p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 13.0px 'Helvetica Neue'}p.p2 {margin: 0.0px 0.0px 0.0px 0.0px; font: 13.0px 'Helvetica Neue'; min-height: 15.0px}class ArxivInput(BaseModel):    """Input for the Arxiv tool."""    query: str = Field(description="search query to look up")class ArxivQueryRun(BaseTool):  # type: ignore[override, override]    """Tool that searches the Arxiv API."""    name: str = "arxiv"    description: str = (        "A wrapper around Arxiv.org "        "Useful for when you need to answer questions about Physics, Mathematics, "        "Computer Science, Quantitative Biology, Quantitative Finance, Statistics, "        "Electrical Engineering, and Economics "        "from scientific articles on arxiv.org. "        "Input should be a search query."    )    api_wrapper: ArxivAPIWrapper = Field(default_factory=ArxivAPIWrapper)  # type: ignore[arg-type]    args_schema: Type[BaseModel] = ArxivInput    def _run(        self,        query: str,        run_manager: Optional[CallbackManagerForToolRun] = None,    ) -> str:        """Use the Arxiv tool."""        return self.api_wrapper.run(query)p

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 13.0px 'Helvetica Neue'}p.p2 {margin: 0.0px 0.0px 0.0px 0.0px; font: 13.0px 'Helvetica Neue'; min-height: 15.0px}def orchestrator(llm_agent, llm_output, tools, user_query):    """    Orchestrates the response based on LLM output and iterates if necessary.    Parameters:    - llm_agent (callable): The LLM agent function for processing tool outputs.    - llm_output (dict): Initial output from the LLM, specifying the next action.    - tools (dict): Dictionary of available tools with their execution methods.    - user_query (str): The original user query.    Returns:    - str: The final response to the user.    """    while True:        action = llm_output.get("action")        if action == "tool_call":            # Extract tool name and parameters            tool_name = llm_output.get("tool_name")            tool_params = llm_output.get("tool_params", {})            if tool_name in tools:                try:                    # Execute the tool                    tool_result = tools[tool_name](**tool_params)                    # Send tool output back to the LLM agent for further processing                    llm_output = llm_agent({"tool_output": tool_result})                except Exception as e:                    return f"Error executing tool '{tool_name}': {str(e)}"            else:                return f"Error: Tool '{tool_name}' not found."        elif action == "return_answer":            # Return the final answer to the user            return llm_output.get("answer", "No answer provided.")        else:            return "Error: Unrecognized action type from LLM output."恭喜你！现在，你已经拥有了一个能够应对各种用例的强大系统——从竞争分析和高级研究，到自动化复杂工作流程，通通不在话下！无论是提升工作效率，还是解锁更多创新机会，这个系统都能为你带来前所未有的助力。你准备好开始探索它的无限潜力了吗？在实现目标的过程中，你最期待的是什么呢？欢迎在评论区分享你的想法和体验，让我们一起讨论如何更好地利用这一强大工具！