hankin
423baff73b
- Docker bridge 网络隔离（8000 端口封死） - Gunicorn 4 Worker 多进程 - Alembic 数据库迁移基线 - 日志轮转 20m×3 - JWT 密钥 + DB 密码 + CORS 收紧 - 3-2-1 备份链路（NAS + R740-B 冷备） - 连接池 pool_pre_ping + pool_recycle=3600
59 lines
1.3 KiB
Python
59 lines
1.3 KiB
Python
"""
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AI 对话历史服务 — 持久化 + 查询
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"""
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from __future__ import annotations
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import uuid
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from sqlalchemy import select, desc
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.models.ai import AiChatSession
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async def save_message(
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db: AsyncSession,
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user_id: uuid.UUID,
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role: str,
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content: str,
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msg_type: str = "text",
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) -> AiChatSession:
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"""保存一条对话消息"""
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msg = AiChatSession(
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user_id=user_id,
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role=role,
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content=content,
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msg_type=msg_type,
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)
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db.add(msg)
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await db.commit()
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await db.refresh(msg)
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return msg
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async def load_history(
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db: AsyncSession,
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user_id: uuid.UUID,
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limit: int = 50,
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) -> list[dict]:
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"""加载用户最近 N 条对话（时间正序返回，方便前端渲染）"""
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stmt = (
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select(AiChatSession)
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.where(AiChatSession.user_id == user_id)
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.order_by(desc(AiChatSession.created_at))
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.limit(limit)
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)
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result = await db.execute(stmt)
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rows = result.scalars().all()
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# 反转为时间正序
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rows = list(reversed(rows))
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return [
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{
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"id": str(r.id),
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"role": r.role,
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"content": r.content,
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"type": r.msg_type,
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"created_at": r.created_at.isoformat() if r.created_at else None,
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}
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for r in rows
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]
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