def predict_and_call(
        self,
        tools: List["BaseTool"],
        user_msg: Optional[Union[str, ChatMessage]] = None,
        chat_history: Optional[List[ChatMessage]] = None,
        verbose: bool = False,
        **kwargs: Any,
    ) -> "AgentChatResponse":
        """
        Predict and call the tool.

        By default uses a ReAct agent to do tool calling (through text prompting),
        but function calling LLMs will implement this differently.

        """
        from llama_index.core.agent.react import ReActAgentWorker
        from llama_index.core.agent.types import Task
        from llama_index.core.chat_engine.types import AgentChatResponse
        from llama_index.core.memory import ChatMemoryBuffer

        worker = ReActAgentWorker(
            tools,
            llm=self,
            callback_manager=self.callback_manager,
            verbose=verbose,
            max_iterations=kwargs.get("max_iterations", 10),
            react_chat_formatter=kwargs.get("react_chat_formatter"),
            output_parser=kwargs.get("output_parser"),
            tool_retriever=kwargs.get("tool_retriever"),
            handle_reasoning_failure_fn=kwargs.get("handle_reasoning_failure_fn"),
        )

        if isinstance(user_msg, ChatMessage) and isinstance(user_msg.content, str):
            user_msg = user_msg.content
        elif isinstance(user_msg, str):
            pass
        elif (
            not user_msg
            and chat_history is not None
            and len(chat_history) > 0
            and isinstance(chat_history[-1].content, str)
        ):
            user_msg = chat_history[-1].content
        else:
            raise ValueError("No user message provided or found in chat history.")

        task = Task(
            input=user_msg,
            memory=ChatMemoryBuffer.from_defaults(chat_history=chat_history),
            extra_state={},
            callback_manager=self.callback_manager,
        )
        step = worker.initialize_step(task)

        try:
            output = worker.run_step(step, task).output

            # react agent worker inserts a "Observation: " prefix to the response
            if output.response and output.response.startswith("Observation: "):
                output.response = output.response.replace("Observation: ", "")
        except Exception as e:
            output = AgentChatResponse(
                response="An error occurred while running the tool: " + str(e),
                sources=[],
            )

        return output

        Predict and call the tool.

        By default uses a ReAct agent to do tool calling (through text prompting),
        but function calling LLMs will implement this differently.

        
llama-index-core/llama_index/core/llms/llm.py