refactor(cache): 重构工具缓存机制并优化LLM请求重试逻辑

将工具缓存的实现从`ToolExecutor`的装饰器模式重构为直接集成。缓存逻辑被移出`cache_manager.py`并整合进`ToolExecutor.execute_tool_call`方法中，简化了代码结构并使其更易于维护。

主要变更：
- 从`cache_manager.py`中移除了`wrap_tool_executor`函数。
- 在`tool_use.py`中，`execute_tool_call`现在包含完整的缓存检查和设置逻辑。
- 调整了`llm_models/utils_model.py`中的LLM请求逻辑，为模型生成的空回复或截断响应增加了内部重试机制，增强了稳定性。
- 清理了项目中未使用的导入和过时的文档文件，以保持代码库的整洁。

src/chat/chat_loop/heartFC_chat.py

@@ -10,7 +10,6 @@ from src.chat.express.expression_learner import expression_learner_manager
 from src.plugin_system.base.component_types import ChatMode
 from src.schedule.schedule_manager import schedule_manager
 from src.plugin_system.apis import message_api
-from src.mood.mood_manager import mood_manager
 
 from .hfc_context import HfcContext
 from .energy_manager import EnergyManager

src/common/cache_manager.py

@@ -4,7 +4,7 @@ import hashlib
 from pathlib import Path
 import numpy as np
 import faiss
-from typing import Any, Dict, Optional, Union, List
+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
@@ -14,6 +14,7 @@ from src.common.vector_db import vector_db_service
 
 logger = get_logger("cache_manager")
 
+
 class CacheManager:
     """
     一个支持分层和语义缓存的通用工具缓存管理器。
@@ -21,6 +22,7 @@ class CacheManager:
     L1缓存: 内存字典 (KV) + FAISS (Vector)。
     L2缓存: 数据库 (KV) + ChromaDB (Vector)。
     """
+
     _instance = None
 
     def __new__(cls, *args, **kwargs):
@@ -32,7 +34,7 @@ class CacheManager:
         """
         初始化缓存管理器。
         """
-        if not hasattr(self, '_initialized'):
+        if not hasattr(self, "_initialized"):
             self.default_ttl = default_ttl
             self.semantic_cache_collection_name = "semantic_cache"
 
@@ -41,7 +43,7 @@ class CacheManager:
             embedding_dim = global_config.lpmm_knowledge.embedding_dimension
             self.l1_vector_index = faiss.IndexFlatIP(embedding_dim)
             self.l1_vector_id_to_key: Dict[int, str] = {}
-            
+
             # L2 向量缓存 (使用新的服务)
             vector_db_service.get_or_create_collection(self.semantic_cache_collection_name)
 
@@ -58,32 +60,32 @@ class CacheManager:
         try:
             if embedding_result is None:
                 return None
-                
+
             # 确保embedding_result是一维数组或列表
             if isinstance(embedding_result, (list, tuple, np.ndarray)):
                 # 转换为numpy数组进行处理
                 embedding_array = np.array(embedding_result)
-                
+
                 # 如果是多维数组，展平它
                 if embedding_array.ndim > 1:
                     embedding_array = embedding_array.flatten()
-                
+
                 # 检查维度是否符合预期
                 expected_dim = global_config.lpmm_knowledge.embedding_dimension
                 if embedding_array.shape[0] != expected_dim:
                     logger.warning(f"嵌入向量维度不匹配: 期望 {expected_dim}, 实际 {embedding_array.shape[0]}")
                     return None
-                
+
                 # 检查是否包含有效的数值
                 if np.isnan(embedding_array).any() or np.isinf(embedding_array).any():
                     logger.warning("嵌入向量包含无效的数值 (NaN 或 Inf)")
                     return None
-                
-                return embedding_array.astype('float32')
+
+                return embedding_array.astype("float32")
             else:
                 logger.warning(f"嵌入结果格式不支持: {type(embedding_result)}")
                 return None
-                
+
         except Exception as e:
             logger.error(f"验证嵌入向量时发生错误: {e}")
             return None
@@ -102,14 +104,20 @@ class CacheManager:
         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 = orjson.dumps(function_args, option=orjson.OPT_SORT_KEYS).decode('utf-8')
+            sorted_args = orjson.dumps(function_args, option=orjson.OPT_SORT_KEYS).decode("utf-8")
         except TypeError:
             sorted_args = repr(sorted(function_args.items()))
         return f"{tool_name}::{sorted_args}::{file_hash}"
 
-    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]:
+    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。
         """
@@ -136,13 +144,13 @@ class CacheManager:
                 embedding_vector = embedding_result[0] if isinstance(embedding_result, tuple) else embedding_result
                 validated_embedding = self._validate_embedding(embedding_vector)
                 if validated_embedding is not None:
-                    query_embedding = np.array([validated_embedding], dtype='float32')
+                    query_embedding = np.array([validated_embedding], dtype="float32")
 
         # 步骤 2a: L1 语义缓存 (FAISS)
         if query_embedding is not None and self.l1_vector_index.ntotal > 0:
             faiss.normalize_L2(query_embedding)
-            distances, indices = self.l1_vector_index.search(query_embedding, 1) # type: ignore
-            if indices.size > 0 and distances[0][0] > 0.75: # IP 越大越相似
+            distances, indices = self.l1_vector_index.search(query_embedding, 1)  # type: ignore
+            if indices.size > 0 and distances[0][0] > 0.75:  # IP 越大越相似
                 hit_index = indices[0][0]
                 l1_hit_key = self.l1_vector_id_to_key.get(hit_index)
                 if l1_hit_key and l1_hit_key in self.l1_kv_cache:
@@ -151,12 +159,9 @@ class CacheManager:
 
         # 步骤 2b: L2 精确缓存 (数据库)
         cache_results_obj = await db_query(
-            model_class=CacheEntries,
-            query_type="get",
-            filters={"cache_key": key},
-            single_result=True
+            model_class=CacheEntries, query_type="get", filters={"cache_key": key}, single_result=True
         )
-        
+
         if cache_results_obj:
             # 使用 getattr 安全访问属性，避免 Pylance 类型检查错误
             expires_at = getattr(cache_results_obj, "expires_at", 0)
@@ -164,7 +169,7 @@ class CacheManager:
                 logger.info(f"命中L2键值缓存: {key}")
                 cache_value = getattr(cache_results_obj, "cache_value", "{}")
                 data = orjson.loads(cache_value)
-                
+
                 # 更新访问统计
                 await db_query(
                     model_class=CacheEntries,
@@ -172,20 +177,16 @@ class CacheManager:
                     filters={"cache_key": key},
                     data={
                         "last_accessed": time.time(),
-                        "access_count": getattr(cache_results_obj, "access_count", 0) + 1
-                    }
+                        "access_count": getattr(cache_results_obj, "access_count", 0) + 1,
+                    },
                 )
-                
+
                 # 回填 L1
                 self.l1_kv_cache[key] = {"data": data, "expires_at": expires_at}
                 return data
             else:
                 # 删除过期的缓存条目
-                await db_query(
-                    model_class=CacheEntries,
-                    query_type="delete",
-                    filters={"cache_key": key}
-                )
+                await db_query(model_class=CacheEntries, query_type="delete", filters={"cache_key": key})
 
