# encoding:utf-8
from bot.bot import Bot
from bridge.context import ContextType
from bridge.reply import Reply, ReplyType
from config import conf, load_config
from common.log import logger
from common.token_bucket import TokenBucket
from common.expired_dict import ExpiredDict
import openai
import time
# OpenAI对话模型API (可用)
class ChatGPTBot(Bot):
def __init__(self):
openai.api_key = conf().get('open_ai_api_key')
if conf().get('open_ai_api_base'):
openai.api_base = conf().get('open_ai_api_base')
proxy = conf().get('proxy')
self.sessions = SessionManager(model= conf().get("model") or "gpt-3.5-turbo")
if proxy:
openai.proxy = proxy
if conf().get('rate_limit_chatgpt'):
self.tb4chatgpt = TokenBucket(conf().get('rate_limit_chatgpt', 20))
if conf().get('rate_limit_dalle'):
self.tb4dalle = TokenBucket(conf().get('rate_limit_dalle', 50))
def reply(self, query, context=None):
# acquire reply content
if context.type == ContextType.TEXT:
logger.info("[OPEN_AI] 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.build_session_query(query, session_id)
logger.debug("[OPEN_AI] session query={}".format(session))
# if context.get('stream'):
# # reply in stream
# return self.reply_text_stream(query, new_query, session_id)
reply_content = self.reply_text(session, session_id, 0)
logger.debug("[OPEN_AI] new_query={}, session_id={}, reply_cont={}, completion_tokens={}".format(session, 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.save_session(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("[OPEN_AI] reply {} used 0 tokens.".format(reply_content))
return reply
elif context.type == ContextType.IMAGE_CREATE:
ok, retstring = self.create_img(query, 0)
reply = None
if ok:
reply = Reply(ReplyType.IMAGE_URL, retstring)
else:
reply = Reply(ReplyType.ERROR, retstring)
return reply
else:
reply = Reply(ReplyType.ERROR, 'Bot不支持处理{}类型的消息'.format(context.type))
return reply
def compose_args(self):
return {
"model": conf().get("model") or "gpt-3.5-turbo", # 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":1,
"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
}
def reply_text(self, session, session_id, 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():
return {"completion_tokens": 0, "content": "提问太快啦，请休息一下再问我吧"}
response = openai.ChatCompletion.create(
messages=session, **self.compose_args()
)
# logger.info("[ChatGPT] 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 openai.error.RateLimitError as e:
# rate limit exception
logger.warn(e)
if retry_count < 1:
time.sleep(5)
logger.warn("[OPEN_AI] RateLimit exceed, 第{}次重试".format(retry_count+1))
return self.reply_text(session, session_id, retry_count+1)
else:
return {"completion_tokens": 0, "content": "提问太快啦，请休息一下再问我吧"}
except openai.error.APIConnectionError as e:
# api connection exception
logger.warn(e)
logger.warn("[OPEN_AI] APIConnection failed")
return {"completion_tokens": 0, "content": "我连接不到你的网络"}
except openai.error.Timeout as e:
logger.warn(e)
logger.warn("[OPEN_AI] Timeout")
return {"completion_tokens": 0, "content": "我没有收到你的消息"}
except Exception as e:
# unknown exception
logger.exception(e)
self.sessions.clear_session(session_id)
return {"completion_tokens": 0, "content": "请再问我一次吧"}
def create_img(self, query, retry_count=0):
try:
if conf().get('rate_limit_dalle') and not self.tb4dalle.get_token():
return False, "请求太快了，请休息一下再问我吧"
logger.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']
logger.info("[OPEN_AI] image_url={}".format(image_url))
return True, image_url
except openai.error.RateLimitError as e:
logger.warn(e)
if retry_count < 1:
time.sleep(5)
logger.warn("[OPEN_AI] ImgCreate RateLimit exceed, 第{}次重试".format(retry_count+1))
return self.create_img(query, retry_count+1)
else:
return False, "提问太快啦，请休息一下再问我吧"
except Exception as e:
logger.exception(e)
return False, str(e)
class AzureChatGPTBot(ChatGPTBot):
def __init__(self):
super().__init__()
openai.api_type = "azure"
openai.api_version = "2023-03-15-preview"
def compose_args(self):
args = super().compose_args()
args["engine"] = args["model"]
del(args["model"])
