Implement Stage 3: Enhance content generation and metadata features

- Updated AI prompts to include metadata context, cluster roles, and product attributes for improved content generation.
- Enhanced GenerateContentFunction to incorporate taxonomy and keyword objects for richer context.
- Introduced new metadata fields in frontend components for better content organization and filtering.
- Added cluster match, taxonomy match, and relevance score to LinkResults for improved link management.
- Implemented metadata completeness scoring and recommended actions in AnalysisPreview for better content optimization.
- Updated API services to support new metadata structures and site progress tracking.

backend/igny8_core/ai/functions/generate_content.py

@@ -63,9 +63,10 @@ class GenerateContentFunction(BaseAIFunction):
             queryset = queryset.filter(account=account)
         
         # Preload all relationships to avoid N+1 queries
+        # Stage 3: Include taxonomy and keyword_objects for metadata
         tasks = list(queryset.select_related(
-            'account', 'site', 'sector', 'cluster', 'idea'
-        ))
+            'account', 'site', 'sector', 'cluster', 'idea', 'taxonomy'
+        ).prefetch_related('keyword_objects'))
         
         if not tasks:
             raise ValueError("No tasks found")
@@ -125,12 +126,54 @@ class GenerateContentFunction(BaseAIFunction):
                 cluster_data += f"Description: {task.cluster.description}\n"
             cluster_data += f"Status: {task.cluster.status or 'active'}\n"
         
+        # Stage 3: Build cluster role context
+        cluster_role_data = ''
+        if hasattr(task, 'cluster_role') and task.cluster_role:
+            role_descriptions = {
+                'hub': 'Hub Page - Main authoritative resource for this topic cluster. Should be comprehensive, overview-focused, and link to supporting content.',
+                'supporting': 'Supporting Page - Detailed content that supports the hub page. Focus on specific aspects, use cases, or subtopics.',
+                'attribute': 'Attribute Page - Content focused on specific attributes, features, or specifications. Include detailed comparisons and specifications.',
+            }
+            role_desc = role_descriptions.get(task.cluster_role, f'Role: {task.cluster_role}')
+            cluster_role_data = f"Cluster Role: {role_desc}\n"
+        
+        # Stage 3: Build taxonomy context
+        taxonomy_data = ''
+        if hasattr(task, 'taxonomy') and task.taxonomy:
+            taxonomy_data = f"Taxonomy: {task.taxonomy.name or ''}\n"
+            if task.taxonomy.taxonomy_type:
+                taxonomy_data += f"Taxonomy Type: {task.taxonomy.get_taxonomy_type_display() or task.taxonomy.taxonomy_type}\n"
+            if task.taxonomy.description:
+                taxonomy_data += f"Description: {task.taxonomy.description}\n"
+        
+        # Stage 3: Build attributes context from keywords
+        attributes_data = ''
+        if hasattr(task, 'keyword_objects') and task.keyword_objects.exists():
+            attribute_list = []
+            for keyword in task.keyword_objects.all():
+                if hasattr(keyword, 'attribute_values') and keyword.attribute_values:
+                    if isinstance(keyword.attribute_values, dict):
+                        for attr_name, attr_value in keyword.attribute_values.items():
+                            attribute_list.append(f"{attr_name}: {attr_value}")
+                    elif isinstance(keyword.attribute_values, list):
+                        for attr_item in keyword.attribute_values:
+                            if isinstance(attr_item, dict):
+                                for attr_name, attr_value in attr_item.items():
+                                    attribute_list.append(f"{attr_name}: {attr_value}")
+                            else:
+                                attribute_list.append(str(attr_item))
+            
+            if attribute_list:
+                attributes_data = "Product/Service Attributes:\n"
+                attributes_data += "\n".join(f"- {attr}" for attr in attribute_list) + "\n"
+        
         # Build keywords string
         keywords_data = task.keywords or ''
         if not keywords_data and task.idea:
             keywords_data = task.idea.target_keywords or ''
         
         # Get prompt from registry with context
+        # Stage 3: Include cluster_role, taxonomy, and attributes in context
         prompt = PromptRegistry.get_prompt(
             function_name='generate_content',
             account=account,
@@ -138,6 +181,9 @@ class GenerateContentFunction(BaseAIFunction):
             context={
                 'IDEA': idea_data,
                 'CLUSTER': cluster_data,
+                'CLUSTER_ROLE': cluster_role_data,
+                'TAXONOMY': taxonomy_data,
+                'ATTRIBUTES': attributes_data,
                 'KEYWORDS': keywords_data,
             }
         )