backend/core/agent_learning_enhanced.py

"""
Enhanced Agent Learning with Feedback

Integrates user feedback into agent confidence scoring and learning.
Provides feedback-weighted confidence adjustments and learning signals.

Usage:
    from core.agent_learning_enhanced import AgentLearningEnhanced

    learning = AgentLearningEnhanced(db)

    # Adjust confidence based on feedback
    new_confidence = learning.adjust_confidence_with_feedback(
        agent_id="agent-1",
        feedback=feedback_obj
    )

    # Get learning signals from feedback
    signals = learning.get_learning_signals("agent-1", days=30)
"""

from datetime import datetime, timedelta, timezone
import logging
import json
from typing import Any, Dict, List, Optional
import uuid
from sqlalchemy.orm import Session

from core.agent_world_model import AgentExperience, WorldModelService
from core.models import AgentExecution, AgentFeedback, AgentRegistry, CognitiveExperience, AgentLearning
from core.continuous_learning_service import ContinuousLearningService

logger = logging.getLogger(__name__)


class AgentLearningEnhanced:
    """
    Enhanced learning service with feedback integration.

    Incorporates user feedback (thumbs up/down, ratings, corrections)
    into agent confidence scoring and world model learning.
    """

    def __init__(self, db: Session):
        """
        Initialize enhanced learning service.

        Args:
            db: Database session
        """
        self.db = db
        self.world_model = WorldModelService()
        self.continuous_learning = ContinuousLearningService(db)

    def adjust_confidence_with_feedback(
        self,
        agent_id: str,
        feedback: AgentFeedback,
        current_confidence: float
    ) -> float:
        """
        Adjust agent confidence based on user feedback.

        Feedback weights:
        - Thumbs up: +0.05
        - Thumbs down: -0.05
        - 5-star rating: +0.10
        - 4-star rating: +0.05
        - 3-star rating: 0.00
        - 2-star rating: -0.05
        - 1-star rating: -0.10
        - Correction: -0.03 (indicates mistake)

        Args:
            agent_id: ID of the agent
            feedback: Feedback object
            current_confidence: Current confidence score

        Returns:
            Adjusted confidence score (0.0 to 1.0)
        """
        adjustment = 0.0

        # Thumbs up/down
        if feedback.thumbs_up_down is True:
            adjustment += 0.05
        elif feedback.thumbs_up_down is False:
            adjustment -= 0.05

        # Star rating
        if feedback.rating is not None:
            rating_weights = {
                1: -0.10,
                2: -0.05,
                3: 0.00,
                4: 0.05,
                5: 0.10
            }
            adjustment += rating_weights.get(feedback.rating, 0.0)

        # Correction feedback
        if feedback.feedback_type == "correction":
            adjustment -= 0.03

        # Apply adjustment and clamp to [0.0, 1.0]
        new_confidence = max(0.0, min(1.0, current_confidence + adjustment))

        logger.info(
            f"Adjusted confidence for agent {agent_id}: "
            f"{current_confidence:.3f} -> {new_confidence:.3f} "
            f"(adjustment: {adjustment:+.3f})"
        )

        return new_confidence

    def get_learning_signals(
        self,
        agent_id: str,
        days: int = 30
    ) -> Dict[str, Any]:
        """
        Get learning signals from recent feedback.

        Analyzes feedback patterns to provide insights for agent improvement.

        Args:
            agent_id: ID of the agent
            days: Number of days to analyze

        Returns:
            Dictionary with learning signals and insights
        """
        cutoff_date = datetime.now() - timedelta(days=days)

        # Get recent feedback
        feedback = self.db.query(AgentFeedback).filter(
            AgentFeedback.agent_id == agent_id,
            AgentFeedback.created_at >= cutoff_date
        ).all()

        if not feedback:
            # Check if we have aggregate learning data even if no recent feedback
            learning_record = self.db.query(AgentLearning).filter(
                AgentLearning.agent_id == agent_id
            ).first()
            
            if not learning_record:
                return {
                    "agent_id": agent_id,
                    "total_feedback": 0,
                    "learning_signals": [],
                    "improvement_suggestions": []
                }
            
            # Use aggregate data if no recent feedback
            return {
                "agent_id": agent_id,
                "total_feedback": learning_record.total_feedback or 0,
                "positive_ratio": learning_record.success_rate or 0,
                "parameters": learning_record.parameters_json or {},
                "learning_signals": [{
                    "type": "info",
                    "message": "Aggregate learning data available, but no feedback in specified period.",
                    "confidence_impact": "neutral"
                }],
                "improvement_suggestions": []
            }

