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Zhongwei Li
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integrations/learning_feedback.py
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390
integrations/learning_feedback.py
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#!/usr/bin/env python3
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"""
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Learning Feedback System - Subtle Progress Indicators
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Provides subtle, non-intrusive feedback about agent learning progress.
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Users see natural improvement without being overwhelmed with technical details.
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All feedback is designed to feel like "smart magic" rather than "system notifications".
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"""
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import json
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import time
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import logging
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from pathlib import Path
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from typing import Dict, Any, List, Optional
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from dataclasses import dataclass
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from datetime import datetime, timedelta
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from agentdb_bridge import get_agentdb_bridge
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from validation_system import get_validation_system
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logger = logging.getLogger(__name__)
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@dataclass
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class LearningMilestone:
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"""Represents a learning milestone achieved by an agent"""
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milestone_type: str
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description: str
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impact: str # How this benefits the user
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confidence: float
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timestamp: datetime
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class LearningFeedbackSystem:
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"""
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Provides subtle feedback about agent learning progress.
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All feedback is designed to feel natural and helpful,
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not technical or overwhelming.
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"""
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def __init__(self):
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self.agentdb_bridge = get_agentdb_bridge()
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self.validation_system = get_validation_system()
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self.feedback_history = []
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self.user_patterns = {}
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self.milestones_achieved = []
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def analyze_agent_usage(self, agent_name: str, user_input: str, execution_time: float,
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success: bool, result_quality: float) -> Optional[str]:
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"""
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Analyze agent usage and provide subtle feedback if appropriate.
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Returns feedback message or None if no feedback needed.
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"""
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try:
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# Track user patterns
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self._track_user_pattern(agent_name, user_input, execution_time)
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# Check for learning milestones
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milestone = self._check_for_milestone(agent_name, execution_time, success, result_quality)
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if milestone:
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self.milestones_achieved.append(milestone)
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return self._format_milestone_feedback(milestone)
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# Check for improvement indicators
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improvement = self._detect_improvement(agent_name, execution_time, result_quality)
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if improvement:
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return self._format_improvement_feedback(improvement)
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# Check for pattern recognition
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pattern_feedback = self._generate_pattern_feedback(agent_name, user_input)
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if pattern_feedback:
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return pattern_feedback
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except Exception as e:
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logger.debug(f"Failed to analyze agent usage: {e}")
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return None
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def _track_user_pattern(self, agent_name: str, user_input: str, execution_time: float):
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"""Track user interaction patterns"""
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if agent_name not in self.user_patterns:
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self.user_patterns[agent_name] = {
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"queries": [],
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"times": [],
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"successes": [],
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"execution_times": [],
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"first_interaction": datetime.now()
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}
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pattern = self.user_patterns[agent_name]
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pattern["queries"].append(user_input)
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pattern["times"].append(execution_time)
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pattern["successes"].append(success)
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pattern["execution_times"].append(execution_time)
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# Keep only last 100 interactions
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for key in ["queries", "times", "successes", "execution_times"]:
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if len(pattern[key]) > 100:
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pattern[key] = pattern[key][-100:]
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def _check_for_milestone(self, agent_name: str, execution_time: float,
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success: bool, result_quality: float) -> Optional[LearningMilestone]:
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"""Check if user achieved a learning milestone"""
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pattern = self.user_patterns.get(agent_name, {})
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# Milestone 1: First successful execution
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if len(pattern.get("successes", [])) == 1 and success:
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return LearningMilestone(
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milestone_type="first_success",
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description="First successful execution",
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impact=f"Agent {agent_name} is now active and learning",
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confidence=0.9,
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timestamp=datetime.now()
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)
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# Milestone 2: Consistency (10 successful uses)
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success_count = len([s for s in pattern.get("successes", []) if s])
