Merge branch 'r-dev' of github.com:Mai-with-u/MaiBot into r-dev

bot.py

@@ -50,6 +50,7 @@ print("警告：Dev进入不稳定开发状态，任何插件与WebUI均可能
 print("\n\n\n\n\n")
 print("-----------------------------------------")
 
+
 def run_runner_process():
     """
     Runner 进程逻辑：作为守护进程运行，负责启动和监控 Worker 进程。

plugins/MaiBot_MCPBridgePlugin/core/__init__.py

@@ -1,2 +1 @@
 """Core helpers for MCP Bridge Plugin."""
-

plugins/MaiBot_MCPBridgePlugin/core/claude_config.py

@@ -167,4 +167,3 @@ def legacy_servers_list_to_claude_config(servers_list_json: str) -> str:
     if not mcp_servers:
         return ""
     return json.dumps({"mcpServers": mcp_servers}, ensure_ascii=False, indent=2)
-

plugins/MaiBot_MCPBridgePlugin/tool_chain.py

@@ -22,21 +22,24 @@ from typing import Any, Dict, List, Optional, Tuple
 
 try:
     from src.common.logger import get_logger
+
     logger = get_logger("mcp_tool_chain")
 except ImportError:
     import logging
+
     logger = logging.getLogger("mcp_tool_chain")
 
 
 @dataclass
 class ToolChainStep:
     """工具链步骤"""
+
     tool_name: str  # 要调用的工具名（如 mcp_server_tool）
     args_template: Dict[str, Any] = field(default_factory=dict)  # 参数模板，支持变量替换
     output_key: str = ""  # 输出存储的键名，供后续步骤引用
     description: str = ""  # 步骤描述
     optional: bool = False  # 是否可选（失败时继续执行）
-    
+
     def to_dict(self) -> Dict[str, Any]:
         return {
             "tool_name": self.tool_name,
@@ -45,7 +48,7 @@ class ToolChainStep:
             "description": self.description,
             "optional": self.optional,
         }
-    
+
     @classmethod
     def from_dict(cls, data: Dict[str, Any]) -> "ToolChainStep":
         return cls(
@@ -60,12 +63,13 @@ class ToolChainStep:
 @dataclass
 class ToolChainDefinition:
     """工具链定义"""
+
     name: str  # 工具链名称（将作为组合工具的名称）
     description: str  # 工具链描述（供 LLM 理解）
     steps: List[ToolChainStep] = field(default_factory=list)  # 执行步骤
     input_params: Dict[str, str] = field(default_factory=dict)  # 输入参数定义 {参数名: 描述}
     enabled: bool = True  # 是否启用
-    
+
     def to_dict(self) -> Dict[str, Any]:
         return {
             "name": self.name,
@@ -74,7 +78,7 @@ class ToolChainDefinition:
             "input_params": self.input_params,
             "enabled": self.enabled,
         }
-    
+
     @classmethod
     def from_dict(cls, data: Dict[str, Any]) -> "ToolChainDefinition":
         steps = [ToolChainStep.from_dict(s) for s in data.get("steps", [])]
@@ -90,12 +94,13 @@ class ToolChainDefinition:
 @dataclass
 class ChainExecutionResult:
     """工具链执行结果"""
+
     success: bool
     final_output: str  # 最终输出（最后一个步骤的结果）
     step_results: List[Dict[str, Any]] = field(default_factory=list)  # 每个步骤的结果
     error: str = ""
     total_duration_ms: float = 0.0
-    
+
     def to_summary(self) -> str:
         """生成执行摘要"""
         lines = []
@@ -103,7 +108,7 @@ class ChainExecutionResult:
             status = "✅" if step.get("success") else "❌"
             tool = step.get("tool_name", "unknown")
             duration = step.get("duration_ms", 0)
-            lines.append(f"{status} 步骤{i+1}: {tool} ({duration:.0f}ms)")
+            lines.append(f"{status} 步骤{i + 1}: {tool} ({duration:.0f}ms)")
             if not step.get("success") and step.get("error"):
                 lines.append(f"   错误: {step['error'][:50]}")
         return "\n".join(lines)
@@ -111,49 +116,49 @@ class ChainExecutionResult:
 
 class ToolChainExecutor:
     """工具链执行器"""
-    
+
     # 变量替换模式: ${step.output_key} 或 ${input.param_name} 或 ${prev}
-    VAR_PATTERN = re.compile(r'\$\{([^}]+)\}')
-    
+    VAR_PATTERN = re.compile(r"\$\{([^}]+)\}")
+
     def __init__(self, mcp_manager):
         self._mcp_manager = mcp_manager
-    
+
     def _resolve_tool_key(self, tool_name: str) -> Optional[str]:
         """解析工具名，返回有效的 tool_key
-        
+
         支持:
         - 直接使用 tool_key（如 mcp_server_tool）
         - 使用注册后的工具名（会自动转换 - 和 . 为 _）
         """
         all_tools = self._mcp_manager.all_tools
-        
+
         # 直接匹配
         if tool_name in all_tools:
             return tool_name
-        
+
         # 尝试转换后匹配（用户可能使用了注册后的名称）
         normalized = tool_name.replace("-", "_").replace(".", "_")
         if normalized in all_tools:
             return normalized
-        
+
         # 尝试查找包含该名称的工具
         for key in all_tools.keys():
             if key.endswith(f"_{tool_name}") or key.endswith(f"_{normalized}"):
                 return key
-        
+
         return None
-    
+
     async def execute(
         self,
         chain: ToolChainDefinition,
         input_args: Dict[str, Any],
     ) -> ChainExecutionResult:
         """执行工具链
-        
+
         Args:
             chain: 工具链定义
             input_args: 用户输入的参数
-            
+
         Returns:
             ChainExecutionResult: 执行结果
         """
@@ -164,15 +169,15 @@ class ToolChainExecutor:
             "step": {},  # 各步骤输出，按 output_key 存储
             "prev": "",  # 上一步的输出
         }
-        
+
         final_output = ""
-        
+
         # 验证必需的输入参数
         missing_params = []
         for param_name in chain.input_params.keys():
             if param_name not in context["input"]:
                 missing_params.append(param_name)
-        
+
         if missing_params:
             return ChainExecutionResult(
                 success=False,
@@ -180,7 +185,7 @@ class ToolChainExecutor:
                 error=f"缺少必需参数: {', '.join(missing_params)}",
                 total_duration_ms=(time.time() - start_time) * 1000,
             )
-        
+
         for i, step in enumerate(chain.steps):
             step_start = time.time()
             step_result = {
@@ -191,96 +196,96 @@ class ToolChainExecutor:
                 "error": "",
                 "duration_ms": 0,
             }
-            
+
             try:
                 # 替换参数中的变量
                 resolved_args = self._resolve_args(step.args_template, context)
                 step_result["resolved_args"] = resolved_args
-                
+
                 # 解析工具名
                 tool_key = self._resolve_tool_key(step.tool_name)
                 if not tool_key:
                     step_result["error"] = f"工具 {step.tool_name} 不存在"
-                    logger.warning(f"工具链步骤 {i+1}: 工具 {step.tool_name} 不存在")
-                    
+                    logger.warning(f"工具链步骤 {i + 1}: 工具 {step.tool_name} 不存在")
+
                     if not step.optional:
                         step_results.append(step_result)
                         return ChainExecutionResult(
                             success=False,
                             final_output="",
                             step_results=step_results,
-                            error=f"步骤 {i+1}: 工具 {step.tool_name} 不存在",
+                            error=f"步骤 {i + 1}: 工具 {step.tool_name} 不存在",
                             total_duration_ms=(time.time() - start_time) * 1000,
                         )
                     step_results.append(step_result)
                     continue
-                
-                logger.debug(f"工具链步骤 {i+1}: 调用 {tool_key}，参数: {resolved_args}")
-                
+
+                logger.debug(f"工具链步骤 {i + 1}: 调用 {tool_key}，参数: {resolved_args}")
+
                 # 调用工具
                 result = await self._mcp_manager.call_tool(tool_key, resolved_args)
-                
+
                 step_duration = (time.time() - step_start) * 1000
                 step_result["duration_ms"] = step_duration
-                
+
                 if result.success:
                     step_result["success"] = True
                     # 确保 content 不为 None
                     content = result.content if result.content is not None else ""
                     step_result["output"] = content
-                    
+
                     # 更新上下文
                     context["prev"] = content
                     if step.output_key:
                         context["step"][step.output_key] = content
-                    
+
                     final_output = content
                     content_preview = content[:100] if content else "(空)"
-                    logger.debug(f"工具链步骤 {i+1} 成功: {content_preview}...")
+                    logger.debug(f"工具链步骤 {i + 1} 成功: {content_preview}...")
                 else:
                     step_result["error"] = result.error or "未知错误"
-                    logger.warning(f"工具链步骤 {i+1} 失败: {result.error}")
-                    
+                    logger.warning(f"工具链步骤 {i + 1} 失败: {result.error}")
+
                     if not step.optional:
                         step_results.append(step_result)
                         return ChainExecutionResult(
                             success=False,
                             final_output="",
                             step_results=step_results,
-                            error=f"步骤 {i+1} ({step.tool_name}) 失败: {result.error}",
+                            error=f"步骤 {i + 1} ({step.tool_name}) 失败: {result.error}",
                             total_duration_ms=(time.time() - start_time) * 1000,
                         )
-                    
+
             except Exception as e:
                 step_duration = (time.time() - step_start) * 1000
                 step_result["duration_ms"] = step_duration
                 step_result["error"] = str(e)
-                logger.error(f"工具链步骤 {i+1} 异常: {e}")
-                
+                logger.error(f"工具链步骤 {i + 1} 异常: {e}")
+
                 if not step.optional:
                     step_results.append(step_result)
                     return ChainExecutionResult(
                         success=False,
                         final_output="",
                         step_results=step_results,
-                        error=f"步骤 {i+1} ({step.tool_name}) 异常: {e}",
+                        error=f"步骤 {i + 1} ({step.tool_name}) 异常: {e}",
                         total_duration_ms=(time.time() - start_time) * 1000,
                     )
-            
+
             step_results.append(step_result)
-        
+
         total_duration = (time.time() - start_time) * 1000
-        
+
         return ChainExecutionResult(
             success=True,
             final_output=final_output,
             step_results=step_results,
             total_duration_ms=total_duration,
         )
-    
+
     def _resolve_args(self, args_template: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]:
         """解析参数模板，替换变量
-        
+
         支持的变量格式:
         - ${input.param_name}: 用户输入的参数
         - ${step.output_key}: 某个步骤的输出
@@ -288,50 +293,48 @@ class ToolChainExecutor:
         - ${prev.field}: 上一步输出（JSON）的某个字段
         """
         resolved = {}
-        
+
         for key, value in args_template.items():
             if isinstance(value, str):
                 resolved[key] = self._substitute_vars(value, context)
             elif isinstance(value, dict):
                 resolved[key] = self._resolve_args(value, context)
             elif isinstance(value, list):
-                resolved[key] = [
-                    self._substitute_vars(v, context) if isinstance(v, str) else v
-                    for v in value
-                ]
+                resolved[key] = [self._substitute_vars(v, context) if isinstance(v, str) else v for v in value]
             else:
                 resolved[key] = value
-        
+
         return resolved
-    
+
     def _substitute_vars(self, template: str, context: Dict[str, Any]) -> str:
         """替换字符串中的变量"""
+
         def replacer(match):
             var_path = match.group(1)
             return self._get_var_value(var_path, context)
-        
+
         return self.VAR_PATTERN.sub(replacer, template)
-    
+
     def _get_var_value(self, var_path: str, context: Dict[str, Any]) -> str:
         """获取变量值
-        
+
         Args:
             var_path: 变量路径，如 "input.query", "step.search_result", "prev", "prev.id"
             context: 上下文
         """
         parts = self._parse_var_path(var_path)
-        
+
         if not parts:
             return ""
-        
+
         # 获取根对象
         root = parts[0]
         if root not in context:
             logger.warning(f"变量 {var_path} 的根 '{root}' 不存在")
             return ""
-        
+
         value = context[root]
-        
+
         # 遍历路径
         for part in parts[1:]:
             if isinstance(value, str):
@@ -349,7 +352,7 @@ class ToolChainExecutor:
                     value = ""
             else:
                 value = ""
-        
+
         # 确保返回字符串
         if isinstance(value, (dict, list)):
             return json.dumps(value, ensure_ascii=False)
@@ -448,39 +451,39 @@ class ToolChainExecutor:
 
 class ToolChainManager:
     """工具链管理器"""
-    
+
     _instance: Optional["ToolChainManager"] = None
-    
+
     def __new__(cls):
         if cls._instance is None:
             cls._instance = super().__new__(cls)
             cls._instance._initialized = False
         return cls._instance
-    
+
     def __init__(self):
         if self._initialized:
             return
         self._initialized = True
         self._chains: Dict[str, ToolChainDefinition] = {}
         self._executor: Optional[ToolChainExecutor] = None
-    
+
     def set_executor(self, mcp_manager) -> None:
         """设置执行器"""
         self._executor = ToolChainExecutor(mcp_manager)
-    
+
     def add_chain(self, chain: ToolChainDefinition) -> bool:
         """添加工具链"""
         if not chain.name:
             logger.error("工具链名称不能为空")
             return False
-        
+
         if chain.name in self._chains:
             logger.warning(f"工具链 {chain.name} 已存在，将被覆盖")
-        
+
         self._chains[chain.name] = chain
         logger.info(f"已添加工具链: {chain.name} ({len(chain.steps)} 个步骤)")
         return True
-    
+
     def remove_chain(self, name: str) -> bool:
         """移除工具链"""
         if name in self._chains:
@@ -488,19 +491,19 @@ class ToolChainManager:
             logger.info(f"已移除工具链: {name}")
             return True
         return False
-    
+
     def get_chain(self, name: str) -> Optional[ToolChainDefinition]:
         """获取工具链"""
         return self._chains.get(name)
-    
+
     def get_all_chains(self) -> Dict[str, ToolChainDefinition]:
         """获取所有工具链"""
         return self._chains.copy()
-    
+
     def get_enabled_chains(self) -> Dict[str, ToolChainDefinition]:
         """获取所有启用的工具链"""
         return {name: chain for name, chain in self._chains.items() if chain.enabled}
-    
+
     async def execute_chain(
         self,
         chain_name: str,
@@ -514,64 +517,64 @@ class ToolChainManager:
                 final_output="",
                 error=f"工具链 {chain_name} 不存在",
             )
-        
+
         if not chain.enabled:
             return ChainExecutionResult(
                 success=False,
                 final_output="",
                 error=f"工具链 {chain_name} 已禁用",
             )
-        
+
         if not self._executor:
             return ChainExecutionResult(
                 success=False,
                 final_output="",
                 error="工具链执行器未初始化",
             )
-        
+
         return await self._executor.execute(chain, input_args)
-    
+
     def load_from_json(self, json_str: str) -> Tuple[int, List[str]]:
         """从 JSON 字符串加载工具链配置
-        
+
         Returns:
             (成功加载数量, 错误列表)
         """
         errors = []
         loaded = 0
-        
+
         try:
             data = json.loads(json_str) if json_str.strip() else []
         except json.JSONDecodeError as e:
             return 0, [f"JSON 解析失败: {e}"]
-        
+
         if not isinstance(data, list):
             data = [data]
-        
+
         for i, item in enumerate(data):
             try:
                 chain = ToolChainDefinition.from_dict(item)
                 if not chain.name:
-                    errors.append(f"第 {i+1} 个工具链缺少名称")
+                    errors.append(f"第 {i + 1} 个工具链缺少名称")
                     continue
                 if not chain.steps:
                     errors.append(f"工具链 {chain.name} 没有步骤")
                     continue
-                
+
                 self.add_chain(chain)
                 loaded += 1
             except Exception as e:
-                errors.append(f"第 {i+1} 个工具链解析失败: {e}")
-        
+                errors.append(f"第 {i + 1} 个工具链解析失败: {e}")
+
         return loaded, errors
-    
+
     def export_to_json(self, pretty: bool = True) -> str:
         """导出所有工具链为 JSON"""
         chains_data = [chain.to_dict() for chain in self._chains.values()]
         if pretty:
             return json.dumps(chains_data, ensure_ascii=False, indent=2)
         return json.dumps(chains_data, ensure_ascii=False)
-    
+
     def clear(self) -> None:
         """清空所有工具链"""
         self._chains.clear()

plugins/hello_world_plugin/plugin.py

@@ -238,7 +238,7 @@ class TestCommand(BaseCommand):
                 chat_stream=self.message.chat_stream,
                 reply_reason=reply_reason,
                 enable_chinese_typo=False,
-                extra_info=f"{reply_reason}用于测试bot的功能是否正常。请你按设定的人设表达一句\"测试正常\"",
+                extra_info=f'{reply_reason}用于测试bot的功能是否正常。请你按设定的人设表达一句"测试正常"',
             )
             if result_status:
                 # 发送生成的回复

pytests/config_test/test_config_base.py

@@ -46,6 +46,7 @@ def patch_attrdoc_post_init():
 
 config_base_module.logger = logging.getLogger("config_base_test_logger")
 
+
 class SimpleClass(ConfigBase):
     a: int = 1
     b: str = "test"
@@ -282,7 +283,7 @@ class TestConfigBase:
                 True,
                 "ConfigBase is not Hashable",
                 id="listset-validation-set-configbase-element_reject",
-            )
+            ),
         ],
     )
     def test_validate_list_set_type(self, annotation, expect_error, error_fragment):
@@ -340,7 +341,7 @@ class TestConfigBase:
                 False,
                 None,
                 id="dict-validation-happy-configbase-value",
-            )
+            ),
         ],
     )
     def test_validate_dict_type(self, annotation, expect_error, error_fragment):
@@ -353,13 +354,11 @@ class TestConfigBase:
         field_name = "mapping"
 
         if expect_error:
-
             # Act / Assert
             with pytest.raises(TypeError) as exc_info:
                 dummy._validate_dict_type(annotation, field_name)
             assert error_fragment in str(exc_info.value)
         else:
-
             # Act
             dummy._validate_dict_type(annotation, field_name)
 
@@ -392,7 +391,7 @@ class TestConfigBase:
 
         # Assert
         assert "字段'field_y'中使用了 Any 类型注解" in caplog.text
-    
+
     def test_discourage_any_usage_suppressed_warning(self, caplog):
         class Sample(ConfigBase):
             _validate_any: bool = False

pytests/image_sys_test/image_manager_test.py

@@ -4,7 +4,6 @@ import importlib
 import pytest
 from pathlib import Path
 import importlib.util
-import asyncio
 
 
 class DummyLogger:
@@ -71,6 +70,7 @@ class DummyLLMRequest:
     async def generate_response_for_image(self, prompt, image_base64, image_format, temp):
         return ("dummy description", {})
 
+
 class DummySelect:
     def __init__(self, *a, **k):
         pass
@@ -81,6 +81,7 @@ class DummySelect:
     def limit(self, n):
         return self
 
+
 @pytest.fixture(autouse=True)
 def patch_external_dependencies(monkeypatch):
     # Provide dummy implementations as modules so that importing image_manager is safe
@@ -103,11 +104,11 @@ def patch_external_dependencies(monkeypatch):
     # Patch MaiImage data model
     data_model_mod = types.SimpleNamespace(MaiImage=DummyMaiImage)
     monkeypatch.setitem(sys.modules, "src.common.data_models.image_data_model", data_model_mod)
- 
+
     # Patch SQLModel select function
     sql_mod = types.SimpleNamespace(select=lambda *a, **k: DummySelect())
     monkeypatch.setitem(sys.modules, "sqlmodel", sql_mod)
-    
+
     # Patch config values used at import-time
     cfg = types.SimpleNamespace(personality=types.SimpleNamespace(visual_style="test-style"))
     model_cfg = types.SimpleNamespace(model_task_config=types.SimpleNamespace(vlm="test-vlm"))
@@ -134,7 +135,7 @@ def _load_image_manager_module(tmp_path=None):
         if tmp_path is not None:
             tmpdir = Path(tmp_path)
             tmpdir.mkdir(parents=True, exist_ok=True)
-            setattr(mod, "IMAGE_DIR", tmpdir)
+            mod.IMAGE_DIR = tmpdir
     except Exception:
         pass
     return mod
@@ -197,4 +198,3 @@ async def test_save_image_and_process_and_cleanup(tmp_path):
 
     # cleanup should run without error
     mgr.cleanup_invalid_descriptions_in_db()
-

pytests/webui/test_config_schema.py

@@ -1,5 +1,3 @@
-import pytest
-
 from src.config.official_configs import ChatConfig
 from src.config.config import Config
 from src.webui.config_schema import ConfigSchemaGenerator

pytests/webui/test_emoji_routes.py

@@ -387,7 +387,7 @@ def test_auth_required_list(client):
     """测试未认证访问列表端点（401）"""
     # Without mock_token_verify fixture
     with patch("src.webui.routers.emoji.verify_auth_token", return_value=False):
-        response = client.get("/emoji/list")
+        client.get("/emoji/list")
         # verify_auth_token 返回 False 会触发 HTTPException
         # 但具体状态码取决于 verify_auth_token_from_cookie_or_header 的实现
         # 这里假设它抛出 401
@@ -397,7 +397,7 @@ def test_auth_required_update(client, sample_emojis):
     """测试未认证访问更新端点（401）"""
     with patch("src.webui.routers.emoji.verify_auth_token", return_value=False):
         emoji_id = sample_emojis[0].id
-        response = client.patch(f"/emoji/{emoji_id}", json={"description": "test"})
+        client.patch(f"/emoji/{emoji_id}", json={"description": "test"})
         # Should be unauthorized
 
 

pytests/webui/test_expression_routes.py

@@ -1,6 +1,5 @@
 """Expression routes pytest tests"""
 
-from datetime import datetime
 from typing import Generator
 from unittest.mock import MagicMock
 
@@ -12,7 +11,6 @@ from sqlalchemy import text
 from sqlmodel import Session, SQLModel, create_engine, select
 
 from src.common.database.database_model import Expression
-from src.common.database.database import get_db_session
 
 
 def create_test_app() -> FastAPI:

scripts/analyze_evaluation_stats.py

@@ -19,7 +19,7 @@ from typing import Dict, List, Set, Tuple
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 sys.path.insert(0, project_root)
 
-from src.common.logger import get_logger
+from src.common.logger import get_logger  # noqa: E402
 
 logger = get_logger("evaluation_stats_analyzer")
 
@@ -38,10 +38,10 @@ def parse_datetime(dt_str: str) -> datetime | None:
 def analyze_single_file(file_path: str) -> Dict:
     """
     分析单个JSON文件的统计信息
-    
+
     Args:
         file_path: JSON文件路径
-        
+
     Returns:
         统计信息字典
     """
@@ -65,40 +65,40 @@ def analyze_single_file(file_path: str) -> Dict:
         "has_reason": False,
         "reason_count": 0,
     }
-    
+
     try:
         with open(file_path, "r", encoding="utf-8") as f:
             data = json.load(f)
-        
+
         # 基本信息
         stats["last_updated"] = data.get("last_updated")
         stats["total_count"] = data.get("total_count", 0)
-        
+
         results = data.get("manual_results", [])
         stats["actual_count"] = len(results)
-        
+
         if not results:
             return stats
-        
+
         # 统计通过/不通过
         suitable_count = sum(1 for r in results if r.get("suitable") is True)
         unsuitable_count = sum(1 for r in results if r.get("suitable") is False)
         stats["suitable_count"] = suitable_count
         stats["unsuitable_count"] = unsuitable_count
         stats["suitable_rate"] = (suitable_count / len(results) * 100) if results else 0.0
-        
+
         # 统计唯一的(situation, style)对
         pairs: Set[Tuple[str, str]] = set()
         for r in results:
             if "situation" in r and "style" in r:
                 pairs.add((r["situation"], r["style"]))
         stats["unique_pairs"] = len(pairs)
-        
+
         # 统计评估者
         for r in results:
             evaluator = r.get("evaluator", "unknown")
             stats["evaluators"][evaluator] += 1
-        
+
         # 统计评估时间
         evaluation_dates = []
         for r in results:
@@ -107,7 +107,7 @@ def analyze_single_file(file_path: str) -> Dict:
                 dt = parse_datetime(evaluated_at)
                 if dt:
                     evaluation_dates.append(dt)
-        
+
         stats["evaluation_dates"] = evaluation_dates
         if evaluation_dates:
             min_date = min(evaluation_dates)
@@ -115,18 +115,18 @@ def analyze_single_file(file_path: str) -> Dict:
             stats["date_range"] = {
                 "start": min_date.isoformat(),
                 "end": max_date.isoformat(),
-                "duration_days": (max_date - min_date).days + 1
+                "duration_days": (max_date - min_date).days + 1,
             }
-        
+
         # 检查字段存在性
         stats["has_expression_id"] = any("expression_id" in r for r in results)
         stats["has_reason"] = any(r.get("reason") for r in results)
         stats["reason_count"] = sum(1 for r in results if r.get("reason"))
-        
+
     except Exception as e:
         stats["error"] = str(e)
         logger.error(f"分析文件 {file_name} 时出错: {e}")
-    
+
     return stats
 
 
@@ -136,57 +136,57 @@ def print_file_stats(stats: Dict, index: int = None):
     print(f"\n{'=' * 80}")
     print(f"{prefix}文件: {stats['file_name']}")
     print(f"{'=' * 80}")
-    
+
     if stats["error"]:
         print(f"✗ 错误: {stats['error']}")
         return
-    
+
     print(f"文件路径: {stats['file_path']}")
     print(f"文件大小: {stats['file_size']:,} 字节 ({stats['file_size'] / 1024:.2f} KB)")
-    
+
     if stats["last_updated"]:
         print(f"最后更新: {stats['last_updated']}")
-    
+
     print("\n【记录统计】")
     print(f"  文件中的 total_count: {stats['total_count']}")
     print(f"  实际记录数: {stats['actual_count']}")
-    
-    if stats['total_count'] != stats['actual_count']:
-        diff = stats['total_count'] - stats['actual_count']
+
+    if stats["total_count"] != stats["actual_count"]:
+        diff = stats["total_count"] - stats["actual_count"]
         print(f"  ⚠️  数量不一致，差值: {diff:+d}")
-    
+
     print("\n【评估结果统计】")
     print(f"  通过 (suitable=True): {stats['suitable_count']} 条 ({stats['suitable_rate']:.2f}%)")
     print(f"  不通过 (suitable=False): {stats['unsuitable_count']} 条 ({100 - stats['suitable_rate']:.2f}%)")
-    
+
     print("\n【唯一性统计】")
     print(f"  唯一 (situation, style) 对: {stats['unique_pairs']} 条")
-    if stats['actual_count'] > 0:
-        duplicate_count = stats['actual_count'] - stats['unique_pairs']
-        duplicate_rate = (duplicate_count / stats['actual_count'] * 100) if stats['actual_count'] > 0 else 0
+    if stats["actual_count"] > 0:
+        duplicate_count = stats["actual_count"] - stats["unique_pairs"]
+        duplicate_rate = (duplicate_count / stats["actual_count"] * 100) if stats["actual_count"] > 0 else 0
         print(f"  重复记录: {duplicate_count} 条 ({duplicate_rate:.2f}%)")
-    
+
     print("\n【评估者统计】")
-    if stats['evaluators']:
-        for evaluator, count in stats['evaluators'].most_common():
-            rate = (count / stats['actual_count'] * 100) if stats['actual_count'] > 0 else 0
+    if stats["evaluators"]:
+        for evaluator, count in stats["evaluators"].most_common():
+            rate = (count / stats["actual_count"] * 100) if stats["actual_count"] > 0 else 0
             print(f"  {evaluator}: {count} 条 ({rate:.2f}%)")
     else:
         print("  无评估者信息")
-    
+
     print("\n【时间统计】")
-    if stats['date_range']:
+    if stats["date_range"]:
         print(f"  最早评估时间: {stats['date_range']['start']}")
         print(f"  最晚评估时间: {stats['date_range']['end']}")
         print(f"  评估时间跨度: {stats['date_range']['duration_days']} 天")
     else:
         print("  无时间信息")
-    
+
     print("\n【字段统计】")
     print(f"  包含 expression_id: {'是' if stats['has_expression_id'] else '否'}")
     print(f"  包含 reason: {'是' if stats['has_reason'] else '否'}")
-    if stats['has_reason']:
-        rate = (stats['reason_count'] / stats['actual_count'] * 100) if stats['actual_count'] > 0 else 0
+    if stats["has_reason"]:
+        rate = (stats["reason_count"] / stats["actual_count"] * 100) if stats["actual_count"] > 0 else 0
         print(f"  有理由的记录: {stats['reason_count']} 条 ({rate:.2f}%)")
 
