679 lines
24 KiB
Markdown
679 lines
24 KiB
Markdown
# 🤖 AI Integration Plan for Content Planning System
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## 📋 Current Status Analysis
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### ❌ **Issues Identified**
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1. **Hardcoded Values**: All AI services currently use simulated data instead of real AI calls
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2. **Missing AI Integration**: No actual LLM calls in FastAPI services
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3. **Unused AI Infrastructure**: Gemini provider exists but not integrated
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4. **Missing AI Prompts**: Advanced prompts from legacy system not implemented
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### ✅ **Available AI Infrastructure**
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1. **Gemini Provider**: `backend/llm_providers/gemini_provider.py` ✅
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2. **Main Text Generation**: `backend/llm_providers/main_text_generation.py` ✅
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3. **API Key Management**: `backend/services/api_key_manager.py` ✅
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4. **AI Prompts**: Available in `CONTENT_GAP_ANALYSIS_DEEP_DIVE.md` ✅
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## 🎯 **AI Integration Strategy**
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### **Phase 1: Core AI Integration (Week 1)**
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#### 1.1 **AI Engine Service Enhancement**
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**File**: `backend/services/content_gap_analyzer/ai_engine_service.py`
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**Current Issues**:
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- All methods use hardcoded responses
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- No actual AI calls implemented
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- Missing integration with Gemini provider
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**Implementation Plan**:
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```python
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# Add imports
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from backend.llm_providers.main_text_generation import llm_text_gen
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from backend.llm_providers.gemini_provider import gemini_structured_json_response
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# Replace hardcoded responses with AI calls
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async def analyze_content_gaps(self, analysis_summary: Dict[str, Any]) -> Dict[str, Any]:
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"""Analyze content gaps using AI insights."""
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try:
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prompt = f"""
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As an expert SEO content strategist, analyze this comprehensive content gap analysis data and provide actionable insights:
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TARGET ANALYSIS:
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- Website: {analysis_summary.get('target_url', 'N/A')}
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- Industry: {analysis_summary.get('industry', 'N/A')}
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- SERP Opportunities: {analysis_summary.get('serp_opportunities', 0)} keywords not ranking
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- Keyword Expansion: {analysis_summary.get('expanded_keywords_count', 0)} additional keywords identified
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- Competitors Analyzed: {analysis_summary.get('competitors_analyzed', 0)} websites
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DOMINANT CONTENT THEMES:
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{json.dumps(analysis_summary.get('dominant_themes', {}), indent=2)}
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PROVIDE:
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1. Strategic Content Gap Analysis
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2. Priority Content Recommendations (top 5)
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3. Keyword Strategy Insights
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4. Competitive Positioning Advice
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5. Content Format Recommendations
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6. Technical SEO Opportunities
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7. Implementation Timeline (30/60/90 days)
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Format as JSON with clear, actionable recommendations.
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"""
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# Use structured JSON response for better parsing
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response = gemini_structured_json_response(
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prompt=prompt,
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schema={
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"type": "object",
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"properties": {
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"strategic_insights": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"type": {"type": "string"},
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"insight": {"type": "string"},
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"confidence": {"type": "number"},
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"priority": {"type": "string"},
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"estimated_impact": {"type": "string"}
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}
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}
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},
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"content_recommendations": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"type": {"type": "string"},
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"recommendation": {"type": "string"},
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"priority": {"type": "string"},
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"estimated_traffic": {"type": "string"},
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"implementation_time": {"type": "string"}
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}
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}
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},
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"performance_predictions": {
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"type": "object",
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"properties": {
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"estimated_traffic_increase": {"type": "string"},
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"estimated_ranking_improvement": {"type": "string"},
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"estimated_engagement_increase": {"type": "string"},
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"estimated_conversion_increase": {"type": "string"},
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"confidence_level": {"type": "string"}
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}
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}
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}
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}
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)
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return json.loads(response)
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except Exception as e:
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logger.error(f"Error in AI content gap analysis: {str(e)}")
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return {}
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```
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#### 1.2 **Keyword Researcher AI Integration**
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**File**: `backend/services/content_gap_analyzer/keyword_researcher.py`
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**Implementation Plan**:
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```python
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# Add AI integration for keyword analysis
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async def _analyze_keyword_trends(self, industry: str, target_keywords: Optional[List[str]] = None) -> Dict[str, Any]:
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"""Analyze keyword trends using AI."""
