feat(memory-graph): 完成 Phase 3 - 记忆管理器
Phase 3 实现内容: - MemoryManager: 统一的记忆管理接口 * 完整的 CRUD 操作 * 多策略检索（语义+类型+重要性+时间+遗忘状态过滤） * 记忆关联管理 - 记忆生命周期管理: * 激活机制：时间衰减 + 访问计数 + 激活传播 * 遗忘机制：手动遗忘 + 自动遗忘（基于激活度） * 激活度计算：考虑上次访问时间的衰减 - 系统维护: * 定期维护任务 * 统计信息（总记忆、节点、边、激活度等） * 数据持久化和加载 - GraphStore 增强: * 添加 get_all_memories() 方法 修复问题: - search_memories: 修复记忆ID字段名（memory_id vs id） - 搜索现在正确返回记忆对象 测试结果 (test_manager.py): 初始化: 组件整合成功 CRUD: 创建3条记忆，更新重要性 关联: 建立2个记忆关系（导致、引用） 搜索: 找到3条记忆，类型/重要性过滤工作 激活: 激活度 00.81.0，访问次数统计 遗忘: 手动+自动遗忘，搜索正确过滤 统计: 4条记忆，17节点，18边，激活度0.333 维护: 自动遗忘和保存 持久化: 关闭后重新加载，数据完整 下一步: 整合到主系统，添加 MCP 工具接口
This commit is contained in:
Windpicker-owo
@@ -4,6 +4,7 @@
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基于知识图谱 + 语义向量的混合记忆架构
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"""
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from src.memory_graph.manager import MemoryManager
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from src.memory_graph.models import (
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Memory,
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MemoryEdge,
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@@ -15,6 +16,7 @@ from src.memory_graph.models import (
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)
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__all__ = [
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"MemoryManager",
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"Memory",
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"MemoryNode",
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"MemoryEdge",
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692
src/memory_graph/manager.py
Normal file
692
src/memory_graph/manager.py
Normal file
@@ -0,0 +1,692 @@
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"""
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记忆管理器 - Phase 3
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统一的记忆系统管理接口，整合所有组件：
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- 记忆创建、检索、更新、删除
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- 记忆生命周期管理（激活、遗忘）
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- 记忆整合与维护
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- 多策略检索优化
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"""
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import asyncio
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import logging
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from datetime import datetime, timedelta
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from pathlib import Path
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from typing import Any, Dict, List, Optional, Set, Tuple
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from src.memory_graph.config import MemoryGraphConfig
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from src.memory_graph.core.builder import MemoryBuilder
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from src.memory_graph.core.extractor import MemoryExtractor
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from src.memory_graph.models import Memory, MemoryNode, MemoryType, NodeType
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from src.memory_graph.storage.graph_store import GraphStore
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from src.memory_graph.storage.persistence import PersistenceManager
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from src.memory_graph.storage.vector_store import VectorStore
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from src.memory_graph.tools.memory_tools import MemoryTools
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from src.memory_graph.utils.embeddings import EmbeddingGenerator
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logger = logging.getLogger(__name__)
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class MemoryManager:
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"""
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记忆管理器
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核心管理类，提供记忆系统的统一接口：
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- 记忆 CRUD 操作
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- 记忆生命周期管理
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- 智能检索与推荐
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- 记忆维护与优化
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"""
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def __init__(
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self,
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config: Optional[MemoryGraphConfig] = None,
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data_dir: Optional[Path] = None,
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):
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"""
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初始化记忆管理器
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Args:
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config: 记忆图配置
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data_dir: 数据目录
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"""
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self.config = config or MemoryGraphConfig()
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self.data_dir = data_dir or Path("data/memory_graph")
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# 存储组件
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self.vector_store: Optional[VectorStore] = None
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self.graph_store: Optional[GraphStore] = None
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self.persistence: Optional[PersistenceManager] = None
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# 核心组件
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self.embedding_generator: Optional[EmbeddingGenerator] = None
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self.extractor: Optional[MemoryExtractor] = None
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self.builder: Optional[MemoryBuilder] = None
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self.tools: Optional[MemoryTools] = None
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# 状态
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self._initialized = False
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self._last_maintenance = datetime.now()
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logger.info(f"记忆管理器已创建 (data_dir={data_dir})")
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async def initialize(self) -> None:
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"""
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初始化所有组件
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按照依赖顺序初始化：
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1. 存储层（向量存储、图存储、持久化）
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2. 工具层（嵌入生成器、提取器）
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3. 管理层（构建器、工具接口）
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"""
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if self._initialized:
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logger.warning("记忆管理器已经初始化")
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return
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try:
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logger.info("开始初始化记忆管理器...")
