AI Blog Writer - Implement modular architecture with research, outline, and core services

backend/services/blog_writer/research/content_angle_generator.py

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+"""
+Content Angle Generator - AI-powered content angle discovery.
+
+Generates strategic content angles from research content for blog posts.
+"""
+
+from typing import List
+from loguru import logger
+
+
+class ContentAngleGenerator:
+    """Generates strategic content angles from research content."""
+    
+    def generate(self, content: str, topic: str, industry: str) -> List[str]:
+        """Parse strategic content angles from the research content using AI."""
+        angles_prompt = f"""
+        Analyze the following research content and create strategic content angles for: {topic} in {industry}
+        
+        Research Content:
+        {content[:3000]}
+        
+        Create 7 compelling content angles that:
+        1. Leverage current trends and data from the research
+        2. Address content gaps and opportunities
+        3. Appeal to different audience segments
+        4. Include unique perspectives not covered by competitors
+        5. Incorporate specific statistics, case studies, or expert insights
+        6. Create emotional connection and urgency
+        7. Provide actionable value to readers
+        
+        Each angle should be:
+        - Specific and data-driven
+        - Unique and differentiated
+        - Compelling and click-worthy
+        - Actionable for readers
+        
+        Respond with JSON:
+        {{
+            "content_angles": [
+                "Specific angle 1 with data/trends",
+                "Specific angle 2 with unique perspective",
+                "Specific angle 3 with actionable insights",
+                "Specific angle 4 with case study focus",
+                "Specific angle 5 with future outlook",
+                "Specific angle 6 with problem-solving focus",
+                "Specific angle 7 with industry insights"
+            ]
+        }}
+        """
+        
+        from services.llm_providers.gemini_provider import gemini_structured_json_response
+        
+        angles_schema = {
+            "type": "object",
+            "properties": {
+                "content_angles": {
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "minItems": 5,
+                    "maxItems": 7
+                }
+            },
+            "required": ["content_angles"]
+        }
+        
+        angles_result = gemini_structured_json_response(
+            prompt=angles_prompt,
+            schema=angles_schema,
+            temperature=0.7,
+            max_tokens=800
+        )
+        
+        if isinstance(angles_result, dict) and 'content_angles' in angles_result:
+            return angles_result['content_angles'][:7]
+        else:
+            # Fail gracefully - no fallback data
+            logger.error(f"AI content angles generation failed: {angles_result}")
+            raise ValueError(f"Content angles generation failed: {angles_result.get('error', 'Unknown error')}")
+