Base code
This commit is contained in:
Kunthawat Greethong
307
backend/models/enhanced_strategy_models.py
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307
backend/models/enhanced_strategy_models.py
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"""
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Enhanced Strategy Database Models
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Defines the enhanced database schema for content strategy with 30+ strategic inputs.
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"""
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from sqlalchemy import Column, Integer, String, Text, DateTime, Float, JSON, ForeignKey, Boolean
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from sqlalchemy.ext.declarative import declarative_base
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from sqlalchemy.orm import relationship
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from datetime import datetime
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Base = declarative_base()
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class EnhancedContentStrategy(Base):
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"""Enhanced Content Strategy model with 30+ strategic inputs."""
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__tablename__ = "enhanced_content_strategies"
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# Primary fields
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id = Column(Integer, primary_key=True)
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user_id = Column(Integer, nullable=False)
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name = Column(String(255), nullable=False)
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industry = Column(String(100), nullable=True)
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# Business Context (8 inputs)
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business_objectives = Column(JSON, nullable=True) # Primary and secondary business goals
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target_metrics = Column(JSON, nullable=True) # KPIs and success metrics
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content_budget = Column(Float, nullable=True) # Monthly/annual content budget
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team_size = Column(Integer, nullable=True) # Content team size
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implementation_timeline = Column(String(100), nullable=True) # 3 months, 6 months, 1 year, etc.
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market_share = Column(String(50), nullable=True) # Current market share percentage
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competitive_position = Column(String(50), nullable=True) # Leader, challenger, niche, emerging
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performance_metrics = Column(JSON, nullable=True) # Current performance data
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# Audience Intelligence (6 inputs)
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content_preferences = Column(JSON, nullable=True) # Preferred content formats and topics
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consumption_patterns = Column(JSON, nullable=True) # When and how audience consumes content
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audience_pain_points = Column(JSON, nullable=True) # Key challenges and pain points
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buying_journey = Column(JSON, nullable=True) # Customer journey stages and touchpoints
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seasonal_trends = Column(JSON, nullable=True) # Seasonal content opportunities
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engagement_metrics = Column(JSON, nullable=True) # Current engagement data
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# Competitive Intelligence (5 inputs)
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top_competitors = Column(JSON, nullable=True) # List of main competitors
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competitor_content_strategies = Column(JSON, nullable=True) # Analysis of competitor approaches
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market_gaps = Column(JSON, nullable=True) # Identified market opportunities
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industry_trends = Column(JSON, nullable=True) # Current industry trends
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emerging_trends = Column(JSON, nullable=True) # Upcoming trends and opportunities
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# Content Strategy (7 inputs)
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preferred_formats = Column(JSON, nullable=True) # Blog posts, videos, infographics, etc.
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content_mix = Column(JSON, nullable=True) # Distribution of content types
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content_frequency = Column(String(50), nullable=True) # Daily, weekly, monthly, etc.
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optimal_timing = Column(JSON, nullable=True) # Best times for publishing
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quality_metrics = Column(JSON, nullable=True) # Content quality standards
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editorial_guidelines = Column(JSON, nullable=True) # Style and tone guidelines
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brand_voice = Column(JSON, nullable=True) # Brand personality and voice
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# Performance & Analytics (4 inputs)
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traffic_sources = Column(JSON, nullable=True) # Primary traffic sources
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conversion_rates = Column(JSON, nullable=True) # Current conversion data
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content_roi_targets = Column(JSON, nullable=True) # ROI goals and targets
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ab_testing_capabilities = Column(Boolean, default=False) # A/B testing availability
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# Legacy fields for backward compatibility
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target_audience = Column(JSON, nullable=True) # Store audience demographics and preferences
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content_pillars = Column(JSON, nullable=True) # Store content pillar definitions
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ai_recommendations = Column(JSON, nullable=True) # Store AI-generated recommendations
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# Enhanced AI Analysis fields
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comprehensive_ai_analysis = Column(JSON, nullable=True) # Enhanced AI analysis results
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onboarding_data_used = Column(JSON, nullable=True) # Track onboarding data integration
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strategic_scores = Column(JSON, nullable=True) # Strategic performance scores
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market_positioning = Column(JSON, nullable=True) # Market positioning analysis
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competitive_advantages = Column(JSON, nullable=True) # Identified competitive advantages
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strategic_risks = Column(JSON, nullable=True) # Risk assessment
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opportunity_analysis = Column(JSON, nullable=True) # Opportunity identification
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# Metadata
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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completion_percentage = Column(Float, default=0.0) # Track input completion
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data_source_transparency = Column(JSON, nullable=True) # Track data sources for auto-population
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# Relationships
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autofill_insights = relationship("ContentStrategyAutofillInsights", back_populates="strategy", cascade="all, delete-orphan")
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# Monitoring relationships
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monitoring_plans = relationship("StrategyMonitoringPlan", back_populates="strategy", cascade="all, delete-orphan")
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monitoring_tasks = relationship("MonitoringTask", back_populates="strategy", cascade="all, delete-orphan")
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performance_metrics = relationship("StrategyPerformanceMetrics", back_populates="strategy", cascade="all, delete-orphan")
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activation_status = relationship("StrategyActivationStatus", back_populates="strategy", cascade="all, delete-orphan")
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def __repr__(self):
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return f"<EnhancedContentStrategy(id={self.id}, name='{self.name}', industry='{self.industry}')>"
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def to_dict(self):
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"""Convert model to dictionary with enhanced structure."""
