mirror of
https://github.com/zhayujie/chatgpt-on-wechat.git
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215 lines
9.3 KiB
Python
215 lines
9.3 KiB
Python
# encoding:utf-8
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import json
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import time
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from typing import List, Tuple
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import openai
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import openai.error
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import broadscope_bailian
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from broadscope_bailian import ChatQaMessage
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from bot.bot import Bot
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from bot.ali.ali_qwen_session import AliQwenSession
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from bot.session_manager import SessionManager
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from bridge.context import ContextType
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from bridge.reply import Reply, ReplyType
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from common.log import logger
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from common import const
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from config import conf, load_config
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class AliQwenBot(Bot):
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def __init__(self):
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super().__init__()
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self.api_key_expired_time = self.set_api_key()
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self.sessions = SessionManager(AliQwenSession, model=conf().get("model", const.QWEN))
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def api_key_client(self):
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return broadscope_bailian.AccessTokenClient(access_key_id=self.access_key_id(), access_key_secret=self.access_key_secret())
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def access_key_id(self):
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return conf().get("qwen_access_key_id")
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def access_key_secret(self):
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return conf().get("qwen_access_key_secret")
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def agent_key(self):
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return conf().get("qwen_agent_key")
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def app_id(self):
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return conf().get("qwen_app_id")
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def node_id(self):
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return conf().get("qwen_node_id", "")
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def temperature(self):
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return conf().get("temperature", 0.2 )
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def top_p(self):
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return conf().get("top_p", 1)
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def reply(self, query, context=None):
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# acquire reply content
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if context.type == ContextType.TEXT:
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logger.info("[QWEN] query={}".format(query))
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session_id = context["session_id"]
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reply = None
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clear_memory_commands = conf().get("clear_memory_commands", ["#清除记忆"])
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if query in clear_memory_commands:
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self.sessions.clear_session(session_id)
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reply = Reply(ReplyType.INFO, "记忆已清除")
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elif query == "#清除所有":
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self.sessions.clear_all_session()
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reply = Reply(ReplyType.INFO, "所有人记忆已清除")
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elif query == "#更新配置":
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load_config()
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reply = Reply(ReplyType.INFO, "配置已更新")
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if reply:
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return reply
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session = self.sessions.session_query(query, session_id)
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logger.debug("[QWEN] session query={}".format(session.messages))
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reply_content = self.reply_text(session)
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logger.debug(
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"[QWEN] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(
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session.messages,
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session_id,
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reply_content["content"],
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reply_content["completion_tokens"],
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)
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)
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if reply_content["completion_tokens"] == 0 and len(reply_content["content"]) > 0:
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reply = Reply(ReplyType.ERROR, reply_content["content"])
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elif reply_content["completion_tokens"] > 0:
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self.sessions.session_reply(reply_content["content"], session_id, reply_content["total_tokens"])
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reply = Reply(ReplyType.TEXT, reply_content["content"])
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else:
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reply = Reply(ReplyType.ERROR, reply_content["content"])
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logger.debug("[QWEN] reply {} used 0 tokens.".format(reply_content))
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return reply
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else:
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reply = Reply(ReplyType.ERROR, "Bot不支持处理{}类型的消息".format(context.type))
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return reply
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def reply_text(self, session: AliQwenSession, retry_count=0) -> dict:
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"""
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call bailian's ChatCompletion to get the answer
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:param session: a conversation session
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:param retry_count: retry count
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:return: {}
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"""
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try:
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prompt, history = self.convert_messages_format(session.messages)
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self.update_api_key_if_expired()
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# NOTE 阿里百炼的call()函数未提供temperature参数，考虑到temperature和top_p参数作用相同，取两者较小的值作为top_p参数传入，详情见文档 https://help.aliyun.com/document_detail/2587502.htm
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response = broadscope_bailian.Completions().call(app_id=self.app_id(), prompt=prompt, history=history, top_p=min(self.temperature(), self.top_p()))
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completion_content = self.get_completion_content(response, self.node_id())
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completion_tokens, total_tokens = self.calc_tokens(session.messages, completion_content)
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return {
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"total_tokens": total_tokens,
