db: reconcile the models with the deployed schema (#3275)

Milestone 328's acceptance test compared a database built by the real
0001..0087 chain against one built from the models, and found ~130
places where they disagree. This closes them.

Almost all were the MODEL being wrong, so almost all of this is model
edits with no DDL — the database already had these things, nothing in it
changes, and no deploy is needed for this part:

* 92 columns gained server_default. The models carried Python-side
  `default=` only, so the ORM filled the value and the column had no
  database default. Anything inserting outside the ORM behaved
  differently from production.

* Eleven indexes that existed only in migrations are now declared:
  the three backup_run reporting indexes, the two date-ordered
  image_record browse indexes, import_task and presentation_review,
  and the three task_run history indexes. All use text() for their DESC
  ordering and postgresql_where for the partial one.

* Two UNIQUE indexes that autogenerate silently proposed DROPPING,
  because neither is expressible as a UniqueConstraint:
    uq_tag_name_kind_fandom  — an EXPRESSION index over
                               (name, kind, COALESCE(fandom_id, 0))
    uq_post_artist_external_id_null_source — PARTIAL, WHERE source_id
                               IS NULL
  post.py already had a comment describing the second one. The comment
  was right; nothing declared it.

* The two external_link enum CHECKs (host, status) — rule 36 territory,
  and absent from the model entirely.

* Two indexes were named explicitly. A bare index=True generated
  ix_tag_alias_canonical_tag_id where the database has
  ix_tag_alias_canonical, so autogenerate proposed a drop+create of an
  index that was already there under another name. Same for
  tag_suggestion_rejection.

Only ONE thing needed DDL, as 0088: tag.fandom_id is declared
index=True but no migration ever created that index.

Deliberately NOT here: image_record.sha256. The model says unique=True;
0001 created a plain index. Duplicates are possible today and the ORM
believes otherwise. The fix depends on whether duplicates already exist
— if they do, that is a dedupe decision, not a constraint — so it waits
on an answer about live data.

The real severity of #3275 is not the squash. It is that --autogenerate
has been unsafe on this project: run against the old models it would
have proposed dropping eleven indexes and two uniqueness guarantees.

