_template:
- content = self._build_content_with_few_shot_examples(
- prompt_template,
- document_text,
- class_label,
- attribute_descriptions,
- page_images, # Pass images to the content builder
- )
- else:
- # Use the unified content builder for DOCUMENT_IMAGE placeholder support
- try:
- content = (
- self._build_content_with_or_without_image_placeholder(
- prompt_template,
- document_text,
- class_label,
- attribute_descriptions,
- page_images, # Pass images to the content builder
- )
- )
- except ValueError as e:
- logger.warning(
- f"Error formatting prompt template: {str(e)}. Using default prompt."
- )
- # Fall back to default prompt if template validation fails
- task_prompt = f"""
- Extract the following fields from this {class_label} document:
-
- {attribute_descriptions}
-
- Document text:
- {document_text}
-
- Respond with a JSON object containing each field name and its extracted value.
- """
- content = [{"text": task_prompt}]
-
- # Add image attachments for fallback case
- if page_images:
- logger.info(
- f"Attaching images to prompt, for {len(page_images)} pages."
- )
- # Limit to 20 images as per Bedrock constraints
- for img in page_images[:20]:
- content.append(
- image.prepare_bedrock_image_attachment(img)
- )
+ message_prompt = content
+
+ logger.info("Using Agentic extraction")
+ logger.debug(f"Using input: {str(message_prompt)}")
+
+ structured_data, response_with_metering = structured_output(
+ model_id=model_id,
+ data_format=dynamic_model,
+ prompt=message_prompt,
+ page_images=self._page_images,
+ config=self.config,
+ context="Extraction",
+ )
- logger.info(
- f"Extracting fields for {class_label} document, section {section_id}"
+ extracted_fields = structured_data.model_dump()
+ metering = response_with_metering["metering"]
+ parsing_succeeded = True
+ else:
+ # Standard Bedrock invocation
+ 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="Extraction",
)
- # Time the model invocation
- request_start_time = time.time()
+ extracted_text = bedrock.extract_text_from_response(
+ dict(response_with_metering)
+ )
+ metering = response_with_metering["metering"]
- # Type-safe boolean access - no string conversion needed!
- if self.config.extraction.agentic.enabled:
- if not AGENTIC_AVAILABLE:
- raise ImportError(
- "Agentic extraction requires Python 3.10+ and strands-agents dependencies. "
- "Install with: pip install 'idp_common[agents]' or use agentic=False"
- )
+ # Parse response into JSON
+ extracted_fields = {}
+ parsing_succeeded = True
- # Create dynamic Pydantic model from JSON Schema
- # Schema is already cleaned by _clean_schema_for_prompt before being passed here
- dynamic_model = create_pydantic_model_from_json_schema(
- schema=class_schema,
- class_label=class_label,
- clean_schema=False, # Already cleaned
+ try:
+ extracted_fields = json.loads(extract_json_from_text(extracted_text))
+ except Exception as e:
+ logger.error(
+ f"Error parsing LLM output - invalid JSON?: {extracted_text} - {e}"
)
+ logger.info("Using unparsed LLM output.")
+ extracted_fields = {"raw_output": extracted_text}
+ parsing_succeeded = False
+
+ total_duration = time.time() - request_start_time
+ logger.info(f"Time taken for extraction: {total_duration:.2f} seconds")
+
+ return ExtractionResult(
+ extracted_fields=extracted_fields,
+ metering=metering,
+ parsing_succeeded=parsing_succeeded,
+ total_duration=total_duration,
+ )
- # Log the Pydantic model schema for debugging
- model_schema = dynamic_model.model_json_schema()
- logger.debug(f"Pydantic model schema for {class_label}:")
- logger.debug(json.dumps(model_schema, indent=2))
-
- # Use agentic extraction with the dynamic model
- # Wrap content list in proper Message format for agentic_idp compatibility
- if isinstance(content, list):
- message_prompt = {"role": "user", "content": content}
- else:
- message_prompt = content
- logger.info("Using Agentic extraction")
- logger.debug(f"Using input: {str(message_prompt)}")
- structured_data, response_with_metering = structured_output( # pyright: ignore[reportPossiblyUnboundVariable]
- model_id=model_id,
- data_format=dynamic_model,
- prompt=message_prompt, # pyright: ignore[reportArgumentType]
- custom_instruction=system_prompt,
- review_agent=self.config.extraction.agentic.review_agent, # Type-safe boolean!
- context="Extraction",
- )
+ def _save_results(
+ self,
+ document: Document,
+ section: Any,
+ result: ExtractionResult,
+ section_info: SectionInfo,
+ section_id: str,
+ t0: float,
+ ) -> None:
+ """
+ Save extraction results to S3 and update document.
- # Extract the structured data as dict for compatibility with existing code
- extracted_fields = structured_data.model_dump()
- # Extract metering from BedrockInvokeModelResponse
- metering = response_with_metering["metering"]
- parsing_succeeded = True # Agentic approach always succeeds in parsing since it returns structured data
+ Args:
+ document: Document being processed
+ section: Section being processed
+ result: Extraction result
+ section_info: Section metadata
+ section_id: Section ID
+ t0: Start time
+ """
+ # Write to S3
+ output = {
+ "document_class": {"type": section_info.class_label},
+ "split_document": {"page_indices": section_info.page_indices},
+ "inference_result": result.extracted_fields,
+ "metadata": {
+ "parsing_succeeded": result.parsing_succeeded,
+ "extraction_time_seconds": result.total_duration,
+ },
+ }
+ s3.write_content(
+ output,
+ section_info.output_bucket,
+ section_info.output_key,
+ content_type="application/json",
+ )
- else:
- # 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="Extraction",
- )
- # For non-agentic approach, response_with_metering is BedrockInvokeModelResponse
- # Extract text from response for non-agentic approach
- extracted_text = bedrock.extract_text_from_response(
- dict(response_with_metering)
- )
- metering = response_with_metering["metering"]
+ # Update section and document
+ section.extraction_result_uri = section_info.output_uri
+ document.metering = utils.merge_metering_data(
+ document.metering, result.metering or {}
+ )
- # Parse response into JSON
- extracted_fields = {}
- parsing_succeeded = True # Flag to track if parsing was successful
+ t3 = time.time()
+ logger.info(
+ f"Total extraction time for section {section_id}: {t3 - t0:.2f} seconds"
+ )
- try:
- # Try to parse the extracted text as JSON
- extracted_fields = json.loads(
- extract_json_from_text(extracted_text)
- )
- except Exception as e:
- # Handle parsing error
- logger.error(
- f"Error parsing LLM output - invalid JSON?: {extracted_text} - {e}"
- )
- logger.info("Using unparsed LLM output.")
- extracted_fields = {"raw_output": extracted_text}
- parsing_succeeded = False # Mark that parsing failed
-
- total_duration = time.time() - request_start_time
- logger.info(f"Time taken for extraction: {total_duration:.2f} seconds")
-
- # Write to S3
- output = {
- "document_class": {"type": class_label},
- "split_document": {"page_indices": page_indices},
- "inference_result": extracted_fields,
- "metadata": {
- "parsing_succeeded": parsing_succeeded,
- "extraction_time_seconds": total_duration,
- },
- }
- s3.write_content(
- output, output_bucket, output_key, content_type="application/json"
- )
+ def process_document_section(self, document: Document, section_id: str) -> Document:
+ """
+ Process a single section from a Document object.
