WIN-JHFT4D3SIVT\caoxiaozhu
11160ec4d2
- M.2: ForgettingCurve, MemoryDecay, MemoryReinforcement (selective forgetting) - M.3: DailyDigestGenerator, ReminderScheduler, ProactiveInformer (proactive reminders) - M.4: MemoryExtractor with LLM-based memory extraction from conversations - M.5: MemoryRecallInjector with token budget control for prompt injection - All phases include comprehensive unit tests (109 tests passing) - Updated checklist.md to mark all tasks complete
73 lines
2.4 KiB
Python
73 lines
2.4 KiB
Python
"""
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MemoryReinforcement
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Triggers memory reinforcement on recall and handles auto-reinforcement.
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"""
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from datetime import UTC, datetime
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from app.models.memory import UserMemory
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class MemoryReinforcement:
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"""Reinforce memories on recall to prevent forgetting."""
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MAX_FREQUENCY = 10
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AUTO_REINFORCE_BOOST = 1.1 # 10% boost per week
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def trigger(self, memory: "UserMemory") -> "UserMemory":
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"""Called when memory is recalled: reset decay_score, increment frequency.
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This is the core reinforcement mechanism - each recall makes the memory
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stickier by resetting its decay curve and incrementing frequency.
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"""
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from app.services.memory.forgetting_curve import ForgettingCurve
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# Increment frequency count (capped at MAX_FREQUENCY)
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current_freq = getattr(memory, "frequency_count", 0) or 0
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memory.frequency_count = min(current_freq + 1, self.MAX_FREQUENCY)
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# Update last accessed time
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now = datetime.now(UTC)
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memory.last_accessed_at = now
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memory.last_recalled_at = now
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# Reset decay score to near 1.0 (fully retained)
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curve = ForgettingCurve()
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memory.decay_score = min(0.95, curve.calculate_decay(memory) + 0.1)
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return memory
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def auto_reinforce(self, memories: list["UserMemory"]) -> list["UserMemory"]:
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"""Weekly auto-reinforce for high-importance memories.
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Applies a 10% boost to frequency_count for high-importance memories
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that haven't been accessed recently, keeping them fresh.
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"""
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reinforced = []
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now = datetime.now(UTC)
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for memory in memories:
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importance_level = getattr(memory, "importance_level", "medium") or "medium"
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if importance_level != "high":
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continue
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current_freq = getattr(memory, "frequency_count", 0) or 0
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if current_freq >= self.MAX_FREQUENCY:
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continue
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# Apply 10% boost, capped at MAX_FREQUENCY
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new_freq = min(int(current_freq * self.AUTO_REINFORCE_BOOST + 1), self.MAX_FREQUENCY)
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memory.frequency_count = new_freq
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# Slightly improve decay score
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current_decay = getattr(memory, "decay_score", 0.5) or 0.5
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memory.decay_score = min(0.95, current_decay * 1.05)
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memory.last_accessed_at = now
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reinforced.append(memory)
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return reinforced
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