af17290595
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.
129 lines
4.7 KiB
Python
129 lines
4.7 KiB
Python
from abc import ABC, abstractmethod
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from typing import Any, List, Optional, Tuple
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from rich.traceback import install
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from src.common.logger import get_logger
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from src.plugin_system.base.component_types import ComponentType, ToolInfo, ToolParamType
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install(extra_lines=3)
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logger = get_logger("base_tool")
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class BaseTool(ABC):
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"""所有工具的基类"""
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name: str = ""
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"""工具的名称"""
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description: str = ""
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"""工具的描述"""
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parameters: List[Tuple[str, ToolParamType, str, bool, List[str] | None]] = []
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"""工具的参数定义，为[("param_name", param_type, "description", required, enum_values)]格式
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param_name: 参数名称
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param_type: 参数类型
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description: 参数描述
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required: 是否必填
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enum_values: 枚举值列表
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例如: [("arg1", ToolParamType.STRING, "参数1描述", True, None), ("arg2", ToolParamType.INTEGER, "参数2描述", False, ["1", "2", "3"])]
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"""
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available_for_llm: bool = False
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"""是否可供LLM使用"""
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history_ttl: int = 5
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"""工具调用历史记录的TTL值，默认为5。设为0表示不记录历史"""
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enable_cache: bool = False
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"""是否为该工具启用缓存"""
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cache_ttl: int = 3600
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"""缓存的TTL值（秒），默认为3600秒（1小时）"""
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semantic_cache_query_key: Optional[str] = None
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"""用于语义缓存的查询参数键名。如果设置，将使用此参数的值进行语义相似度搜索"""
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def __init__(self, plugin_config: Optional[dict] = None):
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self.plugin_config = plugin_config or {} # 直接存储插件配置字典
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@classmethod
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def get_tool_definition(cls) -> dict[str, Any]:
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"""获取工具定义，用于LLM工具调用
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Returns:
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dict: 工具定义字典
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"""
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if not cls.name or not cls.description or not cls.parameters:
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raise NotImplementedError(f"工具类 {cls.__name__} 必须定义 name, description 和 parameters 属性")
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return {"name": cls.name, "description": cls.description, "parameters": cls.parameters}
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@classmethod
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def get_tool_info(cls) -> ToolInfo:
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"""获取工具信息"""
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if not cls.name or not cls.description or not cls.parameters:
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raise NotImplementedError(f"工具类 {cls.__name__} 必须定义 name, description 和 parameters 属性")
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return ToolInfo(
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name=cls.name,
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tool_description=cls.description,
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enabled=cls.available_for_llm,
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tool_parameters=cls.parameters,
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component_type=ComponentType.TOOL,
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)
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@abstractmethod
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async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
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"""执行工具函数(供llm调用)
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通过该方法，maicore会通过llm的tool call来调用工具
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传入的是json格式的参数，符合parameters定义的格式
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Args:
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function_args: 工具调用参数
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Returns:
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dict: 工具执行结果
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"""
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raise NotImplementedError("子类必须实现execute方法")
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async def direct_execute(self, **kwargs: dict[str, Any]) -> dict[str, Any]:
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"""直接执行工具函数(供插件调用)
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通过该方法，插件可以直接调用工具，而不需要传入字典格式的参数
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插件可以直接调用此方法，用更加明了的方式传入参数
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示例: result = await tool.direct_execute(arg1="参数",arg2="参数2")
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工具开发者可以重写此方法以实现与llm调用差异化的执行逻辑
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Args:
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**function_args: 工具调用参数
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Returns:
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dict: 工具执行结果
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"""
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parameter_required = [param[0] for param in self.parameters if param[3]] # 获取所有必填参数名
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for param_name in parameter_required:
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if param_name not in kwargs:
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raise ValueError(f"工具类 {self.__class__.__name__} 缺少必要参数: {param_name}")
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return await self.execute(kwargs)
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def get_config(self, key: str, default=None):
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"""获取插件配置值，使用嵌套键访问
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Args:
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key: 配置键名，使用嵌套访问如 "section.subsection.key"
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default: 默认值
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Returns:
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Any: 配置值或默认值
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"""
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if not self.plugin_config:
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return default
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# 支持嵌套键访问
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keys = key.split(".")
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current = self.plugin_config
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for k in keys:
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if isinstance(current, dict) and k in current:
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current = current[k]
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else:
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return default
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return current
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