feat: code tidying up

bot/zhipu/chat_glm_bot.py

@@ -1,155 +0,0 @@
-# encoding:utf-8
-
-import time
-
-import openai
-import openai.error
-import requests
-
-from bot.bot import Bot
-from bot.zhipu.chat_glm_session import ChatGLMSession
-from bot.openai.open_ai_image import OpenAIImage
-from bot.session_manager import SessionManager
-from bridge.context import ContextType
-from bridge.reply import Reply, ReplyType
-from common.log import logger
-# from common.token_bucket import TokenBucket
-from config import conf, load_config
-from zhipuai import ZhipuAI
-
-
-# ZhipuAI对话模型API
-class ChatGLMBot(Bot):
-    def __init__(self):
-        super().__init__()
-        # set the default api_key
-        self.api_key = conf().get("zhipu_ai_api_key")
-        if conf().get("zhipu_ai_api_base"):
-            openai.api_base = conf().get("zhipu_ai_api_base")
-        # if conf().get("rate_limit_chatgpt"):
-        #     self.tb4chatgpt = TokenBucket(conf().get("rate_limit_chatgpt", 20))
-
-        self.sessions = SessionManager(ChatGLMSession, model=conf().get("model") or "chatglm")
-        self.args = {
-            "model": "glm-4",  # 对话模型的名称
-            "temperature": conf().get("temperature", 0.9),  # 值在[0,1]之间，越大表示回复越具有不确定性
-            # "max_tokens":4096,  # 回复最大的字符数
-            "top_p": conf().get("top_p", 0.7),
-            # "frequency_penalty": conf().get("frequency_penalty", 0.0),  # [-2,2]之间，该值越大则更倾向于产生不同的内容
-            # "presence_penalty": conf().get("presence_penalty", 0.0),  # [-2,2]之间，该值越大则更倾向于产生不同的内容
-            # "request_timeout": conf().get("request_timeout", None),  # 请求超时时间，openai接口默认设置为600，对于难问题一般需要较长时间
-            # "timeout": conf().get("request_timeout", None),  # 重试超时时间，在这个时间内，将会自动重试
-        }
-        self.client = ZhipuAI(api_key=self.api_key)
-
-    def reply(self, query, context=None):
-        # acquire reply content
-        if context.type == ContextType.TEXT:
-            logger.info("[CHATGLM] query={}".format(query))
-
-            session_id = context["session_id"]
-            reply = None
-            clear_memory_commands = conf().get("clear_memory_commands", ["#清除记忆"])
-            if query in clear_memory_commands:
-                self.sessions.clear_session(session_id)
-                reply = Reply(ReplyType.INFO, "记忆已清除")
-            elif query == "#清除所有":
-                self.sessions.clear_all_session()
-                reply = Reply(ReplyType.INFO, "所有人记忆已清除")
-            elif query == "#更新配置":
-                load_config()
-                reply = Reply(ReplyType.INFO, "配置已更新")
-            if reply:
-                return reply
-            session = self.sessions.session_query(query, session_id)
-            logger.debug("[CHATGLM] session query={}".format(session.messages))
-
-            api_key = context.get("openai_api_key") or openai.api_key
-            model = context.get("gpt_model")
-            new_args = None
-            if model:
-                new_args = self.args.copy()
-                new_args["model"] = model
-            # if context.get('stream'):
-            #     # reply in stream
-            #     return self.reply_text_stream(query, new_query, session_id)
-
-            reply_content = self.reply_text(session, api_key, args=new_args)
-            logger.debug(
-                "[CHATGLM] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(
-                    session.messages,
-                    session_id,
-                    reply_content["content"],
-                    reply_content["completion_tokens"],
-                )
-            )
-            if reply_content["completion_tokens"] == 0 and len(reply_content["content"]) > 0:
-                reply = Reply(ReplyType.ERROR, reply_content["content"])
-            elif reply_content["completion_tokens"] > 0:
-                self.sessions.session_reply(reply_content["content"], session_id, reply_content["total_tokens"])
-                reply = Reply(ReplyType.TEXT, reply_content["content"])
-            else:
-                reply = Reply(ReplyType.ERROR, reply_content["content"])
-                logger.debug("[CHATGLM] reply {} used 0 tokens.".format(reply_content))
-            return reply
-        else:
-            reply = Reply(ReplyType.ERROR, "Bot不支持处理{}类型的消息".format(context.type))
-            return reply
-
-    def reply_text(self, session: ChatGLMSession, api_key=None, args=None, retry_count=0) -> dict:
-        """
-        call openai's ChatCompletion to get the answer
-        :param session: a conversation session
-        :param session_id: session id
-        :param retry_count: retry count
-        :return: {}
-        """
-        try:
-            # if conf().get("rate_limit_chatgpt") and not self.tb4chatgpt.get_token():
-            #     raise openai.error.RateLimitError("RateLimitError: rate limit exceeded")
-            # if api_key == None, the default openai.api_key will be used
-            if args is None:
-                args = self.args
-            # response = openai.ChatCompletion.create(api_key=api_key, messages=session.messages, **args)
-            response = self.client.chat.completions.create(messages=session.messages, **args)
-            # logger.debug("[CHATGLM] response={}".format(response))
-            # logger.info("[CHATGLM] reply={}, total_tokens={}".format(response.choices[0]['message']['content'], response["usage"]["total_tokens"]))
-            return {
-                "total_tokens": response.usage.total_tokens,
-                "completion_tokens": response.usage.completion_tokens,
-                "content": response.choices[0].message.content,
-            }
-        except Exception as e:
-            need_retry = retry_count < 2
-            result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
-            if isinstance(e, openai.error.RateLimitError):
-                logger.warn("[CHATGLM] RateLimitError: {}".format(e))
-                result["content"] = "提问太快啦，请休息一下再问我吧"
-                if need_retry:
-                    time.sleep(20)
-            elif isinstance(e, openai.error.Timeout):
-                logger.warn("[CHATGLM] Timeout: {}".format(e))
-                result["content"] = "我没有收到你的消息"
-                if need_retry:
-                    time.sleep(5)
-            elif isinstance(e, openai.error.APIError):
-                logger.warn("[CHATGLM] Bad Gateway: {}".format(e))
-                result["content"] = "请再问我一次"
-                if need_retry:
-                    time.sleep(10)
-            elif isinstance(e, openai.error.APIConnectionError):
-                logger.warn("[CHATGLM] APIConnectionError: {}".format(e))
-                result["content"] = "我连接不到你的网络"
-                if need_retry:
-                    time.sleep(5)
-            else:
-                logger.exception("[CHATGLM] Exception: {}".format(e), e)
-                need_retry = False
-                self.sessions.clear_session(session.session_id)
-
-            if need_retry:
-                logger.warn("[CHATGLM] 第{}次重试".format(retry_count + 1))
-                return self.reply_text(session, api_key, args, retry_count + 1)
-            else:
-                return result
-