- # Update the section with extraction result URI only (not the attributes themselves)
- section.extraction_result_uri = output_uri
+ Args:
+ document: Document object containing section to process
+ section_id: ID of the section to process
+
+ Returns:
+ Document: Updated Document object with extraction results for the section
+ """
+ # Reset state
+ self._reset_context()
+
+ # Validate and get section
+ section = self._validate_and_find_section(document, section_id)
+ if not section:
+ return document
- # Update document with metering data
- document.metering = utils.merge_metering_data(
- document.metering, metering or {}
+ # Prepare section metadata
+ try:
+ section_info = self._prepare_section_info(document, section)
+ except ValueError:
+ return document
+
+ try:
+ t0 = time.time()
+
+ # Load document content
+ document_text = self._load_document_text(
+ document, section_info.sorted_page_ids
+ )
+ page_images = self._load_document_images(
+ document, section_info.sorted_page_ids
)
- t3 = time.time()
- logger.info(
- f"Total extraction time for section {section_id}: {t3 - t0:.2f} seconds"
+ # Initialize extraction context
+ class_schema, attribute_descriptions = self._initialize_extraction_context(
+ section_info.class_label,
+ document_text,
+ page_images,
+ section_info.sorted_page_ids,
+ document,
)
+ # Handle empty schema case (early return)
+ if (
+ not class_schema.get(SCHEMA_PROPERTIES)
+ or not attribute_descriptions.strip()
+ ):
+ return self._handle_empty_schema(
+ document, section, section_info, section_id, t0
+ )
+
+ # Build prompt content
+ content, system_prompt = self._build_extraction_content(
+ document, page_images
+ )
+
+ # Invoke model
+ result = self._invoke_extraction_model(content, system_prompt, section_info)
+
+ # Save results
+ self._save_results(document, section, result, section_info, section_id, t0)
+
except Exception as e:
error_msg = f"Error processing section {section_id}: {str(e)}"
logger.error(error_msg)
diff --git a/lib/idp_common_pkg/idp_common/image/__init__.py b/lib/idp_common_pkg/idp_common/image/__init__.py
index da8678909..eb43f7269 100644
--- a/lib/idp_common_pkg/idp_common/image/__init__.py
+++ b/lib/idp_common_pkg/idp_common/image/__init__.py
@@ -1,185 +1,237 @@
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: MIT-0
-from PIL import Image, ImageFilter, ImageChops, ImageOps
+from PIL import Image, ImageFilter, ImageChops, ImageOps, ImageDraw, ImageFont
import io
-import logging
-from typing import Tuple, Optional, Dict, Any, Union
+import os
+from typing import Tuple, Optional, Dict, Any, Union, List
+from aws_lambda_powertools import Logger
from ..s3 import get_binary_content
from ..utils import parse_s3_uri
-logger = logging.getLogger(__name__)
+logger = Logger(service="image", level=os.getenv("LOG_LEVEL", "INFO"))
-def resize_image(image_data: bytes,
- target_width: Optional[int] = None,
- target_height: Optional[int] = None,
- allow_upscale: bool = False) -> bytes:
+
+def resize_image(
+ image_data: bytes,
+ target_width: Optional[int] = None,
+ target_height: Optional[int] = None,
+ allow_upscale: bool = False,
+ output_format: Optional[str] = None,
+) -> bytes:
"""
Resize an image to fit within target dimensions while preserving aspect ratio.
No padding, no distortion - pure proportional scaling.
Preserves original format when possible.
-
+
Args:
image_data: Raw image bytes
target_width: Target width in pixels (None or empty string = no resize)
target_height: Target height in pixels (None or empty string = no resize)
allow_upscale: Whether to allow making the image larger than original
-
+ output_format: Force output format (e.g. 'PNG', 'JPEG'). If None, preserves original format.
+
Returns:
- Resized image bytes in original format (or JPEG if format cannot be preserved)
+ Resized image bytes in specified format (or original format if output_format is None)
"""
# Handle empty strings - convert to None
if isinstance(target_width, str) and not target_width.strip():
target_width = None
if isinstance(target_height, str) and not target_height.strip():
target_height = None
-
+
# If either dimension is None, return original image unchanged
if target_width is None or target_height is None:
- logger.info("No resize requested (width or height is None/empty), returning original image")
+ logger.info(
+ "No resize requested (width or height is None/empty), returning original image"
+ )
return image_data
-
+
# Convert to int if needed
try:
target_width = int(target_width)
target_height = int(target_height)
except (ValueError, TypeError):
- logger.warning(f"Invalid resize dimensions: width={target_width}, height={target_height}, returning original image")
+ logger.warning(
+ f"Invalid resize dimensions: width={target_width}, height={target_height}, returning original image"
+ )
return image_data
image = Image.open(io.BytesIO(image_data))
current_width, current_height = image.size
original_format = image.format # Store original format
-
+
+ logger.info(
+ f"resize_image: original={current_width}x{current_height} ({original_format}), "
+ f"target={target_width}x{target_height}, output_format={output_format}"
+ )
+
# Calculate scaling factor to fit within bounds while preserving aspect ratio
width_ratio = target_width / current_width
height_ratio = target_height / current_height
scale_factor = min(width_ratio, height_ratio) # Fit within bounds
-
+
# Determine if resizing is needed
needs_resize = (scale_factor < 1.0) or (allow_upscale and scale_factor > 1.0)
-
+
if needs_resize:
new_width = int(current_width * scale_factor)
new_height = int(current_height * scale_factor)
- logger.info(f"Resizing image from {current_width}x{current_height} to {new_width}x{new_height} (scale: {scale_factor:.3f})")
+ 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']:
+
+ # Determine save format
+ if output_format:
+ # Use explicitly requested format
+ save_format = output_format.upper()
+ logger.info(
+ f"Converting from {original_format or 'unknown'} to {save_format}"
+ )
+ elif original_format and original_format in [
+ "JPEG",
+ "PNG",
+ "GIF",
+ "BMP",
+ "TIFF",
+ "WEBP",
+ ]:
save_format = original_format
else:
- save_format = 'JPEG'
+ save_format = "JPEG"
logger.info(f"Converting from {original_format or 'unknown'} to JPEG")
-
+
# Prepare save parameters
save_kwargs = {"format": save_format}
-
+
# Add quality parameters for JPEG
- if save_format in ['JPEG', 'JPG']:
+ if save_format in ["JPEG", "JPG"]:
save_kwargs["quality"] = 95 # High quality
save_kwargs["optimize"] = True
-
+ elif save_format == "PNG":
+ save_kwargs["optimize"] = True
+
# Handle format-specific requirements
- if save_format == 'PNG' and image.mode not in ['RGBA', 'LA', 'L', 'P']:
+ if save_format == "PNG" and image.mode not in ["RGBA", "LA", "L", "P"]:
# PNG requires specific modes
- if image.mode == 'CMYK':
- image = image.convert('RGB')
-
+ if image.mode == "CMYK":
+ image = image.convert("RGB")
+
image.save(img_byte_array, **save_kwargs)
- return img_byte_array.getvalue()
+ result_bytes = img_byte_array.getvalue()
+ logger.info(
+ f"resize_image: resized to {image.width}x{image.height}, "
+ f"saved as {save_format}, size={len(result_bytes):,} bytes"
+ )
+ return result_bytes
else:
# No resizing needed - return original data unchanged
- logger.info(f"Image {current_width}x{current_height} already fits within {target_width}x{target_height}, returning original")
+ logger.info(
+ f"resize_image: image {current_width}x{current_height} already fits within "
+ f"{target_width}x{target_height}, returning original ({len(image_data):,} bytes)"
+ )
return image_data
-def prepare_image(image_source: Union[str, bytes],
- target_width: Optional[int] = None,
- target_height: Optional[int] = None,
- allow_upscale: bool = False) -> bytes:
+
+def prepare_image(
+ image_source: Union[str, bytes],
+ target_width: Optional[int] = None,
+ target_height: Optional[int] = None,
+ allow_upscale: bool = False,
+ output_format: Optional[str] = None,
+) -> bytes:
"""
Prepare an image for model input from either S3 URI or raw bytes
-
+
Args:
image_source: Either an S3 URI (s3://bucket/key) or raw image bytes
target_width: Target width in pixels (None or empty string = no resize)
target_height: Target height in pixels (None or empty string = no resize)
allow_upscale: Whether to allow making the image larger than original
-
+ output_format: Force output format (e.g. 'PNG', 'JPEG'). If None, preserves original format.
+
Returns:
- Processed image bytes ready for model input (preserves format when possible)
+ Processed image bytes ready for model input
"""
# Get the image data
- if isinstance(image_source, str) and image_source.startswith('s3://'):
+ if isinstance(image_source, str) and image_source.startswith("s3://"):
image_data = get_binary_content(image_source)
elif isinstance(image_source, bytes):
image_data = image_source
else:
- raise ValueError(f"Invalid image source: {type(image_source)}. Must be S3 URI or bytes.")
-
+ raise ValueError(
+ f"Invalid image source: {type(image_source)}. Must be S3 URI or bytes."
+ )
+
# Resize and process
- return resize_image(image_data, target_width, target_height, allow_upscale)
+ return resize_image(
+ image_data, target_width, target_height, allow_upscale, output_format
+ )
+
def apply_adaptive_binarization(image_data: bytes) -> bytes:
"""
Apply adaptive binarization using Pillow-only implementation.
-
+
This preprocessing step can significantly improve OCR accuracy on documents with:
- Uneven lighting or shadows
- Low contrast text
- Background noise or gradients
-
+
Implements adaptive mean thresholding similar to OpenCV's ADAPTIVE_THRESH_MEAN_C
with block_size=15 and C=10.
