renjianbo
cffe4a52d6
Backend:
- New /api/v1/agents/{id}/memory endpoints: CRUD for global_knowledge,
knowledge_entities, learning_patterns, vector_memories + import/export
- Fix scope_id column overflow: 3 model columns expanded to hold compound
keys (user_id:agent_id format, 73 chars vs old VARCHAR(36))
- Config: allow unknown env vars (extra="ignore") for optional overrides
Android:
- MemoryManageScreen: 4-tab UI (全局知识/知识实体/学习模式/对话记忆)
with search, delete, and FAB to add new entries
- Import/export via ShareSheet and file picker
- AgentListScreen: long-press dropdown menu → 记忆管理 entry point
- NavGraph: memory_manage/{agentId}/{agentName} route with URL encoding
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
46 lines
1.7 KiB
Python
46 lines
1.7 KiB
Python
"""
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Agent 向量记忆模型。
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存储对话片段的 embedding 向量，支持语义检索。
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"""
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from __future__ import annotations
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import uuid
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from datetime import datetime
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from sqlalchemy import Column, String, Text, DateTime, Index
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from sqlalchemy.dialects.mysql import JSON as MySQLJSON
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from app.core.database import Base
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class AgentVectorMemory(Base):
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"""Agent 向量记忆 — 存储对话文本及 embedding"""
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__tablename__ = "agent_vector_memories"
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id = Column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
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scope_kind = Column(String(16), nullable=False, index=True, comment="作用域类型: agent / bare")
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scope_id = Column(String(255), nullable=False, index=True, comment="作用域 ID: agent_id / user_id")
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session_key = Column(String(128), nullable=False, default="", comment="会话标识")
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content_text = Column(Text, nullable=False, comment="原始对话文本")
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embedding = Column(Text, nullable=True, comment="JSON 序列化的 embedding 向量")
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metadata_ = Column("metadata", MySQLJSON, nullable=True, comment="元数据: {type, iteration, ...}")
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created_at = Column(DateTime, default=datetime.utcnow, nullable=False)
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__table_args__ = (
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Index("ix_agent_vector_memory_scope", "scope_kind", "scope_id"),
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)
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def to_dict(self) -> dict:
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return {
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"id": self.id,
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"scope_kind": self.scope_kind,
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"scope_id": self.scope_id,
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"session_key": self.session_key,
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"content_text": self.content_text,
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"embedding": self.embedding,
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"metadata": self.metadata_ or {},
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"created_at": self.created_at.isoformat() if self.created_at else None,
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}
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