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ALwrity version 0.5.5

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ajaysi
2025-08-15 08:28:34 +05:30
parent 2b8c66c4d0
commit 55a97b2fd4
77 changed files with 8717 additions and 7567 deletions
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56
backend/api/content_planning/services/ai_analytics_service.py
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@@ -137,7 +137,7 @@ class ContentPlanningAIAnalyticsService:
raise ContentPlanningErrorHandler.handle_general_error(e, "generate_strategic_intelligence")
async def get_ai_analytics(self, user_id: Optional[int] = None, strategy_id: Optional[int] = None, force_refresh: bool = False) -> Dict[str, Any]:
"""Get AI analytics with real personalized insights - Database first approach."""
"""Get AI analytics with real personalized insights - FORCE FRESH AI GENERATION."""
try:
logger.info(f"🚀 Starting AI analytics for user: {user_id}, strategy: {strategy_id}, force_refresh: {force_refresh}")
start_time = time.time()
@@ -145,37 +145,51 @@ class ContentPlanningAIAnalyticsService:
# Use user_id or default to 1
current_user_id = user_id or 1
# Skip database check if force_refresh is True
# 🚨 CRITICAL: Always force fresh AI generation for refresh operations
if force_refresh:
logger.info(f"🔄 FORCE REFRESH: Deleting all cached AI analysis for user {current_user_id}")
try:
await self.ai_analysis_db_service.delete_old_ai_analyses(days_old=0)
logger.info(f"✅ Deleted all cached AI analysis for user {current_user_id}")
except Exception as e:
logger.warning(f"⚠️ Failed to delete cached analysis: {str(e)}")
# 🚨 CRITICAL: Skip database check for refresh operations to ensure fresh AI generation
if not force_refresh:
# First, try to get existing AI analysis from database
# Only check database for non-refresh operations
logger.info(f"🔍 Checking database for existing AI analysis for user {current_user_id}")
existing_analysis = await self.ai_analysis_db_service.get_latest_ai_analysis(
user_id=current_user_id,
analysis_type="comprehensive_analysis",
strategy_id=strategy_id,
max_age_hours=24 # Use cached results up to 24 hours old
max_age_hours=1 # 🚨 CRITICAL: Reduced from 24 hours to 1 hour to minimize stale data
)
if existing_analysis:
logger.info(f"✅ Found existing AI analysis in database: {existing_analysis.get('id', 'unknown')}")
cache_age_hours = (datetime.utcnow() - existing_analysis.get('created_at', datetime.utcnow())).total_seconds() / 3600
logger.info(f"✅ Found existing AI analysis in database: {existing_analysis.get('id', 'unknown')} (age: {cache_age_hours:.1f} hours)")
# Return cached results
return {
"insights": existing_analysis.get('insights', []),
"recommendations": existing_analysis.get('recommendations', []),
"total_insights": len(existing_analysis.get('insights', [])),
"total_recommendations": len(existing_analysis.get('recommendations', [])),
"generated_at": existing_analysis.get('created_at', datetime.utcnow()).isoformat(),
"ai_service_status": existing_analysis.get('ai_service_status', 'operational'),
"processing_time": f"{existing_analysis.get('processing_time', 0):.2f}s" if existing_analysis.get('processing_time') else "cached",
"personalized_data_used": True if existing_analysis.get('personalized_data_used') else False,
"data_source": "database_cache",
"cache_age_hours": (datetime.utcnow() - existing_analysis.get('created_at', datetime.utcnow())).total_seconds() / 3600,
"user_profile": existing_analysis.get('personalized_data_used', {})
}
# Return cached results only if very recent (less than 1 hour)
if cache_age_hours < 1:
logger.info(f"📋 Using cached AI analysis (age: {cache_age_hours:.1f} hours)")
return {
"insights": existing_analysis.get('insights', []),
"recommendations": existing_analysis.get('recommendations', []),
"total_insights": len(existing_analysis.get('insights', [])),
"total_recommendations": len(existing_analysis.get('recommendations', [])),
"generated_at": existing_analysis.get('created_at', datetime.utcnow()).isoformat(),
"ai_service_status": existing_analysis.get('ai_service_status', 'operational'),
"processing_time": f"{existing_analysis.get('processing_time', 0):.2f}s" if existing_analysis.get('processing_time') else "cached",
"personalized_data_used": True if existing_analysis.get('personalized_data_used') else False,
"data_source": "database_cache",
"cache_age_hours": cache_age_hours,
"user_profile": existing_analysis.get('personalized_data_used', {})
}
else:
logger.info(f"🔄 Cached analysis too old ({cache_age_hours:.1f} hours) - generating fresh AI analysis")
# No recent analysis found or force refresh requested, run new AI analysis
logger.info(f"🔄 Running new AI analysis for user {current_user_id} (force_refresh: {force_refresh})")
# 🚨 CRITICAL: Always run fresh AI analysis for refresh operations
logger.info(f"🔄 Running FRESH AI analysis for user {current_user_id} (force_refresh: {force_refresh})")
# Get personalized inputs from onboarding data
personalized_inputs = self.onboarding_service.get_personalized_ai_inputs(current_user_id)