Improve the frontend 3
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Urtzi Alfaro
240
services/production/app/api/orchestrator.py
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240
services/production/app/api/orchestrator.py
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# ================================================================
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# services/production/app/api/orchestrator.py
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# ================================================================
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"""
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Production Orchestrator API - Endpoints for orchestrated production scheduling
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Called by the Orchestrator Service to generate production schedules from forecast data
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"""
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from fastapi import APIRouter, Depends, HTTPException, Path
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from typing import Optional, Dict, Any, List
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from datetime import date
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from uuid import UUID
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from pydantic import BaseModel, Field
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import structlog
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from shared.routing import RouteBuilder
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from app.services.production_service import ProductionService
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from app.schemas.production import ProductionScheduleResponse
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from app.core.config import settings
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logger = structlog.get_logger()
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route_builder = RouteBuilder('production')
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router = APIRouter(tags=["production-orchestrator"])
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def get_production_service() -> ProductionService:
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"""Dependency injection for production service"""
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from app.core.database import database_manager
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return ProductionService(database_manager, settings)
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# ================================================================
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# REQUEST/RESPONSE SCHEMAS
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# ================================================================
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class GenerateScheduleRequest(BaseModel):
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"""
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Request to generate production schedule (called by Orchestrator)
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The Orchestrator calls Forecasting Service first, then passes forecast data here.
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Production Service uses this data to determine what to produce.
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NEW: Accepts cached data snapshots from Orchestrator to eliminate duplicate API calls.
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"""
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forecast_data: Dict[str, Any] = Field(..., description="Forecast data from Forecasting Service")
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target_date: Optional[date] = Field(None, description="Target production date")
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planning_horizon_days: int = Field(default=1, ge=1, le=7, description="Planning horizon in days")
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# NEW: Cached data from Orchestrator
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inventory_data: Optional[Dict[str, Any]] = Field(None, description="Cached inventory snapshot from Orchestrator")
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recipes_data: Optional[Dict[str, Any]] = Field(None, description="Cached recipes snapshot from Orchestrator")
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class Config:
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json_schema_extra = {
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"example": {
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"forecast_data": {
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"forecasts": [
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{
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"product_id": "uuid-here",
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"predicted_demand": 100.0,
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"confidence_score": 0.85
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}
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],
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"forecast_id": "uuid-here",
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"generated_at": "2025-01-30T10:00:00Z"
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},
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"target_date": "2025-01-31",
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"planning_horizon_days": 1
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}
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}
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class GenerateScheduleResponse(BaseModel):
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"""Response from generate_schedule endpoint"""
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success: bool
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message: str
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schedule_id: Optional[UUID] = None
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schedule_number: Optional[str] = None
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batches_created: int = 0
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total_planned_quantity: float = 0.0
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warnings: List[str] = []
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errors: List[str] = []
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class Config:
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json_schema_extra = {
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"example": {
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"success": True,
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"message": "Production schedule generated successfully",
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"schedule_id": "uuid-here",
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"schedule_number": "PROD-2025-01-30-001",
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"batches_created": 5,
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"total_planned_quantity": 500.0,
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"warnings": [],
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"errors": []
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}
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}
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# ================================================================
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# ORCHESTRATOR ENTRY POINT
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# ================================================================
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@router.post(
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route_builder.build_nested_resource_route("", None, "generate-schedule"),
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response_model=GenerateScheduleResponse
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)
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async def generate_production_schedule(
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tenant_id: UUID = Path(...),
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request_data: GenerateScheduleRequest = ...,
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production_service: ProductionService = Depends(get_production_service)
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):
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"""
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Generate production schedule from forecast data (called by Orchestrator)
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This is the main entry point for orchestrated production planning.
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The Orchestrator calls Forecasting Service first, then passes forecast data here.
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Flow:
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1. Receive forecast data from orchestrator
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2. Parse forecast to extract product demands
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3. Check inventory levels for each product
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4. Calculate production quantities needed
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5. Create production schedule and batches
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6. Return schedule summary
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Args:
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tenant_id: Tenant UUID
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request_data: Schedule generation request with forecast data
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Returns:
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GenerateScheduleResponse with schedule details and created batches
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"""
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try:
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logger.info("Generate production schedule endpoint called",
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tenant_id=str(tenant_id),
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has_forecast_data=bool(request_data.forecast_data))
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target_date = request_data.target_date or date.today()
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forecast_data = request_data.forecast_data
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# Parse forecast data from orchestrator
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forecasts = _parse_forecast_data(forecast_data)
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if not forecasts:
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return GenerateScheduleResponse(
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success=False,
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message="No forecast data provided",
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errors=["Forecast data is empty or invalid"]
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)
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# Generate production schedule using the service (with cached data if available)
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result = await production_service.generate_production_schedule_from_forecast(
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tenant_id=tenant_id,
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target_date=target_date,
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forecasts=forecasts,
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planning_horizon_days=request_data.planning_horizon_days,
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inventory_data=request_data.inventory_data, # NEW: Pass cached inventory
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recipes_data=request_data.recipes_data # NEW: Pass cached recipes
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)
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logger.info("Production schedule generated successfully",
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tenant_id=str(tenant_id),
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schedule_id=str(result.get('schedule_id')) if result.get('schedule_id') else None,
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batches_created=result.get('batches_created', 0))
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return GenerateScheduleResponse(
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success=True,
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message="Production schedule generated successfully",
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schedule_id=result.get('schedule_id'),
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schedule_number=result.get('schedule_number'),
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batches_created=result.get('batches_created', 0),
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total_planned_quantity=result.get('total_planned_quantity', 0.0),
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warnings=result.get('warnings', []),
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errors=[]
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)
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except Exception as e:
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logger.error("Error generating production schedule",
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error=str(e), tenant_id=str(tenant_id))
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return GenerateScheduleResponse(
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success=False,
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message="Failed to generate production schedule",
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errors=[str(e)]
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)
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# ================================================================
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# HELPER FUNCTIONS
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# ================================================================
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def _parse_forecast_data(forecast_data: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""
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Parse forecast data received from orchestrator
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Expected format from Forecasting Service via Orchestrator:
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{
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"forecasts": [
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{
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"product_id": "uuid",
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"inventory_product_id": "uuid", # Alternative field name
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"predicted_demand": 100.0,
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"predicted_value": 100.0, # Alternative field name
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"confidence_score": 0.85,
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...
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}
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],
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"forecast_id": "uuid",
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"generated_at": "2025-01-30T10:00:00Z"
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}
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"""
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forecasts = []
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forecast_list = forecast_data.get('forecasts', [])
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for forecast_item in forecast_list:
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# Extract product ID (try multiple field names)
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product_id = (
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forecast_item.get('product_id') or
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forecast_item.get('inventory_product_id') or
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forecast_item.get('item_id')
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)
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# Extract predicted demand (try multiple field names)
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predicted_demand = (
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forecast_item.get('predicted_demand') or
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forecast_item.get('predicted_value') or
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forecast_item.get('demand') or
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0
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)
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if product_id and predicted_demand > 0:
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forecasts.append({
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'product_id': product_id,
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'predicted_demand': float(predicted_demand),
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'confidence_score': forecast_item.get('confidence_score', 0.8),
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'lower_bound': forecast_item.get('lower_bound', 0),
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'upper_bound': forecast_item.get('upper_bound', 0),
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'forecast_id': forecast_data.get('forecast_id'),
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})
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return forecasts
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