87 lines
5.4 KiB
Python
87 lines
5.4 KiB
Python
"""ORM model for roundtable-planning business chains.
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A "business chain" (业务链条) is a named, reusable, *ordered* list of agents the
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user assembles in the chain editor. In Step 2 the coordinator dispatches to the
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seats following this order (see ``multi-agent-business-chain-dev.md``).
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Persisted per user (like ``roundtable_drafts``) so it follows the account across
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devices/sessions. ``seats`` is stored as a JSON array serialized into
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``PortableLongText`` (MySQL ``LONGTEXT``) rather than ``PortableJSON`` for
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headroom, mirroring the sibling roundtable persistence modules.
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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 Boolean, 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 RoundtableChainRow(Base):
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__tablename__ = "roundtable_chains"
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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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# Human-readable chain title shown on the picker card.
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title: Mapped[str] = mapped_column(String(512), nullable=False, default="")
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# Optional free-text description.
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description: Mapped[str | None] = mapped_column(String(2048), nullable=True)
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# JSON: ordered [{"agent_id": str, "name": str, "description": str}, ...].
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# seats[0] is the first seat to speak. Stored as LONGTEXT-backed JSON string.
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seats: Mapped[str | None] = mapped_column(PortableLongText, nullable=True)
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# Optional JSON: layered orchestration grouping over ``seats`` — a list of
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# stages, each a list of agent_ids run in parallel; stages run serially in
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# order. e.g. [["a","b"],["c"]] = (a‖b) → c. NULL/absent = linear chain
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# (each seat its own stage). Drives Step 2 dag-mode orchestration; see
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# frontend ``lib/dag-types.ts``. Stored as LONGTEXT-backed JSON string.
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stages: Mapped[str | None] = mapped_column(PortableLongText, nullable=True)
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# Optional JSON: per-stage goals aligned by index to ``stages`` — a list where
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# ``stage_goals[i]`` is the「本阶段目标」for ``stages[i]`` (what that serial stage
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# should produce / how it depends on the previous stage's output). Injected to the
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# coordinator dispatch prompt (and seat tasks as fallback) in dag-mode only. NULL =
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# no goals (and always NULL when ``stages`` is NULL/linear). Stored as JSON string.
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stage_goals: Mapped[str | None] = mapped_column(PortableLongText, nullable=True)
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# 可选「总控编排提示」：用户为本链条写给**总控（协调）智能体**的一段指引，告诉总控该
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# 怎么派活、做什么（整体编排策略 / 风格 / 约束）。**仅分层 DAG 模式生效**：注入到总控
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# 每阶段派活提示（``buildDagStageLeaderTask``）。NULL = 不注入。纯文本，存为 LONGTEXT。
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coordinator_prompt: Mapped[str | None] = mapped_column(PortableLongText, nullable=True)
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# 可选「默认席位执行模式」：flash/thinking/pro/ultra（见前端 lib/seat-mode.ts）。选入研讨时
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# 弹窗默认用它（仍可临时改）。NULL = 未设，调用方回退默认(pro)。短串，VARCHAR(16)。
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seat_mode: Mapped[str | None] = mapped_column(String(16), nullable=True)
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# 可选「研讨前并行取数」开关：True = 选入研讨时，研讨前先跑一轮全席位并行『取数』（各席位只调
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# 技能取数、不研讨），产出作为共享资料注入后续研讨轮，把最慢的技能延迟前置并行、给串行研讨段提速。
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# 默认 False（不变更现有行为）。逐条 opt-in。BOOLEAN，server_default "0"。
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gather_first: Mapped[bool] = mapped_column(
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Boolean, nullable=False, default=False, server_default="0"
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)
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# 发布开关：True = 公共（所有账号可在「公共」筛选里查看 / 选用）；False = 仅本人可见。
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# 编辑 / 删除始终只限 owner；发布只是放开「查看 + 选入研讨」。索引以支持「公共」列表筛选。
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is_public: Mapped[bool] = mapped_column(
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Boolean, nullable=False, default=False, server_default="0", index=True
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)
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# 启停开关：True = 启用（可在选链弹窗 / 公共入口里选用）；False = 停用（管理列表仍可见，
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# 但不可选入研讨）。默认启用，存量链条补列后也为 True。``server_default`` 必须显式声明，
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# 否则既有表上启动期 ``_ensure_orm_columns_sync`` 会跳过「NOT NULL 且无 server_default」的列。
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enabled: Mapped[bool] = mapped_column(
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Boolean, nullable=False, default=True, server_default="1"
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)
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# 「待审核」标记：True = owner 已在编辑器提交审核（发布前需管理员在「待审核」筛选里确认
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# 发布）；管理员发布时置回 False 并同时置 ``is_public=True``。缺省 False，存量链条不受影响。
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# 索引以支持管理端「待审核」列表筛选。
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pending_review: Mapped[bool] = mapped_column(
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Boolean, nullable=False, default=False, server_default="0", index=True
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)
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created_at: Mapped[datetime] = mapped_column(
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BeijingDateTime(), nullable=False, default=lambda: datetime.now(UTC)
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)
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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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