text: ChatGLM-6B and ChatGLM2-6B has the same api specs, so this example should work with both.from langchain.llms import ChatGLMfrom langchain import PromptTemplate, LLMChain# import ostemplate = """{question}"""prompt = PromptTemplate(template=template, input_variables=["question"])# default endpoint_url for a local deployed ChatGLM api serverendpoint_url = "http://127.0.0.1:8000"# direct access endpoint in a proxied environment# os.environ['NO_PROXY'] = '127.0.0.1'llm = ChatGLM(    endpoint_url=endpoint_url,    max_token=80000,    history=[["æˆ‘å°†ä»�ç¾�å›½åˆ°ä¸­å›½æ�¥æ—…æ¸¸ï¼Œå‡ºè¡Œå‰�å¸Œæœ›äº†è§£ä¸­å›½çš„åŸ�å¸‚", "æ¬¢è¿�é—®æˆ‘ä»»ä½•é—®é¢˜ã€‚"]],    top_p=0.9,    model_kwargs={"sample_model_args": False},)# turn on with_history only when you want the LLM object to keep track of the conversation history# and send the accumulated context to the backend model api, which make it stateful. By default it is stateless.# llm.with_history = Truellm_chain = LLMChain(prompt=prompt, llm=llm)question =