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Production-grade media metadata backend per docs/aurora-core-spec.md: Aurora-owned UUIDv7 identity system, normalized movie/TV/people/credits schema, provider abstraction with a TMDb adapter, an idempotent async ingestion pipeline (normalize -> match -> upsert -> provenance), a Postgres FTS/trigram search layer, a stateless FastAPI v1 API with Redis caching/rate-limiting, arq workers, dev/prod Docker Compose topologies, unit + integration + load tests, CI, and architecture/ database/API/ERD documentation. Co-Authored-By: Claude Sonnet 5 <[email protected]>
56 lines
1.2 KiB
Python
56 lines
1.2 KiB
Python
"""Person response schemas."""
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from __future__ import annotations
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from datetime import date, datetime
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from pydantic import BaseModel
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from app.schemas.common import ArtworkOut
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class PersonSummaryOut(BaseModel):
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id: str
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type: str = "person"
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name: str
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known_for_department: str | None = None
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profile_path: str | None = None
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popularity: float | None = None
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class FilmographyCreditOut(BaseModel):
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id: str
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media_id: str
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media_type: str
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title: str
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department: str
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job: str | None = None
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character: str | None = None
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release_date: date | None = None
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poster_path: str | None = None
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class PersonDetailOut(BaseModel):
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id: str
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type: str = "person"
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name: str
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aliases: list[str] = []
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biography: str | None = None
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birthday: date | None = None
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deathday: date | None = None
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birthplace: str | None = None
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gender: str | None = None
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known_for_department: str | None = None
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popularity: float | None = None
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profile_images: list[ArtworkOut] = []
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external_ids: dict[str, str] = {}
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created_at: datetime
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updated_at: datetime
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class PersonCreditsOut(BaseModel):
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id: str
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cast: list[FilmographyCreditOut] = []
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crew: list[FilmographyCreditOut] = []
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