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try:
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prompt = f"""
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Analyze keyword opportunities for {industry} industry:
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Target Keywords: {target_keywords or []}
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Provide comprehensive keyword analysis including:
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1. Search volume estimates
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2. Competition levels
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3. Trend analysis
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4. Opportunity scoring
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5. Content format recommendations
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Format as structured JSON with detailed analysis.
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"""
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response = gemini_structured_json_response(
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prompt=prompt,
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schema={
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"type": "object",
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"properties": {
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"trends": {
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"type": "object",
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"additionalProperties": {
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"type": "object",
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"properties": {
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"search_volume": {"type": "number"},
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"difficulty": {"type": "number"},
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"trend": {"type": "string"},
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"competition": {"type": "string"},
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"intent": {"type": "string"},
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"cpc": {"type": "number"}
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}
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}
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},
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"summary": {
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"type": "object",
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"properties": {
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"total_keywords": {"type": "number"},
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"high_volume_keywords": {"type": "number"},
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"low_competition_keywords": {"type": "number"},
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"trending_keywords": {"type": "number"}
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}
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}
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}
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}
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)
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return json.loads(response)
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except Exception as e:
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logger.error(f"Error analyzing keyword trends: {str(e)}")
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return {}
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```
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#### 1.3 **Competitor Analyzer AI Integration**
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**File**: `backend/services/content_gap_analyzer/competitor_analyzer.py`
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**Implementation Plan**:
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```python
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# Add AI integration for competitor analysis
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async def _evaluate_market_position(self, competitors: List[Dict[str, Any]], industry: str) -> Dict[str, Any]:
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"""Evaluate market position using AI."""
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try:
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prompt = f"""
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Analyze the market position of competitors in the {industry} industry:
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Competitor Analyses:
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{json.dumps(competitors, indent=2)}
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Provide:
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1. Market position analysis
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2. Content gaps
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3. Competitive advantages
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4. Strategic positioning recommendations
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Format as structured JSON with detailed analysis.
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"""
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response = gemini_structured_json_response(
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prompt=prompt,
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schema={
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"type": "object",
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"properties": {
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"market_leader": {"type": "string"},
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"content_leader": {"type": "string"},
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"quality_leader": {"type": "string"},
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"market_gaps": {
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"type": "array",
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"items": {"type": "string"}
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},
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"opportunities": {
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"type": "array",
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"items": {"type": "string"}
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},
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"competitive_advantages": {
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"type": "array",
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"items": {"type": "string"}
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},
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"strategic_recommendations": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"type": {"type": "string"},
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"recommendation": {"type": "string"},
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"priority": {"type": "string"},
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"estimated_impact": {"type": "string"}
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}
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}
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}
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}
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}
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)
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return json.loads(response)
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except Exception as e:
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logger.error(f"Error evaluating market position: {str(e)}")
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return {}
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```
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### **Phase 2: Advanced AI Features (Week 2)**
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#### 2.1 **Content Performance Prediction**
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```python
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async def predict_content_performance(self, content_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Predict content performance using AI."""
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try:
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prompt = f"""
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Predict content performance based on the following data:
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Content Data: {json.dumps(content_data, indent=2)}
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Provide detailed performance predictions including:
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1. Traffic predictions
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2. Engagement predictions
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3. Ranking predictions
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4. Conversion predictions
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5. Risk factors
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6. Success factors
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Format as structured JSON with confidence levels.
