CONPORT implementation - Autotest fix

.roo/mcp.json

@@ -0,0 +1,45 @@
+{
+  "mcpServers": {
+    "conport": {
+      "command": "C:\\Users\\theis\\context-portal\\.venv\\Scripts\\python.exe",
+      "args": [
+        "C:\\Users\\theis\\context-portal\\src\\context_portal_mcp\\main.py",
+        "--mode",
+        "stdio",
+        "--workspace_id",
+        "${workspaceFolder}"
+      ],
+      "alwaysAllow": [
+        "get_product_context",
+        "update_product_context",
+        "get_active_context",
+        "update_active_context",
+        "log_decision",
+        "get_decisions",
+        "search_decisions_fts",
+        "log_progress",
+        "get_progress",
+        "update_progress",
+        "delete_progress_by_id",
+        "log_system_pattern",
+        "get_system_patterns",
+        "log_custom_data",
+        "get_custom_data",
+        "delete_custom_data",
+        "search_project_glossary_fts",
+        "export_conport_to_markdown",
+        "import_markdown_to_conport",
+        "link_conport_items",
+        "search_custom_data_value_fts",
+        "get_linked_items",
+        "batch_log_items",
+        "get_item_history",
+        "delete_decision_by_id",
+        "delete_system_pattern_by_id",
+        "get_conport_schema",
+        "get_recent_activity_summary",
+        "semantic_search_conport"
+      ]
+    }
+  }
+}

TestFiles/Test-BoucleChunky001.json

@@ -26,7 +26,7 @@
             },
             {
               "file_path": "BoucleChunky001_DISP_1K_METALNESS.png",
-              "item_type": "MAP_DISP", 
+              "item_type": "EXTRA", 
               "target_asset_name_override": "BoucleChunky001"
             },
             {

autotest.py

@@ -584,63 +584,59 @@ class AutoTester(QObject):
                         logger.error(f"Value mismatch for field '{key}' in {item_type_name} ({current_context}): Actual='{actual_value}', Expected='{expected_value}'.")
                         item_match = False
             
