Base code
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Kunthawat Greethong
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backend/models/persona_models.py
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247
backend/models/persona_models.py
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"""
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Writing Persona Database Models
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Defines database schema for storing writing personas based on onboarding data analysis.
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Each persona represents a platform-specific writing style derived from user's onboarding data.
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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 WritingPersona(Base):
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"""Main writing persona model that stores the core persona profile."""
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__tablename__ = "writing_personas"
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# Primary fields
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id = Column(Integer, primary_key=True)
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user_id = Column(String(255), nullable=False) # Changed to String to support Clerk user IDs
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persona_name = Column(String(255), nullable=False) # e.g., "Professional LinkedIn Voice", "Casual Blog Writer"
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# Core Identity
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archetype = Column(String(100), nullable=True) # e.g., "The Pragmatic Futurist", "The Thoughtful Educator"
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core_belief = Column(Text, nullable=True) # Central philosophy or belief system
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brand_voice_description = Column(Text, nullable=True) # Detailed brand voice description
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# Linguistic Fingerprint - Quantitative Analysis
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linguistic_fingerprint = Column(JSON, nullable=True) # Complete linguistic analysis
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# Platform-specific adaptations
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platform_adaptations = Column(JSON, nullable=True) # How persona adapts across platforms
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# Source data tracking
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onboarding_session_id = Column(Integer, nullable=True) # Link to onboarding session
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source_website_analysis = Column(JSON, nullable=True) # Website analysis data used
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source_research_preferences = Column(JSON, nullable=True) # Research preferences used
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# AI Analysis metadata
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ai_analysis_version = Column(String(50), nullable=True) # Version of AI analysis used
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confidence_score = Column(Float, nullable=True) # AI confidence in persona accuracy
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analysis_date = Column(DateTime, default=datetime.utcnow)
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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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is_active = Column(Boolean, default=True)
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# Relationships
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platform_personas = relationship("PlatformPersona", back_populates="writing_persona", cascade="all, delete-orphan")
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def __repr__(self):
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return f"<WritingPersona(id={self.id}, name='{self.persona_name}', 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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'persona_name': self.persona_name,
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'archetype': self.archetype,
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'core_belief': self.core_belief,
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'brand_voice_description': self.brand_voice_description,
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'linguistic_fingerprint': self.linguistic_fingerprint,
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'platform_adaptations': self.platform_adaptations,
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'onboarding_session_id': self.onboarding_session_id,
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'source_website_analysis': self.source_website_analysis,
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'source_research_preferences': self.source_research_preferences,
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'ai_analysis_version': self.ai_analysis_version,
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'confidence_score': self.confidence_score,
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'analysis_date': self.analysis_date.isoformat() if self.analysis_date else None,
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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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'is_active': self.is_active
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}
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class PlatformPersona(Base):
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"""Platform-specific persona adaptations for different social media platforms and blogging."""
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__tablename__ = "platform_personas"
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# Primary fields
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id = Column(Integer, primary_key=True)
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writing_persona_id = Column(Integer, ForeignKey("writing_personas.id"), nullable=False)
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platform_type = Column(String(50), nullable=False) # twitter, linkedin, instagram, facebook, blog, medium, substack
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# Platform-specific linguistic constraints
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sentence_metrics = Column(JSON, nullable=True) # Platform-optimized sentence structure
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lexical_features = Column(JSON, nullable=True) # Platform-specific vocabulary and phrases
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rhetorical_devices = Column(JSON, nullable=True) # Platform-appropriate rhetorical patterns
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tonal_range = Column(JSON, nullable=True) # Permitted tones for this platform
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stylistic_constraints = Column(JSON, nullable=True) # Platform formatting rules
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# Platform-specific content guidelines
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content_format_rules = Column(JSON, nullable=True) # Character limits, hashtag usage, etc.
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engagement_patterns = Column(JSON, nullable=True) # How to engage on this platform
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posting_frequency = Column(JSON, nullable=True) # Optimal posting schedule
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content_types = Column(JSON, nullable=True) # Preferred content types for platform
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# Performance optimization
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platform_best_practices = Column(JSON, nullable=True) # Platform-specific best practices
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algorithm_considerations = Column(JSON, nullable=True) # Platform algorithm optimization
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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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is_active = Column(Boolean, default=True)
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# Relationships
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writing_persona = relationship("WritingPersona", back_populates="platform_personas")
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def __repr__(self):
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return f"<PlatformPersona(id={self.id}, platform='{self.platform_type}', persona_id={self.writing_persona_id})>"
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def to_dict(self):
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"""Convert model to dictionary."""