-
+
Args:
image_data: Raw image bytes
-
+
Returns:
Processed image as JPEG bytes with adaptive binarization applied
"""
try:
# Convert bytes to PIL Image
pil_image = Image.open(io.BytesIO(image_data))
-
+
# Convert to grayscale if not already
- if pil_image.mode != 'L':
- pil_image = pil_image.convert('L')
-
+ if pil_image.mode != "L":
+ pil_image = pil_image.convert("L")
+
# Apply adaptive thresholding using Pillow operations
block_size = 15
C = 10
-
+
# Create a blurred version for local mean calculation
# Use BoxBlur with radius = block_size // 2 to approximate local mean
radius = block_size // 2
blurred = pil_image.filter(ImageFilter.BoxBlur(radius))
-
+
# Apply adaptive threshold: original > (blurred - C) ? 255 : 0
# Load pixel data for efficient access
width, height = pil_image.size
original_pixels = list(pil_image.getdata())
blurred_pixels = list(blurred.getdata())
-
+
binary_pixels = []
# Apply thresholding pixel by pixel
for orig, blur in zip(original_pixels, blurred_pixels):
threshold = blur - C
binary_pixels.append(255 if orig > threshold else 0)
-
+
# Create binary image
- binary_image = Image.new('L', (width, height))
+ binary_image = Image.new("L", (width, height))
binary_image.putdata(binary_pixels)
-
+
# Convert to JPEG bytes
img_byte_array = io.BytesIO()
binary_image.save(img_byte_array, format="JPEG")
-
- logger.debug("Applied adaptive binarization preprocessing (Pillow implementation)")
+
+ logger.debug(
+ "Applied adaptive binarization preprocessing (Pillow implementation)"
+ )
return img_byte_array.getvalue()
-
+
except Exception as e:
logger.error(f"Error applying adaptive binarization: {str(e)}")
# Return original image if preprocessing fails
@@ -190,28 +242,18 @@ def apply_adaptive_binarization(image_data: bytes) -> bytes:
def prepare_bedrock_image_attachment(image_data: bytes) -> Dict[str, Any]:
"""
Format an image for Bedrock API attachment
-
+
Args:
image_data: Raw image bytes
-
+
Returns:
Formatted image attachment for Bedrock API
"""
# Detect image format from image data
image = Image.open(io.BytesIO(image_data))
- format_mapping = {
- 'JPEG': 'jpeg',
- 'PNG': 'png',
- 'GIF': 'gif',
- 'WEBP': 'webp'
- }
+ format_mapping = {"JPEG": "jpeg", "PNG": "png", "GIF": "gif", "WEBP": "webp"}
detected_format = format_mapping.get(image.format)
if not detected_format:
raise ValueError(f"Unsupported image format: {image.format}")
logger.info(f"Detected image format: {detected_format}")
- return {
- "image": {
- "format": detected_format,
- "source": {"bytes": image_data}
- }
- }
+ return {"image": {"format": detected_format, "source": {"bytes": image_data}}}
diff --git a/lib/idp_common_pkg/idp_common/utils/bedrock_utils.py b/lib/idp_common_pkg/idp_common/utils/bedrock_utils.py
new file mode 100644
index 000000000..04885781f
--- /dev/null
+++ b/lib/idp_common_pkg/idp_common/utils/bedrock_utils.py
@@ -0,0 +1,409 @@
+import asyncio
+import json
+import logging
+import os
+import random
+import time
+from collections.abc import Awaitable, Callable
+from functools import wraps
+from typing import 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 pydantic_core import ArgsKwargs
+
+# Configure logger
+logger = logging.getLogger(__name__)
+logger.setLevel(os.environ.get("LOG_LEVEL", "INFO"))
+
+
+def async_exponential_backoff_retry[T, **P](
+ max_retries: int = 5,
+ initial_delay: float = 1.0,
+ max_delay: float = 32.0,
+ exponential_base: float = 2.0,
+ jitter: float = 0.1,
+ retryable_errors: list[str] | None = None,
+) -> Callable[[Callable[P, Awaitable[T]]], Callable[P, Awaitable[T]]]:
+ if not retryable_errors:
+ retryable_errors = [
+ "ThrottlingException",
+ "throttlingException",
+ "ModelErrorException",
+ "ValidationException",
+ ]
+
+ def decorator(func: Callable[P, Awaitable[T]]) -> Callable[P, Awaitable[T]]:
+ @wraps(func)
+ async def wrapper(*args, **kwargs) -> T:
+ delay = initial_delay
+
+ def log_bedrock_invocation_error(error: Exception, attempt_num: int):
+ """Log bedrock invocation details when an error occurs"""
+ # Fallback logging if extraction fails
+ logger.error(
+ "Bedrock invocation error",
+ extra={
+ "function_name": func.__name__,
+ "original_error": str(error),
+ "max_attempts": max_retries,
+ "attempt_num":attempt_num
+ },
+ )
+
+ for attempt in range(max_retries):
+ try:
+ return await func(*args, **kwargs)
+ except botocore.exceptions.ClientError as e:
+ error_code = e.response.get("Error", {}).get("Code")
+
+ # Log bedrock invocation details for all errors
+ log_bedrock_invocation_error(e, attempt + 1)
+
+ if (
+ error_code == "ValidationException"
+ and "Output blocked by content filtering policy"
+ not in e.response.get("Error", {}).get("Message", "")
+ ):
+ raise
+ if error_code not in retryable_errors or attempt == max_retries - 1:
+ raise
+
+ jitter_value = random.uniform(-jitter, jitter)
+ sleep_time = max(0.1, delay * (1 + jitter_value))
+ logger.warning(
+ f"{error_code}:{e.response.get('Error', {}).get('Message', '')} encountered in {func.__name__}. Retrying in {sleep_time:.2f} seconds. "
+ f"Attempt {attempt + 1}/{max_retries}"
+ )
+ await asyncio.sleep(sleep_time)
+ delay = min(delay * exponential_base, max_delay)
+ except Exception as e:
+ # Log bedrock invocation details for non-ClientError exceptions too
+ log_bedrock_invocation_error(e, attempt + 1)
+ raise
+
+ return await func(*args, **kwargs)
+
+ return wrapper
+
+ return decorator
+
+
+def exponential_backoff_retry[T, **P](
+ max_retries: int = 5,
+ initial_delay: float = 1.0,
+ max_delay: float = 32.0,
+ exponential_base: float = 2.0,
+ jitter: float = 0.1,
+) -> Callable[[Callable[P, T]], Callable[P, T]]:
+ def decorator(func: Callable[P, T]) -> Callable[P, T]:
+ @wraps(func)
+ def wrapper(*args, **kwargs) -> T:
+ delay = initial_delay
+
+ def log_bedrock_invocation_error(error: Exception, attempt_num: int):
+ """Log bedrock invocation details when an error occurs"""
+ try:
+ # Check for invoke_model API (has 'body' parameter)
+ if "body" in kwargs:
+ logger.error(
+ "Bedrock invoke_model failed",
+ extra={
+ "attempt_number": attempt_num,
+ "max_retries": max_retries,
+ "function_name": func.__name__,
+ "error": str(error),
+ "body": kwargs["body"],
+ },
+ )
+ # Check for converse API (has structured parameters)
+ elif any(
+ key in kwargs
+ for key in [
+ "messages",
+ "inferenceConfig",
+ "system",
+ "toolConfig",
+ ]
+ ):
+ # Log converse API parameters
+ converse_data = {
+ k: v
+ for k, v in kwargs.items()
+ if k
+ in [
+ "messages",
+ "inferenceConfig",
+ "system",
+ "toolConfig",
+ "additionalModelRequestFields",
+ "guardrailConfig",
+ "performanceConfig",
+ "promptVariables",
+ "requestMetadata",
+ ]
+ }
+ logger.error(
+ "Bedrock converse failed",
+ extra={
+ "attempt_number": attempt_num,
+ "max_retries": max_retries,
+ "function_name": func.__name__,
+ "error": str(error),
+ "parameters": json.dumps(converse_data, default=str),
+ },
+ )
+ else:
+ # Generic bedrock error logging
+ logger.error(
+ "Bedrock invocation failed",
+ extra={
+ "attempt_number": attempt_num,
+ "max_retries": max_retries,
+ "function_name": func.__name__,
+ "error": str(error),
+ },
+ )
+
+ except Exception as log_error:
+ # Fallback logging if extraction fails
+ logger.error(
+ "Failed to log bedrock invocation details",
+ extra={
+ "function_name": func.__name__,
+ "log_error": str(log_error),
+ "original_error": str(error),
+ },
+ )
+
+ for attempt in range(max_retries):
+ try:
+ return func(*args, **kwargs)
+ except botocore.exceptions.ClientError as e:
+ error_code = e.response.get("Error", {}).get("Code")
+
+ # Log bedrock invocation details for all errors
+ log_bedrock_invocation_error(e, attempt + 1)
+
+ if (
+ error_code == "ValidationException"
+ and "Output blocked by content filtering policy"
+ not in e.response.get("Error", {}).get("Message", "")
+ ):
+ raise
+ if (
+ error_code
+ not in [
+ "ThrottlingException",
+ "ModelErrorException",
+ "ValidationException",
+ ]
+ or attempt == max_retries - 1
+ ):
+ raise
+
+ jitter_value = random.uniform(-jitter, jitter)
+ sleep_time = max(0.1, delay * (1 + jitter_value))
+ logger.warning(
+ f"{error_code}:{e.response.get('Error', {}).get('Message', '')} encountered in {func.__name__}. Retrying in {sleep_time:.2f} seconds. "
+ f"Attempt {attempt + 1}/{max_retries}"
+ )
+ time.sleep(sleep_time)
+ delay = min(delay * exponential_base, max_delay)
+ except Exception as e:
+ # Log bedrock invocation details for non-ClientError exceptions too
+ log_bedrock_invocation_error(e, attempt + 1)
+ raise
+
+ return func(*args, **kwargs)
+
+ return wrapper
+
+ return decorator
+
+
+class BedrockClientWrapper:
+ """
+ A wrapper around AWS Bedrock Runtime Client that provides automatic retry logic
+ with exponential backoff for handling transient errors and rate limiting.