        # Analyze patterns
        total = len(feedback)

        # Positive vs negative
        positive = sum(
            1 for f in feedback
            if f.thumbs_up_down is True or (f.rating is not None and f.rating >= 4)
        )

        negative = sum(
            1 for f in feedback
            if f.thumbs_up_down is False or (f.rating is not None and f.rating <= 2)
        )

        positive_ratio = positive / total if total > 0 else 0

        # Correction analysis
        corrections = [f for f in feedback if f.feedback_type == "correction"]

        # Generate learning signals
        signals: List[Dict[str, Any]] = []

        if positive_ratio >= 0.8:
            signals.append({
                "type": "strength",
                "message": "Agent is performing well with high positive feedback",
                "confidence_impact": "positive"
            })
        elif positive_ratio <= 0.4:
            signals.append({
                "type": "weakness",
                "message": "Agent is struggling with low positive feedback",
                "confidence_impact": "negative"
            })

        if len(corrections) >= 5:
            signals.append({
                "type": "pattern",
                "message": f"Agent received {len(corrections)} corrections - may need retraining",
                "confidence_impact": "negative",
                "correction_count": len(corrections)
            })

        # Rating analysis
        ratings = [f.rating for f in feedback if f.rating is not None]
        if ratings:
            avg_rating = sum(ratings) / len(ratings)
            if avg_rating >= 4.5:
                signals.append({
                    "type": "strength",
                    "message": f"Excellent average rating: {avg_rating:.1f}/5.0",
                    "confidence_impact": "positive"
                })
            elif avg_rating <= 2.5:
                signals.append({
                    "type": "weakness",
                    "message": f"Poor average rating: {avg_rating:.1f}/5.0",
                    "confidence_impact": "negative"
                })

        # Improvement suggestions
        suggestions = []

        if len(corrections) > 0:
            suggestions.append({
                "type": "training",
                "message": "Review common correction patterns to identify knowledge gaps",
                "priority": "high"
            })

        if positive_ratio < 0.6:
            suggestions.append({
                "type": "supervision",
                "message": "Increase human supervision until performance improves",
                "priority": "medium"
            })

        # Add aggregate signals if available
        learning_record = self.db.query(AgentLearning).filter(
            AgentLearning.agent_id == agent_id
        ).first()
        
        aggregate_data = {}
        if learning_record:
            # Calculate success rate from aggregate stats
            success_rate = 0.0
            if learning_record.total_feedback > 0:
                success_rate = learning_record.positive_feedback / learning_record.total_feedback
                
            aggregate_data = {
                "aggregate_total": learning_record.total_feedback,
                "aggregate_success_rate": success_rate,
                "current_parameters": learning_record.parameters_json
            }
            
            if success_rate < 0.5:
                signals.append({
                    "type": "warning",
                    "message": f"Long-term success rate for agent is low: {success_rate:.1%}",
                    "confidence_impact": "negative"
                })

        return {
            "agent_id": agent_id,
            "total_feedback_in_period": total,
            "positive_ratio_in_period": positive_ratio,
            "correction_count_in_period": len(corrections),
            "aggregate_data": aggregate_data,
            "learning_signals": signals,
            "improvement_suggestions": suggestions
        }

    async def record_feedback_in_world_model(
        self,
        feedback: AgentFeedback
    ) -> bool:
        """
        Record feedback as a learning experience in the world model.

        This enables agents to learn from past feedback and avoid repeating mistakes.

        Args:
            feedback: Feedback object to record

        Returns:
            True if successfully recorded, False otherwise
        """
        try:
            # Get execution context if available
            execution = None
            if feedback.agent_execution_id:
                execution = self.db.query(AgentExecution).filter(
                    AgentExecution.id == feedback.agent_execution_id
                ).first()

            # Determine outcome based on feedback
            if feedback.thumbs_up_down is True or (feedback.rating and feedback.rating >= 4):
                outcome = "Success"
            elif feedback.thumbs_up_down is False or (feedback.rating and feedback.rating <= 2):
                outcome = "Failure"
            else:
                outcome = "Mixed"

            # Calculate feedback score (-1.0 to 1.0)
            feedback_score = 0.0

            if feedback.thumbs_up_down is not None:
                feedback_score += 0.5 if feedback.thumbs_up_down else -0.5

            if feedback.rating is not None:
                # Map 1-5 to -1.0 to 1.0
                feedback_score += (feedback.rating - 3) / 2.0

