在langchain访问清华知谱chatglm的大模型后台

from langchain.chains import LLMChain
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.chat_models import ChatZhipuAI
# from langchain_community.chat_models import ChatOpenAI
from langchain_openai import ChatOpenAI
import jwt
import time
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage


zhipuai_api_key = "bdc59e310deb29d48e6be230d487c518.n88YR9GP76XUePoL"

def generate_token(apikey: str, exp_seconds: int):
        try:
            id, secret = apikey.split(".")
        except Exception as e:
            raise Exception("invalid apikey", e)

        payload = {
            "api_key": id,
            "exp": int(round(time.time() * 1000)) + exp_seconds * 1000,
            "timestamp": int(round(time.time() * 1000)),
        }

        return jwt.encode(
            payload,
            secret,
            algorithm="HS256",
            headers={"alg": "HS256", "sign_type": "SIGN"},
        )

chat = ChatOpenAI(
        model_name="glm-4",
        openai_api_base="https://open.bigmodel.cn/api/paas/v4",
        openai_api_key=generate_token(zhipuai_api_key,10),
        streaming=False,
        verbose=True
    )
messages = [
    # AIMessage(content="Hi."),
    # SystemMessage(content="Your role is a poet."),
    # HumanMessage(content="深圳2008年的GDP多少亿"),
    HumanMessage(content="only give me the result,no other words:the result of add 3 to 4"),
]

response = chat.invoke(messages)
print(response)




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