 
@@ -195,35 +195,35 @@ def print_summary(all_stats: List[Dict]):
     print(f"\n{'=' * 80}")
     print("汇总统计")
     print(f"{'=' * 80}")
-    
+
     total_files = len(all_stats)
     valid_files = [s for s in all_stats if not s.get("error")]
     error_files = [s for s in all_stats if s.get("error")]
-    
+
     print("\n【文件统计】")
     print(f"  总文件数: {total_files}")
     print(f"  成功解析: {len(valid_files)}")
     print(f"  解析失败: {len(error_files)}")
-    
+
     if error_files:
         print("\n  失败文件列表:")
         for stats in error_files:
             print(f"    - {stats['file_name']}: {stats['error']}")
-    
+
     if not valid_files:
         print("\n没有成功解析的文件")
         return
-    
+
     # 汇总记录统计
-    total_records = sum(s['actual_count'] for s in valid_files)
-    total_suitable = sum(s['suitable_count'] for s in valid_files)
-    total_unsuitable = sum(s['unsuitable_count'] for s in valid_files)
+    total_records = sum(s["actual_count"] for s in valid_files)
+    total_suitable = sum(s["suitable_count"] for s in valid_files)
+    total_unsuitable = sum(s["unsuitable_count"] for s in valid_files)
     total_unique_pairs = set()
-    
+
     # 收集所有唯一的(situation, style)对
     for stats in valid_files:
         try:
-            with open(stats['file_path'], "r", encoding="utf-8") as f:
+            with open(stats["file_path"], "r", encoding="utf-8") as f:
                 data = json.load(f)
                 results = data.get("manual_results", [])
                 for r in results:
@@ -231,23 +231,31 @@ def print_summary(all_stats: List[Dict]):
                         total_unique_pairs.add((r["situation"], r["style"]))
         except Exception:
             pass
-    
+
     print("\n【记录汇总】")
     print(f"  总记录数: {total_records:,} 条")
-    print(f"  通过: {total_suitable:,} 条 ({total_suitable / total_records * 100:.2f}%)" if total_records > 0 else "  通过: 0 条")
-    print(f"  不通过: {total_unsuitable:,} 条 ({total_unsuitable / total_records * 100:.2f}%)" if total_records > 0 else "  不通过: 0 条")
+    print(
+        f"  通过: {total_suitable:,} 条 ({total_suitable / total_records * 100:.2f}%)"
+        if total_records > 0
+        else "  通过: 0 条"
+    )
+    print(
+        f"  不通过: {total_unsuitable:,} 条 ({total_unsuitable / total_records * 100:.2f}%)"
+        if total_records > 0
+        else "  不通过: 0 条"
+    )
     print(f"  唯一 (situation, style) 对: {len(total_unique_pairs):,} 条")
-    
+
     if total_records > 0:
         duplicate_count = total_records - len(total_unique_pairs)
         duplicate_rate = (duplicate_count / total_records * 100) if total_records > 0 else 0
         print(f"  重复记录: {duplicate_count:,} 条 ({duplicate_rate:.2f}%)")
-    
+
     # 汇总评估者统计
     all_evaluators = Counter()
     for stats in valid_files:
-        all_evaluators.update(stats['evaluators'])
-    
+        all_evaluators.update(stats["evaluators"])
+
     print("\n【评估者汇总】")
     if all_evaluators:
         for evaluator, count in all_evaluators.most_common():
@@ -255,12 +263,12 @@ def print_summary(all_stats: List[Dict]):
             print(f"  {evaluator}: {count:,} 条 ({rate:.2f}%)")
     else:
         print("  无评估者信息")
-    
+
     # 汇总时间范围
     all_dates = []
     for stats in valid_files:
-        all_dates.extend(stats['evaluation_dates'])
-    
+        all_dates.extend(stats["evaluation_dates"])
+
     if all_dates:
         min_date = min(all_dates)
         max_date = max(all_dates)
@@ -268,9 +276,9 @@ def print_summary(all_stats: List[Dict]):
         print(f"  最早评估时间: {min_date.isoformat()}")
         print(f"  最晚评估时间: {max_date.isoformat()}")
         print(f"  总时间跨度: {(max_date - min_date).days + 1} 天")
-    
+
     # 文件大小汇总
-    total_size = sum(s['file_size'] for s in valid_files)
+    total_size = sum(s["file_size"] for s in valid_files)
     avg_size = total_size / len(valid_files) if valid_files else 0
     print("\n【文件大小汇总】")
     print(f"  总大小: {total_size:,} 字节 ({total_size / 1024 / 1024:.2f} MB)")
@@ -282,35 +290,35 @@ def main():
     logger.info("=" * 80)
     logger.info("开始分析评估结果统计信息")
     logger.info("=" * 80)
-    
+
     if not os.path.exists(TEMP_DIR):
         print(f"\n✗ 错误：未找到temp目录: {TEMP_DIR}")
         logger.error(f"未找到temp目录: {TEMP_DIR}")
         return
-    
+
     # 查找所有JSON文件
     json_files = glob.glob(os.path.join(TEMP_DIR, "*.json"))
-    
+
     if not json_files:
         print(f"\n✗ 错误：temp目录下未找到JSON文件: {TEMP_DIR}")
         logger.error(f"temp目录下未找到JSON文件: {TEMP_DIR}")
         return
-    
+
     json_files.sort()  # 按文件名排序
-    
+
     print(f"\n找到 {len(json_files)} 个JSON文件")
     print("=" * 80)
-    
+
     # 分析每个文件
     all_stats = []
     for i, json_file in enumerate(json_files, 1):
         stats = analyze_single_file(json_file)
         all_stats.append(stats)
         print_file_stats(stats, index=i)
-    
+
     # 打印汇总统计
     print_summary(all_stats)
-    
+
     print(f"\n{'=' * 80}")
     print("分析完成")
     print(f"{'=' * 80}")
@@ -318,5 +326,3 @@ def main():
 
 if __name__ == "__main__":
     main()
-
-

scripts/delete_lpmm_items.py

@@ -171,7 +171,9 @@ def main():
             sys.exit(1)
 
         if not args.raw_index:
-            logger.info(f"{raw_path} 共解析出 {len(paragraphs)} 个段落，请通过 --raw-index 指定要删除的段落，例如 --raw-index 1,3")
+            logger.info(
+                f"{raw_path} 共解析出 {len(paragraphs)} 个段落，请通过 --raw-index 指定要删除的段落，例如 --raw-index 1,3"
+            )
             sys.exit(1)
 
         # 解析索引列表（1-based）

scripts/evaluate_expressions_count_analysis.py

@@ -22,11 +22,11 @@ from collections import defaultdict
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 sys.path.insert(0, project_root)
 
-from src.common.database.database_model import Expression
-from src.common.database.database import db
-from src.common.logger import get_logger
-from src.llm_models.utils_model import LLMRequest
-from src.config.config import model_config
+from src.common.database.database_model import Expression  # noqa: E402
+from src.common.database.database import db  # noqa: E402
+from src.common.logger import get_logger  # noqa: E402
+from src.llm_models.utils_model import LLMRequest  # noqa: E402
+from src.config.config import model_config  # noqa: E402
 
 logger = get_logger("expression_evaluator_count_analysis_llm")
 
@@ -38,13 +38,13 @@ COUNT_ANALYSIS_FILE = os.path.join(TEMP_DIR, "count_analysis_evaluation_results.
 def load_existing_results() -> tuple[List[Dict], Set[Tuple[str, str]]]:
     """
     加载已有的评估结果
-    
+
     Returns:
         (已有结果列表, 已评估的项目(situation, style)元组集合)
     """
     if not os.path.exists(COUNT_ANALYSIS_FILE):
         return [], set()
-    
+
     try:
         with open(COUNT_ANALYSIS_FILE, "r", encoding="utf-8") as f:
             data = json.load(f)
@@ -61,22 +61,22 @@ def load_existing_results() -> tuple[List[Dict], Set[Tuple[str, str]]]:
 def save_results(evaluation_results: List[Dict]):
     """
     保存评估结果到文件
-    
+
     Args:
         evaluation_results: 评估结果列表
     """
     try:
         os.makedirs(TEMP_DIR, exist_ok=True)
-        
+
         data = {
             "last_updated": datetime.now().isoformat(),
             "total_count": len(evaluation_results),
-            "evaluation_results": evaluation_results
+            "evaluation_results": evaluation_results,
         }
-        
+
         with open(COUNT_ANALYSIS_FILE, "w", encoding="utf-8") as f:
             json.dump(data, f, ensure_ascii=False, indent=2)
-        
+
         logger.info(f"评估结果已保存到: {COUNT_ANALYSIS_FILE}")
         print(f"\n✓ 评估结果已保存（共 {len(evaluation_results)} 条）")
     except Exception as e:
@@ -84,70 +84,70 @@ def save_results(evaluation_results: List[Dict]):
         print(f"\n✗ 保存评估结果失败: {e}")
 
 
-def select_expressions_for_evaluation(
-    evaluated_pairs: Set[Tuple[str, str]] = None
-) -> List[Expression]:
+def select_expressions_for_evaluation(evaluated_pairs: Set[Tuple[str, str]] = None) -> List[Expression]:
     """
     选择用于评估的表达方式
     选择所有count>1的项目，然后选择两倍数量的count=1的项目
-    
+
     Args:
         evaluated_pairs: 已评估的项目集合，用于避免重复
-        
+
     Returns:
         选中的表达方式列表
     """
     if evaluated_pairs is None:
         evaluated_pairs = set()
-    
+
     try:
         # 查询所有表达方式
         all_expressions = list(Expression.select())
-        
+
         if not all_expressions:
             logger.warning("数据库中没有表达方式记录")
             return []
-        
+
         # 过滤出未评估的项目
-        unevaluated = [
-            expr for expr in all_expressions 
-            if (expr.situation, expr.style) not in evaluated_pairs
-        ]
-        
+        unevaluated = [expr for expr in all_expressions if (expr.situation, expr.style) not in evaluated_pairs]
+
         if not unevaluated:
             logger.warning("所有项目都已评估完成")
             return []
-        
+
         # 按count分组
         count_eq1 = [expr for expr in unevaluated if expr.count == 1]
         count_gt1 = [expr for expr in unevaluated if expr.count > 1]
-        
+
         logger.info(f"未评估项目中：count=1的有{len(count_eq1)}条，count>1的有{len(count_gt1)}条")
-        
+
         # 选择所有count>1的项目
         selected_count_gt1 = count_gt1.copy()
-        
+
         # 选择count=1的项目，数量为count>1数量的2倍
         count_gt1_count = len(selected_count_gt1)
         count_eq1_needed = count_gt1_count * 2
-        
+
         if len(count_eq1) < count_eq1_needed:
-            logger.warning(f"count=1的项目只有{len(count_eq1)}条，少于需要的{count_eq1_needed}条，将选择全部{len(count_eq1)}条")
+            logger.warning(
+                f"count=1的项目只有{len(count_eq1)}条，少于需要的{count_eq1_needed}条，将选择全部{len(count_eq1)}条"
+            )
             count_eq1_needed = len(count_eq1)
-        
+
         # 随机选择count=1的项目
         selected_count_eq1 = random.sample(count_eq1, count_eq1_needed) if count_eq1 and count_eq1_needed > 0 else []
-        
+
         selected = selected_count_gt1 + selected_count_eq1
         random.shuffle(selected)  # 打乱顺序
-        
-        logger.info(f"已选择{len(selected)}条表达方式：count>1的有{len(selected_count_gt1)}条（全部），count=1的有{len(selected_count_eq1)}条（2倍）")
-        
+
+        logger.info(
+            f"已选择{len(selected)}条表达方式：count>1的有{len(selected_count_gt1)}条（全部），count=1的有{len(selected_count_eq1)}条（2倍）"
+        )
+
         return selected
-        
+
     except Exception as e:
         logger.error(f"选择表达方式失败: {e}")
         import traceback
+
         logger.error(traceback.format_exc())
         return []
 
@@ -155,11 +155,11 @@ def select_expressions_for_evaluation(
 def create_evaluation_prompt(situation: str, style: str) -> str:
     """
     创建评估提示词
-    
+
     Args:
         situation: 情境
         style: 风格
-        
+
     Returns:
         评估提示词
     """
@@ -181,34 +181,32 @@ def create_evaluation_prompt(situation: str, style: str) -> str:
 }}
 如果合适，suitable设为true；如果不合适，suitable设为false，并在reason中说明原因。
 请严格按照JSON格式输出，不要包含其他内容。"""
-    
+
     return prompt
 
 
 async def _single_llm_evaluation(situation: str, style: str, llm: LLMRequest) -> tuple[bool, str, str | None]:
     """
     执行单次LLM评估
-    
+
     Args:
         situation: 情境
         style: 风格
         llm: LLM请求实例
-        
+
     Returns:
         (suitable, reason, error) 元组，如果出错则 suitable 为 False，error 包含错误信息
     """
     try:
         prompt = create_evaluation_prompt(situation, style)
         logger.debug(f"正在评估表达方式: situation={situation}, style={style}")
-        
+
         response, (reasoning, model_name, _) = await llm.generate_response_async(
-            prompt=prompt,
-            temperature=0.6,
-            max_tokens=1024
+            prompt=prompt, temperature=0.6, max_tokens=1024
         )
-        
+
         logger.debug(f"LLM响应: {response}")
-        
+
         # 解析JSON响应
         try:
             evaluation = json.loads(response)
@@ -218,13 +216,13 @@ async def _single_llm_evaluation(situation: str, style: str, llm: LLMRequest) ->
                 evaluation = json.loads(json_match.group())
             else:
                 raise ValueError("无法从响应中提取JSON格式的评估结果") from e
-        
+
         suitable = evaluation.get("suitable", False)
         reason = evaluation.get("reason", "未提供理由")
-        
+
         logger.debug(f"评估结果: {'通过' if suitable else '不通过'}")
         return suitable, reason, None
-            
+
     except Exception as e:
         logger.error(f"评估表达方式 (situation={situation}, style={style}) 时出错: {e}")
         return False, f"评估过程出错: {str(e)}", str(e)
@@ -233,23 +231,25 @@ async def _single_llm_evaluation(situation: str, style: str, llm: LLMRequest) ->
 async def llm_evaluate_expression(expression: Expression, llm: LLMRequest) -> Dict:
     """
     使用LLM评估单个表达方式
-    
+
     Args:
         expression: 表达方式对象
         llm: LLM请求实例
-        
+
     Returns:
         评估结果字典
     """
-    logger.info(f"开始评估表达方式: situation={expression.situation}, style={expression.style}, count={expression.count}")
-    
+    logger.info(
+        f"开始评估表达方式: situation={expression.situation}, style={expression.style}, count={expression.count}"
+    )
+
     suitable, reason, error = await _single_llm_evaluation(expression.situation, expression.style, llm)
-    
+
     if error:
         suitable = False
-    
+
     logger.info(f"评估完成: {'通过' if suitable else '不通过'}")
-    
+
     return {
         "situation": expression.situation,
         "style": expression.style,
@@ -258,28 +258,28 @@ async def llm_evaluate_expression(expression: Expression, llm: LLMRequest) -> Di
         "reason": reason,
         "error": error,
         "evaluator": "llm",
-        "evaluated_at": datetime.now().isoformat()
+        "evaluated_at": datetime.now().isoformat(),
     }
 
 
 def perform_statistical_analysis(evaluation_results: List[Dict]):
     """
     对评估结果进行统计分析
-    
+
     Args:
         evaluation_results: 评估结果列表
     """
     if not evaluation_results:
         print("\n没有评估结果可供分析")
         return
-    
+
     print("\n" + "=" * 60)
     print("统计分析结果")
     print("=" * 60)
-    
+
     # 按count分组统计
     count_groups = defaultdict(lambda: {"total": 0, "suitable": 0, "unsuitable": 0})
-    
+
     for result in evaluation_results:
         count = result.get("count", 1)
         suitable = result.get("suitable", False)
@@ -288,7 +288,7 @@ def perform_statistical_analysis(evaluation_results: List[Dict]):
             count_groups[count]["suitable"] += 1
         else:
             count_groups[count]["unsuitable"] += 1
-    
+
     # 显示每个count的统计
     print("\n【按count分组统计】")
     print("-" * 60)
@@ -298,21 +298,21 @@ def perform_statistical_analysis(evaluation_results: List[Dict]):
         suitable = group["suitable"]
         unsuitable = group["unsuitable"]
         pass_rate = (suitable / total * 100) if total > 0 else 0
-        
+
         print(f"Count = {count}:")
         print(f"  总数: {total}")
         print(f"  通过: {suitable} ({pass_rate:.2f}%)")
-        print(f"  不通过: {unsuitable} ({100-pass_rate:.2f}%)")
+        print(f"  不通过: {unsuitable} ({100 - pass_rate:.2f}%)")
         print()
-    
+
     # 比较count=1和count>1
     count_eq1_group = {"total": 0, "suitable": 0, "unsuitable": 0}
     count_gt1_group = {"total": 0, "suitable": 0, "unsuitable": 0}
-    
+
     for result in evaluation_results:
         count = result.get("count", 1)
         suitable = result.get("suitable", False)
-        
+
         if count == 1:
             count_eq1_group["total"] += 1
             if suitable:
@@ -325,34 +325,34 @@ def perform_statistical_analysis(evaluation_results: List[Dict]):
                 count_gt1_group["suitable"] += 1
             else:
                 count_gt1_group["unsuitable"] += 1
-    
+
     print("\n【Count=1 vs Count>1 对比】")
     print("-" * 60)
-    
+
     eq1_total = count_eq1_group["total"]
     eq1_suitable = count_eq1_group["suitable"]
     eq1_pass_rate = (eq1_suitable / eq1_total * 100) if eq1_total > 0 else 0
-    
+
     gt1_total = count_gt1_group["total"]
     gt1_suitable = count_gt1_group["suitable"]
     gt1_pass_rate = (gt1_suitable / gt1_total * 100) if gt1_total > 0 else 0
-    
+
     print("Count = 1:")
     print(f"  总数: {eq1_total}")
     print(f"  通过: {eq1_suitable} ({eq1_pass_rate:.2f}%)")
-    print(f"  不通过: {eq1_total - eq1_suitable} ({100-eq1_pass_rate:.2f}%)")
+    print(f"  不通过: {eq1_total - eq1_suitable} ({100 - eq1_pass_rate:.2f}%)")
     print()
     print("Count > 1:")
     print(f"  总数: {gt1_total}")
     print(f"  通过: {gt1_suitable} ({gt1_pass_rate:.2f}%)")
-    print(f"  不通过: {gt1_total - gt1_suitable} ({100-gt1_pass_rate:.2f}%)")
+    print(f"  不通过: {gt1_total - gt1_suitable} ({100 - gt1_pass_rate:.2f}%)")
     print()
-    
+
     # 进行卡方检验（简化版，使用2x2列联表）
     if eq1_total > 0 and gt1_total > 0:
         print("【统计显著性检验】")
         print("-" * 60)
-        
+
         # 构建2x2列联表
         #         通过  不通过
         # count=1  a     b
@@ -361,7 +361,7 @@ def perform_statistical_analysis(evaluation_results: List[Dict]):
         b = eq1_total - eq1_suitable
         c = gt1_suitable
         d = gt1_total - gt1_suitable
-        
+
         # 计算卡方统计量（简化版，使用Pearson卡方检验）
         n = eq1_total + gt1_total
         if n > 0:
@@ -370,13 +370,13 @@ def perform_statistical_analysis(evaluation_results: List[Dict]):
             e_b = (eq1_total * (b + d)) / n
             e_c = (gt1_total * (a + c)) / n
             e_d = (gt1_total * (b + d)) / n
-            
+
             # 检查期望频数是否足够大（卡方检验要求每个期望频数>=5）
             min_expected = min(e_a, e_b, e_c, e_d)
             if min_expected < 5:
                 print("警告：期望频数小于5，卡方检验可能不准确")
                 print("建议使用Fisher精确检验")
-            
+
             # 计算卡方值
             chi_square = 0
             if e_a > 0:
@@ -387,26 +387,26 @@ def perform_statistical_analysis(evaluation_results: List[Dict]):
                 chi_square += ((c - e_c) ** 2) / e_c
             if e_d > 0:
                 chi_square += ((d - e_d) ** 2) / e_d
-            
+
             # 自由度 = (行数-1) * (列数-1) = 1
             df = 1
-            
+
             # 临界值（α=0.05）
             chi_square_critical_005 = 3.841
             chi_square_critical_001 = 6.635
-            
+
             print(f"卡方统计量: {chi_square:.4f}")
             print(f"自由度: {df}")
             print(f"临界值 (α=0.05): {chi_square_critical_005}")
             print(f"临界值 (α=0.01): {chi_square_critical_001}")
-            
+
             if chi_square >= chi_square_critical_001:
                 print("结论: 在α=0.01水平下，count=1和count>1的合格率存在显著差异（p<0.01）")
             elif chi_square >= chi_square_critical_005:
                 print("结论: 在α=0.05水平下，count=1和count>1的合格率存在显著差异（p<0.05）")
             else:
                 print("结论: 在α=0.05水平下，count=1和count>1的合格率不存在显著差异（p≥0.05）")
-            
+
             # 计算差异大小
             diff = abs(eq1_pass_rate - gt1_pass_rate)
             print(f"\n合格率差异: {diff:.2f}%")
@@ -420,16 +420,16 @@ def perform_statistical_analysis(evaluation_results: List[Dict]):
             print("数据不足，无法进行统计检验")
     else:
         print("数据不足，无法进行count=1和count>1的对比分析")
-    
+
     # 保存统计分析结果
     analysis_result = {
         "analysis_time": datetime.now().isoformat(),
         "count_groups": {str(k): v for k, v in count_groups.items()},
         "count_eq1": count_eq1_group,
         "count_gt1": count_gt1_group,
-        "total_evaluated": len(evaluation_results)
+        "total_evaluated": len(evaluation_results),
     }
-    
+
     try:
         analysis_file = os.path.join(TEMP_DIR, "count_analysis_statistics.json")
         with open(analysis_file, "w", encoding="utf-8") as f:
@@ -444,7 +444,7 @@ async def main():
     logger.info("=" * 60)
     logger.info("开始表达方式按count分组的LLM评估和统计分析")
     logger.info("=" * 60)
-    
+
     # 初始化数据库连接
     try:
         db.connect(reuse_if_open=True)
@@ -452,97 +452,95 @@ async def main():
     except Exception as e:
         logger.error(f"数据库连接失败: {e}")
         return
-    
+
     # 加载已有评估结果
     existing_results, evaluated_pairs = load_existing_results()
     evaluation_results = existing_results.copy()
-    
+
     if evaluated_pairs:
         print(f"\n已加载 {len(existing_results)} 条已有评估结果")
         print(f"已评估项目数: {len(evaluated_pairs)}")
-    
+
     # 检查是否需要继续评估（检查是否还有未评估的count>1项目）
     # 先查询未评估的count>1项目数量
     try:
         all_expressions = list(Expression.select())
         unevaluated_count_gt1 = [
-            expr for expr in all_expressions 
-            if expr.count > 1 and (expr.situation, expr.style) not in evaluated_pairs
+            expr for expr in all_expressions if expr.count > 1 and (expr.situation, expr.style) not in evaluated_pairs
         ]
         has_unevaluated = len(unevaluated_count_gt1) > 0
     except Exception as e:
         logger.error(f"查询未评估项目失败: {e}")
         has_unevaluated = False
-    
+
     if has_unevaluated:
         print("\n" + "=" * 60)
         print("开始LLM评估")
         print("=" * 60)
         print("评估结果会自动保存到文件\n")
-        
+
         # 创建LLM实例
         print("创建LLM实例...")
         try:
             llm = LLMRequest(
                 model_set=model_config.model_task_config.tool_use,
-                request_type="expression_evaluator_count_analysis_llm"
+                request_type="expression_evaluator_count_analysis_llm",
             )
             print("✓ LLM实例创建成功\n")
         except Exception as e:
             logger.error(f"创建LLM实例失败: {e}")
             import traceback
+
             logger.error(traceback.format_exc())
             print(f"\n✗ 创建LLM实例失败: {e}")
             db.close()
             return
-        
+
         # 选择需要评估的表达方式（选择所有count>1的项目，然后选择两倍数量的count=1的项目）
-        expressions = select_expressions_for_evaluation(
-            evaluated_pairs=evaluated_pairs
-        )
-        
+        expressions = select_expressions_for_evaluation(evaluated_pairs=evaluated_pairs)
+
         if not expressions:
             print("\n没有可评估的项目")
         else:
             print(f"\n已选择 {len(expressions)} 条表达方式进行评估")
             print(f"其中 count>1 的有 {sum(1 for e in expressions if e.count > 1)} 条")
             print(f"其中 count=1 的有 {sum(1 for e in expressions if e.count == 1)} 条\n")
-            
+
             batch_results = []
             for i, expression in enumerate(expressions, 1):
                 print(f"LLM评估进度: {i}/{len(expressions)}")
                 print(f"  Situation: {expression.situation}")
                 print(f"  Style: {expression.style}")
                 print(f"  Count: {expression.count}")
-                
+
                 llm_result = await llm_evaluate_expression(expression, llm)
-                
+
                 print(f"  结果: {'通过' if llm_result['suitable'] else '不通过'}")
-                if llm_result.get('error'):
+                if llm_result.get("error"):
                     print(f"  错误: {llm_result['error']}")
                 print()
-                
+
                 batch_results.append(llm_result)
                 # 使用 (situation, style) 作为唯一标识
                 evaluated_pairs.add((llm_result["situation"], llm_result["style"]))
-                
+
                 # 添加延迟以避免API限流
                 await asyncio.sleep(0.3)
-            
+
             # 将当前批次结果添加到总结果中
             evaluation_results.extend(batch_results)
-            
+
             # 保存结果
             save_results(evaluation_results)
     else:
         print(f"\n所有count>1的项目都已评估完成，已有 {len(evaluation_results)} 条评估结果")
-    
+
     # 进行统计分析
     if len(evaluation_results) > 0:
         perform_statistical_analysis(evaluation_results)
     else:
         print("\n没有评估结果可供分析")
-    
+
     # 关闭数据库连接
     try:
         db.close()
@@ -553,4 +551,3 @@ async def main():
 
 if __name__ == "__main__":
     asyncio.run(main())
-

scripts/evaluate_expressions_llm_v6.py

@@ -20,9 +20,9 @@ from typing import List, Dict, Set, Tuple
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 sys.path.insert(0, project_root)
 
-from src.llm_models.utils_model import LLMRequest
-from src.config.config import model_config
-from src.common.logger import get_logger
+from src.llm_models.utils_model import LLMRequest  # noqa: E402
+from src.config.config import model_config  # noqa: E402
+from src.common.logger import get_logger  # noqa: E402
 
 logger = get_logger("expression_evaluator_llm")
 