-            return item_match
-    
-        def _compare_list_of_rules(self, actual_list: List[Dict[str, Any]], expected_list: List[Dict[str, Any]], item_type_name: str, parent_context: str, item_key_field: str) -> bool:
-            """
-            Compares a list of actual rule items against a list of expected rule items.
-            Items are matched by a key field (e.g., 'asset_name' or 'file_path').
-            Order independent for matching, but logs count mismatches.
-            """
-            list_match = True # Corrected indentation
-            if not isinstance(actual_list, list) or not isinstance(expected_list, list):
-                logger.error(f"Type mismatch for list of {item_type_name}s in {parent_context}. Expected lists.")
-                return False
-    
-            if len(actual_list) != len(expected_list):
-                logger.error(f"Mismatch in number of {item_type_name}s for {parent_context}. Actual: {len(actual_list)}, Expected: {len(expected_list)}.")
-                list_match = False # Count mismatch is an error
-                # If counts differ, we still try to match what we can to provide more detailed feedback,
-                # but the overall list_match will remain False.
-    
-            actual_items_map = {item.get(item_key_field): item for item in actual_list if item.get(item_key_field) is not None}
-            
-            # Keep track of expected items that found a match to identify missing ones more easily
-            matched_expected_keys = set()
-    
-            for expected_item in expected_list:
-                expected_key_value = expected_item.get(item_key_field)
-                if expected_key_value is None:
-                    logger.error(f"Expected {item_type_name} in {parent_context} is missing key field '{item_key_field}'. Cannot compare this item: {expected_item}")
-                    list_match = False # This specific expected item cannot be processed
-                    continue
-    
-                actual_item = actual_items_map.get(expected_key_value)
-                if actual_item:
-                    matched_expected_keys.add(expected_key_value)
-                    if not self._compare_rule_item(actual_item, expected_item, item_type_name, parent_context):
-                        list_match = False # Individual item comparison failed
-                else:
-                    logger.error(f"Expected {item_type_name} with {item_key_field} '{expected_key_value}' not found in actual items for {parent_context}.")
-                    list_match = False
-            
-            # Identify actual items that were not matched by any expected item
-            # This is useful if len(actual_list) >= len(expected_list) but some actual items are "extra"
-            for actual_key_value, actual_item_data in actual_items_map.items():
-                if actual_key_value not in matched_expected_keys:
-                    logger.debug(f"Extra actual {item_type_name} with {item_key_field} '{actual_key_value}' found in {parent_context} (not in expected list or already matched).")
-                    if len(actual_list) != len(expected_list): # If counts already flagged a mismatch, this is just detail
-                        pass
-                    else: # Counts matched, but content didn't align perfectly by key
-                        list_match = False
-    
-    
-            return list_match # Corrected indentation
-    
-        def _compare_rules(self, actual_rules_data: Dict[str, Any], expected_rules_data: Dict[str, Any]) -> bool: # Corrected structure: moved out
-                    item_match = False
-        
         return item_match
+    
+    def _compare_list_of_rules(self, actual_list: List[Dict[str, Any]], expected_list: List[Dict[str, Any]], item_type_name: str, parent_context: str, item_key_field: str) -> bool:
+        """
+        Compares a list of actual rule items against a list of expected rule items.
+        Items are matched by a key field (e.g., 'asset_name' or 'file_path').
+        Order independent for matching, but logs count mismatches.
+        """
+        list_match = True
+        if not isinstance(actual_list, list) or not isinstance(expected_list, list):
+            logger.error(f"Type mismatch for list of {item_type_name}s in {parent_context}. Expected lists.")
+            return False
+
+        if len(actual_list) != len(expected_list):
+            logger.error(f"Mismatch in number of {item_type_name}s for {parent_context}. Actual: {len(actual_list)}, Expected: {len(expected_list)}.")
+            list_match = False # Count mismatch is an error
+            # If counts differ, we still try to match what we can to provide more detailed feedback,
+            # but the overall list_match will remain False.
+
+        actual_items_map = {item.get(item_key_field): item for item in actual_list if item.get(item_key_field) is not None}
+        
+        # Keep track of expected items that found a match to identify missing ones more easily
+        matched_expected_keys = set()
+
+        for expected_item in expected_list:
+            expected_key_value = expected_item.get(item_key_field)
+            if expected_key_value is None:
+                logger.error(f"Expected {item_type_name} in {parent_context} is missing key field '{item_key_field}'. Cannot compare this item: {expected_item}")
+                list_match = False # This specific expected item cannot be processed
+                continue
+
+            actual_item = actual_items_map.get(expected_key_value)
+            if actual_item:
+                matched_expected_keys.add(expected_key_value)
+                if not self._compare_rule_item(actual_item, expected_item, item_type_name, parent_context):
+                    list_match = False # Individual item comparison failed
+            else:
+                logger.error(f"Expected {item_type_name} with {item_key_field} '{expected_key_value}' not found in actual items for {parent_context}.")
+                list_match = False
+        
+        # Identify actual items that were not matched by any expected item
+        # This is useful if len(actual_list) >= len(expected_list) but some actual items are "extra"
+        for actual_key_value, actual_item_data in actual_items_map.items():
+            if actual_key_value not in matched_expected_keys:
+                logger.debug(f"Extra actual {item_type_name} with {item_key_field} '{actual_key_value}' found in {parent_context} (not in expected list or already matched).")
+                if len(actual_list) != len(expected_list): # If counts already flagged a mismatch, this is just detail
+                    pass
+                else: # Counts matched, but content didn't align perfectly by key
+                    list_match = False
+
+
+        return list_match
+    
 
     def _compare_rules(self, actual_rules_data: Dict[str, Any], expected_rules_data: Dict[str, Any]) -> bool:
         """

