314 lines
13 KiB
Python
314 lines
13 KiB
Python
# ============================================================
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# Module: Memory Decay Engine (decay_engine.py)
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# 模块：记忆衰减引擎
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#
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# Simulates human forgetting curve; auto-decays inactive memories and archives them.
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# 模拟人类遗忘曲线，自动衰减不活跃记忆并归档。
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#
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# Core formula (improved Ebbinghaus + emotion coordinates):
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# 核心公式（改进版艾宾浩斯遗忘曲线 + 情感坐标）：
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# Score = Importance × (activation_count^0.3) × e^(-λ×days) × emotion_weight
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#
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# Emotion weight (continuous coordinate, not discrete labels):
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# 情感权重（基于连续坐标而非离散列举）：
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# emotion_weight = base + (arousal × arousal_boost)
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# Higher arousal → higher emotion weight → slower decay
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# 唤醒度越高 → 情感权重越大 → 记忆衰减越慢
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#
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# Depended on by: server.py
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# 被谁依赖：server.py
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# ============================================================
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import math
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import asyncio
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import logging
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from datetime import datetime
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logger = logging.getLogger("ombre_brain.decay")
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class DecayEngine:
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"""
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Memory decay engine — periodically scans all dynamic buckets,
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calculates decay scores, auto-archives low-activity buckets
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to simulate natural forgetting.
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记忆衰减引擎 —— 定期扫描所有动态桶，
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计算衰减得分，将低活跃桶自动归档，模拟自然遗忘。
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"""
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def __init__(self, config: dict, bucket_mgr):
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# --- Load decay parameters / 加载衰减参数 ---
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decay_cfg = config.get("decay", {})
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self.decay_lambda = decay_cfg.get("lambda", 0.05)
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self.threshold = decay_cfg.get("threshold", 0.3)
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self.check_interval = decay_cfg.get("check_interval_hours", 24)
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# --- Emotion weight params (continuous arousal coordinate) ---
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# --- 情感权重参数（基于连续 arousal 坐标）---
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emotion_cfg = decay_cfg.get("emotion_weights", {})
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self.emotion_base = emotion_cfg.get("base", 1.0)
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self.arousal_boost = emotion_cfg.get("arousal_boost", 0.8)
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self.bucket_mgr = bucket_mgr
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# --- Background task control / 后台任务控制 ---
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self._task: asyncio.Task | None = None
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self._running = False
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@property
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def is_running(self) -> bool:
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"""Whether the decay engine is running in the background.
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衰减引擎是否正在后台运行。"""
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return self._running
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# ---------------------------------------------------------
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# Core: calculate decay score for a single bucket
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# 核心：计算单个桶的衰减得分
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#
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# Higher score = more vivid memory; below threshold → archive
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# 得分越高 = 记忆越鲜活，低于阈值则归档
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# Permanent buckets never decay / 固化桶永远不衰减
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# ---------------------------------------------------------
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# ---------------------------------------------------------
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# Freshness bonus: continuous exponential decay
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# 新鲜度加成：连续指数衰减
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# bonus = 1.0 + 1.0 × e^(-t/36), t in hours
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# t=0 → 2.0×, t≈25h(半衰) → 1.5×, t≈72h → ≈1.14×, t→∞ → 1.0×
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# ---------------------------------------------------------
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@staticmethod
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def _calc_time_weight(days_since: float) -> float:
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"""
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Freshness bonus multiplier: 1.0 + e^(-t/36), t in hours.
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新鲜度加成乘数：刚存入×2.0，~36小时半衰，72小时后趋近×1.0。
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"""
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hours = days_since * 24.0
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return 1.0 + 1.0 * math.exp(-hours / 36.0)
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def calculate_score(self, metadata: dict) -> float:
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"""
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Calculate current activity score for a memory bucket.
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计算一个记忆桶的当前活跃度得分。
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New model: short-term vs long-term weight separation.