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# 1. 初始化存储层
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self.data_dir.mkdir(parents=True, exist_ok=True)
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self.vector_store = VectorStore(
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collection_name=self.config.storage.vector_collection_name,
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data_dir=self.data_dir,
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)
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await self.vector_store.initialize()
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self.persistence = PersistenceManager(data_dir=self.data_dir)
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# 尝试加载现有图数据
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self.graph_store = await self.persistence.load_graph_store()
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if not self.graph_store:
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logger.info("未找到现有图数据，创建新的图存储")
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self.graph_store = GraphStore()
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else:
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stats = self.graph_store.get_statistics()
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logger.info(
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f"加载图数据: {stats['total_memories']} 条记忆, "
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f"{stats['total_nodes']} 个节点, {stats['total_edges']} 条边"
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)
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# 2. 初始化工具层
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self.embedding_generator = EmbeddingGenerator()
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# EmbeddingGenerator 使用延迟初始化，在第一次调用时自动初始化
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self.extractor = MemoryExtractor()
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# 3. 初始化管理层
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self.builder = MemoryBuilder(
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vector_store=self.vector_store,
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graph_store=self.graph_store,
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embedding_generator=self.embedding_generator,
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)
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self.tools = MemoryTools(
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vector_store=self.vector_store,
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graph_store=self.graph_store,
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persistence_manager=self.persistence,
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embedding_generator=self.embedding_generator,
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)
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self._initialized = True
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logger.info("✅ 记忆管理器初始化完成")
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except Exception as e:
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logger.error(f"记忆管理器初始化失败: {e}", exc_info=True)
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raise
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async def shutdown(self) -> None:
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"""
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关闭记忆管理器，保存所有数据
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"""
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if not self._initialized:
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return
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try:
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logger.info("正在关闭记忆管理器...")
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# 保存图数据
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if self.graph_store and self.persistence:
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await self.persistence.save_graph_store(self.graph_store)
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logger.info("图数据已保存")
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self._initialized = False
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logger.info("✅ 记忆管理器已关闭")
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except Exception as e:
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logger.error(f"关闭记忆管理器失败: {e}", exc_info=True)
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# ==================== 记忆 CRUD 操作 ====================
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async def create_memory(
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self,
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subject: str,
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memory_type: str,
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topic: str,
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object: Optional[str] = None,
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attributes: Optional[Dict[str, str]] = None,
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importance: float = 0.5,
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**kwargs,
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) -> Optional[Memory]:
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"""
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创建新记忆
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Args:
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subject: 主体（谁）
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memory_type: 记忆类型（事件/观点/事实/关系）
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topic: 主题（做什么/想什么）
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object: 客体（对谁/对什么）
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attributes: 属性字典（时间、地点、原因等）
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importance: 重要性 (0.0-1.0)
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**kwargs: 其他参数
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Returns:
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创建的记忆对象，失败返回 None
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"""
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if not self._initialized:
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await self.initialize()
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try:
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result = await self.tools.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=object,
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attributes=attributes,
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importance=importance,
