diff --git a/lightrag/api/lightrag_server.py b/lightrag/api/lightrag_server.py
index 8f3fbae1..eab614c3 100644
--- a/lightrag/api/lightrag_server.py
+++ b/lightrag/api/lightrag_server.py
@@ -917,6 +917,11 @@ def create_app(args):
     else:
         logger.info("Embedding max_token_size: not set (90% token warning disabled)")
 
+    # Set max_token_size if EMBEDDING_TOKEN_LIMIT is provided
+    if args.embedding_token_limit is not None:
+        embedding_func.max_token_size = args.embedding_token_limit
+        logger.info(f"Set embedding max_token_size to {args.embedding_token_limit}")
+
     # Configure rerank function based on args.rerank_bindingparameter
     rerank_model_func = None
     if args.rerank_binding != "null":
diff --git a/lightrag/lightrag.py b/lightrag/lightrag.py
index a8a183df..72a4dc6d 100644
--- a/lightrag/lightrag.py
+++ b/lightrag/lightrag.py
@@ -64,10 +64,9 @@ from lightrag.kg import (
 
 from lightrag.kg.shared_storage import (
     get_namespace_data,
+    get_pipeline_status_lock,
     get_graph_db_lock,
     get_data_init_lock,
-    get_storage_keyed_lock,
-    initialize_pipeline_status,
 )
 
 from lightrag.base import (
@@ -277,8 +276,12 @@ class LightRAG:
     embedding_func: EmbeddingFunc | None = field(default=None)
     """Function for computing text embeddings. Must be set before use."""
 
-    embedding_token_limit: int | None = field(default=None, init=False)
-    """Token limit for embedding model. Set automatically from embedding_func.max_token_size in __post_init__."""
+    @property
+    def embedding_token_limit(self) -> int | None:
+        """Get the token limit for embedding model from embedding_func."""
+        if self.embedding_func and hasattr(self.embedding_func, "max_token_size"):
+            return self.embedding_func.max_token_size
+        return None
 
     embedding_batch_num: int = field(default=int(os.getenv("EMBEDDING_BATCH_NUM", 10)))
     """Batch size for embedding computations."""
@@ -523,28 +526,12 @@ class LightRAG:
         logger.debug(f"LightRAG init with param:\n  {_print_config}\n")
 
         # Init Embedding
-        # Step 1: Capture max_token_size before applying decorator (decorator strips dataclass attributes)
-        embedding_max_token_size = None
-        if self.embedding_func and hasattr(self.embedding_func, "max_token_size"):
-            embedding_max_token_size = self.embedding_func.max_token_size
-            logger.debug(
-                f"Captured embedding max_token_size: {embedding_max_token_size}"
-            )
-        self.embedding_token_limit = embedding_max_token_size
-
-        # Step 2: Apply priority wrapper decorator
         self.embedding_func = priority_limit_async_func_call(
             self.embedding_func_max_async,
             llm_timeout=self.default_embedding_timeout,
             queue_name="Embedding func",
         )(self.embedding_func)
 
-        # Initialize embedding_token_limit from embedding_func
-        if self.embedding_func and hasattr(self.embedding_func, "max_token_size"):
-            self.embedding_token_limit = self.embedding_func.max_token_size
-        else:
-            self.embedding_token_limit = None
-
         # Initialize all storages
         self.key_string_value_json_storage_cls: type[BaseKVStorage] = (
             self._get_storage_class(self.kv_storage)
@@ -665,13 +652,6 @@ class LightRAG:
     async def initialize_storages(self):
         """Storage initialization must be called one by one to prevent deadlock"""
         if self._storages_status == StoragesStatus.CREATED:
-            # Set default workspace for backward compatibility
-            # This allows initialize_pipeline_status() called without parameters
-            # to use the correct workspace
-            from lightrag.kg.shared_storage import set_default_workspace
-
-            set_default_workspace(self.workspace)
-
             for storage in (
                 self.full_docs,
                 self.text_chunks,
@@ -1606,22 +1586,8 @@ class LightRAG:
         """
 
         # Get pipeline status shared data and lock
-        # Step 1: Get workspace
-        workspace = self.workspace
-
-        # Step 2: Construct namespace (following GraphDB pattern)
-        namespace = f"{workspace}:pipeline" if workspace else "pipeline_status"
-
-        # Step 3: Ensure initialization (on first access)
-        await initialize_pipeline_status(workspace)
-
-        # Step 4: Get lock
-        pipeline_status_lock = get_storage_keyed_lock(
-            keys="status", namespace=namespace, enable_logging=False
-        )
-
-        # Step 5: Get data
-        pipeline_status = await get_namespace_data(namespace)
+        pipeline_status = await get_namespace_data("pipeline_status")
+        pipeline_status_lock = get_pipeline_status_lock()
 
         # Check if another process is already processing the queue
         async with pipeline_status_lock:
@@ -2973,22 +2939,8 @@ class LightRAG:
         doc_llm_cache_ids: list[str] = []
 
         # Get pipeline status shared data and lock for status updates
-        # Step 1: Get workspace
-        workspace = self.workspace
-
-        # Step 2: Construct namespace (following GraphDB pattern)
-        namespace = f"{workspace}:pipeline" if workspace else "pipeline_status"
-
-        # Step 3: Ensure initialization (on first access)
-        await initialize_pipeline_status(workspace)
-
-        # Step 4: Get lock
-        pipeline_status_lock = get_storage_keyed_lock(
-            keys="status", namespace=namespace, enable_logging=False
-        )
-
-        # Step 5: Get data
-        pipeline_status = await get_namespace_data(namespace)
+        pipeline_status = await get_namespace_data("pipeline_status")
+        pipeline_status_lock = get_pipeline_status_lock()
 
         async with pipeline_status_lock:
             log_message = f"Starting deletion process for document {doc_id}"