bvandeusen
3f92669f12
Promotes the prediction store-floor from the TAGGER_STORE_FLOOR env (default 0.05) to a DB-backed, Settings-UI-tunable ml_settings column (default 0.70). Storing every tag down to 0.05 from a ~10k-tag tagger is what grew image_record's TOAST to ~100 GB; the suggestion path already filters at 0.70 and the centroid/learned path covers lower-confidence preferred tags, so the sub-0.70 tail is redundant. Foundation for plan-task #764 (backfill + reclaim land next; this only changes the write gate for NEW imports). - ml_settings.tagger_store_floor (migration 0044, default 0.70) - tagger.Tagger.infer(store_floor=...); ml task passes settings.tagger_store_floor - ML admin GET/PATCH expose it; PATCH rejects a category suggestion threshold below the floor (nothing below the floor is stored, so the gap surfaces nothing) — server backstop for the UI slider clamp - Settings → ML: store-floor slider + caption; category sliders min-bound to it Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
52 lines
2.2 KiB
Python
52 lines
2.2 KiB
Python
"""MLSettings — single-row table holding ML pipeline tunables."""
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from datetime import datetime
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from sqlalchemy import CheckConstraint, DateTime, Float, Integer, String, func
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from sqlalchemy.orm import Mapped, mapped_column
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from .base import Base
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class MLSettings(Base):
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__tablename__ = "ml_settings"
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# Bare name — Base.metadata's naming convention prepends ck_<table>_,
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# producing the final ck_ml_settings_singleton (matches migration 0003).
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__table_args__ = (CheckConstraint("id = 1", name="singleton"),)
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id: Mapped[int] = mapped_column(Integer, primary_key=True)
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suggestion_threshold_character: Mapped[float] = mapped_column(
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Float, nullable=False, default=0.70
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)
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# Default raised 0.50 → 0.70 on 2026-06-02 — operator-flagged 0.50
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# surfaced too many low-confidence picks; 0.70 keeps the rail
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# signal-rich while still surfacing more than the original 0.95
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# which hid almost everything. Operator-tunable via Settings → ML.
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suggestion_threshold_general: Mapped[float] = mapped_column(
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Float, nullable=False, default=0.70
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)
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centroid_similarity_threshold: Mapped[float] = mapped_column(
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Float, nullable=False, default=0.55
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)
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# Ingest floor: tagger predictions below this confidence are not stored
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# (tagger.Tagger.infer). Default 0.70 — the suggestion path already
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# filters at 0.70 and the centroid/learned path covers low-confidence
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# preferred tags, so the sub-0.70 tail is redundant weight (it had
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# bloated image_record's TOAST to ~100 GB; plan-task #764). Operator-
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# tunable via Settings → ML; must stay ≤ the suggestion thresholds.
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tagger_store_floor: Mapped[float] = mapped_column(
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Float, nullable=False, default=0.70
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)
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min_reference_images: Mapped[int] = mapped_column(
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Integer, nullable=False, default=5
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)
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tagger_model_version: Mapped[str] = mapped_column(
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String(128), nullable=False, default="camie-tagger-v2"
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)
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embedder_model_version: Mapped[str] = mapped_column(
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String(128), nullable=False, default="siglip-so400m-patch14-384"
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)
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updated_at: Mapped[datetime] = mapped_column(
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DateTime(timezone=True), nullable=False, server_default=func.now()
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)
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