@@ -33,7 +33,7 @@ TEMP_DIR = os.path.join(os.path.dirname(__file__), "temp")
 def load_manual_results() -> List[Dict]:
     """
     加载人工评估结果（自动读取temp目录下所有JSON文件并合并）
-    
+
     Returns:
         人工评估结果列表（已去重）
     """
@@ -42,62 +42,62 @@ def load_manual_results() -> List[Dict]:
         print("\n✗ 错误：未找到temp目录")
         print("   请先运行 evaluate_expressions_manual.py 进行人工评估")
         return []
-    
+
     # 查找所有JSON文件
     json_files = glob.glob(os.path.join(TEMP_DIR, "*.json"))
-    
+
     if not json_files:
         logger.error(f"temp目录下未找到JSON文件: {TEMP_DIR}")
         print("\n✗ 错误：temp目录下未找到JSON文件")
         print("   请先运行 evaluate_expressions_manual.py 进行人工评估")
         return []
-    
+
     logger.info(f"找到 {len(json_files)} 个JSON文件")
     print(f"\n找到 {len(json_files)} 个JSON文件:")
     for json_file in json_files:
         print(f"  - {os.path.basename(json_file)}")
-    
+
     # 读取并合并所有JSON文件
     all_results = []
     seen_pairs: Set[Tuple[str, str]] = set()  # 用于去重
-    
+
     for json_file in json_files:
         try:
             with open(json_file, "r", encoding="utf-8") as f:
                 data = json.load(f)
                 results = data.get("manual_results", [])
-                
+
                 # 去重：使用(situation, style)作为唯一标识
                 for result in results:
                     if "situation" not in result or "style" not in result:
                         logger.warning(f"跳过无效数据（缺少必要字段）: {result}")
                         continue
-                    
+
                     pair = (result["situation"], result["style"])
                     if pair not in seen_pairs:
                         seen_pairs.add(pair)
                         all_results.append(result)
-                
+
                 logger.info(f"从 {os.path.basename(json_file)} 加载了 {len(results)} 条结果")
         except Exception as e:
             logger.error(f"加载文件 {json_file} 失败: {e}")
             print(f"  警告：加载文件 {os.path.basename(json_file)} 失败: {e}")
             continue
-    
+
     logger.info(f"成功合并 {len(all_results)} 条人工评估结果（去重后）")
     print(f"\n✓ 成功合并 {len(all_results)} 条人工评估结果（已去重）")
-    
+
     return all_results
 
 
 def create_evaluation_prompt(situation: str, style: str) -> str:
     """
     创建评估提示词
-    
+
     Args:
         situation: 情境
         style: 风格
-        
+
     Returns:
         评估提示词
     """
@@ -119,51 +119,50 @@ def create_evaluation_prompt(situation: str, style: str) -> str:
 }}
 如果合适，suitable设为true；如果不合适，suitable设为false，并在reason中说明原因。
 请严格按照JSON格式输出，不要包含其他内容。"""
-    
+
     return prompt
 
 
 async def _single_llm_evaluation(situation: str, style: str, llm: LLMRequest) -> tuple[bool, str, str | None]:
     """
     执行单次LLM评估
-    
+
     Args:
         situation: 情境
         style: 风格
         llm: LLM请求实例
-        
+
     Returns:
         (suitable, reason, error) 元组，如果出错则 suitable 为 False，error 包含错误信息
     """
     try:
         prompt = create_evaluation_prompt(situation, style)
         logger.debug(f"正在评估表达方式: situation={situation}, style={style}")
-        
+
         response, (reasoning, model_name, _) = await llm.generate_response_async(
-            prompt=prompt,
-            temperature=0.6,
-            max_tokens=1024
+            prompt=prompt, temperature=0.6, max_tokens=1024
         )
-        
+
         logger.debug(f"LLM响应: {response}")
-        
+
         # 解析JSON响应
         try:
             evaluation = json.loads(response)
         except json.JSONDecodeError as e:
             import re
+
             json_match = re.search(r'\{[^{}]*"suitable"[^{}]*\}', response, re.DOTALL)
             if json_match:
                 evaluation = json.loads(json_match.group())
             else:
                 raise ValueError("无法从响应中提取JSON格式的评估结果") from e
-        
+
         suitable = evaluation.get("suitable", False)
         reason = evaluation.get("reason", "未提供理由")
-        
+
         logger.debug(f"评估结果: {'通过' if suitable else '不通过'}")
         return suitable, reason, None
-            
+
     except Exception as e:
         logger.error(f"评估表达方式 (situation={situation}, style={style}) 时出错: {e}")
         return False, f"评估过程出错: {str(e)}", str(e)
@@ -172,68 +171,68 @@ async def _single_llm_evaluation(situation: str, style: str, llm: LLMRequest) ->
 async def evaluate_expression_llm(situation: str, style: str, llm: LLMRequest) -> Dict:
     """
     使用LLM评估单个表达方式
-    
+
     Args:
         situation: 情境
         style: 风格
         llm: LLM请求实例
-        
+
     Returns:
         评估结果字典
     """
     logger.info(f"开始评估表达方式: situation={situation}, style={style}")
-    
+
     suitable, reason, error = await _single_llm_evaluation(situation, style, llm)
-    
+
     if error:
         suitable = False
-    
+
     logger.info(f"评估完成: {'通过' if suitable else '不通过'}")
-    
+
     return {
         "situation": situation,
         "style": style,
         "suitable": suitable,
         "reason": reason,
         "error": error,
-        "evaluator": "llm"
+        "evaluator": "llm",
     }
 
 
 def compare_evaluations(manual_results: List[Dict], llm_results: List[Dict], method_name: str) -> Dict:
     """
     对比人工评估和LLM评估的结果
-    
+
     Args:
         manual_results: 人工评估结果列表
         llm_results: LLM评估结果列表
         method_name: 评估方法名称（用于标识）
-        
+
     Returns:
         对比分析结果字典
     """
     # 按(situation, style)建立映射
     llm_dict = {(r["situation"], r["style"]): r for r in llm_results}
-    
+
     total = len(manual_results)
     matched = 0
     true_positives = 0
     true_negatives = 0
     false_positives = 0
     false_negatives = 0
-    
+
     for manual_result in manual_results:
         pair = (manual_result["situation"], manual_result["style"])
         llm_result = llm_dict.get(pair)
         if llm_result is None:
             continue
-        
+
         manual_suitable = manual_result["suitable"]
         llm_suitable = llm_result["suitable"]
-        
+
         if manual_suitable == llm_suitable:
             matched += 1
-        
+
         if manual_suitable and llm_suitable:
             true_positives += 1
         elif not manual_suitable and not llm_suitable:
@@ -242,30 +241,36 @@ def compare_evaluations(manual_results: List[Dict], llm_results: List[Dict], met
             false_positives += 1
         elif manual_suitable and not llm_suitable:
             false_negatives += 1
-    
+
     accuracy = (matched / total * 100) if total > 0 else 0
-    precision = (true_positives / (true_positives + false_positives) * 100) if (true_positives + false_positives) > 0 else 0
-    recall = (true_positives / (true_positives + false_negatives) * 100) if (true_positives + false_negatives) > 0 else 0
+    precision = (
+        (true_positives / (true_positives + false_positives) * 100) if (true_positives + false_positives) > 0 else 0
+    )
+    recall = (
+        (true_positives / (true_positives + false_negatives) * 100) if (true_positives + false_negatives) > 0 else 0
+    )
     f1_score = (2 * precision * recall / (precision + recall)) if (precision + recall) > 0 else 0
-    specificity = (true_negatives / (true_negatives + false_positives) * 100) if (true_negatives + false_positives) > 0 else 0
-    
+    specificity = (
+        (true_negatives / (true_negatives + false_positives) * 100) if (true_negatives + false_positives) > 0 else 0
+    )
+
     # 计算人工效标的不合适率
     manual_unsuitable_count = true_negatives + false_positives  # 人工评估不合适的总数
     manual_unsuitable_rate = (manual_unsuitable_count / total * 100) if total > 0 else 0
-    
+
     # 计算经过LLM删除后剩余项目中的不合适率
     # 在所有项目中，移除LLM判定为不合适的项目后，剩下的项目 = TP + FP（LLM判定为合适的项目）
     # 在这些剩下的项目中，按人工评定的不合适项目 = FP（人工认为不合适，但LLM认为合适）
     llm_kept_count = true_positives + false_positives  # LLM判定为合适的项目总数（保留的项目）
     llm_kept_unsuitable_rate = (false_positives / llm_kept_count * 100) if llm_kept_count > 0 else 0
-    
+
     # 两者百分比相减（评估LLM评定修正后的不合适率是否有降低）
     rate_difference = manual_unsuitable_rate - llm_kept_unsuitable_rate
-    
+
     random_baseline = 50.0
     accuracy_above_random = accuracy - random_baseline
     accuracy_improvement_ratio = (accuracy / random_baseline) if random_baseline > 0 else 0
-    
+
     return {
         "method": method_name,
         "total": total,
@@ -283,29 +288,29 @@ def compare_evaluations(manual_results: List[Dict], llm_results: List[Dict], met
         "specificity": specificity,
         "manual_unsuitable_rate": manual_unsuitable_rate,
         "llm_kept_unsuitable_rate": llm_kept_unsuitable_rate,
-        "rate_difference": rate_difference
+        "rate_difference": rate_difference,
     }
 
 
 async def main(count: int | None = None):
     """
     主函数
-    
+
     Args:
         count: 随机选取的数据条数，如果为None则使用全部数据
     """
     logger.info("=" * 60)
     logger.info("开始表达方式LLM评估")
     logger.info("=" * 60)
-    
+
     # 1. 加载人工评估结果
     print("\n步骤1: 加载人工评估结果")
     manual_results = load_manual_results()
     if not manual_results:
         return
-    
+
     print(f"成功加载 {len(manual_results)} 条人工评估结果")
-    
+
     # 如果指定了数量，随机选择指定数量的数据
     if count is not None:
         if count <= 0:
@@ -317,7 +322,7 @@ async def main(count: int | None = None):
             random.seed()  # 使用系统时间作为随机种子
             manual_results = random.sample(manual_results, count)
             print(f"随机选取 {len(manual_results)} 条数据进行评估")
-    
+
     # 验证数据完整性
     valid_manual_results = []
     for r in manual_results:
@@ -325,62 +330,58 @@ async def main(count: int | None = None):
             valid_manual_results.append(r)
         else:
             logger.warning(f"跳过无效数据: {r}")
-    
+
     if len(valid_manual_results) != len(manual_results):
         print(f"警告：{len(manual_results) - len(valid_manual_results)} 条数据缺少必要字段，已跳过")
-    
+
     print(f"有效数据: {len(valid_manual_results)} 条")
-    
+
     # 2. 创建LLM实例并评估
     print("\n步骤2: 创建LLM实例")
     try:
-        llm = LLMRequest(
-            model_set=model_config.model_task_config.tool_use,
-            request_type="expression_evaluator_llm"
-        )
+        llm = LLMRequest(model_set=model_config.model_task_config.tool_use, request_type="expression_evaluator_llm")
     except Exception as e:
         logger.error(f"创建LLM实例失败: {e}")
         import traceback
+
         logger.error(traceback.format_exc())
         return
-    
+
     print("\n步骤3: 开始LLM评估")
     llm_results = []
     for i, manual_result in enumerate(valid_manual_results, 1):
         print(f"LLM评估进度: {i}/{len(valid_manual_results)}")
-        llm_results.append(await evaluate_expression_llm(
-            manual_result["situation"],
-            manual_result["style"],
-            llm
-        ))
+        llm_results.append(await evaluate_expression_llm(manual_result["situation"], manual_result["style"], llm))
         await asyncio.sleep(0.3)
-    
+
     # 5. 输出FP和FN项目（在评估结果之前）
     llm_dict = {(r["situation"], r["style"]): r for r in llm_results}
-    
+
     # 5.1 输出FP项目（人工评估不通过但LLM误判为通过）
     print("\n" + "=" * 60)
     print("人工评估不通过但LLM误判为通过的项目（FP - False Positive）")
     print("=" * 60)
-    
+
     fp_items = []
     for manual_result in valid_manual_results:
         pair = (manual_result["situation"], manual_result["style"])
         llm_result = llm_dict.get(pair)
         if llm_result is None:
             continue
-        
+
         # 人工评估不通过，但LLM评估通过（FP情况）
         if not manual_result["suitable"] and llm_result["suitable"]:
-            fp_items.append({
-                "situation": manual_result["situation"],
-                "style": manual_result["style"],
-                "manual_suitable": manual_result["suitable"],
-                "llm_suitable": llm_result["suitable"],
-                "llm_reason": llm_result.get("reason", "未提供理由"),
-                "llm_error": llm_result.get("error")
-            })
-    
+            fp_items.append(
+                {
+                    "situation": manual_result["situation"],
+                    "style": manual_result["style"],
+                    "manual_suitable": manual_result["suitable"],
+                    "llm_suitable": llm_result["suitable"],
+                    "llm_reason": llm_result.get("reason", "未提供理由"),
+                    "llm_error": llm_result.get("error"),
+                }
+            )
+
     if fp_items:
         print(f"\n共找到 {len(fp_items)} 条误判项目：\n")
         for idx, item in enumerate(fp_items, 1):
@@ -389,36 +390,38 @@ async def main(count: int | None = None):
             print(f"Style: {item['style']}")
             print("人工评估: 不通过 ❌")
             print("LLM评估: 通过 ✅ (误判)")
-            if item.get('llm_error'):
+            if item.get("llm_error"):
                 print(f"LLM错误: {item['llm_error']}")
             print(f"LLM理由: {item['llm_reason']}")
             print()
     else:
         print("\n✓ 没有误判项目（所有人工评估不通过的项目都被LLM正确识别为不通过）")
-    
+
     # 5.2 输出FN项目（人工评估通过但LLM误判为不通过）
     print("\n" + "=" * 60)
     print("人工评估通过但LLM误判为不通过的项目（FN - False Negative）")
     print("=" * 60)
-    
+
     fn_items = []
     for manual_result in valid_manual_results:
         pair = (manual_result["situation"], manual_result["style"])
         llm_result = llm_dict.get(pair)
         if llm_result is None:
             continue
-        
+
         # 人工评估通过，但LLM评估不通过（FN情况）
         if manual_result["suitable"] and not llm_result["suitable"]:
-            fn_items.append({
-                "situation": manual_result["situation"],
-                "style": manual_result["style"],
-                "manual_suitable": manual_result["suitable"],
-                "llm_suitable": llm_result["suitable"],
-                "llm_reason": llm_result.get("reason", "未提供理由"),
-                "llm_error": llm_result.get("error")
-            })
-    
+            fn_items.append(
+                {
+                    "situation": manual_result["situation"],
+                    "style": manual_result["style"],
+                    "manual_suitable": manual_result["suitable"],
+                    "llm_suitable": llm_result["suitable"],
+                    "llm_reason": llm_result.get("reason", "未提供理由"),
+                    "llm_error": llm_result.get("error"),
+                }
+            )
+
     if fn_items:
         print(f"\n共找到 {len(fn_items)} 条误删项目：\n")
         for idx, item in enumerate(fn_items, 1):
@@ -427,33 +430,41 @@ async def main(count: int | None = None):
             print(f"Style: {item['style']}")
             print("人工评估: 通过 ✅")
             print("LLM评估: 不通过 ❌ (误删)")
-            if item.get('llm_error'):
+            if item.get("llm_error"):
                 print(f"LLM错误: {item['llm_error']}")
             print(f"LLM理由: {item['llm_reason']}")
             print()
     else:
         print("\n✓ 没有误删项目（所有人工评估通过的项目都被LLM正确识别为通过）")
-    
+
     # 6. 对比分析并输出结果
     comparison = compare_evaluations(valid_manual_results, llm_results, "LLM评估")
-    
+
     print("\n" + "=" * 60)
     print("评估结果（以人工评估为标准）")
     print("=" * 60)
-    
+
     # 详细评估结果（核心指标优先）
     print(f"\n--- {comparison['method']} ---")
     print(f"  总数: {comparison['total']} 条")
     print()
     # print("  【核心能力指标】")
     print(f"  特定负类召回率: {comparison['specificity']:.2f}% (将不合适项目正确提取出来的能力)")
-    print(f"     - 计算: TN / (TN + FP) = {comparison['true_negatives']} / ({comparison['true_negatives']} + {comparison['false_positives']})")
-    print(f"     - 含义: 在 {comparison['true_negatives'] + comparison['false_positives']} 个实际不合适的项目中，正确识别出 {comparison['true_negatives']} 个")
+    print(
+        f"     - 计算: TN / (TN + FP) = {comparison['true_negatives']} / ({comparison['true_negatives']} + {comparison['false_positives']})"
+    )
+    print(
+        f"     - 含义: 在 {comparison['true_negatives'] + comparison['false_positives']} 个实际不合适的项目中，正确识别出 {comparison['true_negatives']} 个"
+    )
     # print(f"     - 随机水平: 50.00% (当前高于随机: {comparison['specificity'] - 50.0:+.2f}%)")
     print()
     print(f"  召回率: {comparison['recall']:.2f}% (尽可能少的误删合适项目的能力)")
-    print(f"     - 计算: TP / (TP + FN) = {comparison['true_positives']} / ({comparison['true_positives']} + {comparison['false_negatives']})")
-    print(f"     - 含义: 在 {comparison['true_positives'] + comparison['false_negatives']} 个实际合适的项目中，正确识别出 {comparison['true_positives']} 个")
+    print(
+        f"     - 计算: TP / (TP + FN) = {comparison['true_positives']} / ({comparison['true_positives']} + {comparison['false_negatives']})"
+    )
+    print(
+        f"     - 含义: 在 {comparison['true_positives'] + comparison['false_negatives']} 个实际合适的项目中，正确识别出 {comparison['true_positives']} 个"
+    )
     # print(f"     - 随机水平: 50.00% (当前高于随机: {comparison['recall'] - 50.0:+.2f}%)")
     print()
     print("  【其他指标】")
@@ -464,12 +475,18 @@ async def main(count: int | None = None):
     print()
     print("  【不合适率分析】")
     print(f"  人工效标的不合适率: {comparison['manual_unsuitable_rate']:.2f}%")
-    print(f"     - 计算: (TN + FP) / 总数 = ({comparison['true_negatives']} + {comparison['false_positives']}) / {comparison['total']}")
+    print(
+        f"     - 计算: (TN + FP) / 总数 = ({comparison['true_negatives']} + {comparison['false_positives']}) / {comparison['total']}"
+    )
     print(f"     - 含义: 在人工评估中，有 {comparison['manual_unsuitable_rate']:.2f}% 的项目被判定为不合适")
     print()
     print(f"  经过LLM删除后剩余项目中的不合适率: {comparison['llm_kept_unsuitable_rate']:.2f}%")
-    print(f"     - 计算: FP / (TP + FP) = {comparison['false_positives']} / ({comparison['true_positives']} + {comparison['false_positives']})")
-    print(f"     - 含义: 在所有项目中，移除LLM判定为不合适的项目后，在剩下的 {comparison['true_positives'] + comparison['false_positives']} 个项目中，人工认为不合适的项目占 {comparison['llm_kept_unsuitable_rate']:.2f}%")
+    print(
+        f"     - 计算: FP / (TP + FP) = {comparison['false_positives']} / ({comparison['true_positives']} + {comparison['false_positives']})"
+    )
+    print(
+        f"     - 含义: 在所有项目中，移除LLM判定为不合适的项目后，在剩下的 {comparison['true_positives'] + comparison['false_positives']} 个项目中，人工认为不合适的项目占 {comparison['llm_kept_unsuitable_rate']:.2f}%"
+    )
     print()
     # print(f"  两者百分比差值: {comparison['rate_difference']:+.2f}%")
     # print(f"     - 计算: 人工效标不合适率 - LLM删除后剩余项目不合适率 = {comparison['manual_unsuitable_rate']:.2f}% - {comparison['llm_kept_unsuitable_rate']:.2f}%")
@@ -480,21 +497,22 @@ async def main(count: int | None = None):
     print(f"  TN (正确识别为不合适): {comparison['true_negatives']} ⭐")
     print(f"  FP (误判为合适): {comparison['false_positives']} ⚠️")
     print(f"  FN (误删合适项目): {comparison['false_negatives']} ⚠️")
-    
+
     # 7. 保存结果到JSON文件
     output_file = os.path.join(project_root, "data", "expression_evaluation_llm.json")
     try:
         os.makedirs(os.path.dirname(output_file), exist_ok=True)
         with open(output_file, "w", encoding="utf-8") as f:
-            json.dump({
-                "manual_results": valid_manual_results,
-                "llm_results": llm_results,
-                "comparison": comparison
-            }, f, ensure_ascii=False, indent=2)
+            json.dump(
+                {"manual_results": valid_manual_results, "llm_results": llm_results, "comparison": comparison},
+                f,
+                ensure_ascii=False,
+                indent=2,
+            )
         logger.info(f"\n评估结果已保存到: {output_file}")
     except Exception as e:
         logger.warning(f"保存结果到文件失败: {e}")
-    
+
     print("\n" + "=" * 60)
     print("评估完成")
     print("=" * 60)
@@ -509,15 +527,9 @@ if __name__ == "__main__":
   python evaluate_expressions_llm_v6.py              # 使用全部数据
   python evaluate_expressions_llm_v6.py -n 50       # 随机选取50条数据
   python evaluate_expressions_llm_v6.py --count 100 # 随机选取100条数据
-        """
+        """,
     )
-    parser.add_argument(
-        "-n", "--count",
-        type=int,
-        default=None,
-        help="随机选取的数据条数（默认：使用全部数据）"
-    )
-    
+    parser.add_argument("-n", "--count", type=int, default=None, help="随机选取的数据条数（默认：使用全部数据）")
+
     args = parser.parse_args()
     asyncio.run(main(count=args.count))
-

scripts/evaluate_expressions_manual.py

@@ -18,9 +18,9 @@ from datetime import datetime
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 sys.path.insert(0, project_root)
 
-from src.common.database.database_model import Expression
-from src.common.database.database import db
-from src.common.logger import get_logger
+from src.common.database.database_model import Expression  # noqa: E402
+from src.common.database.database import db  # noqa: E402
+from src.common.logger import get_logger  # noqa: E402
 
 logger = get_logger("expression_evaluator_manual")
 
@@ -32,13 +32,13 @@ MANUAL_EVAL_FILE = os.path.join(TEMP_DIR, "manual_evaluation_results.json")
 def load_existing_results() -> tuple[List[Dict], Set[Tuple[str, str]]]:
     """
     加载已有的评估结果
-    
+
     Returns:
         (已有结果列表, 已评估的项目(situation, style)元组集合)
     """
     if not os.path.exists(MANUAL_EVAL_FILE):
         return [], set()
-    
+
     try:
         with open(MANUAL_EVAL_FILE, "r", encoding="utf-8") as f:
             data = json.load(f)
@@ -55,22 +55,22 @@ def load_existing_results() -> tuple[List[Dict], Set[Tuple[str, str]]]:
 def save_results(manual_results: List[Dict]):
     """
     保存评估结果到文件
-    
+
     Args:
         manual_results: 评估结果列表
     """
     try:
         os.makedirs(TEMP_DIR, exist_ok=True)
-        
+
         data = {
             "last_updated": datetime.now().isoformat(),
             "total_count": len(manual_results),
-            "manual_results": manual_results
+            "manual_results": manual_results,
         }
-        
+
         with open(MANUAL_EVAL_FILE, "w", encoding="utf-8") as f:
             json.dump(data, f, ensure_ascii=False, indent=2)
-        
+
         logger.info(f"评估结果已保存到: {MANUAL_EVAL_FILE}")
         print(f"\n✓ 评估结果已保存（共 {len(manual_results)} 条）")
     except Exception as e:
@@ -81,45 +81,43 @@ def save_results(manual_results: List[Dict]):
 def get_unevaluated_expressions(evaluated_pairs: Set[Tuple[str, str]], batch_size: int = 10) -> List[Expression]:
     """
     获取未评估的表达方式
-    
+
     Args:
         evaluated_pairs: 已评估的项目(situation, style)元组集合
         batch_size: 每次获取的数量
-        
+
     Returns:
         未评估的表达方式列表
     """
     try:
         # 查询所有表达方式
         all_expressions = list(Expression.select())
-        
+
         if not all_expressions:
             logger.warning("数据库中没有表达方式记录")
             return []
-        
+
         # 过滤出未评估的项目：匹配 situation 和 style 均一致
-        unevaluated = [
-            expr for expr in all_expressions 
-            if (expr.situation, expr.style) not in evaluated_pairs
-        ]
-        
+        unevaluated = [expr for expr in all_expressions if (expr.situation, expr.style) not in evaluated_pairs]
+
         if not unevaluated:
             logger.info("所有项目都已评估完成")
             return []
-        
+
         # 如果未评估数量少于请求数量，返回所有
         if len(unevaluated) <= batch_size:
             logger.info(f"剩余 {len(unevaluated)} 条未评估项目，全部返回")
             return unevaluated
-        
+
         # 随机选择指定数量
         selected = random.sample(unevaluated, batch_size)
         logger.info(f"从 {len(unevaluated)} 条未评估项目中随机选择了 {len(selected)} 条")
         return selected
-        
+
     except Exception as e:
         logger.error(f"获取未评估表达方式失败: {e}")
         import traceback
+
         logger.error(traceback.format_exc())
         return []
 
@@ -127,12 +125,12 @@ def get_unevaluated_expressions(evaluated_pairs: Set[Tuple[str, str]], batch_siz
 def manual_evaluate_expression(expression: Expression, index: int, total: int) -> Dict:
     """
     人工评估单个表达方式
-    
+
     Args:
         expression: 表达方式对象
         index: 当前索引（从1开始）
         total: 总数
-        
+
     Returns:
         评估结果字典，如果用户退出则返回 None
     """
@@ -146,38 +144,38 @@ def manual_evaluate_expression(expression: Expression, index: int, total: int) -
     print("  输入 'n' 或 'no' 或 '0' 表示不合适（不通过）")
     print("  输入 'q' 或 'quit' 退出评估")
     print("  输入 's' 或 'skip' 跳过当前项目")
-    
+
     while True:
         user_input = input("\n您的评估 (y/n/q/s): ").strip().lower()
-        
-        if user_input in ['q', 'quit']:
+
+        if user_input in ["q", "quit"]:
             print("退出评估")
             return None
-        
-        if user_input in ['s', 'skip']:
+
+        if user_input in ["s", "skip"]:
             print("跳过当前项目")
             return "skip"
-        
-        if user_input in ['y', 'yes', '1', '是', '通过']:
+
+        if user_input in ["y", "yes", "1", "是", "通过"]:
             suitable = True
             break
-        elif user_input in ['n', 'no', '0', '否', '不通过']:
+        elif user_input in ["n", "no", "0", "否", "不通过"]:
             suitable = False
             break
         else:
             print("输入无效，请重新输入 (y/n/q/s)")
-    
+
     result = {
         "situation": expression.situation,
         "style": expression.style,
         "suitable": suitable,
         "reason": None,
         "evaluator": "manual",
-        "evaluated_at": datetime.now().isoformat()
+        "evaluated_at": datetime.now().isoformat(),
     }
-    
+
     print(f"\n✓ 已记录：{'通过' if suitable else '不通过'}")
-    
+
     return result
 
 
@@ -186,7 +184,7 @@ def main():
     logger.info("=" * 60)
     logger.info("开始表达方式人工评估")
     logger.info("=" * 60)
-    
+
     # 初始化数据库连接
     try:
         db.connect(reuse_if_open=True)
@@ -194,41 +192,41 @@ def main():
     except Exception as e:
         logger.error(f"数据库连接失败: {e}")
         return
-    
+
     # 加载已有评估结果
     existing_results, evaluated_pairs = load_existing_results()
     manual_results = existing_results.copy()
-    
+
     if evaluated_pairs:
         print(f"\n已加载 {len(existing_results)} 条已有评估结果")
         print(f"已评估项目数: {len(evaluated_pairs)}")
-    
+
     print("\n" + "=" * 60)
     print("开始人工评估")
     print("=" * 60)
     print("提示：可以随时输入 'q' 退出，输入 's' 跳过当前项目")
     print("评估结果会自动保存到文件\n")
-    
+
     batch_size = 10
     batch_count = 0
-    
+
     while True:
         # 获取未评估的项目
         expressions = get_unevaluated_expressions(evaluated_pairs, batch_size)
-        
+
         if not expressions:
             print("\n" + "=" * 60)
             print("所有项目都已评估完成！")
             print("=" * 60)
             break
-        
+
         batch_count += 1
         print(f"\n--- 批次 {batch_count}：评估 {len(expressions)} 条项目 ---")
-        
+
         batch_results = []
         for i, expression in enumerate(expressions, 1):
             manual_result = manual_evaluate_expression(expression, i, len(expressions))
-            
+
             if manual_result is None:
                 # 用户退出
                 print("\n评估已中断")
@@ -237,34 +235,34 @@ def main():
                     manual_results.extend(batch_results)
                     save_results(manual_results)
                 return
-            
+
             if manual_result == "skip":
                 # 跳过当前项目
                 continue
-            
+
             batch_results.append(manual_result)
             # 使用 (situation, style) 作为唯一标识
             evaluated_pairs.add((manual_result["situation"], manual_result["style"]))
-        
+
         # 将当前批次结果添加到总结果中
         manual_results.extend(batch_results)
-        
+
         # 保存结果
         save_results(manual_results)
-        
+
         print(f"\n当前批次完成，已评估总数: {len(manual_results)} 条")
-        
+
         # 询问是否继续
         while True:
             continue_input = input("\n是否继续评估下一批？(y/n): ").strip().lower()
-            if continue_input in ['y', 'yes', '1', '是', '继续']:
+            if continue_input in ["y", "yes", "1", "是", "继续"]:
                 break
-            elif continue_input in ['n', 'no', '0', '否', '退出']:
+            elif continue_input in ["n", "no", "0", "否", "退出"]:
                 print("\n评估结束")
                 return
             else:
                 print("输入无效，请重新输入 (y/n)")
-    
+
     # 关闭数据库连接
     try:
         db.close()
@@ -275,4 +273,3 @@ def main():
 
 if __name__ == "__main__":
     main()
-

scripts/import_openie.py

@@ -134,9 +134,7 @@ def handle_import_openie(
         # 在非交互模式下，不再询问用户，而是直接报错终止
         logger.info(f"\n检测到非法文段，共{len(missing_idxs)}条。")
         if non_interactive:
-            logger.error(
-                "检测到非法文段且当前处于非交互模式，无法询问是否删除非法文段，导入终止。"
-            )
+            logger.error("检测到非法文段且当前处于非交互模式，无法询问是否删除非法文段，导入终止。")
             sys.exit(1)
         logger.info("\n是否删除所有非法文段后继续导入？(y/n): ", end="")
         user_choice = input().strip().lower()
@@ -189,9 +187,7 @@ def handle_import_openie(
 async def main_async(non_interactive: bool = False) -> bool:  # sourcery skip: dict-comprehension
     # 新增确认提示
     if non_interactive:
-        logger.warning(
-            "当前处于非交互模式，将跳过导入开销确认提示，直接开始执行 OpenIE 导入。"
-        )
+        logger.warning("当前处于非交互模式，将跳过导入开销确认提示，直接开始执行 OpenIE 导入。")
     else:
         print("=== 重要操作确认 ===")
         print("OpenIE导入时会大量发送请求，可能会撞到请求速度上限，请注意选用的模型")
@@ -261,10 +257,7 @@ async def main_async(non_interactive: bool = False) -> bool:  # sourcery skip: d
 def main(argv: Optional[list[str]] = None) -> None:
     """主函数 - 解析参数并运行异步主流程。"""
     parser = argparse.ArgumentParser(
-        description=(
-            "OpenIE 导入脚本：读取 data/openie 中的 OpenIE JSON 批次，"
-            "将其导入到 LPMM 的向量库与知识图中。"
-        )
+        description=("OpenIE 导入脚本：读取 data/openie 中的 OpenIE JSON 批次，将其导入到 LPMM 的向量库与知识图中。")
     )
     parser.add_argument(
         "--non-interactive",

scripts/info_extraction.py

@@ -123,9 +123,7 @@ def _run(non_interactive: bool = False) -> None:  # sourcery skip: comprehension
     ensure_dirs()  # 确保目录存在
     # 新增用户确认提示
     if non_interactive:
-        logger.warning(
-            "当前处于非交互模式，将跳过费用与时长确认提示，直接开始进行实体提取操作。"
-        )
+        logger.warning("当前处于非交互模式，将跳过费用与时长确认提示，直接开始进行实体提取操作。")
     else:
         print("=== 重要操作确认，请认真阅读以下内容哦 ===")
         print("实体提取操作将会花费较多api余额和时间，建议在空闲时段执行。")

scripts/inspect_lpmm_global.py

@@ -1,6 +1,5 @@
 import os
 import sys
-from typing import Set
 
 # 保证可以导入 src.*
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
@@ -32,7 +31,6 @@ def main() -> None:
     # KG 统计
     nodes = kg.graph.get_node_list()
     edges = kg.graph.get_edge_list()
-    node_set: Set[str] = set(nodes)
 
     para_nodes = [n for n in nodes if n.startswith("paragraph-")]
     ent_nodes = [n for n in nodes if n.startswith("entity-")]
@@ -68,4 +66,3 @@ def main() -> None:
 
 if __name__ == "__main__":
     main()
-

scripts/lpmm_interactive_manager.py

@@ -29,6 +29,7 @@ except ImportError as e:
 
 logger = get_logger("lpmm_interactive_manager")
 
