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import time
import re
from typing import Dict, Any, List, Optional
from datetime import datetime
from utils.config import config
class SmartContextDetector:
"""Intelligently detects when context is relevant to user queries"""
def __init__(self):
# Keywords that suggest time/weather relevance
self.time_indicators = [
'time', 'date', 'today', 'now', 'current', 'moment',
'morning', 'afternoon', 'evening', 'night', 'weekend',
'what time', 'what day', 'what year', 'what month'
]
self.weather_indicators = [
'weather', 'temperature', 'rain', 'snow', 'sunny', 'cloudy',
'forecast', 'climate', 'season', 'hot', 'cold', 'warm', 'cool',
'umbrella', 'jacket', 'outdoor', 'outside'
]
self.location_indicators = [
'in', 'at', 'near', 'around', 'local', 'here', 'there'
]
def should_include_time_context(self, user_query: str,
conversation_history: List[Dict] = None) -> bool:
"""Determine if time context is relevant to the query"""
query_lower = user_query.lower()
# Direct time references
if any(indicator in query_lower for indicator in self.time_indicators):
return True
# Check conversation context for time-related discussions
if conversation_history:
recent_messages = conversation_history[-3:] # Last 3 messages
context_text = " ".join([msg.get('content', '') for msg in recent_messages]).lower()
if any(word in context_text for word in self.time_indicators):
return True
return False
def should_include_weather_context(self, user_query: str,
conversation_history: List[Dict] = None) -> bool:
"""Determine if weather context is relevant to the query"""
query_lower = user_query.lower()
# Direct weather references
if any(indicator in query_lower for indicator in self.weather_indicators):
return True
# Location + activity combinations that often involve weather
location_mentioned = any(loc.lower() in query_lower for loc in
['new york', 'london', 'tokyo', 'paris', 'los angeles', 'sydney', 'singapore', 'mumbai'])
activity_mentioned = any(activity in query_lower for activity in
['outdoor', 'outside', 'walk', 'run', 'travel', 'trip', 'plans', 'going'])
if location_mentioned and activity_mentioned:
return True
# Check conversation context
if conversation_history:
recent_messages = conversation_history[-3:]
context_text = " ".join([msg.get('content', '') for msg in recent_messages]).lower()
if any(word in context_text for word in self.weather_indicators):
return True
return False
def extract_location_if_relevant(self, user_query: str) -> Optional[str]:
"""Extract location if query suggests location-specific context"""
# Common locations
locations = {
'new york': ['new york', 'ny', 'nyc'],
'london': ['london', 'uk', 'england'],
'tokyo': ['tokyo', 'japan', 'jp'],
'paris': ['paris', 'france', 'fr'],
'los angeles': ['los angeles', 'la', 'california'],
'sydney': ['sydney', 'australia', 'au'],
'singapore': ['singapore', 'sg'],
'mumbai': ['mumbai', 'india', 'in']
}
query_lower = user_query.lower()
for location, aliases in locations.items():
if any(alias in query_lower for alias in aliases):
return location
return None
def get_relevant_context(self, user_query: str,
conversation_history: List[Dict] = None) -> Dict[str, Any]:
"""Get only the context that's actually relevant"""
context = {
'include_time': self.should_include_time_context(user_query, conversation_history),
'include_weather': self.should_include_weather_context(user_query, conversation_history),
'detected_location': self.extract_location_if_relevant(user_query),
'timestamp': time.time()
}
# Add actual context data only if needed
if context['include_time']:
now = datetime.now()
context['time_data'] = {
'current_time': now.strftime("%A, %B %d, %Y at %I:%M %p"),
'day_of_week': now.strftime("%A"),
'is_business_hours': 9 <= now.hour <= 17
}
return context
# Global instance
smart_context = SmartContextDetector()
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