diff --git a/scripts/import_openie.py b/scripts/import_openie.py
index f81cbb5c7..472667c14 100644
--- a/scripts/import_openie.py
+++ b/scripts/import_openie.py
@@ -204,7 +204,8 @@ def main():
             logger.error("请保证你的嵌入模型从未更改,并且在导入时使用相同的模型")
             # print("检测到嵌入模型与本地存储不一致，已终止导入。请检查模型设置或清空嵌入库后重试。")
             sys.exit(1)
-        logger.error("如果你是第一次导入知识，请忽略此错误")
+        if "不存在" in str(e):
+            logger.error("如果你是第一次导入知识，请忽略此错误")
     logger.info("Embedding库加载完成")
     # 初始化KG
     kg_manager = KGManager()
diff --git a/src/plugins/knowledge/src/embedding_store.py b/src/plugins/knowledge/src/embedding_store.py
index 5ee92a869..7d012b19b 100644
--- a/src/plugins/knowledge/src/embedding_store.py
+++ b/src/plugins/knowledge/src/embedding_store.py
@@ -6,7 +6,7 @@ from typing import Dict, List, Tuple
 
 import numpy as np
 import pandas as pd
-import tqdm
+# import tqdm
 import faiss
 
 from .llm_client import LLMClient
@@ -194,11 +194,25 @@ class EmbeddingStore:
         """从文件中加载"""
         if not os.path.exists(self.embedding_file_path):
             raise Exception(f"文件{self.embedding_file_path}不存在")
-
         logger.info(f"正在从文件{self.embedding_file_path}中加载{self.namespace}嵌入库")
         data_frame = pd.read_parquet(self.embedding_file_path, engine="pyarrow")
-        for _, row in tqdm.tqdm(data_frame.iterrows(), total=len(data_frame)):
-            self.store[row["hash"]] = EmbeddingStoreItem(row["hash"], row["embedding"], row["str"])
+        total = len(data_frame)
+        with Progress(
+            SpinnerColumn(),
+            TextColumn("[progress.description]{task.description}"),
+            BarColumn(),
+            TaskProgressColumn(),
+            MofNCompleteColumn(),
+            "•",
+            TimeElapsedColumn(),
+            "<",
+            TimeRemainingColumn(),
+            transient=False,
+        ) as progress:
+            task = progress.add_task("加载嵌入库", total=total)
+            for _, row in data_frame.iterrows():
+                self.store[row["hash"]] = EmbeddingStoreItem(row["hash"], row["embedding"], row["str"])
+                progress.update(task, advance=1)
         logger.info(f"{self.namespace}嵌入库加载成功")
 
         try: