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

2025-09-12 16:53:16 +05:30
parent c0a366269d
commit 2ae0c4a8b9
29 changed files with 3210 additions and 907 deletions
--- a/backend/services/blog_writer/research/competitor_analyzer.py
+++ b/backend/services/blog_writer/research/competitor_analyzer.py
@@ -0,0 +1,71 @@
+"""
+Competitor Analyzer - AI-powered competitor analysis for research content.
+
+Extracts competitor insights and market intelligence from research content.
+"""
+
+from typing import Dict, Any
+from loguru import logger
+
+
+class CompetitorAnalyzer:
+    """Analyzes competitors and market intelligence from research content."""
+    
+    def analyze(self, content: str) -> Dict[str, Any]:
+        """Parse comprehensive competitor analysis from the research content using AI."""
+        competitor_prompt = f"""
+        Analyze the following research content and extract competitor insights:
+        
+        Research Content:
+        {content[:3000]}
+        
+        Extract and analyze:
+        1. Top competitors mentioned (companies, brands, platforms)
+        2. Content gaps (what competitors are missing)
+        3. Market opportunities (untapped areas)
+        4. Competitive advantages (what makes content unique)
+        5. Market positioning insights
+        6. Industry leaders and their strategies
+        
+        Respond with JSON:
+        {{
+            "top_competitors": ["competitor1", "competitor2"],
+            "content_gaps": ["gap1", "gap2"],
+            "opportunities": ["opportunity1", "opportunity2"],
+            "competitive_advantages": ["advantage1", "advantage2"],
+            "market_positioning": "positioning insights",
+            "industry_leaders": ["leader1", "leader2"],
+            "analysis_notes": "Comprehensive competitor analysis summary"
+        }}
+        """
+        
+        from services.llm_providers.gemini_provider import gemini_structured_json_response
+        
+        competitor_schema = {
+            "type": "object",
+            "properties": {
+                "top_competitors": {"type": "array", "items": {"type": "string"}},
+                "content_gaps": {"type": "array", "items": {"type": "string"}},
+                "opportunities": {"type": "array", "items": {"type": "string"}},
+                "competitive_advantages": {"type": "array", "items": {"type": "string"}},
+                "market_positioning": {"type": "string"},
+                "industry_leaders": {"type": "array", "items": {"type": "string"}},
+                "analysis_notes": {"type": "string"}
+            },
+            "required": ["top_competitors", "content_gaps", "opportunities", "competitive_advantages", "market_positioning", "industry_leaders", "analysis_notes"]
+        }
+        
+        competitor_analysis = gemini_structured_json_response(
+            prompt=competitor_prompt,
+            schema=competitor_schema,
+            temperature=0.3,
+            max_tokens=1000
+        )
+        
+        if isinstance(competitor_analysis, dict) and 'error' not in competitor_analysis:
+            return competitor_analysis
+        else:
+            # Fail gracefully - no fallback data
+            logger.error(f"AI competitor analysis failed: {competitor_analysis}")
+            raise ValueError(f"Competitor analysis failed: {competitor_analysis.get('error', 'Unknown error')}")
+