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新模型：短期/长期权重分离。
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- Short-term (≤3 days): time_weight dominates, emotion amplifies
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- Long-term (>3 days): emotion_weight dominates, time decays to floor
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短期（≤3天）：时间权重主导，情感放大
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长期（>3天）：情感权重主导，时间衰减到底线
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"""
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if not isinstance(metadata, dict):
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return 0.0
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# --- Pinned/protected buckets: never decay, importance locked to 10 ---
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if metadata.get("pinned") or metadata.get("protected"):
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return 999.0
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# --- Permanent buckets never decay ---
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if metadata.get("type") == "permanent":
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return 999.0
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# --- Feel buckets: never decay, fixed moderate score ---
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if metadata.get("type") == "feel":
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return 50.0
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importance = max(1, min(10, int(metadata.get("importance", 5))))
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activation_count = max(1, int(metadata.get("activation_count", 1)))
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# --- Days since last activation ---
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last_active_str = metadata.get("last_active", metadata.get("created", ""))
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try:
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last_active = datetime.fromisoformat(str(last_active_str))
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days_since = max(0.0, (datetime.now() - last_active).total_seconds() / 86400)
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except (ValueError, TypeError):
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days_since = 30
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# --- Emotion weight ---
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try:
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arousal = max(0.0, min(1.0, float(metadata.get("arousal", 0.3))))
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except (ValueError, TypeError):
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arousal = 0.3
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emotion_weight = self.emotion_base + arousal * self.arousal_boost
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# --- Time weight ---
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time_weight = self._calc_time_weight(days_since)
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# --- Short-term vs Long-term weight separation ---
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# 短期（≤3天）：time_weight 占 70%，emotion 占 30%
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# 长期（>3天）：emotion 占 70%，time_weight 占 30%
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if days_since <= 3.0:
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# Short-term: time dominates, emotion amplifies
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combined_weight = time_weight * 0.7 + emotion_weight * 0.3
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else:
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# Long-term: emotion dominates, time provides baseline
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combined_weight = emotion_weight * 0.7 + time_weight * 0.3
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# --- Base score ---
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base_score = (
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importance
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* (activation_count ** 0.3)
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* math.exp(-self.decay_lambda * days_since)
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* combined_weight
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)
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# --- Weight pool modifiers ---
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# resolved + digested (has feel) → accelerated fade: ×0.02
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# resolved only → ×0.05
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# 已处理+已消化（写过feel）→ 加速淡化：×0.02
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# 仅已处理 → ×0.05
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resolved = metadata.get("resolved", False)
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digested = metadata.get("digested", False) # set when feel is written for this memory
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if resolved and digested:
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resolved_factor = 0.02
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elif resolved:
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resolved_factor = 0.05
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else:
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resolved_factor = 1.0
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urgency_boost = 1.5 if (arousal > 0.7 and not resolved) else 1.0
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return round(base_score * resolved_factor * urgency_boost, 4)
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# ---------------------------------------------------------
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# Execute one decay cycle
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# 执行一轮衰减周期
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# Scan all dynamic buckets → score → archive those below threshold
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# 扫描所有动态桶 → 算分 → 低于阈值的归档
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# ---------------------------------------------------------
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async def run_decay_cycle(self) -> dict:
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"""
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Execute one decay cycle: iterate dynamic buckets, archive those
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scoring below threshold.
