src/config/prompt_management/data_models.py

"""
Data models for the prompt management system.
"""

from dataclasses import dataclass, field
from datetime import datetime
from typing import List, Dict, Optional, Any
from enum import Enum


class IndicatorCategory(Enum):
    """Categories for spiritual distress indicators."""
    EMOTIONAL = "emotional"
    SPIRITUAL = "spiritual"
    SOCIAL = "social"
    EXISTENTIAL = "existential"
    PHYSICAL = "physical"


class ScenarioType(Enum):
    """Types of YELLOW scenarios for targeted questioning."""
    LOSS_OF_INTEREST = "loss_of_interest"
    LOSS_OF_LOVED_ONE = "loss_of_loved_one"
    NO_SUPPORT = "no_support"
    VAGUE_STRESS = "vague_stress"
    SLEEP_ISSUES = "sleep_issues"
    SPIRITUAL_PRACTICE_CHANGE = "spiritual_practice_change"


@dataclass
class Indicator:
    """Represents a spiritual distress indicator."""
    name: str
    category: IndicatorCategory
    definition: str
    examples: List[str]
    severity_weight: float
    context_requirements: List[str] = field(default_factory=list)
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary for serialization."""
        return {
            'name': self.name,
            'category': self.category.value,
            'definition': self.definition,
            'examples': self.examples,
            'severity_weight': self.severity_weight,
            'context_requirements': self.context_requirements
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'Indicator':
        """Create from dictionary."""
        return cls(
            name=data['name'],
            category=IndicatorCategory(data['category']),
            definition=data['definition'],
            examples=data['examples'],
            severity_weight=data['severity_weight'],
            context_requirements=data.get('context_requirements', [])
        )


@dataclass
class Rule:
    """Represents a classification rule."""
    rule_id: str
    description: str
    condition: str
    action: str
    priority: int
    examples: List[str] = field(default_factory=list)
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary for serialization."""
        return {
            'rule_id': self.rule_id,
            'description': self.description,
            'condition': self.condition,
            'action': self.action,
            'priority': self.priority,
            'examples': self.examples
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'Rule':
        """Create from dictionary."""
        return cls(
            rule_id=data['rule_id'],
            description=data['description'],
            condition=data['condition'],
            action=data['action'],
            priority=data['priority'],
            examples=data.get('examples', [])
        )


@dataclass
class Template:
    """Represents a reusable prompt template."""
    template_id: str
    name: str
    content: str
    variables: List[str]
    category: str
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary for serialization."""
        return {
            'template_id': self.template_id,
            'name': self.name,
            'content': self.content,
            'variables': self.variables,
            'category': self.category
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'Template':
        """Create from dictionary."""
        return cls(
            template_id=data['template_id'],
            name=data['name'],
            content=data['content'],
            variables=data['variables'],
            category=data['category']
        )


@dataclass
class QuestionPattern:
    """Represents a question pattern for YELLOW scenarios."""
    pattern_id: str
    scenario_type: ScenarioType
    template: str
    target_clarification: str
    examples: List[str] = field(default_factory=list)
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary for serialization."""
        return {
            'pattern_id': self.pattern_id,
            'scenario_type': self.scenario_type.value,
            'template': self.template,
            'target_clarification': self.target_clarification,
            'examples': self.examples
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'QuestionPattern':
        """Create from dictionary."""
        return cls(
            pattern_id=data['pattern_id'],
            scenario_type=ScenarioType(data['scenario_type']),
            template=data['template'],
            target_clarification=data['target_clarification'],
            examples=data.get('examples', [])
        )


