code: import os
  import langchain
  from config import *
  from util import *
  from langchain.llms import OpenAI, Cohere, HuggingFaceHub
  from langchain.chat_models import ChatOpenAI
  from langchain.agents import AgentType, initialize_agent, load_tools
  from typing import Optional, Type
  from langchain.callbacks.manager import AsyncCallbackManagerForToolRun, CallbackManagerForToolRun
  from langchain import LLMMathChain, SerpAPIWrapper
  from langchain.agents import AgentType, Tool, initialize_agent, tool
  from langchain.chat_models import ChatOpenAI
  from langchain.tools import BaseTool
  from logging import getLogger
  
  os.environ["OPENAI_API_KEY"] = openai_api_key
  os.environ["SERPAPI_API_KEY"] = SERPAPI_API_KEY
  
  prompt = PromptTemplate(
      input_variables=["text"],
      template="{text}",
  )
  
  llm = OpenAI(temperature=0)
  chat = ChatOpenAI(temperature=0)
  
  llm_chain = LLMChain(llm=llm, prompt=prompt)
  chat_model_chain = LLMChain(llm=chat, prompt=prompt)
  
  logger = getLogger()
  
  
  class CustomSearchTool(BaseTool):
      name = "search tool"
      description = "一个搜索引擎。 当你需要回答有关实时的问题或者计算的问题调用该工具，否则不要使用该工具。 输入应该是搜索查询。"
  
      def _run(self, query: str) -> str:
          """Use the tool."""
          return search(query)
  
      async def _arun(self, query: str) -> str:
          """Use the tool asynchronously."""
          raise NotImplementedError("BingSearchRun does not support async")
  
  
  # You can create the tool to pass to an agent
  chat_tool = Tool(
      name="Chat",
      description="一个非常有用的助理，你可以回答除了实时问题或者计算问题以外的任何问题，用中文回答问题",
      func=chat_model_chain.run,
      return_direct=True
  
  )
  
  
  def get_free_dialogue_answer(user_id, query):
      try:
          logger.info(f"******** get_free_dialogue_answer ***************")
          logger.info(f"user_id = {user_id}, user_query = {query} ")
          tools = [chat_tool, CustomSearchTool()]
          agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
          result = agent.run(query)
          logger.info("******** get_free_dialogue_answer  done ***************")
          logger.info(f"user_id = {user_id}, user_query = {query} , response= {result}")
          return result
      except Exception as e:
          logger.warning(f"An error occurred during dialogue processing:{e}")
          return common_responses
  
  
  if __name__ == '__main__':
      query = "北京时间"
      user_id = "122324"
      res = get_free_dialogue_answer(user_id, query)
      print(str(res))
  
apis: langchain.llms.OpenAI langchain.agents.initialize_agent langchain.chat_models.ChatOpenAI langchain.agents.Tool
extract_api: [((786, 807), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (792, 807), False, 'from langchain.llms import OpenAI, Cohere, HuggingFaceHub\n'), ((815, 840), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (825, 840), False, 'from langchain.chat_models import ChatOpenAI\n'), ((950, 961), 'logging.getLogger', 'getLogger', ([], {}), '()\n', (959, 961), False, 'from logging import getLogger\n'), ((1420, 1546), 'langchain.agents.Tool', 'Tool', ([], {'name': '"""Chat"""', 'description': '"""一个非常有用的助理，你可以回答除了实时问题或者计算问题以外的任何问题，用中文回答问题"""', 'func': 'chat_model_chain.run', 'return_direct': '(True)'}), "(name='Chat', description='一个非常有用的助理，你可以回答除了实时问题或者计算问题以外的任何问题，用中文回答问题',\n    func=chat_model_chain.run, return_direct=True)\n", (1424, 1546), False, 'from langchain.agents import AgentType, Tool, initialize_agent, tool\n'), ((1824, 1915), 'langchain.agents.initialize_agent', 'initialize_agent', (['tools', 'llm'], {'agent': 'AgentType.ZERO_SHOT_REACT_DESCRIPTION', 'verbose': '(True)'}), '(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n    verbose=True)\n', (1840, 1915), False, 'from langchain.agents import AgentType, Tool, initialize_agent, tool\n')]