Updates

ETB-API/knowledge_learning/management/commands/generate_recommendations.py

@@ -0,0 +1,138 @@
+from django.core.management.base import BaseCommand, CommandError
+from django.utils import timezone
+from datetime import timedelta
+from incident_intelligence.models import Incident
+from knowledge_learning.services.recommendation_engine import RecommendationEngine
+from knowledge_learning.models import IncidentRecommendation
+
+
+class Command(BaseCommand):
+    help = 'Generate recommendations for incidents'
+
+    def add_arguments(self, parser):
+        parser.add_argument(
+            '--days',
+            type=int,
+            default=1,
+            help='Number of days back to look for incidents (default: 1)'
+        )
+        parser.add_argument(
+            '--status',
+            type=str,
+            choices=['OPEN', 'IN_PROGRESS', 'RESOLVED', 'CLOSED'],
+            default='OPEN',
+            help='Only generate recommendations for incidents with specific status (default: OPEN)'
+        )
+        parser.add_argument(
+            '--severity',
+            type=str,
+            choices=['LOW', 'MEDIUM', 'HIGH', 'CRITICAL', 'EMERGENCY'],
+            help='Only generate recommendations for incidents with specific severity'
+        )
+        parser.add_argument(
+            '--force',
+            action='store_true',
+            help='Force generation even if recommendations already exist'
+        )
+        parser.add_argument(
+            '--dry-run',
+            action='store_true',
+            help='Show what would be generated without actually creating recommendations'
+        )
+        parser.add_argument(
+            '--max-recommendations',
+            type=int,
+            default=5,
+            help='Maximum number of recommendations per incident (default: 5)'
+        )
+
+    def handle(self, *args, **options):
+        days = options['days']
+        status = options['status']
+        severity = options['severity']
+        force = options['force']
+        dry_run = options['dry_run']
+        max_recommendations = options['max_recommendations']
+
+        # Calculate date range
+        end_date = timezone.now()
+        start_date = end_date - timedelta(days=days)
+
+        self.stdout.write(
+            self.style.SUCCESS(f'Looking for {status} incidents from {start_date.date()} to {end_date.date()}')
+        )
+
+        # Build queryset for incidents
+        queryset = Incident.objects.filter(
+            status=status,
+            created_at__gte=start_date,
+            created_at__lte=end_date
+        )
+
+        if severity:
+            queryset = queryset.filter(severity=severity)
+
+        # Filter out incidents that already have recommendations (unless force is used)
+        if not force:
+            existing_recommendation_incidents = IncidentRecommendation.objects.values_list('incident_id', flat=True)
+            queryset = queryset.exclude(id__in=existing_recommendation_incidents)
+
+        incidents = list(queryset)
+        
+        if not incidents:
+            self.stdout.write(
+                self.style.WARNING('No incidents found matching criteria')
+            )
+            return
+
+        self.stdout.write(
+            self.style.SUCCESS(f'Found {len(incidents)} incidents to process')
+        )
+
+        if dry_run:
+            self.stdout.write(self.style.WARNING('DRY RUN - No recommendations will be created'))
+            for incident in incidents:
+                self.stdout.write(f'  - {incident.title} ({incident.severity}) - {incident.created_at}')
+            return
+
+        # Initialize recommendation engine
+        recommendation_engine = RecommendationEngine()
+        
+        success_count = 0
+        error_count = 0
+        total_recommendations = 0
+
+        for incident in incidents:
+            try:
+                self.stdout.write(f'Generating recommendations for: {incident.title}')
+                
+                recommendations = recommendation_engine.generate_recommendations(
+                    incident_id=str(incident.id),
+                    max_recommendations=max_recommendations,
+                    min_confidence=0.5
+                )
+                
+                self.stdout.write(
+                    self.style.SUCCESS(f'  ✓ Generated {len(recommendations)} recommendations')
+                )
+                success_count += 1
+                total_recommendations += len(recommendations)
+                
+            except Exception as e:
+                self.stdout.write(
+                    self.style.ERROR(f'  ✗ Failed to generate recommendations: {str(e)}')
+                )
+                error_count += 1
+
+        # Summary
+        self.stdout.write('\n' + '='*50)
+        self.stdout.write(
+            self.style.SUCCESS(f'Recommendation generation completed:')
+        )
+        self.stdout.write(f'  Incidents processed: {success_count}')
+        self.stdout.write(f'  Total recommendations generated: {total_recommendations}')
+        if error_count > 0:
+            self.stdout.write(
+                self.style.ERROR(f'  Failed: {error_count}')
+            )
+        self.stdout.write('='*50)