+
 async def interactive_add():
     """交互式导入知识"""
     print("\n" + "=" * 40)
@@ -38,7 +39,7 @@ async def interactive_add():
     print("      - 支持多段落，段落间请保留空行。")
     print("      - 输入完成后，在新起的一行输入 'EOF' 并回车结束输入。")
     print("-" * 40)
-    
+
     lines = []
     while True:
         try:
@@ -48,7 +49,7 @@ async def interactive_add():
             lines.append(line)
         except EOFError:
             break
-            
+
     text = "\n".join(lines).strip()
     if not text:
         print("\n[!] 内容为空，操作已取消。")
@@ -58,7 +59,7 @@ async def interactive_add():
     try:
         # 使用 lpmm_ops.py 中的接口
         result = await lpmm_ops.add_content(text)
-        
+
         if result["status"] == "success":
             print(f"\n[√] 成功：{result['message']}")
             print(f"    实际新增段落数: {result.get('count', 0)}")
@@ -68,6 +69,7 @@ async def interactive_add():
         print(f"\n[×] 发生异常: {e}")
         logger.error(f"add_content 异常: {e}", exc_info=True)
 
+
 async def interactive_delete():
     """交互式删除知识"""
     print("\n" + "=" * 40)
@@ -77,10 +79,10 @@ async def interactive_delete():
     print("  1. 关键词模糊匹配（删除包含关键词的所有段落）")
     print("  2. 完整文段匹配（删除完全匹配的段落）")
     print("-" * 40)
-    
+
     mode = input("请选择删除模式 (1/2): ").strip()
     exact_match = False
-    
+
     if mode == "2":
         exact_match = True
         print("\n[完整文段匹配模式]")
@@ -102,14 +104,18 @@ async def interactive_delete():
             print("\n[!] 无效选择，默认使用关键词模糊匹配模式。")
         print("\n[关键词模糊匹配模式]")
         keyword = input("请输入匹配关键词: ").strip()
-    
+
     if not keyword:
         print("\n[!] 输入为空，操作已取消。")
         return
-        
+
     print("-" * 40)
-    confirm = input(f"危险确认：确定要删除所有匹配 '{keyword[:50]}{'...' if len(keyword) > 50 else ''}' 的知识吗？(y/N): ").strip().lower()
-    if confirm != 'y':
+    confirm = (
+        input(f"危险确认：确定要删除所有匹配 '{keyword[:50]}{'...' if len(keyword) > 50 else ''}' 的知识吗？(y/N): ")
+        .strip()
+        .lower()
+    )
+    if confirm != "y":
         print("\n[!] 已取消删除操作。")
         return
 
@@ -117,7 +123,7 @@ async def interactive_delete():
     try:
         # 使用 lpmm_ops.py 中的接口
         result = await lpmm_ops.delete(keyword, exact_match=exact_match)
-        
+
         if result["status"] == "success":
             print(f"\n[√] 成功：{result['message']}")
             print(f"    删除条数: {result.get('deleted_count', 0)}")
@@ -129,6 +135,7 @@ async def interactive_delete():
         print(f"\n[×] 发生异常: {e}")
         logger.error(f"delete 异常: {e}", exc_info=True)
 
+
 async def interactive_clear():
     """交互式清空知识库"""
     print("\n" + "=" * 40)
@@ -141,40 +148,45 @@ async def interactive_clear():
     print("      - 整个知识图谱")
     print("      - 此操作不可恢复！")
     print("-" * 40)
-    
+
     # 双重确认
     confirm1 = input("⚠️  第一次确认：确定要清空整个知识库吗？(输入 'YES' 继续): ").strip()
     if confirm1 != "YES":
         print("\n[!] 已取消清空操作。")
         return
-    
+
     print("\n" + "=" * 40)
     confirm2 = input("⚠️  第二次确认：此操作不可恢复，请再次输入 'CLEAR' 确认: ").strip()
     if confirm2 != "CLEAR":
         print("\n[!] 已取消清空操作。")
         return
-    
+
     print("\n[进度] 正在清空知识库...")
     try:
         # 使用 lpmm_ops.py 中的接口
         result = await lpmm_ops.clear_all()
-        
+
         if result["status"] == "success":
             print(f"\n[√] 成功：{result['message']}")
             stats = result.get("stats", {})
             before = stats.get("before", {})
             after = stats.get("after", {})
             print("\n[统计信息]")
-            print(f"  清空前: 段落={before.get('paragraphs', 0)}, 实体={before.get('entities', 0)}, "
-                  f"关系={before.get('relations', 0)}, KG节点={before.get('kg_nodes', 0)}, KG边={before.get('kg_edges', 0)}")
-            print(f"  清空后: 段落={after.get('paragraphs', 0)}, 实体={after.get('entities', 0)}, "
-                  f"关系={after.get('relations', 0)}, KG节点={after.get('kg_nodes', 0)}, KG边={after.get('kg_edges', 0)}")
+            print(
+                f"  清空前: 段落={before.get('paragraphs', 0)}, 实体={before.get('entities', 0)}, "
+                f"关系={before.get('relations', 0)}, KG节点={before.get('kg_nodes', 0)}, KG边={before.get('kg_edges', 0)}"
+            )
+            print(
+                f"  清空后: 段落={after.get('paragraphs', 0)}, 实体={after.get('entities', 0)}, "
+                f"关系={after.get('relations', 0)}, KG节点={after.get('kg_nodes', 0)}, KG边={after.get('kg_edges', 0)}"
+            )
         else:
             print(f"\n[×] 失败：{result['message']}")
     except Exception as e:
         print(f"\n[×] 发生异常: {e}")
         logger.error(f"clear_all 异常: {e}", exc_info=True)
 
+
 async def interactive_search():
     """交互式查询知识"""
     print("\n" + "=" * 40)
@@ -182,25 +194,25 @@ async def interactive_search():
     print("=" * 40)
     print("说明：输入查询问题或关键词，系统会返回相关的知识段落。")
     print("-" * 40)
-    
+
     # 确保 LPMM 已初始化
     if not global_config.lpmm_knowledge.enable:
         print("\n[!] 警告：LPMM 知识库在配置中未启用。")
         return
-    
+
     try:
         lpmm_start_up()
     except Exception as e:
         print(f"\n[!] LPMM 初始化失败: {e}")
         logger.error(f"LPMM 初始化失败: {e}", exc_info=True)
         return
-    
+
     query = input("请输入查询问题或关键词: ").strip()
-    
+
     if not query:
         print("\n[!] 查询内容为空，操作已取消。")
         return
-    
+
     # 询问返回条数
     print("-" * 40)
     limit_str = input("希望返回的相关知识条数（默认3，直接回车使用默认值）: ").strip()
@@ -210,11 +222,11 @@ async def interactive_search():
     except ValueError:
         limit = 3
         print("[!] 输入无效，使用默认值 3。")
-    
+
     print("\n[进度] 正在查询知识库...")
     try:
         result = await query_lpmm_knowledge(query, limit=limit)
-        
+
         print("\n" + "=" * 60)
         print("[查询结果]")
         print("=" * 60)
@@ -224,6 +236,7 @@ async def interactive_search():
         print(f"\n[×] 查询失败: {e}")
         logger.error(f"查询异常: {e}", exc_info=True)
 
+
 async def main():
     """主循环"""
     while True:
@@ -236,9 +249,9 @@ async def main():
         print("║  4. 清空知识库 (Clear All) ⚠️        ║")
         print("║  0. 退出 (Exit)                     ║")
         print("╚" + "═" * 38 + "╝")
-        
+
         choice = input("请选择操作编号: ").strip()
-        
+
         if choice == "1":
             await interactive_add()
         elif choice == "2":
@@ -253,6 +266,7 @@ async def main():
         else:
             print("\n[!] 无效的选择，请输入 0, 1, 2, 3 或 4。")
 
+
 if __name__ == "__main__":
     try:
         # 运行主循环
@@ -262,4 +276,3 @@ if __name__ == "__main__":
     except Exception as e:
         print(f"\n[!] 程序运行出错: {e}")
         logger.error(f"Main loop 异常: {e}", exc_info=True)
-

scripts/lpmm_manager.py

@@ -21,18 +21,18 @@ PROJECT_ROOT = os.path.abspath(os.path.join(CURRENT_DIR, ".."))
 if PROJECT_ROOT not in sys.path:
     sys.path.append(PROJECT_ROOT)
 
-from src.common.logger import get_logger  # type: ignore
-from src.config.config import global_config, model_config  # type: ignore
+from src.common.logger import get_logger  # type: ignore  # noqa: E402
+from src.config.config import global_config, model_config  # type: ignore  # noqa: E402
 
 # 引入各功能脚本的入口函数
-from import_openie import main as import_openie_main  # type: ignore
-from info_extraction import main as info_extraction_main  # type: ignore
-from delete_lpmm_items import main as delete_lpmm_items_main  # type: ignore
-from inspect_lpmm_batch import main as inspect_lpmm_batch_main  # type: ignore
-from inspect_lpmm_global import main as inspect_lpmm_global_main  # type: ignore
-from refresh_lpmm_knowledge import main as refresh_lpmm_knowledge_main  # type: ignore
-from test_lpmm_retrieval import main as test_lpmm_retrieval_main  # type: ignore
-from raw_data_preprocessor import load_raw_data  # type: ignore
+from import_openie import main as import_openie_main  # type: ignore  # noqa: E402
+from info_extraction import main as info_extraction_main  # type: ignore  # noqa: E402
+from delete_lpmm_items import main as delete_lpmm_items_main  # type: ignore  # noqa: E402
+from inspect_lpmm_batch import main as inspect_lpmm_batch_main  # type: ignore  # noqa: E402
+from inspect_lpmm_global import main as inspect_lpmm_global_main  # type: ignore  # noqa: E402
+from refresh_lpmm_knowledge import main as refresh_lpmm_knowledge_main  # type: ignore  # noqa: E402
+from test_lpmm_retrieval import main as test_lpmm_retrieval_main  # type: ignore  # noqa: E402
+from raw_data_preprocessor import load_raw_data  # type: ignore  # noqa: E402
 
 
 logger = get_logger("lpmm_manager")
@@ -69,15 +69,10 @@ def _check_before_info_extract(non_interactive: bool = False) -> bool:
     raw_dir = Path(PROJECT_ROOT) / "data" / "lpmm_raw_data"
     txt_files = list(raw_dir.glob("*.txt"))
     if not txt_files:
-        msg = (
-            f"[WARN] 未在 {raw_dir} 下找到任何 .txt 原始语料文件，"
-            "info_extraction 可能立即退出或无数据可处理。"
-        )
+        msg = f"[WARN] 未在 {raw_dir} 下找到任何 .txt 原始语料文件，info_extraction 可能立即退出或无数据可处理。"
         print(msg)
         if non_interactive:
-            logger.error(
-                "非交互模式下要求原始语料目录中已存在可用的 .txt 文件，请先准备好数据再重试。"
-            )
+            logger.error("非交互模式下要求原始语料目录中已存在可用的 .txt 文件，请先准备好数据再重试。")
             return False
         cont = input("仍然继续执行信息提取吗？(y/n): ").strip().lower()
         return cont == "y"
@@ -89,15 +84,10 @@ def _check_before_import_openie(non_interactive: bool = False) -> bool:
     openie_dir = Path(PROJECT_ROOT) / "data" / "openie"
     json_files = list(openie_dir.glob("*.json"))
     if not json_files:
-        msg = (
-            f"[WARN] 未在 {openie_dir} 下找到任何 OpenIE JSON 文件，"
-            "import_openie 可能会因为找不到批次而失败。"
-        )
+        msg = f"[WARN] 未在 {openie_dir} 下找到任何 OpenIE JSON 文件，import_openie 可能会因为找不到批次而失败。"
         print(msg)
         if non_interactive:
-            logger.error(
-                "非交互模式下要求 data/openie 目录中已存在可用的 OpenIE JSON 文件，请先执行信息提取脚本。"
-            )
+            logger.error("非交互模式下要求 data/openie 目录中已存在可用的 OpenIE JSON 文件，请先执行信息提取脚本。")
             return False
         cont = input("仍然继续执行导入吗？(y/n): ").strip().lower()
         return cont == "y"
@@ -108,10 +98,7 @@ def _warn_if_lpmm_disabled() -> None:
     """在部分操作前提醒 lpmm_knowledge.enable 状态。"""
     try:
         if not getattr(global_config.lpmm_knowledge, "enable", False):
-            print(
-                "[WARN] 当前配置 lpmm_knowledge.enable = false，"
-                "刷新或检索测试可能无法在聊天侧真正启用 LPMM。"
-            )
+            print("[WARN] 当前配置 lpmm_knowledge.enable = false，刷新或检索测试可能无法在聊天侧真正启用 LPMM。")
     except Exception:
         # 配置异常时不阻断主流程，仅忽略提示
         pass
@@ -131,10 +118,7 @@ def run_action(action: str, extra_args: Optional[List[str]] = None) -> None:
         if action == "prepare_raw":
             logger.info("开始预处理原始语料 (data/lpmm_raw_data/*.txt)...")
             sha_list, raw_data = load_raw_data()
-            print(
-                f"\n[PREPARE_RAW] 完成原始语料预处理：共 {len(raw_data)} 条段落，"
-                f"去重后哈希数 {len(sha_list)}。"
-            )
+            print(f"\n[PREPARE_RAW] 完成原始语料预处理：共 {len(raw_data)} 条段落，去重后哈希数 {len(sha_list)}。")
         elif action == "info_extract":
             if not _check_before_info_extract("--non-interactive" in extra_args):
                 print("已根据用户选择，取消执行信息提取。")
@@ -164,10 +148,7 @@ def run_action(action: str, extra_args: Optional[List[str]] = None) -> None:
             # 一键流水线：预处理原始语料 -> 信息抽取 -> 导入 -> 刷新
             logger.info("开始 full_import：预处理原始语料 -> 信息抽取 -> 导入 -> 刷新")
             sha_list, raw_data = load_raw_data()
-            print(
-                f"\n[FULL_IMPORT] 原始语料预处理完成：共 {len(raw_data)} 条段落，"
-                f"去重后哈希数 {len(sha_list)}。"
-            )
+            print(f"\n[FULL_IMPORT] 原始语料预处理完成：共 {len(raw_data)} 条段落，去重后哈希数 {len(sha_list)}。")
             non_interactive = "--non-interactive" in extra_args
             if not _check_before_info_extract(non_interactive):
                 print("已根据用户选择，取消 full_import（信息提取阶段被取消）。")
@@ -345,9 +326,9 @@ def _interactive_build_delete_args() -> List[str]:
     )
 
     # 快速选项：按推荐方式清理所有相关实体/关系
-    quick_all = input(
-        "是否使用推荐策略：同时删除关联的实体向量/节点、关系向量，并清理孤立实体？(Y/n): "
-    ).strip().lower()
+    quick_all = (
+        input("是否使用推荐策略：同时删除关联的实体向量/节点、关系向量，并清理孤立实体？(Y/n): ").strip().lower()
+    )
     if quick_all in ("", "y", "yes"):
         args.extend(["--delete-entities", "--delete-relations", "--remove-orphan-entities"])
     else:
@@ -375,9 +356,7 @@ def _interactive_build_delete_args() -> List[str]:
 
 def _interactive_build_batch_inspect_args() -> List[str]:
     """为 inspect_lpmm_batch 构造 --openie-file 参数。"""
-    path = _interactive_choose_openie_file(
-        "请输入要检查的 OpenIE JSON 文件路径（回车跳过，由子脚本自行交互）："
-    )
+    path = _interactive_choose_openie_file("请输入要检查的 OpenIE JSON 文件路径（回车跳过，由子脚本自行交互）：")
     if not path:
         return []
     return ["--openie-file", path]
@@ -385,11 +364,7 @@ def _interactive_build_batch_inspect_args() -> List[str]:
 
 def _interactive_build_test_args() -> List[str]:
     """为 test_lpmm_retrieval 构造自定义测试用例参数。"""
-    print(
-        "\n[TEST] 你可以：\n"
-        "- 直接回车使用内置的默认测试用例；\n"
-        "- 或者输入一条自定义问题，并指定期望命中的关键字。"
-    )
+    print("\n[TEST] 你可以：\n- 直接回车使用内置的默认测试用例；\n- 或者输入一条自定义问题，并指定期望命中的关键字。")
     query = input("请输入自定义测试问题（回车则使用默认用例）：").strip()
     if not query:
         return []
@@ -422,9 +397,7 @@ def _run_embedding_helper() -> None:
     print(f"- 当前配置中的嵌入维度 (lpmm_knowledge.embedding_dimension): {current_dim}")
     print(f"- 测试文件路径: {EMBEDDING_TEST_FILE}")
 
-    new_dim = input(
-        "\n如果你计划更换嵌入模型，请在此输入“新的嵌入维度”（仅用于记录与提示，回车则跳过）："
-    ).strip()
+    new_dim = input("\n如果你计划更换嵌入模型，请在此输入“新的嵌入维度”（仅用于记录与提示，回车则跳过）：").strip()
     if new_dim and not new_dim.isdigit():
         print("输入的维度不是纯数字，已取消操作。")
         return
@@ -537,5 +510,3 @@ def main(argv: Optional[list[str]] = None) -> None:
 
 if __name__ == "__main__":
     main()
-
-

scripts/test_memory_retrieval.py

@@ -28,53 +28,55 @@ from maim_message import UserInfo, GroupInfo
 
 logger = get_logger("test_memory_retrieval")
 
+
 # 使用 importlib 动态导入，避免循环导入问题
 def _import_memory_retrieval():
     """使用 importlib 动态导入 memory_retrieval 模块，避免循环导入"""
     try:
         # 先导入 prompt_builder，检查 prompt 是否已经初始化
         from src.chat.utils.prompt_builder import global_prompt_manager
-        
+
         # 检查 memory_retrieval 相关的 prompt 是否已经注册
         # 如果已经注册，说明模块可能已经通过其他路径初始化过了
         prompt_already_init = "memory_retrieval_question_prompt" in global_prompt_manager._prompts
-        
+
         module_name = "src.memory_system.memory_retrieval"
-        
+
         # 如果 prompt 已经初始化，尝试直接使用已加载的模块
         if prompt_already_init and module_name in sys.modules:
             existing_module = sys.modules[module_name]
-            if hasattr(existing_module, 'init_memory_retrieval_prompt'):
+            if hasattr(existing_module, "init_memory_retrieval_prompt"):
                 return (
                     existing_module.init_memory_retrieval_prompt,
                     existing_module._react_agent_solve_question,
                     existing_module._process_single_question,
                 )
-        
+
         # 如果模块已经在 sys.modules 中但部分初始化，先移除它
         if module_name in sys.modules:
             existing_module = sys.modules[module_name]
-            if not hasattr(existing_module, 'init_memory_retrieval_prompt'):
+            if not hasattr(existing_module, "init_memory_retrieval_prompt"):
                 # 模块部分初始化，移除它
                 logger.warning(f"检测到部分初始化的模块 {module_name}，尝试重新导入")
                 del sys.modules[module_name]
                 # 清理可能相关的部分初始化模块
                 keys_to_remove = []
                 for key in sys.modules.keys():
-                    if key.startswith('src.memory_system.') and key != 'src.memory_system':
+                    if key.startswith("src.memory_system.") and key != "src.memory_system":
                         keys_to_remove.append(key)
                 for key in keys_to_remove:
                     try:
                         del sys.modules[key]
                     except KeyError:
                         pass
-        
+
         # 在导入 memory_retrieval 之前，先确保所有可能触发循环导入的模块都已完全加载
         # 这些模块在导入时可能会触发 memory_retrieval 的导入，所以我们需要先加载它们
         try:
             # 先导入可能触发循环导入的模块，让它们完成初始化
             import src.config.config
             import src.chat.utils.prompt_builder
+
             # 尝试导入可能触发循环导入的模块（这些模块可能在模块级别导入了 memory_retrieval）
             # 如果它们已经导入，就确保它们完全初始化
             # 尝试导入可能触发循环导入的模块（这些模块可能在模块级别导入了 memory_retrieval）
@@ -89,11 +91,11 @@ def _import_memory_retrieval():
                 pass  # 如果导入失败，继续
         except Exception as e:
             logger.warning(f"预加载依赖模块时出现警告: {e}")
-        
+
         # 现在尝试导入 memory_retrieval
         # 如果此时仍然触发循环导入，说明有其他模块在模块级别导入了 memory_retrieval
         memory_retrieval_module = importlib.import_module(module_name)
-        
+
         return (
             memory_retrieval_module.init_memory_retrieval_prompt,
             memory_retrieval_module._react_agent_solve_question,
@@ -126,16 +128,16 @@ def create_test_chat_stream(chat_id: str = "test_memory_retrieval") -> ChatStrea
 
 def get_token_usage_since(start_time: float) -> Dict[str, Any]:
     """获取从指定时间开始的token使用情况
-    
+
     Args:
         start_time: 开始时间戳
-        
+
     Returns:
         包含token使用统计的字典
     """
     try:
         start_datetime = datetime.fromtimestamp(start_time)
-        
+
         # 查询从开始时间到现在的所有memory相关的token使用记录
         records = (
             LLMUsage.select()
@@ -150,21 +152,21 @@ def get_token_usage_since(start_time: float) -> Dict[str, Any]:
             )
             .order_by(LLMUsage.timestamp.asc())
         )
-        
+
         total_prompt_tokens = 0
         total_completion_tokens = 0
         total_tokens = 0
         total_cost = 0.0
         request_count = 0
         model_usage = {}  # 按模型统计
-        
+
         for record in records:
             total_prompt_tokens += record.prompt_tokens or 0
             total_completion_tokens += record.completion_tokens or 0
             total_tokens += record.total_tokens or 0
             total_cost += record.cost or 0.0
             request_count += 1
-            
+
             # 按模型统计
             model_name = record.model_name or "unknown"
             if model_name not in model_usage:
@@ -180,7 +182,7 @@ def get_token_usage_since(start_time: float) -> Dict[str, Any]:
             model_usage[model_name]["total_tokens"] += record.total_tokens or 0
             model_usage[model_name]["cost"] += record.cost or 0.0
             model_usage[model_name]["request_count"] += 1
-        
+
         return {
             "total_prompt_tokens": total_prompt_tokens,
             "total_completion_tokens": total_completion_tokens,
@@ -205,25 +207,25 @@ def format_thinking_steps(thinking_steps: list) -> str:
     """格式化思考步骤为可读字符串"""
     if not thinking_steps:
         return "无思考步骤"
-    
+
     lines = []
     for step in thinking_steps:
         iteration = step.get("iteration", "?")
         thought = step.get("thought", "")
         actions = step.get("actions", [])
         observations = step.get("observations", [])
-        
+
         lines.append(f"\n--- 迭代 {iteration} ---")
         if thought:
             lines.append(f"思考: {thought[:200]}...")
-        
+
         if actions:
             lines.append("行动:")
             for action in actions:
                 action_type = action.get("action_type", "unknown")
                 action_params = action.get("action_params", {})
                 lines.append(f"  - {action_type}: {json.dumps(action_params, ensure_ascii=False)}")
-        
+
         if observations:
             lines.append("观察:")
             for obs in observations:
@@ -231,7 +233,7 @@ def format_thinking_steps(thinking_steps: list) -> str:
                 if len(str(obs)) > 200:
                     obs_str += "..."
                 lines.append(f"  - {obs_str}")
-    
+
     return "\n".join(lines)
 
 
@@ -242,31 +244,32 @@ async def test_memory_retrieval(
     max_iterations: Optional[int] = None,
 ) -> Dict[str, Any]:
     """测试记忆检索功能
-    
+
     Args:
         question: 要查询的问题
         chat_id: 聊天ID
         context: 上下文信息
         max_iterations: 最大迭代次数
-        
+
     Returns:
         包含测试结果的字典
     """
     print("\n" + "=" * 80)
-    print(f"[测试] 记忆检索测试")
+    print("[测试] 记忆检索测试")
     print(f"[问题] {question}")
     print("=" * 80)
-    
+
     # 记录开始时间
     start_time = time.time()
-    
+
     # 延迟导入并初始化记忆检索prompt（这会自动加载 global_config）
     # 注意：必须在函数内部调用，避免在模块级别触发循环导入
     try:
         init_memory_retrieval_prompt, _react_agent_solve_question, _ = _import_memory_retrieval()
-        
+
         # 检查 prompt 是否已经初始化，避免重复初始化
         from src.chat.utils.prompt_builder import global_prompt_manager
+
         if "memory_retrieval_question_prompt" not in global_prompt_manager._prompts:
             init_memory_retrieval_prompt()
         else:
@@ -274,24 +277,24 @@ async def test_memory_retrieval(
     except Exception as e:
         logger.error(f"初始化记忆检索模块失败: {e}", exc_info=True)
         raise
-    
+
     # 获取 global_config（此时应该已经加载）
     from src.config.config import global_config
-    
+
     # 直接调用 _react_agent_solve_question 来获取详细的迭代信息
     if max_iterations is None:
         max_iterations = global_config.memory.max_agent_iterations
-    
+
     timeout = global_config.memory.agent_timeout_seconds
-    
-    print(f"\n[配置]")
+
+    print("\n[配置]")
     print(f"  最大迭代次数: {max_iterations}")
     print(f"  超时时间: {timeout}秒")
     print(f"  聊天ID: {chat_id}")
-    
+
     # 执行检索
     print(f"\n[开始检索] {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime())}")
-    
+
     found_answer, answer, thinking_steps, is_timeout = await _react_agent_solve_question(
         question=question,
         chat_id=chat_id,
@@ -299,14 +302,14 @@ async def test_memory_retrieval(
         timeout=timeout,
         initial_info="",
     )
-    
+
     # 记录结束时间
     end_time = time.time()
     elapsed_time = end_time - start_time
-    
+
     # 获取token使用情况
     token_usage = get_token_usage_since(start_time)
-    
+
     # 构建结果
     result = {
         "question": question,
@@ -318,41 +321,41 @@ async def test_memory_retrieval(
         "iteration_count": len(thinking_steps),
         "token_usage": token_usage,
     }
-    
+
     # 输出结果
     print(f"\n[检索完成] {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime())}")
-    print(f"\n[结果]")
+    print("\n[结果]")
     print(f"  是否找到答案: {'是' if found_answer else '否'}")
     if found_answer and answer:
         print(f"  答案: {answer}")
     else:
-        print(f"  答案: (未找到答案)")
+        print("  答案: (未找到答案)")
     print(f"  是否超时: {'是' if is_timeout else '否'}")
     print(f"  迭代次数: {len(thinking_steps)}")
     print(f"  总耗时: {elapsed_time:.2f}秒")
-    
-    print(f"\n[Token使用情况]")
+
+    print("\n[Token使用情况]")
     print(f"  总请求数: {token_usage['request_count']}")
     print(f"  总Prompt Tokens: {token_usage['total_prompt_tokens']:,}")
     print(f"  总Completion Tokens: {token_usage['total_completion_tokens']:,}")
     print(f"  总Tokens: {token_usage['total_tokens']:,}")
     print(f"  总成本: ${token_usage['total_cost']:.6f}")
-    
-    if token_usage['model_usage']:
-        print(f"\n[按模型统计]")
-        for model_name, usage in token_usage['model_usage'].items():
+
+    if token_usage["model_usage"]:
+        print("\n[按模型统计]")
+        for model_name, usage in token_usage["model_usage"].items():
             print(f"  {model_name}:")
             print(f"    请求数: {usage['request_count']}")
             print(f"    Prompt Tokens: {usage['prompt_tokens']:,}")
             print(f"    Completion Tokens: {usage['completion_tokens']:,}")
             print(f"    总Tokens: {usage['total_tokens']:,}")
             print(f"    成本: ${usage['cost']:.6f}")
-    
-    print(f"\n[迭代详情]")
+
+    print("\n[迭代详情]")
     print(format_thinking_steps(thinking_steps))
-    
+
     print("\n" + "=" * 80)
-    
+
     return result
 
