# encoding:utf-8 from bot.bot import Bot from bridge.context import ContextType from bridge.reply import Reply, ReplyType from config import conf, load_config from common.log import logger from common.token_bucket import TokenBucket from common.expired_dict import ExpiredDict import openai import time # OpenAI对话模型API (可用) class ChatGPTBot(Bot): def __init__(self): openai.api_key = conf().get('open_ai_api_key') if conf().get('open_ai_api_base'): openai.api_base = conf().get('open_ai_api_base') proxy = conf().get('proxy') self.sessions = SessionManager() if proxy: openai.proxy = proxy if conf().get('rate_limit_chatgpt'): self.tb4chatgpt = TokenBucket(conf().get('rate_limit_chatgpt', 20)) if conf().get('rate_limit_dalle'): self.tb4dalle = TokenBucket(conf().get('rate_limit_dalle', 50)) def reply(self, query, context=None): # acquire reply content if context.type == ContextType.TEXT: logger.info("[OPEN_AI] 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.build_session_query(query, session_id) logger.debug("[OPEN_AI] session query={}".format(session)) # if context.get('stream'): # # reply in stream # return self.reply_text_stream(query, new_query, session_id) reply_content = self.reply_text(session, session_id, 0) logger.debug("[OPEN_AI] new_query={}, session_id={}, reply_cont={}".format(session, session_id, reply_content["content"])) 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.save_session(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("[OPEN_AI] reply {} used 0 tokens.".format(reply_content)) return reply elif context.type == ContextType.IMAGE_CREATE: ok, retstring = self.create_img(query, 0) reply = None if ok: reply = Reply(ReplyType.IMAGE_URL, retstring) else: reply = Reply(ReplyType.ERROR, retstring) return reply else: reply = Reply(ReplyType.ERROR, 'Bot不支持处理{}类型的消息'.format(context.type)) return reply def compose_args(self): return { "model": conf().get("model") or "gpt-3.5-turbo", # 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":1, "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 } def reply_text(self, session, session_id, 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(): return {"completion_tokens": 0, "content": "提问太快啦，请休息一下再问我吧"} response = openai.ChatCompletion.create( messages=session, **self.compose_args() ) # logger.info("[ChatGPT] 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 openai.error.RateLimitError as e: # rate limit exception logger.warn(e) if retry_count < 1: time.sleep(5) logger.warn("[OPEN_AI] RateLimit exceed, 第{}次重试".format(retry_count+1)) return self.reply_text(session, session_id, retry_count+1) else: return {"completion_tokens": 0, "content": "提问太快啦，请休息一下再问我吧"} except openai.error.APIConnectionError as e: # api connection exception logger.warn(e) logger.warn("[OPEN_AI] APIConnection failed") return {"completion_tokens": 0, "content": "我连接不到你的网络"} except openai.error.Timeout as e: logger.warn(e) logger.warn("[OPEN_AI] Timeout") return {"completion_tokens": 0, "content": "我没有收到你的消息"} except Exception as e: # unknown exception logger.exception(e) self.sessions.clear_session(session_id) return {"completion_tokens": 0, "content": "请再问我一次吧"} def create_img(self, query, retry_count=0): try: if conf().get('rate_limit_dalle') and not self.tb4dalle.get_token(): return False, "请求太快了，请休息一下再问我吧" logger.info("[OPEN_AI] image_query={}".format(query)) response = openai.Image.create( prompt=query, #图片描述 n=1, #每次生成图片的数量 size="256x256" #图片大小,可选有 256x256, 512x512, 1024x1024 ) image_url = response['data'][0]['url'] logger.info("[OPEN_AI] image_url={}".format(image_url)) return True, image_url except openai.error.RateLimitError as e: logger.warn(e) if retry_count < 1: time.sleep(5) logger.warn("[OPEN_AI] ImgCreate RateLimit exceed, 第{}次重试".format(retry_count+1)) return self.create_img(query, retry_count+1) else: return False, "提问太快啦，请休息一下再问我吧" except Exception as e: logger.exception(e) return False, str(e) class AzureChatGPTBot(ChatGPTBot): def __init__(self): super().__init__() openai.api_type = "azure" openai.api_version = "2023-03-15-preview" def compose_args(self): args = super().compose_args() args["engine"] = args["model"] del(args["model"]) return args class SessionManager(object): def __init__(self): if conf().get('expires_in_seconds'): sessions = ExpiredDict(conf().get('expires_in_seconds')) else: sessions = dict() self.sessions = sessions def build_session(self, session_id, system_prompt=None): session = self.sessions.get(session_id, []) if len(session) == 0: if system_prompt is None: system_prompt = conf().get("character_desc", "") system_item = {'role': 'system', 'content': system_prompt} session.append(system_item) self.sessions[session_id] = session return session def build_session_query(self, query, session_id): ''' build query with conversation history e.g. [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Who won the world series in 2020?"}, {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."}, {"role": "user", "content": "Where was it played?"} ] :param query: query content :param session_id: session id :return: query content with conversaction ''' session = self.build_session(session_id) user_item = {'role': 'user', 'content': query} session.append(user_item) return session def save_session(self, answer, session_id, total_tokens): max_tokens = conf().get("conversation_max_tokens") if not max_tokens: # default 3000 max_tokens = 1000 max_tokens = int(max_tokens) session = self.sessions.get(session_id) if session: # append conversation gpt_item = {'role': 'assistant', 'content': answer} session.append(gpt_item) # discard exceed limit conversation self.discard_exceed_conversation(session, max_tokens, total_tokens) def discard_exceed_conversation(self, session, max_tokens, total_tokens): dec_tokens = int(total_tokens) # logger.info("prompt tokens used={},max_tokens={}".format(used_tokens,max_tokens)) while dec_tokens > max_tokens: # pop first conversation if len(session) > 3: session.pop(1) session.pop(1) else: break dec_tokens = dec_tokens - max_tokens def clear_session(self, session_id): self.sessions[session_id] = [] def clear_all_session(self): self.sessions.clear()