New alert system and panel de control page
This commit is contained in:
Urtzi Alfaro
@@ -40,6 +40,10 @@ from app.models.orchestration_run import (
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OrchestrationRun, OrchestrationStatus
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)
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# Add shared utilities to path for demo dates
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sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent))
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from shared.utils.demo_dates import BASE_REFERENCE_DATE
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# Configure logging
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structlog.configure(
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processors=[
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@@ -55,8 +59,8 @@ logger = structlog.get_logger()
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DEMO_TENANT_SAN_PABLO = uuid.UUID("a1b2c3d4-e5f6-47a8-b9c0-d1e2f3a4b5c6") # Individual bakery
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DEMO_TENANT_LA_ESPIGA = uuid.UUID("b2c3d4e5-f6a7-48b9-c0d1-e2f3a4b5c6d7") # Central bakery
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# Base reference date for date calculations
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BASE_REFERENCE_DATE = datetime(2025, 1, 15, 12, 0, 0, tzinfo=timezone.utc)
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# BASE_REFERENCE_DATE now imported from shared utilities to ensure consistency
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# between seeding and cloning operations
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# Hardcoded orchestration run configurations
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ORCHESTRATION_CONFIG = {
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@@ -152,7 +156,93 @@ def generate_run_number(tenant_id: uuid.UUID, index: int, run_type: str) -> str:
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"""Generate a unique run number"""
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tenant_prefix = "SP" if tenant_id == DEMO_TENANT_SAN_PABLO else "LE"
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type_code = run_type[0:3].upper()
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return f"ORCH-{tenant_prefix}-{type_code}-{BASE_REFERENCE_DATE.year}-{index:03d}"
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current_year = datetime.now(timezone.utc).year
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return f"ORCH-{tenant_prefix}-{type_code}-{current_year}-{index:03d}"
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def generate_reasoning_metadata(
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forecasts_generated: int,
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production_batches_created: int,
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procurement_plans_created: int,
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purchase_orders_created: int
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) -> dict:
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"""
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Generate reasoning metadata for orchestration run that will be used by alert processor.
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This creates structured reasoning data that the alert processor can use to provide
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context when showing AI reasoning to users.
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"""
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reasoning_metadata = {
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'reasoning': {
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'type': 'daily_orchestration_summary',
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'timestamp': datetime.now(timezone.utc).isoformat(),
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'summary': 'Daily orchestration run completed successfully',
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'details': {
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'forecasting': {
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'forecasts_created': forecasts_generated,
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'method': 'automated_daily_forecast',
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'reasoning': 'Generated forecasts based on historical patterns and seasonal trends'
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},
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'production': {
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'batches_created': production_batches_created,
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'method': 'demand_based_scheduling',
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'reasoning': 'Scheduled production batches based on forecasted demand and inventory levels'
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},
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'procurement': {
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'requirements_created': procurement_plans_created,
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'pos_created': purchase_orders_created,
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'method': 'automated_procurement',
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'reasoning': 'Generated procurement plan based on production needs and inventory optimization'
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}
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}
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},
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'purchase_orders': [],
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'production_batches': [],
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'ai_insights': {
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'generated': 0,
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'posted': 0
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}
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}
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# Add sample purchase order reasoning if any POs were created
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if purchase_orders_created > 0:
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for i in range(min(purchase_orders_created, 3)): # Limit to 3 sample POs
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po_reasoning = {
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'id': f'po-sample-{i+1}',
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'status': 'created',
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'reasoning': {
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'type': 'inventory_optimization',
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'parameters': {
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'trigger': 'low_stock_prediction',
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'min_depletion_days': random.randint(2, 5),
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'quantity': random.randint(100, 500),
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'unit': 'kg',
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'supplier': 'Demo Supplier',
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'financial_impact_eur': random.randint(100, 1000)
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}
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}
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}
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reasoning_metadata['purchase_orders'].append(po_reasoning)
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# Add sample production batch reasoning if any batches were created
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if production_batches_created > 0:
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for i in range(min(production_batches_created, 3)): # Limit to 3 sample batches
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batch_reasoning = {
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'id': f'batch-sample-{i+1}',
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'status': 'scheduled',
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'reasoning': {
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'type': 'demand_forecasting',
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'parameters': {
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'trigger': 'forecasted_demand',
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'forecasted_quantity': random.randint(50, 200),
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'product_name': 'Demo Product',
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'financial_impact_eur': random.randint(50, 500)
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}
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}
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}
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reasoning_metadata['production_batches'].append(batch_reasoning)
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return reasoning_metadata
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async def generate_orchestration_for_tenant(
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@@ -251,8 +341,8 @@ async def generate_orchestration_for_tenant(
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# For today's run (i==0), use current datetime instead of BASE_REFERENCE_DATE
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if i == 0 and today_run_created:
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now = datetime.now(timezone.utc)
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started_at = now - timedelta(hours=2) # Started 2 hours ago
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completed_at = now - timedelta(minutes=30) # Completed 30 minutes ago
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started_at = now - timedelta(minutes=90) # Started 90 minutes ago (1.5 hours)
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completed_at = now - timedelta(minutes=30) # Completed 30 minutes ago, so 60-minute duration
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duration_seconds = int((completed_at - started_at).total_seconds())
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else:
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started_at = calculate_datetime_from_offset(offset_days - 1)
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@@ -261,13 +351,21 @@ async def generate_orchestration_for_tenant(
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if status in ["completed", "partial_success"]:
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completed_at = calculate_datetime_from_offset(offset_days)
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# Calculate duration based on realistic processing times
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duration_seconds = int((completed_at - started_at).total_seconds())
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# Cap duration to reasonable values
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if duration_seconds > 5400: # More than 1.5 hours
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duration_seconds = random.randint(1800, 3600) # 30-60 minutes
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elif status == "failed":
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completed_at = calculate_datetime_from_offset(offset_days - 0.5)
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duration_seconds = int((completed_at - started_at).total_seconds())
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if duration_seconds > 3600: # More than 1 hour
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duration_seconds = random.randint(300, 1800) # 5-30 minutes
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elif status == "cancelled":
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completed_at = calculate_datetime_from_offset(offset_days - 0.2)
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duration_seconds = int((completed_at - started_at).total_seconds())
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if duration_seconds > 1800: # More than 30 minutes
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duration_seconds = random.randint(60, 600) # 1-10 minutes
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# Generate step timing
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forecasting_started_at = started_at
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@@ -352,6 +450,14 @@ async def generate_orchestration_for_tenant(
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metrics["quality_score"]["max"]
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)))
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# Generate reasoning metadata for the orchestrator context
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reasoning_metadata = generate_reasoning_metadata(
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forecasts_generated,
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production_batches_created,
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procurement_plans_created,
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purchase_orders_created
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)
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# Create orchestration run
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run = OrchestrationRun(
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id=uuid.uuid4(),
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@@ -388,6 +494,7 @@ async def generate_orchestration_for_tenant(
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on_time_delivery_rate=on_time_delivery_rate,
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cost_accuracy=cost_accuracy,
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quality_score=quality_score,
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run_metadata=reasoning_metadata,
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created_at=calculate_datetime_from_offset(offset_days - 2),
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updated_at=calculate_datetime_from_offset(offset_days),
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triggered_by="scheduler" if run_type == "scheduled" else "user" if run_type == "manual" else "test-runner"
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