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

agent/fc_agent/client.py

@@ -40,6 +40,19 @@ class FcClient:
         r.raise_for_status()
         return r.json()
 
+    def submit_embedding(self, job_id: int, embedding: list, version: str) -> dict:
+        """Post a whole-image SigLIP embedding (the 'embed' task) → image_record."""
+        r = self.s.post(
+            f"{self.base}/api/gpu/jobs/submit_embedding",
+            json={
+                "agent_id": self.agent_id, "job_id": job_id,
+                "embedding": embedding, "embedding_version": version,
+            },
+            timeout=120,
+        )
+        r.raise_for_status()
+        return r.json()
+
     def heartbeat(self, job_ids: list[int]) -> None:
         try:
             self.s.post(

agent/fc_agent/worker.py

@@ -11,6 +11,7 @@ orphaned work is re-picked at once rather than waiting out the lease.
 """
 import threading
 
+import numpy as np
 import requests
 
 from . import media, models
@@ -193,28 +194,42 @@ class Worker:
             else:
                 frames = [(None, media.load_image(data))]
 
+            task = job.get("task") or "ccip"
+            embed_version = job.get("embed_version") or DEFAULT_EMBED_VERSION
+            model_name = (
+                self.cfg.embed_model_override
+                or job.get("embed_model_name")
+                or DEFAULT_EMBED_MODEL
+            )
+
+            # 'embed' = WHOLE-IMAGE SigLIP embedding (re-embed the library under a
+            # new model, #1190) → image_record.siglip_embedding. Mean-pool video
+            # frames, matching the server's tag_and_embed. No regions.
+            if task == "embed":
+                embedder = self._ensure_embedder(model_name)
+                vecs = [embedder.embed(frame) for _, frame in frames]
+                if len(vecs) > 1:
+                    vec = np.mean(
+                        np.asarray(vecs, dtype=np.float32), axis=0
+                    ).tolist()
+                else:
+                    vec = vecs[0]
+                self.client.submit_embedding(job["job_id"], vec, embed_version)
+                self._bump(processed=1)
+                return True
+
             # task picks what to produce per crop:
             #   'siglip' (backfill existing images) → concept (SigLIP) regions
             #     ONLY, so it never churns their figure/CCIP regions or the
             #     character-reference cache.
             #   'ccip' / 'both' (a new image's first pass) → figure (CCIP) AND
             #     concept (SigLIP) in one go, off the same crop.
-            task = job.get("task") or "ccip"
             want_ccip = task in ("ccip", "both")
             want_siglip = task in ("ccip", "siglip", "both")
             replace_kinds = (
                 ["concept"] if task == "siglip" else ["figure", "face", "concept"]
             )
-
-            embed_version = job.get("embed_version") or DEFAULT_EMBED_VERSION
-            embedder = None
-            if want_siglip:
-                model_name = (
-                    self.cfg.embed_model_override
-                    or job.get("embed_model_name")
-                    or DEFAULT_EMBED_MODEL
-                )
-                embedder = self._ensure_embedder(model_name)
+            embedder = self._ensure_embedder(model_name) if want_siglip else None
 
             regions = []
             ccip_ev = self.cfg.ccip_model or "ccip-default"