80 lines
4.5 KiB
Python
80 lines
4.5 KiB
Python
"""ORM models for roundtable-planning drafts and recommendation history.
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A "draft" is one full roundtable-planning session (Step 1 intent + Step 2
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multi-agent roundtable), persisted per user so it follows the account across
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devices/sessions — replacing the old browser-only ``localStorage`` storage.
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``step1`` / ``step2`` are stored as JSON serialized into ``PortableLongText``
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(MySQL ``LONGTEXT``) rather than ``PortableJSON`` (MySQL ``TEXT``, 64 KB cap),
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because the Step 2 snapshot can carry a long roundtable transcript plus per-seat
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stub messages that easily exceed 64 KB — mirroring ``ai_writing_sessions``.
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"""
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from __future__ import annotations
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from datetime import UTC, datetime
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from sqlalchemy import Integer, String
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from sqlalchemy.orm import Mapped, mapped_column
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from deerflow.persistence.base import Base
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from deerflow.persistence.types import BeijingDateTime, PortableLongText
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class RoundtableDraftRow(Base):
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__tablename__ = "roundtable_drafts"
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id: Mapped[str] = mapped_column(String(64), primary_key=True)
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user_id: Mapped[str] = mapped_column(String(64), nullable=False, index=True)
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# 外部任务 id（无界嵌入抽屉传入）。非空时该草稿按 task_id 共享、不再按 user_id 分权
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# （读/写/删放开归属校验，见 sql.py）；为空时保持原「按登录用户隔离」语义。
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task_id: Mapped[str | None] = mapped_column(String(128), nullable=True, index=True)
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# Auto-derived task summary (objective → first user message → timestamp).
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title: Mapped[str] = mapped_column(String(512), nullable=False, default="")
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# Furthest step reached when this snapshot was taken (1 | 2 | 3).
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furthest_step: Mapped[int] = mapped_column(Integer, nullable=False, default=1)
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# JSON blobs matching the frontend Step1Snapshot / Step2Snapshot shapes.
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step1: Mapped[str | None] = mapped_column(PortableLongText, nullable=True) # JSON object
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step2: Mapped[str | None] = mapped_column(PortableLongText, nullable=True) # JSON object
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# Step 3「结果绘制」snapshot: { html, generatedAt, model, summary? }. The full
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# report HTML is stored inline so a reloaded draft can show the report without
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# depending on the ephemeral report thread (LONGTEXT — HTML can exceed 64 KB).
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step3: Mapped[str | None] = mapped_column(PortableLongText, nullable=True) # JSON object
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created_at: Mapped[datetime] = mapped_column(BeijingDateTime(), nullable=False, default=lambda: datetime.now(UTC))
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updated_at: Mapped[datetime] = mapped_column(
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BeijingDateTime(),
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nullable=False,
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default=lambda: datetime.now(UTC),
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onupdate=lambda: datetime.now(UTC),
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)
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# 乐观并发控制版本号：每次 update_draft 自增；传入 expected_version 时校验，命中
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# 0 行即并发冲突（防 step2.runs 读-改-写丢失更新——后台作业与前台 PUT 并发）。
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version: Mapped[int] = mapped_column(Integer, nullable=False, default=0, server_default="0")
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class RoundtableRecommendHistoryRow(Base):
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"""One agent-recommendation run, bound to a draft/session.
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Lets the recommend dialog show "last recommendation for this session" on
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open, while still letting the user re-analyze (which appends a new row).
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"""
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__tablename__ = "roundtable_recommend_history"
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id: Mapped[str] = mapped_column(String(64), primary_key=True)
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user_id: Mapped[str] = mapped_column(String(64), nullable=False, index=True)
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draft_id: Mapped[str] = mapped_column(String(64), nullable=False, index=True)
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# Denormalized intent objective at recommend time (list display / context).
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objective: Mapped[str] = mapped_column(String(512), nullable=False, default="")
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# How the run ended on the backend: "done" | "fallback" | "asking" | "error".
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status: Mapped[str] = mapped_column(String(32), nullable=False, default="done")
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model: Mapped[str | None] = mapped_column(String(128), nullable=True)
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# Natural-language rationale (may be long → LONGTEXT).
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rationale: Mapped[str | None] = mapped_column(PortableLongText, nullable=True)
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# JSON: [{"agent_id": str, "reason": str}, ...]
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picks: Mapped[str | None] = mapped_column(PortableLongText, nullable=True)
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# JSON snapshot of the candidate pool at recommend time:
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# [{"agent_id": str, "name": str, "description": str}, ...]
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candidates: Mapped[str | None] = mapped_column(PortableLongText, nullable=True)
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created_at: Mapped[datetime] = mapped_column(BeijingDateTime(), nullable=False, default=lambda: datetime.now(UTC))
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