diff --git a/lightrag/kg/milvus_impl.py b/lightrag/kg/milvus_impl.py
index c72498c5..fe046eb4 100644
--- a/lightrag/kg/milvus_impl.py
+++ b/lightrag/kg/milvus_impl.py
@@ -56,16 +56,16 @@ class MilvusVectorDBStorge(BaseVectorStorage):
             contents[i : i + self._max_batch_size]
             for i in range(0, len(contents), self._max_batch_size)
         ]
-        embedding_tasks = [self.embedding_func(batch) for batch in batches]
-        embeddings_list = []
-        for f in tqdm_async(
-            await asyncio.gather(*embedding_tasks),
-            total=len(embedding_tasks),
-            desc="Generating embeddings",
-            unit="batch",
-        ):
-            embeddings = await f
-            embeddings_list.append(embeddings)
+
+        async def wrapped_task(batch):
+            result = await self.embedding_func(batch)
+            pbar.update(1)
+            return result
+
+        embedding_tasks = [wrapped_task(batch) for batch in batches]
+        pbar = tqdm_async(total=len(embedding_tasks), desc="Generating embeddings", unit="batch")
+        embeddings_list = await asyncio.gather(*embedding_tasks)
+
         embeddings = np.concatenate(embeddings_list)
         for i, d in enumerate(list_data):
             d["vector"] = embeddings[i]
diff --git a/lightrag/storage.py b/lightrag/storage.py
index 534c6e2e..037a9c2f 100644
--- a/lightrag/storage.py
+++ b/lightrag/storage.py
@@ -96,16 +96,16 @@ class NanoVectorDBStorage(BaseVectorStorage):
             contents[i : i + self._max_batch_size]
             for i in range(0, len(contents), self._max_batch_size)
         ]
-        embedding_tasks = [self.embedding_func(batch) for batch in batches]
-        embeddings_list = []
-        for f in tqdm_async(
-            await asyncio.gather(*embedding_tasks),
-            total=len(embedding_tasks),
-            desc="Generating embeddings",
-            unit="batch",
-        ):
-            embeddings = await f
-            embeddings_list.append(embeddings)
+
+        async def wrapped_task(batch):
+            result = await self.embedding_func(batch)
+            pbar.update(1)
+            return result
+
+        embedding_tasks = [wrapped_task(batch) for batch in batches]
+        pbar = tqdm_async(total=len(embedding_tasks), desc="Generating embeddings", unit="batch")
+        embeddings_list = await asyncio.gather(*embedding_tasks)
+
         embeddings = np.concatenate(embeddings_list)
         if len(embeddings) == len(list_data):
             for i, d in enumerate(list_data):