         # 步骤 2c: L2 语义缓存 (VectorDB Service)
         if query_embedding is not None:
@@ -193,31 +194,33 @@ class CacheManager:
                 results = vector_db_service.query(
                     collection_name=self.semantic_cache_collection_name,
                     query_embeddings=query_embedding.tolist(),
-                    n_results=1
+                    n_results=1,
                 )
-                if results and results.get('ids') and results['ids'][0]:
-                    distance = results['distances'][0][0] if results.get('distances') and results['distances'][0] else 'N/A'
+                if results and results.get("ids") and results["ids"][0]:
+                    distance = (
+                        results["distances"][0][0] if results.get("distances") and results["distances"][0] else "N/A"
+                    )
                     logger.debug(f"L2语义搜索找到最相似的结果: id={results['ids'][0]}, 距离={distance}")
-                    
-                    if distance != 'N/A' and distance < 0.75:
-                        l2_hit_key = results['ids'][0][0] if isinstance(results['ids'][0], list) else results['ids'][0]
+
+                    if distance != "N/A" and distance < 0.75:
+                        l2_hit_key = results["ids"][0][0] if isinstance(results["ids"][0], list) else results["ids"][0]
                         logger.info(f"命中L2语义缓存: key='{l2_hit_key}', 距离={distance:.4f}")
-                        
+
                         # 从数据库获取缓存数据
                         semantic_cache_results_obj = await db_query(
                             model_class=CacheEntries,
                             query_type="get",
                             filters={"cache_key": l2_hit_key},
-                            single_result=True
+                            single_result=True,
                         )
-                        
+
                         if semantic_cache_results_obj:
                             expires_at = getattr(semantic_cache_results_obj, "expires_at", 0)
                             if time.time() < expires_at:
                                 cache_value = getattr(semantic_cache_results_obj, "cache_value", "{}")
                                 data = orjson.loads(cache_value)
                                 logger.debug(f"L2语义缓存返回的数据: {data}")
-                                
+
                                 # 回填 L1
                                 self.l1_kv_cache[key] = {"data": data, "expires_at": expires_at}
                                 if query_embedding is not None:
@@ -235,7 +238,15 @@ 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_file_path: Union[str, Path], 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
@@ -244,27 +255,22 @@ class CacheManager:
 
         key = self._generate_key(tool_name, function_args, tool_file_path)
         expires_at = time.time() + ttl
-        
+
         # 写入 L1
         self.l1_kv_cache[key] = {"data": data, "expires_at": expires_at}
 
         # 写入 L2 (数据库)
         cache_data = {
             "cache_key": key,
-            "cache_value": orjson.dumps(data).decode('utf-8'),
+            "cache_value": orjson.dumps(data).decode("utf-8"),
             "expires_at": expires_at,
             "tool_name": tool_name,
             "created_at": time.time(),
             "last_accessed": time.time(),
-            "access_count": 1
+            "access_count": 1,
         }
-        
-        await db_save(
-            model_class=CacheEntries,
-            data=cache_data,
-            key_field="cache_key",
-            key_value=key
-        )
+
+        await db_save(model_class=CacheEntries, data=cache_data, key_field="cache_key", key_value=key)
 
         # 写入语义缓存
         if semantic_query and self.embedding_model:
@@ -274,19 +280,19 @@ class CacheManager:
                     embedding_vector = embedding_result[0] if isinstance(embedding_result, tuple) else embedding_result
                     validated_embedding = self._validate_embedding(embedding_vector)
                     if validated_embedding is not None:
-                        embedding = np.array([validated_embedding], dtype='float32')
-                        
+                        embedding = np.array([validated_embedding], dtype="float32")
+
                         # 写入 L1 Vector
                         new_id = self.l1_vector_index.ntotal
                         faiss.normalize_L2(embedding)
                         self.l1_vector_index.add(x=embedding)  # type: ignore
                         self.l1_vector_id_to_key[new_id] = key
-                        
+
                         # 写入 L2 Vector (使用新的服务)
                         vector_db_service.add(
                             collection_name=self.semantic_cache_collection_name,
                             embeddings=embedding.tolist(),
-                            ids=[key]
+                            ids=[key],
                         )
             except Exception as e:
                 logger.warning(f"语义缓存写入失败: {e}")
@@ -306,16 +312,16 @@ class CacheManager:
         await db_query(
             model_class=CacheEntries,
             query_type="delete",
-            filters={}  # 删除所有记录
+            filters={},  # 删除所有记录
         )
-        
+
         # 清空 VectorDB
         try:
             vector_db_service.delete_collection(name=self.semantic_cache_collection_name)
             vector_db_service.get_or_create_collection(name=self.semantic_cache_collection_name)
         except Exception as e:
             logger.warning(f"清空 VectorDB 集合失败: {e}")
-        
+
         logger.info("L2 (数据库 & VectorDB) 缓存已清空。")
 