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"completion_tokens": completion_tokens,
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"content": completion_content,
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}
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except Exception as e:
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need_retry = retry_count < 2
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result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
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if isinstance(e, openai.error.RateLimitError):
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logger.warn("[QWEN] RateLimitError: {}".format(e))
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result["content"] = "提问太快啦，请休息一下再问我吧"
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if need_retry:
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time.sleep(20)
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elif isinstance(e, openai.error.Timeout):
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logger.warn("[QWEN] Timeout: {}".format(e))
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result["content"] = "我没有收到你的消息"
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if need_retry:
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time.sleep(5)
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elif isinstance(e, openai.error.APIError):
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logger.warn("[QWEN] Bad Gateway: {}".format(e))
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result["content"] = "请再问我一次"
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if need_retry:
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time.sleep(10)
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elif isinstance(e, openai.error.APIConnectionError):
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logger.warn("[QWEN] APIConnectionError: {}".format(e))
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need_retry = False
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result["content"] = "我连接不到你的网络"
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else:
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logger.exception("[QWEN] Exception: {}".format(e))
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need_retry = False
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self.sessions.clear_session(session.session_id)
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if need_retry:
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logger.warn("[QWEN] 第{}次重试".format(retry_count + 1))
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return self.reply_text(session, retry_count + 1)
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else:
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return result
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def set_api_key(self):
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api_key, expired_time = self.api_key_client().create_token(agent_key=self.agent_key())
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broadscope_bailian.api_key = api_key
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return expired_time
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def update_api_key_if_expired(self):
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if time.time() > self.api_key_expired_time:
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self.api_key_expired_time = self.set_api_key()
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def convert_messages_format(self, messages) -> Tuple[str, List[ChatQaMessage]]:
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history = []
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user_content = ''
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assistant_content = ''
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system_content = ''
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for message in messages:
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role = message.get('role')
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if role == 'user':
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user_content += message.get('content')
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elif role == 'assistant':
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assistant_content = message.get('content')
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history.append(ChatQaMessage(user_content, assistant_content))
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user_content = ''
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assistant_content = ''
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elif role =='system':
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system_content += message.get('content')
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if user_content == '':
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raise Exception('no user message')
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if system_content != '':
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# NOTE 模拟系统消息，测试发现人格描述以"你需要扮演ChatGPT"开头能够起作用，而以"你是ChatGPT"开头模型会直接否认
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system_qa = ChatQaMessage(system_content, '好的，我会严格按照你的设定回答问题')
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history.insert(0, system_qa)
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logger.debug("[QWEN] converted qa messages: {}".format([item.to_dict() for item in history]))
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logger.debug("[QWEN] user content as prompt: {}".format(user_content))
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return user_content, history
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def get_completion_content(self, response, node_id):
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if not response['Success']:
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return f"[ERROR]\n{response['Code']}:{response['Message']}"
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text = response['Data']['Text']
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if node_id == '':
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return text
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# TODO: 当使用流程编排创建大模型应用时，响应结构如下，最终结果在['finalResult'][node_id]['response']['text']中，暂时先这么写
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# {
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# 'Success': True,
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# 'Code': None,
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# 'Message': None,
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# 'Data': {
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# 'ResponseId': '9822f38dbacf4c9b8daf5ca03a2daf15',
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# 'SessionId': 'session_id',
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# 'Text': '{"finalResult":{"LLM_T7islK":{"params":{"modelId":"qwen-plus-v1","prompt":"${systemVars.query}${bizVars.Text}"},"response":{"text":"作为一个AI语言模型，我没有年龄，因为我没有生日。\n我只是一个程序，没有生命和身体。"}}}}',
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# 'Thoughts': [],
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# 'Debug': {},
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# 'DocReferences': []
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# },
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# 'RequestId': '8e11d31551ce4c3f83f49e6e0dd998b0',
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# 'Failed': None
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# }
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text_dict = json.loads(text)
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completion_content = text_dict['finalResult'][node_id]['response']['text']
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return completion_content
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def calc_tokens(self, messages, completion_content):
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completion_tokens = len(completion_content)
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prompt_tokens = 0
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for message in messages:
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prompt_tokens += len(message["content"])
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return completion_tokens, prompt_tokens + completion_tokens
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