# encoding:utf-8 from model.model import Model from config import model_conf, common_conf_val from common import const from common import log import openai import time user_session = dict() # OpenAI对话模型API (可用) class OpenAIModel(Model): def __init__(self): openai.api_key = model_conf(const.OPEN_AI).get('api_key') api_base = model_conf(const.OPEN_AI).get('api_base') if api_base: openai.api_base = api_base log.info("[OPEN_AI] api_base={}".format(openai.api_base)) self.model = model_conf(const.OPEN_AI).get('model', 'text-davinci-003') proxy = model_conf(const.OPEN_AI).get('proxy') if proxy: openai.proxy = proxy def reply(self, query, context=None): # acquire reply content if not context or not context.get('type') or context.get('type') == 'TEXT': log.info("[OPEN_AI] query={}".format(query)) from_user_id = context['from_user_id'] clear_memory_commands = common_conf_val('clear_memory_commands', ['#清除记忆']) if query in clear_memory_commands: Session.clear_session(from_user_id) return '记忆已清除' new_query = Session.build_session_query(query, from_user_id) log.debug("[OPEN_AI] session query={}".format(new_query)) if context.get('stream'): # reply in stream return self.reply_text_stream(query, new_query, from_user_id) reply_content = self.reply_text(new_query, from_user_id, 0) log.debug("[OPEN_AI] new_query={}, user={}, reply_cont={}".format(new_query, from_user_id, reply_content)) if reply_content and query: Session.save_session(query, reply_content, from_user_id) return reply_content elif context.get('type', None) == 'IMAGE_CREATE': return self.create_img(query, 0) def reply_text(self, query, user_id, retry_count=0): try: response = openai.Completion.create( model=self.model, # 对话模型的名称 prompt=query, temperature=model_conf(const.OPEN_AI).get("temperature", 0.75), # 熵值，在[0,1]之间，越大表示选取的候选词越随机，回复越具有不确定性，建议和top_p参数二选一使用，创意性任务越大越好，精确性任务越小越好 #max_tokens=4096, # 回复最大的字符数，为输入和输出的总数 #top_p=model_conf(const.OPEN_AI).get("top_p", 0.7),, #候选词列表。0.7 意味着只考虑前70%候选词的标记，建议和temperature参数二选一使用 frequency_penalty=model_conf(const.OPEN_AI).get("frequency_penalty", 0.0), # [-2,2]之间，该值越大则越降低模型一行中的重复用词，更倾向于产生不同的内容 presence_penalty=model_conf(const.OPEN_AI).get("presence_penalty", 1.0), # [-2,2]之间，该值越大则越不受输入限制，将鼓励模型生成输入中不存在的新词，更倾向于产生不同的内容 stop=["\n\n\n"] ) res_content = response.choices[0]['text'].strip().replace('<|endoftext|>', '') log.info("[OPEN_AI] reply={}".format(res_content)) return res_content except openai.error.RateLimitError as e: # rate limit exception log.warn(e) if retry_count < 1: time.sleep(5) log.warn("[OPEN_AI] RateLimit exceed, 第{}次重试".format(retry_count+1)) return self.reply_text(query, user_id, retry_count+1) else: return "提问太快啦，请休息一下再问我吧" except Exception as e: # unknown exception log.exception(e) Session.clear_session(user_id) return "请再问我一次吧" async def reply_text_stream(self, query, context, retry_count=0): try: user_id=context['from_user_id'] new_query = Session.build_session_query(query, user_id) res = openai.Completion.create( model= "text-davinci-003", # 对话模型的名称 prompt=new_query, temperature=model_conf(const.OPEN_AI).get("temperature", 0.75), # 熵值，在[0,1]之间，越大表示选取的候选词越随机，回复越具有不确定性，建议和top_p参数二选一使用，创意性任务越大越好，精确性任务越小越好 max_tokens=model_conf(const.OPEN_AI).get("conversation_max_tokens", 3000), # 回复最大的字符数，为输入和输出的总数,davinci的流式对话需要启用这属性，不然对话会断流 #top_p=model_conf(const.OPEN_AI).get("top_p", 0.7),, #候选词列表。