     async def clear_all(self):
@@ -327,85 +333,23 @@ class CacheManager:
     async def clean_expired(self):
         """清理过期的缓存条目"""
         current_time = time.time()
-        
+
         # 清理L1过期条目
         expired_keys = []
         for key, entry in self.l1_kv_cache.items():
             if current_time >= entry["expires_at"]:
                 expired_keys.append(key)
-        
+
         for key in expired_keys:
             del self.l1_kv_cache[key]
-        
+
         # 清理L2过期条目
-        await db_query(
-            model_class=CacheEntries,
-            query_type="delete",
-            filters={"expires_at": {"$lt": current_time}}
-        )
-        
+        await db_query(model_class=CacheEntries, query_type="delete", filters={"expires_at": {"$lt": current_time}})
+
         if expired_keys:
             logger.info(f"清理了 {len(expired_keys)} 个过期的L1缓存条目")
 
+
 # 全局实例
 tool_cache = CacheManager()
 
-import inspect
-import time
-
-def wrap_tool_executor():
-    """
-    包装工具执行器以添加缓存功能
-    这个函数应该在系统启动时被调用一次
-    """
-    from src.plugin_system.core.tool_use import ToolExecutor
-    from src.plugin_system.apis.tool_api import get_tool_instance
-    original_execute = ToolExecutor.execute_tool_call
-
-    async def wrapped_execute_tool_call(self, tool_call, tool_instance=None):
-        if not tool_instance:
-            tool_instance = get_tool_instance(tool_call.func_name)
-
-        if not tool_instance or not tool_instance.enable_cache:
-            return await original_execute(self, tool_call, tool_instance)
-
-        try:
-            tool_file_path = inspect.getfile(tool_instance.__class__)
-            semantic_query = None
-            if tool_instance.semantic_cache_query_key:
-                semantic_query = tool_call.args.get(tool_instance.semantic_cache_query_key)
-
-            cached_result = await tool_cache.get(
-                tool_name=tool_call.func_name,
-                function_args=tool_call.args,
-                tool_file_path=tool_file_path,
-                semantic_query=semantic_query
-            )
-            if cached_result:
-                logger.info(f"{getattr(self, 'log_prefix', '')}使用缓存结果，跳过工具 {tool_call.func_name} 执行")
-                return cached_result
-        except Exception as e:
-            logger.error(f"{getattr(self, 'log_prefix', '')}检查工具缓存时出错: {e}")
-
-        result = await original_execute(self, tool_call, tool_instance)
-
-        try:
-            tool_file_path = inspect.getfile(tool_instance.__class__)
-            semantic_query = None
-            if tool_instance.semantic_cache_query_key:
-                semantic_query = tool_call.args.get(tool_instance.semantic_cache_query_key)
-            
-            await tool_cache.set(
-                tool_name=tool_call.func_name,
-                function_args=tool_call.args,
-                tool_file_path=tool_file_path,
-                data=result,
-                ttl=tool_instance.cache_ttl,
-                semantic_query=semantic_query
-            )
-        except Exception as e:
-            logger.error(f"{getattr(self, 'log_prefix', '')}设置工具缓存时出错: {e}")
-
-        return result
-
-    ToolExecutor.execute_tool_call = wrapped_execute_tool_call

src/llm_models/utils_model.py

@@ -310,82 +310,94 @@ class LLMRequest:
                 message_builder.add_text_content(processed_prompt)
                 messages = [message_builder.build()]
                 tool_built = self._build_tool_options(tools)
-                
-                response = await self._execute_request(
-                    api_provider=api_provider,
-                    client=client,
-                    request_type=RequestType.RESPONSE,
-                    model_info=model_info,
-                    message_list=messages,
-                    tool_options=tool_built,
-                    temperature=temperature,
-                    max_tokens=max_tokens,
-                )
 
