bvandeusen
bc6d43d3f2
Hygiene follow-up to the Camie retirement (#1189) — these were left inert to bound that change; nothing reads them now. Migration 0068 drops: - ml_settings: tagger_store_floor, tagger_model_version, suggestion_threshold_ character/general (already dead pre-retirement — scoring uses per-head thresholds), video_min_tag_frames (only the deleted video-prediction aggregator used it). - image_record: tagger_model_version (no writer), centroid_scores (dead JSON cache, no reader). Also: ml_admin _EDITABLE/GET/_validate pruned (dropped the store-floor invariant + video_min_tag_frames check); MLThresholdSliders trimmed to a video-embedding card (interval + max frames only); importer no longer resets the dropped cols; download_models drops the Camie fetch; stale CASCADE comments in cleanup_service no longer name the removed tables. Tests updated. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
86 lines
3.8 KiB
Python
86 lines
3.8 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 (
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Boolean,
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CheckConstraint,
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DateTime,
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Float,
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Integer,
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String,
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func,
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)
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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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# Video embedding (#747). Sample one frame every N seconds (fixed CADENCE, not
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# a fixed count) so coverage reflects real screen time regardless of length;
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# cap the total so a long video can't explode into hundreds of embeds. The
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# per-frame SigLIP embeddings are mean-pooled. Operator-tunable.
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video_frame_interval_seconds: Mapped[float] = mapped_column(
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Float, nullable=False, default=4.0
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)
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video_max_frames: Mapped[int] = mapped_column(
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Integer, nullable=False, default=64
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)
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# Tagging-v2 head training (#114). The head is the suggestion source that
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# LEARNS from the operator's tags (replacing Camie + centroid). A concept
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# needs >= head_min_positives labelled images before a head is trained;
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# head_auto_apply_precision is the precision bar a head must clear (at some
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# operating point) to "graduate" into earned auto-apply. Operator-tunable.
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head_min_positives: Mapped[int] = mapped_column(
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Integer, nullable=False, default=8
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)
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head_auto_apply_precision: Mapped[float] = mapped_column(
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Float, nullable=False, default=0.97
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)
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# Earned auto-apply (#114). A graduated head fires (tags images without a
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# human) when this master switch is on AND the head has at least
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# head_auto_apply_min_positives clean labels — so a precise-looking but
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# under-supported low-N head can't spray tags across the library. ON by
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# default (operator-asked 2026-06-29: opt-OUT, not opt-in); the support +
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# measured-precision gates keep it safe, and every auto-tag is reversible.
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head_auto_apply_enabled: Mapped[bool] = mapped_column(
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Boolean, nullable=False, default=True
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)
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head_auto_apply_min_positives: Mapped[int] = mapped_column(
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Integer, nullable=False, default=30
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)
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# CCIP character-match cosine cut (#114). 0.85 default — the v1 flat 0.75
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# over-fired (high-reference characters matched a scatter of images); 0.85
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# keeps the confident single-character matches. Tunable from the agent card.
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ccip_match_threshold: Mapped[float] = mapped_column(
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Float, nullable=False, default=0.85
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)
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# CCIP auto-apply (#114). Confident matches (>= ccip_auto_apply_threshold,
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# above the suggest cut) auto-tag on a daily sweep. ON by default (opt-out);
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# single-character references + the high bar keep it safe, every tag reversible.
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ccip_auto_apply_enabled: Mapped[bool] = mapped_column(
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Boolean, nullable=False, default=True
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)
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ccip_auto_apply_threshold: Mapped[float] = mapped_column(
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Float, nullable=False, default=0.92
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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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# The HF model NAME the embedder loads (server CPU embed + announced to the
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# GPU agent in the lease). Operator-settable so the embedder is a choice, not
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# a hardcode (#1190): set name + version together, then re-embed + retrain.
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embedder_model_name: Mapped[str] = mapped_column(
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String(128), nullable=False, default="google/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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