init: first commit to Gitea mirror, update README with Docker quick start and new repo URL

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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P0lar1s
2026-04-15 15:44:25 +08:00
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# ============================================================
# Module: Memory Decay Engine (decay_engine.py)
# 模块:记忆衰减引擎
#
# Simulates human forgetting curve; auto-decays inactive memories and archives them.
# 模拟人类遗忘曲线,自动衰减不活跃记忆并归档。
#
# Core formula (improved Ebbinghaus + emotion coordinates):
# 核心公式(改进版艾宾浩斯遗忘曲线 + 情感坐标):
# Score = Importance × (activation_count^0.3) × e^(-λ×days) × emotion_weight
#
# Emotion weight (continuous coordinate, not discrete labels):
# 情感权重(基于连续坐标而非离散列举):
# emotion_weight = base + (arousal × arousal_boost)
# Higher arousal → higher emotion weight → slower decay
# 唤醒度越高 → 情感权重越大 → 记忆衰减越慢
#
# Depended on by: server.py
# 被谁依赖server.py
# ============================================================
import math
import asyncio
import logging
from datetime import datetime
logger = logging.getLogger("ombre_brain.decay")
class DecayEngine:
"""
Memory decay engine — periodically scans all dynamic buckets,
calculates decay scores, auto-archives low-activity buckets
to simulate natural forgetting.
记忆衰减引擎 —— 定期扫描所有动态桶,
计算衰减得分,将低活跃桶自动归档,模拟自然遗忘。
"""
def __init__(self, config: dict, bucket_mgr):
# --- Load decay parameters / 加载衰减参数 ---
decay_cfg = config.get("decay", {})
self.decay_lambda = decay_cfg.get("lambda", 0.05)
self.threshold = decay_cfg.get("threshold", 0.3)
self.check_interval = decay_cfg.get("check_interval_hours", 24)
# --- Emotion weight params (continuous arousal coordinate) ---
# --- 情感权重参数(基于连续 arousal 坐标)---
emotion_cfg = decay_cfg.get("emotion_weights", {})
self.emotion_base = emotion_cfg.get("base", 1.0)
self.arousal_boost = emotion_cfg.get("arousal_boost", 0.8)
self.bucket_mgr = bucket_mgr
# --- Background task control / 后台任务控制 ---
self._task: asyncio.Task | None = None
self._running = False
@property
def is_running(self) -> bool:
"""Whether the decay engine is running in the background.
衰减引擎是否正在后台运行。"""
return self._running
# ---------------------------------------------------------
# Core: calculate decay score for a single bucket
# 核心:计算单个桶的衰减得分
#
# Higher score = more vivid memory; below threshold → archive
# 得分越高 = 记忆越鲜活,低于阈值则归档
# Permanent buckets never decay / 固化桶永远不衰减
# ---------------------------------------------------------
def calculate_score(self, metadata: dict) -> float:
"""
Calculate current activity score for a memory bucket.
计算一个记忆桶的当前活跃度得分。
Formula: Score = Importance × (act_count^0.3) × e^(-λ×days) × (base + arousal×boost)
"""
if not isinstance(metadata, dict):
return 0.0
# --- Permanent buckets never decay / 固化桶永不衰减 ---
if metadata.get("type") == "permanent":
return 999.0
importance = max(1, min(10, int(metadata.get("importance", 5))))
activation_count = max(1, int(metadata.get("activation_count", 1)))
# --- Days since last activation / 距离上次激活过了多少天 ---
last_active_str = metadata.get("last_active", metadata.get("created", ""))
try:
last_active = datetime.fromisoformat(str(last_active_str))
days_since = max(0.0, (datetime.now() - last_active).total_seconds() / 86400)
except (ValueError, TypeError):
days_since = 30 # Parse failure → assume 30 days / 解析失败假设已过 30 天
# --- Emotion weight: continuous arousal coordinate ---
# --- 情感权重:基于连续 arousal 坐标计算 ---
# Higher arousal → stronger emotion → higher weight → slower decay
# arousal 越高 → 情感越强烈 → 权重越大 → 衰减越慢
try:
arousal = max(0.0, min(1.0, float(metadata.get("arousal", 0.3))))
except (ValueError, TypeError):
arousal = 0.3
emotion_weight = self.emotion_base + arousal * self.arousal_boost
# --- Apply decay formula / 套入衰减公式 ---
score = (
importance
* (activation_count ** 0.3)
* math.exp(-self.decay_lambda * days_since)
* emotion_weight
)
# --- Weight pool modifiers / 权重池修正因子 ---
# Resolved events drop to 5%, sink to bottom awaiting keyword reactivation
# 已解决的事件权重骤降到 5%,沉底等待关键词激活
resolved_factor = 0.05 if metadata.get("resolved", False) else 1.0
# High-arousal unresolved buckets get urgency boost for priority surfacing
# 高唤醒未解决桶额外加成,优先浮现
urgency_boost = 1.5 if (arousal > 0.7 and not metadata.get("resolved", False)) else 1.0
return round(score * resolved_factor * urgency_boost, 4)
# ---------------------------------------------------------
# Execute one decay cycle
# 执行一轮衰减周期
# Scan all dynamic buckets → score → archive those below threshold
# 扫描所有动态桶 → 算分 → 低于阈值的归档
# ---------------------------------------------------------
async def run_decay_cycle(self) -> dict:
"""
Execute one decay cycle: iterate dynamic buckets, archive those
scoring below threshold.
