# encoding:utf-8
import time
import openai
import openai.error
import requests
from bot.bot import Bot
from bot.zhipu.chat_glm_session import ChatGLMSession
from bot.openai.open_ai_image import OpenAIImage
from bot.session_manager import SessionManager
from bridge.context import ContextType
from bridge.reply import Reply, ReplyType
from common.log import logger
# from common.token_bucket import TokenBucket
from config import conf, load_config
from zhipuai import ZhipuAI
# ZhipuAI对话模型API
class ChatGLMBot(Bot):
def __init__(self):
super().__init__()
# set the default api_key
self.api_key = conf().get("zhipu_ai_api_key")
if conf().get("zhipu_ai_api_base"):
openai.api_base = conf().get("zhipu_ai_api_base")
# if conf().get("rate_limit_chatgpt"):
# self.tb4chatgpt = TokenBucket(conf().get("rate_limit_chatgpt", 20))
self.sessions = SessionManager(ChatGLMSession, model=conf().get("model") or "chatglm")
self.args = {
"model": "glm-4", # 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": conf().get("top_p", 0.7),
# "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
# "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
# "timeout": conf().get("request_timeout", None), # 重试超时时间，在这个时间内，将会自动重试
}
self.client = ZhipuAI(api_key=self.api_key)
def reply(self, query, context=None):
# acquire reply content
if context.type == ContextType.TEXT:
logger.info("[CHATGLM] 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.session_query(query, session_id)
logger.debug("[CHATGLM] session query={}".format(session.messages))
api_key = context.get("openai_api_key") or openai.api_key
model = context.get("gpt_model")
new_args = None
if model:
new_args = self.args.copy()
new_args["model"] = model
# if context.get('stream'):
# # reply in stream
# return self.reply_text_stream(query, new_query, session_id)
reply_content = self.reply_text(session, api_key, args=new_args)
logger.debug(
"[CHATGLM] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(
session.messages,
session_id,
reply_content["content"],
reply_content["completion_tokens"],
)
)
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.session_reply(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("[CHATGLM] reply {} used 0 tokens.".format(reply_content))
return reply
else:
reply = Reply(ReplyType.ERROR, "Bot不支持处理{}类型的消息".format(context.type))
return reply
def reply_text(self, session: ChatGLMSession, api_key=None, args=None, 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():
# raise openai.error.RateLimitError("RateLimitError: rate limit exceeded")
# if api_key == None, the default openai.api_key will be used
if args is None:
args = self.args
# response = openai.ChatCompletion.create(api_key=api_key, messages=session.messages, **args)
response = self.client.chat.completions.create(messages=session.messages, **args)
# logger.debug("[CHATGLM] response={}".format(response))
# logger.info("[CHATGLM] 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 Exception as e:
need_retry = retry_count < 2
result = {"completion_tokens": 0, "content": "我现在有点累了，等会再来吧"}
if isinstance(e, openai.error.RateLimitError):
logger.warn("[CHATGLM] RateLimitError: {}".format(e))
result["content"] = "提问太快啦，请休息一下再问我吧"
if need_retry:
time.sleep(20)
elif isinstance(e, openai.error.Timeout):
logger.warn("[CHATGLM] Timeout: {}".format(e))
result["content"] = "我没有收到你的消息"
if need_retry:
time.sleep(5)
elif isinstance(e, openai.error.APIError):
logger.warn("[CHATGLM] Bad Gateway: {}".format(e))
result["content"] = "请再问我一次"
if need_retry:
time.sleep(10)
elif isinstance(e, openai.error.APIConnectionError):
logger.warn("[CHATGLM] APIConnectionError: {}".format(e))
result["content"] = "我连接不到你的网络"
if need_retry:
time.sleep(5)
else:
logger.exception("[CHATGLM] Exception: {}".format(e), e)
need_retry = False
self.sessions.clear_session(session.session_id)
if need_retry:
logger.warn("[CHATGLM] 第{}次重试".format(retry_count + 1))
return self.reply_text(session, api_key, args, retry_count + 1)
else:
return result