 
@@ -375,12 +378,12 @@ def main() -> None:
         "-o",
         help="将结果保存到JSON文件（可选）",
     )
-    
+
     args = parser.parse_args()
-    
+
     # 初始化日志（使用较低的详细程度，避免输出过多日志）
     initialize_logging(verbose=False)
-    
+
     # 交互式输入问题
     print("\n" + "=" * 80)
     print("记忆检索测试工具")
@@ -389,7 +392,7 @@ def main() -> None:
     if not question:
         print("错误: 问题不能为空")
         return
-    
+
     # 交互式输入最大迭代次数
     max_iterations_input = input("\n请输入最大迭代次数（直接回车使用配置默认值）: ").strip()
     max_iterations = None
@@ -402,7 +405,7 @@ def main() -> None:
         except ValueError:
             print("警告: 无效的迭代次数，将使用配置默认值")
             max_iterations = None
-    
+
     # 连接数据库
     try:
         db.connect(reuse_if_open=True)
@@ -410,7 +413,7 @@ def main() -> None:
         logger.error(f"数据库连接失败: {e}")
         print(f"错误: 数据库连接失败: {e}")
         return
-    
+
     # 运行测试
     try:
         result = asyncio.run(
@@ -421,7 +424,7 @@ def main() -> None:
                 max_iterations=max_iterations,
             )
         )
-        
+
         # 如果指定了输出文件，保存结果
         if args.output:
             # 将thinking_steps转换为可序列化的格式
@@ -429,7 +432,7 @@ def main() -> None:
             with open(args.output, "w", encoding="utf-8") as f:
                 json.dump(output_result, f, ensure_ascii=False, indent=2)
             print(f"\n[结果已保存] {args.output}")
-        
+
     except KeyboardInterrupt:
         print("\n\n[中断] 用户中断测试")
     except Exception as e:
@@ -444,4 +447,3 @@ def main() -> None:
 
 if __name__ == "__main__":
     main()
-

src/bw_learner/expression_selector.py

@@ -455,6 +455,7 @@ class ExpressionSelector:
                 expr_obj.save()
                 logger.debug("表达方式激活: 更新last_active_time in db")
 
+
 try:
     expression_selector = ExpressionSelector()
 except Exception as e:

src/bw_learner/jargon_explainer.py

@@ -17,6 +17,7 @@ from src.bw_learner.learner_utils import (
 
 logger = get_logger("jargon")
 
+
 class JargonExplainer:
     """黑话解释器，用于在回复前识别和解释上下文中的黑话"""
 

src/bw_learner/learner_utils.py

@@ -60,31 +60,31 @@ def calculate_style_similarity(style1: str, style2: str) -> float:
     """
     计算两个 style 的相似度，返回0-1之间的值
     在计算前会移除"使用"和"句式"这两个词（参考 expression_similarity_analysis.py）
-    
+
     Args:
         style1: 第一个 style
         style2: 第二个 style
-    
+
     Returns:
         float: 相似度值，范围0-1
     """
     if not style1 or not style2:
         return 0.0
-    
+
     # 移除"使用"和"句式"这两个词
     def remove_ignored_words(text: str) -> str:
         """移除需要忽略的词"""
         text = text.replace("使用", "")
         text = text.replace("句式", "")
         return text.strip()
-    
+
     cleaned_style1 = remove_ignored_words(style1)
     cleaned_style2 = remove_ignored_words(style2)
-    
+
     # 如果清理后文本为空，返回0
     if not cleaned_style1 or not cleaned_style2:
         return 0.0
-    
+
     return difflib.SequenceMatcher(None, cleaned_style1, cleaned_style2).ratio()
 
 
@@ -495,4 +495,4 @@ def parse_expression_response(response: str) -> Tuple[List[Tuple[str, str, str]]
             if content and source_id:
                 jargon_entries.append((content, source_id))
 
-    return expressions, jargon_entries
+    return expressions, jargon_entries

src/bw_learner/message_recorder.py

@@ -2,7 +2,6 @@ import time
 import asyncio
 from typing import List, Any
 from src.common.logger import get_logger
-from src.config.config import global_config
 from src.chat.message_receive.chat_stream import get_chat_manager
 from src.chat.utils.chat_message_builder import get_raw_msg_by_timestamp_with_chat_inclusive
 from src.chat.utils.common_utils import TempMethodsExpression
@@ -119,9 +118,7 @@ class MessageRecorder:
 
                 # 触发 expression_learner 和 jargon_miner 的处理
                 if self.enable_expression_learning:
-                    asyncio.create_task(
-                        self._trigger_expression_learning(messages)
-                    )
+                    asyncio.create_task(self._trigger_expression_learning(messages))
 
             except Exception as e:
                 logger.error(f"为聊天流 {self.chat_name} 提取和分发消息失败: {e}")
@@ -130,9 +127,7 @@ class MessageRecorder:
                 traceback.print_exc()
                 # 即使失败也保持时间戳更新，避免频繁重试
 
-    async def _trigger_expression_learning(
-        self, messages: List[Any]
-    ) -> None:
+    async def _trigger_expression_learning(self, messages: List[Any]) -> None:
         """
         触发 expression 学习，使用指定的消息列表
 

src/chat/brain_chat/PFC/action_planner.py

@@ -1,5 +1,5 @@
 import time
-from typing import Tuple, Optional, Dict, Any  # 增加了 Optional
+from typing import Tuple, Optional  # 增加了 Optional
 from src.common.logger import get_logger
 from src.llm_models.utils_model import LLMRequest
 from src.config.config import global_config, model_config
@@ -120,7 +120,7 @@ class ActionPlanner:
     def _get_personality_prompt(self) -> str:
         """获取个性提示信息"""
         prompt_personality = global_config.personality.personality
-        
+
         # 检查是否需要随机替换为状态
         if (
             global_config.personality.states
@@ -128,7 +128,7 @@ class ActionPlanner:
             and random.random() < global_config.personality.state_probability
         ):
             prompt_personality = random.choice(global_config.personality.states)
-        
+
         bot_name = global_config.bot.nickname
         return f"你的名字是{bot_name},你{prompt_personality};"
 
@@ -170,13 +170,10 @@ class ActionPlanner:
                             )
                         break
             else:
-                logger.debug(
-                    f"[私聊][{self.private_name}]聊天历史为空或尚未加载，跳过 Bot 发言时间检查。"
-                )
+                logger.debug(f"[私聊][{self.private_name}]聊天历史为空或尚未加载，跳过 Bot 发言时间检查。")
         except Exception as e:
             logger.debug(f"[私聊][{self.private_name}]获取 Bot 上次发言时间时出错: {e}")
 
-
         # --- 获取超时提示信息 ---
         # (这部分逻辑不变)
         timeout_context = ""

src/chat/brain_chat/PFC/conversation.py

@@ -112,10 +112,10 @@ class Conversation:
                             "user_nickname": msg.user_info.user_nickname if msg.user_info else "",
                             "user_cardname": msg.user_info.user_cardname if msg.user_info else None,
                             "platform": msg.user_info.platform if msg.user_info else "",
-                        }
+                        },
                     }
                     initial_messages_dict.append(msg_dict)
-                
+
                 # 将加载的消息填充到 ObservationInfo 的 chat_history
                 self.observation_info.chat_history = initial_messages_dict
                 self.observation_info.chat_history_str = chat_talking_prompt + "\n"

src/chat/brain_chat/PFC/message_sender.py

@@ -66,9 +66,9 @@ class DirectMessageSender:
 
             # 发送消息（直接调用底层 API）
             from src.chat.message_receive.uni_message_sender import _send_message
-            
+
             sent = await _send_message(message, show_log=True)
-            
+
             if sent:
                 # 存储消息
                 await self.storage.store_message(message, chat_stream)

src/chat/brain_chat/PFC/observation_info.py

@@ -5,7 +5,7 @@ from src.common.logger import get_logger
 from .chat_observer import ChatObserver
 from .chat_states import NotificationHandler, NotificationType, Notification
 from src.chat.utils.chat_message_builder import build_readable_messages
-from src.common.data_models.database_data_model import DatabaseMessages, DatabaseUserInfo
+from src.common.data_models.database_data_model import DatabaseMessages
 import traceback  # 导入 traceback 用于调试
 
 logger = get_logger("observation_info")
@@ -13,15 +13,15 @@ logger = get_logger("observation_info")
 
 def dict_to_database_message(msg_dict: Dict[str, Any]) -> DatabaseMessages:
     """Convert PFC dict format to DatabaseMessages object
-    
+
     Args:
         msg_dict: Message in PFC dict format with nested user_info
-        
+
     Returns:
         DatabaseMessages object compatible with build_readable_messages()
     """
     user_info_dict: Dict[str, Any] = msg_dict.get("user_info", {})
-    
+
     return DatabaseMessages(
         message_id=msg_dict.get("message_id", ""),
         time=msg_dict.get("time", 0.0),

src/chat/brain_chat/PFC/pfc.py

@@ -42,9 +42,7 @@ class GoalAnalyzer:
     """对话目标分析器"""
 
     def __init__(self, stream_id: str, private_name: str):
-        self.llm = LLMRequest(
-            model_set=model_config.model_task_config.planner, request_type="conversation_goal"
-        )
+        self.llm = LLMRequest(model_set=model_config.model_task_config.planner, request_type="conversation_goal")
 
         self.personality_info = self._get_personality_prompt()
         self.name = global_config.bot.nickname
@@ -60,7 +58,7 @@ class GoalAnalyzer:
     def _get_personality_prompt(self) -> str:
         """获取个性提示信息"""
         prompt_personality = global_config.personality.personality
-        
+
         # 检查是否需要随机替换为状态
         if (
             global_config.personality.states
@@ -68,7 +66,7 @@ class GoalAnalyzer:
             and random.random() < global_config.personality.state_probability
         ):
             prompt_personality = random.choice(global_config.personality.states)
-        
+
         bot_name = global_config.bot.nickname
         return f"你的名字是{bot_name},你{prompt_personality};"
 

src/chat/brain_chat/PFC/pfc_KnowledgeFetcher.py

@@ -1,13 +1,11 @@
 from typing import List, Tuple, Dict, Any
 from src.common.logger import get_logger
+
 # NOTE: HippocampusManager doesn't exist in v0.12.2 - memory system was redesigned
 # from src.plugins.memory_system.Hippocampus import HippocampusManager
 from src.llm_models.utils_model import LLMRequest
-from src.config.config import global_config, model_config
-from src.chat.message_receive.message import Message
+from src.config.config import model_config
 from src.chat.knowledge import qa_manager
-from src.chat.utils.chat_message_builder import build_readable_messages
-from src.chat.brain_chat.PFC.observation_info import dict_to_database_message
 
 logger = get_logger("knowledge_fetcher")
 
@@ -16,9 +14,7 @@ class KnowledgeFetcher:
     """知识调取器"""
 
     def __init__(self, private_name: str):
-        self.llm = LLMRequest(
-            model_set=model_config.model_task_config.utils
-        )
+        self.llm = LLMRequest(model_set=model_config.model_task_config.utils)
         self.private_name = private_name
 
     def _lpmm_get_knowledge(self, query: str) -> str:
@@ -50,13 +46,7 @@ class KnowledgeFetcher:
         Returns:
             Tuple[str, str]: (获取的知识, 知识来源)
         """
-        db_messages = [dict_to_database_message(m) for m in chat_history]
-        chat_history_text = build_readable_messages(
-            db_messages,
-            replace_bot_name=True,
-            timestamp_mode="relative",
-            read_mark=0.0,
-        )
+        _ = chat_history
 
         # NOTE: Hippocampus memory system was redesigned in v0.12.2
         # The old get_memory_from_text API no longer exists
@@ -64,7 +54,7 @@ class KnowledgeFetcher:
         # TODO: Integrate with new memory system if needed
         knowledge_text = ""
         sources_text = "无记忆匹配"  # 默认值
-        
+
         # # 从记忆中获取相关知识 (DISABLED - old Hippocampus API)
         # related_memory = await HippocampusManager.get_instance().get_memory_from_text(
         #     text=f"{query}\n{chat_history_text}",

src/chat/brain_chat/PFC/reply_checker.py

@@ -14,10 +14,7 @@ class ReplyChecker:
     """回复检查器"""
 
     def __init__(self, stream_id: str, private_name: str):
-        self.llm = LLMRequest(
-            model_set=model_config.model_task_config.utils,
-            request_type="reply_check"
-        )
+        self.llm = LLMRequest(model_set=model_config.model_task_config.utils, request_type="reply_check")
         self.personality_info = self._get_personality_prompt()
         self.name = global_config.bot.nickname
         self.private_name = private_name
@@ -27,7 +24,7 @@ class ReplyChecker:
     def _get_personality_prompt(self) -> str:
         """获取个性提示信息"""
         prompt_personality = global_config.personality.personality
-        
+
         # 检查是否需要随机替换为状态
         if (
             global_config.personality.states
@@ -35,7 +32,7 @@ class ReplyChecker:
             and random.random() < global_config.personality.state_probability
         ):
             prompt_personality = random.choice(global_config.personality.states)
-        
+
         bot_name = global_config.bot.nickname
         return f"你的名字是{bot_name},你{prompt_personality};"
 

src/chat/brain_chat/PFC/reply_generator.py

@@ -99,7 +99,7 @@ class ReplyGenerator:
     def _get_personality_prompt(self) -> str:
         """获取个性提示信息"""
         prompt_personality = global_config.personality.personality
-        
+
         # 检查是否需要随机替换为状态
         if (
             global_config.personality.states
@@ -107,7 +107,7 @@ class ReplyGenerator:
             and random.random() < global_config.personality.state_probability
         ):
             prompt_personality = random.choice(global_config.personality.states)
-        
+
         bot_name = global_config.bot.nickname
         return f"你的名字是{bot_name},你{prompt_personality};"
 

src/chat/brain_chat/brain_chat.py

@@ -704,10 +704,7 @@ class BrainChatting:
 
                             # 等待指定时间，但可被新消息打断
                             try:
-                                await asyncio.wait_for(
-                                    self._new_message_event.wait(),
-                                    timeout=wait_seconds
-                                )
+                                await asyncio.wait_for(self._new_message_event.wait(), timeout=wait_seconds)
                                 # 如果事件被触发，说明有新消息到达
                                 logger.info(f"{self.log_prefix} wait 动作被新消息打断，提前结束等待")
                             except asyncio.TimeoutError:
@@ -731,7 +728,9 @@ class BrainChatting:
                             # 使用默认等待时间
                             wait_seconds = 3
 
-                            logger.info(f"{self.log_prefix} 执行 listening（转换为 wait）动作，等待 {wait_seconds} 秒（可被新消息打断）")
+                            logger.info(
+                                f"{self.log_prefix} 执行 listening（转换为 wait）动作，等待 {wait_seconds} 秒（可被新消息打断）"
+                            )
 
                             # 清除事件状态，准备等待新消息
                             self._new_message_event.clear()
@@ -749,10 +748,7 @@ class BrainChatting:
 
                             # 等待指定时间，但可被新消息打断
                             try:
-                                await asyncio.wait_for(
-                                    self._new_message_event.wait(),
-                                    timeout=wait_seconds
-                                )
+                                await asyncio.wait_for(self._new_message_event.wait(), timeout=wait_seconds)
                                 # 如果事件被触发，说明有新消息到达
                                 logger.info(f"{self.log_prefix} listening 动作被新消息打断，提前结束等待")
                             except asyncio.TimeoutError:

src/chat/brain_chat/brain_planner.py

@@ -431,15 +431,21 @@ class BrainPlanner:
         except Exception as req_e:
             logger.error(f"{self.log_prefix}LLM 请求执行失败: {req_e}")
             extracted_reasoning = f"LLM 请求失败，模型出现问题: {req_e}"
-            return extracted_reasoning, [
-                ActionPlannerInfo(
-                    action_type="complete_talk",
-                    reasoning=extracted_reasoning,
-                    action_data={},
-                    action_message=None,
-                    available_actions=available_actions,
-                )
-            ], llm_content, llm_reasoning, llm_duration_ms
+            return (
+                extracted_reasoning,
+                [
+                    ActionPlannerInfo(
+                        action_type="complete_talk",
+                        reasoning=extracted_reasoning,
+                        action_data={},
+                        action_message=None,
+                        available_actions=available_actions,
+                    )
+                ],
+                llm_content,
+                llm_reasoning,
+                llm_duration_ms,
+            )
 
         # 解析LLM响应
         if llm_content:

src/chat/knowledge/embedding_store.py

@@ -105,7 +105,7 @@ class EmbeddingStore:
         self.embedding_file_path = f"{dir_path}/{namespace}.parquet"
         self.index_file_path = f"{dir_path}/{namespace}.index"
         self.idx2hash_file_path = f"{dir_path}/{namespace}_i2h.json"
-        
+
         self.dirty = False  # 标记是否有新增数据需要重建索引
 
         # 多线程配置参数验证和设置

src/chat/knowledge/ie_process.py

@@ -1,7 +1,7 @@
 import asyncio
 import json
 import time
-from typing import List, Union, Dict, Any
+from typing import List, Union
 
 from .global_logger import logger
 from . import prompt_template
@@ -192,17 +192,15 @@ class IEProcess:
 
         results = []
         total = len(paragraphs)
-        
+
         for i, pg in enumerate(paragraphs, start=1):
             # 打印进度日志，让用户知道没有卡死
             logger.info(f"[IEProcess] 正在处理第 {i}/{total} 段文本 (长度: {len(pg)})...")
-            
+
             # 使用 asyncio.to_thread 包装同步阻塞调用，防止死锁
             # 这样 info_extract_from_str 内部的 asyncio.run 会在独立线程的新 loop 中运行
             try:
-                entities, triples = await asyncio.to_thread(
-                    info_extract_from_str, self.llm_ner, self.llm_rdf, pg
-                )
+                entities, triples = await asyncio.to_thread(info_extract_from_str, self.llm_ner, self.llm_rdf, pg)
 
                 if entities is not None:
                     results.append(

src/chat/knowledge/kg_manager.py

@@ -395,8 +395,7 @@ class KGManager:
             appear_cnt = self.ent_appear_cnt.get(ent_hash)
             if not appear_cnt or appear_cnt <= 0:
                 logger.debug(
-                    f"实体 {ent_hash} 在 ent_appear_cnt 中不存在或计数为 0，"
-                    f"将使用 1.0 作为默认出现次数参与权重计算"
+                    f"实体 {ent_hash} 在 ent_appear_cnt 中不存在或计数为 0，将使用 1.0 作为默认出现次数参与权重计算"
                 )
                 appear_cnt = 1.0
             ent_weights[ent_hash] = float(np.sum(scores)) / float(appear_cnt)

src/chat/knowledge/lpmm_ops.py

@@ -11,31 +11,30 @@ from src.chat.knowledge import get_qa_manager, lpmm_start_up
 
 logger = get_logger("LPMM-Plugin-API")
 
+
 class LPMMOperations:
     """
     LPMM 内部操作接口。
     封装了 LPMM 的核心操作，供插件系统 API 或其他内部组件调用。
     """
-    
+
     def __init__(self):
         self._initialized = False
 
-    async def _run_cancellable_executor(
-        self, func: Callable, *args, **kwargs
-    ) -> Any:
+    async def _run_cancellable_executor(self, func: Callable, *args, **kwargs) -> Any:
         """
         在线程池中执行可取消的同步操作。
         当任务被取消时（如 Ctrl+C），会立即响应并抛出 CancelledError。
         注意：线程池中的操作可能仍在运行，但协程会立即返回，不会阻塞主进程。
-        
+
         Args:
             func: 要执行的同步函数
             *args: 函数的位置参数
             **kwargs: 函数的关键字参数
-            
+
         Returns:
             函数的返回值
-            
+
         Raises:
             asyncio.CancelledError: 当任务被取消时
         """
@@ -51,42 +50,42 @@ class LPMMOperations:
             # 如果全局没初始化，尝试初始化
             if not global_config.lpmm_knowledge.enable:
                 logger.warning("LPMM 知识库在全局配置中未启用，操作可能受限。")
-            
+
             lpmm_start_up()
             qa_mgr = get_qa_manager()
-            
+
         if qa_mgr is None:
             raise RuntimeError("无法获取 LPMM QAManager，请检查 LPMM 是否已正确安装和配置。")
-            
+
         return qa_mgr.embed_manager, qa_mgr.kg_manager, qa_mgr
 
     async def add_content(self, text: str, auto_split: bool = True) -> dict:
         """
         向知识库添加新内容。
-        
+
         Args:
             text: 原始文本。
             auto_split: 是否自动按双换行符分割段落。
                 - True: 自动分割（默认），支持多段文本（用双换行分隔）
                 - False: 不分割，将整个文本作为完整一段处理
-            
+
         Returns:
             dict: {"status": "success/error", "count": 导入段落数, "message": "描述"}
         """
         try:
             embed_mgr, kg_mgr, _ = await self._get_managers()
-            
+
             # 1. 分段处理
             if auto_split:
                 # 自动按双换行符分割
-                paragraphs = [p.strip() for p in text.split('\n\n') if p.strip()]
+                paragraphs = [p.strip() for p in text.split("\n\n") if p.strip()]
             else:
                 # 不分割，作为完整一段
                 text_stripped = text.strip()
                 if not text_stripped:
                     return {"status": "error", "message": "文本内容为空"}
                 paragraphs = [text_stripped]
-            
+
             if not paragraphs:
                 return {"status": "error", "message": "文本内容为空"}
 
@@ -94,14 +93,16 @@ class LPMMOperations:
             from src.chat.knowledge.ie_process import IEProcess
             from src.llm_models.utils_model import LLMRequest
             from src.config.config import model_config
-            
-            llm_ner = LLMRequest(model_set=model_config.model_task_config.lpmm_entity_extract, request_type="lpmm.entity_extract")
+
+            llm_ner = LLMRequest(
+                model_set=model_config.model_task_config.lpmm_entity_extract, request_type="lpmm.entity_extract"
+            )
             llm_rdf = LLMRequest(model_set=model_config.model_task_config.lpmm_rdf_build, request_type="lpmm.rdf_build")
             ie_process = IEProcess(llm_ner, llm_rdf)
-            
+
             logger.info(f"[Plugin API] 正在对 {len(paragraphs)} 段文本执行信息抽取...")
             extracted_docs = await ie_process.process_paragraphs(paragraphs)
-            
+
             # 3. 构造并导入数据
             # 这里我们手动实现导入逻辑，不依赖外部脚本
             # a. 准备段落
@@ -115,7 +116,7 @@ class LPMMOperations:
             # store_new_data_set 期望的格式：raw_paragraphs 的键是段落hash（不带前缀），值是段落文本
             new_raw_paragraphs = {}
             new_triple_list_data = {}
-            
+
             for pg_hash, passage in raw_paragraphs.items():
                 key = f"paragraph-{pg_hash}"
                 if key not in embed_mgr.stored_pg_hashes:
@@ -128,26 +129,22 @@ class LPMMOperations:
             # 2. 使用 EmbeddingManager 的标准方法存储段落、实体和关系的嵌入
             # store_new_data_set 会自动处理嵌入生成和存储
             # 将同步阻塞操作放到线程池中执行，避免阻塞事件循环
-            await self._run_cancellable_executor(
-                embed_mgr.store_new_data_set,
-                new_raw_paragraphs,
-                new_triple_list_data
-            )
+            await self._run_cancellable_executor(embed_mgr.store_new_data_set, new_raw_paragraphs, new_triple_list_data)
 
             # 3. 构建知识图谱（只需要三元组数据和embedding_manager）
-            await self._run_cancellable_executor(
-                kg_mgr.build_kg,
-                new_triple_list_data,
-                embed_mgr
-            )
+            await self._run_cancellable_executor(kg_mgr.build_kg, new_triple_list_data, embed_mgr)
 
             # 4. 持久化
             await self._run_cancellable_executor(embed_mgr.rebuild_faiss_index)
             await self._run_cancellable_executor(embed_mgr.save_to_file)
             await self._run_cancellable_executor(kg_mgr.save_to_file)
-            
-            return {"status": "success", "count": len(new_raw_paragraphs), "message": f"成功导入 {len(new_raw_paragraphs)} 条知识"}
-            
+
+            return {
+                "status": "success",
+                "count": len(new_raw_paragraphs),
+                "message": f"成功导入 {len(new_raw_paragraphs)} 条知识",
+            }
+
         except asyncio.CancelledError:
             logger.warning("[Plugin API] 导入操作被用户中断")
             return {"status": "cancelled", "message": "导入操作已被用户中断"}
@@ -158,11 +155,11 @@ class LPMMOperations:
     async def search(self, query: str, top_k: int = 3) -> List[str]:
         """
         检索知识库。
-        
+
         Args:
             query: 查询问题。
             top_k: 返回最相关的条目数。
-            
+
         Returns:
             List[str]: 相关文段列表。
         """
@@ -179,21 +176,21 @@ class LPMMOperations:
     async def delete(self, keyword: str, exact_match: bool = False) -> dict:
         """
         根据关键词或完整文段删除知识库内容。
-        
+
         Args:
             keyword: 匹配关键词或完整文段。
             exact_match: 是否使用完整文段匹配（True=完全匹配，False=关键词模糊匹配）。
-            
+
         Returns:
             dict: {"status": "success/info", "deleted_count": 删除条数, "message": "描述"}
         """
         try:
             embed_mgr, kg_mgr, _ = await self._get_managers()
-            
+
             # 1. 查找匹配的段落
             to_delete_keys = []
             to_delete_hashes = []
-            
+
             for key, item in embed_mgr.paragraphs_embedding_store.store.items():
                 if exact_match:
                     # 完整文段匹配
@@ -205,29 +202,25 @@ class LPMMOperations:
                     if keyword in item.str:
                         to_delete_keys.append(key)
                         to_delete_hashes.append(key.replace("paragraph-", "", 1))
-            
+
             if not to_delete_keys:
                 match_type = "完整文段" if exact_match else "关键词"
                 return {"status": "info", "deleted_count": 0, "message": f"未找到匹配的内容（{match_type}匹配）"}
 
             # 2. 执行删除
             # 将同步阻塞操作放到线程池中执行，避免阻塞事件循环
-            
+
             # a. 从向量库删除
             deleted_count, _ = await self._run_cancellable_executor(
-                embed_mgr.paragraphs_embedding_store.delete_items,
-                to_delete_keys
+                embed_mgr.paragraphs_embedding_store.delete_items, to_delete_keys
             )
             embed_mgr.stored_pg_hashes = set(embed_mgr.paragraphs_embedding_store.store.keys())
-            
+
             # b. 从知识图谱删除
             # 注意：必须使用关键字参数，避免 True 被误当作 ent_hashes 参数
             # 使用 partial 来传递关键字参数，因为 run_in_executor 不支持 **kwargs
             delete_func = partial(
-                kg_mgr.delete_paragraphs,
-                to_delete_hashes,
-                ent_hashes=None,
-                remove_orphan_entities=True
+                kg_mgr.delete_paragraphs, to_delete_hashes, ent_hashes=None, remove_orphan_entities=True
             )
             await self._run_cancellable_executor(delete_func)
 
