Assistant API（下线中）

# 示例代码，仅供参考
def submit_message(thread, assistant, message):
    Messages.create(
        thread_id=thread.id,
        role="user",
        content=message
    )

    run = Runs.create(
        thread_id=thread.id,
        assistant_id=assistant.id,
        stream=True
    )
    
    for event, data in run:
        if event == 'thread.message.delta':
            yield data.delta.content.text.value
        if event == 'thread.message.completed':
            yield '\n'
        if event == 'thread.run.step.delta':
            # 第一次检测到step.delta时输出工具名称
            if not hasattr(submit_message, 'tool_name_shown'):
                submit_message.tool_name_shown = True
                tool_name = data.delta.step_details.tool_calls[0]['type']
                yield f"\n正在使用工具: {tool_name}\n\n"
            
            formatted_output = format_tool_output(data.delta.step_details.tool_calls[0])
            if formatted_output is not None:
                yield formatted_output

# 示例代码，仅供参考
while True:
    for event, data in run:
        elif event == 'thread.run.requires_action':
            # 工具调用 => 可能需要提交工具输出 => 导致新的 run 生成器
            tool_calls = data.required_action.submit_tool_outputs.tool_calls
            if not tool_calls:
                continue  
         
            tool_outputs = []
            for tool_call in tool_calls:
                name = tool_call.function.name
                arguments = json.loads(tool_call.function.arguments)
                output = tools_map[name](**arguments)
                # 普通工具
                tool_outputs.append({"output": output})

            # 将工具输出提交给 run，会返回一个新的 run 生成器
            run = Runs.submit_tool_outputs(
                thread_id=thread.id,  # 原生的对话线程，无需手动管理
                run_id=data.id,
                tool_outputs=tool_outputs,
                stream=True
            )
            yield "转接中...\n"
            break  # 跳出当前事件循环，用返回的 new_run 接着处理

        elif event in ('thread.run.completed', 'thread.run.cancelled',
                    'thread.run.expired', 'thread.run.failed'):
            # 当前 run 结束
            break
    else:
        # for循环正常结束(没有 break)，说明 run 流耗尽
        break

# 示例代码，仅供参考
# 获得Multi Agent的回复，输入与输出需要与Gradio前端展示界面中的参数对齐
def get_multi_agent_response(query,history):
    # 处理输入为空的情况
    if len(query) == 0:
        return "",history+[("","")],"",""
    # 获取Agent的运行顺序
    assistant_order = get_agent_response(PlannerAssistant,query)
    try:
        order_stk = ast.literal_eval(assistant_order)
        cur_query = query
        # 依次运行Agent
        for i in range(len(order_stk)):
            yield "----->".join(order_stk),history+[(query,"multi agent正在努力工作中...")],f"{order_stk[i]}正在处理信息...",""
            cur_assistant = assistant_mapper[order_stk[i]]
            response = get_agent_response(cur_assistant,cur_query)
            yield "----->".join(order_stk),history+[(query,"multi agent正在努力工作中...")],response,""
            # 如果当前Agent为最后一个Agent，则将其输出作为Multi Agent的输出
            if i == len(order_stk)-1:
                yield "----->".join(order_stk),history+[(query,response)],"assistant已处理完毕",""
            # 如果当前Agent不是最后一个Agent，则将上一个Agent的输出response添加到下一轮的query中，作为参考信息
            else:
                # 在参考信息前后加上特殊标识符，可以防止大模型混淆参考信息与提问
                cur_query = f"你可以参考已知的信息：\n{response}\n你要完整地回答用户的问题。问题是：{query}。"
    # 兜底策略，如果上述程序运行失败，则直接调用ChatAssistant
    except Exception as e:
        yield "ChatAssistant",[(query,get_agent_response(ChatAssistant,query))],"",""