Files
AI_NovelGenerator/novel_generator/common.py
T
YILING0013 ba10e6dd66 进行文件的逻辑拆分
初步对ui.py以及novel_generator.py进行了拆分
2025-02-16 22:32:32 +08:00

60 lines
2.1 KiB
Python

#novel_generator/common.py
# -*- coding: utf-8 -*-
"""
通用重试、清洗、日志工具
"""
import logging
import re
import time
import traceback
def call_with_retry(func, max_retries=3, sleep_time=2, fallback_return=None, **kwargs):
"""
通用的重试机制封装。
:param func: 要执行的函数
:param max_retries: 最大重试次数
:param sleep_time: 重试前的等待秒数
:param fallback_return: 如果多次重试仍失败时的返回值
:param kwargs: 传给func的命名参数
:return: func的结果,若失败则返回 fallback_return
"""
for attempt in range(1, max_retries + 1):
try:
return func(**kwargs)
except Exception as e:
logging.warning(f"[call_with_retry] Attempt {attempt} failed with error: {e}")
traceback.print_exc()
if attempt < max_retries:
time.sleep(sleep_time)
else:
logging.error("Max retries reached, returning fallback_return.")
return fallback_return
def remove_think_tags(text: str) -> str:
"""移除 <think>...</think> 包裹的内容"""
return re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL)
def debug_log(prompt: str, response_content: str):
logging.info(
f"\n[######################################### Prompt #########################################]\n{prompt}\n"
)
logging.info(
f"\n[######################################### Response #########################################]\n{response_content}\n"
)
def invoke_with_cleaning(llm_adapter, prompt: str) -> str:
"""
调用 LLM,增加重试和清洗逻辑
如果多次失败,则返回空字符串以继续流程,而不是中断。
"""
def _invoke(prompt):
return llm_adapter.invoke(prompt)
response = call_with_retry(func=_invoke, max_retries=3, fallback_return="", prompt=prompt)
if not response:
logging.warning("No response from model after retry. Return empty.")
return ""
cleaned_text = remove_think_tags(response)
debug_log(prompt, cleaned_text)
return cleaned_text.strip()