-                content = response.content or ""
-                reasoning_content = response.reasoning_content or ""
-                tool_calls = response.tool_calls
+                # 针对当前模型的空回复/截断重试逻辑
+                empty_retry_count = 0
+                max_empty_retry = api_provider.max_retry
+                empty_retry_interval = api_provider.retry_interval
 
-                if not reasoning_content and content:
-                    content, extracted_reasoning = self._extract_reasoning(content)
-                    reasoning_content = extracted_reasoning
-
-                is_empty_reply = not tool_calls and (not content or content.strip() == "")
-                is_truncated = False
-                if use_anti_truncation:
-                    if content.endswith("[done]"):
-                        content = content[:-6].strip()
-                    else:
-                        is_truncated = True
-
-                if is_empty_reply or is_truncated:
-                    # 空回复或截断不进行模型切换，仅记录错误后抛出或返回
-                    reason = "空回复" if is_empty_reply else "截断"
-                    msg = f"模型 '{model_name}' 生成了{reason}的回复"
-                    logger.error(msg)
-                    if raise_when_empty:
-                        raise RuntimeError(msg)
-                    return msg, (reasoning_content, model_name, tool_calls)
-
-
-                # 成功获取响应
-                if usage := response.usage:
-                    llm_usage_recorder.record_usage_to_database(
-                        model_info=model_info, model_usage=usage, time_cost=time.time() - start_time,
-                        user_id="system", request_type=self.request_type, endpoint="/chat/completions",
+                while empty_retry_count <= max_empty_retry:
+                    response = await self._execute_request(
+                        api_provider=api_provider,
+                        client=client,
+                        request_type=RequestType.RESPONSE,
+                        model_info=model_info,
+                        message_list=messages,
+                        tool_options=tool_built,
+                        temperature=temperature,
+                        max_tokens=max_tokens,
                     )
 
-                if not content and not tool_calls:
-                    if raise_when_empty:
-                        raise RuntimeError("生成空回复")
-                    content = "生成的响应为空"
-                
-                logger.info(f"模型 '{model_name}' 成功生成回复。")
-                return content, (reasoning_content, model_name, tool_calls)
+                    content = response.content or ""
+                    reasoning_content = response.reasoning_content or ""
+                    tool_calls = response.tool_calls
+
+                    if not reasoning_content and content:
+                        content, extracted_reasoning = self._extract_reasoning(content)
+                        reasoning_content = extracted_reasoning
+
+                    is_empty_reply = not tool_calls and (not content or content.strip() == "")
+                    is_truncated = False
+                    if use_anti_truncation:
+                        if content.endswith("[done]"):
+                            content = content[:-6].strip()
+                        else:
+                            is_truncated = True
+
+                    if is_empty_reply or is_truncated:
+                        empty_retry_count += 1
+                        if empty_retry_count <= max_empty_retry:
+                            reason = "空回复" if is_empty_reply else "截断"
+                            logger.warning(f"模型 '{model_name}' 检测到{reason}，正在进行第 {empty_retry_count}/{max_empty_retry} 次重新生成...")
+                            if empty_retry_interval > 0:
+                                await asyncio.sleep(empty_retry_interval)
+                            continue  # 继续使用当前模型重试
+                        else:
+                            # 当前模型重试次数用尽，跳出内层循环，触发外层循环切换模型
+                            reason = "空回复" if is_empty_reply else "截断"
+                            logger.error(f"模型 '{model_name}' 经过 {max_empty_retry} 次重试后仍然是{reason}的回复。")
+                            raise RuntimeError(f"模型 '{model_name}' 达到最大空回复/截断重试次数")
+
+                    # 成功获取响应
+                    if usage := response.usage:
+                        llm_usage_recorder.record_usage_to_database(
+                            model_info=model_info, model_usage=usage, time_cost=time.time() - start_time,
+                            user_id="system", request_type=self.request_type, endpoint="/chat/completions",
+                        )
+
+                    if not content and not tool_calls:
+                        if raise_when_empty:
+                            raise RuntimeError("生成空回复")
+                        content = "生成的响应为空"
+                    
+                    logger.info(f"模型 '{model_name}' 成功生成回复。")
+                    return content, (reasoning_content, model_name, tool_calls)
 