@@ -235,9 +228,13 @@ class LPMMOperations:
             await self._run_cancellable_executor(embed_mgr.rebuild_faiss_index)
             await self._run_cancellable_executor(embed_mgr.save_to_file)
             await self._run_cancellable_executor(kg_mgr.save_to_file)
-            
+
             match_type = "完整文段" if exact_match else "关键词"
-            return {"status": "success", "deleted_count": deleted_count, "message": f"已成功删除 {deleted_count} 条相关知识（{match_type}匹配）"}
+            return {
+                "status": "success",
+                "deleted_count": deleted_count,
+                "message": f"已成功删除 {deleted_count} 条相关知识（{match_type}匹配）",
+            }
 
         except asyncio.CancelledError:
             logger.warning("[Plugin API] 删除操作被用户中断")
@@ -249,13 +246,13 @@ class LPMMOperations:
     async def clear_all(self) -> dict:
         """
         清空整个LPMM知识库（删除所有段落、实体、关系和知识图谱数据）。
-        
+
         Returns:
             dict: {"status": "success/error", "message": "描述", "stats": {...}}
         """
         try:
             embed_mgr, kg_mgr, _ = await self._get_managers()
-            
+
             # 记录清空前的统计信息
             before_stats = {
                 "paragraphs": len(embed_mgr.paragraphs_embedding_store.store),
@@ -264,40 +261,37 @@ class LPMMOperations:
                 "kg_nodes": len(kg_mgr.graph.get_node_list()),
                 "kg_edges": len(kg_mgr.graph.get_edge_list()),
             }
-            
+
             # 将同步阻塞操作放到线程池中执行，避免阻塞事件循环
-            
+
             # 1. 清空所有向量库
             # 获取所有keys
             para_keys = list(embed_mgr.paragraphs_embedding_store.store.keys())
             ent_keys = list(embed_mgr.entities_embedding_store.store.keys())
             rel_keys = list(embed_mgr.relation_embedding_store.store.keys())
-            
+
             # 删除所有段落向量
             para_deleted, _ = await self._run_cancellable_executor(
-                embed_mgr.paragraphs_embedding_store.delete_items,
-                para_keys
+                embed_mgr.paragraphs_embedding_store.delete_items, para_keys
             )
             embed_mgr.stored_pg_hashes.clear()
-            
+
             # 删除所有实体向量
             if ent_keys:
                 ent_deleted, _ = await self._run_cancellable_executor(
-                    embed_mgr.entities_embedding_store.delete_items,
-                    ent_keys
+                    embed_mgr.entities_embedding_store.delete_items, ent_keys
                 )
             else:
                 ent_deleted = 0
-            
+
             # 删除所有关系向量
             if rel_keys:
                 rel_deleted, _ = await self._run_cancellable_executor(
-                    embed_mgr.relation_embedding_store.delete_items,
-                    rel_keys
+                    embed_mgr.relation_embedding_store.delete_items, rel_keys
                 )
             else:
                 rel_deleted = 0
-            
+
             # 2. 清空所有 embedding store 的索引和映射
             # 确保 faiss_index 和 idx2hash 也被重置，并删除旧的索引文件
             def _clear_embedding_indices():
@@ -310,7 +304,7 @@ class LPMMOperations:
                     os.remove(embed_mgr.paragraphs_embedding_store.index_file_path)
                 if os.path.exists(embed_mgr.paragraphs_embedding_store.idx2hash_file_path):
                     os.remove(embed_mgr.paragraphs_embedding_store.idx2hash_file_path)
-                
+
                 # 清空实体索引
                 embed_mgr.entities_embedding_store.faiss_index = None
                 embed_mgr.entities_embedding_store.idx2hash = None
@@ -320,7 +314,7 @@ class LPMMOperations:
                     os.remove(embed_mgr.entities_embedding_store.index_file_path)
                 if os.path.exists(embed_mgr.entities_embedding_store.idx2hash_file_path):
                     os.remove(embed_mgr.entities_embedding_store.idx2hash_file_path)
-                
+
                 # 清空关系索引
                 embed_mgr.relation_embedding_store.faiss_index = None
                 embed_mgr.relation_embedding_store.idx2hash = None
@@ -330,9 +324,9 @@ class LPMMOperations:
                     os.remove(embed_mgr.relation_embedding_store.index_file_path)
                 if os.path.exists(embed_mgr.relation_embedding_store.idx2hash_file_path):
                     os.remove(embed_mgr.relation_embedding_store.idx2hash_file_path)
-            
+
             await self._run_cancellable_executor(_clear_embedding_indices)
-            
+
             # 3. 清空知识图谱
             # 获取所有段落hash
             all_pg_hashes = list(kg_mgr.stored_paragraph_hashes)
@@ -341,24 +335,22 @@ class LPMMOperations:
                 # 注意：必须使用关键字参数，避免 True 被误当作 ent_hashes 参数
                 # 使用 partial 来传递关键字参数，因为 run_in_executor 不支持 **kwargs
                 delete_func = partial(
-                    kg_mgr.delete_paragraphs,
-                    all_pg_hashes,
-                    ent_hashes=None,
-                    remove_orphan_entities=True
+                    kg_mgr.delete_paragraphs, all_pg_hashes, ent_hashes=None, remove_orphan_entities=True
                 )
                 await self._run_cancellable_executor(delete_func)
-            
+
             # 完全清空KG：创建新的空图（无论是否有段落hash都要执行）
             from quick_algo import di_graph
+
             kg_mgr.graph = di_graph.DiGraph()
             kg_mgr.stored_paragraph_hashes.clear()
             kg_mgr.ent_appear_cnt.clear()
-            
+
             # 4. 保存所有数据（此时所有store都是空的，索引也是None）
             # 注意：即使store为空，save_to_file也会保存空的DataFrame，这是正确的
             await self._run_cancellable_executor(embed_mgr.save_to_file)
             await self._run_cancellable_executor(kg_mgr.save_to_file)
-            
+
             after_stats = {
                 "paragraphs": len(embed_mgr.paragraphs_embedding_store.store),
                 "entities": len(embed_mgr.entities_embedding_store.store),
@@ -366,14 +358,14 @@ class LPMMOperations:
                 "kg_nodes": len(kg_mgr.graph.get_node_list()),
                 "kg_edges": len(kg_mgr.graph.get_edge_list()),
             }
-            
+
             return {
                 "status": "success",
                 "message": f"已成功清空LPMM知识库（删除 {para_deleted} 个段落、{ent_deleted} 个实体、{rel_deleted} 个关系）",
                 "stats": {
                     "before": before_stats,
                     "after": after_stats,
-                }
+                },
             }
 
         except asyncio.CancelledError:
@@ -383,6 +375,6 @@ class LPMMOperations:
             logger.error(f"[Plugin API] 清空知识库失败: {e}", exc_info=True)
             return {"status": "error", "message": str(e)}
 
+
 # 内部使用的单例
 lpmm_ops = LPMMOperations()
-

src/chat/logger/plan_reply_logger.py

@@ -136,4 +136,3 @@ class PlanReplyLogger:
             return str(value)
         # Fallback to string for other complex types
         return str(value)
-

src/chat/message_receive/bot.py

@@ -85,17 +85,17 @@ class ChatBot:
 
     async def _create_pfc_chat(self, message: MessageRecv):
         """创建或获取PFC对话实例
-        
+
         Args:
             message: 消息对象
         """
         try:
             chat_id = str(message.chat_stream.stream_id)
             private_name = str(message.message_info.user_info.user_nickname)
-            
+
             logger.debug(f"[私聊][{private_name}]创建或获取PFC对话: {chat_id}")
             await self.pfc_manager.get_or_create_conversation(chat_id, private_name)
-            
+
         except Exception as e:
             logger.error(f"创建PFC聊天失败: {e}")
             logger.error(traceback.format_exc())

src/chat/message_receive/message.py

@@ -96,7 +96,7 @@ class Message(MessageBase):
                 if processed_text:
                     return f"{global_config.bot.nickname}: {processed_text}"
                 return None
-            
+
             tasks = [process_forward_node(node_dict) for node_dict in segment.data]
             results = await asyncio.gather(*tasks, return_exceptions=True)
             segments_text = []

src/chat/message_receive/uni_message_sender.py

@@ -189,7 +189,7 @@ async def _send_message(message: MessageSending, show_log=True) -> bool:
 
                 # 如果未开启 API Server，直接跳过 Fallback
                 if not global_config.maim_message.enable_api_server:
-                    logger.debug(f"[API Server Fallback] API Server未开启，跳过fallback")
+                    logger.debug("[API Server Fallback] API Server未开启，跳过fallback")
                     if legacy_exception:
                         raise legacy_exception
                     return False
@@ -198,13 +198,13 @@ async def _send_message(message: MessageSending, show_log=True) -> bool:
                 extra_server = getattr(global_api, "extra_server", None)
 
                 if not extra_server:
-                    logger.warning(f"[API Server Fallback] extra_server不存在")
+                    logger.warning("[API Server Fallback] extra_server不存在")
                     if legacy_exception:
                         raise legacy_exception
                     return False
 
                 if not extra_server.is_running():
-                    logger.warning(f"[API Server Fallback] extra_server未运行")
+                    logger.warning("[API Server Fallback] extra_server未运行")
                     if legacy_exception:
                         raise legacy_exception
                     return False
@@ -253,7 +253,7 @@ async def _send_message(message: MessageSending, show_log=True) -> bool:
                 )
 
                 # 直接调用 Server 的 send_message 接口，它会自动处理路由
-                logger.debug(f"[API Server Fallback] 正在通过extra_server发送消息...")
+                logger.debug("[API Server Fallback] 正在通过extra_server发送消息...")
                 results = await extra_server.send_message(api_message)
                 logger.debug(f"[API Server Fallback] 发送结果: {results}")
 

src/chat/planner_actions/planner.py

@@ -35,6 +35,7 @@ logger = get_logger("planner")
 
 install(extra_lines=3)
 
+
 class ActionPlanner:
     def __init__(self, chat_id: str, action_manager: ActionManager):
         self.chat_id = chat_id
@@ -48,7 +49,7 @@ class ActionPlanner:
         self.last_obs_time_mark = 0.0
 
         self.plan_log: List[Tuple[str, float, Union[List[ActionPlannerInfo], str]]] = []
-        
+
         # 黑话缓存：使用 OrderedDict 实现 LRU，最多缓存10个
         self.unknown_words_cache: OrderedDict[str, None] = OrderedDict()
         self.unknown_words_cache_limit = 10
@@ -111,20 +112,29 @@ class ActionPlanner:
 
             # 替换 [picid:xxx] 为 [图片：描述]
             pic_pattern = r"\[picid:([^\]]+)\]"
+
             def replace_pic_id(pic_match: re.Match) -> str:
                 pic_id = pic_match.group(1)
                 description = translate_pid_to_description(pic_id)
                 return f"[图片：{description}]"
+
             msg_text = re.sub(pic_pattern, replace_pic_id, msg_text)
 
             # 替换用户引用格式：回复<aaa:bbb> 和 @<aaa:bbb>
-            platform = getattr(message, "user_info", None) and message.user_info.platform or getattr(message, "chat_info", None) and message.chat_info.platform or "qq"
+            platform = (
+                getattr(message, "user_info", None)
+                and message.user_info.platform
+                or getattr(message, "chat_info", None)
+                and message.chat_info.platform
+                or "qq"
+            )
             msg_text = replace_user_references(msg_text, platform, replace_bot_name=True)
 
             # 替换单独的 <用户名:用户ID> 格式（replace_user_references 已处理回复<和@<格式）
             # 匹配所有 <aaa:bbb> 格式，由于 replace_user_references 已经替换了回复<和@<格式，
             # 这里匹配到的应该都是单独的格式
             user_ref_pattern = r"<([^:<>]+):([^:<>]+)>"
+
             def replace_user_ref(user_match: re.Match) -> str:
                 user_name = user_match.group(1)
                 user_id = user_match.group(2)
@@ -137,6 +147,7 @@ class ActionPlanner:
                 except Exception:
                     # 如果解析失败，使用原始昵称
                     return user_name
+
             msg_text = re.sub(user_ref_pattern, replace_user_ref, msg_text)
 
             preview = msg_text if len(msg_text) <= 100 else f"{msg_text[:97]}..."
@@ -165,7 +176,7 @@ class ActionPlanner:
             else:
                 reasoning = "未提供原因"
             action_data = {key: value for key, value in action_json.items() if key not in ["action"]}
-            
+
             # 非no_reply动作需要target_message_id
             target_message = None
 
@@ -244,7 +255,7 @@ class ActionPlanner:
     def _update_unknown_words_cache(self, new_words: List[str]) -> None:
         """
         更新黑话缓存，将新的黑话加入缓存
-        
+
         Args:
             new_words: 新提取的黑话列表
         """
@@ -254,7 +265,7 @@ class ActionPlanner:
             word = word.strip()
             if not word:
                 continue
-            
+
             # 如果已存在，移到末尾（LRU）
             if word in self.unknown_words_cache:
                 self.unknown_words_cache.move_to_end(word)
@@ -269,10 +280,10 @@ class ActionPlanner:
     def _merge_unknown_words_with_cache(self, new_words: Optional[List[str]]) -> List[str]:
         """
         合并新提取的黑话和缓存中的黑话
-        
+
         Args:
             new_words: 新提取的黑话列表（可能为None）
-            
+
         Returns:
             合并后的黑话列表（去重）
         """
@@ -284,31 +295,29 @@ class ActionPlanner:
                     word = word.strip()
                     if word:
                         cleaned_new_words.append(word)
-        
+
         # 获取缓存中的黑话列表
         cached_words = list(self.unknown_words_cache.keys())
-        
+
         # 合并并去重（保留顺序：新提取的在前，缓存的在后）
         merged_words: List[str] = []
         seen = set()
-        
+
         # 先添加新提取的
         for word in cleaned_new_words:
             if word not in seen:
                 merged_words.append(word)
                 seen.add(word)
-        
+
         # 再添加缓存的（如果不在新提取的列表中）
         for word in cached_words:
             if word not in seen:
                 merged_words.append(word)
                 seen.add(word)
-        
+
         return merged_words
 
-    def _process_unknown_words_cache(
-        self, actions: List[ActionPlannerInfo]
-    ) -> None:
+    def _process_unknown_words_cache(self, actions: List[ActionPlannerInfo]) -> None:
         """
         处理黑话缓存逻辑：
         1. 检查是否有 reply action 提取了 unknown_words
@@ -316,7 +325,7 @@ class ActionPlanner:
         3. 如果缓存数量大于5，移除最老的2个
         4. 对于每个 reply action，合并缓存和新提取的黑话
         5. 更新缓存
-        
+
         Args:
             actions: 解析后的动作列表
         """
@@ -330,7 +339,7 @@ class ActionPlanner:
                     removed_count += 1
             if removed_count > 0:
                 logger.debug(f"{self.log_prefix}缓存数量大于5，移除最老的{removed_count}个缓存")
-        
+
         # 检查是否有 reply action 提取了 unknown_words
         has_extracted_unknown_words = False
         for action in actions:
@@ -340,22 +349,22 @@ class ActionPlanner:
                 if unknown_words and isinstance(unknown_words, list) and len(unknown_words) > 0:
                     has_extracted_unknown_words = True
                     break
-        
+
         # 如果当前 plan 的 reply 没有提取，移除最老的1个
         if not has_extracted_unknown_words:
             if len(self.unknown_words_cache) > 0:
                 self.unknown_words_cache.popitem(last=False)
                 logger.debug(f"{self.log_prefix}当前 plan 的 reply 没有提取黑话，移除最老的1个缓存")
-        
+
         # 对于每个 reply action，合并缓存和新提取的黑话
         for action in actions:
             if action.action_type == "reply":
                 action_data = action.action_data or {}
                 new_words = action_data.get("unknown_words")
-                
+
                 # 合并新提取的和缓存的黑话列表
                 merged_words = self._merge_unknown_words_with_cache(new_words)
-                
+
                 # 更新 action_data
                 if merged_words:
                     action_data["unknown_words"] = merged_words
@@ -366,7 +375,7 @@ class ActionPlanner:
                 else:
                     # 如果没有合并后的黑话，移除 unknown_words 字段
                     action_data.pop("unknown_words", None)
-                
+
                 # 更新缓存（将新提取的黑话加入缓存）
                 if new_words:
                     self._update_unknown_words_cache(new_words)
@@ -442,15 +451,19 @@ class ActionPlanner:
             # 检查是否已经有回复该消息的 action
             has_reply_to_force_message = False
             for action in actions:
-                if action.action_type == "reply" and action.action_message and action.action_message.message_id == force_reply_message.message_id:
+                if (
+                    action.action_type == "reply"
+                    and action.action_message
+                    and action.action_message.message_id == force_reply_message.message_id
+                ):
                     has_reply_to_force_message = True
                     break
-            
+
             # 如果没有回复该消息，强制添加回复 action
             if not has_reply_to_force_message:
                 # 移除所有 no_reply action（如果有）
                 actions = [a for a in actions if a.action_type != "no_reply"]
-                
+
                 # 创建强制回复 action
                 available_actions_dict = dict(current_available_actions)
                 force_reply_action = ActionPlannerInfo(
@@ -577,10 +590,11 @@ class ActionPlanner:
             if global_config.chat.think_mode == "classic":
                 reply_action_example = ""
                 if global_config.chat.llm_quote:
-                    reply_action_example += "5.如果要明确回复消息，使用quote，如果消息不多不需要明确回复，设置quote为false\n"
+                    reply_action_example += (
+                        "5.如果要明确回复消息，使用quote，如果消息不多不需要明确回复，设置quote为false\n"
+                    )
                 reply_action_example += (
-                    '{{"action":"reply", "target_message_id":"消息id(m+数字)", '
-                    '"unknown_words":["词语1","词语2"]'
+                    '{{"action":"reply", "target_message_id":"消息id(m+数字)", "unknown_words":["词语1","词语2"]'
                 )
                 if global_config.chat.llm_quote:
                     reply_action_example += ', "quote":"如果需要引用该message，设置为true"'
@@ -590,7 +604,9 @@ class ActionPlanner:
                     "5.think_level表示思考深度，0表示该回复不需要思考和回忆，1表示该回复需要进行回忆和思考\n"
                 )
                 if global_config.chat.llm_quote:
-                    reply_action_example += "6.如果要明确回复消息，使用quote，如果消息不多不需要明确回复，设置quote为false\n"
+                    reply_action_example += (
+                        "6.如果要明确回复消息，使用quote，如果消息不多不需要明确回复，设置quote为false\n"
+                    )
                 reply_action_example += (
                     '{{"action":"reply", "think_level":数值等级(0或1), '
                     '"target_message_id":"消息id(m+数字)", '
@@ -741,15 +757,21 @@ class ActionPlanner:
 
         except Exception as req_e:
             logger.error(f"{self.log_prefix}LLM 请求执行失败: {req_e}")
-            return f"LLM 请求失败，模型出现问题: {req_e}", [
-                ActionPlannerInfo(
-                    action_type="no_reply",
-                    reasoning=f"LLM 请求失败，模型出现问题: {req_e}",
-                    action_data={},
-                    action_message=None,
-                    available_actions=available_actions,
-                )
-            ], llm_content, llm_reasoning, llm_duration_ms
+            return (
+                f"LLM 请求失败，模型出现问题: {req_e}",
+                [
+                    ActionPlannerInfo(
+                        action_type="no_reply",
+                        reasoning=f"LLM 请求失败，模型出现问题: {req_e}",
+                        action_data={},
+                        action_message=None,
+                        available_actions=available_actions,
+                    )
+                ],
+                llm_content,
+                llm_reasoning,
+                llm_duration_ms,
+            )
 
         # 解析LLM响应
         extracted_reasoning = ""

src/chat/replyer/group_generator.py

@@ -1071,7 +1071,6 @@ class DefaultReplyer:
         chat_target_2_prompt = prompt_manager.get_prompt("chat_target_group2")
         chat_target_2 = await prompt_manager.render_prompt(chat_target_2_prompt)
 
-
         # 根据配置构建最终的 reply_style：支持 multiple_reply_style 按概率随机替换
         reply_style = global_config.personality.reply_style
         multi_styles = global_config.personality.multiple_reply_style

src/chat/replyer/private_generator.py

@@ -26,6 +26,7 @@ from src.chat.utils.chat_message_builder import (
 )
 from src.bw_learner.expression_selector import expression_selector
 from src.plugin_system.apis.message_api import translate_pid_to_description
+
 # from src.memory_system.memory_activator import MemoryActivator
 from src.person_info.person_info import Person, is_person_known
 from src.plugin_system.base.component_types import ActionInfo, EventType
@@ -807,7 +808,7 @@ class PrivateReplyer:
                 reply_style = global_config.personality.reply_style
 
         # 使用统一的 is_bot_self 函数判断是否是机器人自己（支持多平台，包括 WebUI）
-        
+
         if is_bot_self(platform, user_id):
             prompt_template = prompt_manager.get_prompt("private_replyer_self")
             prompt_template.add_context("target", target)

src/chat/utils/chat_message_builder.py

@@ -519,7 +519,7 @@ def _build_readable_messages_internal(
     output_lines: List[str] = []
 
     prev_timestamp: Optional[float] = None
-    for timestamp, name, content, is_action in detailed_message:
+    for timestamp, name, content, _is_action in detailed_message:
         # 检查是否需要插入长时间间隔提示
         if long_time_notice and prev_timestamp is not None:
             time_diff = timestamp - prev_timestamp

src/chat/utils/common_utils.py

@@ -5,6 +5,7 @@ from src.common.logger import get_logger
 
 logger = get_logger("common_utils")
 
+
 class TempMethodsExpression:
     """用于临时存放一些方法的类"""
 

src/common/data_models/chat_session_data_model.py

@@ -4,6 +4,7 @@ from src.common.database.database_model import ChatSession
 
 from . import BaseDatabaseDataModel
 
+
 class MaiChatSession(BaseDatabaseDataModel[ChatSession]):
     def __init__(self, session_id: str, platform: str, user_id: Optional[str] = None, group_id: Optional[str] = None):
         self.session_id = session_id
@@ -33,4 +34,4 @@ class MaiChatSession(BaseDatabaseDataModel[ChatSession]):
             platform=self.platform,
             user_id=self.user_id,
             group_id=self.group_id,
-        )
+        )

src/common/data_models/message_data_model.py

@@ -1,4 +1,4 @@
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from enum import Enum
 from typing import Any, Iterable, List, Optional, Tuple, Union
 

src/common/logger_color_and_mapping.py

@@ -221,5 +221,7 @@ if not supports_truecolor():
         CONVERTED_MODULE_COLORS[name] = escape_str
 else:
     for name, (hex_fore_color, hex_back_color, bold) in MODULE_COLORS.items():
-        escape_str = rgb_pair_to_ansi_truecolor(hex_to_rgb(hex_fore_color), hex_to_rgb(hex_back_color) if hex_back_color else None, bold)
-        CONVERTED_MODULE_COLORS[name] = escape_str
+        escape_str = rgb_pair_to_ansi_truecolor(
+            hex_to_rgb(hex_fore_color), hex_to_rgb(hex_back_color) if hex_back_color else None, bold
+        )
+        CONVERTED_MODULE_COLORS[name] = escape_str

src/common/message_server/api.py

@@ -9,6 +9,7 @@ from .server import get_global_server
 
 global_api = None
 
+
 def get_global_api() -> MessageServer:  # sourcery skip: extract-method
     """获取全局MessageServer实例"""
     global global_api
@@ -80,12 +81,12 @@ def get_global_api() -> MessageServer:  # sourcery skip: extract-method
                     api_logger.warning(f"Rejected connection with invalid API Key: {api_key}")
                     return False
 
-                server_config.on_auth = auth_handler # type: ignore # maim_message库写错类型了
+                server_config.on_auth = auth_handler  # type: ignore # maim_message库写错类型了
 
                 # 3. Setup Message Bridge
                 # Initialize refined route map if not exists
                 if not hasattr(global_api, "platform_map"):
-                    global_api.platform_map = {} # type: ignore # 不知道这是什么神奇写法 
+                    global_api.platform_map = {}  # type: ignore # 不知道这是什么神奇写法
 
                 async def bridge_message_handler(message: APIMessageBase, metadata: dict):
                     # 使用 MessageConverter 转换 APIMessageBase 到 Legacy MessageBase
@@ -108,7 +109,7 @@ def get_global_api() -> MessageServer:  # sourcery skip: extract-method
                             api_logger.debug(f"Bridge received: api_key='{api_key}', platform='{platform}'")
 
                             if platform:
-                                global_api.platform_map[platform] = api_key # type: ignore 
+                                global_api.platform_map[platform] = api_key  # type: ignore
                                 api_logger.info(f"Updated platform_map: {platform} -> {api_key}")
                         except Exception as e:
                             api_logger.warning(f"Failed to update platform map: {e}")
@@ -117,21 +118,21 @@ def get_global_api() -> MessageServer:  # sourcery skip: extract-method
                     if "raw_message" not in msg_dict:
                         msg_dict["raw_message"] = None
 
-                    await global_api.process_message(msg_dict) # type: ignore
+                    await global_api.process_message(msg_dict)  # type: ignore
 
-                server_config.on_message = bridge_message_handler # type: ignore # maim_message库写错类型了
+                server_config.on_message = bridge_message_handler  # type: ignore # maim_message库写错类型了
 
                 # 3.5. Register custom message handlers (bridge to Legacy handlers)
                 # message_id_echo: handles message ID echo from adapters
                 # 兼容新旧两个版本的 maim_message:
                 # - 旧版: handler(payload)
                 # - 新版: handler(payload, metadata)
-                async def custom_message_id_echo_handler(payload: dict, metadata: dict = None): # type: ignore
+                async def custom_message_id_echo_handler(payload: dict, metadata: dict = None):  # type: ignore
                     # Bridge to the Legacy custom handler registered in main.py
                     try:
                         # The Legacy handler expects the payload format directly
                         if hasattr(global_api, "_custom_message_handlers"):
-                            handler = global_api._custom_message_handlers.get("message_id_echo") # type: ignore # 已经不知道这是什么了
+                            handler = global_api._custom_message_handlers.get("message_id_echo")  # type: ignore # 已经不知道这是什么了
                             if handler:
                                 await handler(payload)
                                 api_logger.debug(f"Processed message_id_echo: {payload}")
@@ -140,7 +141,7 @@ def get_global_api() -> MessageServer:  # sourcery skip: extract-method
                     except Exception as e:
                         api_logger.warning(f"Failed to process message_id_echo: {e}")
 
-                server_config.register_custom_handler("message_id_echo", custom_message_id_echo_handler) # type: ignore # maim_message库写错类型了
+                server_config.register_custom_handler("message_id_echo", custom_message_id_echo_handler)  # type: ignore # maim_message库写错类型了
 
                 # 4. Initialize Server
                 extra_server = WebSocketServer(config=server_config)
@@ -167,7 +168,7 @@ def get_global_api() -> MessageServer:  # sourcery skip: extract-method
                 global_api.stop = patched_stop
 
                 # Attach for reference
-                global_api.extra_server = extra_server # type: ignore # 这是什么
+                global_api.extra_server = extra_server  # type: ignore # 这是什么
 
             except ImportError:
                 get_logger("maim_message").error(

src/common/utils/utils_file.py

@@ -9,6 +9,7 @@ from src.common.database.database import get_db_session
 
 logger = get_logger("file_utils")
 
+
 class FileUtils:
     @staticmethod
     def save_binary_to_file(file_path: Path, data: bytes):
@@ -35,7 +36,7 @@ class FileUtils:
         except Exception as e:
             logger.error(f"保存文件 {file_path} 失败: {e}")
             raise e
-        
+
     @staticmethod
     def get_file_path_by_hash(data_hash: str) -> Path:
         """
@@ -52,4 +53,4 @@ class FileUtils:
             if binary_data := session.exec(statement).first():
                 return Path(binary_data.full_path)
             else:
-                raise FileNotFoundError(f"未找到哈希值为 {data_hash} 的数据文件记录")
+                raise FileNotFoundError(f"未找到哈希值为 {data_hash} 的数据文件记录")

src/config/legacy_migration.py

@@ -278,4 +278,3 @@ def try_migrate_legacy_bot_config_dict(data: dict[str, Any]) -> MigrationResult:
 
     reason = ",".join(reasons)
     return MigrationResult(data=data, migrated=migrated_any, reason=reason)
-

src/dream/dream_agent.py

@@ -86,8 +86,8 @@ def init_dream_tools(chat_id: str) -> None:
     finish_maintenance = make_finish_maintenance(chat_id)
 
     search_jargon = make_search_jargon(chat_id)
-    delete_jargon = make_delete_jargon(chat_id)
-    update_jargon = make_update_jargon(chat_id)
+    _delete_jargon = make_delete_jargon(chat_id)
+    _update_jargon = make_update_jargon(chat_id)
 
     _dream_tool_registry.register_tool(
         DreamTool(

src/dream/dream_generator.py

@@ -54,8 +54,6 @@ async def generate_dream_summary(
 ) -> None:
     """生成梦境总结，输出到日志，并根据配置可选地推送给指定用户"""
     try:
-        
-
         # 第一步：建立工具调用结果映射 (call_id -> result)
         tool_results_map: dict[str, str] = {}
         for msg in conversation_messages:

src/dream/tools/__init__.py

@@ -4,4 +4,3 @@ dream agent 工具实现模块。
 每个工具的具体实现放在独立文件中，通过 make_xxx(chat_id) 工厂函数
 生成绑定到特定 chat_id 的协程函数，由 dream_agent.init_dream_tools 统一注册。
 """
-

src/dream/tools/create_chat_history_tool.py

@@ -63,4 +63,3 @@ def make_create_chat_history(chat_id: str):
             return f"create_chat_history 执行失败: {e}"
 
     return create_chat_history
-

src/dream/tools/delete_chat_history_tool.py

@@ -23,4 +23,3 @@ def make_delete_chat_history(chat_id: str):  # chat_id 目前未直接使用，
             return f"delete_chat_history 执行失败: {e}"
 
     return delete_chat_history
-

src/dream/tools/delete_jargon_tool.py

@@ -23,4 +23,3 @@ def make_delete_jargon(chat_id: str):  # chat_id 目前未直接使用，预留
             return f"delete_jargon 执行失败: {e}"
 
     return delete_jargon
-

src/dream/tools/finish_maintenance_tool.py

@@ -14,4 +14,3 @@ def make_finish_maintenance(chat_id: str):  # chat_id 目前未直接使用，
         return msg
 
     return finish_maintenance
-

src/dream/tools/get_chat_history_detail_tool.py

@@ -41,4 +41,3 @@ def make_get_chat_history_detail(chat_id: str):  # chat_id 目前未直接使用
             return f"get_chat_history_detail 执行失败: {e}"
 
     return get_chat_history_detail
-

src/dream/tools/search_chat_history_tool.py

@@ -212,4 +212,3 @@ def make_search_chat_history(chat_id: str):
             return f"search_chat_history 执行失败: {e}"
 
     return search_chat_history
-

src/dream/tools/update_chat_history_tool.py

@@ -46,4 +46,3 @@ def make_update_chat_history(chat_id: str):  # chat_id 目前未直接使用，
             return f"update_chat_history 执行失败: {e}"
 
     return update_chat_history
-

src/dream/tools/update_jargon_tool.py

@@ -49,4 +49,3 @@ def make_update_jargon(chat_id: str):  # chat_id 目前未直接使用，预留
             return f"update_jargon 执行失败: {e}"
 
     return update_jargon
-

src/llm_models/model_client/openai_client.py

@@ -458,8 +458,8 @@ def _default_normal_response_parser(
                 if not isinstance(arguments, dict):
                     # 此时为了调试方便，建议打印出 arguments 的类型
                     raise RespParseException(
-                        resp, 
-                        f"响应解析失败，工具调用参数无法解析为字典类型 type={type(arguments)} arguments={arguments}"
+                        resp,
+                        f"响应解析失败，工具调用参数无法解析为字典类型 type={type(arguments)} arguments={arguments}",
                     )
                 api_response.tool_calls.append(ToolCall(call.id, call.function.name, arguments))
             except json.JSONDecodeError as e:

src/memory_system/memory_retrieval.py

@@ -2,7 +2,7 @@ import time
 import json
 import asyncio
 from datetime import datetime
-from typing import List, Dict, Any, Optional, Tuple, Callable, cast
+from typing import List, Dict, Any, Optional, Tuple, Callable
 from src.common.logger import get_logger
 from src.config.config import global_config, model_config
 from src.prompt.prompt_manager import prompt_manager
@@ -34,7 +34,7 @@ def _cleanup_stale_not_found_thinking_back() -> None:
     try:
         with get_db_session() as session:
             statement = select(ThinkingQuestion).where(
-                (ThinkingQuestion.found_answer == False)
+                col(ThinkingQuestion.found_answer).is_(False)
                 & (ThinkingQuestion.updated_timestamp < datetime.fromtimestamp(threshold_time))
             )
             records = session.exec(statement).all()
@@ -786,8 +786,7 @@ def _get_recent_query_history(chat_id: str, time_window_seconds: float = 600.0)
         str: 格式化的查询历史字符串
     """
     try:
-        current_time = time.time()
-        start_time = current_time - time_window_seconds
+        _current_time = time.time()
 
         with get_db_session() as session:
             statement = (
@@ -838,15 +837,14 @@ def _get_recent_found_answers(chat_id: str, time_window_seconds: float = 600.0)
         List[str]: 格式化的答案列表，每个元素格式为 "问题：xxx\n答案：xxx"
     """
     try:
-        current_time = time.time()
-        start_time = current_time - time_window_seconds
+        _current_time = time.time()
 
