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
56 lines
2.1 KiB
Python
56 lines
2.1 KiB
Python
from sqlalchemy import (
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Column,
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String,
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Text,
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Integer,
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Float,
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ForeignKey,
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Boolean,
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DateTime,
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Enum as SQLEnum,
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JSON,
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)
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from app.models.base import BaseModel, utc_now
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class MemorySummary(BaseModel):
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"""
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对话摘要 — 中期记忆
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当一段对话超过阈值轮数时，自动生成摘要存入此表
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"""
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__tablename__ = "memory_summaries"
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user_id = Column(String(36), ForeignKey("users.id"), nullable=False, index=True)
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conversation_id = Column(String(36), ForeignKey("conversations.id"), nullable=False, index=True)
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summary_text = Column(Text, nullable=False) # 摘要内容
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turn_count = Column(Integer, default=0) # 摘要时累计轮数
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summary_at = Column(DateTime, default=utc_now, nullable=False)
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class UserMemory(BaseModel):
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"""
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用户画像记忆 — 长期记忆
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从对话中提取的用户事实、偏好、目标
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"""
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__tablename__ = "user_memories"
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user_id = Column(String(36), ForeignKey("users.id"), nullable=False, index=True)
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memory_type = Column(String(50), nullable=False) # fact | preference | goal | habit | other
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content = Column(Text, nullable=False) # 记忆内容
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importance = Column(Integer, default=5) # 重要程度 1-10 (legacy, replaced by importance_score)
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is_recalled = Column(Boolean, default=False) # 是否在当前对话中被召回
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recall_count = Column(Integer, default=0) # 被召回次数
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source_conversation_id = Column(String(36), nullable=True) # 来源对话
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extracted_at = Column(DateTime, default=utc_now, nullable=False)
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last_recalled_at = Column(DateTime, nullable=True)
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# M.1: 重要性评分系统
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frequency_count = Column(
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Integer, default=0
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) # 被召回次数 (duplicate of recall_count, for scoring clarity)
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emotion_tags = Column(JSON, nullable=True) # List of emotion keywords
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importance_score = Column(Float, default=0.5) # 重要性分数 0.0-1.0
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importance_level = Column(String(20), default="medium") # high | medium | low
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associated_topics = Column(JSON, nullable=True) # List of topic strings
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