             except RespNotOkException as e:
                 if e.status_code in [401, 403]:
                     logger.error(f"模型 '{model_name}' 遇到认证/权限错误 (Code: {e.status_code})，将尝试下一个模型。")
                     failed_models.add(model_name)
                     last_exception = e
-                    continue
+                    continue # 切换到下一个模型
                 else:
-                    # 对于其他HTTP错误，不切换模型，直接抛出
                     logger.error(f"模型 '{model_name}' 请求失败，HTTP状态码: {e.status_code}")
-                    last_exception = e
                     if raise_when_empty:
                         raise
-                    break
+                    # 对于其他HTTP错误，直接抛出，不再尝试其他模型
+                    return f"请求失败: {e}", ("", model_name, None)
+
             except RuntimeError as e:
+                # 捕获所有重试失败（包括空回复和网络问题）
                 logger.error(f"模型 '{model_name}' 在所有重试后仍然失败: {e}，将尝试下一个模型。")
                 failed_models.add(model_name)
                 last_exception = e
-                continue
+                continue # 切换到下一个模型
+
             except Exception as e:
                 logger.error(f"使用模型 '{model_name}' 时发生未知异常: {e}")
                 failed_models.add(model_name)
                 last_exception = e
-                continue
+                continue # 切换到下一个模型
 
         # 所有模型都尝试失败
         logger.error("所有可用模型都已尝试失败。")

src/plugin_system/apis/tool_api.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional, Type
+from typing import Optional, Type
 from src.plugin_system.base.base_tool import BaseTool
 from src.plugin_system.base.component_types import ComponentType
 

src/plugin_system/base/base_plugin.py

@@ -1,5 +1,5 @@
 from abc import abstractmethod
-from typing import List, Type, Tuple, Union, TYPE_CHECKING
+from typing import List, Type, Tuple, Union
 from .plugin_base import PluginBase
 
 from src.common.logger import get_logger

src/plugin_system/base/plus_command.py

@@ -4,7 +4,7 @@
 """
 
 from abc import ABC, abstractmethod
-from typing import Dict, Tuple, Optional, List
+from typing import Tuple, Optional, List
 import re
 
 from src.common.logger import get_logger

src/plugin_system/core/tool_use.py

@@ -7,8 +7,10 @@ from src.llm_models.utils_model import LLMRequest
 from src.llm_models.payload_content import ToolCall
 from src.config.config import global_config, model_config
 from src.chat.utils.prompt_builder import Prompt, global_prompt_manager
+import inspect
 from src.chat.message_receive.chat_stream import get_chat_manager
 from src.common.logger import get_logger
+from src.common.cache_manager import tool_cache
 
 logger = get_logger("tool_use")
 
@@ -184,28 +186,71 @@ class ToolExecutor:
         return tool_results, used_tools
 
     async def execute_tool_call(self, tool_call: ToolCall, tool_instance: Optional[BaseTool] = None) -> Optional[Dict[str, Any]]:
-        # sourcery skip: use-assigned-variable
-        """执行单个工具调用
+        """执行单个工具调用，并处理缓存"""
+        
+        function_args = tool_call.args or {}
+        tool_instance = tool_instance or get_tool_instance(tool_call.func_name)
 
-        Args:
-            tool_call: 工具调用对象
+        # 如果工具不存在或未启用缓存，则直接执行
+        if not tool_instance or not tool_instance.enable_cache:
+            return await self._original_execute_tool_call(tool_call, tool_instance)
 
