except json.JSONDecodeError:
            raise ValueError(
                f'"{self.llm.model}" did not respond with valid JSON. Please try again.'
            )
        called_tool_name = parsed_chat_result["tool"]
        called_tool_arguments = parsed_chat_result["tool_input"]
        called_tool = next(
            (fn for fn in functions if fn["name"] == called_tool_name), None
        )
        if called_tool is None:
            raise ValueError(
                f"Failed to parse a function call from {self.llm.model} \
output: {chat_generation_content}"
            )
        if called_tool["name"] == DEFAULT_RESPONSE_FUNCTION["name"]:
            return ChatResult(
                generations=[
                    ChatGeneration(
                        message=AIMessage(
                            content=called_tool_arguments["response"],
                        )
                    )
                ]
            )
        response_message_with_functions = AIMessage(
            content="",
            additional_kwargs={
                "function_call": {
                    "name": called_tool_name,
                    "arguments": json.dumps(called_tool_arguments)
                    if called_tool_arguments
                    else "",
                },
            },
        )
        return ChatResult(
            generations=[ChatGeneration(message=response_message_with_functions)]
        )
    @property
    def _llm_type(self) -> str:
        return "ollama_functions"