diff --git a/dsSchoolBuddy/ElasticSearch/T2_BgeM3.py b/dsSchoolBuddy/ElasticSearch/T2_BgeM3.py
index 2d79e644..44799a92 100644
--- a/dsSchoolBuddy/ElasticSearch/T2_BgeM3.py
+++ b/dsSchoolBuddy/ElasticSearch/T2_BgeM3.py
@@ -1,15 +1,11 @@
-import os
-
+# pip install pydantic
 from langchain_core.documents import Document
 from langchain_core.vectorstores import InMemoryVectorStore
 from langchain_openai import OpenAIEmbeddings
 from langchain_text_splitters import RecursiveCharacterTextSplitter
-
+from pydantic import SecretStr  # 导入 SecretStr
 from Config.Config import EMBED_MODEL_NAME, EMBED_BASE_URL, EMBED_API_KEY
 
-# 设置环境变量
-os.environ["OPENAI_BASE_URL"] = EMBED_BASE_URL
-os.environ["OPENAI_API_KEY"] = EMBED_API_KEY
 
 # 模拟长字符串文档内容
 long_text = """混凝土是一种广泛使用的建筑材料，由水泥、砂、石子和水混合而成。它具有高强度、耐久性和良好的可塑性，被广泛应用于建筑、桥梁、道路等土木工程领域。
@@ -37,7 +33,11 @@ all_splits = text_splitter.split_documents(docs)
 print(f"切割后的文档块数量：{len(all_splits)}")
 
 # 嵌入模型
-embeddings = OpenAIEmbeddings(model=EMBED_MODEL_NAME)
+embeddings = OpenAIEmbeddings(
+    model=EMBED_MODEL_NAME,
+    base_url=EMBED_BASE_URL,
+    api_key=SecretStr(EMBED_API_KEY)  # 包装成 SecretStr 类型
+)
 
 # 向量存储
 vector_store = InMemoryVectorStore(embeddings)