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https://github.com/zhayujie/chatgpt-on-wechat.git
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formatting code
This commit is contained in:
lanvent
@@ -1,41 +1,52 @@
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# encoding:utf-8
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import time
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import openai
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import openai.error
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from bot.bot import Bot
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from bot.openai.open_ai_image import OpenAIImage
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from bot.openai.open_ai_session import OpenAISession
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from bot.session_manager import SessionManager
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from bridge.context import ContextType
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from bridge.reply import Reply, ReplyType
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from config import conf
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from common.log import logger
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import openai
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import openai.error
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import time
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from config import conf
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user_session = dict()
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# OpenAI对话模型API (可用)
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class OpenAIBot(Bot, OpenAIImage):
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def __init__(self):
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super().__init__()
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openai.api_key = conf().get('open_ai_api_key')
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if conf().get('open_ai_api_base'):
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openai.api_base = conf().get('open_ai_api_base')
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proxy = conf().get('proxy')
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openai.api_key = conf().get("open_ai_api_key")
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if conf().get("open_ai_api_base"):
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openai.api_base = conf().get("open_ai_api_base")
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proxy = conf().get("proxy")
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if proxy:
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openai.proxy = proxy
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self.sessions = SessionManager(OpenAISession, model= conf().get("model") or "text-davinci-003")
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self.sessions = SessionManager(
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OpenAISession, model=conf().get("model") or "text-davinci-003"
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)
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self.args = {
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"model": conf().get("model") or "text-davinci-003", # 对话模型的名称
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"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
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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":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
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"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
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"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
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"timeout": conf().get('request_timeout', None), #重试超时时间，在这个时间内，将会自动重试
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"stop":["\n\n\n"]
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"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
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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": conf().get(
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"frequency_penalty", 0.0
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), # [-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": conf().get(
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"presence_penalty", 0.0
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), # [-2,2]之间，该值越大则更倾向于产生不同的内容
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"request_timeout": conf().get(
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"request_timeout", None
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), # 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
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"timeout": conf().get("request_timeout", None), # 重试超时时间，在这个时间内，将会自动重试
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"stop": ["\n\n\n"],
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}
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def reply(self, query, context=None):
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@@ -43,24 +54,34 @@ class OpenAIBot(Bot, OpenAIImage):
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if context and context.type:
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if context.type == ContextType.TEXT:
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logger.info("[OPEN_AI] query={}".format(query))
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session_id = context['session_id']
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session_id = context["session_id"]
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reply = None
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if query == '#清除记忆':
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if query == "#清除记忆":
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self.sessions.clear_session(session_id)
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reply = Reply(ReplyType.INFO, '记忆已清除')
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elif query == '#清除所有':
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reply = Reply(ReplyType.INFO, "记忆已清除")
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elif query == "#清除所有":
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self.sessions.clear_all_session()
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reply = Reply(ReplyType.INFO, '所有人记忆已清除')
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reply = Reply(ReplyType.INFO, "所有人记忆已清除")
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else:
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session = self.sessions.session_query(query, session_id)
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result = self.reply_text(session)
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total_tokens, completion_tokens, reply_content = result['total_tokens'], result['completion_tokens'], result['content']
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logger.debug("[OPEN_AI] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(str(session), session_id, reply_content, completion_tokens))
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total_tokens, completion_tokens, reply_content = (
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result["total_tokens"],
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result["completion_tokens"],
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result["content"],
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)
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logger.debug(
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"[OPEN_AI] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(
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str(session), session_id, reply_content, completion_tokens
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)
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)
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if total_tokens == 0 :
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if total_tokens == 0:
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reply = Reply(ReplyType.ERROR, reply_content)
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else:
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self.sessions.session_reply(reply_content, session_id, total_tokens)
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self.sessions.session_reply(
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reply_content, session_id, total_tokens
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)
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reply = Reply(ReplyType.TEXT, reply_content)
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return reply
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elif context.type == ContextType.IMAGE_CREATE:
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@@ -72,42 +93,44 @@ class OpenAIBot(Bot, OpenAIImage):
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reply = Reply(ReplyType.ERROR, retstring)
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return reply
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def reply_text(self, session:OpenAISession, retry_count=0):
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def reply_text(self, session: OpenAISession, retry_count=0):
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try:
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response = openai.Completion.create(
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prompt=str(session), **self.args
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response = openai.Completion.create(prompt=str(session), **self.args)
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res_content = (
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response.choices[0]["text"].strip().replace("<|endoftext|>", "")
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)
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res_content = response.choices[0]['text'].strip().replace('<|endoftext|>', '')
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total_tokens = response["usage"]["total_tokens"]
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completion_tokens = response["usage"]["completion_tokens"]
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logger.info("[OPEN_AI] reply={}".format(res_content))
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return {"total_tokens": total_tokens,
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"completion_tokens": completion_tokens,
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"content": res_content}
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return {
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"total_tokens": total_tokens,
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"completion_tokens": completion_tokens,
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"content": res_content,
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}
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except Exception as e:
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need_retry = retry_count < 2
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result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
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if isinstance(e, openai.error.RateLimitError):
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logger.warn("[OPEN_AI] RateLimitError: {}".format(e))
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result['content'] = "提问太快啦，请休息一下再问我吧"
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result["content"] = "提问太快啦，请休息一下再问我吧"
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if need_retry:
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time.sleep(5)
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elif isinstance(e, openai.error.Timeout):
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logger.warn("[OPEN_AI] Timeout: {}".format(e))
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result['content'] = "我没有收到你的消息"
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result["content"] = "我没有收到你的消息"
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if need_retry:
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time.sleep(5)
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elif isinstance(e, openai.error.APIConnectionError):