@dataclass
class YellowScenario:
    """Represents a YELLOW scenario for targeted questioning."""
    scenario_type: ScenarioType
    patient_statement: str
    context_clues: List[str]
    target_clarification: str
    question_patterns: List[QuestionPattern]
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary for serialization."""
        return {
            'scenario_type': self.scenario_type.value,
            'patient_statement': self.patient_statement,
            'context_clues': self.context_clues,
            'target_clarification': self.target_clarification,
            'question_patterns': [p.to_dict() for p in self.question_patterns]
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'YellowScenario':
        """Create from dictionary."""
        return cls(
            scenario_type=ScenarioType(data['scenario_type']),
            patient_statement=data['patient_statement'],
            context_clues=data['context_clues'],
            target_clarification=data['target_clarification'],
            question_patterns=[QuestionPattern.from_dict(p) for p in data['question_patterns']]
        )


@dataclass
class PromptConfig:
    """Configuration for a specific AI agent prompt."""
    agent_type: str
    base_prompt: str
    shared_indicators: List[Indicator]
    shared_rules: List[Rule]
    templates: List[Template]
    version: str
    last_updated: datetime
    session_override: Optional[str] = None
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary for serialization."""
        return {
            'agent_type': self.agent_type,
            'base_prompt': self.base_prompt,
            'shared_indicators': [i.to_dict() for i in self.shared_indicators],
            'shared_rules': [r.to_dict() for r in self.shared_rules],
            'templates': [t.to_dict() for t in self.templates],
            'version': self.version,
            'last_updated': self.last_updated.isoformat(),
            'session_override': self.session_override
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'PromptConfig':
        """Create from dictionary."""
        return cls(
            agent_type=data['agent_type'],
            base_prompt=data['base_prompt'],
            shared_indicators=[Indicator.from_dict(i) for i in data['shared_indicators']],
            shared_rules=[Rule.from_dict(r) for r in data['shared_rules']],
            templates=[Template.from_dict(t) for t in data['templates']],
            version=data['version'],
            last_updated=datetime.fromisoformat(data['last_updated']),
            session_override=data.get('session_override')
        )


@dataclass
class ValidationResult:
    """Result of prompt validation."""
    is_valid: bool
    errors: List[str] = field(default_factory=list)
    warnings: List[str] = field(default_factory=list)
    
    def add_error(self, error: str):
        """Add an error to the result."""
        self.errors.append(error)
        self.is_valid = False
    
    def add_warning(self, warning: str):
        """Add a warning to the result."""
        self.warnings.append(warning)


@dataclass
class Message:
    """Represents a single message in conversation history."""
    content: str
    classification: str
    timestamp: datetime
    confidence: float = 0.0
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary for serialization."""
        return {
            'content': self.content,
            'classification': self.classification,
            'timestamp': self.timestamp.isoformat(),
            'confidence': self.confidence
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'Message':
        """Create from dictionary."""
        return cls(
            content=data['content'],
            classification=data['classification'],
            timestamp=datetime.fromisoformat(data['timestamp']),
            confidence=data.get('confidence', 0.0)
        )


@dataclass
class Classification:
    """Represents a classification result with context."""
    category: str
    confidence: float
    reasoning: str
    indicators_found: List[str] = None
    context_factors: List[str] = None
    
    def __post_init__(self):
        if self.indicators_found is None:
            self.indicators_found = []
        if self.context_factors is None:
            self.context_factors = []
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary for serialization."""
        return {
            'category': self.category,
            'confidence': self.confidence,
            'reasoning': self.reasoning,
            'indicators_found': self.indicators_found,
            'context_factors': self.context_factors
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'Classification':
        """Create from dictionary."""
        return cls(
            category=data['category'],
            confidence=data['confidence'],
            reasoning=data['reasoning'],
            indicators_found=data.get('indicators_found', []),
            context_factors=data.get('context_factors', [])
        )