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if success_count == 10:
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return LearningMilestone(
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milestone_type="consistency",
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description="10 successful executions",
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impact=f"Agent {agent_name} is reliable and consistent",
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confidence=0.85,
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timestamp=datetime.now()
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)
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# Milestone 3: Speed improvement (20% faster than average)
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if len(pattern.get("execution_times", [])) >= 10:
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recent_times = pattern["execution_times"][-5:]
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early_times = pattern["execution_times"][:5]
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recent_avg = sum(recent_times) / len(recent_times)
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early_avg = sum(early_times) / len(early_times)
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if early_avg > 0 and recent_avg < early_avg * 0.8: # 20% improvement
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return LearningMilestone(
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milestone_type="speed_improvement",
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description="20% faster execution speed",
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impact=f"Agent {agent_name} has optimized and become faster",
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confidence=0.8,
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timestamp=datetime.now()
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)
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# Milestone 4: Long-term relationship (30 days)
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if pattern.get("first_interaction"):
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days_since_first = (datetime.now() - pattern["first_interaction"]).days
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if days_since_first >= 30:
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return LearningMilestone(
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milestone_type="long_term_usage",
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description="30 days of consistent usage",
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impact=f"Agent {agent_name} has learned your preferences over time",
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confidence=0.95,
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timestamp=datetime.now()
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)
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return None
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def _detect_improvement(self, agent_name: str, execution_time: float,
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result_quality: float) -> Optional[Dict[str, Any]]:
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"""Detect if agent shows improvement signs"""
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pattern = self.user_patterns.get(agent_name, {})
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if len(pattern.get("execution_times", [])) < 5:
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return None
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recent_times = pattern["execution_times"][-3:]
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avg_recent = sum(recent_times) / len(recent_times)
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# Check speed improvement
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if avg_recent < 2.0: # Fast execution
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return {
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"type": "speed",
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"message": f"⚡ Agent is responding quickly",
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"detail": f"Average time: {avg_recent:.1f}s"
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}
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# Check quality improvement
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if result_quality > 0.9:
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return {
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"type": "quality",
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"message": f"✨ High quality results detected",
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"detail": f"Result quality: {result_quality:.1%}"
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}
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return None
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def _generate_pattern_feedback(self, agent_name: str, user_input: str) -> Optional[str]:
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"""Generate feedback based on user interaction patterns"""
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pattern = self.user_patterns.get(agent_name, {})
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if len(pattern.get("queries", [])) < 5:
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return None
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queries = pattern["queries"]
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# Check for time-based patterns
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hour = datetime.now().hour
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weekday = datetime.now().weekday()
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# Morning patterns
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if 6 <= hour <= 9 and len([q for q in queries[-5:] if "morning" in q.lower() or "today" in q.lower()]) >= 3:
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return f"🌅 Good morning! {agent_name} is ready for your daily analysis"
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# Friday patterns
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if weekday == 4 and len([q for q in queries[-10:] if "week" in q.lower() or "friday" in q.lower()]) >= 2:
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return f"📊 {agent_name} is preparing your weekly summary"
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# End of month patterns
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day_of_month = datetime.now().day
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if day_of_month >= 28 and len([q for q in queries[-10:] if "month" in q.lower()]) >= 2:
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return f"📈 {agent_name} is ready for your monthly reports"
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return None
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def _format_milestone_feedback(self, milestone: LearningMilestone) -> str:
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"""Format milestone feedback to feel natural and encouraging"""
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messages = {
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"first_success": [
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f"🎉 Congratulations! {milestone.description}",
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f"🎉 Agent is now active and ready to assist you!"
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],
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"consistency": [
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f"🎯 Excellent! {milestone.description}",
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f"🎯 Your agent has proven its reliability"
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],
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"speed_improvement": [
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f"⚡ Amazing! {milestone.description}",
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f"⚡ Your agent is getting much faster with experience"
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],
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"long_term_usage": [
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f"🌟 Fantastic! {milestone.description}",
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f"🌟 Your agent has learned your preferences and patterns"
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]
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}
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message_set = messages.get(milestone.milestone_type, ["✨ Milestone achieved!"])