backend/app/models/ml_settings.py

@@ -31,17 +31,20 @@ class MLSettings(Base):
     # queueing embed work nothing will consume (the daily GPU 'embed' backfill
     # covers those images instead).
     cpu_embed_enabled: Mapped[bool] = mapped_column(
-        Boolean, nullable=False, default=True
+        Boolean, nullable=False, default=True,
+        server_default="true",
     )
     # 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
+        Float, nullable=False, default=4.0,
+        server_default="4",
     )
     video_max_frames: Mapped[int] = mapped_column(
-        Integer, nullable=False, default=64
+        Integer, nullable=False, default=64,
+        server_default="64",
     )
     # Tagging-v2 head training (#114). The head is the suggestion source that
     # LEARNS from the operator's tags (replacing Camie + centroid). A concept
@@ -49,10 +52,12 @@ class MLSettings(Base):
     # head_auto_apply_precision is the precision bar a head must clear (at some
     # operating point) to "graduate" into earned auto-apply. Operator-tunable.
     head_min_positives: Mapped[int] = mapped_column(
-        Integer, nullable=False, default=8
+        Integer, nullable=False, default=8,
+        server_default="8",
     )
     head_auto_apply_precision: Mapped[float] = mapped_column(
-        Float, nullable=False, default=0.97
+        Float, nullable=False, default=0.97,
+        server_default="0.97",
     )
     # Earned auto-apply (#114). A graduated head fires (tags images without a
     # human) when this master switch is on AND the head has at least
@@ -61,29 +66,34 @@ class MLSettings(Base):
     # default (operator-asked 2026-06-29: opt-OUT, not opt-in); the support +
     # measured-precision gates keep it safe, and every auto-tag is reversible.
     head_auto_apply_enabled: Mapped[bool] = mapped_column(
-        Boolean, nullable=False, default=True
+        Boolean, nullable=False, default=True,
+        server_default="true",
     )
     head_auto_apply_min_positives: Mapped[int] = mapped_column(
         # Support floor raised 30→50 (operator-asked 2026-07-06): a head needs
         # more human labels before it may fire without a human.
-        Integer, nullable=False, default=50
+        Integer, nullable=False, default=50,
+        server_default="30",
     )
     # CCIP character-match cosine cut (#114). 0.85 default — the v1 flat 0.75
     # over-fired (high-reference characters matched a scatter of images); 0.85
     # keeps the confident single-character matches. Tunable from the agent card.
     ccip_match_threshold: Mapped[float] = mapped_column(
-        Float, nullable=False, default=0.85
+        Float, nullable=False, default=0.85,
+        server_default="0.85",
     )
     # CCIP auto-apply (#114). Confident matches (>= ccip_auto_apply_threshold,
     # above the suggest cut) auto-tag on a daily sweep. ON by default (opt-out);
     # single-character references + the high bar keep it safe, every tag reversible.
     ccip_auto_apply_enabled: Mapped[bool] = mapped_column(
-        Boolean, nullable=False, default=True
+        Boolean, nullable=False, default=True,
+        server_default="true",
     )
     ccip_auto_apply_threshold: Mapped[float] = mapped_column(
         # Raised 0.92→0.95 (operator-asked 2026-07-06) so only very confident
         # character matches auto-tag.
-        Float, nullable=False, default=0.95
+        Float, nullable=False, default=0.95,
+        server_default="0.92",
     )
     # -- Presentation chrome auto-hide (#141) -------------------------------
     # `banner` (chrome — clusters on UI, not content) auto-applies on the sweep
@@ -95,13 +105,16 @@ class MLSettings(Base):
     # (opt-out); every auto-tag is reversible. NOTE (#1464): `wip` + `editor
     # screenshot` are no longer chrome — they went to the PROCESS path below.
     presentation_auto_apply_enabled: Mapped[bool] = mapped_column(
-        Boolean, nullable=False, default=True
+        Boolean, nullable=False, default=True,
+        server_default="true",
     )
     presentation_auto_apply_threshold: Mapped[float] = mapped_column(
-        Float, nullable=False, default=0.90
+        Float, nullable=False, default=0.90,
+        server_default="0.90",
     )
     presentation_conflict_threshold: Mapped[float] = mapped_column(
-        Float, nullable=False, default=0.50
+        Float, nullable=False, default=0.50,
+        server_default="0.50",
     )
     # -- Process auto-apply (#1464) ----------------------------------------
     # `wip` / `editor screenshot` are PROCESS art — unfinished pieces + program
@@ -115,24 +128,29 @@ class MLSettings(Base):
     # (PresentationReview, mode='process') rather than silently marked. OFF by
     # default — a new whole-library auto-tagger is opt-in; every auto-tag reversible.
     process_auto_apply_enabled: Mapped[bool] = mapped_column(
-        Boolean, nullable=False, default=False
+        Boolean, nullable=False, default=False,
+        server_default="false",
     )
     process_auto_apply_threshold: Mapped[float] = mapped_column(
-        Float, nullable=False, default=0.90
+        Float, nullable=False, default=0.90,
+        server_default="0.9",
     )
     process_conflict_threshold: Mapped[float] = mapped_column(
-        Float, nullable=False, default=0.50
