Improve AI logic

shared/clients/forecast_client.py

@@ -141,6 +141,27 @@ class ForecastServiceClient(BaseServiceClient):
         }
         return await self.post("forecasting/operations/validate-predictions", params=params, tenant_id=tenant_id)
 
+    async def validate_forecasts(
+        self,
+        tenant_id: str,
+        date: date
+    ) -> Optional[Dict[str, Any]]:
+        """
+        Validate forecasts for a specific date against actual sales.
+        Calculates MAPE, RMSE, MAE and identifies products with poor accuracy.
+
+        Args:
+            tenant_id: Tenant UUID
+            date: Date to validate
+
+        Returns:
+            Dict with overall metrics and poor accuracy products list
+        """
+        params = {
+            "validation_date": date.isoformat()
+        }
+        return await self.post("forecasting/operations/validate-forecasts", params=params, tenant_id=tenant_id)
+
     async def get_forecast_statistics(
         self,
         tenant_id: str,
@@ -179,10 +200,81 @@ class ForecastServiceClient(BaseServiceClient):
 
         return await self.get("forecasting/analytics/predictions-performance", tenant_id=tenant_id, params=params)
 
+    # ================================================================
+    # ML INSIGHTS: Dynamic Rules Generation
+    # ================================================================
+
+    async def trigger_rules_generation(
+        self,
+        tenant_id: str,
+        product_ids: Optional[List[str]] = None,
+        lookback_days: int = 90,
+        min_samples: int = 10
+    ) -> Optional[Dict[str, Any]]:
+        """
+        Trigger dynamic business rules learning for demand forecasting.
+
+        Args:
+            tenant_id: Tenant UUID
+            product_ids: Specific product IDs to analyze. If None, analyzes all products
+            lookback_days: Days of historical data to analyze (30-365)
+            min_samples: Minimum samples required for rule learning (5-100)
+
+        Returns:
+            Dict with rules generation results including insights posted
+        """
+        data = {
+            "product_ids": product_ids,
+            "lookback_days": lookback_days,
+            "min_samples": min_samples
+        }
+        return await self.post("forecasting/ml/insights/generate-rules", data=data, tenant_id=tenant_id)
+
     # ================================================================
     # Legacy/Compatibility Methods (deprecated)
     # ================================================================
 
+    async def generate_forecasts(
+        self,
+        tenant_id: str,
+        forecast_days: int = 7,
+        inventory_product_ids: Optional[List[str]] = None
+    ) -> Optional[Dict[str, Any]]:
+        """
+        COMPATIBILITY: Orchestrator-friendly method to generate forecasts
+
+        This method is called by the orchestrator service and generates batch forecasts
+        for either specified products or all products.
+
+        Args:
+            tenant_id: Tenant UUID
+            forecast_days: Number of days to forecast (default 7)
+            inventory_product_ids: Optional list of product IDs. If None, forecasts all products.
+
+        Returns:
+            Dict with forecast results
+        """
+        from datetime import datetime
+
+        # If no product IDs specified, let the backend handle it
+        if not inventory_product_ids:
+            # Call the batch operation endpoint to forecast all products
+            # The forecasting service will handle fetching all products internally
+            data = {
+                "batch_name": f"orchestrator-batch-{datetime.now().strftime('%Y%m%d')}",
+                "inventory_product_ids": [],  # Empty list will trigger fetching all products
+                "forecast_days": forecast_days
+            }
+            return await self.post("forecasting/operations/batch", data=data, tenant_id=tenant_id)
+
+        # Otherwise use the standard batch forecast
+        return await self.generate_batch_forecast(
+            tenant_id=tenant_id,
+            inventory_product_ids=inventory_product_ids,
+            forecast_date=datetime.now().date(),
+            forecast_days=forecast_days
+        )
+
     async def create_forecast(
         self,
         tenant_id: str,