Add AI insights feature

services/inventory/app/api/ml_insights.py

@@ -7,7 +7,7 @@ Provides endpoints to trigger ML insight generation for:
 - Demand pattern analysis
 """
 
-from fastapi import APIRouter, Depends, HTTPException
+from fastapi import APIRouter, Depends, HTTPException, Request
 from pydantic import BaseModel, Field
 from typing import Optional, List
 from uuid import UUID
@@ -71,6 +71,7 @@ class SafetyStockOptimizationResponse(BaseModel):
 async def trigger_safety_stock_optimization(
     tenant_id: str,
     request_data: SafetyStockOptimizationRequest,
+    request: Request,
     db: AsyncSession = Depends(get_db)
 ):
     """
@@ -81,10 +82,12 @@ async def trigger_safety_stock_optimization(
     2. Runs the SafetyStockInsightsOrchestrator to optimize levels
     3. Generates insights about safety stock recommendations
     4. Posts insights to AI Insights Service
+    5. Publishes recommendation events to RabbitMQ
 
     Args:
         tenant_id: Tenant UUID
         request_data: Optimization parameters
+        request: FastAPI request (for app state access)
         db: Database session
 
     Returns:
@@ -103,8 +106,13 @@ async def trigger_safety_stock_optimization(
         from app.models.inventory import Ingredient
         from sqlalchemy import select
 
+        # Get event publisher from app state (if available)
+        event_publisher = getattr(request.app.state, 'event_publisher', None) if hasattr(request, 'app') else None
+
         # Initialize orchestrator
-        orchestrator = SafetyStockInsightsOrchestrator()
+        orchestrator = SafetyStockInsightsOrchestrator(
+            event_publisher=event_publisher
+        )
 
         # Get products to optimize
         if request_data.product_ids:
@@ -378,6 +386,7 @@ async def generate_safety_stock_insights_internal(
         result = await trigger_safety_stock_optimization(
             tenant_id=tenant_id,
             request_data=request_data,
+            request=request,
             db=db
         )
 

services/inventory/app/main.py

@@ -126,6 +126,7 @@ class InventoryService(StandardFastAPIService):
             # Store services in app state
             app.state.alert_service = alert_service
             app.state.inventory_scheduler = inventory_scheduler  # Store scheduler for manual triggering
+            app.state.event_publisher = self.event_publisher  # Store event publisher for ML insights
         else:
             self.logger.error("Event publisher not initialized, alert service unavailable")
 

services/inventory/app/ml/safety_stock_insights_orchestrator.py

@@ -14,6 +14,7 @@ import os
 # Add shared clients to path
 sys.path.append(os.path.join(os.path.dirname(__file__), '../../../..'))
 from shared.clients.ai_insights_client import AIInsightsClient
+from shared.messaging import UnifiedEventPublisher
 
 from app.ml.safety_stock_optimizer import SafetyStockOptimizer
 
@@ -28,15 +29,18 @@ class SafetyStockInsightsOrchestrator:
     1. Optimize safety stock from demand history and cost parameters
     2. Generate insights comparing optimal vs hardcoded approach
     3. Post insights to AI Insights Service
-    4. Provide optimized safety stock levels for inventory management
+    4. Publish recommendation events to RabbitMQ
+    5. Provide optimized safety stock levels for inventory management
     """
 
     def __init__(
         self,
-        ai_insights_base_url: str = "http://ai-insights-service:8000"
+        ai_insights_base_url: str = "http://ai-insights-service:8000",
+        event_publisher: Optional[UnifiedEventPublisher] = None
     ):
         self.optimizer = SafetyStockOptimizer()
         self.ai_insights_client = AIInsightsClient(ai_insights_base_url)
+        self.event_publisher = event_publisher
 
     async def optimize_and_post_insights(
         self,
@@ -109,6 +113,17 @@ class SafetyStockInsightsOrchestrator:
                 successful=post_results['successful'],
                 failed=post_results['failed']
             )
+
+            # Step 4: Publish recommendation events to RabbitMQ
+            created_insights = post_results.get('created_insights', [])
+            if created_insights:
+                product_context = product_characteristics.copy() if product_characteristics else {}
+                product_context['inventory_product_id'] = inventory_product_id
+                await self._publish_insight_events(
+                    tenant_id=tenant_id,
+                    insights=created_insights,
+                    product_context=product_context
+                )
         else:
             post_results = {'total': 0, 'successful': 0, 'failed': 0}
             logger.info("No insights to post for product", inventory_product_id=inventory_product_id)
@@ -167,6 +182,84 @@ class SafetyStockInsightsOrchestrator:
 
         return enriched
 
+    async def _publish_insight_events(
+        self,
+        tenant_id: str,
+        insights: List[Dict[str, Any]],
+        product_context: Optional[Dict[str, Any]] = None
+    ) -> None:
+        """
+        Publish recommendation events to RabbitMQ for each insight.
+
+        Args:
+            tenant_id: Tenant identifier
+            insights: List of created insights (with insight_id from AI Insights Service)
+            product_context: Optional product context (name, id, etc.)
+        """
+        if not self.event_publisher:
+            logger.warning("Event publisher not configured, skipping event publication")
+            return
+
+        for insight in insights:
+            try:
+                # Determine severity based on confidence and priority
+                confidence = insight.get('confidence', 0)
+                priority = insight.get('priority', 'medium')
+
+                if priority == 'urgent' or confidence >= 90:
+                    severity = 'urgent'
+                elif priority == 'high' or confidence >= 70:
+                    severity = 'high'
+                elif priority == 'medium' or confidence >= 50:
+                    severity = 'medium'
+                else:
+                    severity = 'low'
+
+                # Build event metadata
+                event_metadata = {
+                    'insight_id': insight.get('id'),
+                    'insight_type': insight.get('insight_type'),
+                    'inventory_product_id': insight.get('metrics_json', {}).get('inventory_product_id'),
+                    'ingredient_name': product_context.get('ingredient_name') if product_context else None,
+                    'suggested_safety_stock': insight.get('metrics_json', {}).get('suggested_safety_stock'),
+                    'current_safety_stock': insight.get('metrics_json', {}).get('current_safety_stock'),
+                    'estimated_savings': insight.get('impact_value'),
+                    'confidence': confidence,
+                    'recommendation': insight.get('recommendation'),
+                    'impact_type': insight.get('impact_type'),
+                    'source_service': 'inventory',
+                    'source_model': 'safety_stock_optimizer'
+                }
+
+                # Remove None values
+                event_metadata = {k: v for k, v in event_metadata.items() if v is not None}
+
+                # Publish recommendation event
+                await self.event_publisher.publish_recommendation(
+                    event_type='ai_safety_stock_optimization',
+                    tenant_id=tenant_id,
+                    severity=severity,
+                    data=event_metadata
+                )
+
+                logger.info(
+                    "Published safety stock insight recommendation event",
+                    tenant_id=tenant_id,
+                    insight_id=insight.get('id'),
+                    insight_type=insight.get('insight_type'),
+                    severity=severity
+                )
+
+            except Exception as e:
+                logger.error(
+                    "Failed to publish insight event",
+                    tenant_id=tenant_id,
+                    insight_id=insight.get('id'),
+                    error=str(e),
+                    exc_info=True
+                )
+                # Don't raise - we don't want to fail the whole workflow if event publishing fails
+
     async def optimize_all_products(
         self,
         tenant_id: str,