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