NuklearRabbit
a7cbeaae3b
Five rule scanners (duplicate customers, missing fields, odometer regression, booking overlap, status conflict) run automatically after seed and via an explicit scan endpoint. Issue defer/reject/merge-customers endpoints with transactional customer merge (booking rewiring, tombstone, audit). Data Quality nav + workbench UI with two-column duplicate comparison and inline (non-native) confirm. Dashboard attention items now link to issues. 35 backend tests passing, ruff clean. Fixed a real false-positive bug in odometer-regression detection found through iteration on seed data, and two TS narrowing errors. Verified end-to-end via browser: S2 merge and S4 overlap scenarios.
191 lines
4.1 KiB
Python
191 lines
4.1 KiB
Python
from __future__ import annotations
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from datetime import datetime
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from typing import Any, Literal
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from pydantic import BaseModel, Field, conint
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Role = Literal["operations_manager", "rental_employee"]
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class DemoLoginRequest(BaseModel):
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role: Role
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class CurrentUser(BaseModel):
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public_ref: str
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display_name: str
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role: Role
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class VehicleOut(BaseModel):
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public_ref: str
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make: str
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model: str
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model_year: int
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registration_number: str
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location: str
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operational_status: str
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odometer_km: int
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next_service_km: int
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active: bool
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attention: bool = False
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class BookingSummaryOut(BaseModel):
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public_ref: str
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customer_ref: str
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vehicle_ref: str
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starts_at: datetime
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ends_at: datetime
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status: str
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class BookingOut(BookingSummaryOut):
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start_odometer_km: int | None
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end_odometer_km: int | None
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requirements_complete: bool
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customer_name: str
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class RegisterReturnRequest(BaseModel):
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end_odometer_km: conint(ge=0)
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fuel_level_percent: conint(ge=0, le=100)
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cleanliness_ok: bool
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damage_reported: bool
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technical_warning: bool
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notes: str | None = Field(default=None, max_length=2000)
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class NextBookingRisk(BaseModel):
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booking_ref: str
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starts_at: datetime
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at_risk: bool
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class RegisterReturnResult(BaseModel):
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booking_ref: str
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vehicle_ref: str
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inspection_ref: str
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resulting_vehicle_status: str
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odometer_regression: bool
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quality_issue_ref: str | None
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workflow_event_id: str
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next_booking_risk: NextBookingRisk | None
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class InspectionOut(BaseModel):
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public_ref: str
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booking_ref: str
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type: str
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fuel_level_percent: int
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cleanliness_ok: bool
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damage_reported: bool
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technical_warning: bool
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odometer_km: int
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completed_at: datetime
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class MaintenanceOut(BaseModel):
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public_ref: str
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occurred_at: datetime
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odometer_km: int
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category: str
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summary: str
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class DataQualityIssueOut(BaseModel):
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public_ref: str
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rule_type: str
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entity_type: str
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entity_ref: str
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severity: str
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status: str
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evidence: dict[str, Any]
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detected_at: datetime
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resolved_at: datetime | None = None
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class DataQualityIssueDetailOut(DataQualityIssueOut):
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entity_snapshot: dict[str, Any] | None = None
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related_snapshots: list[dict[str, Any]] = Field(default_factory=list)
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class MergeCustomersRequest(BaseModel):
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survivor_ref: str
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field_overrides: dict[str, str] | None = None
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class MergeCustomersResult(BaseModel):
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issue_ref: str
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survivor_ref: str
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loser_ref: str
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rewired_bookings: int
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class ScanResultOut(BaseModel):
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created: dict[str, int]
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class VehicleDetailOut(VehicleOut):
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bookings: list[BookingSummaryOut] = Field(default_factory=list)
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inspections: list[InspectionOut] = Field(default_factory=list)
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maintenance: list[MaintenanceOut] = Field(default_factory=list)
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quality_issues: list[DataQualityIssueOut] = Field(default_factory=list)
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class DashboardMetrics(BaseModel):
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available: int
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rented: int
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cleaning: int
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maintenance: int
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blocked: int
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open_quality_issues: int
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pending_or_failed_workflows: int
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class AttentionItem(BaseModel):
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kind: Literal["quality_issue", "vehicle"]
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severity: str
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title: str
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detail: str
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link_type: Literal["vehicle", "booking", "customer"]
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link_ref: str
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issue_ref: str | None = None
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class TodayItem(BaseModel):
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kind: Literal["departure", "return"]
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booking_ref: str
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vehicle_ref: str
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scheduled_at: datetime
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class AutomationRunOut(BaseModel):
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event_id: str
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event_type: str
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aggregate_ref: str
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status: str
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attempts: int
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last_error: str | None
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occurred_at: datetime
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class DashboardOut(BaseModel):
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metrics: DashboardMetrics
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attention_items: list[AttentionItem]
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today: list[TodayItem]
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recent_automation: list[AutomationRunOut]
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class AuditEventOut(BaseModel):
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id: str
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actor_type: str
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actor_label: str
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action: str
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entity_type: str
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entity_id: str | None
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correlation_id: str
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occurred_at: datetime
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metadata: dict[str, Any] | None = None
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