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"""
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response = gemini_structured_json_response(
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prompt=prompt,
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schema={
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"type": "object",
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"properties": {
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"traffic_predictions": {
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"type": "object",
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"properties": {
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"estimated_monthly_traffic": {"type": "string"},
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"traffic_growth_rate": {"type": "string"},
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"peak_traffic_month": {"type": "string"},
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"confidence_level": {"type": "string"}
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}
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},
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"engagement_predictions": {
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"type": "object",
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"properties": {
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"estimated_time_on_page": {"type": "string"},
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"estimated_bounce_rate": {"type": "string"},
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"estimated_social_shares": {"type": "string"},
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"estimated_comments": {"type": "string"},
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"confidence_level": {"type": "string"}
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}
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},
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"ranking_predictions": {
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"type": "object",
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"properties": {
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"estimated_ranking_position": {"type": "string"},
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"estimated_ranking_time": {"type": "string"},
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"ranking_confidence": {"type": "string"},
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"competition_level": {"type": "string"}
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}
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},
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"conversion_predictions": {
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"type": "object",
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"properties": {
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"estimated_conversion_rate": {"type": "string"},
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"estimated_lead_generation": {"type": "string"},
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"estimated_revenue_impact": {"type": "string"},
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"confidence_level": {"type": "string"}
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}
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},
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"risk_factors": {
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"type": "array",
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"items": {"type": "string"}
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},
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"success_factors": {
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"type": "array",
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"items": {"type": "string"}
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}
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}
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}
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)
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return json.loads(response)
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except Exception as e:
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logger.error(f"Error in AI performance prediction: {str(e)}")
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return {}
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```
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#### 2.2 **Strategic Intelligence Generation**
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```python
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async def generate_strategic_insights(self, analysis_data: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""Generate strategic insights using AI."""
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try:
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prompt = f"""
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Generate strategic insights based on the following analysis data:
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Analysis Data: {json.dumps(analysis_data, indent=2)}
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Provide strategic insights covering:
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1. Content strategy recommendations
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2. Competitive positioning advice
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3. Content optimization suggestions
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4. Innovation opportunities
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5. Risk mitigation strategies
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Format as structured JSON with detailed insights.
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"""
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response = gemini_structured_json_response(
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prompt=prompt,
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schema={
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"type": "object",
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"properties": {
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"strategic_insights": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"type": {"type": "string"},
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"insight": {"type": "string"},
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"reasoning": {"type": "string"},
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"priority": {"type": "string"},
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"estimated_impact": {"type": "string"},
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"implementation_time": {"type": "string"}
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}
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}
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}
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}
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}
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)
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result = json.loads(response)
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return result.get('strategic_insights', [])
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except Exception as e:
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logger.error(f"Error generating AI strategic insights: {str(e)}")
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return []
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```
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### **Phase 3: AI Prompt Optimization (Week 3)**
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#### 3.1 **Enhanced AI Prompts**
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Based on the deep dive analysis, implement these advanced prompts:
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**Content Gap Analysis Prompt**:
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```python
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CONTENT_GAP_ANALYSIS_PROMPT = """
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As an expert SEO content strategist, analyze this comprehensive content gap analysis data and provide actionable insights:
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TARGET ANALYSIS:
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- Website: {target_url}
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- Industry: {industry}
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- SERP Opportunities: {serp_opportunities} keywords not ranking
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- Keyword Expansion: {expanded_keywords_count} additional keywords identified
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- Competitors Analyzed: {competitors_analyzed} websites
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DOMINANT CONTENT THEMES:
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{dominant_themes}
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PROVIDE:
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1. Strategic Content Gap Analysis
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2. Priority Content Recommendations (top 5)
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3. Keyword Strategy Insights
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4. Competitive Positioning Advice
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5. Content Format Recommendations
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6. Technical SEO Opportunities
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7. Implementation Timeline (30/60/90 days)
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Format as JSON with clear, actionable recommendations.
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"""
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```
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**Market Position Analysis Prompt**:
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```python
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MARKET_POSITION_PROMPT = """
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Analyze the market position of competitors in the {industry} industry:
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Competitor Analyses:
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{competitor_analyses}
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Provide:
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1. Market position analysis
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2. Content gaps
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3. Competitive advantages
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4. Strategic positioning recommendations
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Format as JSON with detailed analysis.
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"""
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```
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**Keyword Analysis Prompt**:
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```python
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KEYWORD_ANALYSIS_PROMPT = """
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Analyze keyword opportunities for {industry} industry:
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Keyword Trends: {trend_analysis}
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Search Intent: {intent_analysis}
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Opportunities: {opportunities}
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Provide:
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1. High-priority keyword recommendations
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2. Content format suggestions
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3. Topic cluster development
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4. Search intent optimization
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Format as JSON with detailed analysis.
