feat(cache): 用文件修改时间替换源码哈希生成缓存键

BREAKING CHANGE: CacheManager 的 _generate_key/get/set 方法签名变更，现在需要传入 tool_file_path 而非 tool_class 实例，所有调用方需跟进适配。

src/common/cache_manager.py

@@ -2,10 +2,12 @@ import time
 import json
 import hashlib
 import inspect
+import os
+from pathlib import Path
 import numpy as np
 import faiss
 import chromadb
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Optional, Union
 from src.common.logger import get_logger
 from src.llm_models.utils_model import LLMRequest
 from src.config.config import global_config, model_config
@@ -90,26 +92,33 @@ class CacheManager:
             logger.error(f"验证嵌入向量时发生错误: {e}")
             return None
 
-    def _generate_key(self, tool_name: str, function_args: Dict[str, Any], tool_class: Any) -> str:
-        """生成确定性的缓存键，包含代码哈希以实现自动失效。"""
+    def _generate_key(self, tool_name: str, function_args: Dict[str, Any], tool_file_path: Union[str, Path]) -> str:
+        """生成确定性的缓存键，包含文件修改时间以实现自动失效。"""
         try:
-            source_code = inspect.getsource(tool_class)
-            code_hash = hashlib.md5(source_code.encode()).hexdigest()
-        except (TypeError, OSError) as e:
-            code_hash = "unknown"
-            logger.warning(f"无法获取 {tool_class.__name__} 的源代码，代码哈希将为 'unknown'。错误: {e}")
+            tool_file_path = Path(tool_file_path)
+            if tool_file_path.exists():
+                file_name = tool_file_path.name
+                file_mtime = tool_file_path.stat().st_mtime
+                file_hash = hashlib.md5(f"{file_name}:{file_mtime}".encode()).hexdigest()
+            else:
+                file_hash = "unknown"
+                logger.warning(f"工具文件不存在: {tool_file_path}")
+        except (OSError, TypeError) as e:
+            file_hash = "unknown"
+            logger.warning(f"无法获取文件信息: {tool_file_path}，错误: {e}")
+        
         try:
             sorted_args = json.dumps(function_args, sort_keys=True)
         except TypeError:
             sorted_args = repr(sorted(function_args.items()))
-        return f"{tool_name}::{sorted_args}::{code_hash}"
+        return f"{tool_name}::{sorted_args}::{file_hash}"
 
-    async def get(self, tool_name: str, function_args: Dict[str, Any], tool_class: Any, semantic_query: Optional[str] = None) -> Optional[Any]:
+    async def get(self, tool_name: str, function_args: Dict[str, Any], tool_file_path: Union[str, Path], semantic_query: Optional[str] = None) -> Optional[Any]:
         """
         从缓存获取结果，查询顺序: L1-KV -> L1-Vector -> L2-KV -> L2-Vector。
         """
         # 步骤 1: L1 精确缓存查询
-        key = self._generate_key(tool_name, function_args, tool_class)
+        key = self._generate_key(tool_name, function_args, tool_file_path)
         logger.debug(f"生成的缓存键: {key}")
         if semantic_query:
             logger.debug(f"使用的语义查询: '{semantic_query}'")
@@ -220,14 +229,14 @@ class CacheManager:
         logger.debug(f"缓存未命中: {key}")
         return None
 
-    async def set(self, tool_name: str, function_args: Dict[str, Any], tool_class: Any, data: Any, ttl: Optional[int] = None, semantic_query: Optional[str] = None):
+    async def set(self, tool_name: str, function_args: Dict[str, Any], tool_file_path: Union[str, Path], data: Any, ttl: Optional[int] = None, semantic_query: Optional[str] = None):
         """将结果存入所有缓存层。"""
         if ttl is None:
             ttl = self.default_ttl
         if ttl <= 0:
             return
 
-        key = self._generate_key(tool_name, function_args, tool_class)
+        key = self._generate_key(tool_name, function_args, tool_file_path)
         expires_at = time.time() + ttl
         
         # 写入 L1

src/plugins/built_in/web_search_tool/plugin.py

@@ -87,9 +87,13 @@ class WebSurfingTool(BaseTool):
         if not query:
             return {"error": "搜索查询不能为空。"}
 
+        # 获取当前文件路径用于缓存键
+        import os
+        current_file_path = os.path.abspath(__file__)
+
         # 检查缓存
         query = function_args.get("query")
-        cached_result = await tool_cache.get(self.name, function_args, tool_class=self.__class__, semantic_query=query)
+        cached_result = await tool_cache.get(self.name, function_args, current_file_path, semantic_query=query)
         if cached_result:
             logger.info(f"缓存命中: {self.name} -> {function_args}")
             return cached_result
@@ -111,7 +115,7 @@ class WebSurfingTool(BaseTool):
         # 保存到缓存
         if "error" not in result:
             query = function_args.get("query")
-            await tool_cache.set(self.name, function_args, self.__class__, result, semantic_query=query)
+            await tool_cache.set(self.name, function_args, current_file_path, result, semantic_query=query)
             
         return result
 
@@ -464,8 +468,12 @@ class URLParserTool(BaseTool):
         """
         执行URL内容提取和总结。优先使用Exa，失败后尝试本地解析。
         """
+        # 获取当前文件路径用于缓存键
+        import os
+        current_file_path = os.path.abspath(__file__)
+
         # 检查缓存
-        cached_result = await tool_cache.get(self.name, function_args, tool_class=self.__class__)
+        cached_result = await tool_cache.get(self.name, function_args, current_file_path)
         if cached_result:
             logger.info(f"缓存命中: {self.name} -> {function_args}")
             return cached_result

template/bot_config_template.toml

@@ -1,5 +1,5 @@
 [inner]
-version = "6.3.7"
+version = "6.3.8"
 
 #----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读----
 #如果你想要修改配置文件，请递增version的值
@@ -162,8 +162,8 @@ ban_msgs_regex = [
 
 [anti_prompt_injection] # LLM反注入系统配置
 enabled = true # 是否启用反注入系统
-enabled_rules = false # 是否启用规则检测
-enabled_LLM = true # 是否启用LLM检测
+enabled_rules = true # 是否启用规则检测
+enabled_LLM = false # 是否启用LLM检测
 process_mode = "lenient" # 处理模式：strict(严格模式，直接丢弃), lenient(宽松模式，消息加盾)
 
 # 白名单配置