code: import langchain
  from dotenv import load_dotenv
  from langchain.agents import initialize_agent, AgentType
  from langchain.chat_models import ChatOpenAI
  from datetime import timedelta, datetime
  import chainlit as cl
  from utils.custom_tools import CustomTrinoListTable, CustomTrinoTableSchema, CustomTrinoSqlQuery, CustomTrinoSqlCheck, CustomTrinoTableJoin
  
  # 加载.env文件中的环境变量
  load_dotenv()
  langchain.debug = True
  today = (datetime.now()).strftime("%Y%m%d")
  yeaterday = (datetime.now() - timedelta(days=1)).strftime("%Y%m%d")
  today_d = (datetime.now()).strftime("%Y-%m-%d")
  yeaterday_d = (datetime.now() - timedelta(days=1)).strftime("%Y-%m-%d")
  custom_prefix = f""" You are an agent designed to interact with a Trino SQL database.
  Please do not answer others questions,use Chinese to answer questions,think it step by step. 
  NOTE: 
  data rule:
  date string format YYYYMMDD. for example, today is {today}, then yeaterday is {yeaterday}, and so on.
  date string format YYYY-MM-DD. for example, today is {today_d}, then yeaterday is {yeaterday_d}, and so on.
  column dt is table data version string, not a business date.
  sql rule:
  sql generation not need to end with ; only sql itself.
  get answer from single sql after plan logics step by step. 
  You have access to the following tools:"""
  
  
  @cl.on_chat_start
  async def start():
      # Initialize model
      model = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, verbose=False, streaming=True)
  
      custom_tool_list = [CustomTrinoListTable(), CustomTrinoTableSchema(), CustomTrinoSqlQuery(), CustomTrinoSqlCheck(), CustomTrinoTableJoin()]
  
      agent_executor = initialize_agent(
          custom_tool_list,
          llm=model,
          agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
          verbose=True,
          max_iterations=6,
          agent_kwargs={"prefix": custom_prefix},
          handle_parsing_errors="Check your output and make sure it conforms"
      )
      cl.user_session.set("agent", agent_executor)
      # Send the initial message
      elements = [
          cl.Text(name="提问：", content="计算订单明细表的dt为昨天,销售日期为本月的总销售数量", display="inline"),
          cl.Text(name="我能生成SQL脚本：", content=f"SELECT SUM(num) AS total_sales_quantity FROM gjdw.dw_sale_tr_goods_dt WHERE dt = '{today}' AND dates >= '2023-10-01' AND dates <= '2023-10-31'", display="inline", language="SQL"),
          cl.Text(name="最终结果：", content="订单明细表的dt为昨天，销售日期为本月的总销售数量是0。", display="inline"),
      ]
      content = "Hi,我是 Trino SQL Agent ,我能帮助你查询trino数据库。您可以向我提问，例如："
      await cl.Message(content=content, elements=elements).send()
  
  
  @cl.on_message
  async def main(message: cl.Message):
      agent = cl.user_session.get("agent")  # type: #AgentExecutor
      cb = cl.LangchainCallbackHandler(stream_final_answer=True)
      print(message)
      await cl.make_async(agent.run)(message, callbacks=[cb])
  
  # def ask(input: str) -> str:
  #     print("-- Serving request for input: %s" % input)
  #     try:
  #         response = agent_executor.run(input)
  #     except Exception as e:
  #         response = str(e)
  #         if response.startswith("Could not parse LLM output: `"):
  #             response = response.removeprefix("Could not parse LLM output: `").removesuffix("`")
  #     return response
  
  
  # agent_executor.run(" table gjdw.dw_sale_tr_goods_dt has column named bill_code ?")
  # ask("计算订单明细表的dt为昨天,销售日期为本月初的总销售数量")
  
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