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return message_set[0] if message_set else f"✨ {milestone.description}"
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def _format_improvement_feedback(self, improvement: Dict[str, Any]) -> str:
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"""Format improvement feedback to feel helpful but not overwhelming"""
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if improvement["type"] == "speed":
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return f"{improvement['message']} ({improvement['detail']})"
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elif improvement["type"] == "quality":
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return f"{improvement['message']} ({improvement['detail']})"
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else:
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return improvement["message"]
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def get_learning_summary(self, agent_name: str) -> Dict[str, Any]:
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"""Get comprehensive learning summary for an agent"""
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try:
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# Get AgentDB learning summary
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agentdb_summary = self.agentdb_bridge.get_learning_summary(agent_name)
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# Get validation summary
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validation_summary = self.validation_system.get_validation_summary()
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# Get user patterns
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pattern = self.user_patterns.get(agent_name, {})
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# Calculate user statistics
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total_queries = len(pattern.get("queries", []))
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success_rate = (sum(pattern.get("successes", [])) / len(pattern.get("successes", [False])) * 100) if pattern.get("successes") else 0
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avg_time = sum(pattern.get("execution_times", [])) / len(pattern.get("execution_times", [1])) if pattern.get("execution_times") else 0
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# Get milestones
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milestones = [m for m in self.milestones_achieved if m.description and agent_name.lower() in m.description.lower()]
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return {
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"agent_name": agent_name,
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"agentdb_learning": agentdb_summary,
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"validation_performance": validation_summary,
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"user_statistics": {
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"total_queries": total_queries,
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"success_rate": success_rate,
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"average_time": avg_time,
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"first_interaction": pattern.get("first_interaction"),
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"last_interaction": datetime.now() if pattern else None
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},
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"milestones_achieved": [
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{
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"type": m.milestone_type,
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"description": m.description,
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"impact": m.impact,
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"confidence": m.confidence,
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"timestamp": m.timestamp.isoformat()
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}
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for m in milestones
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],
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"learning_progress": self._calculate_progress_score(agent_name)
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}
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except Exception as e:
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logger.error(f"Failed to get learning summary: {e}")
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return {"error": str(e)}
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def _calculate_progress_score(self, agent_name: str) -> float:
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"""Calculate overall learning progress score"""
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score = 0.0
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# AgentDB contributions (40%)
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try:
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agentdb_summary = self.agentdb_bridge.get_learning_summary(agent_name)
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if agentdb_summary and agentdb_summary.get("total_sessions", 0) > 0:
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score += min(0.4, agentdb_summary["success_rate"] * 0.4)
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except:
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pass
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# User engagement (30%)
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pattern = self.user_patterns.get(agent_name, {})
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if pattern.get("successes"):
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engagement_rate = sum(pattern["successes"]) / len(pattern["successes"])
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score += min(0.3, engagement_rate * 0.3)
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# Milestones (20%)
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milestone_score = min(len(self.milestones_achieved) / 4, 0.2) # Max 4 milestones
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score += milestone_score
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# Consistency (10%)
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if len(pattern.get("successes", [])) >= 10:
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consistency = sum(pattern["successes"][-10:]) / 10
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score += min(0.1, consistency * 0.1)
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return min(score, 1.0)
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def suggest_personalization(self, agent_name: str) -> Optional[str]:
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"""
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Suggest personalization based on learned patterns.
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Returns subtle suggestion or None.
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"""
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try:
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pattern = self.user_patterns.get(agent_name, {})
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# Check if user always asks for similar things
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recent_queries = pattern.get("queries", [])[-10:]
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# Look for common themes
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themes = {}
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for query in recent_queries:
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words = query.lower().split()
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for word in words:
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if len(word) > 3: # Ignore short words
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themes[word] = themes.get(word, 0) + 1
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# Find most common theme
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if themes:
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top_theme = max(themes, key=themes.get)
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if themes[top_theme] >= 3: # Appears in 3+ recent queries
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return f"🎯 I notice you often ask about {top_theme}. Consider creating a specialized agent for this."
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except Exception as e:
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logger.debug(f"Failed to suggest personalization: {e}")
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return None
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# Global feedback system (invisible to users)
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_learning_feedback_system = None
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def get_learning_feedback_system() -> LearningFeedbackSystem:
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"""Get the global learning feedback system instance"""
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global _learning_feedback_system
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if _learning_feedback_system is None:
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_learning_feedback_system = LearningFeedbackSystem()
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return _learning_feedback_system
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def analyze_agent_execution(agent_name: str, user_input: str, execution_time: float,
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success: bool, result_quality: float) -> Optional[str]:
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"""
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Analyze agent execution and provide learning feedback.
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Called automatically after each agent execution.
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"""
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system = get_learning_feedback_system()
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return system.analyze_agent_usage(agent_name, user_input, execution_time, success, result_quality)
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def get_agent_learning_summary(agent_name: str) -> Dict[str, Any]:
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"""
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Get comprehensive learning summary for an agent.
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Used internally for progress tracking.
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"""
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system = get_learning_feedback_system()
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return system.get_learning_summary(agent_name)
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def suggest_agent_personalization(agent_name: str) -> Optional[str]:
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"""
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Suggest personalization based on learned patterns.
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Used when appropriate to enhance user experience.
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"""
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system = get_learning_feedback_system()
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return system.suggest_personalization(agent_name)
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# Auto-initialize when module is imported
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get_learning_feedback_system()
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Reference in New Issue
Block a user