WIN-JHFT4D3SIVT\caoxiaozhu
9bfa0dcc11
- Add FrequencyTracker: increment(), get_frequency_score(), get_recency_score(), get_time_decay() - Add EmotionAnalyzer: EMOTION_KEYWORDS dict, extract(), calculate_score(), get_emotion_profile() - Add ImpactEvaluator: evaluate(), get_topic_overlap(), rank_by_impact() - Add ImportanceScorer: composite scoring (freq 35% + recency 20% + emotion 25% + impact 20%) - Update UserMemory model: frequency_count, emotion_tags, importance_score, importance_level, associated_topics - Integrate ImportanceScorer into memory_service.py (recall + importance update) - Add 37 tests for all memory scoring components - Fix urgency patterns: remove overly broad '今天' that matched neutral text - Update memory-update checklist: mark all M.1 tasks complete
85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
"""
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FrequencyTracker
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Tracks how often a memory is recalled and calculates frequency/recency scores.
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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 FrequencyTracker:
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"""Track and score memory recall frequency"""
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# Score weights
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MAX_FREQUENCY = 10 # Cap frequency count for scoring
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RECENCY_DECAY_DAYS = 30 # After 30 days, recency score drops significantly
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def increment(self, memory: "UserMemory") -> "UserMemory":
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"""Increment recall count and update last recalled timestamp"""
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memory.frequency_count = (memory.frequency_count or 0) + 1
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memory.last_recalled_at = datetime.now(UTC)
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return memory
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def get_frequency_score(self, memory: "UserMemory") -> float:
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"""Calculate normalized frequency score (0.0 - 1.0)
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Uses logarithmic scaling to prevent high-frequency memories
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from dominating completely.
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"""
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count = memory.frequency_count or 0
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if count == 0:
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return 0.0
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# Logarithmic scaling: more recalls have diminishing returns
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# log(1+x) / log(1+MAX) gives 0-1 range
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import math
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score = math.log(1 + count) / math.log(1 + self.MAX_FREQUENCY)
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return min(1.0, max(0.0, score))
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def get_recency_score(self, memory: "UserMemory") -> float:
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"""Calculate recency score (0.0 - 1.0)
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Memory recalled recently scores higher. Uses exponential decay.
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"""
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last_recalled = memory.last_recalled_at
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if last_recalled is None:
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return 0.0
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now = datetime.now(UTC)
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if isinstance(last_recalled, datetime):
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if last_recalled.tzinfo is None:
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last_recalled = last_recalled.replace(tzinfo=UTC)
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days_since = (now - last_recalled).total_seconds() / 86400
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else:
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days_since = self.RECENCY_DECAY_DAYS
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# Exponential decay: half-life of RECENCY_DECAY_DAYS
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import math
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decay = math.exp(-days_since / self.RECENCY_DECAY_DAYS)
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return min(1.0, max(0.0, decay))
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def get_time_decay(self, memory: "UserMemory") -> float:
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"""Calculate time-based decay factor for forgetting curve"""
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last_accessed = getattr(memory, "last_accessed_at", None)
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if last_accessed is None:
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last_accessed = memory.last_recalled_at
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if last_accessed is None:
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return 1.0
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now = datetime.now(UTC)
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if isinstance(last_accessed, datetime):
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if last_accessed.tzinfo is None:
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last_accessed = last_accessed.replace(tzinfo=UTC)
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days_since = (now - last_accessed).total_seconds() / 86400
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else:
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days_since = 0
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import math
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return math.exp(-days_since / self.RECENCY_DECAY_DAYS)
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