feat(memory-graph): Step 1 - 集成记忆工具到插件系统
完成记忆系统工具的插件化集成： 1. 创建记忆工具适配器 (memory_plugin_tools.py) - CreateMemoryTool: 创建新记忆 - LinkMemoriesTool: 关联两条记忆 - SearchMemoriesTool: 搜索相关记忆 - 适配 BaseTool 接口，支持 LLM 调用 2. 创建全局 MemoryManager 单例 (manager_singleton.py) - initialize_memory_manager(): 初始化全局实例 - get_memory_manager(): 获取单例实例 - shutdown_memory_manager(): 关闭管理器 - 线程安全的单例模式 3. 创建记忆系统插件 (plugins/memory_graph_plugin/) - MemoryGraphPlugin: 插件主类 - 自动注册3个记忆工具到系统 - on_plugin_loaded(): 初始化 MemoryManager - on_unload(): 清理资源 4. 修复类型问题 - ToolParamType.OBJECT STRING (JSON格式) - ToolParamType.NUMBER FLOAT - attributes 参数支持 JSON 字符串解析 - 修复 min_importance None 比较错误 5. 添加集成测试 (test_plugin_integration.py) - 测试工具导入和定义 - 测试 MemoryManager 初始化 - 测试工具执行（创建、搜索记忆） - 测试单例模式 测试结果: 所有测试通过 LLM 现在可以通过工具调用主动管理记忆！
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Windpicker-owo
@@ -362,7 +362,7 @@ class MemoryManager:
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continue
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# 重要性过滤
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if memory.importance < min_importance:
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if min_importance is not None and memory.importance < min_importance:
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continue
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# 遗忘状态过滤
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93
src/memory_graph/manager_singleton.py
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93
src/memory_graph/manager_singleton.py
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@@ -0,0 +1,93 @@
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"""
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记忆系统管理单例
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提供全局访问的 MemoryManager 实例
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"""
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from __future__ import annotations
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from pathlib import Path
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from typing import Optional
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from src.common.logger import get_logger
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from src.memory_graph.manager import MemoryManager
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logger = get_logger(__name__)
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# 全局 MemoryManager 实例
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_memory_manager: Optional[MemoryManager] = None
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_initialized: bool = False
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async def initialize_memory_manager(data_dir: Optional[Path | str] = None) -> MemoryManager:
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"""
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初始化全局 MemoryManager
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Args:
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data_dir: 数据目录，默认使用 data/memory_graph
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Returns:
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MemoryManager 实例
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"""
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global _memory_manager, _initialized
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if _initialized and _memory_manager:
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logger.info("MemoryManager 已经初始化，返回现有实例")
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return _memory_manager
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try:
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if data_dir is None:
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data_dir = Path("data/memory_graph")
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elif isinstance(data_dir, str):
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data_dir = Path(data_dir)
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logger.info(f"正在初始化全局 MemoryManager (data_dir={data_dir})...")
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_memory_manager = MemoryManager(data_dir=data_dir)
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await _memory_manager.initialize()
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_initialized = True
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logger.info("✅ 全局 MemoryManager 初始化成功")
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return _memory_manager
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except Exception as e:
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logger.error(f"初始化 MemoryManager 失败: {e}", exc_info=True)
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_initialized = False
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_memory_manager = None
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raise
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def get_memory_manager() -> Optional[MemoryManager]:
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"""
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获取全局 MemoryManager 实例
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Returns:
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MemoryManager 实例，如果未初始化则返回 None
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"""
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if not _initialized or _memory_manager is None:
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logger.warning("MemoryManager 尚未初始化，请先调用 initialize_memory_manager()")
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return None
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return _memory_manager
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async def shutdown_memory_manager():
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"""关闭全局 MemoryManager"""
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global _memory_manager, _initialized
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if _memory_manager:
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try:
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logger.info("正在关闭全局 MemoryManager...")