         # 查询最近时间窗口内已找到答案的记录，按更新时间倒序
         with get_db_session() as session:
             statement = (
                 select(ThinkingQuestion)
                 .where(col(ThinkingQuestion.context) == chat_id)
-                .where(col(ThinkingQuestion.found_answer) == True)
+                .where(col(ThinkingQuestion.found_answer))
                 .where(col(ThinkingQuestion.answer).is_not(None))
                 .where(col(ThinkingQuestion.answer) != "")
                 .order_by(col(ThinkingQuestion.updated_timestamp).desc())

src/memory_system/retrieval_tools/query_chat_history.py

@@ -105,25 +105,27 @@ async def search_chat_history(
         # 检查参数
         if not keyword and not participant and not start_time and not end_time:
             return "未指定查询参数（需要提供keyword、participant、start_time或end_time之一）"
-        
+
         # 解析时间参数
         start_timestamp = None
         end_timestamp = None
-        
+
         if start_time:
             try:
                 from src.memory_system.memory_utils import parse_datetime_to_timestamp
+
                 start_timestamp = parse_datetime_to_timestamp(start_time)
             except ValueError as e:
                 return f"开始时间格式错误: {str(e)}，支持格式如：'2025-01-01' 或 '2025-01-01 12:00:00' 或 '2025/01/01'"
-        
+
         if end_time:
             try:
                 from src.memory_system.memory_utils import parse_datetime_to_timestamp
+
                 end_timestamp = parse_datetime_to_timestamp(end_time)
             except ValueError as e:
                 return f"结束时间格式错误: {str(e)}，支持格式如：'2025-01-01' 或 '2025-01-01 12:00:00' 或 '2025/01/01'"
-        
+
         # 验证时间范围
         if start_timestamp and end_timestamp and start_timestamp > end_timestamp:
             return "开始时间不能晚于结束时间"
@@ -158,23 +160,20 @@ async def search_chat_history(
                     f"search_chat_history 当前聊天流在黑名单中，强制使用本地查询，chat_id={chat_id}, keyword={keyword}, participant={participant}"
                 )
             query = ChatHistory.select().where(ChatHistory.chat_id == chat_id)
-        
+
         # 添加时间过滤条件
         if start_timestamp is not None and end_timestamp is not None:
             # 查询指定时间段内的记录（记录的时间范围与查询时间段有交集）
             # 记录的开始时间在查询时间段内，或记录的结束时间在查询时间段内，或记录完全包含查询时间段
             query = query.where(
                 (
-                    (ChatHistory.start_time >= start_timestamp)
-                    & (ChatHistory.start_time <= end_timestamp)
+                    (ChatHistory.start_time >= start_timestamp) & (ChatHistory.start_time <= end_timestamp)
                 )  # 记录开始时间在查询时间段内
                 | (
-                    (ChatHistory.end_time >= start_timestamp)
-                    & (ChatHistory.end_time <= end_timestamp)
+                    (ChatHistory.end_time >= start_timestamp) & (ChatHistory.end_time <= end_timestamp)
                 )  # 记录结束时间在查询时间段内
                 | (
-                    (ChatHistory.start_time <= start_timestamp)
-                    & (ChatHistory.end_time >= end_timestamp)
+                    (ChatHistory.start_time <= start_timestamp) & (ChatHistory.end_time >= end_timestamp)
                 )  # 记录完全包含查询时间段
             )
             logger.debug(
@@ -302,7 +301,7 @@ async def search_chat_history(
                     time_desc = f"时间<='{end_str}'"
                 if time_desc:
                     conditions.append(time_desc)
-            
+
             if conditions:
                 conditions_str = "且".join(conditions)
                 return f"未找到满足条件（{conditions_str}）的聊天记录"

src/memory_system/retrieval_tools/query_words.py

@@ -30,7 +30,7 @@ async def query_words(chat_id: str, words: str) -> str:
             if separator in words:
                 words_list = [w.strip() for w in words.split(separator) if w.strip()]
                 break
-        
+
         # 如果没有找到分隔符，整个字符串作为一个词语
         if not words_list:
             words_list = [words.strip()]
@@ -76,4 +76,3 @@ def register_tool():
         ],
         execute_func=query_words,
     )
-

src/plugin_system/apis/generator_api.py

@@ -123,7 +123,7 @@ async def generate_reply(
         # 如果 reply_time_point 未传入，设置为当前时间戳
         if reply_time_point is None:
             reply_time_point = time.time()
-        
+
         # 获取回复器
         logger.debug("[GeneratorAPI] 开始生成回复")
         replyer = get_replyer(chat_stream, chat_id, request_type=request_type)

src/plugin_system/apis/plugin_service_api.py

@@ -39,6 +39,18 @@ def unregister_service(service_name: str, plugin_name: Optional[str] = None) ->
     return plugin_service_registry.unregister_service(service_name, plugin_name)
 
 
-async def call_service(service_name: str, *args: Any, plugin_name: Optional[str] = None, **kwargs: Any) -> Any:
+async def call_service(
+    service_name: str,
+    *args: Any,
+    plugin_name: Optional[str] = None,
+    caller_plugin: Optional[str] = None,
+    **kwargs: Any,
+) -> Any:
     """调用插件服务。"""
-    return await plugin_service_registry.call_service(service_name, *args, plugin_name=plugin_name, **kwargs)
+    return await plugin_service_registry.call_service(
+        service_name,
+        *args,
+        plugin_name=plugin_name,
+        caller_plugin=caller_plugin,
+        **kwargs,
+    )

src/plugin_system/base/plugin_base.py

@@ -558,7 +558,9 @@ class PluginBase(ABC):
             if version_spec:
                 is_ok, msg = self._is_version_spec_satisfied(dep_version, version_spec)
                 if not is_ok:
-                    logger.error(f"{self.log_prefix} 依赖插件版本不满足: {dep_name} {version_spec}, 当前版本={dep_version} ({msg})")
+                    logger.error(
+                        f"{self.log_prefix} 依赖插件版本不满足: {dep_name} {version_spec}, 当前版本={dep_version} ({msg})"
+                    )
                     return False
 
             if min_version or max_version:

src/plugin_system/base/service_types.py

@@ -1,5 +1,5 @@
 from dataclasses import dataclass, field
-from typing import Any, Dict
+from typing import Any, Dict, List
 
 
 @dataclass
@@ -11,6 +11,8 @@ class PluginServiceInfo:
     version: str = "1.0.0"
     description: str = ""
     enabled: bool = True
+    public: bool = False
+    allowed_callers: List[str] = field(default_factory=list)
     params_schema: Dict[str, Any] = field(default_factory=dict)
     return_schema: Dict[str, Any] = field(default_factory=dict)
     metadata: Dict[str, Any] = field(default_factory=dict)

src/plugin_system/core/component_registry.py

@@ -274,6 +274,23 @@ class ComponentRegistry:
             logger.error(f"移除组件 {component_name} 时发生错误: {e}")
             return False
 
+    async def remove_components_by_plugin(self, plugin_name: str) -> int:
+        """移除某插件注册的所有组件。"""
+        targets = [
+            (component_info.name, component_info.component_type)
+            for component_info in self._components.values()
+            if component_info.plugin_name == plugin_name
+        ]
+
+        removed_count = 0
+        for component_name, component_type in targets:
+            if await self.remove_component(component_name, component_type, plugin_name):
+                removed_count += 1
+
+        if removed_count:
+            logger.info(f"已移除插件 {plugin_name} 的组件数量: {removed_count}")
+        return removed_count
+
     def remove_plugin_registry(self, plugin_name: str) -> bool:
         """移除插件注册信息
 
@@ -734,9 +751,7 @@ class ComponentRegistry:
             "enabled_plugins": len([p for p in self._plugins.values() if p.enabled]),
             "workflow_steps": workflow_step_count,
             "enabled_workflow_steps": enabled_workflow_step_count,
-            "workflow_steps_by_stage": {
-                stage.value: len(steps) for stage, steps in self._workflow_steps.items()
-            },
+            "workflow_steps_by_stage": {stage.value: len(steps) for stage, steps in self._workflow_steps.items()},
         }
 
 

src/plugin_system/core/plugin_manager.py

@@ -200,13 +200,43 @@ class PluginManager:
         """
         重载插件模块
         """
+        old_instance = self.loaded_plugins.get(plugin_name)
+        if not old_instance:
+            logger.warning(f"插件 {plugin_name} 未加载，无法重载")
+            return False
+
         if not await self.remove_registered_plugin(plugin_name):
             return False
+
         if not self.load_registered_plugin_classes(plugin_name)[0]:
+            logger.error(f"插件 {plugin_name} 重载失败，开始回滚旧实例")
+            rollback_ok = await self._rollback_failed_reload(plugin_name, old_instance)
+            if rollback_ok:
+                logger.info(f"插件 {plugin_name} 已回滚到旧版本实例")
+            else:
+                logger.error(f"插件 {plugin_name} 回滚失败，插件当前不可用")
             return False
+
         logger.debug(f"插件 {plugin_name} 重载成功")
         return True
 
+    async def _rollback_failed_reload(self, plugin_name: str, old_instance: PluginBase) -> bool:
+        """重载失败后回滚旧实例。"""
+        try:
+            await component_registry.remove_components_by_plugin(plugin_name)
+            component_registry.remove_plugin_registry(plugin_name)
+            plugin_service_registry.remove_services_by_plugin(plugin_name)
+
+            if not old_instance.register_plugin():
+                logger.error(f"插件 {plugin_name} 回滚失败: 旧实例重新注册失败")
+                return False
+
+            self.loaded_plugins[plugin_name] = old_instance
+            return True
+        except Exception as e:
+            logger.error(f"插件 {plugin_name} 回滚异常: {e}", exc_info=True)
+            return False
+
     def rescan_plugin_directory(self) -> Tuple[int, int]:
         """
         重新扫描插件根目录
@@ -399,7 +429,9 @@ class PluginManager:
 
     def _resolve_plugin_load_order(self, dependency_graph: Dict[str, Set[str]]) -> Tuple[List[str], Set[str]]:
         """根据依赖图计算加载顺序，并检测循环依赖。"""
-        indegree: Dict[str, int] = {plugin_name: len(dependencies) for plugin_name, dependencies in dependency_graph.items()}
+        indegree: Dict[str, int] = {
+            plugin_name: len(dependencies) for plugin_name, dependencies in dependency_graph.items()
+        }
         reverse_graph: Dict[str, Set[str]] = {plugin_name: set() for plugin_name in dependency_graph}
 
         for plugin_name, dependencies in dependency_graph.items():

src/plugin_system/core/plugin_service_registry.py

@@ -26,6 +26,9 @@ class PluginServiceRegistry:
         if "." in service_info.plugin_name:
             logger.error(f"插件服务所属插件名称 '{service_info.plugin_name}' 包含非法字符 '.'，请使用下划线替代")
             return False
+        if invalid_callers := [caller for caller in service_info.allowed_callers if "." in caller]:
+            logger.error(f"插件服务白名单包含非法调用方名称: {invalid_callers}")
+            return False
 
         full_name = service_info.full_name
         if full_name in self._services:
@@ -52,7 +55,9 @@ class PluginServiceRegistry:
         full_name = self._resolve_full_name(service_name, plugin_name)
         return self._service_handlers.get(full_name) if full_name else None
 
-    def list_services(self, plugin_name: Optional[str] = None, enabled_only: bool = False) -> Dict[str, PluginServiceInfo]:
+    def list_services(
+        self, plugin_name: Optional[str] = None, enabled_only: bool = False
+    ) -> Dict[str, PluginServiceInfo]:
         """列出插件服务。"""
         services = self._services.copy()
         if plugin_name:
@@ -103,12 +108,33 @@ class PluginServiceRegistry:
             logger.info(f"已移除插件 {plugin_name} 的服务数量: {removed_count}")
         return removed_count
 
-    async def call_service(self, service_name: str, *args: Any, plugin_name: Optional[str] = None, **kwargs: Any) -> Any:
+    async def call_service(
+        self,
+        service_name: str,
+        *args: Any,
+        plugin_name: Optional[str] = None,
+        caller_plugin: Optional[str] = None,
+        **kwargs: Any,
+    ) -> Any:
         """调用插件服务（支持同步/异步handler）。"""
         service_info = self.get_service(service_name, plugin_name)
         if not service_info:
             target_name = f"{plugin_name}.{service_name}" if plugin_name and "." not in service_name else service_name
             raise ValueError(f"插件服务未注册: {target_name}")
+
+        if (
+            "." not in service_name
+            and plugin_name is None
+            and caller_plugin
+            and service_info.plugin_name != caller_plugin
+        ):
+            raise PermissionError("跨插件服务调用必须使用完整服务名或显式指定plugin_name")
+
+        if not self._is_call_authorized(service_info, caller_plugin):
+            raise PermissionError(
+                f"调用被拒绝: caller={caller_plugin or 'anonymous'} 无权限访问服务 {service_info.full_name}"
+            )
+
         if not service_info.enabled:
             raise RuntimeError(f"插件服务已禁用: {service_info.full_name}")
 
@@ -116,8 +142,93 @@ class PluginServiceRegistry:
         if not handler:
             raise RuntimeError(f"插件服务处理器不存在: {service_info.full_name}")
 
+        self._validate_input_contract(service_info, args, kwargs)
+
         result = handler(*args, **kwargs)
-        return await result if inspect.isawaitable(result) else result
+        resolved_result = await result if inspect.isawaitable(result) else result
+        self._validate_output_contract(service_info, resolved_result)
+        return resolved_result
+
+    def _is_call_authorized(self, service_info: PluginServiceInfo, caller_plugin: Optional[str]) -> bool:
+        """检查服务调用权限。"""
+        if caller_plugin is None:
+            return service_info.public
+        if caller_plugin == service_info.plugin_name:
+            return True
+        if service_info.public:
+            return True
+        allowed_callers = {caller.strip() for caller in service_info.allowed_callers if caller.strip()}
+        return "*" in allowed_callers or caller_plugin in allowed_callers
+
+    def _validate_input_contract(
+        self, service_info: PluginServiceInfo, args: tuple[Any, ...], kwargs: dict[str, Any]
+    ) -> None:
+        """校验服务入参契约。"""
+        schema = service_info.params_schema
+        if not schema:
+            return
+
+        properties = schema.get("properties", {}) if isinstance(schema, dict) else {}
+        is_invocation_schema = "args" in properties or "kwargs" in properties
+
+        if is_invocation_schema:
+            payload = {"args": list(args), "kwargs": kwargs}
+            self._validate_by_schema(payload, schema, path="params")
+            return
+
+        if args:
+            raise ValueError(f"服务 {service_info.full_name} 的入参契约不允许位置参数")
+        self._validate_by_schema(kwargs, schema, path="params")
+
+    def _validate_output_contract(self, service_info: PluginServiceInfo, value: Any) -> None:
+        """校验服务返回值契约。"""
+        if not service_info.return_schema:
+            return
+        self._validate_by_schema(value, service_info.return_schema, path="return")
+
+    def _validate_by_schema(self, value: Any, schema: Dict[str, Any], path: str) -> None:
+        """基于简化JSON-Schema校验数据。"""
+        expected_type = schema.get("type")
+        if expected_type:
+            self._validate_type(value, expected_type, path)
+
+        enum_values = schema.get("enum")
+        if enum_values is not None and value not in enum_values:
+            raise ValueError(f"{path} 不在枚举范围内: {value}")
+
+        if expected_type == "object":
+            properties = schema.get("properties", {})
+            required = schema.get("required", [])
+
+            for field in required:
+                if field not in value:
+                    raise ValueError(f"{path}.{field} 为必填字段")
+
+            for field, field_value in value.items():
+                if field in properties:
+                    self._validate_by_schema(field_value, properties[field], f"{path}.{field}")
+                elif schema.get("additionalProperties", True) is False:
+                    raise ValueError(f"{path}.{field} 不允许额外字段")
+
+        if expected_type == "array":
+            if item_schema := schema.get("items"):
+                for index, item in enumerate(value):
+                    self._validate_by_schema(item, item_schema, f"{path}[{index}]")
+
+    def _validate_type(self, value: Any, expected_type: str, path: str) -> None:
+        """校验基础类型。"""
+        type_checkers: Dict[str, Callable[[Any], bool]] = {
+            "string": lambda item: isinstance(item, str),
+            "number": lambda item: isinstance(item, (int, float)) and not isinstance(item, bool),
+            "integer": lambda item: isinstance(item, int) and not isinstance(item, bool),
+            "boolean": lambda item: isinstance(item, bool),
+            "object": lambda item: isinstance(item, dict),
+            "array": lambda item: isinstance(item, list),
+            "null": lambda item: item is None,
+        }
+        checker = type_checkers.get(expected_type)
+        if checker and not checker(value):
+            raise TypeError(f"{path} 类型不匹配，期望 {expected_type}，实际 {type(value).__name__}")
 
     def _resolve_full_name(self, service_name: str, plugin_name: Optional[str] = None) -> Optional[str]:
         """解析服务全名。"""

src/plugin_system/core/workflow_engine.py

@@ -1,7 +1,8 @@
 from typing import Any, Awaitable, Callable, List, Optional, Tuple, Union
+import asyncio
+import inspect
 import time
 import uuid
-import inspect
 
 from src.common.logger import get_logger
 from src.plugin_system.base.component_types import EventType, MaiMessages
@@ -95,7 +96,9 @@ class WorkflowEngine:
             except Exception as e:
                 workflow_context.timings[stage_key] = time.perf_counter() - stage_start
                 workflow_context.errors.append(f"{stage_key}: {e}")
-                logger.error(f"[trace_id={workflow_context.trace_id}] Workflow阶段 {stage_key} 执行异常: {e}", exc_info=True)
+                logger.error(
+                    f"[trace_id={workflow_context.trace_id}] Workflow阶段 {stage_key} 执行异常: {e}", exc_info=True
+                )
                 self._execution_history[workflow_context.trace_id]["status"] = "failed"
                 self._execution_history[workflow_context.trace_id]["errors"] = workflow_context.errors.copy()
                 return (
@@ -144,11 +147,19 @@ class WorkflowEngine:
 
             step_timing_key = f"{stage.value}:{step_info.full_name}"
             step_start = time.perf_counter()
+            timeout_seconds = step_info.timeout_ms / 1000 if step_info.timeout_ms > 0 else None
 
             try:
-                result = handler(context, message)
-                if inspect.isawaitable(result):
-                    result = await result
+                if inspect.iscoroutinefunction(handler):
+                    coroutine = handler(context, message)
+                    result = await asyncio.wait_for(coroutine, timeout_seconds) if timeout_seconds else await coroutine
+                else:
+                    if timeout_seconds:
+                        result = await asyncio.wait_for(asyncio.to_thread(handler, context, message), timeout_seconds)
+                    else:
+                        result = handler(context, message)
+                    if inspect.isawaitable(result):
+                        result = await asyncio.wait_for(result, timeout_seconds) if timeout_seconds else await result
                 context.timings[step_timing_key] = time.perf_counter() - step_start
 
                 normalized_result = self._normalize_step_result(result)
@@ -165,10 +176,30 @@ class WorkflowEngine:
                     normalized_result.diagnostics.setdefault("error_code", WorkflowErrorCode.DOWNSTREAM_FAILED.value)
                 return normalized_result
 
+            except asyncio.TimeoutError:
+                context.timings[step_timing_key] = time.perf_counter() - step_start
+                timeout_message = f"workflow step timeout after {step_info.timeout_ms}ms"
+                context.errors.append(f"{step_info.full_name}: {timeout_message}")
+                logger.error(
+                    f"[trace_id={context.trace_id}] Workflow step {step_info.full_name} 超时: {timeout_message}"
+                )
+                return WorkflowStepResult(
+                    status="failed",
+                    return_message=timeout_message,
+                    diagnostics={
+                        "stage": stage.value,
+                        "step": step_info.full_name,
+                        "trace_id": context.trace_id,
+                        "error_code": WorkflowErrorCode.STEP_TIMEOUT.value,
+                    },
+                )
+
             except Exception as e:
                 context.timings[step_timing_key] = time.perf_counter() - step_start
                 context.errors.append(f"{step_info.full_name}: {e}")
-                logger.error(f"[trace_id={context.trace_id}] Workflow step {step_info.full_name} 执行异常: {e}", exc_info=True)
+                logger.error(
+                    f"[trace_id={context.trace_id}] Workflow step {step_info.full_name} 执行异常: {e}", exc_info=True
+                )
                 return WorkflowStepResult(
                     status="failed",
                     return_message=str(e),

src/prompt/prompt_manager.py

@@ -117,7 +117,7 @@ class PromptManager:
     def add_context_construct_function(self, name: str, func: Callable[[str], str | Coroutine[Any, Any, str]]) -> None:
         """
         添加一个上下文构造函数
-        
+
         Args:
             name (str): 上下文名称
             func (Callable[[str], str | Coroutine[Any, Any, str]]): 构造函数，接受 Prompt 名称作为参数，返回字符串或返回字符串的协程
@@ -144,7 +144,7 @@ class PromptManager:
     def get_prompt(self, prompt_name: str) -> Prompt:
         """
         获取指定名称的 Prompt 实例的克隆
-        
+
         Args:
             prompt_name (str): 要获取的 Prompt 名称
         Returns:
@@ -161,7 +161,7 @@ class PromptManager:
     async def render_prompt(self, prompt: Prompt) -> str:
         """
         渲染一个 Prompt 实例
-        
+
         Args:
             prompt (Prompt): 要渲染的 Prompt 实例
         Returns:

src/webui/api/planner.py

@@ -7,6 +7,7 @@
 2. 日志列表使用文件名解析时间戳，只在需要时读取完整内容
 3. 详情按需加载
 """
+
 import json
 from pathlib import Path
 from typing import List, Dict, Optional
@@ -21,6 +22,7 @@ PLAN_LOG_DIR = Path("logs/plan")
 
 class ChatSummary(BaseModel):
     """聊天摘要 - 轻量级，不读取文件内容"""
+
     chat_id: str
     plan_count: int
     latest_timestamp: float
@@ -29,6 +31,7 @@ class ChatSummary(BaseModel):
 
 class PlanLogSummary(BaseModel):
     """规划日志摘要"""
+
     chat_id: str
     timestamp: float
     filename: str
@@ -41,6 +44,7 @@ class PlanLogSummary(BaseModel):
 
 class PlanLogDetail(BaseModel):
     """规划日志详情"""
+
     type: str
     chat_id: str
     timestamp: float
@@ -54,6 +58,7 @@ class PlanLogDetail(BaseModel):
 
 class PlannerOverview(BaseModel):
     """规划器总览 - 轻量级统计"""
+
     total_chats: int
     total_plans: int
     chats: List[ChatSummary]
@@ -61,6 +66,7 @@ class PlannerOverview(BaseModel):
 
 class PaginatedChatLogs(BaseModel):
     """分页的聊天日志列表"""
+
     data: List[PlanLogSummary]
     total: int
     page: int
@@ -71,7 +77,7 @@ class PaginatedChatLogs(BaseModel):
 def parse_timestamp_from_filename(filename: str) -> float:
     """从文件名解析时间戳: 1766497488220_af92bdb1.json -> 1766497488.220"""
     try:
-        timestamp_str = filename.split('_')[0]
+        timestamp_str = filename.split("_")[0]
         # 时间戳是毫秒级，需要转换为秒
         return float(timestamp_str) / 1000
     except (ValueError, IndexError):
@@ -86,41 +92,39 @@ async def get_planner_overview():
     """
     if not PLAN_LOG_DIR.exists():
         return PlannerOverview(total_chats=0, total_plans=0, chats=[])
-    
+
     chats = []
     total_plans = 0
-    
+
     for chat_dir in PLAN_LOG_DIR.iterdir():
         if not chat_dir.is_dir():
             continue
-        
+
         # 只统计json文件数量
         json_files = list(chat_dir.glob("*.json"))
         plan_count = len(json_files)
         total_plans += plan_count
-        
+
         if plan_count == 0:
             continue
-        
+
         # 从文件名获取最新时间戳
         latest_file = max(json_files, key=lambda f: parse_timestamp_from_filename(f.name))
         latest_timestamp = parse_timestamp_from_filename(latest_file.name)
-        
-        chats.append(ChatSummary(
-            chat_id=chat_dir.name,
-            plan_count=plan_count,
-            latest_timestamp=latest_timestamp,
-            latest_filename=latest_file.name
-        ))
-    
+
+        chats.append(
+            ChatSummary(
+                chat_id=chat_dir.name,
+                plan_count=plan_count,
+                latest_timestamp=latest_timestamp,
+                latest_filename=latest_file.name,
+            )
+        )
+
     # 按最新时间戳排序
     chats.sort(key=lambda x: x.latest_timestamp, reverse=True)
-    
-    return PlannerOverview(
-        total_chats=len(chats),
-        total_plans=total_plans,
-        chats=chats
-    )
+
+    return PlannerOverview(total_chats=len(chats), total_plans=total_plans, chats=chats)
 
 
 @router.get("/chat/{chat_id}/logs", response_model=PaginatedChatLogs)
@@ -128,7 +132,7 @@ async def get_chat_plan_logs(
     chat_id: str,
     page: int = Query(1, ge=1),
     page_size: int = Query(20, ge=1, le=100),
-    search: Optional[str] = Query(None, description="搜索关键词，匹配提示词内容")
+    search: Optional[str] = Query(None, description="搜索关键词，匹配提示词内容"),
 ):
     """
     获取指定聊天的规划日志列表（分页）
@@ -137,73 +141,69 @@ async def get_chat_plan_logs(
     """
     chat_dir = PLAN_LOG_DIR / chat_id
     if not chat_dir.exists():
-        return PaginatedChatLogs(
-            data=[], total=0, page=page, page_size=page_size, chat_id=chat_id
-        )
-    
+        return PaginatedChatLogs(data=[], total=0, page=page, page_size=page_size, chat_id=chat_id)
+
     # 先获取所有文件并按时间戳排序
     json_files = list(chat_dir.glob("*.json"))
     json_files.sort(key=lambda f: parse_timestamp_from_filename(f.name), reverse=True)
-    
+
     # 如果有搜索关键词，需要过滤文件
     if search:
         search_lower = search.lower()
         filtered_files = []
         for log_file in json_files:
             try:
-                with open(log_file, 'r', encoding='utf-8') as f:
+                with open(log_file, "r", encoding="utf-8") as f:
                     data = json.load(f)
-                    prompt = data.get('prompt', '')
+                    prompt = data.get("prompt", "")
                     if search_lower in prompt.lower():
                         filtered_files.append(log_file)
             except Exception:
                 continue
         json_files = filtered_files
-    
+
     total = len(json_files)
-    
+
     # 分页 - 只读取当前页的文件
     offset = (page - 1) * page_size
-    page_files = json_files[offset:offset + page_size]
-    
+    page_files = json_files[offset : offset + page_size]
+
     logs = []
     for log_file in page_files:
         try:
-            with open(log_file, 'r', encoding='utf-8') as f:
+            with open(log_file, "r", encoding="utf-8") as f:
                 data = json.load(f)
-                reasoning = data.get('reasoning', '')
-                actions = data.get('actions', [])
-                action_types = [a.get('action_type', '') for a in actions if a.get('action_type')]
-                logs.append(PlanLogSummary(
-                    chat_id=data.get('chat_id', chat_id),
-                    timestamp=data.get('timestamp', parse_timestamp_from_filename(log_file.name)),
-                    filename=log_file.name,
-                    action_count=len(actions),
-                    action_types=action_types,
-                    total_plan_ms=data.get('timing', {}).get('total_plan_ms', 0),
-                    llm_duration_ms=data.get('timing', {}).get('llm_duration_ms', 0),
-                    reasoning_preview=reasoning[:100] if reasoning else ''
-                ))
+                reasoning = data.get("reasoning", "")
+                actions = data.get("actions", [])
+                action_types = [a.get("action_type", "") for a in actions if a.get("action_type")]
+                logs.append(
+                    PlanLogSummary(
+                        chat_id=data.get("chat_id", chat_id),
+                        timestamp=data.get("timestamp", parse_timestamp_from_filename(log_file.name)),
+                        filename=log_file.name,
+                        action_count=len(actions),
+                        action_types=action_types,
+                        total_plan_ms=data.get("timing", {}).get("total_plan_ms", 0),
+                        llm_duration_ms=data.get("timing", {}).get("llm_duration_ms", 0),
+                        reasoning_preview=reasoning[:100] if reasoning else "",
+                    )
+                )
         except Exception:
             # 文件读取失败时使用文件名信息
-            logs.append(PlanLogSummary(
-                chat_id=chat_id,
-                timestamp=parse_timestamp_from_filename(log_file.name),
-                filename=log_file.name,
-                action_count=0,
-                action_types=[],
-                total_plan_ms=0,
-                llm_duration_ms=0,
-                reasoning_preview='[读取失败]'
-            ))
-    
-    return PaginatedChatLogs(
-        data=logs,
-        total=total,
-        page=page,
-        page_size=page_size,
-        chat_id=chat_id
-    )
+            logs.append(
+                PlanLogSummary(
+                    chat_id=chat_id,
+                    timestamp=parse_timestamp_from_filename(log_file.name),
+                    filename=log_file.name,
+                    action_count=0,
+                    action_types=[],
+                    total_plan_ms=0,
+                    llm_duration_ms=0,
+                    reasoning_preview="[读取失败]",
+                )
+            )
+
+    return PaginatedChatLogs(data=logs, total=total, page=page, page_size=page_size, chat_id=chat_id)
 
