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"""
Pattern Detection Module
Detects risk signals and warning patterns in conversations.
"""
from typing import List, Dict, Any
import re
class PatternDetector:
"""Detects risk signals in message patterns."""
def __init__(self):
"""Initialize pattern detector."""
self.comparison_keywords = [
'competitor', 'alternative', 'better', 'cheaper', 'faster',
'other', 'someone else', 'another', 'different', 'switch',
'change', 'similar', 'compare', 'versus', 'instead'
]
self.frustration_keywords = [
'slow', 'late', 'delayed', 'wait', 'frustrated', 'annoyed',
'angry', 'upset', 'disappointed', 'problem', 'issue', 'bug',
'broken', 'not working', 'fail', 'error', 'impossible'
]
self.disengagement_keywords = [
'cancel', 'stop', 'end', 'quit', 'leave', 'exit', 'goodbye',
'farewell', 'thanks anyway', 'no thanks', 'decline', 'refuse',
'not interested', 'moving on', 'consider', 'think about',
'evaluate', 'looking at'
]
self.price_keywords = [
'expensive', 'cost', 'price', 'cheap', 'expensive', 'fee',
'charge', 'budget', 'discount', 'negotiate', 'lower'
]
def detect_signals(self, messages: List[Dict[str, Any]],
context: str = "general") -> Dict[str, Any]:
"""
Detect risk signals in conversations.
Args:
messages: List of messages
context: Type of relationship (customer/employee/investor/general)
Returns:
Dictionary with detected signals and risk assessment
"""
if not messages:
return self._empty_signals()
signals = []
risk_scores = []
breaking_point = None
key_phrases = []
for i, msg in enumerate(messages):
# Handle both strings and dicts
if isinstance(msg, dict):
text = msg.get('text', '').lower()
timestamp = msg.get('timestamp', f'Message {i+1}')
elif isinstance(msg, str):
text = msg.lower()
timestamp = f'Message {i+1}'
else:
text = str(msg).lower()
timestamp = f'Message {i+1}'
# Check for various signal types
comparison_signal = self._check_comparisons(text)
frustration_signal = self._check_frustration(text)
disengagement_signal = self._check_disengagement(text)
price_signal = self._check_price(text)
msg_signals = []
msg_risk = 0
if comparison_signal['found']:
msg_signals.append(comparison_signal)
msg_risk += 25
key_phrases.append(comparison_signal['text'])
if breaking_point is None:
breaking_point = i + 1
if frustration_signal['found']:
msg_signals.append(frustration_signal)
msg_risk += 30
key_phrases.append(frustration_signal['text'])
if breaking_point is None and msg_risk > 30:
breaking_point = i + 1
if disengagement_signal['found']:
msg_signals.append(disengagement_signal)
msg_risk += 35
key_phrases.append(disengagement_signal['text'])
if breaking_point is None:
breaking_point = i + 1
if price_signal['found']:
msg_signals.append(price_signal)
msg_risk += 20
key_phrases.append(price_signal['text'])
if msg_signals:
signals.append({
'message_index': i + 1,
'timestamp': timestamp,
'signals': msg_signals,
'risk_score': min(100, msg_risk)
})
risk_scores.append(msg_risk)
# Calculate overall risk
overall_risk = max(risk_scores) if risk_scores else 0
risk_level = self._assess_risk_level(overall_risk)
# Generate recommendations
recommendations = self._generate_recommendations(
context, risk_level, signals, breaking_point
)
return {
'signals': signals,
'risk_level': risk_level,
'confidence': min(100, len(signals) * 15),
'breaking_point': breaking_point,
'key_phrases': list(set(key_phrases))[:5], # Top 5 unique phrases
'recommendations': recommendations,
'total_risk_score': min(100, overall_risk)
}
def _check_comparisons(self, text: str) -> Dict[str, Any]:
"""Check for competitor/alternative mentions."""
for keyword in self.comparison_keywords:
if keyword in text:
return {
'found': True,
'type': 'COMPETITOR_COMPARISON',
'text': keyword,
'description': 'Comparing with alternatives or competitors'
}
return {'found': False}
def _check_frustration(self, text: str) -> Dict[str, Any]:
"""Check for frustration indicators."""
for keyword in self.frustration_keywords:
if keyword in text:
return {
'found': True,
'type': 'FRUSTRATION',
'text': keyword,
'description': 'Expressing dissatisfaction or frustration'
}
return {'found': False}
def _check_disengagement(self, text: str) -> Dict[str, Any]:
"""Check for disengagement signals."""
for keyword in self.disengagement_keywords:
if keyword in text:
return {
'found': True,
'type': 'DISENGAGEMENT',
'text': keyword,
'description': 'Showing intent to leave or end relationship'
}
return {'found': False}
def _check_price(self, text: str) -> Dict[str, Any]:
"""Check for price-related concerns."""
for keyword in self.price_keywords:
if keyword in text:
return {
'found': True,
'type': 'PRICE_CONCERN',
'text': keyword,
'description': 'Mentioning cost or pricing concerns'
}
return {'found': False}
def _assess_risk_level(self, risk_score: float) -> str:
"""Assess overall risk level."""
if risk_score >= 70:
return "CRITICAL"
elif risk_score >= 50:
return "HIGH"
elif risk_score >= 30:
return "MEDIUM"
else:
return "LOW"
def _generate_recommendations(self, context: str, risk_level: str,
signals: List[Dict], breaking_point: int) -> List[str]:
"""Generate actionable recommendations."""
recommendations = []
if risk_level == "CRITICAL":
recommendations.append("⚠️ URGENT: Immediate intervention required")
if breaking_point:
recommendations.append(f"Breaking point detected at message {breaking_point}")
if context == "customer":
if risk_level in ["CRITICAL", "HIGH"]:
recommendations.append("Contact customer immediately to address concerns")
recommendations.append("Prepare retention offer (discount/upgrade)")
recommendations.append("Escalate to account manager")
elif context == "employee":
if risk_level in ["CRITICAL", "HIGH"]:
recommendations.append("Schedule 1-on-1 with HR or manager")
recommendations.append("Identify root cause of dissatisfaction")
recommendations.append("Prepare retention plan")
elif context == "investor":
if risk_level in ["CRITICAL", "HIGH"]:
recommendations.append("Prepare detailed response addressing concerns")
recommendations.append("Schedule follow-up meeting")
# Add general recommendations
if any(s.get('type') == 'COMPETITOR_COMPARISON' for s in signals):
recommendations.append("Counter competitive threats with unique value proposition")
if any(s.get('type') == 'PRICE_CONCERN' for s in signals):
recommendations.append("Review pricing strategy and alternative plans")
return recommendations[:5] # Top 5 recommendations
def _empty_signals(self) -> Dict[str, Any]:
"""Return empty signals structure."""
return {
'signals': [],
'risk_level': 'UNKNOWN',
'confidence': 0,
'breaking_point': None,
'key_phrases': [],
'recommendations': []
}
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