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logger.warn("[OPEN_AI] APIConnectionError: {}".format(e))
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need_retry = False
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result['content'] = "我连接不到你的网络"
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result["content"] = "我连接不到你的网络"
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else:
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logger.warn("[OPEN_AI] Exception: {}".format(e))
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need_retry = False
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self.sessions.clear_session(session.session_id)
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if need_retry:
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logger.warn("[OPEN_AI] 第{}次重试".format(retry_count+1))
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return self.reply_text(session, retry_count+1)
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logger.warn("[OPEN_AI] 第{}次重试".format(retry_count + 1))
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return self.reply_text(session, retry_count + 1)
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else:
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return result
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return result
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@@ -1,38 +1,45 @@
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import time
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import openai
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import openai.error
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from common.token_bucket import TokenBucket
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from common.log import logger
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from common.token_bucket import TokenBucket
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from config import conf
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# OPENAI提供的画图接口
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class OpenAIImage(object):
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def __init__(self):
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openai.api_key = conf().get('open_ai_api_key')
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if conf().get('rate_limit_dalle'):
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self.tb4dalle = TokenBucket(conf().get('rate_limit_dalle', 50))
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openai.api_key = conf().get("open_ai_api_key")
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if conf().get("rate_limit_dalle"):
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self.tb4dalle = TokenBucket(conf().get("rate_limit_dalle", 50))
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def create_img(self, query, retry_count=0):
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try:
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if conf().get('rate_limit_dalle') and not self.tb4dalle.get_token():
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if conf().get("rate_limit_dalle") and not self.tb4dalle.get_token():
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return False, "请求太快了，请休息一下再问我吧"
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logger.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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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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image_url = response["data"][0]["url"]
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logger.info("[OPEN_AI] image_url={}".format(image_url))
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return True, image_url
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except openai.error.RateLimitError as e:
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logger.warn(e)
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if retry_count < 1:
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time.sleep(5)
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logger.warn("[OPEN_AI] ImgCreate RateLimit exceed, 第{}次重试".format(retry_count+1))
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return self.create_img(query, retry_count+1)
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logger.warn(
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"[OPEN_AI] ImgCreate RateLimit exceed, 第{}次重试".format(
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retry_count + 1
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)
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)
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return self.create_img(query, retry_count + 1)
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else:
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return False, "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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logger.exception(e)
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return False, str(e)
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return False, str(e)
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@@ -1,32 +1,34 @@
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from bot.session_manager import Session
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from common.log import logger
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class OpenAISession(Session):
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def __init__(self, session_id, system_prompt=None, model= "text-davinci-003"):
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def __init__(self, session_id, system_prompt=None, model="text-davinci-003"):
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super().__init__(session_id, system_prompt)
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self.model = model
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self.reset()
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def __str__(self):
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# 构造对话模型的输入
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'''
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"""
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e.g. Q: xxx
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A: xxx
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Q: xxx
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'''
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"""
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prompt = ""
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for item in self.messages:
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if item['role'] == 'system':
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prompt += item['content'] + "<|endoftext|>\n\n\n"
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elif item['role'] == 'user':
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prompt += "Q: " + item['content'] + "\n"
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elif item['role'] == 'assistant':
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prompt += "\n\nA: " + item['content'] + "<|endoftext|>\n"
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if item["role"] == "system":
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prompt += item["content"] + "<|endoftext|>\n\n\n"
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elif item["role"] == "user":
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prompt += "Q: " + item["content"] + "\n"
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elif item["role"] == "assistant":
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prompt += "\n\nA: " + item["content"] + "<|endoftext|>\n"
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if len(self.messages) > 0 and self.messages[-1]['role'] == 'user':
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if len(self.messages) > 0 and self.messages[-1]["role"] == "user":
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prompt += "A: "
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return prompt
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def discard_exceeding(self, max_tokens, cur_tokens= None):
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def discard_exceeding(self, max_tokens, cur_tokens=None):
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precise = True
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try:
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cur_tokens = self.calc_tokens()
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@@ -34,7 +36,9 @@ class OpenAISession(Session):
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precise = False
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if cur_tokens is None:
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raise e
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logger.debug("Exception when counting tokens precisely for query: {}".format(e))
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logger.debug(
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"Exception when counting tokens precisely for query: {}".format(e)
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)
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while cur_tokens > max_tokens:
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if len(self.messages) > 1:
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self.messages.pop(0)
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@@ -46,24 +50,34 @@ class OpenAISession(Session):
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cur_tokens = len(str(self))
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break
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elif len(self.messages) == 1 and self.messages[0]["role"] == "user":
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logger.warn("user question exceed max_tokens. total_tokens={}".format(cur_tokens))
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logger.warn(
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"user question exceed max_tokens. total_tokens={}".format(
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cur_tokens
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)
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)
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break
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else:
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logger.debug("max_tokens={}, total_tokens={}, len(conversation)={}".format(max_tokens, cur_tokens, len(self.messages)))
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logger.debug(
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"max_tokens={}, total_tokens={}, len(conversation)={}".format(
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max_tokens, cur_tokens, len(self.messages)
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)
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)
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break
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if precise:
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cur_tokens = self.calc_tokens()
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else:
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cur_tokens = len(str(self))
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return cur_tokens
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def calc_tokens(self):
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return num_tokens_from_string(str(self), self.model)
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# refer to https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
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def num_tokens_from_string(string: str, model: str) -> int:
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"""Returns the number of tokens in a text string."""
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import tiktoken
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encoding = tiktoken.encoding_for_model(model)
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num_tokens = len(encoding.encode(string,disallowed_special=()))
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return num_tokens
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num_tokens = len(encoding.encode(string, disallowed_special=()))
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return num_tokens
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