@dataclass
class ConversationHistory:
    """Represents conversation history for context-aware classification."""
    messages: List[Message]
    distress_indicators_found: List[str]
    context_flags: List[str]
    medical_context: Dict[str, Any] = None
    
    def __post_init__(self):
        if self.medical_context is None:
            self.medical_context = {'conditions': [], 'medications': []}
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary for serialization."""
        return {
            'messages': [msg.to_dict() for msg in self.messages],
            'distress_indicators_found': self.distress_indicators_found,
            'context_flags': self.context_flags,
            'medical_context': self.medical_context
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'ConversationHistory':
        """Create from dictionary."""
        return cls(
            messages=[Message.from_dict(msg) for msg in data['messages']],
            distress_indicators_found=data['distress_indicators_found'],
            context_flags=data['context_flags'],
            medical_context=data.get('medical_context', {'conditions': [], 'medications': []})
        )


class ErrorType(Enum):
    """Types of classification errors for structured feedback."""
    WRONG_CLASSIFICATION = "wrong_classification"
    SEVERITY_MISJUDGMENT = "severity_misjudgment"
    MISSED_INDICATORS = "missed_indicators"
    FALSE_POSITIVE = "false_positive"
    CONTEXT_MISUNDERSTANDING = "context_misunderstanding"
    LANGUAGE_INTERPRETATION = "language_interpretation"


class ErrorSubcategory(Enum):
    """Subcategories for classification errors."""
    # Wrong Classification subcategories
    GREEN_TO_YELLOW = "green_to_yellow"
    GREEN_TO_RED = "green_to_red"
    YELLOW_TO_GREEN = "yellow_to_green"
    YELLOW_TO_RED = "yellow_to_red"
    RED_TO_GREEN = "red_to_green"
    RED_TO_YELLOW = "red_to_yellow"
    
    # Severity Misjudgment subcategories
    UNDERESTIMATED_DISTRESS = "underestimated_distress"
    OVERESTIMATED_DISTRESS = "overestimated_distress"
    
    # Missed Indicators subcategories
    EMOTIONAL_INDICATORS = "emotional_indicators"
    SPIRITUAL_INDICATORS = "spiritual_indicators"
    SOCIAL_INDICATORS = "social_indicators"
    
    # False Positive subcategories
    MISINTERPRETED_STATEMENT = "misinterpreted_statement"
    CULTURAL_MISUNDERSTANDING = "cultural_misunderstanding"
    
    # Context Misunderstanding subcategories
    IGNORED_HISTORY = "ignored_history"
    MISSED_DEFENSIVE_RESPONSE = "missed_defensive_response"
    
    # Language Interpretation subcategories
    LITERAL_INTERPRETATION = "literal_interpretation"
    MISSED_SUBTEXT = "missed_subtext"


class QuestionIssueType(Enum):
    """Types of issues with triage questions."""
    INAPPROPRIATE_QUESTION = "inappropriate_question"
    INSENSITIVE_LANGUAGE = "insensitive_language"
    WRONG_SCENARIO_TARGETING = "wrong_scenario_targeting"
    UNCLEAR_QUESTION = "unclear_question"
    LEADING_QUESTION = "leading_question"


class ReferralProblemType(Enum):
    """Types of problems with referral generation."""
    INCOMPLETE_SUMMARY = "incomplete_summary"
    MISSING_CONTACT_INFO = "missing_contact_info"
    INCORRECT_URGENCY = "incorrect_urgency"
    POOR_CONTEXT_DESCRIPTION = "poor_context_description"


@dataclass
class ClassificationError:
    """Represents a classification error for structured feedback."""
    error_id: str
    error_type: ErrorType
    subcategory: ErrorSubcategory
    expected_category: str  # GREEN, YELLOW, RED
    actual_category: str    # GREEN, YELLOW, RED
    message_content: str
    reviewer_comments: str
    confidence_level: float  # 0.0 to 1.0
    timestamp: datetime
    session_id: Optional[str] = None
    additional_context: Dict[str, Any] = field(default_factory=dict)
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary for serialization."""
        return {
            'error_id': self.error_id,
            'error_type': self.error_type.value,
            'subcategory': self.subcategory.value,
            'expected_category': self.expected_category,
            'actual_category': self.actual_category,
            'message_content': self.message_content,
            'reviewer_comments': self.reviewer_comments,
            'confidence_level': self.confidence_level,
            'timestamp': self.timestamp.isoformat(),
            'session_id': self.session_id,
            'additional_context': self.additional_context
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'ClassificationError':
        """Create from dictionary."""
        return cls(
            error_id=data['error_id'],
            error_type=ErrorType(data['error_type']),
            subcategory=ErrorSubcategory(data['subcategory']),
            expected_category=data['expected_category'],
            actual_category=data['actual_category'],
            message_content=data['message_content'],
            reviewer_comments=data['reviewer_comments'],
            confidence_level=data['confidence_level'],
            timestamp=datetime.fromisoformat(data['timestamp']),
            session_id=data.get('session_id'),
            additional_context=data.get('additional_context', {})
        )


