feat(ml): operator model swap — GPU re-embed + embedder as a setting (#1190)

Make the SigLIP embedder an operator choice (drop-in to SigLIP 2:
google/siglip2-so400m-patch16-512 is a verified 1152-d model at 512px → no
schema change, better small-cue fidelity). A swap = set model + re-embed +
retrain, all operator-driven; the GPU agent does the re-embed so it's fast.

- settings: embedder_model_name is now a setting (migration 0065) alongside the
  existing embedder_model_version; both editable + validated (non-empty) in the
  ml admin API. The server embedder loads by HF name (AutoImageProcessor/Model,
  model-agnostic), preferring the pre-downloaded local dir for the default so
  existing deploys don't re-download; rebuilds on a name change.
- agent: new 'embed' job = whole-image SigLIP embedding (mean-pool video frames)
  under the lease-announced model → POST /jobs/submit_embedding writes
  image_record.siglip_embedding + siglip_model_version. The lease now announces
  the model FROM THE SETTING (not a constant).
- re-embed routing: enqueue_gpu_backfill('embed') selects unembedded + stale-
  version images; 'siglip' now re-embeds concept crops whose version != current
  (so a swap re-triggers crops, not just the never-embedded back-catalogue). The
  CPU ml-worker backfill no longer re-embeds on a version mismatch (it can't
  churn the library at 512px) — the GPU agent owns version re-embeds. Daily
  'embed' + 'siglip' beats self-heal.
- scoring: score_image only bags embeddings in the CURRENT model's space (whole-
  image gated by siglip_model_version, concept regions by embedding_version) so a
  mid-swap stale vector isn't scored by new-space heads; legacy NULL = current.
- UI: GpuAgentCard "Embedding model (advanced)" — edit name/version, Save, and
  "Re-embed library (GPU)" (queues embed + siglip); points at SigLIP 2.

Tests: lease announces model + submit_embedding round-trip; enqueue 'embed'
selects stale/unembedded; stale-version excluded from scoring; embedder model
settable + empty rejected; siglip gate updated to current-version concept.

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

backend/app/api/gpu.py

@@ -17,7 +17,6 @@ from sqlalchemy.dialects.postgresql import insert as pg_insert
 from ..extensions import get_session
 from ..models import AppSetting, GpuJob, ImageRecord, MLSettings
 from ..services.gallery_service import image_url
-from ..services.ml.embedder import MODEL_NAME as EMBED_MODEL_NAME
 from ..services.ml.gpu_jobs import GpuJobService
 from ..services.ml.regions import RegionService
 
@@ -138,11 +137,12 @@ async def lease():
                 # For video/animated: the agent samples at this cadence.
                 "frame_interval_seconds": ml.video_frame_interval_seconds,
                 "max_frames": ml.video_max_frames,
-                # The embedding model the agent must use for concept crops, so
-                # its region vectors land in the SAME space the heads trained in.
-                # Server-announced → the agent stays model-agnostic; a swap is a
-                # server setting + a re-embed migration, never an agent change.
-                "embed_model_name": EMBED_MODEL_NAME,
+                # The embedding model the agent must use for concept crops + the
+                # whole-image 'embed' task, so its vectors land in the SAME space
+                # the heads trained in. Server-announced FROM THE SETTING → the
+                # agent stays model-agnostic; an operator swap is a setting + a
+                # re-embed, never an agent change.
+                "embed_model_name": ml.embedder_model_name,
                 "embed_version": ml.embedder_model_version,
             })
     return jsonify({"jobs": out})
@@ -188,6 +188,34 @@ async def submit():
     return jsonify({"ok": True, "stored": len(regions)})
 
 
+@gpu_bp.route("/jobs/submit_embedding", methods=["POST"])
+async def submit_embedding():
+    """Store a whole-image SigLIP embedding (the 'embed' task) on image_record +
+    close the job. Body: {agent_id, job_id, embedding:[...], embedding_version}.
+    This is how the GPU agent re-embeds the library under a new model (#1190) —
+    much faster than the CPU ml-worker at higher resolutions."""
+    body = await request.get_json(silent=True) or {}
+    agent_id = str(body.get("agent_id") or "agent")
+    job_id = body.get("job_id")
+    embedding = body.get("embedding")
+    version = body.get("embedding_version")
+    if job_id is None or not embedding or not version:
+        return jsonify({"error": "job_id, embedding, embedding_version required"}), 400
+    async with get_session() as session:
+        if not await _agent_authed(session):
+            return jsonify({"error": "unauthorized"}), 401
+        job = await session.get(GpuJob, int(job_id))
+        if job is None or job.status != "leased" or job.lease_token != agent_id:
+            return jsonify({"error": "lease_invalid"}), 409
+        img = await session.get(ImageRecord, job.image_record_id)
+        if img is not None:
+            img.siglip_embedding = embedding
+            img.siglip_model_version = version
+        await GpuJobService(session).complete(agent_id, int(job_id))
+        await session.commit()
+    return jsonify({"ok": True})
+
+
 @gpu_bp.route("/jobs/fail", methods=["POST"])
 async def fail():
     body = await request.get_json(silent=True) or {}