ALwrity AI Blog Writer - Added Google Grounding UI Implementation

backend/services/blog_writer/outline/outline_optimizer.py

@@ -17,61 +17,64 @@ class OutlineOptimizer:
         """Optimize entire outline for better flow, SEO, and engagement."""
         outline_text = "\n".join([f"{i+1}. {s.heading}" for i, s in enumerate(outline)])
         
-        optimization_prompt = f"""
-        Optimize this blog outline for better flow, engagement, and SEO:
-        
-        Current Outline:
-        {outline_text}
-        
-        Optimization Focus: {focus}
-        
-        Optimization Goals:
-        - Improve narrative flow and logical progression
-        - Enhance SEO with better keyword distribution
-        - Increase engagement with compelling headings
-        - Ensure comprehensive coverage of the topic
-        - Optimize for featured snippets and voice search
-        
-        Respond with JSON array of optimized sections:
-        [
-            {{
-                "heading": "Optimized heading",
-                "subheadings": ["subheading 1", "subheading 2"],
-                "key_points": ["point 1", "point 2"],
-                "target_words": 300,
-                "keywords": ["keyword1", "keyword2"]
-            }}
-        ]
-        """
+        optimization_prompt = f"""Optimize this blog outline for better flow, engagement, and SEO:
+
+Current Outline:
+{outline_text}
+
+Optimization Focus: {focus}
+
+Goals: Improve narrative flow, enhance SEO, increase engagement, ensure comprehensive coverage.
+
+Return JSON format:
+{{
+    "outline": [
+        {{
+            "heading": "Optimized heading",
+            "subheadings": ["subheading 1", "subheading 2"],
+            "key_points": ["point 1", "point 2"],
+            "target_words": 300,
+            "keywords": ["keyword1", "keyword2"]
+        }}
+    ]
+}}"""
         
         try:
             from services.llm_providers.gemini_provider import gemini_structured_json_response
             
             optimization_schema = {
-                "type": "array",
-                "items": {
-                    "type": "object",
-                    "properties": {
-                        "heading": {"type": "string"},
-                        "subheadings": {"type": "array", "items": {"type": "string"}},
-                        "key_points": {"type": "array", "items": {"type": "string"}},
-                        "target_words": {"type": "integer"},
-                        "keywords": {"type": "array", "items": {"type": "string"}}
-                    },
-                    "required": ["heading", "subheadings", "key_points", "target_words", "keywords"]
-                }
+                "type": "object",
+                "properties": {
+                    "outline": {
+                        "type": "array",
+                        "items": {
+                            "type": "object",
+                            "properties": {
+                                "heading": {"type": "string"},
+                                "subheadings": {"type": "array", "items": {"type": "string"}},
+                                "key_points": {"type": "array", "items": {"type": "string"}},
+                                "target_words": {"type": "integer"},
+                                "keywords": {"type": "array", "items": {"type": "string"}}
+                            },
+                            "required": ["heading", "subheadings", "key_points", "target_words", "keywords"]
+                        }
+                    }
+                },
+                "required": ["outline"],
+                "propertyOrdering": ["outline"]
             }
             
             optimized_data = gemini_structured_json_response(
                 prompt=optimization_prompt,
                 schema=optimization_schema,
                 temperature=0.3,
-                max_tokens=2000
+                max_tokens=6000  # Match main outline generator
             )
             
-            if isinstance(optimized_data, list):
+            # Handle the new schema format with "outline" wrapper
+            if isinstance(optimized_data, dict) and 'outline' in optimized_data:
                 optimized_sections = []
-                for i, section_data in enumerate(optimized_data):
+                for i, section_data in enumerate(optimized_data['outline']):
                     section = BlogOutlineSection(
                         id=f"s{i+1}",
                         heading=section_data.get('heading', f'Section {i+1}'),
@@ -82,9 +85,14 @@ class OutlineOptimizer:
                         keywords=section_data.get('keywords', [])
                     )
                     optimized_sections.append(section)
+                logger.info(f"✅ Outline optimization completed: {len(optimized_sections)} sections optimized")
                 return optimized_sections
+            else:
+                logger.warning(f"Invalid optimization response format: {type(optimized_data)}")
+                
         except Exception as e:
             logger.warning(f"AI outline optimization failed: {e}")
+            logger.info("Returning original outline without optimization")
         
         return outline