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
         )