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return {
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'id': self.id,
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'user_id': self.user_id,
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'name': self.name,
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'industry': self.industry,
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# Business Context
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'business_objectives': self.business_objectives,
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'target_metrics': self.target_metrics,
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'content_budget': self.content_budget,
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'team_size': self.team_size,
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'implementation_timeline': self.implementation_timeline,
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'market_share': self.market_share,
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'competitive_position': self.competitive_position,
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'performance_metrics': self.performance_metrics,
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# Audience Intelligence
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'content_preferences': self.content_preferences,
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'consumption_patterns': self.consumption_patterns,
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'audience_pain_points': self.audience_pain_points,
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'buying_journey': self.buying_journey,
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'seasonal_trends': self.seasonal_trends,
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'engagement_metrics': self.engagement_metrics,
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# Competitive Intelligence
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'top_competitors': self.top_competitors,
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'competitor_content_strategies': self.competitor_content_strategies,
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'market_gaps': self.market_gaps,
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'industry_trends': self.industry_trends,
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'emerging_trends': self.emerging_trends,
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# Content Strategy
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'preferred_formats': self.preferred_formats,
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'content_mix': self.content_mix,
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'content_frequency': self.content_frequency,
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'optimal_timing': self.optimal_timing,
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'quality_metrics': self.quality_metrics,
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'editorial_guidelines': self.editorial_guidelines,
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'brand_voice': self.brand_voice,
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# Performance & Analytics
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'traffic_sources': self.traffic_sources,
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'conversion_rates': self.conversion_rates,
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'content_roi_targets': self.content_roi_targets,
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'ab_testing_capabilities': self.ab_testing_capabilities,
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# Legacy fields
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'target_audience': self.target_audience,
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'content_pillars': self.content_pillars,
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'ai_recommendations': self.ai_recommendations,
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# Enhanced AI Analysis
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'comprehensive_ai_analysis': self.comprehensive_ai_analysis,
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'onboarding_data_used': self.onboarding_data_used,
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'strategic_scores': self.strategic_scores,
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'market_positioning': self.market_positioning,
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'competitive_advantages': self.competitive_advantages,
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'strategic_risks': self.strategic_risks,
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'opportunity_analysis': self.opportunity_analysis,
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# Metadata
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'created_at': self.created_at.isoformat() if self.created_at else None,
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'updated_at': self.updated_at.isoformat() if self.updated_at else None,
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'completion_percentage': self.completion_percentage,
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'data_source_transparency': self.data_source_transparency
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}
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def calculate_completion_percentage(self):
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"""Calculate the percentage of required fields that have been filled."""
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required_fields = [
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'business_objectives', 'target_metrics', 'content_budget', 'team_size',
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'implementation_timeline', 'market_share', 'competitive_position',
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'content_preferences', 'consumption_patterns', 'audience_pain_points',
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'buying_journey', 'seasonal_trends', 'engagement_metrics',
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'top_competitors', 'competitor_content_strategies', 'market_gaps',
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'industry_trends', 'emerging_trends', 'preferred_formats',
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'content_mix', 'content_frequency', 'optimal_timing',
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'quality_metrics', 'editorial_guidelines', 'brand_voice',
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'traffic_sources', 'conversion_rates', 'content_roi_targets'
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]
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filled_fields = sum(1 for field in required_fields if getattr(self, field) is not None)
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self.completion_percentage = (filled_fields / len(required_fields)) * 100
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return self.completion_percentage
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class EnhancedAIAnalysisResult(Base):
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"""Enhanced AI Analysis Result model for storing comprehensive AI-generated insights."""
