Add forecasting demand insights trigger + fix RabbitMQ cleanup

Issue 1: Forecasting demand insights not triggered in demo workflow
- Created internal ML endpoint: /forecasting/internal/ml/generate-demand-insights
- Added trigger_demand_insights_internal() to ForecastServiceClient
- Integrated forecasting insights into demo session post-clone workflow
- Now triggers 4 AI insight types: price, safety stock, yield, + demand

Issue 2: RabbitMQ client cleanup error in procurement service
- Fixed: rabbitmq_client.close() → rabbitmq_client.disconnect()
- Added proper cleanup in exception handler
- Error: "'RabbitMQClient' object has no attribute 'close'"

Files modified:
- services/forecasting/app/api/ml_insights.py (new internal_router)
- services/forecasting/app/main.py (register internal router)
- shared/clients/forecast_client.py (new trigger method)
- services/demo_session/app/services/clone_orchestrator.py (+ demand insights)
- services/procurement/app/api/internal_demo.py (fix disconnect)

Expected impact:
- Demo sessions will now generate demand forecasting insights
- No more RabbitMQ cleanup errors in logs
- AI insights count should increase from 1 to 2-3 per session

🤖 Generated with Claude Code

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

services/forecasting/app/api/ml_insights.py

@@ -767,3 +767,172 @@ async def ml_insights_health():
             "POST /ml/insights/analyze-business-rules"
         ]
     }
+
+
+# ================================================================
+# INTERNAL ML INSIGHTS ENDPOINTS (for demo session service)
+# ================================================================
+
+internal_router = APIRouter(tags=["Internal ML"])
+
+
+@internal_router.post("/api/v1/tenants/{tenant_id}/forecasting/internal/ml/generate-demand-insights")
+async def trigger_demand_insights_internal(
+    tenant_id: str,
+    request: Request,
+    db: AsyncSession = Depends(get_db)
+):
+    """
+    Internal endpoint to trigger demand forecasting insights for a tenant.
+
+    This endpoint is called by the demo-session service after cloning to generate
+    AI insights from the seeded forecast data.
+
+    Args:
+        tenant_id: Tenant UUID
+        request: FastAPI request object to access app state
+        db: Database session
+
+    Returns:
+        Dict with insights generation results
+    """
+    logger.info(
+        "Internal demand insights generation triggered",
+        tenant_id=tenant_id
+    )
+
+    try:
+        # Import ML orchestrator and clients
+        from app.ml.demand_insights_orchestrator import DemandInsightsOrchestrator
+        from shared.clients.sales_client import SalesServiceClient
+        from shared.clients.inventory_client import InventoryServiceClient
+        from app.core.config import settings
+
+        # Get event publisher from app state
+        event_publisher = getattr(request.app.state, 'event_publisher', None)
+
+        # Initialize orchestrator and clients
+        orchestrator = DemandInsightsOrchestrator(event_publisher=event_publisher)
+        inventory_client = InventoryServiceClient(settings)
+
+        # Get all products for tenant (limit to 10 for performance)
+        all_products = await inventory_client.get_all_ingredients(tenant_id=tenant_id)
+        products = all_products[:10] if all_products else []
+
+        logger.info(
+            "Retrieved products from inventory service",
+            tenant_id=tenant_id,
+            product_count=len(products)
+        )
+
+        if not products:
+            return {
+                "success": False,
+                "message": "No products found for analysis",
+                "tenant_id": tenant_id,
+                "products_analyzed": 0,
+                "insights_posted": 0
+            }
+
+        # Initialize sales client
+        sales_client = SalesServiceClient(config=settings, calling_service_name="forecasting")
+
+        # Calculate date range (90 days lookback)
+        end_date = datetime.utcnow()
+        start_date = end_date - timedelta(days=90)
+
+        # Process each product
+        total_insights_generated = 0
+        total_insights_posted = 0
+
+        for product in products:
+            try:
+                product_id = str(product['id'])
+                product_name = product.get('name', 'Unknown Product')
+
+                logger.debug(
+                    "Analyzing demand for product",
+                    tenant_id=tenant_id,
+                    product_id=product_id,
+                    product_name=product_name
+                )
+
+                # Fetch historical sales data
+                sales_data_raw = await sales_client.get_product_sales(
+                    tenant_id=tenant_id,
+                    product_id=product_id,
+                    start_date=start_date,
+                    end_date=end_date
+                )
+
+                if not sales_data_raw or len(sales_data_raw) < 10:
+                    logger.debug(
+                        "Insufficient sales data for product",
+                        product_id=product_id,
+                        sales_records=len(sales_data_raw) if sales_data_raw else 0
+                    )
+                    continue
+
+                # Convert to DataFrame
+                sales_df = pd.DataFrame(sales_data_raw)
+
+                # Run demand insights orchestrator
+                insights = await orchestrator.analyze_and_generate_insights(
+                    tenant_id=tenant_id,
+                    product_id=product_id,
+                    product_name=product_name,
+                    sales_data=sales_df,
+                    lookback_days=90,
+                    db=db
+                )
+
+                if insights:
+                    total_insights_generated += len(insights)
+                    total_insights_posted += len(insights)
+
+                    logger.info(
+                        "Demand insights generated for product",
+                        tenant_id=tenant_id,
+                        product_id=product_id,
+                        insights_count=len(insights)
+                    )
+
+            except Exception as e:
+                logger.warning(
+                    "Failed to analyze product demand (non-fatal)",
+                    tenant_id=tenant_id,
+                    product_id=product_id,
+                    error=str(e)
+                )
+                continue
+
+        logger.info(
+            "Internal demand insights generation complete",
+            tenant_id=tenant_id,
+            products_analyzed=len(products),
+            insights_generated=total_insights_generated,
+            insights_posted=total_insights_posted
+        )
+
+        return {
+            "success": True,
+            "message": f"Generated {total_insights_posted} demand forecasting insights",
+            "tenant_id": tenant_id,
+            "products_analyzed": len(products),
+            "insights_posted": total_insights_posted
+        }
+
+    except Exception as e:
+        logger.error(
+            "Internal demand insights generation failed",
+            tenant_id=tenant_id,
+            error=str(e),
+            exc_info=True
+        )
+        return {
+            "success": False,
+            "message": f"Demand insights generation failed: {str(e)}",
+            "tenant_id": tenant_id,
+            "products_analyzed": 0,
+            "insights_posted": 0
+        }

services/forecasting/app/main.py

@@ -193,6 +193,7 @@ service.add_router(analytics.router)
 service.add_router(scenario_operations.router)
 service.add_router(internal_demo.router, tags=["internal-demo"])
 service.add_router(ml_insights.router)  # ML insights endpoint
+service.add_router(ml_insights.internal_router)  # Internal ML insights endpoint
 service.add_router(validation.router)  # Validation endpoint
 service.add_router(historical_validation.router)  # Historical validation endpoint
 service.add_router(webhooks.router)  # Webhooks endpoint