projectBrief.md

@@ -0,0 +1,44 @@
+# Project Brief: Asset Processor Tool
+
+## 1. Main Goal & Purpose
+
+The primary goal of the Asset Processor Tool is to provide **CG artists and 3D content teams with a friendly, fast, and flexible interface to process and organize 3D asset source files into a standardized library format.** It automates repetitive and complex tasks involved in preparing assets from various suppliers for use in production pipelines.
+
+## 2. Key Features & Components
+
+*   **Automated Asset Processing:** Ingests 3D asset source files (texture sets, models, etc.) from `.zip`, `.rar`, `.7z` archives, or folders.
+*   **Preset-Driven Workflow:** Utilizes configurable JSON presets to interpret different asset sources (e.g., from various online vendors or internal standards), defining rules for file classification and processing.
+*   **Comprehensive File Operations:**
+    *   **Classification:** Automatically identifies map types (Color, Normal, Roughness, etc.), models, and other file categories based on preset rules.
+    *   **Image Processing:** Performs tasks like image resizing (to standard resolutions like 1K, 2K, 4K, avoiding upscaling), glossiness-to-roughness conversion, normal map green channel inversion (OpenGL/DirectX handling), alpha channel extraction, bit-depth adjustments, and low-resolution fallback generation for small source images.
+    *   **Channel Merging:** Combines channels from different source maps into packed textures (e.g., Normal + Roughness + Metallic into a single NRMRGH map).
+*   **Metadata Generation:** Creates a detailed `metadata.json` file for each processed asset, containing information about maps, categories, processing settings, and more, for downstream tool integration.
+*   **Flexible Output Organization:** Generates a clean, structured output directory based on user-configurable naming patterns and tokens.
+*   **Multiple User Interfaces:**
+    *   **Graphical User Interface (GUI):** The primary interface, designed to be user-friendly, offering drag-and-drop functionality, an integrated preset editor, a live preview table for rule validation and overrides, and clear processing controls.
+    *   **Directory Monitor:** An automated script that watches a specified folder for new asset archives and processes them based on preset names embedded in the archive filename.
+    *   **Command-Line Interface (CLI):** Intended for batch processing and scripting (currently with limited core functionality).
+*   **Optional Blender Integration:** Can automatically run Blender scripts post-processing to create PBR node groups and materials in specified `.blend` files, linking to the newly processed textures.
+*   **Hierarchical Rule System:** Allows for dynamic, granular overrides of preset configurations at the source, asset, or individual file level via the GUI.
+*   **Experimental LLM Prediction:** Includes an option to use a Large Language Model for file interpretation and rule prediction.
+
+## 3. Target Audience
+
+*   **CG Artists:** Individual artists looking for an efficient way to manage and prepare their personal or downloaded asset libraries.
+*   **3D Content Creation Teams:** Studios or groups needing a standardized pipeline for processing and organizing assets from multiple sources.
+*   **Technical Artists/Pipeline Developers:** Who may extend or integrate the tool into broader production workflows.
+
+## 4. Overall Architectural Style & Key Technologies
+
+*   **Core Language:** Python
+*   **GUI Framework:** PySide6
+*   **Configuration:** Primarily JSON-based (application settings, user overrides, type definitions, supplier settings, presets, LLM settings).
+*   **Processing Architecture:** A modular, staged processing pipeline orchestrated by a central engine. Each stage performs a discrete task on an `AssetProcessingContext` object.
+*   **Key Libraries:** OpenCV (image processing), NumPy (numerical operations), py7zr/rarfile (archive handling), watchdog (directory monitoring).
+*   **Design Principles:** Modularity, configurability, and user-friendliness (especially for the GUI).
+
+## 5. Foundational Information
+
+*   The tool aims to significantly reduce manual effort and ensure consistency in asset preparation.
+*   It is designed to be adaptable to various asset sources and pipeline requirements through its extensive configuration options and preset system.
+*   The output `metadata.json` is key for enabling further automation and integration with other tools or digital content creation (DCC) applications.