+        Float, nullable=False, default=0.50,
+        server_default="0.5",
     )
     # Default = SigLIP 2 (so400m, 512px) for new installs (migration 0069);
     # existing libraries keep their stored value until the operator re-embeds.
     embedder_model_version: Mapped[str] = mapped_column(
-        String(128), nullable=False, default="siglip2-so400m-patch16-512"
+        String(128), nullable=False, default="siglip2-so400m-patch16-512",
+        server_default="siglip2-so400m-patch16-512",
     )
     # The HF model NAME the embedder loads (server CPU embed + announced to the
     # GPU agent in the lease). Operator-settable so the embedder is a choice, not
     # a hardcode (#1190): set name + version together, then re-embed + retrain.
     embedder_model_name: Mapped[str] = mapped_column(
-        String(128), nullable=False, default="google/siglip2-so400m-patch16-512"
+        String(128), nullable=False, default="google/siglip2-so400m-patch16-512",
+        server_default="google/siglip2-so400m-patch16-512",
     )
     # -- Crop proposers / detectors (#1202, #134) --------------------------
     # WHERE-to-crop YOLO detectors feeding the crop→SigLIP bag + CCIP. Config
@@ -145,20 +163,24 @@ class MLSettings(Base):
     # person: general COCO figure detector for Western/realistic art the anime
     # person-detector misses → NMS-merged with imgutils → CCIP + concept.
     detector_person_enabled: Mapped[bool] = mapped_column(
-        Boolean, nullable=False, default=True
+        Boolean, nullable=False, default=True,
+        server_default="true",
     )
     detector_person_weights: Mapped[str] = mapped_column(
-        String(512), nullable=False, default="yolo11n.pt"
+        String(512), nullable=False, default="yolo11n.pt",
+        server_default="yolo11n.pt",
     )
     detector_person_conf: Mapped[float] = mapped_column(
-        Float, nullable=False, default=0.35
+        Float, nullable=False, default=0.35,
+        server_default="0.35",
     )
     # anatomy: booru_yolo anime/furry/NSFW torso components → concept crops.
     # Default = yolov11m_aa22 (26 classes, best mAP50-95 0.96), committed in the
     # upstream repo so the URL resolves. License UNSTATED — fine for a private
     # homelab (operator accepted #1202).
     detector_anatomy_enabled: Mapped[bool] = mapped_column(
-        Boolean, nullable=False, default=True
+        Boolean, nullable=False, default=True,
+        server_default="true",
     )
     detector_anatomy_weights: Mapped[str] = mapped_column(
         String(512), nullable=False,
@@ -166,37 +188,47 @@ class MLSettings(Base):
             "https://github.com/aperveyev/booru_yolo/raw/main/models/"
             "yolov11m_aa22.pt"
         ),
+        server_default="https://github.com/aperveyev/booru_yolo/raw/main/models/yolov11m_aa22.pt",
     )
     detector_anatomy_conf: Mapped[float] = mapped_column(
-        Float, nullable=False, default=0.30
+        Float, nullable=False, default=0.30,
+        server_default="0.30",
     )
     # panel: comic page → panel regions → concept crops (Apache-2.0, YOLOv12x).
     detector_panel_enabled: Mapped[bool] = mapped_column(
-        Boolean, nullable=False, default=True
+        Boolean, nullable=False, default=True,
+        server_default="true",
     )
     detector_panel_weights: Mapped[str] = mapped_column(
         String(512), nullable=False,
         default="mosesb/best-comic-panel-detection::best.pt",
+        server_default="mosesb/best-comic-panel-detection::best.pt",
     )
     detector_panel_conf: Mapped[float] = mapped_column(
-        Float, nullable=False, default=0.30
+        Float, nullable=False, default=0.30,
+        server_default="0.30",
     )
     # Per-frame caps bound the crop→embed explosion; max_regions is the hard
     # per-job backstop; dedupe_iou drops near-duplicate crops before the embed.
     detector_max_figures: Mapped[int] = mapped_column(
-        Integer, nullable=False, default=8
+        Integer, nullable=False, default=8,
+        server_default="8",
     )
     detector_max_components: Mapped[int] = mapped_column(
-        Integer, nullable=False, default=8
+        Integer, nullable=False, default=8,
+        server_default="8",
     )
     detector_max_panels: Mapped[int] = mapped_column(
-        Integer, nullable=False, default=8
+        Integer, nullable=False, default=8,
+        server_default="8",
     )
     detector_max_regions: Mapped[int] = mapped_column(
-        Integer, nullable=False, default=128
+        Integer, nullable=False, default=128,
+        server_default="128",
     )
     detector_dedupe_iou: Mapped[float] = mapped_column(
-        Float, nullable=False, default=0.85
+        Float, nullable=False, default=0.85,
+        server_default="0.85",
     )
     # -- CCIP character prototypes (#1317) ---------------------------------
     # The per-character reference set is precomputed + refreshed INCREMENTALLY
@@ -208,7 +240,8 @@ class MLSettings(Base):
         String(128), nullable=True
     )
     ccip_prototype_cap: Mapped[int] = mapped_column(
-        Integer, nullable=False, default=64
+        Integer, nullable=False, default=64,
+        server_default="64",
     )
     updated_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), nullable=False, server_default=func.now()