229 lines
5.8 KiB
Markdown
229 lines
5.8 KiB
Markdown
# Phase M.2：遗忘曲线系统
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日期：2026-04-04
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状态：规划中
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依赖：M.1 (重要性评分)
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工作量：3 天
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---
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## 1. 本阶段目的
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实现「选择性遗忘」机制，让 Jarvis 知道「什么可以忘记」。
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核心问题：
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- 冷门知识不应该一直占存储
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- 但不能直接删，要有归档机制
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- 重要记忆应该被强化，不容易忘
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---
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## 2. 核心架构
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```
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┌─────────────────────────────────────────────────────────────┐
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│ ForgettingCurve │
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├─────────────────────────────────────────────────────────────┤
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│ decay_score = exp(-time_since_access / half_life) │
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│ │
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│ 遗忘策略: │
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│ - decay_score < 0.2 → 归档到 cold_storage │
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│ - decay_score < 0.5 → 降权，不参与主动提醒 │
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│ - importance_level=high → 半衰期延长 3x │
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└─────────────────────────────────────────────────────────────┘
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```
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---
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## 3. 艾宾浩斯遗忘曲线模型
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### 3.1 基础遗忘曲线
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```
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保留率
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100% |████████████
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80% |██████████
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60% |███████
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40% |█████
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20% |██
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0% |________________________ 时间
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1天 7天 30天 90天
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```
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### 3.2 Jarvis 遗忘策略
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```python
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# 基础半衰期：30 天
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BASE_HALF_LIFE_DAYS = 30
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# 重要性影响半衰期
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if importance_level == "high":
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half_life = BASE_HALF_LIFE_DAYS * 3 # 90 天
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elif importance_level == "medium":
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half_life = BASE_HALF_LIFE_DAYS * 1 # 30 天
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else:
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half_life = BASE_HALF_LIFE_DAYS * 0.5 # 15 天
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# 遗忘分数
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decay_score = exp(-days_since_access / half_life)
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```
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---
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## 4. 核心文件
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### 4.1 新增文件
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| 文件 | 职责 |
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|------|------|
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| `services/memory/forgetting_curve.py` | 遗忘曲线计算 |
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| `services/memory/memory_decay.py` | 记忆衰减处理 |
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| `services/memory/reinforcement.py` | 记忆强化触发 |
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### 4.2 修改文件
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| 文件 | 修改内容 |
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|------|---------|
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| `models/memory.py` | 增加 decay_score, is_archived, last_accessed_at 字段 |
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| `services/memory_service.py` | 集成遗忘逻辑 |
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---
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## 5. API 设计
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### 5.1 ForgettingCurve
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```python
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class ForgettingCurve:
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def calculate_decay(self, memory: UserMemory) -> float:
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"""返回 0.0-1.0 的保留分数"""
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def should_archive(self, memory: UserMemory) -> bool:
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"""是否应该归档"""
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def should_deprioritize(self, memory: UserMemory) -> bool:
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"""是否应该降权（不参与主动提醒）"""
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```
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### 5.2 Reinforcement
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```python
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class MemoryReinforcement:
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def trigger(self, memory_id: int) -> None:
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"""被召回时触发强化"""
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# 频率 +1
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# decay_score 重置
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def auto_reinforce(self, user_id: int) -> None:
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"""定期自动强化高重要性记忆"""
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# 每周检查，对 high 级别记忆做轻量强化
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```
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---
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## 6. 归档机制
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### 6.1 热/冷存储分离
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```python
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# 热存储：活跃记忆，参与检索和主动提醒
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HOT_MEMORIES = []
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# 冷存储：归档记忆，不参与主动提醒，按需恢复
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COLD_MEMORIES = []
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```
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### 6.2 归档恢复
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```python
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async def restore_from_archive(self, memory_id: int) -> UserMemory:
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"""从归档恢复记忆"""
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# 恢复后 decay_score 重置为 0.8
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# 重新加入热存储
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```
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---
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## 7. 调度任务
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### 7.1 每日遗忘检查
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```python
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# 每天凌晨执行
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@scheduler.scheduled_task("cron", hour=3)
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async def daily_forgetting_check():
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"""每日遗忘检查"""
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# 1. 计算所有记忆的 decay_score
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# 2. 归档 decay < 0.2 的记忆
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# 3. 降权 decay < 0.5 的记忆
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# 4. 强化被召回的记忆
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```
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### 7.2 每周自动强化
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```python
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@scheduler.scheduled_task("cron", day_of_week="mon", hour=4)
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async def weekly_reinforcement():
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"""每周自动强化"""
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# 对 high 重要性记忆做轻量强化
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# frequency_count *= 1.1 (上限 10)
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```
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---
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## 8. 测试设计
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### 8.1 遗忘曲线测试
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```python
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def test_decay_after_30_days():
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memory = create_memory(last_accessed_at=days_ago(30))
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decay = curve.calculate_decay(memory)
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assert 0.4 < decay < 0.6 # 约 50% 保留
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def test_high_importance_slower_decay():
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high = create_memory(importance_level="high", last_accessed_at=days_ago(30))
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low = create_memory(importance_level="low", last_accessed_at=days_ago(30))
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assert curve.calculate_decay(high) > curve.calculate_decay(low)
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```
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### 8.2 归档测试
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```python
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def test_archive_low_decay():
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memory = create_memory(decay_score=0.15)
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assert curve.should_archive(memory) == True
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def test_restore_from_archive():
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memory = service.restore_from_archive(memory_id)
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assert memory.is_archived == False
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assert memory.decay_score > 0.5
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```
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---
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## 9. 验收标准
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| 标准 | 说明 |
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|------|------|
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| 遗忘曲线正确 | 30 天后 decay ≈ 0.5 |
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| 高重要性记忆衰减慢 | high 级别衰减速度是 low 的 1/6 |
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| 归档正常 | decay < 0.2 自动归档 |
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| 恢复正常 | 归档记忆可以恢复 |
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| 调度任务正常 | 每日检查、周强化执行 |
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| 单元测试覆盖率 | > 80% |
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---
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## 10. 工作量估算
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| 任务 | 工作量 |
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|------|--------|
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| ForgettingCurve | 0.5 天 |
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| MemoryDecay | 0.5 天 |
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| Reinforcement | 0.5 天 |
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| 归档机制 | 0.5 天 |
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| 调度任务 | 0.5 天 |
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| 测试 | 0.5 天 |
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| **合计** | **3 天** |
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