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"""
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```
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### **Phase 4: AI Service Integration (Week 4)**
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#### 4.1 **Create AI Service Manager**
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**File**: `backend/services/ai_service_manager.py`
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```python
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"""
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AI Service Manager
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Centralized AI service management for content planning system.
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"""
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from typing import Dict, Any, List, Optional
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from loguru import logger
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import json
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from backend.llm_providers.main_text_generation import llm_text_gen
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from backend.llm_providers.gemini_provider import gemini_structured_json_response
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class AIServiceManager:
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"""Manages AI service interactions and prompt handling."""
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def __init__(self):
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"""Initialize AI service manager."""
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self.logger = logger
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self.prompts = self._load_prompts()
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def _load_prompts(self) -> Dict[str, str]:
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"""Load AI prompts from configuration."""
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return {
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'content_gap_analysis': CONTENT_GAP_ANALYSIS_PROMPT,
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'market_position': MARKET_POSITION_PROMPT,
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'keyword_analysis': KEYWORD_ANALYSIS_PROMPT,
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'performance_prediction': PERFORMANCE_PREDICTION_PROMPT,
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'strategic_insights': STRATEGIC_INSIGHTS_PROMPT
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}
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async def generate_content_gap_analysis(self, analysis_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Generate content gap analysis using AI."""
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try:
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prompt = self.prompts['content_gap_analysis'].format(**analysis_data)
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response = gemini_structured_json_response(
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prompt=prompt,
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schema=CONTENT_GAP_ANALYSIS_SCHEMA
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)
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return json.loads(response)
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except Exception as e:
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self.logger.error(f"Error generating content gap analysis: {str(e)}")
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return {}
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async def generate_market_position_analysis(self, market_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Generate market position analysis using AI."""
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try:
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prompt = self.prompts['market_position'].format(**market_data)
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response = gemini_structured_json_response(
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prompt=prompt,
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schema=MARKET_POSITION_SCHEMA
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)
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return json.loads(response)
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except Exception as e:
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self.logger.error(f"Error generating market position analysis: {str(e)}")
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return {}
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async def generate_keyword_analysis(self, keyword_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Generate keyword analysis using AI."""
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try:
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prompt = self.prompts['keyword_analysis'].format(**keyword_data)
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response = gemini_structured_json_response(
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prompt=prompt,
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schema=KEYWORD_ANALYSIS_SCHEMA
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)
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return json.loads(response)
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except Exception as e:
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self.logger.error(f"Error generating keyword analysis: {str(e)}")
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return {}
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```
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#### 4.2 **Update All Services to Use AI Manager**
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```python
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# In each service file, replace hardcoded responses with AI calls
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from services.ai_service_manager import AIServiceManager
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class AIEngineService:
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def __init__(self):
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self.ai_manager = AIServiceManager()
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logger.info("AIEngineService initialized")
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async def analyze_content_gaps(self, analysis_summary: Dict[str, Any]) -> Dict[str, Any]:
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"""Analyze content gaps using AI insights."""
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return await self.ai_manager.generate_content_gap_analysis(analysis_summary)
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async def analyze_market_position(self, market_data: Dict[str, Any]) -> Dict[str, Any]:
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"""Analyze market position using AI insights."""
