refactor(core): remove tool history manager and integrate cache into tool executor

Removes the `ToolHistoryManager` and its associated functionalities, including history recording, querying, and prompt generation. This simplifies the architecture by decoupling tool execution history from the core logic.

The tool caching mechanism is now directly integrated into the `ToolExecutor` by wrapping the `execute_tool_call` method. This ensures that caching is applied consistently for all tool executions that have it enabled, improving performance and reducing redundant calls.

- Deletes `src/common/tool_history.py`.
- Removes tool history related functions from `prompt_builder.py` and `tool_api.py`.
- Adds a `wrap_tool_executor` function in `cache_manager.py` to apply caching logic directly to the `ToolExecutor`.

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
+from typing import Any, Dict, Optional, Union, List
 from src.common.logger import get_logger
 from src.llm_models.utils_model import LLMRequest
 from src.config.config import global_config, model_config
@@ -141,7 +141,7 @@ class CacheManager:
         # 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)
+            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)
@@ -348,4 +348,64 @@ class CacheManager:
             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()
+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