0.7 意味着只考虑前70%候选词的标记，建议和temperature参数二选一使用 frequency_penalty=model_conf(const.OPEN_AI).get("frequency_penalty", 0.0), # [-2,2]之间，该值越大则越降低模型一行中的重复用词，更倾向于产生不同的内容 presence_penalty=model_conf(const.OPEN_AI).get("presence_penalty", 1.0), # [-2,2]之间，该值越大则越不受输入限制，将鼓励模型生成输入中不存在的新词，更倾向于产生不同的内容 stream=True ) full_response = "" for chunk in res: log.debug(chunk) if (chunk["choices"][0]["finish_reason"]=="stop"): break chunk_message = chunk['choices'][0].get("text") if(chunk_message): full_response+=chunk_message yield False,full_response Session.save_session(query, full_response, user_id) log.info("[chatgpt]: reply={}", full_response) yield True,full_response except openai.error.RateLimitError as e: # rate limit exception log.warn(e) if retry_count < 1: time.sleep(5) log.warn("[CHATGPT] RateLimit exceed, 第{}次重试".format(retry_count+1)) yield True, self.reply_text_stream(query, user_id, retry_count+1) else: yield True, "提问太快啦，请休息一下再问我吧" except openai.error.APIConnectionError as e: log.warn(e) log.warn("[CHATGPT] APIConnection failed") yield True, "我连接不到网络，请稍后重试" except openai.error.Timeout as e: log.warn(e) log.warn("[CHATGPT] Timeout") yield True, "我没有收到消息，请稍后重试" except Exception as e: # unknown exception log.exception(e) Session.clear_session(user_id) yield True, "请再问我一次吧" def _process_reply_stream( self, query: str, reply: dict, user_id: str ) -> str: full_response = "" for response in reply: if response.get("choices") is None or len(response["choices"]) == 0: raise Exception("OpenAI API returned no choices") if response["choices"][0].get("finish_details") is not None: break if response["choices"][0].get("text") is None: raise Exception("OpenAI API returned no text") if response["choices"][0]["text"] == "<|endoftext|>": break yield response["choices"][0]["text"] full_response += response["choices"][0]["text"] if query and full_response: Session.save_session(query, full_response, user_id) def create_img(self, query, retry_count=0): try: log.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'] log.info("[OPEN_AI] image_url={}".format(image_url)) return [image_url] except openai.error.RateLimitError as e: log.warn(e) if retry_count < 1: time.sleep(5) log.warn("[OPEN_AI] ImgCreate RateLimit exceed, 第{}次重试".format(retry_count+1)) return self.reply_text(query, retry_count+1) else: return "提问太快啦，请休息一下再问我吧" except Exception as e: log.exception(e) return None class Session(object): @staticmethod def build_session_query(query, user_id): ''' build query with conversation history e.g. Q: xxx A: xxx Q: xxx :param query: query content :param user_id: from user id :return: query content with conversaction ''' prompt = model_conf(const.OPEN_AI).get("character_desc", "") if prompt: prompt += "<|endoftext|>\n\n\n" session = user_session.get(user_id, None) if session: for conversation in session: prompt += "Q: " + conversation["question"] + "\n\n\nA: " + conversation["answer"] + "<|endoftext|>\n" prompt += "Q: " + query + "\nA: " return prompt else: return prompt + "Q: " + query + "\nA: " @staticmethod def save_session(query, answer, user_id): max_tokens = model_conf(const.OPEN_AI).get("conversation_max_tokens") if not max_tokens: # default 3000 max_tokens = 1000 conversation = dict() conversation["question"] = query conversation["answer"] = answer session = user_session.get(user_id) log.debug(conversation) log.debug(session) if session: # append conversation session.append(conversation) else: # create session queue = list() queue.append(conversation) user_session[user_id] = queue # discard exceed limit conversation Session.discard_exceed_conversation(user_session[user_id], max_tokens) @staticmethod def discard_exceed_conversation(session, max_tokens): count = 0 count_list = list() for i in range(len(session)-1, -1, -1): # count tokens of conversation list history_conv = session[i] count += len(history_conv["question"]) + len(history_conv["answer"]) count_list.append(count) for c in count_list: if c > max_tokens: # pop first conversation session.pop(0) @staticmethod def clear_session(user_id): user_session[user_id] = []