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# 本源代码中, ⭐ = 关键步骤
"""
测试：
- show me the solution of $x^2=cos(x)$, solve this problem with figure, and plot and save image to t.jpg
"""
from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, ProxyNetworkActivate
from toolbox import get_conf, select_api_key, update_ui_lastest_msg, Singleton
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_plugin_arg
from crazy_functions.crazy_utils import input_clipping, try_install_deps
from crazy_functions.agent_fns.persistent import GradioMultiuserManagerForPersistentClasses
from crazy_functions.agent_fns.auto_agent import AutoGenMath
import time
def remove_model_prefix(llm):
if llm.startswith('api2d-'): llm = llm.replace('api2d-', '')
if llm.startswith('azure-'): llm = llm.replace('azure-', '')
return llm
@CatchException
def 多智能体终端(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
"""
txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
plugin_kwargs 插件模型的参数
chatbot 聊天显示框的句柄，用于显示给用户
history 聊天历史，前情提要
system_prompt 给gpt的静默提醒
web_port 当前软件运行的端口号
"""
# 检查当前的模型是否符合要求
supported_llms = [
"gpt-3.5-turbo-16k",
'gpt-3.5-turbo-1106',
"gpt-4",
"gpt-4-32k",
'gpt-4-1106-preview',
"azure-gpt-3.5-turbo-16k",
"azure-gpt-3.5-16k",
"azure-gpt-4",
"azure-gpt-4-32k",
]
from request_llms.bridge_all import model_info
if model_info[llm_kwargs['llm_model']]["max_token"] < 8000: # 至少是8k上下文的模型
chatbot.append([f"处理任务: {txt}", f"当前插件只支持{str(supported_llms)}, 当前模型{llm_kwargs['llm_model']}的最大上下文长度太短, 不能支撑AutoGen运行。"])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
if model_info[llm_kwargs['llm_model']]["endpoint"] is not None: # 如果不是本地模型，加载API_KEY
llm_kwargs['api_key'] = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
# 尝试导入依赖，如果缺少依赖，则给出安装建议
try:
import autogen
if get_conf("AUTOGEN_USE_DOCKER"):
import docker
except:
chatbot.append([ f"处理任务: {txt}",
f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pyautogen docker```。"])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
# 尝试导入依赖，如果缺少依赖，则给出安装建议
try:
import autogen
import glob, os, time, subprocess
if get_conf("AUTOGEN_USE_DOCKER"):
subprocess.Popen(["docker", "--version"])
except:
chatbot.append([f"处理任务: {txt}", f"缺少docker运行环境！"])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
return
# 解锁插件
chatbot.get_cookies()['lock_plugin'] = None
persistent_class_multi_user_manager = GradioMultiuserManagerForPersistentClasses()
user_uuid = chatbot.get_cookies().get('uuid')
persistent_key = f"{user_uuid}->多智能体终端"
if persistent_class_multi_user_manager.already_alive(persistent_key):
# 当已经存在一个正在运行的多智能体终端时，直接将用户输入传递给它，而不是再次启动一个新的多智能体终端
print('[debug] feed new user input')
executor = persistent_class_multi_user_manager.get(persistent_key)
exit_reason = yield from executor.main_process_ui_control(txt, create_or_resume="resume")
else:
# 运行多智能体终端 (首次)
print('[debug] create new executor instance')
history = []
chatbot.append(["正在启动: 多智能体终端", "插件动态生成, 执行开始, 作者 Microsoft & Binary-Husky."])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
executor = AutoGenMath(llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port)
persistent_class_multi_user_manager.set(persistent_key, executor)
exit_reason = yield from executor.main_process_ui_control(txt, create_or_resume="create")
if exit_reason == "wait_feedback":
# 当用户点击了“等待反馈”按钮时，将executor存储到cookie中，等待用户的再次调用
executor.chatbot.get_cookies()['lock_plugin'] = 'crazy_functions.多智能体->多智能体终端'
else:
executor.chatbot.get_cookies()['lock_plugin'] = None
yield from update_ui(chatbot=executor.chatbot, history=executor.history) # 更新状态
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