zhayujie
9 ay önce
2 değiştirilmiş dosya ile 0 ekleme ve 203 silme
+ 0
- 155
bot/zhipu/chat_glm_bot.py
Dosyayı Görüntüle
| @@ -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 | |||
+ 0
- 48
bot/zhipu/chat_glm_session.py
Dosyayı Görüntüle
| @@ -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 | |||
Yükleniyor…