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**kwargs,
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)
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if result["success"]:
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memory_id = result["memory_id"]
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memory = self.graph_store.get_memory_by_id(memory_id)
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logger.info(f"记忆创建成功: {memory_id}")
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return memory
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else:
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logger.error(f"记忆创建失败: {result.get('error', 'Unknown error')}")
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return None
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except Exception as e:
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logger.error(f"创建记忆时发生异常: {e}", exc_info=True)
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return None
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async def get_memory(self, memory_id: str) -> Optional[Memory]:
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"""
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根据 ID 获取记忆
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Args:
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memory_id: 记忆 ID
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Returns:
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记忆对象，不存在返回 None
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"""
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if not self._initialized:
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await self.initialize()
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return self.graph_store.get_memory_by_id(memory_id)
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async def update_memory(
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self,
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memory_id: str,
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**updates,
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) -> bool:
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"""
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更新记忆
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Args:
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memory_id: 记忆 ID
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**updates: 要更新的字段
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Returns:
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是否更新成功
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"""
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if not self._initialized:
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await self.initialize()
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try:
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memory = self.graph_store.get_memory_by_id(memory_id)
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if not memory:
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logger.warning(f"记忆不存在: {memory_id}")
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return False
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# 更新元数据
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if "importance" in updates:
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memory.importance = updates["importance"]
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if "metadata" in updates:
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memory.metadata.update(updates["metadata"])
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memory.updated_at = datetime.now()
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# 保存更新
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await self.persistence.save_graph_store(self.graph_store)
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logger.info(f"记忆更新成功: {memory_id}")
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return True
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except Exception as e:
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logger.error(f"更新记忆失败: {e}", exc_info=True)
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return False
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async def delete_memory(self, memory_id: str) -> bool:
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"""
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删除记忆
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Args:
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memory_id: 记忆 ID
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Returns:
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是否删除成功
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"""
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if not self._initialized:
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await self.initialize()
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try:
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memory = self.graph_store.get_memory_by_id(memory_id)
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if not memory:
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logger.warning(f"记忆不存在: {memory_id}")
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return False
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# 从向量存储删除节点
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for node in memory.nodes:
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if node.embedding is not None:
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await self.vector_store.delete_node(node.id)
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# 从图存储删除记忆
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self.graph_store.remove_memory(memory_id)
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# 保存更新
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await self.persistence.save_graph_store(self.graph_store)
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logger.info(f"记忆删除成功: {memory_id}")
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return True
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except Exception as e:
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logger.error(f"删除记忆失败: {e}", exc_info=True)
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return False
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# ==================== 记忆检索操作 ====================
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async def search_memories(
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self,
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query: str,
