refactor: remove face embedding architecture - single Qdrant _faces collection

- Delete FaceEmbeddingDb module (face_embedding_db.rs)
- Stub match_faces_iterative, generate_seed_embeddings, tmdb_match_handler
- Remove sync_trace_embeddings, populate_face_embeddings_to_qdrant
- Remove embedding from face.json output (face_processor.py)
- Remove embedding from PG UPDATE (store_traced_faces.py)
- Remove workspace traces staging (checkin.rs, qdrant_workspace.rs)
- Fix tests: add pose_angle to Face, hand_nodes to TkgResult

Disabled functions (need reimplement with _faces):
- match_faces_iterative (identity agent)
- generate_seed_embeddings (TMDb seeds)
- tmdb_match_handler (TMDb matching)
- cluster_face_embeddings, search_similar_faces
- merge_traces_within_cuts

scripts/face_processor.py

@@ -225,8 +225,9 @@ class FaceProcessorVision:
                 if face_img.size == 0:
                     continue
 
-                # CoreML embedding
-                emb = self.extract_face_embedding(face_img)
+                # CoreML embedding - TODO: push to Qdrant _faces collection instead
+                # emb = self.extract_face_embedding(face_img)
+                emb = None
                 if emb is not None:
                     embed_count += 1
 
@@ -240,7 +241,6 @@ class FaceProcessorVision:
                 faces.append({
                     "x": x, "y": y, "width": w, "height": h,
                     "confidence": face.get("confidence", 0.5),
-                    "embedding": emb,
                     "pose_angle": {
                         "angle": pose_angle,
                         "roll": pose_info.get("roll", 0),
@@ -262,20 +262,17 @@ class FaceProcessorVision:
 
             if len(face_data["frames"]) % 100 == 0:
                 elapsed = time.time() - t0
-                print(f"[FACE_V2] {len(face_data['frames'])} frames, {embed_count} embeddings, {elapsed:.0f}s")
+                print(f"[FACE_V2] {len(face_data['frames'])} frames, {elapsed:.0f}s")
                 if self.publisher:
                     pct = int(len(face_data["frames"]) * 100 / max(len(frames), 1))
                     if pct > last_pct:
                         last_pct = pct
                         self.publisher.progress("face", len(face_data["frames"]), len(frames),
-                            f"{embed_count} faces", embed_count, "faces")
+                            "", 0, "faces")
 
         self.video.release()
 
-        # Finalize
         face_data["metadata"]["status"] = "completed"
-        face_data["metadata"]["total_embeddings"] = embed_count
-        face_data["metadata"]["embedder"] = "coreml_facenet"
 
         # Convert dict frames to list for Rust FaceResult format
         frames_list = []