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await _memory_manager.shutdown()
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logger.info("✅ 全局 MemoryManager 已关闭")
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except Exception as e:
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logger.error(f"关闭 MemoryManager 时出错: {e}", exc_info=True)
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finally:
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_memory_manager = None
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_initialized = False
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def is_initialized() -> bool:
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"""检查 MemoryManager 是否已初始化"""
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return _initialized and _memory_manager is not None
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224
src/memory_graph/plugin_tools/memory_plugin_tools.py
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224
src/memory_graph/plugin_tools/memory_plugin_tools.py
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@@ -0,0 +1,224 @@
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"""
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记忆系统插件工具
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将 MemoryTools 适配为 BaseTool 格式，供 LLM 使用
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"""
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from __future__ import annotations
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from typing import Any, ClassVar
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from src.common.logger import get_logger
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from src.plugin_system.base.base_tool import BaseTool
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from src.plugin_system.base.component_types import ToolParamType
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logger = get_logger(__name__)
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class CreateMemoryTool(BaseTool):
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"""创建记忆工具"""
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name = "create_memory"
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description = "创建一个新的记忆。记忆由主体、类型、主题、客体（可选）和属性组成。用于记录重要的信息、事件、想法等。"
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parameters: ClassVar[list[tuple[str, ToolParamType, str, bool, list[str] | None]]] = [
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("subject", ToolParamType.STRING, "记忆的主体，通常是'我'、'用户'或具体的人名", True, None),
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("memory_type", ToolParamType.STRING, "记忆类型", True, ["事件", "事实", "关系", "观点"]),
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("topic", ToolParamType.STRING, "记忆的主题，即发生的事情或状态", True, None),
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("object", ToolParamType.STRING, "记忆的客体，即主题作用的对象（可选）", False, None),
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("attributes", ToolParamType.STRING, "记忆的属性（JSON格式字符串），如 {\"时间\":\"今天\",\"地点\":\"家里\"}", False, None),
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("importance", ToolParamType.FLOAT, "记忆的重要性（0.0-1.0），默认0.5", False, None),
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]
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available_for_llm = True
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async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
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"""执行创建记忆"""
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try:
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# 获取全局 memory_manager
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from src.memory_graph.manager_singleton import get_memory_manager
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manager = get_memory_manager()
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if not manager:
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return {
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"name": self.name,
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"content": "记忆系统未初始化"
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}
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# 提取参数
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subject = function_args.get("subject", "")
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memory_type = function_args.get("memory_type", "")
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topic = function_args.get("topic", "")
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obj = function_args.get("object")
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# 处理 attributes（可能是字符串或字典）
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attributes_raw = function_args.get("attributes", {})
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if isinstance(attributes_raw, str):
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import orjson
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try:
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attributes = orjson.loads(attributes_raw)
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except Exception:
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attributes = {}
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else:
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attributes = attributes_raw
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importance = function_args.get("importance", 0.5)
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# 创建记忆
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memory = await manager.create_memory(
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subject=subject,
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memory_type=memory_type,
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topic=topic,
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object_=obj,
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attributes=attributes,
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importance=importance,
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)
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if memory:
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logger.info(f"[CreateMemoryTool] 成功创建记忆: {memory.id}")
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return {
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"name": self.name,
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"content": f"成功创建记忆（ID: {memory.id}）"
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}
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else:
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return {
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"name": self.name,
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"content": "创建记忆失败"
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}
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except Exception as e:
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logger.error(f"[CreateMemoryTool] 执行失败: {e}", exc_info=True)
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return {
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"name": self.name,
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"content": f"创建记忆时出错: {str(e)}"
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}
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class LinkMemoriesTool(BaseTool):
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"""关联记忆工具"""
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name = "link_memories"
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description = "在两个记忆之间建立关联关系。用于连接相关的记忆，形成知识网络。"
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parameters: ClassVar[list[tuple[str, ToolParamType, str, bool, list[str] | None]]] = [
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("source_query", ToolParamType.STRING, "源记忆的搜索查询（如记忆的主题关键词）", True, None),
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("target_query", ToolParamType.STRING, "目标记忆的搜索查询", True, None),