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执行一轮衰减：遍历动态桶，归档得分低于阈值的桶。
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Returns stats: {"checked": N, "archived": N, "lowest_score": X}
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"""
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try:
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buckets = await self.bucket_mgr.list_all(include_archive=False)
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except Exception as e:
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logger.error(f"Failed to list buckets for decay / 衰减周期列桶失败: {e}")
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return {"checked": 0, "archived": 0, "lowest_score": 0, "error": str(e)}
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checked = 0
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archived = 0
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auto_resolved = 0
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lowest_score = float("inf")
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for bucket in buckets:
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meta = bucket.get("metadata", {})
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# Skip permanent / pinned / protected / feel buckets
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# 跳过固化桶、钉选/保护桶和 feel 桶
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if meta.get("type") in ("permanent", "feel") or meta.get("pinned") or meta.get("protected"):
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continue
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checked += 1
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# --- Auto-resolve: imp≤4 + >30 days old + not resolved → auto resolve ---
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# --- 自动结案：重要度≤4 + 超过30天 + 未解决 → 自动 resolve ---
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if not meta.get("resolved", False):
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imp = int(meta.get("importance", 5))
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last_active_str = meta.get("last_active", meta.get("created", ""))
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try:
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last_active = datetime.fromisoformat(str(last_active_str))
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days_since = (datetime.now() - last_active).total_seconds() / 86400
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except (ValueError, TypeError):
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days_since = 999
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if imp <= 4 and days_since > 30:
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try:
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await self.bucket_mgr.update(bucket["id"], resolved=True)
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auto_resolved += 1
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logger.info(
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f"Auto-resolved / 自动结案: "
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f"{meta.get('name', bucket['id'])} "
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f"(imp={imp}, days={days_since:.0f})"
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)
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except Exception as e:
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logger.warning(f"Auto-resolve failed / 自动结案失败: {e}")
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try:
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score = self.calculate_score(meta)
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except Exception as e:
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logger.warning(
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f"Score calculation failed for {bucket.get('id', '?')} / "
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f"计算得分失败: {e}"
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)
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continue
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lowest_score = min(lowest_score, score)
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# --- Below threshold → archive (simulate forgetting) ---
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# --- 低于阈值 → 归档（模拟遗忘）---
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if score < self.threshold:
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try:
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success = await self.bucket_mgr.archive(bucket["id"])
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if success:
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archived += 1
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logger.info(
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f"Decay archived / 衰减归档: "
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f"{meta.get('name', bucket['id'])} "
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f"(score={score:.4f}, threshold={self.threshold})"
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)
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except Exception as e:
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logger.warning(
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f"Archive failed for {bucket.get('id', '?')} / "
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f"归档失败: {e}"
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)
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result = {
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"checked": checked,
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"archived": archived,
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"auto_resolved": auto_resolved,
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"lowest_score": lowest_score if checked > 0 else 0,
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}
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logger.info(f"Decay cycle complete / 衰减周期完成: {result}")
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return result
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# ---------------------------------------------------------
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# Background decay task management
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# 后台衰减任务管理
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# ---------------------------------------------------------
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async def ensure_started(self) -> None:
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"""
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Ensure the decay engine is started (lazy init on first call).
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确保衰减引擎已启动（懒加载，首次调用时启动）。
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"""
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if not self._running:
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await self.start()
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async def start(self) -> None:
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"""Start the background decay loop.
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启动后台衰减循环。"""
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if self._running:
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return
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self._running = True
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self._task = asyncio.create_task(self._background_loop())
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logger.info(
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f"Decay engine started, interval: {self.check_interval}h / "
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f"衰减引擎已启动，检查间隔: {self.check_interval} 小时"
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)
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async def stop(self) -> None:
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"""Stop the background decay loop.
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停止后台衰减循环。"""
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self._running = False
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if self._task:
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self._task.cancel()
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try:
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await self._task
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except asyncio.CancelledError:
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pass
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logger.info("Decay engine stopped / 衰减引擎已停止")
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async def _background_loop(self) -> None:
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"""Background loop: run decay → sleep → repeat.
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后台循环体：执行衰减 → 睡眠 → 重复。"""
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while self._running:
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try:
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await self.run_decay_cycle()
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except Exception as e:
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logger.error(f"Decay cycle error / 衰减周期出错: {e}")
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# --- Wait for next cycle / 等待下一个周期 ---
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try:
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await asyncio.sleep(self.check_interval * 3600)
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except asyncio.CancelledError:
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break
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