修復 AI embedding queue 原子取件

config.py

@@ -320,7 +320,7 @@ YOUTUBE_API_KEY = os.getenv('YOUTUBE_API_KEY', '')
 # ==========================================
 # 系統版本與路徑
 # ==========================================
-SYSTEM_VERSION = "V10.256"
+SYSTEM_VERSION = "V10.257"
 LOG_FILE_PATH = os.path.join(BASE_DIR, 'logs/system.log')
 public_url = PUBLIC_URL  # 用於模板顯示
 

services/openclaw_learning_service.py

@@ -174,42 +174,65 @@ def _process_one_embedding(row_id: int, target_table: str, target_id: int,
         session.close()
 
 
+def _claim_pending_embeddings(limit: int = EMBED_BATCH_SIZE,
+                              max_attempts: int = EMBED_MAX_ATTEMPTS):
+    """Atomically claim pending embedding jobs across multiple workers."""
+    session = get_session()
+    try:
+        session.execute(
+            text("""
+                UPDATE embedding_retry_queue
+                   SET status = 'pending',
+                       updated_at = :now,
+                       last_error = COALESCE(last_error, '') || ' | reset stale processing'
+                 WHERE status = 'processing'
+                   AND updated_at < :cutoff
+            """),
+            {
+                "now": datetime.now(),
+                "cutoff": datetime.fromtimestamp(time.time() - 15 * 60),
+            },
+        )
+        session.commit()
+        rows = session.execute(
+            text("""
+                WITH picked AS (
+                    SELECT id
+                      FROM embedding_retry_queue
+                     WHERE status = 'pending'
+                       AND attempts < :max
+                     ORDER BY created_at
+                     FOR UPDATE SKIP LOCKED
+                     LIMIT :lim
+                )
+                UPDATE embedding_retry_queue q
+                   SET status = 'processing',
+                       updated_at = :now
+                  FROM picked
+                 WHERE q.id = picked.id
+             RETURNING q.id, q.target_table, q.target_id, q.text_content, q.model
+            """),
+            {
+                "now": datetime.now(),
+                "max": max_attempts,
+                "lim": limit,
+            },
+        ).fetchall()
+        session.commit()
+        return rows
+    except Exception:
+        session.rollback()
+        raise
+    finally:
+        session.close()
+
+
 def _embedding_worker_loop():
     """背景執行緒：輪詢 embedding_retry_queue，批次處理 pending 項目"""
     sys_log.info("[OCLearn] Hermes Embedding Worker (DB-backed) 啟動")
     while True:
         try:
-            session = get_session()
-            try:
-                session.execute(
-                    text("""
-                        UPDATE embedding_retry_queue
-                           SET status = 'pending',
-                               updated_at = :now,
-                               last_error = COALESCE(last_error, '') || ' | reset stale processing'
-                         WHERE status = 'processing'
-                           AND updated_at < :cutoff
-                    """),
-                    {
-                        "now": datetime.now(),
-                        "cutoff": datetime.fromtimestamp(time.time() - 15 * 60),
-                    },
-                )
-                session.commit()
-                rows = session.execute(
-                    text("""
-                        SELECT id, target_table, target_id, text_content, model
-                          FROM embedding_retry_queue
-                         WHERE status = 'pending'
-                           AND attempts < :max
-                         ORDER BY created_at
-                         LIMIT :lim
-                    """),
-                    {"max": EMBED_MAX_ATTEMPTS, "lim": EMBED_BATCH_SIZE},
-                ).fetchall()
-            finally:
-                session.close()
-
+            rows = _claim_pending_embeddings()
             for row in rows:
                 _process_one_embedding(
                     row[0], row[1], row[2], row[3], row[4] or "bge-m3:latest"

tests/test_ai_insight_embedding_bridge.py

@@ -98,3 +98,41 @@ def test_process_one_embedding_writes_signature(monkeypatch):
     assert target_updates
     assert "embedding_signature" in target_updates[0][0]
     assert target_updates[0][1]["sig"] == get_embedding_signature(model="bge-m3:latest", dim=1024)
+
+
+def test_claim_pending_embeddings_uses_skip_locked(monkeypatch):
+    import services.openclaw_learning_service as learning
+
+    executed = []
+    claimed_rows = [(7, "ai_insights", 42, "測試內容", "bge-m3:latest")]
+
+    class Result:
+        def fetchall(self):
+            return claimed_rows
+
+    class Session:
+        def execute(self, stmt, params=None):
+            executed.append((str(stmt), params or {}))
+            if "RETURNING q.id" in str(stmt):
+                return Result()
+            return Result()
+
+        def commit(self):
+            pass
+
+        def rollback(self):
+            pass
+
+        def close(self):
+            pass
+
+    monkeypatch.setattr(learning, "get_session", lambda: Session())
+
+    rows = learning._claim_pending_embeddings(limit=3, max_attempts=5)
+
+    assert rows == claimed_rows
+    claim_sql = [stmt for stmt, _ in executed if "RETURNING q.id" in stmt][0]
+    assert "FOR UPDATE SKIP LOCKED" in claim_sql
+    assert "UPDATE embedding_retry_queue q" in claim_sql
+    assert executed[-1][1]["lim"] == 3
+    assert executed[-1][1]["max"] == 5