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__tablename__ = "enhanced_ai_analysis_results"
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id = Column(Integer, primary_key=True)
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user_id = Column(Integer, nullable=False)
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strategy_id = Column(Integer, ForeignKey("enhanced_content_strategies.id"), nullable=True)
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# Analysis type for the 5 specialized prompts
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analysis_type = Column(String(50), nullable=False) # comprehensive_strategy, audience_intelligence, competitive_intelligence, performance_optimization, content_calendar_optimization
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# Comprehensive analysis results
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comprehensive_insights = Column(JSON, nullable=True) # Holistic strategy insights
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audience_intelligence = Column(JSON, nullable=True) # Detailed audience analysis
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competitive_intelligence = Column(JSON, nullable=True) # Competitive landscape analysis
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performance_optimization = Column(JSON, nullable=True) # Performance improvement recommendations
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content_calendar_optimization = Column(JSON, nullable=True) # Calendar optimization insights
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# Enhanced data tracking
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onboarding_data_used = Column(JSON, nullable=True) # Track onboarding data integration
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data_confidence_scores = Column(JSON, nullable=True) # Confidence scores for data sources
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recommendation_quality_scores = Column(JSON, nullable=True) # Quality scores for recommendations
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# Performance metrics
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processing_time = Column(Float, nullable=True) # Processing time in seconds
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ai_service_status = Column(String(20), default="operational") # operational, fallback, error
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prompt_version = Column(String(50), nullable=True) # Version of AI prompt used
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# Metadata
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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def __repr__(self):
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return f"<EnhancedAIAnalysisResult(id={self.id}, type='{self.analysis_type}', user_id={self.user_id})>"
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def to_dict(self):
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"""Convert model to dictionary."""
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return {
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'id': self.id,
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'user_id': self.user_id,
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'strategy_id': self.strategy_id,
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'analysis_type': self.analysis_type,
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'comprehensive_insights': self.comprehensive_insights,
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'audience_intelligence': self.audience_intelligence,
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'competitive_intelligence': self.competitive_intelligence,
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'performance_optimization': self.performance_optimization,
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'content_calendar_optimization': self.content_calendar_optimization,
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'onboarding_data_used': self.onboarding_data_used,
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'data_confidence_scores': self.data_confidence_scores,
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'recommendation_quality_scores': self.recommendation_quality_scores,
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'processing_time': self.processing_time,
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'ai_service_status': self.ai_service_status,
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'prompt_version': self.prompt_version,
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'created_at': self.created_at.isoformat() if self.created_at else None,
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'updated_at': self.updated_at.isoformat() if self.updated_at else None
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}
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class OnboardingDataIntegration(Base):
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"""Model for tracking onboarding data integration with enhanced strategy."""
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__tablename__ = "onboarding_data_integrations"
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id = Column(Integer, primary_key=True)
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user_id = Column(Integer, nullable=False)
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strategy_id = Column(Integer, ForeignKey("enhanced_content_strategies.id"), nullable=True)
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# Legacy onboarding storage fields (match existing DB schema)
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website_analysis_data = Column(JSON, nullable=True) # Data from website analysis
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research_preferences_data = Column(JSON, nullable=True) # Data from research preferences
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api_keys_data = Column(JSON, nullable=True) # API configuration data
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# Integration mapping and user edits
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field_mappings = Column(JSON, nullable=True) # Mapping of onboarding fields to strategy fields
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auto_populated_fields = Column(JSON, nullable=True) # Fields auto-populated from onboarding
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user_overrides = Column(JSON, nullable=True) # Fields manually overridden by user
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# Data quality and transparency
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data_quality_scores = Column(JSON, nullable=True) # Quality scores for each data source
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confidence_levels = Column(JSON, nullable=True) # Confidence levels for auto-populated data
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data_freshness = Column(JSON, nullable=True) # How recent the onboarding data is
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# Metadata
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)
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def to_dict(self):
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"""Convert model to dictionary (legacy fields)."""
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return {
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'id': self.id,
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'user_id': self.user_id,
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'strategy_id': self.strategy_id,
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'website_analysis_data': self.website_analysis_data,
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'research_preferences_data': self.research_preferences_data,
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'api_keys_data': self.api_keys_data,
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'field_mappings': self.field_mappings,
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'auto_populated_fields': self.auto_populated_fields,
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'user_overrides': self.user_overrides,
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'data_quality_scores': self.data_quality_scores,
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'confidence_levels': self.confidence_levels,
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'data_freshness': self.data_freshness,
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'created_at': self.created_at.isoformat() if self.created_at else None,
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'updated_at': self.updated_at.isoformat() if self.updated_at else None
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}
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# New model to persist accepted auto-fill inputs used to create a strategy
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class ContentStrategyAutofillInsights(Base):
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__tablename__ = "content_strategy_autofill_insights"
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id = Column(Integer, primary_key=True)
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strategy_id = Column(Integer, ForeignKey("enhanced_content_strategies.id"), nullable=False)
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user_id = Column(Integer, nullable=False)
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# Full snapshot of accepted inputs and transparency at time of strategy creation/confirmation
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accepted_fields = Column(JSON, nullable=False)
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sources = Column(JSON, nullable=True)
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input_data_points = Column(JSON, nullable=True)
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quality_scores = Column(JSON, nullable=True)
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confidence_levels = Column(JSON, nullable=True)
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data_freshness = Column(JSON, nullable=True)
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created_at = Column(DateTime, default=datetime.utcnow)
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# Relationship back to strategy
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strategy = relationship("EnhancedContentStrategy", back_populates="autofill_insights")
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