 
 @router.get("/log/{chat_id}/{filename}", response_model=PlanLogDetail)
@@ -212,22 +212,23 @@ async def get_log_detail(chat_id: str, filename: str):
     log_file = PLAN_LOG_DIR / chat_id / filename
     if not log_file.exists():
         raise HTTPException(status_code=404, detail="日志文件不存在")
-    
+
     try:
-        with open(log_file, 'r', encoding='utf-8') as f:
+        with open(log_file, "r", encoding="utf-8") as f:
             data = json.load(f)
             return PlanLogDetail(**data)
     except Exception as e:
-        raise HTTPException(status_code=500, detail=f"读取日志失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"读取日志失败: {str(e)}") from e
 
 
 # ========== 兼容旧接口 ==========
 
+
 @router.get("/stats")
 async def get_planner_stats():
     """获取规划器统计信息 - 兼容旧接口"""
     overview = await get_planner_overview()
-    
+
     # 获取最近10条计划的摘要
     recent_plans = []
     for chat in overview.chats[:5]:  # 从最近5个聊天中获取
@@ -236,17 +237,17 @@ async def get_planner_stats():
             recent_plans.extend(chat_logs.data)
         except Exception:
             continue
-    
+
     # 按时间排序取前10
     recent_plans.sort(key=lambda x: x.timestamp, reverse=True)
     recent_plans = recent_plans[:10]
-    
+
     return {
         "total_chats": overview.total_chats,
         "total_plans": overview.total_plans,
         "avg_plan_time_ms": 0,
         "avg_llm_time_ms": 0,
-        "recent_plans": recent_plans
+        "recent_plans": recent_plans,
     }
 
 
@@ -258,44 +259,43 @@ async def get_chat_list():
 
 
 @router.get("/all-logs")
-async def get_all_logs(
-    page: int = Query(1, ge=1),
-    page_size: int = Query(20, ge=1, le=100)
-):
+async def get_all_logs(page: int = Query(1, ge=1), page_size: int = Query(20, ge=1, le=100)):
     """获取所有规划日志 - 兼容旧接口"""
     if not PLAN_LOG_DIR.exists():
         return {"data": [], "total": 0, "page": page, "page_size": page_size}
-    
+
     # 收集所有文件
     all_files = []
     for chat_dir in PLAN_LOG_DIR.iterdir():
         if chat_dir.is_dir():
             for log_file in chat_dir.glob("*.json"):
                 all_files.append((chat_dir.name, log_file))
-    
+
     # 按时间戳排序
     all_files.sort(key=lambda x: parse_timestamp_from_filename(x[1].name), reverse=True)
-    
+
     total = len(all_files)
     offset = (page - 1) * page_size
-    page_files = all_files[offset:offset + page_size]
-    
+    page_files = all_files[offset : offset + page_size]
+
     logs = []
     for chat_id, log_file in page_files:
         try:
-            with open(log_file, 'r', encoding='utf-8') as f:
+            with open(log_file, "r", encoding="utf-8") as f:
                 data = json.load(f)
-                reasoning = data.get('reasoning', '')
-                logs.append({
-                    "chat_id": data.get('chat_id', chat_id),
-                    "timestamp": data.get('timestamp', parse_timestamp_from_filename(log_file.name)),
-                    "filename": log_file.name,
-                    "action_count": len(data.get('actions', [])),
-                    "total_plan_ms": data.get('timing', {}).get('total_plan_ms', 0),
-                    "llm_duration_ms": data.get('timing', {}).get('llm_duration_ms', 0),
-                    "reasoning_preview": reasoning[:100] if reasoning else ''
-                })
+                reasoning = data.get("reasoning", "")
+                logs.append(
+                    {
+                        "chat_id": data.get("chat_id", chat_id),
+                        "timestamp": data.get("timestamp", parse_timestamp_from_filename(log_file.name)),
+                        "filename": log_file.name,
+                        "action_count": len(data.get("actions", [])),
+                        "total_plan_ms": data.get("timing", {}).get("total_plan_ms", 0),
+                        "llm_duration_ms": data.get("timing", {}).get("llm_duration_ms", 0),
+                        "reasoning_preview": reasoning[:100] if reasoning else "",
+                    }
+                )
         except Exception:
             continue
-    
-    return {"data": logs, "total": total, "page": page, "page_size": page_size}
+
+    return {"data": logs, "total": total, "page": page, "page_size": page_size}

src/webui/api/replier.py

@@ -7,6 +7,7 @@
 2. 日志列表使用文件名解析时间戳，只在需要时读取完整内容
 3. 详情按需加载
 """
+
 import json
 from pathlib import Path
 from typing import List, Dict, Optional
@@ -21,6 +22,7 @@ REPLY_LOG_DIR = Path("logs/reply")
 
 class ReplierChatSummary(BaseModel):
     """聊天摘要 - 轻量级，不读取文件内容"""
+
     chat_id: str
     reply_count: int
     latest_timestamp: float
@@ -29,6 +31,7 @@ class ReplierChatSummary(BaseModel):
 
 class ReplyLogSummary(BaseModel):
     """回复日志摘要"""
+
     chat_id: str
     timestamp: float
     filename: str
@@ -41,6 +44,7 @@ class ReplyLogSummary(BaseModel):
 
 class ReplyLogDetail(BaseModel):
     """回复日志详情"""
+
     type: str
     chat_id: str
     timestamp: float
@@ -57,6 +61,7 @@ class ReplyLogDetail(BaseModel):
 
 class ReplierOverview(BaseModel):
     """回复器总览 - 轻量级统计"""
+
     total_chats: int
     total_replies: int
     chats: List[ReplierChatSummary]
@@ -64,6 +69,7 @@ class ReplierOverview(BaseModel):
 
 class PaginatedReplyLogs(BaseModel):
     """分页的回复日志列表"""
+
     data: List[ReplyLogSummary]
     total: int
     page: int
@@ -74,7 +80,7 @@ class PaginatedReplyLogs(BaseModel):
 def parse_timestamp_from_filename(filename: str) -> float:
     """从文件名解析时间戳: 1766497488220_af92bdb1.json -> 1766497488.220"""
     try:
-        timestamp_str = filename.split('_')[0]
+        timestamp_str = filename.split("_")[0]
         # 时间戳是毫秒级，需要转换为秒
         return float(timestamp_str) / 1000
     except (ValueError, IndexError):
@@ -89,41 +95,39 @@ async def get_replier_overview():
     """
     if not REPLY_LOG_DIR.exists():
         return ReplierOverview(total_chats=0, total_replies=0, chats=[])
-    
+
     chats = []
     total_replies = 0
-    
+
     for chat_dir in REPLY_LOG_DIR.iterdir():
         if not chat_dir.is_dir():
             continue
-        
+
         # 只统计json文件数量
         json_files = list(chat_dir.glob("*.json"))
         reply_count = len(json_files)
         total_replies += reply_count
-        
+
         if reply_count == 0:
             continue
-        
+
         # 从文件名获取最新时间戳
         latest_file = max(json_files, key=lambda f: parse_timestamp_from_filename(f.name))
         latest_timestamp = parse_timestamp_from_filename(latest_file.name)
-        
-        chats.append(ReplierChatSummary(
-            chat_id=chat_dir.name,
-            reply_count=reply_count,
-            latest_timestamp=latest_timestamp,
-            latest_filename=latest_file.name
-        ))
-    
+
+        chats.append(
+            ReplierChatSummary(
+                chat_id=chat_dir.name,
+                reply_count=reply_count,
+                latest_timestamp=latest_timestamp,
+                latest_filename=latest_file.name,
+            )
+        )
+
     # 按最新时间戳排序
     chats.sort(key=lambda x: x.latest_timestamp, reverse=True)
-    
-    return ReplierOverview(
-        total_chats=len(chats),
-        total_replies=total_replies,
-        chats=chats
-    )
+
+    return ReplierOverview(total_chats=len(chats), total_replies=total_replies, chats=chats)
 
 
 @router.get("/chat/{chat_id}/logs", response_model=PaginatedReplyLogs)
@@ -131,7 +135,7 @@ async def get_chat_reply_logs(
     chat_id: str,
     page: int = Query(1, ge=1),
     page_size: int = Query(20, ge=1, le=100),
-    search: Optional[str] = Query(None, description="搜索关键词，匹配提示词内容")
+    search: Optional[str] = Query(None, description="搜索关键词，匹配提示词内容"),
 ):
     """
     获取指定聊天的回复日志列表（分页）
@@ -140,71 +144,67 @@ async def get_chat_reply_logs(
     """
     chat_dir = REPLY_LOG_DIR / chat_id
     if not chat_dir.exists():
-        return PaginatedReplyLogs(
-            data=[], total=0, page=page, page_size=page_size, chat_id=chat_id
-        )
-    
+        return PaginatedReplyLogs(data=[], total=0, page=page, page_size=page_size, chat_id=chat_id)
+
     # 先获取所有文件并按时间戳排序
     json_files = list(chat_dir.glob("*.json"))
     json_files.sort(key=lambda f: parse_timestamp_from_filename(f.name), reverse=True)
-    
+
     # 如果有搜索关键词，需要过滤文件
     if search:
         search_lower = search.lower()
         filtered_files = []
         for log_file in json_files:
             try:
-                with open(log_file, 'r', encoding='utf-8') as f:
+                with open(log_file, "r", encoding="utf-8") as f:
                     data = json.load(f)
-                    prompt = data.get('prompt', '')
+                    prompt = data.get("prompt", "")
                     if search_lower in prompt.lower():
                         filtered_files.append(log_file)
             except Exception:
                 continue
         json_files = filtered_files
-    
+
     total = len(json_files)
-    
+
     # 分页 - 只读取当前页的文件
     offset = (page - 1) * page_size
-    page_files = json_files[offset:offset + page_size]
-    
+    page_files = json_files[offset : offset + page_size]
+
     logs = []
     for log_file in page_files:
         try:
-            with open(log_file, 'r', encoding='utf-8') as f:
+            with open(log_file, "r", encoding="utf-8") as f:
                 data = json.load(f)
-                output = data.get('output', '')
-                logs.append(ReplyLogSummary(
-                    chat_id=data.get('chat_id', chat_id),
-                    timestamp=data.get('timestamp', parse_timestamp_from_filename(log_file.name)),
-                    filename=log_file.name,
-                    model=data.get('model', ''),
-                    success=data.get('success', True),
-                    llm_ms=data.get('timing', {}).get('llm_ms', 0),
-                    overall_ms=data.get('timing', {}).get('overall_ms', 0),
-                    output_preview=output[:100] if output else ''
-                ))
+                output = data.get("output", "")
+                logs.append(
+                    ReplyLogSummary(
+                        chat_id=data.get("chat_id", chat_id),
+                        timestamp=data.get("timestamp", parse_timestamp_from_filename(log_file.name)),
+                        filename=log_file.name,
+                        model=data.get("model", ""),
+                        success=data.get("success", True),
+                        llm_ms=data.get("timing", {}).get("llm_ms", 0),
+                        overall_ms=data.get("timing", {}).get("overall_ms", 0),
+                        output_preview=output[:100] if output else "",
+                    )
+                )
         except Exception:
             # 文件读取失败时使用文件名信息
-            logs.append(ReplyLogSummary(
-                chat_id=chat_id,
-                timestamp=parse_timestamp_from_filename(log_file.name),
-                filename=log_file.name,
-                model='',
-                success=False,
-                llm_ms=0,
-                overall_ms=0,
-                output_preview='[读取失败]'
-            ))
-    
-    return PaginatedReplyLogs(
-        data=logs,
-        total=total,
-        page=page,
-        page_size=page_size,
-        chat_id=chat_id
-    )
+            logs.append(
+                ReplyLogSummary(
+                    chat_id=chat_id,
+                    timestamp=parse_timestamp_from_filename(log_file.name),
+                    filename=log_file.name,
+                    model="",
+                    success=False,
+                    llm_ms=0,
+                    overall_ms=0,
+                    output_preview="[读取失败]",
+                )
+            )
+
+    return PaginatedReplyLogs(data=logs, total=total, page=page, page_size=page_size, chat_id=chat_id)
 
 
 @router.get("/log/{chat_id}/{filename}", response_model=ReplyLogDetail)
@@ -213,35 +213,36 @@ async def get_reply_log_detail(chat_id: str, filename: str):
     log_file = REPLY_LOG_DIR / chat_id / filename
     if not log_file.exists():
         raise HTTPException(status_code=404, detail="日志文件不存在")
-    
+
     try:
-        with open(log_file, 'r', encoding='utf-8') as f:
+        with open(log_file, "r", encoding="utf-8") as f:
             data = json.load(f)
             return ReplyLogDetail(
-                type=data.get('type', 'reply'),
-                chat_id=data.get('chat_id', chat_id),
-                timestamp=data.get('timestamp', 0),
-                prompt=data.get('prompt', ''),
-                output=data.get('output', ''),
-                processed_output=data.get('processed_output', []),
-                model=data.get('model', ''),
-                reasoning=data.get('reasoning', ''),
-                think_level=data.get('think_level', 0),
-                timing=data.get('timing', {}),
-                error=data.get('error'),
-                success=data.get('success', True)
+                type=data.get("type", "reply"),
+                chat_id=data.get("chat_id", chat_id),
+                timestamp=data.get("timestamp", 0),
+                prompt=data.get("prompt", ""),
+                output=data.get("output", ""),
+                processed_output=data.get("processed_output", []),
+                model=data.get("model", ""),
+                reasoning=data.get("reasoning", ""),
+                think_level=data.get("think_level", 0),
+                timing=data.get("timing", {}),
+                error=data.get("error"),
+                success=data.get("success", True),
             )
     except Exception as e:
-        raise HTTPException(status_code=500, detail=f"读取日志失败: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"读取日志失败: {str(e)}") from e
 
 
 # ========== 兼容接口 ==========
 
+
 @router.get("/stats")
 async def get_replier_stats():
     """获取回复器统计信息"""
     overview = await get_replier_overview()
-    
+
     # 获取最近10条回复的摘要
     recent_replies = []
     for chat in overview.chats[:5]:  # 从最近5个聊天中获取
@@ -250,15 +251,15 @@ async def get_replier_stats():
             recent_replies.extend(chat_logs.data)
         except Exception:
             continue
-    
+
     # 按时间排序取前10
     recent_replies.sort(key=lambda x: x.timestamp, reverse=True)
     recent_replies = recent_replies[:10]
-    
+
     return {
         "total_chats": overview.total_chats,
         "total_replies": overview.total_replies,
-        "recent_replies": recent_replies
+        "recent_replies": recent_replies,
     }
 
 
@@ -266,4 +267,4 @@ async def get_replier_stats():
 async def get_replier_chat_list():
     """获取所有聊天ID列表"""
     overview = await get_replier_overview()
-    return [chat.chat_id for chat in overview.chats]
+    return [chat.chat_id for chat in overview.chats]

src/webui/dependencies.py

@@ -1,6 +1,6 @@
 from typing import Optional
-from fastapi import Depends, Cookie, Header, Request, HTTPException
-from .core import get_current_token, get_token_manager, check_auth_rate_limit, check_api_rate_limit
+from fastapi import Depends, Cookie, Header, Request
+from .core import get_current_token, get_token_manager, check_auth_rate_limit
 
 
 async def require_auth(

src/webui/middleware/anti_crawler.py

@@ -124,6 +124,7 @@ SCANNER_SPECIFIC_HEADERS = {
 # loose: 宽松模式（较宽松的检测，较高的频率限制）
 # basic: 基础模式（只记录恶意访问，不阻止，不限制请求数，不跟踪IP）
 
+
 # IP白名单配置（从配置文件读取，逗号分隔）
 # 支持格式：
 # - 精确IP：127.0.0.1, 192.168.1.100
@@ -151,7 +152,7 @@ def _parse_allowed_ips(ip_string: str) -> list:
         ip_entry = ip_entry.strip()  # 去除空格
         if not ip_entry:
             continue
-        
+
         # 跳过注释行（以#开头）
         if ip_entry.startswith("#"):
             continue
@@ -237,19 +238,21 @@ def _convert_wildcard_to_regex(wildcard_pattern: str) -> Optional[str]:
 def _get_anti_crawler_config():
     """获取防爬虫配置"""
     from src.config.config import global_config
+
     return {
-        'mode': global_config.webui.anti_crawler_mode,
-        'allowed_ips': _parse_allowed_ips(global_config.webui.allowed_ips),
-        'trusted_proxies': _parse_allowed_ips(global_config.webui.trusted_proxies),
-        'trust_xff': global_config.webui.trust_xff
+        "mode": global_config.webui.anti_crawler_mode,
+        "allowed_ips": _parse_allowed_ips(global_config.webui.allowed_ips),
+        "trusted_proxies": _parse_allowed_ips(global_config.webui.trusted_proxies),
+        "trust_xff": global_config.webui.trust_xff,
     }
 
+
 # 初始化配置（将在模块加载时执行）
 _config = _get_anti_crawler_config()
-ANTI_CRAWLER_MODE = _config['mode']
-ALLOWED_IPS = _config['allowed_ips']
-TRUSTED_PROXIES = _config['trusted_proxies']
-TRUST_XFF = _config['trust_xff']
+ANTI_CRAWLER_MODE = _config["mode"]
+ALLOWED_IPS = _config["allowed_ips"]
+TRUSTED_PROXIES = _config["trusted_proxies"]
+TRUST_XFF = _config["trust_xff"]
 
 
 def _get_mode_config(mode: str) -> dict:

src/webui/routers/annual_report.py

@@ -333,7 +333,7 @@ async def get_social_network(year: int = 2025) -> SocialNetworkData:
             statement = select(func.count()).where(
                 col(Messages.timestamp) >= datetime.fromtimestamp(start_ts),
                 col(Messages.timestamp) <= datetime.fromtimestamp(end_ts),
-                col(Messages.is_at) == True,
+                col(Messages.is_at),
             )
             data.at_count = int(session.exec(statement).first() or 0)
 
@@ -342,7 +342,7 @@ async def get_social_network(year: int = 2025) -> SocialNetworkData:
             statement = select(func.count()).where(
                 col(Messages.timestamp) >= datetime.fromtimestamp(start_ts),
                 col(Messages.timestamp) <= datetime.fromtimestamp(end_ts),
-                col(Messages.is_mentioned) == True,
+                col(Messages.is_mentioned),
             )
             data.mentioned_count = int(session.exec(statement).first() or 0)
 
@@ -552,7 +552,7 @@ async def get_expression_vibe(year: int = 2025) -> ExpressionVibeData:
         # 1. 表情包之王 - 使用次数最多的表情包
         with get_db_session() as session:
             statement = (
-                select(Images).where(col(Images.is_registered) == True).order_by(desc(col(Images.query_count))).limit(5)
+                select(Images).where(col(Images.is_registered)).order_by(desc(col(Images.query_count))).limit(5)
             )
             top_emojis = session.exec(statement).all()
         if top_emojis:
@@ -636,7 +636,7 @@ async def get_expression_vibe(year: int = 2025) -> ExpressionVibeData:
             statement = select(func.count()).where(
                 col(Messages.timestamp) >= datetime.fromtimestamp(start_ts),
                 col(Messages.timestamp) <= datetime.fromtimestamp(end_ts),
-                col(Messages.is_picture) == True,
+                col(Messages.is_picture),
             )
             data.image_processed_count = int(session.exec(statement).first() or 0)
 
@@ -781,12 +781,12 @@ async def get_achievements(year: int = 2025) -> AchievementData:
         # 1. 新学到的黑话数量
         # Jargon 表没有时间字段,统计全部已确认的黑话
         with get_db_session() as session:
-            statement = select(func.count()).where(col(Jargon.is_jargon) == True)
+            statement = select(func.count()).where(col(Jargon.is_jargon))
             data.new_jargon_count = int(session.exec(statement).first() or 0)
 
         # 2. 代表性黑话示例
         with get_db_session() as session:
-            statement = select(Jargon).where(col(Jargon.is_jargon) == True).order_by(desc(col(Jargon.count))).limit(5)
+            statement = select(Jargon).where(col(Jargon.is_jargon)).order_by(desc(col(Jargon.count))).limit(5)
             jargon_samples = session.exec(statement).all()
         data.sample_jargons = [
             {

src/webui/routers/emoji.py

@@ -532,7 +532,7 @@ async def get_emoji_stats(maibot_session: Optional[str] = Cookie(None), authoriz
                 .select_from(Images)
                 .where(
                     col(Images.image_type) == ImageType.EMOJI,
-                    col(Images.is_registered) == True,
+                    col(Images.is_registered),
                 )
             )
             banned_statement = (
@@ -540,7 +540,7 @@ async def get_emoji_stats(maibot_session: Optional[str] = Cookie(None), authoriz
                 .select_from(Images)
                 .where(
                     col(Images.image_type) == ImageType.EMOJI,
-                    col(Images.is_banned) == True,
+                    col(Images.is_banned),
                 )
             )
 
@@ -1283,7 +1283,7 @@ async def preheat_thumbnail_cache(
                 select(Images)
                 .where(
                     col(Images.image_type) == ImageType.EMOJI,
-                    col(Images.is_banned) == False,
+                    col(Images.is_banned).is_(False),
                 )
                 .order_by(col(Images.query_count).desc())
                 .limit(limit * 2)

src/webui/routers/jargon.py

@@ -315,15 +315,15 @@ async def get_jargon_stats():
             total = session.exec(select(fn.count()).select_from(Jargon)).one()
 
             confirmed_jargon = session.exec(
-                select(fn.count()).select_from(Jargon).where(col(Jargon.is_jargon) == True)
+                select(fn.count()).select_from(Jargon).where(col(Jargon.is_jargon))
             ).one()
             confirmed_not_jargon = session.exec(
-                select(fn.count()).select_from(Jargon).where(col(Jargon.is_jargon) == False)
+                select(fn.count()).select_from(Jargon).where(col(Jargon.is_jargon).is_(False))
             ).one()
             pending = session.exec(select(fn.count()).select_from(Jargon).where(col(Jargon.is_jargon).is_(None))).one()
 
             complete_count = session.exec(
-                select(fn.count()).select_from(Jargon).where(col(Jargon.is_complete) == True)
+                select(fn.count()).select_from(Jargon).where(col(Jargon.is_complete))
             ).one()
 
             chat_count = session.exec(

src/webui/routers/knowledge.py

@@ -17,36 +17,36 @@ _paragraph_store_cache = None
 
 def _get_paragraph_store():
     """延迟加载段落 embedding store（只读模式，轻量级）
-    
+
     Returns:
         EmbeddingStore | None: 如果配置启用则返回store，否则返回None
     """
     # 检查配置是否启用
     if not global_config.webui.enable_paragraph_content:
         return None
-    
+
     global _paragraph_store_cache
     if _paragraph_store_cache is not None:
         return _paragraph_store_cache
-    
+
     try:
         from src.chat.knowledge.embedding_store import EmbeddingStore
         import os
-        
+
         # 获取数据路径
         current_dir = os.path.dirname(os.path.abspath(__file__))
         root_path = os.path.abspath(os.path.join(current_dir, "..", ".."))
         embedding_dir = os.path.join(root_path, "data/embedding")
-        
+
         # 只加载段落 embedding store（轻量级）
         paragraph_store = EmbeddingStore(
             namespace="paragraph",
             dir_path=embedding_dir,
             max_workers=1,  # 只读不需要多线程
-            chunk_size=100
+            chunk_size=100,
         )
         paragraph_store.load_from_file()
-        
+
         _paragraph_store_cache = paragraph_store
         logger.info(f"成功加载段落 embedding store，包含 {len(paragraph_store.store)} 个段落")
         return paragraph_store
@@ -57,10 +57,10 @@ def _get_paragraph_store():
 
 def _get_paragraph_content(node_id: str) -> tuple[Optional[str], bool]:
     """从 embedding store 获取段落完整内容
-    
+
     Args:
         node_id: 段落节点ID，格式为 'paragraph-{hash}'
-    
+
     Returns:
         tuple[str | None, bool]: (段落完整内容或None, 是否启用了功能)
     """
@@ -69,12 +69,12 @@ def _get_paragraph_content(node_id: str) -> tuple[Optional[str], bool]:
         if paragraph_store is None:
             # 功能未启用
             return None, False
-        
+
         # 从 store 中获取完整内容
         paragraph_item = paragraph_store.store.get(node_id)
         if paragraph_item is not None:
             # paragraph_item 是 EmbeddingStoreItem，其 str 属性包含完整文本
-            content: str = getattr(paragraph_item, 'str', '')
+            content: str = getattr(paragraph_item, "str", "")
             if content:
                 return content, True
         return None, True
@@ -156,14 +156,18 @@ def _convert_graph_to_json(kg_manager) -> KnowledgeGraph:
             node_data = graph[node_id]
             # 节点类型: "ent" -> "entity", "pg" -> "paragraph"
             node_type = "entity" if ("type" in node_data and node_data["type"] == "ent") else "paragraph"
-            
+
             # 对于段落节点，尝试从 embedding store 获取完整内容
             if node_type == "paragraph":
                 full_content, _ = _get_paragraph_content(node_id)
-                content = full_content if full_content is not None else (node_data["content"] if "content" in node_data else node_id)
+                content = (
+                    full_content
+                    if full_content is not None
+                    else (node_data["content"] if "content" in node_data else node_id)
+                )
             else:
                 content = node_data["content"] if "content" in node_data else node_id
-            
+
             create_time = node_data["create_time"] if "create_time" in node_data else None
 
             nodes.append(KnowledgeNode(id=node_id, type=node_type, content=content, create_time=create_time))
@@ -245,14 +249,18 @@ async def get_knowledge_graph(
             try:
                 node_data = graph[node_id]
                 node_type_val = "entity" if ("type" in node_data and node_data["type"] == "ent") else "paragraph"
-                
+
                 # 对于段落节点，尝试从 embedding store 获取完整内容
                 if node_type_val == "paragraph":
                     full_content, _ = _get_paragraph_content(node_id)
-                    content = full_content if full_content is not None else (node_data["content"] if "content" in node_data else node_id)
+                    content = (
+                        full_content
+                        if full_content is not None
+                        else (node_data["content"] if "content" in node_data else node_id)
+                    )
                 else:
                     content = node_data["content"] if "content" in node_data else node_id
-                
+
                 create_time = node_data["create_time"] if "create_time" in node_data else None
 
                 nodes.append(KnowledgeNode(id=node_id, type=node_type_val, content=content, create_time=create_time))
@@ -368,11 +376,15 @@ async def search_knowledge_node(query: str = Query(..., min_length=1), _auth: bo
             try:
                 node_data = graph[node_id]
                 node_type = "entity" if ("type" in node_data and node_data["type"] == "ent") else "paragraph"
-                
+
                 # 对于段落节点，尝试从 embedding store 获取完整内容
                 if node_type == "paragraph":
                     full_content, _ = _get_paragraph_content(node_id)
-                    content = full_content if full_content is not None else (node_data["content"] if "content" in node_data else node_id)
+                    content = (
+                        full_content
+                        if full_content is not None
+                        else (node_data["content"] if "content" in node_data else node_id)
+                    )
                 else:
                     content = node_data["content"] if "content" in node_data else node_id
 

src/webui/routers/person.py

@@ -370,7 +370,7 @@ async def get_person_stats(maibot_session: Optional[str] = Cookie(None), authori
 
         with get_db_session() as session:
             total = len(session.exec(select(PersonInfo.id)).all())
-            known = len(session.exec(select(PersonInfo.id).where(col(PersonInfo.is_known) == True)).all())
+            known = len(session.exec(select(PersonInfo.id).where(col(PersonInfo.is_known))).all())
         unknown = total - known
 
         # 按平台统计

src/webui/routers/plugin.py

@@ -1762,7 +1762,7 @@ async def update_plugin_config_raw(
         try:
             tomlkit.loads(request.config)
         except Exception as e:
-            raise HTTPException(status_code=400, detail=f"TOML 格式错误: {str(e)}")
+            raise HTTPException(status_code=400, detail=f"TOML 格式错误: {str(e)}") from e
 
         # 备份旧配置
         import shutil

src/webui/services/git_mirror_service.py

@@ -659,4 +659,4 @@ def get_git_mirror_service() -> GitMirrorService:
     global _git_mirror_service
     if _git_mirror_service is None:
         _git_mirror_service = GitMirrorService()
-    return _git_mirror_service
+    return _git_mirror_service