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# encoding:utf-8
import json
import time
from typing import List, Tuple
import openai
import openai.error
import broadscope_bailian
from broadscope_bailian import ChatQaMessage
from bot.bot import Bot
from bot.baidu.baidu_wenxin_session import BaiduWenxinSession
from bot.session_manager import SessionManager
from bridge.context import ContextType
from bridge.reply import Reply, ReplyType
from common.log import logger
from config import conf, load_config
class TongyiQwenBot(Bot):
def __init__(self):
super().__init__()
self.access_key_id = conf().get("qwen_access_key_id")
self.access_key_secret = conf().get("qwen_access_key_secret")
self.agent_key = conf().get("qwen_agent_key")
self.app_id = conf().get("qwen_app_id")
self.node_id = conf().get("qwen_node_id") or ""
self.api_key_client = broadscope_bailian.AccessTokenClient(access_key_id=self.access_key_id, access_key_secret=self.access_key_secret)
self.api_key_expired_time = self.set_api_key()
self.sessions = SessionManager(BaiduWenxinSession, model=conf().get("model") or "qwen")
self.temperature = conf().get("temperature", 0.2) # 值在[0,1]之间，越大表示回复越具有不确定性
self.top_p = conf().get("top_p", 1)
def reply(self, query, context=None):
# acquire reply content
if context.type == ContextType.TEXT:
logger.info("[TONGYI] 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("[TONGYI] session query={}".format(session.messages))
reply_content = self.reply_text(session)
logger.debug(
"[TONGYI] 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("[TONGYI] 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: BaiduWenxinSession, retry_count=0) -> dict:
"""
call bailian's ChatCompletion to get the answer
:param session: a conversation session
:param retry_count: retry count
:return: {}
"""
try:
prompt, history = self.convert_messages_format(session.messages)
self.update_api_key_if_expired()
# NOTE 阿里百炼的call()函数参数比较奇怪, top_k参数表示top_p, top_p参数表示temperature, 可以参考文档 https://help.aliyun.com/document_detail/2587502.htm
response = broadscope_bailian.Completions().call(app_id=self.app_id, prompt=prompt, history=history, top_k=self.top_p, top_p=self.temperature)
completion_content = self.get_completion_content(response, self.node_id)
completion_tokens, total_tokens = self.calc_tokens(session.messages, completion_content)
return {
"total_tokens": total_tokens,
"completion_tokens": completion_tokens,
"content": completion_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("[TONGYI] 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("[TONGYI] 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("[TONGYI] 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("[TONGYI] APIConnectionError: {}".format(e))
need_retry = False
result["content"] = "我连接不到你的网络"
else:
logger.exception("[TONGYI] Exception: {}".format(e))
need_retry = False
self.sessions.clear_session(session.session_id)
if need_retry:
logger.warn("[TONGYI] 第{}次重试".format(retry_count + 1))
return self.reply_text(session, retry_count + 1)
else:
return result
def set_api_key(self):
api_key, expired_time = self.api_key_client.create_token(agent_key=self.agent_key)
broadscope_bailian.api_key = api_key
return expired_time
def update_api_key_if_expired(self):
if time.time() > self.api_key_expired_time:
self.api_key_expired_time = self.set_api_key()
def convert_messages_format(self, messages) -> Tuple[str, List[ChatQaMessage]]:
history = []
user_content = ''
assistant_content = ''
for message in messages:
role = message.get('role')
if role == 'user':
user_content += message.get('content')
elif role == 'assistant':
assistant_content = message.get('content')
history.append(ChatQaMessage(user_content, assistant_content))
user_content = ''
assistant_content = ''
if user_content == '':
raise Exception('no user message')
return user_content, history
def get_completion_content(self, response, node_id):
text = response['Data']['Text']
if node_id == '':
return text
# TODO: 当使用流程编排创建大模型应用时，响应结构如下，最终结果在['finalResult'][node_id]['response']['text']中，暂时先这么写
# {
# 'Success': True,
# 'Code': None,
# 'Message': None,
# 'Data': {
# 'ResponseId': '9822f38dbacf4c9b8daf5ca03a2daf15',
# 'SessionId': 'session_id',
# 'Text': '{"finalResult":{"LLM_T7islK":{"params":{"modelId":"qwen-plus-v1","prompt":"${systemVars.query}${bizVars.Text}"},"response":{"text":"作为一个AI语言模型，我没有年龄，因为我没有生日。\n我只是一个程序，没有生命和身体。"}}}}',
# 'Thoughts': [],
# 'Debug': {},
# 'DocReferences': []
# },
# 'RequestId': '8e11d31551ce4c3f83f49e6e0dd998b0',
# 'Failed': None
# }
text_dict = json.loads(text)
completion_content = text_dict['finalResult'][node_id]['response']['text']
return completion_content
def calc_tokens(self, messages, completion_content):
completion_tokens = len(completion_content)
prompt_tokens = 0
for message in messages:
prompt_tokens += len(message["content"])
return completion_tokens, prompt_tokens + completion_tokens
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