Цвылев Александр Вадимович
591a938e71
POST /radio + /radio/next (stateless infinite feed: seed track / from-likes,
exploration mix, client-passed exclude_ids) and GET /tracks|artists/{id}/similar,
replacing the stubs. Recommender port abstracts the (future) ML service —
NullRecommender is wired now so RecommendationService always uses its metadata
heuristics (genre/artist similarity, random exploration filler), never a hard ML
dependency. Adds TrackRepository.list_similar/sample_playable + Artist.list_similar,
reason codes for the client, RemoteRecommender skeleton (TODO: ML contract).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
44 lines
1.2 KiB
Python
44 lines
1.2 KiB
Python
"""Radio + similarity response schemas (§6.5)."""
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import uuid
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from pydantic import BaseModel, Field
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from app.api.schemas.artist import ArtistOut
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from app.api.schemas.track import TrackOut
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class RadioRequest(BaseModel):
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"""Start or continue a radio. ``seed_track_id`` seeds from a track;
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``from_likes`` seeds from the caller's likes. ``exclude_ids`` are already-
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queued tracks to skip (the client drives the infinite feed). ``exploration``
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biases familiar↔new."""
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seed_track_id: uuid.UUID | None = None
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from_likes: bool = False
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exploration: float = Field(default=0.25, ge=0.0, le=1.0)
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count: int = Field(default=20, ge=1, le=50)
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exclude_ids: list[uuid.UUID] = Field(default_factory=list)
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class RadioTrackOut(BaseModel):
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track: TrackOut
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# Short code the client localizes: ml | similar | from_likes | discover.
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reason: str
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class RadioResponse(BaseModel):
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# Where the picks came from: "ml" or "metadata" (fallback).
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source: str
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tracks: list[RadioTrackOut]
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class SimilarTracksOut(BaseModel):
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source: str
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tracks: list[TrackOut]
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class SimilarArtistsOut(BaseModel):
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source: str
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artists: list[ArtistOut]
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