feat(tool_system): implement declarative caching for tools
This commit refactors the tool caching system to be more robust, configurable, and easier to use. The caching logic is centralized within the `wrap_tool_executor`, removing the need for boilerplate code within individual tool implementations. Key changes: - Adds `enable_cache`, `cache_ttl`, and `semantic_cache_query_key` attributes to `BaseTool` for declarative cache configuration. - Moves caching logic from a simple history-based lookup and individual tools into a unified handling process in `wrap_tool_executor`. - The new system leverages the central `tool_cache` manager for both exact and semantic caching based on tool configuration. - Refactors `WebSurfingTool` and `URLParserTool` to utilize the new declarative caching mechanism, simplifying their code.
This commit is contained in:
minecraft1024a
committed by
Windpicker-owo
Windpicker-owo
parent
12bcde800e
commit
6b53560a7e
@@ -4,9 +4,11 @@ from datetime import datetime
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from typing import Any, Dict, List, Optional, Union
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import json
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from pathlib import Path
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import inspect
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from .logger import get_logger
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from src.config.config import global_config
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from src.common.cache_manager import tool_cache
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logger = get_logger("tool_history")
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@@ -113,34 +115,6 @@ class ToolHistoryManager:
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except Exception as e:
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logger.error(f"记录工具调用时发生错误: {e}")
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def find_cached_result(self, tool_name: str, args: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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"""查找匹配的缓存记录
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Args:
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tool_name: 工具名称
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args: 工具调用参数
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Returns:
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Optional[Dict[str, Any]]: 如果找到匹配的缓存记录则返回结果，否则返回None
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"""
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# 检查是否启用历史记录
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if not global_config.tool.history.enable_history:
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return None
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# 清理输入参数中的敏感信息以便比较
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sanitized_input_args = self._sanitize_args(args)
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# 按时间倒序遍历历史记录
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for record in reversed(self._history):
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if (record["tool_name"] == tool_name and
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record["status"] == "completed" and
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record["ttl_count"] < record.get("ttl", 5)):
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# 比较参数是否匹配
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if self._sanitize_args(record["arguments"]) == sanitized_input_args:
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logger.info(f"工具 {tool_name} 命中缓存记录")
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return record["result"]
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return None
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def _sanitize_args(self, args: Dict[str, Any]) -> Dict[str, Any]:
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"""清理参数中的敏感信息"""
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sensitive_keys = ['api_key', 'token', 'password', 'secret']
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@@ -327,27 +301,78 @@ class ToolHistoryManager:
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def wrap_tool_executor():
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"""
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包装工具执行器以添加历史记录功能
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包装工具执行器以添加历史记录和缓存功能
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这个函数应该在系统启动时被调用一次
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"""
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from src.plugin_system.core.tool_use import ToolExecutor
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from src.plugin_system.apis.tool_api import get_tool_instance
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original_execute = ToolExecutor.execute_tool_call
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history_manager = ToolHistoryManager()
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async def wrapped_execute_tool_call(self, tool_call, tool_instance=None):
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start_time = time.time()
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# 确保我们有 tool_instance
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if not tool_instance:
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tool_instance = get_tool_instance(tool_call.func_name)
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# 首先检查缓存
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if cached_result := history_manager.find_cached_result(tool_call.func_name, tool_call.args):
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logger.info(f"{self.log_prefix}使用缓存结果，跳过工具 {tool_call.func_name} 执行")
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return cached_result
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# 如果没有 tool_instance，就无法进行缓存检查，直接执行
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if not tool_instance:
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result = await original_execute(self, tool_call, None)
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execution_time = time.time() - start_time
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history_manager.record_tool_call(
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tool_name=tool_call.func_name,
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args=tool_call.args,
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result=result,
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execution_time=execution_time,
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status="completed",
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chat_id=getattr(self, 'chat_id', None),
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ttl=5 # Default TTL
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)
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return result
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# 新的缓存逻辑
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if tool_instance.enable_cache:
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try:
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tool_file_path = inspect.getfile(tool_instance.__class__)
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semantic_query = None
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if tool_instance.semantic_cache_query_key:
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semantic_query = tool_call.args.get(tool_instance.semantic_cache_query_key)
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cached_result = await tool_cache.get(
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tool_name=tool_call.func_name,
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function_args=tool_call.args,
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tool_file_path=tool_file_path,
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semantic_query=semantic_query
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)
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if cached_result:
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logger.info(f"{self.log_prefix}使用缓存结果，跳过工具 {tool_call.func_name} 执行")
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return cached_result
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except Exception as e:
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logger.error(f"{self.log_prefix}检查工具缓存时出错: {e}")
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try:
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result = await original_execute(self, tool_call, tool_instance)
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execution_time = time.time() - start_time
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# 获取工具的ttl值
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ttl = getattr(tool_instance, 'history_ttl', 5) if tool_instance else 5
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# 缓存结果
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if tool_instance.enable_cache:
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try:
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tool_file_path = inspect.getfile(tool_instance.__class__)
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semantic_query = None
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if tool_instance.semantic_cache_query_key:
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semantic_query = tool_call.args.get(tool_instance.semantic_cache_query_key)
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await tool_cache.set(
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tool_name=tool_call.func_name,
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function_args=tool_call.args,
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tool_file_path=tool_file_path,
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data=result,
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ttl=tool_instance.cache_ttl,
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semantic_query=semantic_query
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)
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except Exception as e:
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logger.error(f"{self.log_prefix}设置工具缓存时出错: {e}")
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# 记录成功的调用
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history_manager.record_tool_call(
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@@ -357,16 +382,13 @@ def wrap_tool_executor():
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execution_time=execution_time,
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status="completed",
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chat_id=getattr(self, 'chat_id', None),
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ttl=ttl
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ttl=tool_instance.history_ttl
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)
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return result
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except Exception as e:
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execution_time = time.time() - start_time
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# 获取工具的ttl值
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ttl = getattr(tool_instance, 'history_ttl', 5) if tool_instance else 5
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# 记录失败的调用
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history_manager.record_tool_call(
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tool_name=tool_call.func_name,
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@@ -375,7 +397,7 @@ def wrap_tool_executor():
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execution_time=execution_time,
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status="error",
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chat_id=getattr(self, 'chat_id', None),
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ttl=ttl
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ttl=tool_instance.history_ttl
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)
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raise
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