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return await self.ai_manager.generate_market_position_analysis(market_data)
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```
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## 📊 **Implementation Timeline**
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### **Week 1: Core AI Integration** ✅ **COMPLETED**
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- [x] Replace hardcoded responses in AI Engine Service
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- [x] Integrate Gemini provider calls
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- [x] Implement basic AI prompts
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- [x] Test AI functionality
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### **Week 2: Advanced AI Features** ✅ **COMPLETED**
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- [x] Implement content performance prediction
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- [x] Add strategic intelligence generation
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- [x] Create comprehensive AI schemas
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- [x] Optimize AI prompts
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### **Week 3: AI Prompt Optimization** ✅ **COMPLETED**
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- [x] Implement advanced prompts from deep dive
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- [x] Create structured JSON schemas
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- [x] Optimize prompt performance
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- [x] Add error handling and fallbacks
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|
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**Status Update**: ✅ **AI Prompt Optimizer Service fully implemented**
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- Advanced AI prompts from deep dive analysis implemented
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- Comprehensive JSON schemas for structured responses
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- Optimized prompt performance with expert-level instructions
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- Robust error handling and fallback mechanisms
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- Integration with existing AI engine service
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|
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### **Week 4: AI Service Integration** ✅ **COMPLETED**
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- [x] Create AI Service Manager
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- [x] Update all services to use AI Manager
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- [x] Implement centralized AI configuration
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- [x] Add AI performance monitoring
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|
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**Status Update**: ✅ **AI Service Manager fully implemented**
|
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- Centralized AI service management with performance monitoring
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- All services updated to use AI Service Manager
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- Centralized AI configuration with timeout and retry settings
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- Comprehensive AI performance monitoring with metrics tracking
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- Service breakdown by AI type with success rates and response times
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## ✅ **Phase 4 Status Update**
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### **Completed Tasks**
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1. **✅ AI Service Manager**
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- Centralized AI service management with performance monitoring
|
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- Comprehensive AI configuration with timeout and retry settings
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- Service breakdown by AI type with success rates and response times
|
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- Performance metrics tracking and health monitoring
|
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- Centralized prompt and schema management
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|
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2. **✅ Service Integration**
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- AI Engine Service updated to use AI Service Manager
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- All AI calls routed through centralized manager
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- Performance monitoring and metrics collection
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- Error handling and fallback mechanisms
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- Health check integration
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3. **✅ Performance Monitoring**
|
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- AI call performance metrics tracking
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- Service breakdown by AI type
|
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- Success rate monitoring
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- Response time tracking
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- Error rate monitoring
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|
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### **New Features Implemented**
|
|
- **Centralized AI Management**: Single point of control for all AI services
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- **Performance Monitoring**: Real-time metrics for AI service performance
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- **Service Breakdown**: Detailed metrics by AI service type
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- **Configuration Management**: Centralized AI configuration settings
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- **Health Monitoring**: Comprehensive health checks for AI services
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### **Quality Criteria**
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- [ ] AI response accuracy > 85%
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- [ ] AI response time < 10 seconds
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- [ ] AI error rate < 5%
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- [ ] AI fallback mechanisms working
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- [ ] AI prompts optimized for quality
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## 🔧 **Implementation Steps**
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### **Step 1: Environment Setup**
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1. Verify Gemini API key configuration
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2. Test Gemini provider functionality
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3. Set up AI service monitoring
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4. Configure error handling
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|
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### **Step 2: Core Integration**
|
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1. Update AI Engine Service with real AI calls
|
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2. Implement structured JSON responses
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3. Add comprehensive error handling
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4. Test AI functionality
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|
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### **Step 3: Service Updates**
|
|
1. Update Keyword Researcher with AI integration
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2. Update Competitor Analyzer with AI integration
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3. Update Website Analyzer with AI integration
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4. Test all services with AI
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|
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### **Step 4: Optimization**
|
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1. Optimize AI prompts for better results
|
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2. Implement AI response caching
|
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3. Add AI performance monitoring
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4. Create AI fallback mechanisms
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## 📈 **Expected Outcomes**
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|
|
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### **Immediate Benefits**
|
|
- ✅ Real AI-powered insights instead of hardcoded data
|
|
- ✅ Dynamic content recommendations
|
|
- ✅ Intelligent keyword analysis
|
|
- ✅ Strategic competitive intelligence
|
|
|
|
### **Long-term Benefits**
|
|
- ✅ Improved content strategy accuracy
|
|
- ✅ Better keyword targeting
|
|
- ✅ Enhanced competitive positioning
|
|
- ✅ Optimized content performance
|
|
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---
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|
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**Status**: Ready for Implementation
|
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**Priority**: High
|
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**Estimated Duration**: 4 weeks
|
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**Dependencies**: Gemini API key, existing AI infrastructure |