mirror of
https://github.com/zhayujie/bot-on-anything.git
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218 lines
8.2 KiB
Python
218 lines
8.2 KiB
Python
# encoding:utf-8
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from model.model import Model
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from config import model_conf
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from common import const
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from common import log
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import openai
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import time
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user_session = dict()
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# OpenAI对话模型API (可用)
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class OpenAIModel(Model):
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def __init__(self):
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openai.api_key = model_conf(const.OPEN_AI).get('api_key')
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def reply(self, query, context=None):
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# acquire reply content
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if not context or not context.get('type') or context.get('type') == 'TEXT':
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log.info("[OPEN_AI] query={}".format(query))
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from_user_id = context['from_user_id']
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if query == '#清除记忆':
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Session.clear_session(from_user_id)
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return '记忆已清除'
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new_query = Session.build_session_query(query, from_user_id)
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log.debug("[OPEN_AI] session query={}".format(new_query))
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if context.get('stream'):
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# reply in stream
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return self.reply_text_stream(query, new_query, from_user_id)
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reply_content = self.reply_text(new_query, from_user_id, 0)
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log.debug("[OPEN_AI] new_query={}, user={}, reply_cont={}".format(new_query, from_user_id, reply_content))
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if reply_content and query:
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Session.save_session(query, reply_content, from_user_id)
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return reply_content
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elif context.get('type', None) == 'IMAGE_CREATE':
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return self.create_img(query, 0)
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def reply_text(self, query, user_id, retry_count=0):
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try:
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response = openai.Completion.create(
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model="text-davinci-003", # 对话模型的名称
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prompt=query,
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temperature=0.9, # 值在[0,1]之间，越大表示回复越具有不确定性
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max_tokens=1200, # 回复最大的字符数
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top_p=1,
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frequency_penalty=0.0, # [-2,2]之间，该值越大则更倾向于产生不同的内容
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presence_penalty=0.0, # [-2,2]之间，该值越大则更倾向于产生不同的内容
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stop=["\n\n\n"]
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)
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res_content = response.choices[0]['text'].strip().replace('<|endoftext|>', '')
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log.info("[OPEN_AI] reply={}".format(res_content))
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return res_content
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except openai.error.RateLimitError as e:
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# rate limit exception
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log.warn(e)
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if retry_count < 1:
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time.sleep(5)
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log.warn("[OPEN_AI] RateLimit exceed, 第{}次重试".format(retry_count+1))
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return self.reply_text(query, user_id, retry_count+1)
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else:
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return "提问太快啦，请休息一下再问我吧"
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except Exception as e:
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# unknown exception
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log.exception(e)
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Session.clear_session(user_id)
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return "请再问我一次吧"
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def reply_text_stream(self, query, new_query, user_id, retry_count=0):
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try:
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res = openai.Completion.create(
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model="text-davinci-003", # 对话模型的名称
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prompt=new_query,
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temperature=0.9, # 值在[0,1]之间，越大表示回复越具有不确定性
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max_tokens=1200, # 回复最大的字符数
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top_p=1,
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frequency_penalty=0.0, # [-2,2]之间，该值越大则更倾向于产生不同的内容
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presence_penalty=0.0, # [-2,2]之间，该值越大则更倾向于产生不同的内容
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stop=["\n\n\n"],
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stream=True
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)
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return self._process_reply_stream(query, res, user_id)
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except openai.error.RateLimitError as e:
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# rate limit exception
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log.warn(e)
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if retry_count < 1:
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time.sleep(5)
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log.warn("[OPEN_AI] RateLimit exceed, 第{}次重试".format(retry_count+1))
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return self.reply_text(query, user_id, retry_count+1)
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else:
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return "提问太快啦，请休息一下再问我吧"
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except Exception as e:
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# unknown exception
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log.exception(e)
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Session.clear_session(user_id)
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return "请再问我一次吧"
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def _process_reply_stream(
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self,
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query: str,
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reply: dict,
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user_id: str
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) -> str:
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full_response = ""
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for response in reply:
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if response.get("choices") is None or len(response["choices"]) == 0:
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raise Exception("OpenAI API returned no choices")
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if response["choices"][0].get("finish_details") is not None:
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break
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if response["choices"][0].get("text") is None:
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raise Exception("OpenAI API returned no text")
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if response["choices"][0]["text"] == "<|endoftext|>":
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break
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yield response["choices"][0]["text"]
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full_response += response["choices"][0]["text"]
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if query and full_response:
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Session.save_session(query, full_response, user_id)
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def create_img(self, query, retry_count=0):
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try:
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log.info("[OPEN_AI] image_query={}".format(query))
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response = openai.Image.create(
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prompt=query, #图片描述
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n=1, #每次生成图片的数量
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size="256x256" #图片大小,可选有 256x256, 512x512, 1024x1024
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)
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image_url = response['data'][0]['url']
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log.info("[OPEN_AI] image_url={}".format(image_url))
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return image_url
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except openai.error.RateLimitError as e:
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log.warn(e)
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if retry_count < 1:
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time.sleep(5)
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log.warn("[OPEN_AI] ImgCreate RateLimit exceed, 第{}次重试".format(retry_count+1))
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return self.reply_text(query, retry_count+1)
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else:
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return "提问太快啦，请休息一下再问我吧"
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except Exception as e:
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log.exception(e)
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return None
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class Session(object):
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@staticmethod
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def build_session_query(query, user_id):
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'''
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build query with conversation history
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e.g. Q: xxx
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A: xxx
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Q: xxx
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:param query: query content
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:param user_id: from user id
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:return: query content with conversaction
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'''
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prompt = model_conf(const.OPEN_AI).get("character_desc", "")
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if prompt:
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prompt += "<|endoftext|>\n\n\n"
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session = user_session.get(user_id, None)
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if session:
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for conversation in session:
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prompt += "Q: " + conversation["question"] + "\n\n\nA: " + conversation["answer"] + "<|endoftext|>\n"
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prompt += "Q: " + query + "\nA: "
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return prompt
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else:
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return prompt + "Q: " + query + "\nA: "
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@staticmethod
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def save_session(query, answer, user_id):
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max_tokens = model_conf(const.OPEN_AI).get("conversation_max_tokens")
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if not max_tokens:
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# default 3000
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max_tokens = 1000
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conversation = dict()
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conversation["question"] = query
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conversation["answer"] = answer
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session = user_session.get(user_id)
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log.debug(conversation)
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log.debug(session)
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if session:
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# append conversation
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session.append(conversation)
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else:
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# create session
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queue = list()
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queue.append(conversation)
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user_session[user_id] = queue
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# discard exceed limit conversation
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Session.discard_exceed_conversation(user_session[user_id], max_tokens)
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@staticmethod
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def discard_exceed_conversation(session, max_tokens):
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count = 0
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count_list = list()
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for i in range(len(session)-1, -1, -1):
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# count tokens of conversation list
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history_conv = session[i]
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count += len(history_conv["question"]) + len(history_conv["answer"])
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count_list.append(count)
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for c in count_list:
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if c > max_tokens:
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# pop first conversation
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session.pop(0)
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@staticmethod
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def clear_session(user_id):
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user_session[user_id] = []
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