code: import langchain
  from langchain.agents import load_tools, initialize_agent, AgentType
  from langchain.chat_models import ChatOpenAI
  
  langchain.verbose = True
  langchain.debug = True
  
  
  def get_chat():
      return ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
  
  
  if __name__ == "__main__":
      chat = get_chat()
      tools = load_tools(["terminal"])
      agent_chain = initialize_agent(
          tools, chat, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION
      )
  
      result = agent_chain.run("sample_data ディレクトリにあるファイルの一覧を教えて")
      print(result)
  
  
  
  # {
  #     "prompts": [
  #         "Human: Answer the following questions as best you can. You have access to the following tools:
  # terminal: Run shell commands on this Linux machine.
  # Use the following format:
  #
  # Question: the input question you must answer
  # Thought: you should always think about what to do
  # Action: the action to take, should be one of [terminal]
  # Action Input: the input to the action
  # Observation: the result of the action
  # ... (this Thought/Action/Action Input/Observation can repeat N times)
  # Thought: I now know the final answer
  # Final Answer: the final answer to the original input question
  #
  # Begin!
  #
  # Question: sample_data ディレクトリにあるファイルの一覧を教えて
  # Thought:I need to list the files in the sample_data directory.
  # Action: terminal
  # Action Input: ls sample_data
  # Observation: ls: cannot access 'sample_data': No such file or directory
  #
  # Thought:The sample_data directory does not exist. I need to find the correct directory.
  # Action: terminal
  # Action Input: ls
  # Observation: Dockerfile
  # app
  # docker-compose.yml
  # langchain
  # requirements.txt
  # venv
  #
  # Thought:The sample_data directory is not in the current directory. I need to search for it.
  # Action: terminal
  # Action Input: find / -name sample_data
  # Observation: /usr/src/app/sample_data
  # /opt/project/app/sample_data
  #
  # Thought:"
  #     ]
  # }
apis: langchain.agents.load_tools langchain.agents.initialize_agent langchain.chat_models.ChatOpenAI
extract_api: [((209, 262), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model_name': '"""gpt-3.5-turbo"""', 'temperature': '(0)'}), "(model_name='gpt-3.5-turbo', temperature=0)\n", (219, 262), False, 'from langchain.chat_models import ChatOpenAI\n'), ((326, 350), 'langchain.agents.load_tools', 'load_tools', (["['terminal']"], {}), "(['terminal'])\n", (336, 350), False, 'from langchain.agents import load_tools, initialize_agent, AgentType\n'), ((369, 443), 'langchain.agents.initialize_agent', 'initialize_agent', (['tools', 'chat'], {'agent': 'AgentType.ZERO_SHOT_REACT_DESCRIPTION'}), '(tools, chat, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)\n', (385, 443), False, 'from langchain.agents import load_tools, initialize_agent, AgentType\n')]