from toolbox import trimmed_format_exc, get_conf, ProxyNetworkActivate from crazy_functions.agent_fns.pipe import PluginMultiprocessManager, PipeCom from request_llms.bridge_all import predict_no_ui_long_connection import time def gpt_academic_generate_oai_reply( self, messages, sender, config, ): llm_config = self.llm_config if config is None else config if llm_config is False: return False, None if messages is None: messages = self._oai_messages[sender] inputs = messages[-1]['content'] history = [] for message in messages[:-1]: history.append(message['content']) context=messages[-1].pop("context", None) assert context is None, "预留参数 context 未实现" reply = predict_no_ui_long_connection( inputs=inputs, llm_kwargs=llm_config, history=history, sys_prompt=self._oai_system_message[0]['content'], console_slience=True ) assumed_done = reply.endswith('\nTERMINATE') return True, reply class AutoGenGeneral(PluginMultiprocessManager): def gpt_academic_print_override(self, user_proxy, message, sender): # ⭐⭐ run in subprocess self.child_conn.send(PipeCom("show", sender.name + "\n\n---\n\n" + message["content"])) def gpt_academic_get_human_input(self, user_proxy, message): # ⭐⭐ run in subprocess patience = 300 begin_waiting_time = time.time() self.child_conn.send(PipeCom("interact", message)) while True: time.sleep(0.5) if self.child_conn.poll(): wait_success = True break if time.time() - begin_waiting_time > patience: self.child_conn.send(PipeCom("done", "")) wait_success = False break if wait_success: return self.child_conn.recv().content else: raise TimeoutError("等待用户输入超时") def define_agents(self): raise NotImplementedError def exe_autogen(self, input): # ⭐⭐ run in subprocess input = input.content with ProxyNetworkActivate("AutoGen"): code_execution_config = {"work_dir": self.autogen_work_dir, "use_docker": self.use_docker} agents = self.define_agents() user_proxy = None assistant = None for agent_kwargs in agents: agent_cls = agent_kwargs.pop('cls') kwargs = { 'llm_config':self.llm_kwargs, 'code_execution_config':code_execution_config } kwargs.update(agent_kwargs) agent_handle = agent_cls(**kwargs) agent_handle._print_received_message = lambda a,b: self.gpt_academic_print_override(agent_kwargs, a, b) for d in agent_handle._reply_func_list: if hasattr(d['reply_func'],'__name__') and d['reply_func'].__name__ == 'generate_oai_reply': d['reply_func'] = gpt_academic_generate_oai_reply if agent_kwargs['name'] == 'user_proxy': agent_handle.get_human_input = lambda a: self.gpt_academic_get_human_input(user_proxy, a) user_proxy = agent_handle if agent_kwargs['name'] == 'assistant': assistant = agent_handle try: if user_proxy is None or assistant is None: raise Exception("用户代理或助理代理未定义") user_proxy.initiate_chat(assistant, message=input) except Exception as e: tb_str = '```\n' + trimmed_format_exc() + '```' self.child_conn.send(PipeCom("done", "AutoGen 执行失败: \n\n" + tb_str)) def subprocess_worker(self, child_conn): # ⭐⭐ run in subprocess self.child_conn = child_conn while True: msg = self.child_conn.recv() # PipeCom self.exe_autogen(msg) class AutoGenGroupChat(AutoGenGeneral): def exe_autogen(self, input): # ⭐⭐ run in subprocess import autogen input = input.content with ProxyNetworkActivate("AutoGen"): code_execution_config = {"work_dir": self.autogen_work_dir, "use_docker": self.use_docker} agents = self.define_agents() agents_instances = [] for agent_kwargs in agents: agent_cls = agent_kwargs.pop("cls") kwargs = {"code_execution_config": code_execution_config} kwargs.update(agent_kwargs) agent_handle = agent_cls(**kwargs) agent_handle._print_received_message = lambda a, b: self.gpt_academic_print_override(agent_kwargs, a, b) agents_instances.append(agent_handle) if agent_kwargs["name"] == "user_proxy": user_proxy = agent_handle user_proxy.get_human_input = lambda a: self.gpt_academic_get_human_input(user_proxy, a) try: groupchat = autogen.GroupChat(agents=agents_instances, messages=[], max_round=50) manager = autogen.GroupChatManager(groupchat=groupchat, **self.define_group_chat_manager_config()) manager._print_received_message = lambda a, b: self.gpt_academic_print_override(agent_kwargs, a, b) manager.get_human_input = lambda a: self.gpt_academic_get_human_input(manager, a) if user_proxy is None: raise Exception("user_proxy is not defined") user_proxy.initiate_chat(manager, message=input) except Exception: tb_str = "```\n" + trimmed_format_exc() + "```" self.child_conn.send(PipeCom("done", "AutoGen exe failed: \n\n" + tb_str)) def define_group_chat_manager_config(self): raise NotImplementedError