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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
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