return args
class SessionManager(object):
def __init__(self, model = "gpt-3.5-turbo-0301"):
if conf().get('expires_in_seconds'):
sessions = ExpiredDict(conf().get('expires_in_seconds'))
else:
sessions = dict()
self.sessions = sessions
self.model = model
def build_session(self, session_id, system_prompt=None):
session = self.sessions.get(session_id, [])
if len(session) == 0:
if system_prompt is None:
system_prompt = conf().get("character_desc", "")
system_item = {'role': 'system', 'content': system_prompt}
session.append(system_item)
self.sessions[session_id] = session
return session
def build_session_query(self, query, session_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 session_id: session id
:return: query content with conversaction
'''
session = self.build_session(session_id)
user_item = {'role': 'user', 'content': query}
session.append(user_item)
try:
total_tokens = num_tokens_from_messages(session, self.model)
max_tokens = conf().get("conversation_max_tokens", 1000)
total_tokens = self.discard_exceed_conversation(session, max_tokens, total_tokens)
logger.debug("prompt tokens used={}".format(total_tokens))
except Exception as e:
logger.debug("Exception when counting tokens precisely for prompt: {}".format(str(e)))
return session
def save_session(self, answer, session_id, total_tokens):
max_tokens = conf().get("conversation_max_tokens", 1000)
session = self.sessions.get(session_id)
if session:
# append conversation
gpt_item = {'role': 'assistant', 'content': answer}
session.append(gpt_item)
# discard exceed limit conversation
tokens_cnt = self.discard_exceed_conversation(session, max_tokens, total_tokens)
logger.debug("raw total_tokens={}, savesession tokens={}".format(total_tokens, tokens_cnt))
def discard_exceed_conversation(self, session, max_tokens, total_tokens):
dec_tokens = int(total_tokens)
# logger.info("prompt tokens used={},max_tokens={}".format(used_tokens,max_tokens))
while dec_tokens > max_tokens:
# pop first conversation
if len(session) > 2:
session.pop(1)
elif len(session) == 2 and session[1]["role"] == "assistant":
session.pop(1)
break
elif len(session) == 2 and session[1]["role"] == "user":
logger.warn("user message exceed max_tokens. total_tokens={}".format(dec_tokens))
break
else:
logger.debug("max_tokens={}, total_tokens={}, len(sessions)={}".format(max_tokens, dec_tokens, len(session)))
break
try:
cur_tokens = num_tokens_from_messages(session, self.model)
dec_tokens = cur_tokens
except Exception as e:
logger.debug("Exception when counting tokens precisely for query: {}".format(e))
dec_tokens = dec_tokens - max_tokens
return dec_tokens
def clear_session(self, session_id):
self.sessions[session_id] = []
def clear_all_session(self):
self.sessions.clear()
# refer to https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
def num_tokens_from_messages(messages, model):
"""Returns the number of tokens used by a list of messages."""
import tiktoken
try:
encoding = tiktoken.encoding_for_model(model)
except KeyError:
logger.debug("Warning: model not found. Using cl100k_base encoding.")
encoding = tiktoken.get_encoding("cl100k_base")
if model == "gpt-3.5-turbo":
return num_tokens_from_messages(messages, model="gpt-3.5-turbo-0301")
elif model == "gpt-4":
return num_tokens_from_messages(messages, model="gpt-4-0314")
elif model == "gpt-3.5-turbo-0301":
tokens_per_message = 4 # every message follows <|start|>{role/name}\n{content}<|end|>\n
tokens_per_name = -1 # if there's a name, the role is omitted
elif model == "gpt-4-0314":
tokens_per_message = 3
tokens_per_name = 1
else:
logger.warn(f"num_tokens_from_messages() is not implemented for model {model}. Returning num tokens assuming gpt-3.5-turbo-0301.")
return num_tokens_from_messages(messages, model="gpt-3.5-turbo-0301")
num_tokens = 0
for message in messages:
num_tokens += tokens_per_message
for key, value in message.items():
num_tokens += len(encoding.encode(value))
if key == "name":
num_tokens += tokens_per_name
num_tokens += 3 # every reply is primed with <|start|>assistant<|message|>
return num_tokens