            # Clamp to [-1.0, 1.0]
            feedback_score = max(-1.0, min(1.0, feedback_score))

            # Create experience
            experience = AgentExperience(
                id=str(uuid.uuid4()),  # Will need to import uuid
                agent_id=feedback.agent_id,
                task_type=feedback.feedback_type or "general",
                input_summary=feedback.input_context or "User feedback",
                outcome=outcome,
                learnings=feedback.user_correction or feedback.ai_reasoning or "",
                confidence_score=0.5,
                feedback_score=feedback_score,
                artifacts=[feedback.agent_execution_id] if feedback.agent_execution_id else [],
                agent_role="Agent",  # Could be enhanced
                specialty=None,
                timestamp=datetime.now()
            )

            # Record in world model
            success = await self.world_model.record_experience(experience)

            if success:
                logger.info(
                    f"Recorded feedback in world model: agent={feedback.agent_id}, "
                    f"feedback_score={feedback_score:.2f}"
                )

            return success

        except Exception as e:
            logger.error(f"Failed to record feedback in world model: {e}")
            return False

    def batch_update_confidence_from_feedback(
        self,
        agent_id: str,
        days: int = 30
    ) -> Optional[float]:
        """
        Batch update agent confidence based on recent feedback.

        Aggregates all feedback from the last N days and adjusts confidence.

        Args:
            agent_id: ID of the agent
            days: Number of days to analyze

        Returns:
            New confidence score, or None if agent not found
        """
        agent = self.db.query(AgentRegistry).filter(
            AgentRegistry.id == agent_id
        ).first()

        if not agent:
            return None

        cutoff_date = datetime.now() - timedelta(days=days)

        # Get recent feedback
        feedback = self.db.query(AgentFeedback).filter(
            AgentFeedback.agent_id == agent_id,
            AgentFeedback.created_at >= cutoff_date
        ).all()

        if not feedback:
            return agent.confidence_score

        # Calculate aggregate adjustment
        total_adjustment = 0.0

        for f in feedback:
            # Use individual feedback adjustments
            # Weight by recency (more recent = higher weight)
            days_old = (datetime.now() - f.created_at).days
            recency_weight = max(0.1, 1.0 - (days_old / days))  # Decay to 0.1

            adjustment = 0.0

            if f.thumbs_up_down is True:
                adjustment += 0.05
            elif f.thumbs_up_down is False:
                adjustment -= 0.05

            if f.rating is not None:
                rating_weights = {1: -0.10, 2: -0.05, 3: 0.00, 4: 0.05, 5: 0.10}
                adjustment += rating_weights.get(f.rating, 0.0)

            if f.feedback_type == "correction":
                adjustment -= 0.03

            total_adjustment += adjustment * recency_weight

        # Apply adjustment
        new_confidence = max(0.0, min(1.0, agent.confidence_score + total_adjustment))

        logger.info(
            f"Batch confidence update for agent {agent_id}: "
            f"{agent.confidence_score:.3f} -> {new_confidence:.3f} "
            f"(total adjustment: {total_adjustment:+.3f} from {len(feedback)} feedback)"
        )

        return new_confidence

    async def record_user_correction(
        self,
        agent_id: str,
        tenant_id: str,
        original_action: Dict[str, Any],
        corrected_action: Dict[str, Any],
        context: Optional[str] = None
    ) -> str:
        """
        Record a user correction for agent learning.
        Ported from SaaS LearningService.
        """
        experience_id = str(uuid.uuid4())
        try:
            # Classify correction type
            correction_type = self._classify_correction(original_action, corrected_action)

            experience = CognitiveExperience(
                id=experience_id,
                tenant_id=tenant_id,
                agent_id=agent_id,
                experience_type="user_correction",
                task_type=corrected_action.get("action_type", "unknown"),
                input_summary=context or "User correction in GuidancePanel",
                output_summary=json.dumps({
                    "original": original_action,
                    "corrected": corrected_action
                }),
                outcome="correction",
                learnings={
                    "original_action": original_action,
                    "corrected_action": corrected_action,
                    "correction_type": correction_type,
                    "timestamp": datetime.now(timezone.utc).isoformat()
                },
                effectiveness_score=0.0
            )

            self.db.add(experience)
            
