diff --git a/server/chat/knowledge_base_chat.py b/server/chat/knowledge_base_chat.py
index 60956b44..8a9197f5 100644
--- a/server/chat/knowledge_base_chat.py
+++ b/server/chat/knowledge_base_chat.py
@@ -7,18 +7,16 @@ from configs import (LLM_MODELS,
                      TEMPERATURE,
                      USE_RERANKER,
                      RERANKER_MODEL,
-                     RERANKER_MAX_LENGTH,
-                     MODEL_PATH)
-from server.utils import wrap_done, get_ChatOpenAI
+                     RERANKER_MAX_LENGTH)
+from server.utils import wrap_done, get_ChatOpenAI, get_model_path
 from server.utils import BaseResponse, get_prompt_template
 from langchain.chains import LLMChain
 from langchain.callbacks import AsyncIteratorCallbackHandler
 from typing import AsyncIterable, List, Optional
-import asyncio
+import asyncio, json
 from langchain.prompts.chat import ChatPromptTemplate
 from server.chat.utils import History
 from server.knowledge_base.kb_service.base import KBServiceFactory
-import json
 from urllib.parse import urlencode
 from server.knowledge_base.kb_doc_api import search_docs
 from server.reranker.reranker import LangchainReranker
@@ -86,18 +84,17 @@ async def knowledge_base_chat(query: str = Body(..., description="用户输入",
 
         # 加入reranker
         if USE_RERANKER:
-            reranker_model_path = MODEL_PATH["reranker"].get(RERANKER_MODEL,"BAAI/bge-reranker-large")
-            print("-----------------model path------------------")
-            print(reranker_model_path)
+            reranker_model_path = get_model_path(RERANKER_MODEL)
             reranker_model = LangchainReranker(top_n=top_k,
                                             device=embedding_device(),
                                             max_length=RERANKER_MAX_LENGTH,
                                             model_name_or_path=reranker_model_path
                                             )
+            print("-------------before rerank-----------------")
             print(docs)
             docs = reranker_model.compress_documents(documents=docs,
                                                      query=query)
-            print("---------after rerank------------------")
+            print("------------after rerank------------------")
             print(docs)
         context = "\n".join([doc.page_content for doc in docs])
 
@@ -144,4 +141,3 @@ async def knowledge_base_chat(query: str = Body(..., description="用户输入",
         await task
 
     return EventSourceResponse(knowledge_base_chat_iterator(query, top_k, history,model_name,prompt_name))
-