"""ORM model for the standalone 大屏绘制智能体 session (按 taskId 存取). One row per external ``task_id`` — the standalone dashboard page persists its chat transcript + the latest structured ``report_json`` so that returning to the same task either shows the finished dashboard directly (valid report_json) or restores the generation process. **Global / shared by task** — like ``roundtable_drafts`` task-bound drafts, the store keys on ``task_id`` and does **not** scope by ``user_id`` (open read + shared writes). ``user_id`` is recorded for audit only. ``transcript`` / ``report_json`` are large JSON blobs (chat history + report data) stored as serialized strings in ``PortableLongText`` (MySQL ``LONGTEXT``) to dodge MySQL's 64 KB ``TEXT`` cap — same approach as ``roundtable_drafts``. """ from __future__ import annotations from datetime import UTC, datetime from sqlalchemy import String, UniqueConstraint from sqlalchemy.orm import Mapped, mapped_column from deerflow.persistence.base import Base from deerflow.persistence.types import BeijingDateTime, PortableLongText class DashboardSessionRow(Base): __tablename__ = "dashboard_sessions" # One session per task — keep it unique so upsert-by-task is unambiguous. __table_args__ = (UniqueConstraint("task_id", name="uq_dashboard_sessions_task_id"),) id: Mapped[str] = mapped_column(String(64), primary_key=True) # External task id (deep-link). The session is shared by task, not by user. task_id: Mapped[str] = mapped_column(String(128), nullable=False, index=True) # Audit only — who last wrote. Never used for access control. user_id: Mapped[str | None] = mapped_column(String(64), nullable=True) # Chat transcript JSON (the dashboard agent's dialogue bubbles). transcript: Mapped[str | None] = mapped_column(PortableLongText, nullable=True) # Latest structured dashboard data (report-json string). Presence of a # parseable value = "有效结果" → the page shows the dashboard directly. report_json: Mapped[str | None] = mapped_column(PortableLongText, nullable=True) created_at: Mapped[datetime] = mapped_column(BeijingDateTime(), nullable=False, default=lambda: datetime.now(UTC)) updated_at: Mapped[datetime] = mapped_column( BeijingDateTime(), nullable=False, default=lambda: datetime.now(UTC), onupdate=lambda: datetime.now(UTC), )