36 lines
1.6 KiB
Python
36 lines
1.6 KiB
Python
from sqlalchemy import Column, String, Text, Integer, ForeignKey, Boolean, DateTime, Enum as SQLEnum
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from datetime import datetime
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from app.models.base import BaseModel
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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=datetime.utcnow, 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
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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=datetime.utcnow, nullable=False)
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last_recalled_at = Column(DateTime, nullable=True)
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