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result = {
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'id': self.id,
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'writing_persona_id': self.writing_persona_id,
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'platform_type': self.platform_type,
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'sentence_metrics': self.sentence_metrics,
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'lexical_features': self.lexical_features,
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'rhetorical_devices': self.rhetorical_devices,
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'tonal_range': self.tonal_range,
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'stylistic_constraints': self.stylistic_constraints,
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'content_format_rules': self.content_format_rules,
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'engagement_patterns': self.engagement_patterns,
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'posting_frequency': self.posting_frequency,
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'content_types': self.content_types,
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'platform_best_practices': self.platform_best_practices,
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'algorithm_considerations': self.algorithm_considerations,
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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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'is_active': self.is_active
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}
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# Add LinkedIn-specific fields if this is a LinkedIn persona
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if self.platform_type.lower() == "linkedin" and self.algorithm_considerations:
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linkedin_data = self.algorithm_considerations
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if isinstance(linkedin_data, dict):
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result.update({
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'professional_networking': linkedin_data.get('professional_networking', {}),
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'linkedin_features': linkedin_data.get('linkedin_features', {}),
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'algorithm_optimization': linkedin_data.get('algorithm_optimization', {}),
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'professional_context_optimization': linkedin_data.get('professional_context_optimization', {})
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})
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return result
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class PersonaAnalysisResult(Base):
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"""Stores AI analysis results used to generate personas."""
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__tablename__ = "persona_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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writing_persona_id = Column(Integer, ForeignKey("writing_personas.id"), nullable=True)
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# Analysis input data
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analysis_prompt = Column(Text, nullable=True) # The prompt used for analysis
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input_data = Column(JSON, nullable=True) # Raw input data from onboarding
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# AI Analysis results
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linguistic_analysis = Column(JSON, nullable=True) # Detailed linguistic fingerprint analysis
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personality_analysis = Column(JSON, nullable=True) # Personality and archetype analysis
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platform_recommendations = Column(JSON, nullable=True) # Platform-specific recommendations
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style_guidelines = Column(JSON, nullable=True) # Generated style guidelines
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# Quality metrics
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analysis_confidence = Column(Float, nullable=True) # AI confidence in analysis
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data_sufficiency_score = Column(Float, nullable=True) # How much data was available for analysis
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recommendation_quality = Column(Float, nullable=True) # Quality of generated recommendations
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# AI service metadata
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ai_provider = Column(String(50), nullable=True) # gemini, openai, anthropic
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model_version = Column(String(100), nullable=True) # Specific model version used
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processing_time = Column(Float, nullable=True) # Processing time in seconds
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# Metadata
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created_at = Column(DateTime, default=datetime.utcnow)
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def __repr__(self):
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return f"<PersonaAnalysisResult(id={self.id}, user_id={self.user_id}, provider='{self.ai_provider}')>"
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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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'writing_persona_id': self.writing_persona_id,
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'analysis_prompt': self.analysis_prompt,
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'input_data': self.input_data,
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'linguistic_analysis': self.linguistic_analysis,
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'personality_analysis': self.personality_analysis,
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'platform_recommendations': self.platform_recommendations,
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'style_guidelines': self.style_guidelines,
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'analysis_confidence': self.analysis_confidence,
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'data_sufficiency_score': self.data_sufficiency_score,
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'recommendation_quality': self.recommendation_quality,
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'ai_provider': self.ai_provider,
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'model_version': self.model_version,
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'processing_time': self.processing_time,
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'created_at': self.created_at.isoformat() if self.created_at else None
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}
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class PersonaValidationResult(Base):
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"""Stores validation results for generated personas."""
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__tablename__ = "persona_validation_results"
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id = Column(Integer, primary_key=True)
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writing_persona_id = Column(Integer, ForeignKey("writing_personas.id"), nullable=False)
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platform_persona_id = Column(Integer, ForeignKey("platform_personas.id"), nullable=True)
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# Validation metrics
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stylometric_accuracy = Column(Float, nullable=True) # How well persona matches original style
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consistency_score = Column(Float, nullable=True) # Consistency across generated content
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platform_compliance = Column(Float, nullable=True) # How well adapted to platform constraints
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# Test results
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sample_outputs = Column(JSON, nullable=True) # Sample content generated with persona
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validation_feedback = Column(JSON, nullable=True) # User or automated feedback
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improvement_suggestions = Column(JSON, nullable=True) # Suggestions for persona refinement
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# Metadata
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validation_date = Column(DateTime, default=datetime.utcnow)
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validator_type = Column(String(50), nullable=True) # automated, user, ai_review
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def __repr__(self):
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return f"<PersonaValidationResult(id={self.id}, persona_id={self.writing_persona_id}, accuracy={self.stylometric_accuracy})>"
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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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'writing_persona_id': self.writing_persona_id,
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'platform_persona_id': self.platform_persona_id,
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'stylometric_accuracy': self.stylometric_accuracy,
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'consistency_score': self.consistency_score,
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'platform_compliance': self.platform_compliance,
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'sample_outputs': self.sample_outputs,
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'validation_feedback': self.validation_feedback,
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'improvement_suggestions': self.improvement_suggestions,
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'validation_date': self.validation_date.isoformat() if self.validation_date else None,
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'validator_type': self.validator_type
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}
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