-        Returns:
-            Optional[Dict]: 工具调用结果，如果失败则返回None
-        """
+        # --- 缓存逻辑开始 ---
+        try:
+            tool_file_path = inspect.getfile(tool_instance.__class__)
+            semantic_query = None
+            if tool_instance.semantic_cache_query_key:
+                semantic_query = function_args.get(tool_instance.semantic_cache_query_key)
+
+            cached_result = await tool_cache.get(
+                tool_name=tool_call.func_name,
+                function_args=function_args,
+                tool_file_path=tool_file_path,
+                semantic_query=semantic_query
+            )
+            if cached_result:
+                logger.info(f"{self.log_prefix}使用缓存结果，跳过工具 {tool_call.func_name} 执行")
+                return cached_result
+        except Exception as e:
+            logger.error(f"{self.log_prefix}检查工具缓存时出错: {e}")
+
+        # 缓存未命中，执行原始工具调用
+        result = await self._original_execute_tool_call(tool_call, tool_instance)
+
+        # 将结果存入缓存
+        try:
+            tool_file_path = inspect.getfile(tool_instance.__class__)
+            semantic_query = None
+            if tool_instance.semantic_cache_query_key:
+                semantic_query = function_args.get(tool_instance.semantic_cache_query_key)
+            
+            await tool_cache.set(
+                tool_name=tool_call.func_name,
+                function_args=function_args,
+                tool_file_path=tool_file_path,
+                data=result,
+                ttl=tool_instance.cache_ttl,
+                semantic_query=semantic_query
+            )
+        except Exception as e:
+            logger.error(f"{self.log_prefix}设置工具缓存时出错: {e}")
+        # --- 缓存逻辑结束 ---
+
+        return result
+
+    async def _original_execute_tool_call(self, tool_call: ToolCall, tool_instance: Optional[BaseTool] = None) -> Optional[Dict[str, Any]]:
+        """执行单个工具调用的原始逻辑"""
         try:
             function_name = tool_call.func_name
             function_args = tool_call.args or {}
-            logger.info(f"🤖 {self.log_prefix} 正在执行工具: [bold green]{function_name}[/bold green] | 参数: {function_args}")
-            function_args["llm_called"] = True  # 标记为LLM调用
+            logger.info(f"{self.log_prefix} 正在执行工具: [bold green]{function_name}[/bold green] | 参数: {function_args}")
+            function_args["llm_called"] = True
 
-            # 获取对应工具实例
             tool_instance = tool_instance or get_tool_instance(function_name)
             if not tool_instance:
                 logger.warning(f"未知工具名称: {function_name}")
                 return None
 
-            # 执行工具并记录日志
             logger.debug(f"{self.log_prefix}执行工具 {function_name}，参数: {function_args}")
             result = await tool_instance.execute(function_args)
             if result:

src/plugins/built_in/maizone_refactored/services/content_service.py

@@ -9,12 +9,9 @@ import datetime
 import base64
 import aiohttp
 from src.common.logger import get_logger
-import base64
-import aiohttp
 import imghdr
 import asyncio
-from src.common.logger import get_logger
-from src.plugin_system.apis import llm_api, config_api, generator_api, person_api
+from src.plugin_system.apis import llm_api, config_api, generator_api
 from src.chat.message_receive.chat_stream import get_chat_manager
 from maim_message import UserInfo
 from src.llm_models.utils_model import LLMRequest

src/plugins/built_in/permission_management/plugin.py

@@ -16,7 +16,7 @@ from src.plugin_system.apis.permission_api import permission_api
 from src.plugin_system.apis.logging_api import get_logger
 from src.plugin_system.base.component_types import PlusCommandInfo, ChatType
 from src.plugin_system.base.config_types import ConfigField
-from src.plugin_system.utils.permission_decorators import require_permission, require_master, PermissionChecker
+from src.plugin_system.utils.permission_decorators import require_permission
 
 
 logger = get_logger("Permission")

src/schedule/schedule_manager.py

@@ -411,7 +411,6 @@ class ScheduleManager:
         通过关键词匹配、唤醒度、睡眠压力等综合判断是否处于休眠时间。
         新增弹性睡眠机制，允许在压力低时延迟入睡，并在入睡前发送通知。
         """
-        from src.chat.chat_loop.wakeup_manager import WakeUpManager
         # --- 基础检查 ---
         if not global_config.schedule.enable_is_sleep:
             return False