# 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 ChatGPTModel(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 proxy = model_conf(const.OPEN_AI).get('proxy') if proxy: openai.proxy = proxy log.info("[CHATGPT] api_base={} proxy={}".format( api_base, 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("[CHATGPT] 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("[CHATGPT] 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("[CHATGPT] new_query={}, user={}, reply_cont={}".format(new_query, from_user_id, reply_content)) 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.ChatCompletion.create( model= model_conf(const.OPEN_AI).get("model") or "gpt-3.5-turbo", # 对话模型的名称 messages=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 ) reply_content = response.choices[0]['message']['content'] used_token = response['usage']['total_tokens'] log.debug(response) log.info("[CHATGPT] reply={}", reply_content) if reply_content: # save conversation Session.save_session(query, reply_content, user_id, used_token) return response.choices[0]['message']['content'] 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)) 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 openai.error.APIConnectionError as e: log.warn(e) log.warn("[CHATGPT] APIConnection failed") return "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") return "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) 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.ChatCompletion.create( model= model_conf(const.OPEN_AI).get("model") or "gpt-3.5-turbo", # 对话模型的名称 messages=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=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 stream=True ) full_response = "" for chunk in res: log.debug(chunk) if (chunk["choices"][0]["finish_reason"]=="stop"): break chunk_message = chunk['choices'][0]['delta'].get("content") 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 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. [ {"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 user_id: from user id :return: query content with conversaction ''' session = user_session.get(user_id, []) if len(session) == 0: system_prompt = model_conf(const.OPEN_AI).get("character_desc", "") system_item = {'role': 'system', 'content': system_prompt} session.append(system_item) user_session[user_id] = session user_item = {'role': 'user', 'content': query} session.append(user_item) return session @staticmethod def save_session(query, answer, user_id, used_tokens=0): max_tokens = model_conf(const.OPEN_AI).get('conversation_max_tokens') max_history_num = model_conf(const.OPEN_AI).get('max_history_num', None) if not max_tokens or max_tokens > 4000: # default value max_tokens = 1000 session = user_session.get(user_id) if session: # append conversation gpt_item = {'role': 'assistant', 'content': answer} session.append(gpt_item) if used_tokens > max_tokens and len(session) >= 3: # pop first conversation (TODO: more accurate calculation) session.pop(1) session.pop(1) if max_history_num is not None: while len(session) > max_history_num * 2 + 1: session.pop(1) session.pop(1) @staticmethod def clear_session(user_id): user_session[user_id] = []