fix(queue): preserve embedding message ids across serialization (#1380)

EmbeddingMsg instances are registered with RequestWaitTracker before they are written to the embedding queue. The queue serializes messages via dataclasses.asdict(), but id was only assigned in __init__ and was not declared as a dataclass field, so the payload lost the registered root id. When the worker deserialized the message it generated a fresh id and marked the wrong root as complete, allowing wait=True callers to hang until timeout.

Declare id as a dataclass field so queue payloads retain the registered id, and add a regression test covering the serialize/dequeue round trip with RequestWaitTracker.

Fixes #1379

Author: officialasishkumar

openviking/storage/queuefs/embedding_msg.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2026 Beijing Volcano Engine Technology Co., Ltd.
 # SPDX-License-Identifier: AGPL-3.0
 import json
-from dataclasses import asdict, dataclass
+from dataclasses import asdict, dataclass, field
 from typing import Any, Dict, List, Optional, Union
 from uuid import uuid4
 
@@ -10,6 +10,7 @@
 class EmbeddingMsg:
     message: Union[str, List[Dict[str, Any]]]
     context_data: Dict[str, Any]
+    id: str = field(default_factory=lambda: str(uuid4()))
     telemetry_id: str = ""
     semantic_msg_id: Optional[str] = None
 

tests/storage/test_embedding_msg.py

@@ -0,0 +1,29 @@
+# Copyright (c) 2026 Beijing Volcano Engine Technology Co., Ltd.
+# SPDX-License-Identifier: AGPL-3.0
+
+from uuid import uuid4
+
+from openviking.storage.queuefs.embedding_msg import EmbeddingMsg
+from openviking.telemetry.request_wait_tracker import RequestWaitTracker
+
+
+def test_embedding_msg_roundtrip_preserves_id_for_request_wait_tracker():
+    telemetry_id = f"tm_{uuid4().hex}"
+    tracker = RequestWaitTracker.get_instance()
+    tracker.register_request(telemetry_id)
+
+    try:
+        msg = EmbeddingMsg(
+            "hello",
+            {"uri": "viking://agent/skills/demo"},
+            telemetry_id=telemetry_id,
+        )
+        tracker.register_embedding_root(telemetry_id, msg.id)
+
+        restored = EmbeddingMsg.from_dict(msg.to_dict())
+
+        assert restored.id == msg.id
+        tracker.mark_embedding_done(telemetry_id, restored.id)
+        assert tracker.is_complete(telemetry_id)
+    finally:
+        tracker.cleanup(telemetry_id)