@dataclass
class QuestionIssue:
    """Represents an issue with triage question generation."""
    issue_id: str
    issue_type: QuestionIssueType
    question_content: str
    scenario_type: ScenarioType
    reviewer_comments: str
    severity: str  # low, medium, high
    timestamp: datetime
    session_id: Optional[str] = None
    suggested_improvement: Optional[str] = None
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary for serialization."""
        return {
            'issue_id': self.issue_id,
            'issue_type': self.issue_type.value,
            'question_content': self.question_content,
            'scenario_type': self.scenario_type.value,
            'reviewer_comments': self.reviewer_comments,
            'severity': self.severity,
            'timestamp': self.timestamp.isoformat(),
            'session_id': self.session_id,
            'suggested_improvement': self.suggested_improvement
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'QuestionIssue':
        """Create from dictionary."""
        return cls(
            issue_id=data['issue_id'],
            issue_type=QuestionIssueType(data['issue_type']),
            question_content=data['question_content'],
            scenario_type=ScenarioType(data['scenario_type']),
            reviewer_comments=data['reviewer_comments'],
            severity=data['severity'],
            timestamp=datetime.fromisoformat(data['timestamp']),
            session_id=data.get('session_id'),
            suggested_improvement=data.get('suggested_improvement')
        )


@dataclass
class ReferralProblem:
    """Represents a problem with referral generation."""
    problem_id: str
    problem_type: ReferralProblemType
    referral_content: str
    reviewer_comments: str
    severity: str  # low, medium, high
    timestamp: datetime
    session_id: Optional[str] = None
    missing_fields: List[str] = field(default_factory=list)
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary for serialization."""
        return {
            'problem_id': self.problem_id,
            'problem_type': self.problem_type.value,
            'referral_content': self.referral_content,
            'reviewer_comments': self.reviewer_comments,
            'severity': self.severity,
            'timestamp': self.timestamp.isoformat(),
            'session_id': self.session_id,
            'missing_fields': self.missing_fields
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'ReferralProblem':
        """Create from dictionary."""
        return cls(
            problem_id=data['problem_id'],
            problem_type=ReferralProblemType(data['problem_type']),
            referral_content=data['referral_content'],
            reviewer_comments=data['reviewer_comments'],
            severity=data['severity'],
            timestamp=datetime.fromisoformat(data['timestamp']),
            session_id=data.get('session_id'),
            missing_fields=data.get('missing_fields', [])
        )


@dataclass
class ErrorPattern:
    """Represents a pattern identified in classification errors."""
    pattern_id: str
    pattern_type: str
    description: str
    frequency: int
    affected_scenarios: List[ScenarioType]
    suggested_improvements: List[str]
    confidence_score: float
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary for serialization."""
        return {
            'pattern_id': self.pattern_id,
            'pattern_type': self.pattern_type,
            'description': self.description,
            'frequency': self.frequency,
            'affected_scenarios': [s.value for s in self.affected_scenarios],
            'suggested_improvements': self.suggested_improvements,
            'confidence_score': self.confidence_score
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'ErrorPattern':
        """Create from dictionary."""
        return cls(
            pattern_id=data['pattern_id'],
            pattern_type=data['pattern_type'],
            description=data['description'],
            frequency=data['frequency'],
            affected_scenarios=[ScenarioType(s) for s in data['affected_scenarios']],
            suggested_improvements=data['suggested_improvements'],
            confidence_score=data['confidence_score']
        )