Update app.py

app.py

@@ -15,8 +15,6 @@ from langchain.chains.conversation.prompt import ENTITY_MEMORY_CONVERSATION_TEMP
 
 from langchain import LLMChain
 
-memory = ConversationBufferMemory(memory_key="chat_history")
-
 persist_directory="db"
 llm=OpenAI(model_name = "text-davinci-003", temperature=0)
 model_name = "hkunlp/instructor-large"
@@ -24,7 +22,7 @@ embed_instruction = "Represent the text from the BMW website for retrieval"
 query_instruction = "Query the most relevant text from the BMW website"
 embeddings = HuggingFaceInstructEmbeddings(model_name=model_name, embed_instruction=embed_instruction, query_instruction=query_instruction)
 vectordb = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
-chain = RetrievalQAWithSourcesChain.from_chain_type(llm, chain_type="stuff", retriever=vectordb.as_retriever(), memory=memory)
+chain = RetrievalQAWithSourcesChain.from_chain_type(llm, chain_type="stuff", retriever=vectordb.as_retriever())
 
 def chat(message, history):
     history = history or []