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| 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 | |
| try: | |
| print_msg = sender.name + "\n\n---\n\n" + message["content"] | |
| except: | |
| print_msg = sender.name + "\n\n---\n\n" + message | |
| self.child_conn.send(PipeCom("show", print_msg)) | |
| 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 | |
| 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") | |
| with ProxyNetworkActivate("AutoGen"): | |
| 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 | |