+
+ This wrapper automatically retries failed requests for specific error types:
+ - ThrottlingException: When API rate limits are exceeded
+ - ModelErrorException: When the model encounters temporary errors
+ - ValidationException: When content filtering blocks output (retryable case)
+
+ The retry mechanism uses exponential backoff with jitter to avoid thundering herd
+ problems when multiple clients retry simultaneously.
+
+ Attributes:
+ client (BedrockRuntimeClient): The underlying AWS Bedrock Runtime client
+ max_retries (int): Maximum number of retry attempts
+ initial_delay (float): Initial delay between retries in seconds
+ max_delay (float): Maximum delay between retries in seconds
+ exponential_base (float): Base for exponential backoff calculation
+ jitter (float): Random jitter factor to add variance to retry delays
+ invoke_model: Wrapped invoke_model method with retry logic
+ converse: Wrapped converse method with retry logic
+
+ Example:
+ >>> import boto3
+ >>> from mypy_boto3_bedrock_runtime import BedrockRuntimeClient
+ >>> bedrock_client = boto3.client("bedrock-runtime", region_name="us-east-1")
+ >>> wrapper = BedrockClientWrapper(bedrock_client, max_retries=3)
+ >>> # Use invoke_model with automatic retries
+ >>> response = wrapper.invoke_model(
+ ... modelId="anthropic.claude-3-sonnet-20240229-v1:0",
+ ... body=json.dumps(
+ ... {
+ ... "messages": [{"role": "user", "content": "Hello"}],
+ ... "max_tokens": 100,
+ ... }
+ ... ),
+ ... )
+ >>> # Use converse API with automatic retries
+ >>> response = wrapper.converse(
+ ... modelId="anthropic.claude-3-sonnet-20240229-v1:0",
+ ... messages=[{"role": "user", "content": [{"text": "Hello"}]}],
+ ... )
+ """
+
+ def __init__(
+ self,
+ bedrock_client: BedrockRuntimeClient,
+ max_retries: int = 5,
+ initial_delay: float = 1.0,
+ max_delay: float = 32.0,
+ exponential_base: float = 2.0,
+ jitter: float = 0.1,
+ ):
+ """
+ Initialize the BedrockClientWrapper with retry configuration.
+
+ Args:
+ bedrock_client (BedrockRuntimeClient): The AWS Bedrock Runtime client to wrap
+ max_retries (int, optional): Maximum number of retry attempts. Defaults to 5.
+ initial_delay (float, optional): Initial delay between retries in seconds. Defaults to 1.0.
+ max_delay (float, optional): Maximum delay between retries in seconds. Defaults to 32.0.
+ exponential_base (float, optional): Base for exponential backoff calculation. Defaults to 2.0.
+ jitter (float, optional): Random jitter factor (0.0-1.0) to add variance to retry delays. Defaults to 0.1.
+
+ Raises:
+ TypeError: If bedrock_client is not a BedrockRuntimeClient instance
+ ValueError: If retry parameters are invalid (negative values, jitter > 1.0, etc.)
+ """
+ self.client = bedrock_client
+
+ self.max_retries = max_retries
+ self.initial_delay = initial_delay
+ self.max_delay = max_delay
+ self.exponential_base = exponential_base
+ self.jitter = jitter
+
+ # Apply decorator directly to client methods
+ self._decorated_invoke_model = exponential_backoff_retry(
+ max_retries=max_retries,
+ initial_delay=initial_delay,
+ max_delay=max_delay,
+ exponential_base=exponential_base,
+ jitter=jitter,
+ )(self.client.invoke_model)
+
+ self._decorated_converse = exponential_backoff_retry(
+ max_retries=max_retries,
+ initial_delay=initial_delay,
+ max_delay=max_delay,
+ exponential_base=exponential_base,
+ jitter=jitter,
+ )(self.client.converse)
+
+ self._decorated_converse_stream_async = exponential_backoff_retry(
+ max_retries=max_retries,
+ initial_delay=initial_delay,
+ max_delay=max_delay,
+ exponential_base=exponential_base,
+ jitter=jitter,
+ )(self.client.converse_stream)
+
+ def invoke_model(
+ self, **kwargs: Unpack[InvokeModelRequestTypeDef]
+ ) -> InvokeModelResponseTypeDef:
+ """
+ Invoke a model with automatic retry logic.
+
+ This method has the same signature as BedrockRuntimeClient.invoke_model()
+ but includes automatic retry logic with exponential backoff.
+
+ Args:
+ modelId: The ID or ARN of the model to invoke
+ body: The input data to send to the model
+ contentType: The MIME type of the input data
+ accept: The desired MIME type of the response
+ **kwargs: Additional arguments passed to the underlying API
+
+ Returns:
+ InvokeModelResponseTypeDef: The response from the model invocation
+
+ Raises:
+ botocore.exceptions.ClientError: For non-retryable errors or after max retries
+ """
+ return self._decorated_invoke_model(**kwargs)
+
+ def converse(
+ self,
+ **kwargs: Unpack[ConverseRequestTypeDef],
+ ) -> ConverseResponseTypeDef:
+ """
+ Converse with a model using the conversation API with automatic retry logic.
+
+ This method has the same signature as BedrockRuntimeClient.converse()
+ but includes automatic retry logic with exponential backoff.
+
+ Args:
+ modelId: The ID or ARN of the model to invoke
+ messages: The conversation messages
+ system: System prompts to provide context
+ inferenceConfig: Configuration for model inference parameters
+ toolConfig: Configuration for tool use
+ guardrailConfig: Configuration for content filtering
+ additionalModelRequestFields: Additional model-specific request fields
+ promptVariables: Variables to substitute in prompts
+ additionalModelResponseFieldPaths: Additional response field paths
+ performanceConfig: Performance optimization configuration
+ requestMetadata: Metadata for the request
+ **kwargs: Additional arguments passed to the underlying API
+
+ Returns:
+ ConverseResponseTypeDef: The response from the conversation
+
+ Raises:
+ botocore.exceptions.ClientError: For non-retryable errors or after max retries
+ """
+ return self._decorated_converse(**kwargs)
+
+ def converse_stream(
+ self, **kwargs: Unpack[ConverseStreamRequestTypeDef]
+ ) -> ConverseStreamResponseTypeDef:
+ """
+ Async version of converse_stream with automatic retry logic.
+
+ This method has the same signature as BedrockRuntimeClient.converse_stream()
+ but runs asynchronously with automatic retry logic and exponential backoff.
+
+ Args:
+ **kwargs: All arguments passed to the underlying converse_stream API
+
+ Returns:
+ The streaming response from the conversation
+
+ Raises:
+ botocore.exceptions.ClientError: For non-retryable errors or after max retries
+ """
+ return self._decorated_converse_stream_async(**kwargs)
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 000000000..51c1c802a
--- /dev/null
+++ b/lib/idp_common_pkg/idp_common/utils/grid_overlay.py
@@ -0,0 +1,389 @@
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: MIT-0
+
+"""
+Grid overlay module for adding coordinate references to document images.
+
+This module provides functions to add ruler-style coordinate grids to images,
+enabling LLMs to provide precise bounding box coordinates for extracted fields.
+"""
+
+import io
+import logging
+
+from PIL import Image, ImageDraw, ImageFont
+
+logger = logging.getLogger(__name__)
+
+
+def add_ruler_edges(
+ image_data: bytes,
+ ruler_width: int = 30,
+ tick_interval: int = 50,
+ label_interval: int = 100,
+ ruler_color: tuple[int, int, int, int] = (240, 240, 240, 255),
+ tick_color: str = "black",
+ label_color: str = "black",
+ font_size: int = 12,
+) -> bytes:
+ """
+ Add ruler-style edges to the image (like graph paper margins).
+ Document content remains completely unobscured.
+
+ Args:
+ image_data: Raw image bytes (JPEG, PNG, etc.)