            # Also adjust confidence (penalty for needing correction)
            agent = self.db.query(AgentRegistry).filter(AgentRegistry.id == agent_id).first()
            if agent:
                # Penalty: -0.05
                agent.confidence_score = max(0.0, (agent.confidence_score or 0.5) - 0.05)
                logger.info(f"Penalty for correction: Agent {agent_id} confidence -> {agent.confidence_score:.2f}")

            self.db.commit()
            
            # Continuous learning update (adaptive parameters)
            try:
                self.continuous_learning.update_from_feedback(AgentFeedback(
                    tenant_id=tenant_id,
                    agent_id=agent_id,
                    feedback_type="correction",
                    user_correction=json.dumps(corrected_action),
                    created_at=datetime.now(timezone.utc)
                ))
            except Exception as le:
                logger.warning(f"Continuous learning update failed: {le}")

            logger.info(f"Recorded user correction for agent {agent_id}: {correction_type}")
            return experience_id

        except Exception as e:
            logger.error(f"Failed to record user correction: {e}")
            self.db.rollback()
            raise

    def _classify_correction(self, original: Dict, corrected: Dict) -> str:
        """Classify the type of correction made."""
        if not isinstance(original, dict) or not isinstance(corrected, dict):
            return "other_correction"
        if original.get("action_type") != corrected.get("action_type"):
            return "action_type_change"
        if original.get("parameters") != corrected.get("parameters"):
            return "parameter_adjustment"
        return "other_correction"

    async def record_rejection(
        self,
        agent_id: str,
        tenant_id: str,
        action_type: str,
        action_data: Dict[str, Any],
        reason: Optional[str] = None,
        context: Optional[str] = None
    ) -> str:
        """Record a user rejection for agent learning."""
        experience_id = str(uuid.uuid4())
        try:
            experience = CognitiveExperience(
                id=experience_id,
                tenant_id=tenant_id,
                agent_id=agent_id,
                experience_type="user_rejection",
                task_type=action_type,
                input_summary=context or "User rejection in GuidancePanel",
                output_summary=json.dumps({
                    "proposed_action": action_data,
                    "rejection_reason": reason
                }),
                outcome="rejection",
                learnings={
                    "proposed_action": action_data,
                    "rejection_reason": reason,
                    "rejection_type": "explicit_rejection"
                },
                effectiveness_score=-0.5
            )

            self.db.add(experience)
            
            # Confidence penalty: -0.1 (stronger than correction)
            agent = self.db.query(AgentRegistry).filter(AgentRegistry.id == agent_id).first()
            if agent:
                agent.confidence_score = max(0.0, (agent.confidence_score or 0.5) - 0.1)
                logger.info(f"Penalty for rejection: Agent {agent_id} confidence -> {agent.confidence_score:.2f}")

            self.db.commit()
            
            # Continuous learning update (adaptive parameters)
            try:
                self.continuous_learning.update_from_feedback(AgentFeedback(
                    tenant_id=tenant_id,
                    agent_id=agent_id,
                    feedback_type="rejection",
                    ai_reasoning=reason,
                    created_at=datetime.now(timezone.utc)
                ))
            except Exception as le:
                logger.warning(f"Continuous learning update failed: {le}")

            return experience_id
        except Exception as e:
            logger.error(f"Failed to record rejection: {e}")
            self.db.rollback()
            raise

    async def analyze_failure_patterns(
        self,
        agent_id: str,
        tenant_id: str,
        min_occurrences: int = 3
    ) -> List[Dict[str, Any]]:
        """Identify recurring failure patterns from CognitiveExperience records."""
        try:
            failures = self.db.query(CognitiveExperience).filter(
                CognitiveExperience.agent_id == agent_id,
                CognitiveExperience.tenant_id == tenant_id,
                CognitiveExperience.outcome.in_(["failure", "correction", "rejection"])
            ).order_by(CognitiveExperience.created_at.desc()).limit(100).all()

            patterns: Dict[str, Dict[str, Any]] = {}
            for exp in failures:
                l = exp.learnings or {}
                c_type = l.get("correction_type") or l.get("rejection_type") or "unknown"
                if c_type not in patterns:
                    patterns[c_type] = {"type": c_type, "count": 0, "examples": []}
                patterns[c_type]["count"] += 1
                if len(patterns[c_type]["examples"]) < 3:
                    patterns[c_type]["examples"].append(exp.task_type)

            return [p for p in patterns.values() if p["count"] >= min_occurrences]
        except Exception as e:
            logger.error(f"Failed to analyze failure patterns: {e}")
            return []