"""ORM models for roundtable-planning drafts and recommendation history.

A "draft" is one full roundtable-planning session (Step 1 intent + Step 2
multi-agent roundtable), persisted per user so it follows the account across
devices/sessions — replacing the old browser-only ``localStorage`` storage.

``step1`` / ``step2`` are stored as JSON serialized into ``PortableLongText``
(MySQL ``LONGTEXT``) rather than ``PortableJSON`` (MySQL ``TEXT``, 64 KB cap),
because the Step 2 snapshot can carry a long roundtable transcript plus per-seat
stub messages that easily exceed 64 KB — mirroring ``ai_writing_sessions``.
"""

from __future__ import annotations

from datetime import UTC, datetime

from sqlalchemy import Integer, String
from sqlalchemy.orm import Mapped, mapped_column

from deerflow.persistence.base import Base
from deerflow.persistence.types import BeijingDateTime, PortableLongText


class RoundtableDraftRow(Base):
    __tablename__ = "roundtable_drafts"

    id: Mapped[str] = mapped_column(String(64), primary_key=True)
    user_id: Mapped[str] = mapped_column(String(64), nullable=False, index=True)
    # 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
    # （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。
    task_id: Mapped[str | None] = mapped_column(String(128), nullable=True, index=True)
    # Auto-derived task summary (objective → first user message → timestamp).
    title: Mapped[str] = mapped_column(String(512), nullable=False, default="")
    # Furthest step reached when this snapshot was taken (1 | 2 | 3).
    furthest_step: Mapped[int] = mapped_column(Integer, nullable=False, default=1)
    # JSON blobs matching the frontend Step1Snapshot / Step2Snapshot shapes.
    step1: Mapped[str | None] = mapped_column(PortableLongText, nullable=True)  # JSON object
    step2: Mapped[str | None] = mapped_column(PortableLongText, nullable=True)  # JSON object
    # Step 3「结果绘制」snapshot: { html, generatedAt, model, summary? }. The full
    # report HTML is stored inline so a reloaded draft can show the report without
    # depending on the ephemeral report thread (LONGTEXT — HTML can exceed 64 KB).
    step3: Mapped[str | None] = mapped_column(PortableLongText, nullable=True)  # JSON object
    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),
    )
    # 乐观并发控制版本号：每次 update_draft 自增；传入 expected_version 时校验，命中
    # 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）。
    version: Mapped[int] = mapped_column(Integer, nullable=False, default=0, server_default="0")


class RoundtableRecommendHistoryRow(Base):
    """One agent-recommendation run, bound to a draft/session.

    Lets the recommend dialog show "last recommendation for this session" on
    open, while still letting the user re-analyze (which appends a new row).
    """

    __tablename__ = "roundtable_recommend_history"

    id: Mapped[str] = mapped_column(String(64), primary_key=True)
    user_id: Mapped[str] = mapped_column(String(64), nullable=False, index=True)
    draft_id: Mapped[str] = mapped_column(String(64), nullable=False, index=True)
    # Denormalized intent objective at recommend time (list display / context).
    objective: Mapped[str] = mapped_column(String(512), nullable=False, default="")
    # How the run ended on the backend: "done" | "fallback" | "asking" | "error".
    status: Mapped[str] = mapped_column(String(32), nullable=False, default="done")
    model: Mapped[str | None] = mapped_column(String(128), nullable=True)
    # Natural-language rationale (may be long → LONGTEXT).
    rationale: Mapped[str | None] = mapped_column(PortableLongText, nullable=True)
    # JSON: [{"agent_id": str, "reason": str}, ...]
    picks: Mapped[str | None] = mapped_column(PortableLongText, nullable=True)
    # JSON snapshot of the candidate pool at recommend time:
    # [{"agent_id": str, "name": str, "description": str}, ...]
    candidates: Mapped[str | None] = mapped_column(PortableLongText, nullable=True)
    created_at: Mapped[datetime] = mapped_column(BeijingDateTime(), nullable=False, default=lambda: datetime.now(UTC))