diff --git a/dsSchoolBuddy/ElasticSearch/T6_SelectByVector.py b/dsSchoolBuddy/ElasticSearch/T6_SelectByVector.py
index 368099f0..9c4ca8ed 100644
--- a/dsSchoolBuddy/ElasticSearch/T6_SelectByVector.py
+++ b/dsSchoolBuddy/ElasticSearch/T6_SelectByVector.py
@@ -1,5 +1,4 @@
 import json
-import warnings
 import requests
 from langchain_openai import OpenAIEmbeddings
 from pydantic import SecretStr
@@ -7,16 +6,6 @@ from pydantic import SecretStr
 from Config import Config
 from ElasticSearch.Utils.EsSearchUtil import EsSearchUtil
 
-# 抑制HTTPS相关警告
-warnings.filterwarnings('ignore', message='Connecting to .* using TLS with verify_certs=False is insecure')
-warnings.filterwarnings('ignore', message='Unverified HTTPS request is being made to host')
-
-# 从配置中获取重排模型参数
-RERANK_MODEL = Config.RERANK_MODEL
-RERANK_BASE_URL = Config.RERANK_BASE_URL
-RERANK_BINDING_API_KEY = Config.RERANK_BINDING_API_KEY
-
-
 def get_query_embedding(query: str) -> list:
     """
     将查询文本转换为向量
@@ -99,7 +88,7 @@ def rerank_results(query: str, results: list) -> list:
 
     # 准备重排请求数据
     rerank_data = {
-        "model": RERANK_MODEL,
+        "model": Config.RERANK_MODEL,
         "query": query,
         "documents": [result['_source']['user_input'] for result in results],
         "top_n": len(results)
@@ -108,11 +97,11 @@ def rerank_results(query: str, results: list) -> list:
     # 调用重排API
     headers = {
         "Content-Type": "application/json",
-        "Authorization": f"Bearer {RERANK_BINDING_API_KEY}"
+        "Authorization": f"Bearer {Config.RERANK_BINDING_API_KEY}"
     }
 
     try:
-        response = requests.post(RERANK_BASE_URL, headers=headers, data=json.dumps(rerank_data))
+        response = requests.post(Config.RERANK_BASE_URL, headers=headers, data=json.dumps(rerank_data))
         response.raise_for_status()
         rerank_result = response.json()