refactor(core): 优化类型提示与代码风格

本次提交对项目代码进行了广泛的重构,主要集中在以下几个方面:

1.  **类型提示现代化**:
    -   将 `typing` 模块中的 `Optional[T]`、`List[T]`、`Dict[K, V]` 等旧式类型提示更新为现代的 `T | None`、`list[T]`、`dict[K, V]` 语法。
    -   这提高了代码的可读性,并与较新 Python 版本的风格保持一致。

2.  **代码风格统一**:
    -   移除了多余的空行和不必要的空格,使代码更加紧凑和规范。
    -   统一了部分日志输出的格式,增强了日志的可读性。

3.  **导入语句优化**:
    -   调整了部分模块的 `import` 语句顺序,使其符合 PEP 8 规范。

这些更改不涉及任何功能性变动,旨在提升代码库的整体质量、可维护性和开发体验。
This commit is contained in:
minecraft1024a
2025-10-31 20:56:17 +08:00
committed by Windpicker-owo
parent 6026682a03
commit 2ee6aa3951
46 changed files with 923 additions and 924 deletions

View File

@@ -2,7 +2,6 @@
情境提取器
从聊天历史中提取当前的情境situation用于 StyleLearner 预测
"""
from typing import Optional
from src.chat.utils.prompt import Prompt, global_prompt_manager
from src.common.logger import get_logger
@@ -41,17 +40,17 @@ def init_prompt():
class SituationExtractor:
"""情境提取器,从聊天历史中提取当前情境"""
def __init__(self):
self.llm_model = LLMRequest(
model_set=model_config.model_task_config.utils_small,
request_type="expression.situation_extractor"
)
async def extract_situations(
self,
chat_history: list | str,
target_message: Optional[str] = None,
target_message: str | None = None,
max_situations: int = 3
) -> list[str]:
"""
@@ -68,18 +67,18 @@ class SituationExtractor:
# 转换chat_history为字符串
if isinstance(chat_history, list):
chat_info = "\n".join([
f"{msg.get('sender', 'Unknown')}: {msg.get('content', '')}"
f"{msg.get('sender', 'Unknown')}: {msg.get('content', '')}"
for msg in chat_history
])
else:
chat_info = chat_history
# 构建目标消息信息
if target_message:
target_message_info = f",现在你想要回复消息:{target_message}"
else:
target_message_info = ""
# 构建 prompt
try:
prompt = (await global_prompt_manager.get_prompt_async("situation_extraction_prompt")).format(
@@ -87,31 +86,31 @@ class SituationExtractor:
chat_history=chat_info,
target_message_info=target_message_info
)
# 调用 LLM
response, _ = await self.llm_model.generate_response_async(
prompt=prompt,
temperature=0.3
)
if not response or not response.strip():
logger.warning("LLM返回空响应无法提取情境")
return []
# 解析响应
situations = self._parse_situations(response, max_situations)
if situations:
logger.debug(f"提取到 {len(situations)} 个情境: {situations}")
else:
logger.warning(f"无法从LLM响应中解析出情境。响应:\n{response}")
return situations
except Exception as e:
logger.error(f"提取情境失败: {e}")
return []
@staticmethod
def _parse_situations(response: str, max_situations: int) -> list[str]:
"""
@@ -125,33 +124,33 @@ class SituationExtractor:
情境描述列表
"""
situations = []
for line in response.splitlines():
line = line.strip()
if not line:
continue
# 移除可能的序号、引号等
line = line.lstrip('0123456789.、-*>)】] \t"\'""''')
line = line.rstrip('"\'""''')
line = line.strip()
if not line:
continue
# 过滤掉明显不是情境描述的内容
if len(line) > 30: # 太长
continue
if len(line) < 2: # 太短
continue
if any(keyword in line.lower() for keyword in ['例如', '注意', '', '分析', '总结']):
if any(keyword in line.lower() for keyword in ["例如", "注意", "", "分析", "总结"]):
continue
situations.append(line)
if len(situations) >= max_situations:
break
return situations