raise e
        else:
            run_manager.on_chain_end(response)
            return response
    def _parse_intermediate_steps(
        self, intermediate_steps: List[Tuple[OpenAIAssistantAction, str]]
    ) -> dict:
        last_action, last_output = intermediate_steps[-1]
        run = self._wait_for_run(last_action.run_id, last_action.thread_id)
        required_tool_call_ids = {
            tc.id for tc in run.required_action.submit_tool_outputs.tool_calls
        }
        tool_outputs = [
            {"output": str(output), "tool_call_id": action.tool_call_id}
            for action, output in intermediate_steps
            if action.tool_call_id in required_tool_call_ids
        ]
        submit_tool_outputs = {
            "tool_outputs": tool_outputs,
            "run_id": last_action.run_id,
            "thread_id": last_action.thread_id,
        }
        return submit_tool_outputs
    def _create_run(self, input: dict) -> Any:
        params = {
            k: v
            for k, v in input.items()
            if k in ("instructions", "model", "tools", "run_metadata")
        }
        return self.client.beta.threads.runs.create(
            input["thread_id"],
            assistant_id=self.assistant_id,
            **params,
        )
    def _create_thread_and_run(self, input: dict, thread: dict) -> Any:
        params = {
            k: v
            for k, v in input.items()
            if k in ("instructions", "model", "tools", "run_metadata")
        }
        run = self.client.beta.threads.create_and_run(
            assistant_id=self.assistant_id,
            thread=thread,
            **params,
        )