Update rag_chain.py

app/rag_chain.py

@@ -7,10 +7,12 @@ from weaviate.connect import ConnectionParams
 from weaviate.auth import AuthApiKey
 from weaviate.auth import AuthCredentials
 from weaviate.classes.init import Auth
-from langchain.vectorstores import Weaviate
+# from langchain.vectorstores import Weaviate
 from langchain.chains import RetrievalQA
 from langchain.chat_models import ChatOpenAI
 from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import Weaviate as WeaviateLangchain
+from langchain.embeddings.openai import OpenAIEmbeddings
 
 def build_rag_chain():
     # 1. Weaviate 클라이언트
@@ -95,14 +97,25 @@ def build_rag_chain():
     legacy_client = legacy_weaviate.Client(connection_params=client4.connection_params)  # langchain용
 
 
-    # 2. 벡터스토어
-    vectorstore = Weaviate(
-        client=legacy_client,
-        index_name="LangDocs",
-        text_key="text",
-        embedding=OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
+    client = weaviate.Client(
+        url="http://183.111.96.67:32668",  # 예: http://localhost:8080
+        auth_client_secret=weaviate.AuthApiKey(api_key="01js3q6y7twaxccm5dbh3se9bt"),
+        additional_headers={
+            "X-OpenAI-Api-Key": OPENAI_API_KEY
+        }
     )
 
+    vectorstore = WeaviateLangchain(client=client, index_name="LangDocs", text_key="text", embedding=OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY))
+
+
+    # 2. 벡터스토어
+    # vectorstore = Weaviate(
+    #     client=client,
+    #     index_name="LangDocs",
+    #     text_key="text",
+    #     embedding=OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
+    # )
+
     # 3. HuggingFace LLM (예: mistralai/Mistral-7B-Instruct-v0.2)
     llm = ChatOpenAI(temperature=0, openai_api_key=OPENAI_API_KEY)