diff --git a/src/chat/knowledge/embedding_store.py b/src/chat/knowledge/embedding_store.py
index 2cb9fbdfb..3eb466d21 100644
--- a/src/chat/knowledge/embedding_store.py
+++ b/src/chat/knowledge/embedding_store.py
@@ -26,7 +26,7 @@ from rich.progress import (
     TextColumn,
 )
 from src.manager.local_store_manager import local_storage
-from src.chat.utils.utils import get_embedding_sync
+from src.chat.utils.utils import get_embedding
 from src.config.config import global_config
 
 
@@ -99,7 +99,7 @@ class EmbeddingStore:
         self.idx2hash = None
 
     def _get_embedding(self, s: str) -> List[float]:
-        return get_embedding_sync(s)
+        return get_embedding(s)
 
     def get_test_file_path(self):
         return EMBEDDING_TEST_FILE
diff --git a/src/chat/knowledge/qa_manager.py b/src/chat/knowledge/qa_manager.py
index b4a0dc1fc..c83683b79 100644
--- a/src/chat/knowledge/qa_manager.py
+++ b/src/chat/knowledge/qa_manager.py
@@ -10,7 +10,7 @@ from .kg_manager import KGManager
 # from .lpmmconfig import global_config
 from .utils.dyn_topk import dyn_select_top_k
 from src.llm_models.utils_model import LLMRequest
-from src.chat.utils.utils import get_embedding_sync
+from src.chat.utils.utils import get_embedding
 from src.config.config import global_config
 
 MAX_KNOWLEDGE_LENGTH = 10000  # 最大知识长度
@@ -36,7 +36,7 @@ class QAManager:
 
         # 生成问题的Embedding
         part_start_time = time.perf_counter()
-        question_embedding = await get_embedding_sync(question)
+        question_embedding = await get_embedding(question)
         if question_embedding is None:
             logger.error("生成问题Embedding失败")
             return None
diff --git a/src/chat/utils/utils.py b/src/chat/utils/utils.py
index 045e9e911..a329b3548 100644
--- a/src/chat/utils/utils.py
+++ b/src/chat/utils/utils.py
@@ -122,18 +122,6 @@ async def get_embedding(text, request_type="embedding"):
     return embedding
 
 
-def get_embedding_sync(text, request_type="embedding"):
-    """获取文本的embedding向量（同步版本）"""
-    # TODO: API-Adapter修改标记
-    llm = LLMRequest(model=global_config.model.embedding, request_type=request_type)
-    try:
-        embedding = llm.get_embedding_sync(text)
-    except Exception as e:
-        logger.error(f"获取embedding失败: {str(e)}")
-        embedding = None
-    return embedding
-
-
 def get_recent_group_speaker(chat_stream_id: str, sender, limit: int = 12) -> list:
     # 获取当前群聊记录内发言的人
     filter_query = {"chat_id": chat_stream_id}
diff --git a/src/llm_models/utils_model.py b/src/llm_models/utils_model.py
index e2e37fdbd..1077cfa09 100644
--- a/src/llm_models/utils_model.py
+++ b/src/llm_models/utils_model.py
@@ -827,29 +827,6 @@ class LLMRequest:
         )
         return embedding
 
-    def get_embedding_sync(self, text: str) -> Union[list, None]:
-        """同步方法：获取文本的embedding向量
-
-        Args:
-            text: 需要获取embedding的文本
-
-        Returns:
-            list: embedding向量，如果失败则返回None
-        """
-        return asyncio.run(self.get_embedding(text))
-
-    def generate_response_sync(self, prompt: str, **kwargs) -> Union[str, Tuple]:
-        """同步方式根据输入的提示生成模型的响应
-
-        Args:
-            prompt: 输入的提示文本
-            **kwargs: 额外的参数
-
-        Returns:
-            Union[str, Tuple]: 模型响应内容，如果有工具调用则返回元组
-        """
-        return asyncio.run(self.generate_response_async(prompt, **kwargs))
-
 
 def compress_base64_image_by_scale(base64_data: str, target_size: int = 0.8 * 1024 * 1024) -> str:
     """压缩base64格式的图片到指定大小