refactor(ml): drop dead tagger/suggestion settings + columns (#1199)

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

backend/app/models/image_record.py

@@ -9,7 +9,6 @@ from datetime import datetime
 
 from pgvector.sqlalchemy import Vector
 from sqlalchemy import (
-    JSON,
     BigInteger,
     DateTime,
     Enum,
@@ -77,19 +76,13 @@ class ImageRecord(Base):
         ForeignKey("artist.id", ondelete="SET NULL"), nullable=True, index=True
     )
 
-    # ML fields (populated by FC-2's ml-worker). Per-tag predictions live in the
-    # normalized image_prediction table (#768) — the tagger_predictions JSON
-    # column was dropped in migration 0046. tagger_model_version stays as the
-    # "has this been tagged / is it current?" signal the backfill sweep reads.
-    tagger_model_version: Mapped[str | None] = mapped_column(String(128), nullable=True)
-    # 1152 = SigLIP-so400m embedding dim. Swapping models in FC-2 may require
-    # a column-width migration.
+    # ML fields (populated by the ml-worker / GPU agent). 1152 = SigLIP-so400m
+    # embedding dim; siglip_model_version stamps which model produced it (so an
+    # operator model swap, #1190, can re-embed the stale rows). A different-dim
+    # model would need a column-width migration.
     siglip_embedding: Mapped[list[float] | None] = mapped_column(Vector(1152), nullable=True)
     siglip_model_version: Mapped[str | None] = mapped_column(String(128), nullable=True)
 
-    # Centroid score cache (populated post-tagging)
-    centroid_scores: Mapped[dict | None] = mapped_column(JSON, nullable=True)
-
     created_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), nullable=False, server_default=func.now()
     )

backend/app/models/ml_settings.py

@@ -23,39 +23,16 @@ class MLSettings(Base):
     __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
-    )
-    # Ingest floor: tagger predictions below this confidence are not stored
-    # (tagger.Tagger.infer). Default 0.70 — the suggestion path already filters
-    # there, 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
-    )
-    # Video tagging (#747). Sample one frame every N seconds (fixed CADENCE, not a
-    # fixed count) so a tag's frame-presence reflects real screen time regardless
-    # of video length; cap the total so a long video can't explode into hundreds
-    # of inferences (the cadence stretches past the cap). A tag is kept only if it
-    # appears in >= video_min_tag_frames sampled frames (≈ that many × interval
-    # seconds on screen) — duration-independent noise rejection. Operator-tunable.
+    # Video embedding (#747). Sample one frame every N seconds (fixed CADENCE, not
+    # a fixed count) so coverage reflects real screen time regardless of length;
+    # cap the total so a long video can't explode into hundreds of embeds. The
+    # per-frame SigLIP embeddings are mean-pooled. Operator-tunable.
     video_frame_interval_seconds: Mapped[float] = mapped_column(
         Float, nullable=False, default=4.0
     )
     video_max_frames: Mapped[int] = mapped_column(
         Integer, nullable=False, default=64
     )
-    video_min_tag_frames: Mapped[int] = mapped_column(
-        Integer, nullable=False, default=3
-    )
     # Tagging-v2 head training (#114). The head is the suggestion source that
     # LEARNS from the operator's tags (replacing Camie + centroid). A concept
     # needs >= head_min_positives labelled images before a head is trained;
@@ -94,9 +71,6 @@ class MLSettings(Base):
     ccip_auto_apply_threshold: Mapped[float] = mapped_column(
         Float, nullable=False, default=0.92
     )
-    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"
     )