Ran a dedicated post-M7 release-readiness audit. Found and fixed the one real gap: mypy was a declared dev dependency but had never been run in any milestone's validation loop. Fixed all 43 pre-existing type errors it surfaced, including two genuine defensive- programming gaps (unguarded Optional vehicle/customer lookups that could have crashed with unhandled 500s instead of clean 404/401 responses) rather than suppressing them. make lint now runs ruff + mypy; mypy reports zero errors across 44 source files. Re-verified end to end against a genuinely wiped-volumes clean checkout: automatic migrations, deterministic seed, 66/66 backend tests, and the full user-journey matrix (login, dashboard, vehicle/booking detail, return workflow, invalid-mileage rejection, data-quality review, duplicate-customer merge, audit trail, Knowledge Assistant, n8n, MCP Hub) via curl and Playwright. Live-verified both external-dependency degraded modes, not just unit tests: stopped n8n mid-flow and confirmed a return still commits with the outbox event staying pending and retrying with backoff, then self-healing to succeeded with zero manual intervention once n8n came back; verified RAGcore's unavailable-degradation path against an unreachable host. Added frontend/e2e/interactive-elements.spec.ts (11 tests covering every nav item, filter, tab, and role boundary) alongside the existing demo script test — 12/12 e2e tests passing. Verified no secrets are committed (.env never tracked, clean git history scan) and .env.example covers every operator-configurable setting. Confirmed no placeholders, TODOs, fake responses, hardcoded metrics, or dead routes anywhere in the codebase. Updated README.md with an honest integration-status section and PROJECT_STATE.md with the full audit findings. Added artifacts/final-acceptance/summary.md as the authoritative final evidence document (commands, results, URLs, demo access, integration status per external dependency, known limitations, deployment instructions, five-minute demo flow).
222 lines
4.8 KiB
Python
222 lines
4.8 KiB
Python
from __future__ import annotations
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from datetime import datetime
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from typing import Annotated, Any, Literal
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from pydantic import BaseModel, Field
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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: Annotated[int, Field(ge=0)]
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fuel_level_percent: Annotated[int, Field(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 OperationsSummaryOut(BaseModel):
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tenant: str
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metrics: DashboardMetrics
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class AttentionVehicleOut(BaseModel):
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vehicle_ref: str
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severity: Literal["low", "medium", "high"]
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rule_type: str
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summary: str
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detected_at: datetime
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class McpVehicleDetailOut(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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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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open_quality_issue_count: int
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current_booking_ref: str | None
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class McpKnowledgeSearchRequest(BaseModel):
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question: str = Field(min_length=3, max_length=1000)
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max_sources: int = Field(default=4, ge=1, le=8)
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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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