+ ruler_width: Width of the ruler margin in pixels
+ tick_interval: Spacing between minor tick marks (in 0-1000 scale)
+ label_interval: Spacing between labeled major tick marks (in 0-1000 scale)
+ ruler_color: Background color of ruler (RGBA tuple)
+ tick_color: Color of tick marks
+ label_color: Color of coordinate labels
+ font_size: Font size for labels
+
+ Returns:
+ Image bytes with ruler edges added (JPEG format)
+ """
+ image = Image.open(io.BytesIO(image_data)).convert("RGBA")
+ orig_width, orig_height = image.size
+
+ logger.info(f"Adding ruler edges to image {orig_width}x{orig_height}")
+
+ # Create canvas with ruler margins on top and left
+ new_width = orig_width + ruler_width
+ new_height = orig_height + ruler_width
+
+ canvas = Image.new("RGBA", (new_width, new_height), (255, 255, 255, 255))
+
+ draw = ImageDraw.Draw(canvas)
+
+ # Create ruler backgrounds
+ # Top ruler (horizontal) - for X coordinates
+ draw.rectangle([(ruler_width, 0), (new_width, ruler_width)], fill=ruler_color)
+ # Left ruler (vertical) - for Y coordinates
+ draw.rectangle([(0, ruler_width), (ruler_width, new_height)], fill=ruler_color)
+ # Corner square
+ draw.rectangle([(0, 0), (ruler_width, ruler_width)], fill=ruler_color)
+
+ # Paste original image offset by ruler width
+ canvas.paste(image, (ruler_width, ruler_width))
+
+ # Load font
+ font = _load_font(font_size)
+ small_font = _load_font(max(font_size - 2, 8))
+
+ # Draw tick marks and labels on TOP ruler (X-axis)
+ for i in range(0, 1001, tick_interval):
+ pixel_x = ruler_width + int((i / 1000.0) * orig_width)
+
+ if i % label_interval == 0:
+ # Major tick with label
+ draw.line(
+ [(pixel_x, ruler_width - 12), (pixel_x, ruler_width)],
+ fill=tick_color,
+ width=2,
+ )
+ # Center the label above the tick
+ label = str(i)
+ bbox = draw.textbbox((0, 0), label, font=font)
+ label_width = bbox[2] - bbox[0]
+ draw.text(
+ (pixel_x - label_width // 2, 2),
+ label,
+ fill=label_color,
+ font=font,
+ )
+ else:
+ # Minor tick (no label)
+ draw.line(
+ [(pixel_x, ruler_width - 6), (pixel_x, ruler_width)],
+ fill=tick_color,
+ width=1,
+ )
+
+ # Draw tick marks and labels on LEFT ruler (Y-axis)
+ for i in range(0, 1001, tick_interval):
+ pixel_y = ruler_width + int((i / 1000.0) * orig_height)
+
+ if i % label_interval == 0:
+ # Major tick with label
+ draw.line(
+ [(ruler_width - 12, pixel_y), (ruler_width, pixel_y)],
+ fill=tick_color,
+ width=2,
+ )
+ # Right-align the label
+ label = str(i)
+ bbox = draw.textbbox((0, 0), label, font=font)
+ label_width = bbox[2] - bbox[0]
+ draw.text(
+ (ruler_width - label_width - 14, pixel_y - 6),
+ label,
+ fill=label_color,
+ font=font,
+ )
+ else:
+ # Minor tick
+ draw.line(
+ [(ruler_width - 6, pixel_y), (ruler_width, pixel_y)],
+ fill=tick_color,
+ width=1,
+ )
+
+ # Add origin marker in corner (skip if using default font that doesn't support sizing)
+ try:
+ draw.text((2, 2), "0", fill=label_color, font=small_font)
+ except (OSError, AttributeError):
+ logger.debug("Skipping origin label - font rendering issue")
+
+ canvas = canvas.convert("RGB")
+ img_byte_array = io.BytesIO()
+ # Use PNG with optimization for better compression while preserving quality
+ canvas.save(img_byte_array, format="PNG", optimize=True)
+
+ logger.info(f"Ruler edges added. New size: {new_width}x{new_height}")
+ return img_byte_array.getvalue()
+
+
+def draw_bounding_boxes(
+ image_data: bytes,
+ bboxes: list[dict],
+ 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()
+ # Use PNG with optimization for better compression while preserving quality
+ result.save(img_byte_array, format="PNG", optimize=True)
+
+ logger.info(f"Drew {len(bboxes)} bounding boxes on image")
+ return img_byte_array.getvalue()
+
+
+def add_ruler_and_draw_boxes(
+ image_data: bytes,
+ bboxes: list[dict],
+ ruler_width: int = 30,
+ tick_interval: int = 50,
+ label_interval: int = 100,
+ box_color: str = "red",
+ box_width: int = 3,
+) -> bytes:
+ """
+ Convenience function to add ruler edges and draw bounding boxes in one step.
+
+ Args:
+ image_data: Raw image bytes
+ bboxes: List of bounding box dictionaries
+ ruler_width: Width of ruler margin
+ tick_interval: Spacing between minor ticks
+ label_interval: Spacing between major ticks
+ box_color: Default color for boxes
+ box_width: Line width for boxes
+
+ Returns:
+ Image bytes with ruler and bounding boxes
+ """
+ # First add ruler edges
+ image_with_ruler = add_ruler_edges(
+ image_data,
+ ruler_width=ruler_width,
+ tick_interval=tick_interval,
+ label_interval=label_interval,
+ )
+
+ # Then draw bounding boxes (accounting for 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/idp_common/utils/pdf_helpers.py b/lib/idp_common_pkg/idp_common/utils/pdf_helpers.py
new file mode 100644
index 000000000..c102ee526
--- /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/pyproject.toml b/lib/idp_common_pkg/pyproject.toml
index 6f2fdb826..0cc0c90a4 100644
--- a/lib/idp_common_pkg/pyproject.toml
+++ b/lib/idp_common_pkg/pyproject.toml
@@ -19,12 +19,14 @@ name = "idp_common"
version = "0.4.2"
description = "Common utilities for GenAI IDP Accelerator patterns"
authors = [{ name = "AWS", email = "noreply@amazon.com" }]
-requires-python = ">=3.10,<3.14"
+requires-python = ">=3.12,<3.14"
dependencies = [
- "boto3==1.39.7", # Core dependency for AWS services
+ "boto3==1.39.7", # Core dependency for AWS services
"jsonschema>=4.25.1",
"pydantic>=2.12.0",
"deepdiff>=6.0.0",
+ "mypy-boto3-bedrock-runtime>=1.40.76",
+ "aws-lambda-powertools>=3.21.0",
]
readme = "README.md"
classifiers = [
@@ -66,12 +68,16 @@ classification = [
# Extraction module dependencies
extraction = [
- "Pillow==11.2.1", # For image handling
+ "Pillow==11.2.1", # For image handling
]
# Assessment module dependencies
assessment = [
- "Pillow==11.2.1", # For image handling
+ "Pillow==11.2.1", # For image handling
+ "PyMuPDF==1.25.5", # Required by OcrService for PDF processing
+ "aws-lambda-powertools>=3.2.0", # Structured logging and observability
+ "mypy-boto3-dynamodb>=1.40.56",
+ "strands-agents==1.17.0",
]
# Evaluation module dependencies
@@ -97,26 +103,26 @@ appsync = ["requests==2.32.4"]
# Agents module dependencies
agents = [
- "strands-agents==1.14.0; python_version>='3.10'",
+ "strands-agents==1.17.0; python_version>='3.10'",
"strands-agents-tools==0.2.13; python_version>='3.10'",
- "bedrock-agentcore>=0.1.1; python_version>='3.10'", # Specifically for the code interpreter tool
+ "bedrock-agentcore>=0.1.1; python_version>='3.10'", # Specifically for the code interpreter tool
]
# Code intelligence module dependencies
code_intel = [
- "requests==2.32.4",
- "strands-agents==1.14.0",
- "strands-agents-tools==0.2.13",
- "bedrock-agentcore>=0.1.1",
- "PyYAML>=6.0.0",
- "pathspec>=0.11.0",
- "chardet>=5.0.0"
+ "requests==2.32.4",
+ "strands-agents==1.17.0",
+ "strands-agents-tools==0.2.13",
+ "bedrock-agentcore>=0.1.1",
+ "PyYAML>=6.0.0",
+ "pathspec>=0.11.0",
+ "chardet>=5.0.0",
]
# Document service factory dependencies (includes both appsync and dynamodb support)
# This includes all dependencies needed for both backends
docs_service = [
- "requests==2.32.4", # Required for appsync module (dynamodb only needs boto3 which is core)
+ "requests==2.32.4", # Required for appsync module (dynamodb only needs boto3 which is core)
"aws-xray-sdk>=2.14.0", # Required for X-Ray tracing
]
@@ -139,8 +145,8 @@ test = [
"pytest-asyncio>=1.1.0",
"typer>=0.19.2",
"ruff>=0.14.0",
