Improve AI logic
This commit is contained in:
Urtzi Alfaro
@@ -213,8 +213,7 @@ async def generate_batch_forecast(
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tenant_id: str = Path(..., description="Tenant ID"),
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request_obj: Request = None,
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current_user: dict = Depends(get_current_user_dep),
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enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service),
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rate_limiter = Depends(get_rate_limiter)
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enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
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):
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"""Generate forecasts for multiple products in batch (Admin+ only, quota enforced)"""
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metrics = get_metrics_collector(request_obj)
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@@ -227,30 +226,47 @@ async def generate_batch_forecast(
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if metrics:
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metrics.increment_counter("batch_forecasts_total")
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if not request.inventory_product_ids:
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raise ValueError("inventory_product_ids cannot be empty")
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# Check if we need to get all products instead of specific ones
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inventory_product_ids = request.inventory_product_ids
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if inventory_product_ids is None or len(inventory_product_ids) == 0:
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# If no specific products requested, fetch all products for the tenant
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# from the inventory service to generate forecasts for all of them
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from shared.clients.inventory_client import InventoryServiceClient
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from app.core.config import settings
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inventory_client = InventoryServiceClient(settings)
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all_ingredients = await inventory_client.get_all_ingredients(tenant_id=tenant_id)
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inventory_product_ids = [str(ingredient['id']) for ingredient in all_ingredients] if all_ingredients else []
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# If still no products, return early with success response
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if not inventory_product_ids:
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logger.info("No products found for forecasting", tenant_id=tenant_id)
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from app.schemas.forecasts import BatchForecastResponse
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return BatchForecastResponse(
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batch_id=str(uuid.uuid4()),
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tenant_id=tenant_id,
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products_processed=0,
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forecasts_generated=0,
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success=True,
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message="No products found for forecasting"
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)
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# Get subscription tier and enforce quotas
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tier = current_user.get('subscription_tier', 'starter')
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# Skip rate limiting for service-to-service calls (orchestrator)
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# Rate limiting is handled at the gateway level for user requests
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# Check daily quota for forecast generation
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quota_limit = get_forecast_quota(tier)
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quota_result = await rate_limiter.check_and_increment_quota(
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tenant_id,
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"forecast_generation",
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quota_limit,
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period=86400 # 24 hours
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# Create a copy of the request with the actual list of product IDs to forecast
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# (whether originally provided or fetched from inventory service)
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from app.schemas.forecasts import BatchForecastRequest
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updated_request = BatchForecastRequest(
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tenant_id=tenant_id, # Use the tenant_id from the path parameter
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batch_name=getattr(request, 'batch_name', f"orchestrator-batch-{datetime.now().strftime('%Y%m%d')}"),
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inventory_product_ids=inventory_product_ids,
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forecast_days=getattr(request, 'forecast_days', 7)
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)
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# Validate forecast horizon if specified
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if request.horizon_days:
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await rate_limiter.validate_forecast_horizon(
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tenant_id, request.horizon_days, tier
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)
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batch_result = await enhanced_forecasting_service.generate_batch_forecast(
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batch_result = await enhanced_forecasting_service.generate_batch_forecasts(
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tenant_id=tenant_id,
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request=request
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request=updated_request
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)
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if metrics:
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@@ -258,9 +274,25 @@ async def generate_batch_forecast(
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logger.info("Batch forecast generated successfully",
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tenant_id=tenant_id,
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total_forecasts=batch_result.total_forecasts)
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total_forecasts=batch_result.get('total_forecasts', 0))
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return batch_result
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# Convert the service result to BatchForecastResponse format
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from app.schemas.forecasts import BatchForecastResponse
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now = datetime.now(timezone.utc)
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return BatchForecastResponse(
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id=batch_result.get('batch_id', str(uuid.uuid4())),
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tenant_id=tenant_id,
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batch_name=updated_request.batch_name,
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status="completed",
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total_products=batch_result.get('total_forecasts', 0),
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completed_products=batch_result.get('successful_forecasts', 0),
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failed_products=batch_result.get('failed_forecasts', 0),
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requested_at=now,
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completed_at=now,
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processing_time_ms=0,
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forecasts=[],
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error_message=None
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)
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except ValueError as e:
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if metrics:
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@@ -484,6 +516,174 @@ async def clear_prediction_cache(
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)
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@router.post(
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route_builder.build_operations_route("validate-forecasts"),
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response_model=dict
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)
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@service_only_access
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@track_execution_time("validate_forecasts_duration_seconds", "forecasting-service")
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async def validate_forecasts(
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validation_date: date = Query(..., description="Date to validate forecasts for"),
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tenant_id: str = Path(..., description="Tenant ID"),
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request_obj: Request = None,
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current_user: dict = Depends(get_current_user_dep),
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enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
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):
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"""
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Validate forecasts for a specific date against actual sales.
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Calculates MAPE, RMSE, MAE and identifies products with poor accuracy.
