"""MLSettings — single-row table holding ML pipeline tunables.""" from datetime import datetime from sqlalchemy import CheckConstraint, DateTime, Float, Integer, String, func from sqlalchemy.orm import Mapped, mapped_column from .base import Base class MLSettings(Base): __tablename__ = "ml_settings" # Bare name — Base.metadata's naming convention prepends ck__, # producing the final ck_ml_settings_singleton (matches migration 0003). __table_args__ = (CheckConstraint("id = 1", name="singleton"),) id: Mapped[int] = mapped_column(Integer, primary_key=True) suggestion_threshold_character: Mapped[float] = mapped_column( Float, nullable=False, default=0.70 ) # Default raised 0.50 → 0.70 on 2026-06-02 — operator-flagged 0.50 # surfaced too many low-confidence picks; 0.70 keeps the rail # signal-rich while still surfacing more than the original 0.95 # which hid almost everything. Operator-tunable via Settings → ML. suggestion_threshold_general: Mapped[float] = mapped_column( Float, nullable=False, default=0.70 ) centroid_similarity_threshold: Mapped[float] = mapped_column( Float, nullable=False, default=0.55 ) # Ingest floor: tagger predictions below this confidence are not stored # (tagger.Tagger.infer). Default 0.70 — the suggestion path already # filters at 0.70 and the centroid/learned path covers low-confidence # preferred tags, so the sub-0.70 tail is redundant weight (it had # bloated image_record's TOAST to ~100 GB; plan-task #764). Operator- # tunable via Settings → ML; must stay ≤ the suggestion thresholds. tagger_store_floor: Mapped[float] = mapped_column( Float, nullable=False, default=0.70 ) min_reference_images: Mapped[int] = mapped_column( Integer, nullable=False, default=5 ) tagger_model_version: Mapped[str] = mapped_column( String(128), nullable=False, default="camie-tagger-v2" ) embedder_model_version: Mapped[str] = mapped_column( String(128), nullable=False, default="siglip-so400m-patch14-384" ) updated_at: Mapped[datetime] = mapped_column( DateTime(timezone=True), nullable=False, server_default=func.now() )