- "deepdiff>=6.0.0", # Required for BDA blueprint service tests
- "datamodel-code-generator>=0.25.0", # Required for schema/pydantic generator tests
+ "deepdiff>=6.0.0", # Required for BDA blueprint service tests
+ "datamodel-code-generator>=0.25.0", # Required for schema/pydantic generator tests
]
# Full package with all dependencies
@@ -157,20 +163,20 @@ all = [
"pyarrow==20.0.0",
"openpyxl==3.1.5",
"python-docx==1.2.0",
- "strands-agents==1.14.0; python_version>='3.10'",
+ "strands-agents==1.17.0; python_version>='3.10'",
"strands-agents-tools==0.2.13; python_version>='3.10'",
"bedrock-agentcore>=0.1.1; python_version>='3.10'",
# "s3fs==2023.12.2" - - disabled till we fix package dependencies
]
agentic-extraction = [
"jsonpatch==1.33",
- "strands-agents==1.14.0 ; python_full_version >= '3.10'",
+ "strands-agents==1.17.0 ; python_full_version >= '3.10'",
"pandas>=2.2.3",
- "pymupdf==1.25.5", # Pinned to 1.25.5 - has pre-built ARM64 wheels, 1.26.x requires compilation
+ "pymupdf==1.25.5", # Pinned to 1.25.5 - has pre-built ARM64 wheels, 1.26.x requires compilation
"email-validator>=2.3.0",
"tabulate>=0.9.0",
- "aws-lambda-powertools>=3.2.0", # Structured logging and observability
- "datamodel-code-generator>=0.25.0", # Generate Pydantic models from JSON Schema
+ "aws-lambda-powertools>=3.2.0", # Structured logging and observability
+ "datamodel-code-generator>=0.25.0", # Generate Pydantic models from JSON Schema
]
[project.urls]
diff --git a/lib/idp_common_pkg/tests/conftest.py b/lib/idp_common_pkg/tests/conftest.py
index a83d71d25..f17c0da17 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 000000000..a1b17cd98
--- /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 fc8d6d1c1..96db5c1f9 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 4c4904ef2..5df0b9264 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 1a133d212..76b99f267 100644
--- a/lib/idp_common_pkg/uv.lock
+++ b/lib/idp_common_pkg/uv.lock
@@ -1,12 +1,6 @@
version = 1
revision = 3
-requires-python = ">=3.9, <3.14"
-resolution-markers = [
- "python_full_version >= '3.12'",
- "python_full_version == '3.11.*'",
- "python_full_version == '3.10.*'",
- "python_full_version < '3.10'",
-]
+requires-python = ">=3.12, <3.14"
[[package]]
name = "aiobotocore"
@@ -42,7 +36,6 @@ source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "aiohappyeyeballs" },
{ name = "aiosignal" },
- { name = "async-timeout", marker = "python_full_version < '3.11'" },
{ name = "attrs" },
{ name = "frozenlist" },
{ name = "multidict" },
@@ -51,40 +44,6 @@ dependencies = [
]
sdist = { url = "https://files.pythonhosted.org/packages/62/f1/8515650ac3121a9e55c7b217c60e7fae3e0134b5acfe65691781b5356929/aiohttp-3.13.0.tar.gz", hash = "sha256:378dbc57dd8cf341ce243f13fa1fa5394d68e2e02c15cd5f28eae35a70ec7f67", size = 7832348, upload-time = "2025-10-06T19:58:48.089Z" }
wheels = [
- { url = "https://files.pythonhosted.org/packages/25/18/a3a9c9b7c8d400f71d1ff93c3e1520a5d53dba170f829ca9c6b2b070677b/aiohttp-3.13.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:ca69ec38adf5cadcc21d0b25e2144f6a25b7db7bea7e730bac25075bc305eff0", size = 734428, upload-time = "2025-10-06T19:54:40.285Z" },
- { url = "https://files.pythonhosted.org/packages/aa/02/f1eac06d78997e015030130ccf1c7cf864a919f97d77ff27e89c82fc3186/aiohttp-3.13.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:240f99f88a9a6beb53ebadac79a2e3417247aa756202ed234b1dbae13d248092", size = 491939, upload-time = "2025-10-06T19:54:42.113Z" },
- { url = "https://files.pythonhosted.org/packages/e1/db/5d65af7cbe5f302e23b1ea5cfc156cd0c7738a0d2db531a3837d2754de94/aiohttp-3.13.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a4676b978a9711531e7cea499d4cdc0794c617a1c0579310ab46c9fdf5877702", size = 487229, upload-time = "2025-10-06T19:54:43.978Z" },
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@@ -1289,11 +918,14 @@ wheels = [
[[package]]
name = "idp-common"
-version = "0.3.13"
+version = "0.4.2"
source = { editable = "." }
dependencies = [
+ { name = "aws-lambda-powertools" },
{ name = "boto3" },
+ { name = "deepdiff" },
{ name = "jsonschema" },
+ { name = "mypy-boto3-bedrock-runtime" },
{ name = "pydantic" },
]
@@ -1305,17 +937,18 @@ agentic-extraction = [
{ name = "jsonpatch" },
{ name = "pandas" },
{ name = "pymupdf" },
- { name = "strands-agents", marker = "python_full_version >= '3.10'" },
+ { name = "strands-agents" },
{ name = "tabulate" },
]
agents = [
- { name = "bedrock-agentcore", marker = "python_full_version >= '3.10'" },
- { name = "strands-agents", marker = "python_full_version >= '3.10'" },
- { name = "strands-agents-tools", marker = "python_full_version >= '3.10'" },
+ { name = "bedrock-agentcore" },
+ { name = "strands-agents" },
+ { name = "strands-agents-tools" },
]
all = [
{ name = "amazon-textract-textractor", extra = ["pandas"] },
- { name = "bedrock-agentcore", marker = "python_full_version >= '3.10'" },
+ { name = "bedrock-agentcore" },
+ { name = "genson" },
{ name = "munkres" },
{ name = "numpy" },
{ name = "openpyxl" },
@@ -1325,18 +958,31 @@ all = [
{ name = "pymupdf" },
{ name = "python-docx" },
{ name = "requests" },
- { name = "strands-agents", marker = "python_full_version >= '3.10'" },
- { name = "strands-agents-tools", marker = "python_full_version >= '3.10'" },
+ { name = "stickler-eval" },
+ { name = "strands-agents" },
+ { name = "strands-agents-tools" },
]
appsync = [
{ name = "requests" },
]
assessment = [
+ { name = "aws-lambda-powertools" },
+ { name = "mypy-boto3-dynamodb" },
{ name = "pillow" },
+ { name = "strands-agents" },
]
classification = [
{ name = "pillow" },
]
+code-intel = [
+ { name = "bedrock-agentcore" },
+ { name = "chardet" },
+ { name = "pathspec" },
+ { name = "pyyaml" },
+ { name = "requests" },
+ { name = "strands-agents" },
+ { name = "strands-agents-tools" },
+]
criteria-validation = [
{ name = "s3fs" },
]
@@ -1350,11 +996,12 @@ docs-service = [
{ name = "requests" },
]
evaluation = [
+ { name = "genson" },
{ name = "munkres" },
{ name = "numpy" },
+ { name = "stickler-eval" },
]
extraction = [
- { name = "deepdiff" },
{ name = "pillow" },
]
image = [
@@ -1393,7 +1040,7 @@ test = [
{ name = "requests" },
{ name = "rich" },
{ name = "ruff" },
- { name = "strands-agents", marker = "python_full_version >= '3.10'" },
+ { name = "strands-agents" },
{ name = "tabulate" },
{ name = "typer" },
]
@@ -1402,16 +1049,22 @@ 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" },
{ 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" },
+ { name = "bedrock-agentcore", marker = "extra == 'code-intel'", specifier = ">=0.1.1" },
{ name = "boto3", specifier = "==1.39.7" },
+ { name = "chardet", marker = "extra == 'code-intel'", specifier = ">=5.0.0" },
{ name = "datamodel-code-generator", marker = "extra == 'agentic-extraction'", specifier = ">=0.25.0" },
{ name = "datamodel-code-generator", marker = "extra == 'test'", specifier = ">=0.25.0" },
- { name = "deepdiff", marker = "extra == 'extraction'", specifier = ">=6.0.0" },
+ { name = "deepdiff", specifier = ">=6.0.0" },
{ name = "deepdiff", marker = "extra == 'test'", specifier = ">=6.0.0" },
{ name = "email-validator", marker = "extra == 'agentic-extraction'", specifier = ">=2.3.0" },
+ { name = "genson", marker = "extra == 'all'", specifier = "==1.3.0" },
+ { name = "genson", marker = "extra == 'evaluation'", specifier = "==1.3.0" },
{ name = "idp-common", extras = ["agentic-extraction"], marker = "extra == 'test'" },
{ name = "ipykernel", marker = "extra == 'dev'", specifier = ">=6.29.5,<7.0.0" },
