124 lines
3.9 KiB
Python
124 lines
3.9 KiB
Python
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"""
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Agent记忆数据模型
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定义Agent的记忆存储结构
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"""
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from datetime import datetime
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from typing import Optional, List
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from enum import Enum
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from pydantic import Field
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from beanie import Document
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class MemoryType(str, Enum):
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"""记忆类型枚举"""
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SHORT_TERM = "short_term" # 短期记忆（会话内）
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LONG_TERM = "long_term" # 长期记忆（跨会话）
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EPISODIC = "episodic" # 情景记忆（特定事件）
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SEMANTIC = "semantic" # 语义记忆（知识性）
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class AgentMemory(Document):
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"""
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Agent记忆文档模型
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存储Agent的记忆内容
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"""
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memory_id: str = Field(..., description="唯一标识")
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agent_id: str = Field(..., description="Agent ID")
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# 记忆内容
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memory_type: str = Field(
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default=MemoryType.SHORT_TERM.value,
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description="记忆类型"
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)
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content: str = Field(..., description="记忆内容")
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summary: str = Field(default="", description="内容摘要")
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# 向量嵌入（用于相似度检索）
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embedding: List[float] = Field(default_factory=list, description="向量嵌入")
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# 元数据
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importance: float = Field(default=0.5, ge=0, le=1, description="重要性评分")
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access_count: int = Field(default=0, description="访问次数")
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# 关联信息
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source_room_id: Optional[str] = Field(default=None, description="来源聊天室ID")
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source_discussion_id: Optional[str] = Field(default=None, description="来源讨论ID")
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related_agents: List[str] = Field(default_factory=list, description="相关Agent列表")
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tags: List[str] = Field(default_factory=list, description="标签")
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# 时间戳
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created_at: datetime = Field(default_factory=datetime.utcnow)
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last_accessed: datetime = Field(default_factory=datetime.utcnow)
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expires_at: Optional[datetime] = Field(default=None, description="过期时间")
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class Settings:
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name = "agent_memories"
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indexes = [
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[("agent_id", 1)],
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[("memory_type", 1)],
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[("importance", -1)],
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[("last_accessed", -1)],
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]
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def access(self) -> None:
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"""
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记录访问，更新访问计数和时间
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"""
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self.access_count += 1
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self.last_accessed = datetime.utcnow()
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def is_expired(self) -> bool:
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"""
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检查记忆是否已过期
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Returns:
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是否过期
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"""
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if self.expires_at is None:
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return False
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return datetime.utcnow() > self.expires_at
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def calculate_relevance_score(
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self,
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similarity: float,
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time_decay_factor: float = 0.1
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) -> float:
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"""
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计算综合相关性分数
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结合向量相似度、重要性和时间衰减
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Args:
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similarity: 向量相似度 (0-1)
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time_decay_factor: 时间衰减因子
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Returns:
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综合相关性分数
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"""
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# 计算时间衰减
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hours_since_access = (datetime.utcnow() - self.last_accessed).total_seconds() / 3600
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time_decay = 1.0 / (1.0 + time_decay_factor * hours_since_access)
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# 综合评分
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score = (
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0.5 * similarity +
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0.3 * self.importance +
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0.2 * time_decay
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)
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return min(1.0, max(0.0, score))
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class Config:
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json_schema_extra = {
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"example": {
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"memory_id": "mem-001",
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"agent_id": "product-manager",
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"memory_type": "long_term",
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"content": "在登录系统设计讨论中，团队决定采用OAuth2.0方案",
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"summary": "登录系统采用OAuth2.0",
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"importance": 0.8,
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"access_count": 5,
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"source_room_id": "product-design-room",
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"tags": ["登录", "OAuth", "认证"]
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}
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}
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