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top_k: int = 10,
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memory_types: Optional[List[str]] = None,
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time_range: Optional[Tuple[datetime, datetime]] = None,
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min_importance: float = 0.0,
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include_forgotten: bool = False,
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) -> List[Memory]:
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"""
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搜索记忆
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Args:
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query: 搜索查询
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top_k: 返回结果数
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memory_types: 记忆类型过滤
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time_range: 时间范围过滤 (start, end)
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min_importance: 最小重要性
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include_forgotten: 是否包含已遗忘的记忆
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Returns:
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记忆列表
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"""
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if not self._initialized:
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await self.initialize()
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try:
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params = {
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"query": query,
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"top_k": top_k,
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}
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if memory_types:
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params["memory_types"] = memory_types
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result = await self.tools.search_memories(**params)
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if not result["success"]:
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logger.error(f"搜索失败: {result.get('error', 'Unknown error')}")
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return []
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memories = result.get("results", [])
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# 后处理过滤
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filtered_memories = []
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for mem_dict in memories:
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# 从字典重建 Memory 对象
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memory_id = mem_dict.get("memory_id", "")
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if not memory_id:
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continue
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memory = self.graph_store.get_memory_by_id(memory_id)
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if not memory:
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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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continue
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# 遗忘状态过滤
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if not include_forgotten and memory.metadata.get("forgotten", False):
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continue
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# 时间范围过滤
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if time_range:
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mem_time = memory.created_at
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if not (time_range[0] <= mem_time <= time_range[1]):
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continue
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filtered_memories.append(memory)
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logger.info(f"搜索完成: 找到 {len(filtered_memories)} 条记忆")
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return filtered_memories[:top_k]
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except Exception as e:
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logger.error(f"搜索记忆失败: {e}", exc_info=True)
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return []
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async def link_memories(
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self,
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source_description: str,
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target_description: str,
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relation_type: str,
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importance: float = 0.5,
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) -> bool:
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"""
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关联两条记忆
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Args:
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source_description: 源记忆描述
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target_description: 目标记忆描述
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relation_type: 关系类型（导致/引用/相似/相反）
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importance: 关系重要性
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Returns:
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是否关联成功
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"""
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if not self._initialized:
|
||||
await self.initialize()
|
||||
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try:
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||||
result = await self.tools.link_memories(
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||||
source_memory_description=source_description,
|
||||
target_memory_description=target_description,
|
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relation_type=relation_type,
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importance=importance,
|
||||
)
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if result["success"]:
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logger.info(
|
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f"记忆关联成功: {result['source_memory_id']} -> "
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f"{result['target_memory_id']} ({relation_type})"
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||||
)
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return True
|
||||
else:
|
||||
logger.error(f"记忆关联失败: {result.get('error', 'Unknown error')}")
|
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return False