执行一轮衰减:遍历动态桶,归档得分低于阈值的桶。
Returns stats: {"checked": N, "archived": N, "lowest_score": X}
"""
try:
buckets = await self.bucket_mgr.list_all(include_archive=False)
except Exception as e:
logger.error(f"Failed to list buckets for decay / 衰减周期列桶失败: {e}")
return {"checked": 0, "archived": 0, "lowest_score": 0, "error": str(e)}
checked = 0
archived = 0
lowest_score = float("inf")
for bucket in buckets:
meta = bucket.get("metadata", {})
# Skip permanent buckets / 跳过固化桶
if meta.get("type") == "permanent":
continue
checked += 1
try:
score = self.calculate_score(meta)
except Exception as e:
logger.warning(
f"Score calculation failed for {bucket.get('id', '?')} / "
f"计算得分失败: {e}"
)
continue
lowest_score = min(lowest_score, score)
# --- Below threshold → archive (simulate forgetting) ---
# --- 低于阈值 → 归档(模拟遗忘)---
if score < self.threshold:
try:
success = await self.bucket_mgr.archive(bucket["id"])
if success:
archived += 1
logger.info(
f"Decay archived / 衰减归档: "
f"{meta.get('name', bucket['id'])} "
f"(score={score:.4f}, threshold={self.threshold})"
)
except Exception as e:
logger.warning(
f"Archive failed for {bucket.get('id', '?')} / "
f"归档失败: {e}"
)
result = {
"checked": checked,
"archived": archived,
"lowest_score": lowest_score if checked > 0 else 0,
}
logger.info(f"Decay cycle complete / 衰减周期完成: {result}")
return result
# ---------------------------------------------------------
# Background decay task management
# 后台衰减任务管理
# ---------------------------------------------------------
async def ensure_started(self) -> None:
"""
Ensure the decay engine is started (lazy init on first call).
确保衰减引擎已启动(懒加载,首次调用时启动)。
"""
if not self._running:
await self.start()
async def start(self) -> None:
"""Start the background decay loop.
启动后台衰减循环。"""
if self._running:
return
self._running = True
self._task = asyncio.create_task(self._background_loop())
logger.info(
f"Decay engine started, interval: {self.check_interval}h / "
f"衰减引擎已启动,检查间隔: {self.check_interval} 小时"
)
async def stop(self) -> None:
"""Stop the background decay loop.
停止后台衰减循环。"""
self._running = False
if self._task:
self._task.cancel()
try:
await self._task
except asyncio.CancelledError:
pass
logger.info("Decay engine stopped / 衰减引擎已停止")
async def _background_loop(self) -> None:
"""Background loop: run decay → sleep → repeat.
后台循环体:执行衰减 → 睡眠 → 重复。"""
while self._running:
try:
await self.run_decay_cycle()
except Exception as e:
logger.error(f"Decay cycle error / 衰减周期出错: {e}")
# --- Wait for next cycle / 等待下一个周期 ---
try:
await asyncio.sleep(self.check_interval * 3600)
except asyncio.CancelledError:
break