diff --git a/app/rag_chain.py b/app/rag_chain.py
index 9147dab..99f6425 100644
--- a/app/rag_chain.py
+++ b/app/rag_chain.py
@@ -6,6 +6,8 @@ from langchain.embeddings import HuggingFaceEmbeddings  # 필요 시 HuggingFace
 from langchain.vectorstores import Weaviate
 from langchain.chains import RetrievalQA
 from langchain.llms import HuggingFaceHub
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
 
 def build_rag_chain():
     # 1. Weaviate 클라이언트
@@ -22,24 +24,16 @@ def build_rag_chain():
     # 2. 벡터스토어
     vectorstore = Weaviate(
         client=client,
-        index_name="Test",
+        index_name="LangDocs",
         text_key="text",
-        embedding= HuggingFaceEmbeddings(
-          model_name="sentence-transformers/all-MiniLM-L6-v2"
-        )
+        embedding=OpenAIEmbeddings()
     )
 
     # 3. HuggingFace LLM (예: mistralai/Mistral-7B-Instruct-v0.2)
-    llm = HuggingFaceHub(
-        repo_id="mistralai/Mistral-7B-Instruct-v0.2",
-        model_kwargs={
-            "temperature": 0.1,
-            "max_new_tokens": 512,
-            "top_p": 0.95,
-        }
-    )
+    llm = ChatOpenAI(temperature=0)
 
     retriever = vectorstore.as_retriever()
 
     # 4. RetrievalQA chain 구성
-    return RetrievalQA.from_chain_type(llm=llm, retriever=retriever, chain_type="stuff")
+
+    return RetrievalQA.from_chain_type(llm=llm, retriever=retriever)