feat: Complete Pattern 2 Lambda metering implementation

- Add Lambda metering to Extraction function (both skip and normal paths)
- Add Lambda metering to Assessment function (both skip and normal paths)
- Add Lambda metering to Summarization function (successful execution path)
- Fix robust type handling in lambda_metering utility function
- Support string/numeric conversion for memory_limit_in_mb field

Pattern 2 now has complete Lambda execution cost tracking for all core
processing functions: OCR, Classification, Extraction, Assessment, Summarization.

Each function tracks:
- Lambda invocation counts (for /bin/bash.20 per 1M requests pricing)
- Lambda GB-seconds duration (for 6.67 per 1M GB-seconds pricing)

Provides complete cost visibility for document processing workflows.

Author: Bob Strahan

patterns/pattern-2/src/assessment_function/index.py

@@ -10,7 +10,7 @@
 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
+from idp_common.utils import normalize_boolean_value, calculate_lambda_metering, merge_metering_data
 from assessment_validator import AssessmentValidator
 
 # Custom exception for throttling scenarios
@@ -94,6 +94,7 @@ def handler(event, context):
     This function assesses the confidence of extraction results for a document section
     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)}")
 
     # Load configuration
@@ -166,6 +167,13 @@ def handler(event, context):
                 # Add only the section being processed (preserve existing data)
                 section_document.sections = [section]
 
+                # Add Lambda metering for assessment skip execution
+                try:
+                    lambda_metering = calculate_lambda_metering("Assessment", 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)}")
+                
                 # Return consistent format for Map state collation
                 response = {
                     "section_id": section_id, 
@@ -293,6 +301,13 @@ def handler(event, context):
                     updated_document.errors.extend(validation_errors)
                     logger.error(f"Validation Error: {validation_errors}")
 
+    # Add Lambda metering for successful assessment execution
+    try:
+        lambda_metering = calculate_lambda_metering("Assessment", 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),

patterns/pattern-2/src/extraction_function/index.py

@@ -10,6 +10,7 @@
 from idp_common import metrics, get_config, extraction
 from idp_common.models import Document, Section, Status
 from idp_common.docs_service import create_document_service
+from idp_common.utils import calculate_lambda_metering, merge_metering_data
 
 # Configuration will be loaded in handler function
 
@@ -24,6 +25,7 @@ def handler(event, context):
     """
     Process a single section of a document for information extraction
     """
+    start_time = time.time()  # Capture start time for Lambda metering
     logger.info(f"Event: {json.dumps(event)}")
 
     # Load configuration
@@ -65,6 +67,13 @@ def handler(event, context):
     if section.extraction_result_uri and section.extraction_result_uri.strip():
         logger.info(f"Skipping extraction for section {section_id} - already has extraction data: {section.extraction_result_uri}")
 
+        # Add Lambda metering for extraction skip execution
+        try:
+            lambda_metering = calculate_lambda_metering("Extraction", context, start_time)
+            full_document.metering = merge_metering_data(full_document.metering, lambda_metering)
+        except Exception as e:
+            logger.warning(f"Failed to add Lambda metering for extraction skip: {str(e)}")
+        
         # Return the section without processing
         response = {
             "section_id": section_id,
@@ -117,6 +126,13 @@ def handler(event, context):
         logger.error(error_message)
         raise Exception(error_message)
 
+    # Add Lambda metering for successful extraction execution
+    try:
+        lambda_metering = calculate_lambda_metering("Extraction", 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 extraction: {str(e)}")
+    
     # Prepare output with automatic compression if needed
     response = {
         "section_id": section_id,

patterns/pattern-2/src/summarization_function/index.py

@@ -13,6 +13,7 @@
 from idp_common import get_config, summarization
 from idp_common.models import Document, Status
 from idp_common.docs_service import create_document_service
+from idp_common.utils import calculate_lambda_metering, merge_metering_data
 
 # Configuration will be loaded in handler function
 
@@ -72,6 +73,13 @@ def handler(event, context):
         else:
             logger.warning("Document summarization completed but no summary report URI was set")
 
+        # Add Lambda metering for successful summarization execution
+        try:
+            lambda_metering = calculate_lambda_metering("Summarization", context, start_time)
+            processed_document.metering = merge_metering_data(processed_document.metering, lambda_metering)
+        except Exception as e:
+            logger.warning(f"Failed to add Lambda metering for summarization: {str(e)}")
+        
         # Prepare output with automatic compression if needed
         return {
             'document': processed_document.serialize_document(working_bucket, "summarization", logger),