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/keyword_analyzer.py
+++ b/backend/services/blog_writer/research/keyword_analyzer.py
@@ -0,0 +1,78 @@
+"""
+Keyword Analyzer - AI-powered keyword analysis for research content.
+
+Extracts and analyzes keywords from research content using structured AI responses.
+"""
+
+from typing import Dict, Any, List
+from loguru import logger
+
+
+class KeywordAnalyzer:
+    """Analyzes keywords from research content using AI-powered extraction."""
+    
+    def analyze(self, content: str, original_keywords: List[str]) -> Dict[str, Any]:
+        """Parse comprehensive keyword analysis from the research content using AI."""
+        # Use AI to extract and analyze keywords from the rich research content
+        keyword_prompt = f"""
+        Analyze the following research content and extract comprehensive keyword insights for: {', '.join(original_keywords)}
+        
+        Research Content:
+        {content[:3000]}  # Limit to avoid token limits
+        
+        Extract and analyze:
+        1. Primary keywords (main topic terms)
+        2. Secondary keywords (related terms, synonyms)
+        3. Long-tail opportunities (specific phrases people search for)
+        4. Search intent (informational, commercial, navigational, transactional)
+        5. Keyword difficulty assessment (1-10 scale)
+        6. Content gaps (what competitors are missing)
+        7. Semantic keywords (related concepts)
+        8. Trending terms (emerging keywords)
+        
+        Respond with JSON:
+        {{
+            "primary": ["keyword1", "keyword2"],
+            "secondary": ["related1", "related2"],
+            "long_tail": ["specific phrase 1", "specific phrase 2"],
+            "search_intent": "informational|commercial|navigational|transactional",
+            "difficulty": 7,
+            "content_gaps": ["gap1", "gap2"],
+            "semantic_keywords": ["concept1", "concept2"],
+            "trending_terms": ["trend1", "trend2"],
+            "analysis_insights": "Brief analysis of keyword landscape"
+        }}
+        """
+        
+        from services.llm_providers.gemini_provider import gemini_structured_json_response
+        
+        keyword_schema = {
+            "type": "object",
+            "properties": {
+                "primary": {"type": "array", "items": {"type": "string"}},
+                "secondary": {"type": "array", "items": {"type": "string"}},
+                "long_tail": {"type": "array", "items": {"type": "string"}},
+                "search_intent": {"type": "string"},
+                "difficulty": {"type": "integer"},
+                "content_gaps": {"type": "array", "items": {"type": "string"}},
+                "semantic_keywords": {"type": "array", "items": {"type": "string"}},
+                "trending_terms": {"type": "array", "items": {"type": "string"}},
+                "analysis_insights": {"type": "string"}
+            },
+            "required": ["primary", "secondary", "long_tail", "search_intent", "difficulty", "content_gaps", "semantic_keywords", "trending_terms", "analysis_insights"]
+        }
+        
+        keyword_analysis = gemini_structured_json_response(
+            prompt=keyword_prompt,
+            schema=keyword_schema,
+            temperature=0.3,
+            max_tokens=1000
+        )
+        
+        if isinstance(keyword_analysis, dict) and 'error' not in keyword_analysis:
+            return keyword_analysis
+        else:
+            # Fail gracefully - no fallback data
+            logger.error(f"AI keyword analysis failed: {keyword_analysis}")
+            raise ValueError(f"Keyword analysis failed: {keyword_analysis.get('error', 'Unknown error')}")
+