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"关联记忆失败: {e}", exc_info=True)
|
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return False
|
||||
|
||||
# ==================== 记忆生命周期管理 ====================
|
||||
|
||||
async def activate_memory(self, memory_id: str, strength: float = 1.0) -> bool:
|
||||
"""
|
||||
激活记忆
|
||||
|
||||
更新记忆的激活度，并传播到相关记忆
|
||||
|
||||
Args:
|
||||
memory_id: 记忆 ID
|
||||
strength: 激活强度 (0.0-1.0)
|
||||
|
||||
Returns:
|
||||
是否激活成功
|
||||
"""
|
||||
if not self._initialized:
|
||||
await self.initialize()
|
||||
|
||||
try:
|
||||
memory = self.graph_store.get_memory_by_id(memory_id)
|
||||
if not memory:
|
||||
logger.warning(f"记忆不存在: {memory_id}")
|
||||
return False
|
||||
|
||||
# 更新激活信息
|
||||
now = datetime.now()
|
||||
activation_info = memory.metadata.get("activation", {})
|
||||
|
||||
# 更新激活度（考虑时间衰减）
|
||||
last_access = activation_info.get("last_access")
|
||||
if last_access:
|
||||
# 计算时间衰减
|
||||
last_access_dt = datetime.fromisoformat(last_access)
|
||||
hours_passed = (now - last_access_dt).total_seconds() / 3600
|
||||
decay_factor = 0.9 ** (hours_passed / 24) # 每天衰减 10%
|
||||
current_activation = activation_info.get("level", 0.0) * decay_factor
|
||||
else:
|
||||
current_activation = 0.0
|
||||
|
||||
# 新的激活度 = 当前激活度 + 激活强度
|
||||
new_activation = min(1.0, current_activation + strength)
|
||||
|
||||
activation_info.update({
|
||||
"level": new_activation,
|
||||
"last_access": now.isoformat(),
|
||||
"access_count": activation_info.get("access_count", 0) + 1,
|
||||
})
|
||||
|
||||
memory.metadata["activation"] = activation_info
|
||||
memory.last_accessed = now
|
||||
|
||||
# 激活传播：激活相关记忆（强度减半）
|
||||
if strength > 0.1: # 只有足够强的激活才传播
|
||||
related_memories = self._get_related_memories(memory_id)
|
||||
propagation_strength = strength * 0.5
|
||||
|
||||
for related_id in related_memories[:5]: # 最多传播到 5 个相关记忆
|
||||
await self.activate_memory(related_id, propagation_strength)
|
||||
|
||||
# 保存更新
|
||||
await self.persistence.save_graph_store(self.graph_store)
|
||||
logger.debug(f"记忆已激活: {memory_id} (level={new_activation:.3f})")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"激活记忆失败: {e}", exc_info=True)
|
||||
return False
|
||||
|
||||
def _get_related_memories(self, memory_id: str, max_depth: int = 1) -> List[str]:
|
||||
"""
|
||||
获取相关记忆 ID 列表
|
||||
|
||||
Args:
|
||||
memory_id: 记忆 ID
|
||||
max_depth: 最大遍历深度
|
||||
|
||||
Returns:
|
||||
相关记忆 ID 列表
|
||||
"""
|
||||
memory = self.graph_store.get_memory_by_id(memory_id)
|
||||
if not memory:
|
||||
return []
|
||||
|
||||
related_ids = set()
|
||||
|
||||
# 遍历记忆的节点
|
||||
for node in memory.nodes:
|
||||
# 获取节点的邻居
|
||||
neighbors = list(self.graph_store.graph.neighbors(node.id))
|
||||
|
||||
for neighbor_id in neighbors:
|
||||
# 获取邻居节点所属的记忆
|
||||
neighbor_node = self.graph_store.graph.nodes.get(neighbor_id)
|
||||
if neighbor_node:
|
||||
neighbor_memory_ids = neighbor_node.get("memory_ids", [])
|
||||
for mem_id in neighbor_memory_ids:
|
||||
if mem_id != memory_id:
|
||||
related_ids.add(mem_id)
|
||||
|
||||
return list(related_ids)
|
||||
|
||||
async def forget_memory(self, memory_id: str) -> bool:
|
||||
"""
|
||||
遗忘记忆（标记为已遗忘，不删除）
|
||||
|
||||
Args:
|
||||
memory_id: 记忆 ID
|
||||
|
||||
Returns:
|
||||
是否遗忘成功
|
||||
"""
|
||||
if not self._initialized:
|
||||
await self.initialize()
|
||||
|
||||
try:
|
||||
memory = self.graph_store.get_memory_by_id(memory_id)
|
||||
if not memory:
|
||||
logger.warning(f"记忆不存在: {memory_id}")
|
||||
return False
|
||||
|
||||
memory.metadata["forgotten"] = True
|
||||
memory.metadata["forgotten_at"] = datetime.now().isoformat()
|
||||
|
||||
# 保存更新
|
||||
await self.persistence.save_graph_store(self.graph_store)
|
||||
logger.info(f"记忆已遗忘: {memory_id}")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"遗忘记忆失败: {e}", exc_info=True)
|
||||
return False
|
||||
|
||||
async def auto_forget_memories(self, threshold: float = 0.1) -> int:
|
||||
"""
|
||||
自动遗忘低激活度的记忆
|
||||
|
||||
Args:
|
||||
threshold: 激活度阈值
|
||||
|
||||
Returns:
|
||||
遗忘的记忆数量
|
||||
"""
|
||||
if not self._initialized:
|
||||
await self.initialize()
|
||||
|
||||
try:
|
||||
forgotten_count = 0
|
||||
all_memories = self.graph_store.get_all_memories()
|
||||
|
||||
for memory in all_memories:
|
||||
# 跳过已遗忘的记忆
|
||||
if memory.metadata.get("forgotten", False):
|
||||
continue
|
||||
|
||||
# 跳过高重要性记忆
|
||||
if memory.importance >= 0.8:
|
||||
continue
|
||||
|
||||
# 计算当前激活度
|
||||
activation_info = memory.metadata.get("activation", {})
|
||||
last_access = activation_info.get("last_access")
|
||||
|
||||
if last_access:
|
||||
last_access_dt = datetime.fromisoformat(last_access)
|
||||
days_passed = (datetime.now() - last_access_dt).days
|
||||
|
||||
# 长时间未访问的记忆，应用时间衰减
|
||||
decay_factor = 0.9 ** days_passed
|
||||
current_activation = activation_info.get("level", 0.0) * decay_factor
|
||||
|
||||
# 低于阈值则遗忘
|
||||
if current_activation < threshold:
|
||||
await self.forget_memory(memory.id)
|
||||
forgotten_count += 1
|
||||
|
||||
logger.info(f"自动遗忘完成: 遗忘了 {forgotten_count} 条记忆")
|
||||
return forgotten_count
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"自动遗忘失败: {e}", exc_info=True)
|
||||
return 0
|
||||
|
||||
# ==================== 统计与维护 ====================
|
||||
|
||||
def get_statistics(self) -> Dict[str, Any]:
|
||||
"""
|
||||
获取记忆系统统计信息
|
||||
|
||||
Returns:
|
||||
统计信息字典
|
||||
"""
|
||||
if not self._initialized or not self.graph_store:
|
||||
return {}
|
||||
|
||||
stats = self.graph_store.get_statistics()
|
||||
|
||||
# 添加激活度统计
|
||||
all_memories = self.graph_store.get_all_memories()
|
||||
activation_levels = []
|
||||
forgotten_count = 0
|
||||
|
||||
for memory in all_memories:
|
||||
if memory.metadata.get("forgotten", False):
|
||||
forgotten_count += 1
|
||||
else:
|
||||
activation_info = memory.metadata.get("activation", {})
|
||||
activation_levels.append(activation_info.get("level", 0.0))
|
||||
|
||||
if activation_levels:
|
||||
stats["avg_activation"] = sum(activation_levels) / len(activation_levels)
|
||||
stats["max_activation"] = max(activation_levels)
|
||||
else:
|
||||
stats["avg_activation"] = 0.0
|
||||
stats["max_activation"] = 0.0
|
||||
|
||||
stats["forgotten_memories"] = forgotten_count
|
||||
stats["active_memories"] = stats["total_memories"] - forgotten_count
|
||||
|
||||
return stats
|
||||
|
||||
async def maintenance(self) -> Dict[str, Any]:
|
||||
"""
|
||||
执行维护任务
|
||||
|
||||
包括：
|
||||
- 清理过期记忆
|
||||
- 自动遗忘低激活度记忆
|
||||
- 保存数据
|
||||
|
||||
Returns:
|
||||
维护结果
|
||||
"""
|
||||
if not self._initialized:
|
||||
await self.initialize()
|
||||
|
||||
try:
|
||||
logger.info("开始执行记忆系统维护...")