{ name = "jsonpatch", marker = "extra == 'agentic-extraction'", specifier = "==1.33" },
@@ -1421,6 +1074,8 @@ 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" },
{ name = "numpy", marker = "extra == 'ocr'", specifier = "==1.26.4" },
@@ -1430,6 +1085,7 @@ requires-dist = [
{ name = "pandas", marker = "extra == 'agentic-extraction'", specifier = ">=2.2.3" },
{ name = "pandas", marker = "extra == 'all'", specifier = "==2.2.3" },
{ name = "pandas", marker = "extra == 'ocr'", specifier = "==2.2.3" },
+ { name = "pathspec", marker = "extra == 'code-intel'", specifier = ">=0.11.0" },
{ name = "pillow", marker = "extra == 'all'", specifier = "==11.2.1" },
{ name = "pillow", marker = "extra == 'assessment'", specifier = "==11.2.1" },
{ name = "pillow", marker = "extra == 'classification'", specifier = "==11.2.1" },
@@ -1441,7 +1097,7 @@ requires-dist = [
{ name = "pyarrow", marker = "extra == 'reporting'", specifier = "==20.0.0" },
{ name = "pyarrow", marker = "extra == 'test'", specifier = "==20.0.0" },
{ name = "pydantic", specifier = ">=2.12.0" },
- { name = "pymupdf", marker = "extra == 'agentic-extraction'", specifier = ">=1.25.5" },
+ { name = "pymupdf", marker = "extra == 'agentic-extraction'", specifier = "==1.25.5" },
{ name = "pymupdf", marker = "extra == 'all'", specifier = "==1.25.5" },
{ name = "pymupdf", marker = "extra == 'ocr'", specifier = "==1.25.5" },
{ name = "pytest", marker = "extra == 'test'", specifier = ">=7.4.0" },
@@ -1452,23 +1108,30 @@ requires-dist = [
{ name = "python-docx", marker = "extra == 'ocr'", specifier = "==1.2.0" },
{ name = "python-docx", marker = "extra == 'test'", specifier = "==1.2.0" },
{ name = "python-dotenv", marker = "extra == 'dev'", specifier = ">=1.1.0,<2.0.0" },
+ { name = "pyyaml", marker = "extra == 'code-intel'", specifier = ">=6.0.0" },
{ name = "pyyaml", marker = "extra == 'test'", specifier = "==6.0.2" },
{ name = "requests", marker = "extra == 'all'", specifier = "==2.32.4" },
{ name = "requests", marker = "extra == 'appsync'", specifier = "==2.32.4" },
+ { name = "requests", marker = "extra == 'code-intel'", specifier = "==2.32.4" },
{ name = "requests", marker = "extra == 'docs-service'", specifier = "==2.32.4" },
{ name = "requests", marker = "extra == 'test'", specifier = ">=2.32.3,<3.0.0" },
{ 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 = "strands-agents", marker = "python_full_version >= '3.10' and extra == 'agentic-extraction'", specifier = ">=1.7.1" },
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- { name = "strands-agents-tools", marker = "python_full_version >= '3.10' and extra == 'all'", specifier = ">=0.2.2" },
+ { 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" },
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+ { 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" },
{ name = "tabulate", marker = "extra == 'agentic-extraction'", specifier = ">=0.9.0" },
{ name = "typer", marker = "extra == 'test'", specifier = ">=0.19.2" },
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+provides-extras = ["core", "dev", "image", "ocr", "classification", "extraction", "assessment", "evaluation", "criteria-validation", "reporting", "appsync", "agents", "code-intel", "docs-service", "test", "all", "agentic-extraction"]
[[package]]
name = "importlib-metadata"
@@ -1512,9 +1175,7 @@ dependencies = [
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{ name = "comm" },
{ name = "debugpy" },
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- { name = "ipython", version = "9.6.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+ { name = "ipython" },
{ name = "jupyter-client" },
{ name = "jupyter-core" },
{ name = "matplotlib-inline" },
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diff --git a/lib/idp_common_pkg/verify_stickler.py b/lib/idp_common_pkg/verify_stickler.py
index 16d294106..42d82917c 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": {
diff --git a/patterns/pattern-2/src/assessment_function/index.py b/patterns/pattern-2/src/assessment_function/index.py
index 59ec90041..e1ea12bd2 100644
--- a/patterns/pattern-2/src/assessment_function/index.py
+++ b/patterns/pattern-2/src/assessment_function/index.py
@@ -10,88 +10,99 @@
from idp_common.models import Document, Status
from idp_common.docs_service import create_document_service
from idp_common import s3
-from idp_common.utils import normalize_boolean_value, calculate_lambda_metering, merge_metering_data
+from idp_common.utils import (
+ normalize_boolean_value,
+ calculate_lambda_metering,
+ merge_metering_data,
+)
from assessment_validator import AssessmentValidator
from aws_xray_sdk.core import xray_recorder, patch_all
patch_all()
+
# Custom exception for throttling scenarios
class ThrottlingException(Exception):
"""Exception raised when throttling is detected in document processing results"""
+
pass
+
# Throttling detection constants
THROTTLING_KEYWORDS = [
"throttlingexception",
- "provisionedthroughputexceededexception",
+ "provisionedthroughputexceededexception",
"servicequotaexceededexception",
"toomanyrequestsexception",
"requestlimitexceeded",
"too many tokens",
"please wait before trying again",
- "reached max retries"
+ "reached max retries",
]
THROTTLING_EXCEPTIONS = [
"ThrottlingException",
"ProvisionedThroughputExceededException",
- "ServiceQuotaExceededException",
+ "ServiceQuotaExceededException",
"TooManyRequestsException",
- "RequestLimitExceeded"
+ "RequestLimitExceeded",
]
# Configuration will be loaded in handler function
logger = logging.getLogger()
logger.setLevel(os.environ.get("LOG_LEVEL", "INFO"))
-logging.getLogger('idp_common.bedrock.client').setLevel(os.environ.get("BEDROCK_LOG_LEVEL", "INFO"))
+logging.getLogger("idp_common.bedrock.client").setLevel(
+ os.environ.get("BEDROCK_LOG_LEVEL", "INFO")
+)
+
def is_throttling_exception(exception):
"""
Check if an exception is related to throttling.
-
+
Args:
exception: The exception to check
-
+
Returns:
bool: True if the exception is throttling-related, False otherwise
"""
from botocore.exceptions import ClientError
-
+
if isinstance(exception, ClientError):
- error_code = exception.response.get('Error', {}).get('Code', '')
+ error_code = exception.response.get("Error", {}).get("Code", "")
return error_code in THROTTLING_EXCEPTIONS
-
+
exception_name = type(exception).__name__
exception_message = str(exception).lower()
-
- return (
- exception_name in THROTTLING_EXCEPTIONS or
- any(keyword in exception_message for keyword in THROTTLING_KEYWORDS)
+
+ return exception_name in THROTTLING_EXCEPTIONS or any(
+ keyword in exception_message for keyword in THROTTLING_KEYWORDS
)
+
def check_document_for_throttling_errors(document):
"""
Check if a document has throttling errors in its errors field.
-
+
Args:
document: The document object to check
-
+
Returns:
tuple: (has_throttling_errors: bool, first_throttling_error: str or None)
"""
if document.status != Status.FAILED or not document.errors:
return False, None
-
+
for error_msg in document.errors:
error_lower = str(error_msg).lower()
if any(keyword in error_lower for keyword in THROTTLING_KEYWORDS):
return True, error_msg
-
+
return False, None
-@xray_recorder.capture('assessment_function')
+
+@xray_recorder.capture("assessment_function")
def handler(event, context):
"""
Lambda handler for document assessment.
@@ -99,32 +110,36 @@ def handler(event, context):
using the Assessment service from the idp_common library.
