refactor(ml): remove the dead per-tag centroid subsystem (#1189)

The v2 pivot replaced per-tag SigLIP centroids with learned heads + CCIP.
Centroids were still recomputed (on every tag merge + a daily beat) but NOTHING
read them — suggestions come from heads+CCIP and apply_allowlist_tags applies
via Camie predictions, not centroids. Pure dead wiring; remove it.

Removed: CentroidService, recompute_centroid/recompute_centroids tasks, the
daily beat, POST /api/ml/recompute-centroids, the recompute-on-merge trigger,
the tag_reference_embedding table + model, the centroid_similarity_threshold +
min_reference_images settings (migration 0066), the CentroidRecomputeCard +
its store action + MaintenancePanel tile, and the centroid slider in
MLThresholdSliders. _keep_as_alias drops its vestigial has-centroid branch (the
allowlist branch already covers "could re-emit"); tag merge no longer clears a
table that no longer exists.

NOT touched (still live, parallel to heads): the Camie tagger, ImagePrediction,
and the allowlist bulk-apply — accepting a suggestion still allowlists + applies
it across the library. The tag-eval "centroid" baseline metric is unrelated
(in-memory) and stays. (image_record.centroid_scores JSON column also remains —
separate legacy field, its own micro-cleanup.)

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa

backend/app/models/__init__.py

@@ -38,7 +38,6 @@ from .tag_allowlist import TagAllowlist
 from .tag_eval_run import TagEvalRun
 from .tag_head import TagHead
 from .tag_positive_confirmation import TagPositiveConfirmation
-from .tag_reference_embedding import TagReferenceEmbedding
 from .tag_suggestion_rejection import TagSuggestionRejection
 from .task_run import TaskRun
 
@@ -83,7 +82,6 @@ __all__ = [
     "TagEvalRun",
     "TagHead",
     "TagPositiveConfirmation",
-    "TagReferenceEmbedding",
     "TagSuggestionRejection",
     "TaskRun",
 ]

backend/app/models/ml_settings.py

@@ -33,21 +33,14 @@ class MLSettings(Base):
     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.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
     )
-    min_reference_images: Mapped[int] = mapped_column(
-        Integer, nullable=False, default=5
-    )
     # 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

backend/app/models/tag_reference_embedding.py

@@ -1,23 +0,0 @@
-"""TagReferenceEmbedding — per-tag centroid (mean SigLIP embedding of members)."""
-
-from datetime import datetime
-
-from pgvector.sqlalchemy import Vector
-from sqlalchemy import DateTime, ForeignKey, Integer, String, func
-from sqlalchemy.orm import Mapped, mapped_column
-
-from .base import Base
-
-
-class TagReferenceEmbedding(Base):
-    __tablename__ = "tag_reference_embedding"
-
-    tag_id: Mapped[int] = mapped_column(
-        ForeignKey("tag.id", ondelete="CASCADE"), primary_key=True
-    )
-    embedding: Mapped[list[float]] = mapped_column(Vector(1152), nullable=False)
-    reference_count: Mapped[int] = mapped_column(Integer, nullable=False)
-    model_version: Mapped[str] = mapped_column(String(128), nullable=False)
-    updated_at: Mapped[datetime] = mapped_column(
-        DateTime(timezone=True), nullable=False, server_default=func.now()
-    )