Upload langchain_KB.py

langchain_KB.py

@@ -97,7 +97,7 @@ def langchain_localKB_construct(filepath, username):
 def langchain_RAG(prompt, username):
     ### 用langchain看框架接入本地知识库。   
     embeddings = OpenAIEmbeddings(disallowed_special=()) ## load embedding model again here. 
-    vector_store = FAISS.load_local(f'./{username}/faiss_index', embeddings)
+    vector_store = FAISS.load_local(f'./{username}/faiss_index', embeddings, allow_dangerous_deserialization=True)
     docs = vector_store.similarity_search(prompt, k=5)
     context = [doc.page_content for doc in docs]
     total_prompt = f"已知信息：\n{context}\n 根据这些已知信息来回答问题:\n{prompt}"