"""
start_time = time.time() # Capture start time for Lambda metering
- logger.info(f"Starting assessment processing for event: {json.dumps(event, default=str)}")
+ logger.info(
+ f"Starting assessment processing for event: {json.dumps(event, default=str)}"
+ )
# Load configuration
config = get_config(as_model=True)
# Use default=str to handle Decimal and other non-serializable types
logger.info(f"Config: {json.dumps(config.model_dump(), default=str)}")
-
+
# Extract input from event - handle both compressed and uncompressed
- document_data = event.get('document', {})
- section_id = event.get('section_id')
-
+ document_data = event.get("document", {})
+ section_id = event.get("section_id")
+
# Validate inputs
if not document_data:
raise ValueError("No document provided in event")
-
+
if not section_id:
raise ValueError("No section_id provided in event")
-
+
# Convert document data to Document object - handle compression
- working_bucket = os.environ.get('WORKING_BUCKET')
+ working_bucket = os.environ.get("WORKING_BUCKET")
document = Document.load_document(document_data, working_bucket, logger)
- logger.info(f"Processing assessment for document {document.id}, section {section_id}")
+ logger.info(
+ f"Processing assessment for document {document.id}, section {section_id}"
+ )
# X-Ray annotations
- xray_recorder.put_annotation('document_id', {document.id})
- xray_recorder.put_annotation('processing_stage', 'assessment')
+ xray_recorder.put_annotation("document_id", {document.id})
+ xray_recorder.put_annotation("processing_stage", "assessment")
# Find the section we're processing
section = None
@@ -132,24 +147,32 @@ 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-2/template.yaml b/patterns/pattern-2/template.yaml
index 8dd95c3ef..ba91c3b0b 100644
--- a/patterns/pattern-2/template.yaml
+++ b/patterns/pattern-2/template.yaml
@@ -114,14 +114,14 @@ Parameters:
EnableXRayTracing:
Type: String
- Default: 'true'
- AllowedValues: ['true', 'false']
+ Default: "true"
+ AllowedValues: ["true", "false"]
Description: Enable X-Ray tracing
EnableECRImageScanning:
Type: String
- Default: 'true'
- AllowedValues: ['true', 'false']
+ Default: "true"
+ AllowedValues: ["true", "false"]
Description: Enable automatic vulnerability scanning for Lambda container images in ECR
PermissionsBoundaryArn:
@@ -384,7 +384,7 @@ Resources:
MemorySize: 128
Handler: index.handler
CodeUri: ../../src/lambda/start_codebuild
- Description: CodeBuild trigger Lambda for Docker image builds
+ Description: CodeBuild trigger Lambda for Docker image builds
LoggingConfig:
LogGroup: !Ref CodeBuildTriggerLogGroup
@@ -426,7 +426,7 @@ Resources:
Properties:
ServiceToken: !GetAtt CodeBuildTrigger.Arn
RepositoryName: !Ref ECRRepository
-
+
# Shared IAM policy for Lambda functions to pull container images from ECR
LambdaECRAccessPolicy:
Type: AWS::IAM::ManagedPolicy
@@ -852,7 +852,6 @@ Resources:
- "eu.anthropic.claude-sonnet-4-5-20250929-v1:0"
- "eu.anthropic.claude-sonnet-4-5-20250929-v1:0:1m"
-
order: 1
classificationMethod:
type: string
@@ -925,6 +924,10 @@ Resources:
description: This introduces a second agent to review the first agents work. Only use with highly complex workflows as it increases token usage.
order: 1
default: false
+ review_agent_model:
+ type: string
+ description: Model to review the initial extraction agents work and correct it if needed, if not specified will default to the same as the extraction model.
+ default: ""
image:
type: object
sectionLabel: Image Processing Settings
@@ -1098,7 +1101,7 @@ Resources:
"us.anthropic.claude-sonnet-4-5-20250929-v1:0:1m",
"us.anthropic.claude-opus-4-20250514-v1:0",
"us.anthropic.claude-opus-4-1-20250805-v1:0",
- "eu.amazon.nova-lite-v1:0",
+ "eu.amazon.nova-lite-v1:0",
"eu.amazon.nova-pro-v1:0",
"eu.anthropic.claude-3-haiku-20240307-v1:0",
"eu.anthropic.claude-haiku-4-5-20251001-v1:0",
@@ -1163,7 +1166,7 @@ Resources:
"us.anthropic.claude-sonnet-4-5-20250929-v1:0:1m",
"us.anthropic.claude-opus-4-20250514-v1:0",
"us.anthropic.claude-opus-4-1-20250805-v1:0",
- "eu.amazon.nova-lite-v1:0",
+ "eu.amazon.nova-lite-v1:0",
"eu.amazon.nova-pro-v1:0",
"eu.anthropic.claude-3-haiku-20240307-v1:0",
"eu.anthropic.claude-haiku-4-5-20251001-v1:0",
@@ -1231,7 +1234,7 @@ Resources:
"us.anthropic.claude-sonnet-4-5-20250929-v1:0:1m",
"us.anthropic.claude-opus-4-20250514-v1:0",
"us.anthropic.claude-opus-4-1-20250805-v1:0",
- "eu.amazon.nova-lite-v1:0",
+ "eu.amazon.nova-lite-v1:0",
"eu.amazon.nova-pro-v1:0",
"eu.anthropic.claude-3-haiku-20240307-v1:0",
"eu.anthropic.claude-haiku-4-5-20251001-v1:0",
@@ -1300,7 +1303,7 @@ Resources:
"us.anthropic.claude-sonnet-4-5-20250929-v1:0:1m",
"us.anthropic.claude-opus-4-20250514-v1:0",
"us.anthropic.claude-opus-4-1-20250805-v1:0",
- "eu.amazon.nova-lite-v1:0",
+ "eu.amazon.nova-lite-v1:0",
"eu.amazon.nova-pro-v1:0",
"eu.anthropic.claude-3-haiku-20240307-v1:0",
"eu.anthropic.claude-haiku-4-5-20251001-v1:0",
@@ -1369,7 +1372,7 @@ Resources:
"us.anthropic.claude-sonnet-4-5-20250929-v1:0:1m",
"us.anthropic.claude-opus-4-20250514-v1:0",
"us.anthropic.claude-opus-4-1-20250805-v1:0",
- "eu.amazon.nova-lite-v1:0",
+ "eu.amazon.nova-lite-v1:0",
"eu.amazon.nova-pro-v1:0",
"eu.anthropic.claude-3-haiku-20240307-v1:0",
"eu.anthropic.claude-haiku-4-5-20251001-v1:0",
@@ -1440,7 +1443,28 @@ Resources:
model_id:
type: string
description: Model ID for the chat companion agents
- enum: ["us.amazon.nova-lite-v1:0", "us.amazon.nova-pro-v1:0", "us.amazon.nova-premier-v1:0", "us.anthropic.claude-3-haiku-20240307-v1:0", "us.anthropic.claude-haiku-4-5-20251001-v1:0", "us.anthropic.claude-3-5-sonnet-20241022-v2:0", "us.anthropic.claude-3-7-sonnet-20250219-v1:0", "us.anthropic.claude-sonnet-4-20250514-v1:0", "us.anthropic.claude-sonnet-4-5-20250929-v1:0", "us.anthropic.claude-opus-4-20250514-v1:0", "us.anthropic.claude-opus-4-1-20250805-v1:0", "eu.amazon.nova-lite-v1:0", "eu.amazon.nova-pro-v1:0", "eu.anthropic.claude-3-haiku-20240307-v1:0", "eu.anthropic.claude-haiku-4-5-20251001-v1:0", "eu.anthropic.claude-3-5-sonnet-20241022-v2:0", "eu.anthropic.claude-3-7-sonnet-20250219-v1:0", "eu.anthropic.claude-sonnet-4-20250514-v1:0", "eu.anthropic.claude-sonnet-4-5-20250929-v1:0"]
+ enum:
+ [
+ "us.amazon.nova-lite-v1:0",
+ "us.amazon.nova-pro-v1:0",
+ "us.amazon.nova-premier-v1:0",
+ "us.anthropic.claude-3-haiku-20240307-v1:0",
+ "us.anthropic.claude-haiku-4-5-20251001-v1:0",
+ "us.anthropic.claude-3-5-sonnet-20241022-v2:0",
+ "us.anthropic.claude-3-7-sonnet-20250219-v1:0",
+ "us.anthropic.claude-sonnet-4-20250514-v1:0",
+ "us.anthropic.claude-sonnet-4-5-20250929-v1:0",
+ "us.anthropic.claude-opus-4-20250514-v1:0",
+ "us.anthropic.claude-opus-4-1-20250805-v1:0",
+ "eu.amazon.nova-lite-v1:0",
+ "eu.amazon.nova-pro-v1:0",
+ "eu.anthropic.claude-3-haiku-20240307-v1:0",
+ "eu.anthropic.claude-haiku-4-5-20251001-v1:0",
+ "eu.anthropic.claude-3-5-sonnet-20241022-v2:0",
+ "eu.anthropic.claude-3-7-sonnet-20250219-v1:0",
+ "eu.anthropic.claude-sonnet-4-20250514-v1:0",
+ "eu.anthropic.claude-sonnet-4-5-20250929-v1:0",
+ ]
default: "us.anthropic.claude-sonnet-4-20250514-v1:0"
order: 0
error_analyzer:
@@ -2514,7 +2538,7 @@ Resources:
SAVE_REPORTING_FUNCTION_NAME: !Ref SaveReportingFunctionName
CONFIGURATION_TABLE_NAME: !Ref ConfigurationTable
WORKING_BUCKET: !Ref WorkingBucket
- DOCUMENT_TRACKING_MODE: !If [HasAppSyncApi, 'appsync', 'dynamodb']
+ DOCUMENT_TRACKING_MODE: !If [HasAppSyncApi, "appsync", "dynamodb"]
TRACKING_TABLE: !Ref TrackingTable
LoggingConfig:
LogGroup: !Ref EvaluationFunctionLogGroup
diff --git a/patterns/pattern-3/src/assessment_function/index.py b/patterns/pattern-3/src/assessment_function/index.py
index 38cbcb4af..ad29b5510 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 000000000..1cfeb691a
--- /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()