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This endpoint is called by the orchestrator during Step 5 to validate
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yesterday's forecasts and trigger retraining if needed.
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Args:
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validation_date: Date to validate forecasts for
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tenant_id: Tenant ID
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Returns:
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Dict with overall metrics and poor accuracy products list:
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- overall_mape: Mean Absolute Percentage Error across all products
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- overall_rmse: Root Mean Squared Error across all products
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- overall_mae: Mean Absolute Error across all products
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- products_validated: Number of products validated
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- poor_accuracy_products: List of products with MAPE > 30%
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"""
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metrics = get_metrics_collector(request_obj)
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try:
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logger.info("Validating forecasts for date",
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tenant_id=tenant_id,
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validation_date=validation_date.isoformat())
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if metrics:
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metrics.increment_counter("forecast_validations_total")
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# Get all forecasts for the validation date
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from app.repositories.forecast_repository import ForecastRepository
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from shared.clients.sales_client import SalesServiceClient
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db_manager = create_database_manager(settings.DATABASE_URL, "forecasting-service")
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async with db_manager.get_session() as session:
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forecast_repo = ForecastRepository(session)
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# Get forecasts for the validation date
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forecasts = await forecast_repo.get_forecasts_by_date(
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tenant_id=uuid.UUID(tenant_id),
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forecast_date=validation_date
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)
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if not forecasts:
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logger.warning("No forecasts found for validation date",
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tenant_id=tenant_id,
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validation_date=validation_date.isoformat())
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return {
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"overall_mape": 0,
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"overall_rmse": 0,
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"overall_mae": 0,
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"products_validated": 0,
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"poor_accuracy_products": []
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}
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# Get actual sales for the validation date from sales service
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sales_client = SalesServiceClient(settings, "forecasting-service")
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actual_sales_response = await sales_client.get_sales_by_date_range(
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tenant_id=tenant_id,
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start_date=validation_date,
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end_date=validation_date
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)
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# Create sales lookup dict
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sales_dict = {}
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if actual_sales_response and 'sales' in actual_sales_response:
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for sale in actual_sales_response['sales']:
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product_id = sale.get('inventory_product_id')
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quantity = sale.get('quantity', 0)
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if product_id:
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# Aggregate quantities for the same product
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sales_dict[product_id] = sales_dict.get(product_id, 0) + quantity
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# Calculate metrics for each product
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import numpy as np
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mape_list = []
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rmse_list = []
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mae_list = []
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poor_accuracy_products = []
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for forecast in forecasts:
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product_id = str(forecast.inventory_product_id)
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actual_quantity = sales_dict.get(product_id)
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# Skip if no actual sales data
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if actual_quantity is None:
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continue
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predicted_quantity = forecast.predicted_demand
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# Calculate errors
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absolute_error = abs(predicted_quantity - actual_quantity)
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squared_error = (predicted_quantity - actual_quantity) ** 2
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# Calculate percentage error (avoid division by zero)
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if actual_quantity > 0:
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percentage_error = (absolute_error / actual_quantity) * 100
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else:
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# If actual is 0 but predicted is not, treat as 100% error
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percentage_error = 100 if predicted_quantity > 0 else 0
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mape_list.append(percentage_error)
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rmse_list.append(squared_error)
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mae_list.append(absolute_error)
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# Track products with poor accuracy
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if percentage_error > 30:
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poor_accuracy_products.append({
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"product_id": product_id,
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"mape": round(percentage_error, 2),
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"predicted": round(predicted_quantity, 2),
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"actual": round(actual_quantity, 2)
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})
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# Calculate overall metrics
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overall_mape = np.mean(mape_list) if mape_list else 0
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overall_rmse = np.sqrt(np.mean(rmse_list)) if rmse_list else 0
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overall_mae = np.mean(mae_list) if mae_list else 0
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result = {
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"overall_mape": round(overall_mape, 2),
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"overall_rmse": round(overall_rmse, 2),
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"overall_mae": round(overall_mae, 2),
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"products_validated": len(mape_list),
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"poor_accuracy_products": poor_accuracy_products
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}
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logger.info("Forecast validation complete",
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tenant_id=tenant_id,
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validation_date=validation_date.isoformat(),
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overall_mape=result["overall_mape"],
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products_validated=result["products_validated"],
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poor_accuracy_count=len(poor_accuracy_products))
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if metrics:
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metrics.increment_counter("forecast_validations_completed_total")
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metrics.observe_histogram("forecast_validation_mape", overall_mape)
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return result
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except Exception as e:
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logger.error("Failed to validate forecasts",
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error=str(e),
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tenant_id=tenant_id,
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validation_date=validation_date.isoformat())
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if metrics:
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metrics.increment_counter("forecast_validations_failed_total")
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail=f"Failed to validate forecasts: {str(e)}"
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)
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# ============================================================================
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# Tenant Data Deletion Operations (Internal Service Only)
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# ============================================================================
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