bot/zhipu/chat_glm_session.py

@@ -1,48 +0,0 @@
-from bot.session_manager import Session
-from common.log import logger
-
-class ChatGLMSession(Session):
-    def __init__(self, session_id, system_prompt=None, model="glm-4"):
-        super().__init__(session_id, system_prompt)
-        self.model = model
-        self.reset()
-
-    def discard_exceeding(self, max_tokens, cur_tokens=None):
-        precise = True
-        try:
-            cur_tokens = self.calc_tokens()
-        except Exception as e:
-            precise = False
-            if cur_tokens is None:
-                raise e
-            logger.debug("Exception when counting tokens precisely for query: {}".format(e))
-        while cur_tokens > max_tokens:
-            if len(self.messages) > 2:
-                self.messages.pop(1)
-            elif len(self.messages) == 2 and self.messages[1]["role"] == "assistant":
-                self.messages.pop(1)
-                if precise:
-                    cur_tokens = self.calc_tokens()
-                else:
-                    cur_tokens = cur_tokens - max_tokens
-                break
-            elif len(self.messages) == 2 and self.messages[1]["role"] == "user":
-                logger.warn("user message exceed max_tokens. total_tokens={}".format(cur_tokens))
-                break
-            else:
-                logger.debug("max_tokens={}, total_tokens={}, len(messages)={}".format(max_tokens, cur_tokens, len(self.messages)))
-                break
-            if precise:
-                cur_tokens = self.calc_tokens()
-            else:
-                cur_tokens = cur_tokens - max_tokens
-        return cur_tokens
-
-    def calc_tokens(self):
-        return num_tokens_from_messages(self.messages, self.model)
-
-def num_tokens_from_messages(messages, model):
-    tokens = 0
-    for msg in messages:
-        tokens += len(msg["content"])
-    return tokens