Add AI rules support for document processing and matching; enhance tax analysis with flag_for_review and auto_approve fields

app/main.py

@@ -378,8 +378,11 @@ async def process_document(
     This endpoint uses AI to extract structured data from receipt images,
     including vendor, amount, date, and category information.
 
-    Optionally accepts user_location to guide tax calculations and depreciation
-    based on the user's location (format: "State/Province, Country" e.g., "Ontario, Canada").
+    Optionally accepts:
+    - user_location: Guide tax calculations and depreciation based on location
+      (format: "State/Province, Country" e.g., "Ontario, Canada")
+    - ai_rules: Custom categorization rules to override default logic
+      (e.g., [{"condition": "vendor is Starbucks", "action": "Food"}])
     """
     try:
         # Get file info from database
@@ -387,11 +390,20 @@ async def process_document(
         if not db_uploaded_file:
             raise HTTPException(status_code=404, detail=f"File {file_id} not found")
 
+        # Convert ai_rules from Pydantic models to dictionaries if provided
+        ai_rules_list = None
+        if request.ai_rules:
+            ai_rules_list = [
+                {"condition": rule.condition, "action": rule.action}
+                for rule in request.ai_rules
+            ]
+
         # Process the file using the stored file path
         receipt_data = await document_processor.process_file(
             db_uploaded_file.file_path,
             db_uploaded_file.file_type,
             user_location=request.user_location,
+            ai_rules=ai_rules_list,
         )
 
         # Parse date for database storage
@@ -570,9 +582,21 @@ async def match_specific_receipts(request: MatchSpecificRequest, db: db_dependen
         else:
             logger.info(f"Using default/provided user_location: {user_location}")
 
+        # Convert ai_rules from Pydantic models to dictionaries if provided
+        ai_rules_list = None
+        if request.ai_rules:
+            ai_rules_list = [
+                {"condition": rule.condition, "action": rule.action}
+                for rule in request.ai_rules
+            ]
+            logger.info(f"Applying {len(ai_rules_list)} custom AI rules to matching")
+
         try:
             matching_results = matching_engine.process_matching(
-                receipts, transactions, user_location=user_location
+                receipts,
+                transactions,
+                user_location=user_location,
+                ai_rules=ai_rules_list,
             )
             logger.info(f"Matching completed, got {len(matching_results)} results")
 
@@ -584,6 +608,13 @@ async def match_specific_receipts(request: MatchSpecificRequest, db: db_dependen
                 # if result.tax_analysis and "final_tax_amount" in result.tax_analysis:
                 #     final_tax = result.tax_analysis["final_tax_amount"]
 
+                # Extract flag_for_review and auto_approve from tax_analysis if available
+                flag_for_review = None
+                auto_approve = None
+                if result.tax_analysis:
+                    flag_for_review = result.tax_analysis.get("flag_for_review")
+                    auto_approve = result.tax_analysis.get("auto_approve")
+
                 match_response = MatchResponse(
                     receipt_id=result.receipt.id,
                     transaction_id=result.transaction.id
@@ -603,6 +634,8 @@ async def match_specific_receipts(request: MatchSpecificRequest, db: db_dependen
                     if result.transaction
                     else 0.0,
                     tax_analysis=result.tax_analysis,
+                    flag_for_review=flag_for_review,
+                    auto_approve=auto_approve,
                 )
                 match_responses.append(match_response)