diff --git a/configs/model_config.py.example b/configs/model_config.py.example
index 1908414..9100ad4 100644
--- a/configs/model_config.py.example
+++ b/configs/model_config.py.example
@@ -96,6 +96,7 @@ ONLINE_LLM_MODEL = {
         "version": "qwen-turbo",  # 可选包括 "qwen-turbo", "qwen-plus"
         "api_key": "",  # 请在阿里云控制台模型服务灵积API-KEY管理页面创建
         "provider": "QwenWorker",
+        "embed_model": "text-embedding-v1" # embedding 模型名称
     },
 
     # 百川 API，申请方式请参考 https://www.baichuan-ai.com/home#api-enter
diff --git a/server/embeddings_api.py b/server/embeddings_api.py
index 80cd289..661d97b 100644
--- a/server/embeddings_api.py
+++ b/server/embeddings_api.py
@@ -5,32 +5,32 @@ from server.utils import BaseResponse, get_model_worker_config, list_embed_model
 from fastapi import Body
 from typing import Dict, List
 
-
 online_embed_models = list_online_embed_models()
 
 
 def embed_texts(
-    texts: List[str],
-    embed_model: str = EMBEDDING_MODEL,
-    to_query: bool = False,
+        texts: List[str],
+        embed_model: str = EMBEDDING_MODEL,
+        to_query: bool = False,
 ) -> BaseResponse:
     '''
     对文本进行向量化。返回数据格式：BaseResponse(data=List[List[float]])
     TODO: 也许需要加入缓存机制，减少 token 消耗
     '''
     try:
-        if embed_model in list_embed_models(): # 使用本地Embeddings模型
+        if embed_model in list_embed_models():  # 使用本地Embeddings模型
             from server.utils import load_local_embeddings
 
             embeddings = load_local_embeddings(model=embed_model)
             return BaseResponse(data=embeddings.embed_documents(texts))
 
-        if embed_model in list_online_embed_models(): # 使用在线API
+        if embed_model in list_online_embed_models():  # 使用在线API
             config = get_model_worker_config(embed_model)
             worker_class = config.get("worker_class")
+            embed_model = config.get("embed_model")
             worker = worker_class()
             if worker_class.can_embedding():
-                params = ApiEmbeddingsParams(texts=texts, to_query=to_query)
+                params = ApiEmbeddingsParams(texts=texts, to_query=to_query, embed_model=embed_model)
                 resp = worker.do_embeddings(params)
                 return BaseResponse(**resp)
 
@@ -39,10 +39,12 @@ def embed_texts(
         logger.error(e)
         return BaseResponse(code=500, msg=f"文本向量化过程中出现错误：{e}")
 
+
 def embed_texts_endpoint(
-    texts: List[str] = Body(..., description="要嵌入的文本列表", examples=[["hello", "world"]]),
-    embed_model: str = Body(EMBEDDING_MODEL, description=f"使用的嵌入模型，除了本地部署的Embedding模型，也支持在线API({online_embed_models})提供的嵌入服务。"),
-    to_query: bool = Body(False, description="向量是否用于查询。有些模型如Minimax对存储/查询的向量进行了区分优化。"),
+        texts: List[str] = Body(..., description="要嵌入的文本列表", examples=[["hello", "world"]]),
+        embed_model: str = Body(EMBEDDING_MODEL,
+                                description=f"使用的嵌入模型，除了本地部署的Embedding模型，也支持在线API({online_embed_models})提供的嵌入服务。"),
+        to_query: bool = Body(False, description="向量是否用于查询。有些模型如Minimax对存储/查询的向量进行了区分优化。"),
 ) -> BaseResponse:
     '''
     对文本进行向量化，返回 BaseResponse(data=List[List[float]])
@@ -51,9 +53,9 @@ def embed_texts_endpoint(
 
 
 def embed_documents(
-    docs: List[Document],
-    embed_model: str = EMBEDDING_MODEL,
-    to_query: bool = False,
+        docs: List[Document],
+        embed_model: str = EMBEDDING_MODEL,
+        to_query: bool = False,
 ) -> Dict:
     """
     将 List[Document] 向量化，转化为 VectorStore.add_embeddings 可以接受的参数
diff --git a/server/model_workers/base.py b/server/model_workers/base.py
index 7b456a9..88affb4 100644
--- a/server/model_workers/base.py
+++ b/server/model_workers/base.py
@@ -113,6 +113,9 @@ class ApiModelWorker(BaseModelWorker):
         sys.stdout = sys.__stdout__
         sys.stderr = sys.__stderr__
 
+        new_loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(new_loop)
+
         self.context_len = context_len
         self.semaphore = asyncio.Semaphore(self.limit_worker_concurrency)
         self.version = None