bvandeusen
b91a230f12
Works through the optional CCIP ideas + the "keep moving even if I forget" ask:
AUTOMATION (no button needed):
- Hourly beat auto-enqueues CCIP backfill — new images get embedded (and errored
ones retried) on their own; the queue never goes idle waiting for a click.
- CCIP auto-apply: a daily sweep tags confident matches (source='ccip_auto') so
identity tags keep flowing. ON by default (opt-out, like head auto-apply);
ml_settings.ccip_auto_apply_enabled + _threshold (0.92, above the suggest cut),
migration 0064. Vectorized (one matmul + reduceat per image), reversible, skips
already-applied/rejected. Switch + threshold in the GPU agent card; GET/PATCH
/api/ml/settings; auto_applied count in /api/ccip/overview.
REFERENCE QUALITY (the over-fire root cause):
- character_references now draws ONLY from single-character images — on a
multi-character image the tag is image-level, so every figure would otherwise
pollute each character's prototypes (a 2-char image tagged 'Velma' made
Daphne's figure a Velma reference). This is the contamination behind residual
over-firing.
- Cached on a cheap signature (char-tag count + ccip-region count/max-id) so the
reference load isn't redone on every modal open.
Tests: multi-character image not used as a reference; auto-apply tags a confident
match as ccip_auto.
NEXT (not done, confirmed): comic-panel cropping + SigLIP concept crops ("spot
interesting content").
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
113 lines
5.1 KiB
Python
113 lines
5.1 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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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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# Video tagging (#747). Sample one frame every N seconds (fixed CADENCE, not a
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# fixed count) so a tag's frame-presence reflects real screen time regardless
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# of video length; cap the total so a long video can't explode into hundreds
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# of inferences (the cadence stretches past the cap). A tag is kept only if it
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# appears in >= video_min_tag_frames sampled frames (≈ that many × interval
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# seconds on screen) — duration-independent noise rejection. 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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video_min_tag_frames: Mapped[int] = mapped_column(
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Integer, nullable=False, default=3
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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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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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