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("relation", ToolParamType.STRING, "关系类型", True, ["导致", "引用", "相似", "相反", "部分"]),
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("strength", ToolParamType.FLOAT, "关系强度（0.0-1.0），默认0.7", False, None),
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]
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available_for_llm = True
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async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
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"""执行关联记忆"""
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try:
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from src.memory_graph.manager_singleton import get_memory_manager
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manager = get_memory_manager()
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if not manager:
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return {
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"name": self.name,
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"content": "记忆系统未初始化"
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}
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source_query = function_args.get("source_query", "")
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target_query = function_args.get("target_query", "")
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relation = function_args.get("relation", "引用")
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strength = function_args.get("strength", 0.7)
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# 关联记忆
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success = await manager.link_memories(
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source_description=source_query,
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target_description=target_query,
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relation_type=relation,
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importance=strength,
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)
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if success:
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logger.info(f"[LinkMemoriesTool] 成功关联记忆: {source_query} -> {target_query}")
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return {
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"name": self.name,
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"content": f"成功建立关联: {source_query} --{relation}--> {target_query}"
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}
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else:
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return {
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"name": self.name,
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"content": "关联记忆失败，可能找不到匹配的记忆"
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}
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except Exception as e:
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logger.error(f"[LinkMemoriesTool] 执行失败: {e}", exc_info=True)
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return {
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"name": self.name,
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"content": f"关联记忆时出错: {str(e)}"
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}
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class SearchMemoriesTool(BaseTool):
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"""搜索记忆工具"""
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name = "search_memories"
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description = "搜索相关的记忆。根据查询词搜索记忆库，返回最相关的记忆。"
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parameters: ClassVar[list[tuple[str, ToolParamType, str, bool, list[str] | None]]] = [
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("query", ToolParamType.STRING, "搜索查询词，描述想要找什么样的记忆", True, None),
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("top_k", ToolParamType.INTEGER, "返回的记忆数量，默认5", False, None),
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("min_importance", ToolParamType.FLOAT, "最低重要性阈值（0.0-1.0），只返回重要性不低于此值的记忆", False, None),
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]
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available_for_llm = True
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async def execute(self, function_args: dict[str, Any]) -> dict[str, Any]:
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"""执行搜索记忆"""
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try:
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from src.memory_graph.manager_singleton import get_memory_manager
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manager = get_memory_manager()
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if not manager:
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return {
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"name": self.name,
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"content": "记忆系统未初始化"
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}
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query = function_args.get("query", "")
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top_k = function_args.get("top_k", 5)
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min_importance_raw = function_args.get("min_importance")
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min_importance = float(min_importance_raw) if min_importance_raw is not None else None
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# 搜索记忆
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memories = await manager.search_memories(
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query=query,
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top_k=top_k,
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min_importance=min_importance,
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)
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if memories:
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# 格式化结果
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result_lines = [f"找到 {len(memories)} 条相关记忆：\n"]
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for i, mem in enumerate(memories, 1):
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topic = mem.metadata.get("topic", "N/A")
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mem_type = mem.metadata.get("memory_type", "N/A")
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importance = mem.importance
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result_lines.append(
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f"{i}. [{mem_type}] {topic} (重要性: {importance:.2f})"
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)
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result_text = "\n".join(result_lines)
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logger.info(f"[SearchMemoriesTool] 搜索成功: 查询='{query}', 结果数={len(memories)}")
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return {
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"name": self.name,
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"content": result_text
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}
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else:
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return {
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"name": self.name,
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"content": f"未找到与 '{query}' 相关的记忆"
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}
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except Exception as e:
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logger.error(f"[SearchMemoriesTool] 执行失败: {e}", exc_info=True)
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return {
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"name": self.name,
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"content": f"搜索记忆时出错: {str(e)}"
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}
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