181 lines
8.8 KiB
Python
181 lines
8.8 KiB
Python
#! /usr/bin/env python3
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# -*- coding: utf-8 -*-
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from datetime import datetime
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import openai
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import json
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import os
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import random
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from chatglm.tool_registry import get_tools, dispatch_tool, extract_code
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from chatglm.code_kernel import execute, CodeKernel
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from typing import Dict, Union, Optional, Tuple
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from wcferry import Wcf
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functions = get_tools()
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class ChatGLM():
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def __init__(self, config={}, wcf: Optional[Wcf] = None, max_retry=5) -> None:
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openai.api_key = config.get('key', 'XXX')
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# 自己搭建或第三方代理的接口
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openai.api_base = config.get('api', 'http://localhost:8000/v1')
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if config.get('proxy', None):
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openai.proxy = {"http": config.get(
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'proxy', None), "https": config.get('proxy', None)}
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self.conversation_list = {}
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self.chat_type = {}
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self.max_retry = max_retry
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self.wcf = wcf
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self.filePath = config.get('file_path', 'temp')
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self.kernel = CodeKernel()
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self.system_content_msg = {"chat": [{"role": "system", "content": config.get('prompt', '你是智能聊天机器人，你叫小薇')}],
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"tool": [{"role": "system", "content": "Answer the following questions as best as you can. You have access to the following tools:"}],
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"code": [{"role": "system", "content": "你是一位智能AI助手，你叫ChatGLM，你连接着一台电脑，但请注意不能联网。在使用Python解决任务时，你可以运行代码并得到结果，如果运行结果有错误，你需要尽可能对代码进行改进。你可以处理用户上传到电脑上的文件，文件默认存储路径是{}。".format(self.filePath)}]}
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def get_answer(self, question: str, wxid: str) -> str:
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# wxid或者roomid,个人时为微信id，群消息时为群id
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if '#帮助' == question:
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return '本助手有三种模式，#聊天模式 = #1 ，#工具模式 = #2 ，#代码模式 = #3 , #清除模式会话 = #4 , #清除全部会话 = #5 可用发送#对应模式 或者 #编号 进行切换'
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elif '#聊天模式' == question or '#1' == question:
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self.chat_type[wxid] = 'chat'
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return '已切换#聊天模式'
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elif '#工具模式' == question or '#2' == question:
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self.chat_type[wxid] = 'tool'
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return '已切换#工具模式 \n工具有：查看天气，日期，新闻,comfyUI文生图。例如：\n帮我生成一张小鸟的图片，提示词必须是英文'
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elif '#代码模式' == question or '#3' == question:
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self.chat_type[wxid] = 'code'
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return '已切换#代码模式 \n代码模式可以用于写python代码，例如：\n用python画一个爱心'
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elif '#清除模式会话' == question or '#4' == question:
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self.conversation_list[wxid][self.chat_type[wxid]
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] = self.system_content_msg[self.chat_type[wxid]]
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return '已清除'
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elif '#清除全部会话' == question or '#5' == question:
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self.conversation_list[wxid] = self.system_content_msg
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return '已清除'
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self.updateMessage(wxid, question, "user")
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try:
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params = dict(model="chatglm3", temperature=1.0,
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messages=self.conversation_list[wxid][self.chat_type[wxid]], stream=False)
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if 'tool' == self.chat_type[wxid]:
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params["functions"] = functions
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response = openai.ChatCompletion.create(**params)
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for _ in range(self.max_retry):
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if response.choices[0].message.get("function_call"):
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function_call = response.choices[0].message.function_call
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print(
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f"Function Call Response: {function_call.to_dict_recursive()}")
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function_args = json.loads(function_call.arguments)
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observation = dispatch_tool(
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function_call.name, function_args)
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if isinstance(observation, dict):
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res_type = observation['res_type'] if 'res_type' in observation else 'text'
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res = observation['res'] if 'res_type' in observation else str(
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observation)
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if res_type == 'image':
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filename = observation['filename']
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filePath = os.path.join(self.filePath, filename)
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res.save(filePath)
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self.wcf and self.wcf.send_image(filePath, wxid)
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tool_response = '[Image]' if res_type == 'image' else res
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else:
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tool_response = observation if isinstance(
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observation, str) else str(observation)
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print(f"Tool Call Response: {tool_response}")
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params["messages"].append(response.choices[0].message)
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params["messages"].append(
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{
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"role": "function",
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"name": function_call.name,
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"content": tool_response, # 调用函数返回结果
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}
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)
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self.updateMessage(wxid, tool_response, "function")
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response = openai.ChatCompletion.create(**params)
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elif response.choices[0].message.content.find('interpreter') != -1:
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output_text = response.choices[0].message.content
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code = extract_code(output_text)
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self.wcf and self.wcf.send_text('代码如下：\n' + code, wxid)
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self.wcf and self.wcf.send_text('执行代码...', wxid)
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try:
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res_type, res = execute(code, self.kernel)
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except Exception as e:
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rsp = f'代码执行错误: {e}'
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break
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if res_type == 'image':
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filename = '{}.png'.format(''.join(random.sample(
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'abcdefghijklmnopqrstuvwxyz1234567890', 8)))
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filePath = os.path.join(self.filePath, filename)
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res.save(filePath)
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self.wcf and self.wcf.send_image(filePath, wxid)
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else:
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self.wcf and self.wcf.send_text("执行结果:\n" + res, wxid)
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tool_response = '[Image]' if res_type == 'image' else res
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print("Received:", res_type, res)
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params["messages"].append(response.choices[0].message)
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params["messages"].append(
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{
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"role": "function",
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"name": "interpreter",
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"content": tool_response, # 调用函数返回结果
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}
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)
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self.updateMessage(wxid, tool_response, "function")
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response = openai.ChatCompletion.create(**params)
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else:
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rsp = response.choices[0].message.content
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break
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self.updateMessage(wxid, rsp, "assistant")
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except Exception as e0:
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rsp = "发生未知错误：" + str(e0)
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return rsp
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def updateMessage(self, wxid: str, question: str, role: str) -> None:
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now_time = str(datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
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# 初始化聊天记录,组装系统信息
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if wxid not in self.conversation_list.keys():
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self.conversation_list[wxid] = self.system_content_msg
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if wxid not in self.chat_type.keys():
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self.chat_type[wxid] = 'chat'
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# 当前问题
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content_question_ = {"role": role, "content": question}
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self.conversation_list[wxid][self.chat_type[wxid]].append(
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content_question_)
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# 只存储10条记录，超过滚动清除
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i = len(self.conversation_list[wxid][self.chat_type[wxid]])
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if i > 10:
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print("滚动清除微信记录：" + wxid)
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# 删除多余的记录，倒着删，且跳过第一个的系统消息
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del self.conversation_list[wxid][self.chat_type[wxid]][1]
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if __name__ == "__main__":
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from configuration import Config
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config = Config().CHATGLM
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if not config:
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exit(0)
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chat = ChatGLM(config)
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while True:
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q = input(">>> ")
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try:
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time_start = datetime.now() # 记录开始时间
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print(chat.get_answer(q, "wxid"))
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time_end = datetime.now() # 记录结束时间
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# 计算的时间差为程序的执行时间，单位为秒/s
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print(f"{round((time_end - time_start).total_seconds(), 2)}s")
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except Exception as e:
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print(e)
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