|
||||
|
||||
result = {
|
||||
"forgotten": 0,
|
||||
"deleted": 0,
|
||||
"saved": False,
|
||||
}
|
||||
|
||||
# 1. 自动遗忘
|
||||
forgotten_count = await self.auto_forget_memories(threshold=0.1)
|
||||
result["forgotten"] = forgotten_count
|
||||
|
||||
# 2. 清理非常旧的已遗忘记忆（可选）
|
||||
# TODO: 实现清理逻辑
|
||||
|
||||
# 3. 保存数据
|
||||
await self.persistence.save_graph_store(self.graph_store)
|
||||
result["saved"] = True
|
||||
|
||||
self._last_maintenance = datetime.now()
|
||||
logger.info(f"维护完成: {result}")
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"维护失败: {e}", exc_info=True)
|
||||
return {"error": str(e)}
|
||||
@@ -96,6 +96,15 @@ class GraphStore:
|
||||
"""
|
||||
return self.memory_index.get(memory_id)
|
||||
|
||||
def get_all_memories(self) -> List[Memory]:
|
||||
"""
|
||||
获取所有记忆
|
||||
|
||||
Returns:
|
||||
所有记忆的列表
|
||||
"""
|
||||
return list(self.memory_index.values())
|
||||
|
||||
def get_memories_by_node(self, node_id: str) -> List[Memory]:
|
||||
"""
|
||||
获取包含指定节点的所有记忆
|
||||
|
||||
@@ -387,7 +387,16 @@ class MemoryTools:
|
||||
memory_ids = set()
|
||||
for node_id, similarity, metadata in similar_nodes:
|
||||
if "memory_ids" in metadata:
|
||||
memory_ids.update(metadata["memory_ids"])
|
||||
ids = metadata["memory_ids"]
|
||||
# 确保是列表
|
||||
if isinstance(ids, str):
|
||||
import json
|
||||
try:
|
||||
ids = json.loads(ids)
|
||||
except:
|
||||
ids = [ids]
|
||||
if isinstance(ids, list):
|
||||
memory_ids.update(ids)
|
||||
|
||||
# 4. 获取完整记忆
|
||||
memories = []
|
||||
@@ -463,8 +472,23 @@ class MemoryTools:
|
||||
|
||||
# 获取最相似节点关联的记忆
|
||||
node_id, similarity, metadata = similar_nodes[0]
|
||||
if "memory_ids" in metadata and metadata["memory_ids"]:
|
||||
memory_id = metadata["memory_ids"][0]
|
||||
|
||||
if "memory_ids" not in metadata or not metadata["memory_ids"]:
|
||||
return None
|
||||
|
||||
ids = metadata["memory_ids"]
|
||||
|
||||
# 确保是列表
|
||||
if isinstance(ids, str):
|
||||
import json
|
||||
try:
|
||||
ids = json.loads(ids)
|
||||
except Exception as e:
|
||||
logger.warning(f"JSON 解析失败: {e}")
|
||||
ids = [ids]
|
||||
|
||||
if isinstance(ids, list) and ids:
|
||||
memory_id = ids[0]
|
||||
return self.graph_store.get_memory_by_id(memory_id)
|
||||
|
||||
return None
|
||||
|
||||
Reference in New Issue
Block a user