story writer backend migration complete, Blog writer SEO and story writer backend migration complete, Blog writer SEO and story writer frontend migration complete

backend/services/blog_writer/content/introduction_generator.py

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+"""
+Introduction Generator - Generates varied blog introductions based on content and research.
+
+Generates 3 different introduction options for the user to choose from.
+"""
+
+from typing import Dict, Any, List
+from loguru import logger
+
+from models.blog_models import BlogResearchResponse, BlogOutlineSection
+
+
+class IntroductionGenerator:
+    """Generates blog introductions using research and content data."""
+    
+    def __init__(self):
+        """Initialize the introduction generator."""
+        pass
+    
+    def build_introduction_prompt(
+        self,
+        blog_title: str,
+        research: BlogResearchResponse,
+        outline: List[BlogOutlineSection],
+        sections_content: Dict[str, str],
+        primary_keywords: List[str],
+        search_intent: str
+    ) -> str:
+        """Build a prompt for generating blog introductions."""
+        
+        # Extract key research insights
+        keyword_analysis = research.keyword_analysis or {}
+        content_angles = research.suggested_angles or []
+        
+        # Get a summary of the first few sections for context
+        section_summaries = []
+        for i, section in enumerate(outline[:3], 1):
+            section_id = section.id
+            content = sections_content.get(section_id, '')
+            if content:
+                # Take first 200 chars as summary
+                summary = content[:200] + '...' if len(content) > 200 else content
+                section_summaries.append(f"{i}. {section.heading}: {summary}")
+        
+        sections_text = '\n'.join(section_summaries) if section_summaries else "Content sections are being generated."
+        
+        primary_kw_text = ', '.join(primary_keywords) if primary_keywords else "the topic"
+        content_angle_text = ', '.join(content_angles[:3]) if content_angles else "General insights"
+        
+        return f"""Generate exactly 3 varied blog introductions for the following blog post.
+
+BLOG TITLE: {blog_title}
+
+PRIMARY KEYWORDS: {primary_kw_text}
+SEARCH INTENT: {search_intent}
+CONTENT ANGLES: {content_angle_text}
+
+BLOG CONTENT SUMMARY:
+{sections_text}
+
+REQUIREMENTS FOR EACH INTRODUCTION:
+- 80-120 words in length
+- Hook the reader immediately with a compelling opening
+- Clearly state the value proposition and what readers will learn
+- Include the primary keyword naturally within the first 2 sentences
+- Each introduction should have a different angle/approach:
+  1. First: Problem-focused (highlight the challenge readers face)
+  2. Second: Benefit-focused (emphasize the value and outcomes)
+  3. Third: Story/statistic-focused (use a compelling fact or narrative hook)
+- Maintain a professional yet engaging tone
+- Avoid generic phrases - be specific and benefit-driven
+
+Return ONLY a JSON array of exactly 3 introductions:
+[
+  "First introduction (80-120 words, problem-focused)",
+  "Second introduction (80-120 words, benefit-focused)",
+  "Third introduction (80-120 words, story/statistic-focused)"
+]"""
+    
+    def get_introduction_schema(self) -> Dict[str, Any]:
+        """Get the JSON schema for introduction generation."""
+        return {
+            "type": "array",
+            "items": {
+                "type": "string",
+                "minLength": 80,
+                "maxLength": 150
+            },
+            "minItems": 3,
+            "maxItems": 3
+        }
+    
+    async def generate_introductions(
+        self,
+        blog_title: str,
+        research: BlogResearchResponse,
+        outline: List[BlogOutlineSection],
+        sections_content: Dict[str, str],
+        primary_keywords: List[str],
+        search_intent: str,
+        user_id: str
+    ) -> List[str]:
+        """Generate 3 varied blog introductions.
+        
+        Args:
+            blog_title: The blog post title
+            research: Research data with keywords and insights
+            outline: Blog outline sections
+            sections_content: Dictionary mapping section IDs to their content
+            primary_keywords: Primary keywords for the blog
+            search_intent: Search intent (informational, commercial, etc.)
+            user_id: User ID for API calls
+            
+        Returns:
+            List of 3 introduction options
+        """
+        from services.llm_providers.main_text_generation import llm_text_gen
+        
+        if not user_id:
+            raise ValueError("user_id is required for introduction generation")
+        
+        # Build prompt
+        prompt = self.build_introduction_prompt(
+            blog_title=blog_title,
+            research=research,
+            outline=outline,
+            sections_content=sections_content,
+            primary_keywords=primary_keywords,
+            search_intent=search_intent
+        )
+        
+        # Get schema
+        schema = self.get_introduction_schema()
+        
+        logger.info(f"Generating blog introductions for user {user_id}")
+        
+        try:
+            # Generate introductions using structured JSON response
+            result = llm_text_gen(
+                prompt=prompt,
+                json_struct=schema,
+                system_prompt="You are an expert content writer specializing in creating compelling blog introductions that hook readers and clearly communicate value.",
+                user_id=user_id
+            )
+            
+            # Handle response - could be array directly or wrapped in dict
+            if isinstance(result, list):
+                introductions = result
+            elif isinstance(result, dict):
+                # Try common keys
+                introductions = result.get('introductions', result.get('options', result.get('intros', [])))
+                if not introductions and isinstance(result.get('response'), list):
+                    introductions = result['response']
+            else:
+                logger.warning(f"Unexpected introduction generation result type: {type(result)}")
+                introductions = []
+            
+            # Validate and clean introductions
+            cleaned_introductions = []
+            for intro in introductions:
+                if isinstance(intro, str) and len(intro.strip()) >= 50:  # Minimum reasonable length
+                    cleaned = intro.strip()
+                    # Ensure it's within reasonable bounds
+                    if len(cleaned) <= 200:  # Allow slight overflow for quality
+                        cleaned_introductions.append(cleaned)
+            
+            # Ensure we have exactly 3 introductions
+            if len(cleaned_introductions) < 3:
+                logger.warning(f"Generated only {len(cleaned_introductions)} introductions, expected 3")
+                # Pad with placeholder if needed
+                while len(cleaned_introductions) < 3:
+                    cleaned_introductions.append(f"{blog_title} - A comprehensive guide covering essential insights and practical strategies.")
+            
+            # Return exactly 3 introductions
+            return cleaned_introductions[:3]
+            
+        except Exception as e:
+            logger.error(f"Failed to generate introductions: {e}")
+            # Fallback: generate simple introductions
+            fallback_introductions = [
+                f"In this comprehensive guide, we'll explore {primary_keywords[0] if primary_keywords else 'essential insights'} and provide actionable strategies.",
+                f"Discover everything you need to know about {primary_keywords[0] if primary_keywords else 'this topic'} and how it can transform your approach.",
+                f"Whether you're new to {primary_keywords[0] if primary_keywords else 'this topic'} or looking to deepen your understanding, this guide has you covered."
+            ]
+            return fallback_introductions
+