Improve AI logic

services/tenant/app/schemas/tenant_settings.py

@@ -184,7 +184,7 @@ class SupplierSelectionSettings(BaseModel):
 
     @validator('price_weight', 'lead_time_weight', 'quality_weight', 'reliability_weight')
     def validate_weights_sum(cls, v, values):
-        weights = [values.get('price_weight', 0.40), values.get('lead_time_weight', 0.20), 
+        weights = [values.get('price_weight', 0.40), values.get('lead_time_weight', 0.20),
                   values.get('quality_weight', 0.20), values.get('reliability_weight', 0.20)]
         total = sum(weights)
         if total > 1.0:
@@ -192,6 +192,32 @@ class SupplierSelectionSettings(BaseModel):
         return v
 
 
+class MLInsightsSettings(BaseModel):
+    """ML Insights configuration settings"""
+    # Inventory ML (Safety Stock Optimization)
+    inventory_lookback_days: int = Field(90, ge=30, le=365, description="Days of demand history for safety stock analysis")
+    inventory_min_history_days: int = Field(30, ge=7, le=180, description="Minimum days of history required")
+
+    # Production ML (Yield Prediction)
+    production_lookback_days: int = Field(90, ge=30, le=365, description="Days of production history for yield analysis")
+    production_min_history_runs: int = Field(30, ge=10, le=100, description="Minimum production runs required")
+
+    # Procurement ML (Supplier Analysis & Price Forecasting)
+    supplier_analysis_lookback_days: int = Field(180, ge=30, le=730, description="Days of order history for supplier analysis")
+    supplier_analysis_min_orders: int = Field(10, ge=5, le=100, description="Minimum orders required for analysis")
+    price_forecast_lookback_days: int = Field(180, ge=90, le=730, description="Days of price history for forecasting")
+    price_forecast_horizon_days: int = Field(30, ge=7, le=90, description="Days to forecast ahead")
+
+    # Forecasting ML (Dynamic Rules)
+    rules_generation_lookback_days: int = Field(90, ge=30, le=365, description="Days of sales history for rule learning")
+    rules_generation_min_samples: int = Field(10, ge=5, le=100, description="Minimum samples required for rule generation")
+
+    # Global ML Settings
+    enable_ml_insights: bool = Field(True, description="Enable/disable ML insights generation")
+    ml_insights_auto_trigger: bool = Field(False, description="Automatically trigger ML insights in daily workflow")
+    ml_confidence_threshold: float = Field(0.80, ge=0.0, le=1.0, description="Minimum confidence threshold for ML recommendations")
+
+
 # ================================================================
 # REQUEST/RESPONSE SCHEMAS
 # ================================================================
@@ -210,6 +236,7 @@ class TenantSettingsResponse(BaseModel):
     safety_stock_settings: SafetyStockSettings
     moq_settings: MOQSettings
     supplier_selection_settings: SupplierSelectionSettings
+    ml_insights_settings: MLInsightsSettings
     created_at: datetime
     updated_at: datetime
 
@@ -229,6 +256,7 @@ class TenantSettingsUpdate(BaseModel):
     safety_stock_settings: Optional[SafetyStockSettings] = None
     moq_settings: Optional[MOQSettings] = None
     supplier_selection_settings: Optional[SupplierSelectionSettings] = None
+    ml_insights_settings: Optional[MLInsightsSettings] = None
 
 
 class CategoryUpdateRequest(BaseModel):