fengxiangdebaoziandClaude Opus 4.8
a8da6c2632
taskId 深链会商存储独立化： - 新增独立表 roundtable_task_drafts + 路由 /api/roundtable-task-drafts，只按 task_id 归属、完全不按 user 分权（created_by 仅审计），与个人 roundtable_drafts 物理分离； 个人草稿表恢复严格按用户隔离（去掉原 task 放权）。 - 后台研讨作业按 taskId 共享：roundtable_jobs 加 task_id，get/get_by_draft/stream/ resume/cancel 支持 ?task_id= 跨用户读取与控制，作业报告写回 task-draft 存储。 - 前端：新增 api/task-drafts.ts 客户端，useDraftPersistence / useRoundtableDrafts / roundtable-jobs / DispatchChainJobModal / RoundtablePlanningPage 按 taskId 路由读写。 - 迁移 20260618_01 + column_additions_mysql.sql(jobs.task_id) + 数据搬迁脚本 scripts/migrate_roundtable_task_drafts.py（把历史 task_id 行迁出个人表）。 第三步聊天记录像第二步一样存全： - 总结报告 / 经典看板 / 大屏 三条流的生成过程对话（中间文字 + 步骤条 + stub）落库， 加载草稿整段回显，而非只剩最终 md / 流程图 / HTML。DraftStep3Snapshot 扩展 dialogues/seatStubMessages 槽位；存稿走页面 render 期 ref，恢复走各 hook hydrateSaved （旧草稿无字段则回退单条「历史记录」气泡）。 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
82 lines
5.2 KiB
Python
82 lines
5.2 KiB
Python
"""ORM model for roundtable background-orchestration jobs.
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A "job" = one **backend** run of the roundtable orchestration pipeline kicked off
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from Step 2「后台挂起」: it drives the leader→dispatch→sub-agents loop to consensus
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and then Step 3 report drawing, entirely server-side, surviving page refresh/close.
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Bound 1:1 to a ``roundtable_drafts`` row (``draft_id``) so the history dropdown can
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show "this task is running" and the dispatch-chain modal can render live progress.
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Big JSON columns (``dispatch_chain`` / ``thread_ids`` / ``chain``) are stored as
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serialized strings in ``PortableLongText`` (MySQL ``LONGTEXT``) — same approach as
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``ai_writing_sessions`` / ``roundtable_drafts`` — to avoid MySQL's 64 KB ``TEXT`` cap.
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See ``frontend-web/docs/multi-agent-background-run-dev.md`` §2.5 for the field
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contract and §Phase 1 for the build steps.
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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, Text
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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 RoundtableJobRow(Base):
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__tablename__ = "roundtable_jobs"
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id: Mapped[str] = mapped_column(String(64), primary_key=True)
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# The draft/session this job runs for. Indexed for "active job of draft" lookups.
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draft_id: Mapped[str | None] = mapped_column(String(64), nullable=True, index=True)
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user_id: Mapped[str | None] = mapped_column(String(64), nullable=True, index=True)
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# 外部任务 id（taskId 深链会商）。非空 = 这是一条 task 作业：读取按 task_id 共享、
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# **不按 user 分权**（任何人打开同一 task 都能看在跑的研讨）；其 draft_id 指向独立的
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# ``roundtable_task_drafts`` 表。为空 = 普通个人作业（按 user 分权）。
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task_id: Mapped[str | None] = mapped_column(String(128), nullable=True, index=True)
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# Job status machine (§2.1): queued / running / awaiting_input / done / error / cancelled.
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status: Mapped[str] = mapped_column(String(32), nullable=False, default="queued", index=True)
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# Fine-grained phase (§2.2): initializing / leader_thinking / dispatching /
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# consensus / report_drawing / report_done.
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phase: Mapped[str] = mapped_column(String(32), nullable=False, default="initializing")
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# Current orchestration cycle (0..MAX_CYCLES).
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cycle: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
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# Dispatch chain nodes (§2.3): JSON array of
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# { agentId, name, avatarType, order, state, lastSpokeCycle? }.
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dispatch_chain: Mapped[str | None] = mapped_column(PortableLongText, nullable=True)
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# Currently-speaking seat agent_id (null = coordinator / none).
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active_agent_id: Mapped[str | None] = mapped_column(String(128), nullable=True)
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consensus_percentage: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
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# When status=awaiting_input: the coordinator's clarification question text.
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pending_clarification: Mapped[str | None] = mapped_column(Text, nullable=True)
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# ThreadIdMap: JSON object { "<agent_id>": "<thread_id>", ... }.
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thread_ids: Mapped[str | None] = mapped_column(PortableLongText, nullable=True)
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coordinator_name: Mapped[str | None] = mapped_column(String(128), nullable=True)
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# "recommend"（总控自由派活）| "chain"（按业务链条顺序派活）| "dag"（分层 DAG：stage 间串行、stage 内并行）.
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orchestration_mode: Mapped[str] = mapped_column(String(16), nullable=False, default="recommend")
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# chain 模式来源链条快照：JSON object { id, title }（recommend 模式为 null）.
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chain: Mapped[str | None] = mapped_column(PortableLongText, nullable=True)
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# dag 模式分层编排计划：JSON object { mode, stages:[{id,agentIds,goal?}], finalSynthesis,
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# coordinatorPrompt? }（对齐前端 OrchestrationPlan）。仅 dag 模式有值；驱动后端 _run_dag
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# （stage 串行 / stage 内并行）+ 前端大弹窗按 stage 分层画流程图 + resume 时重建计划。
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orchestration_plan: Mapped[str | None] = mapped_column(PortableLongText, nullable=True)
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# 研讨对话记录：JSON array of { id, role('leader'|'seat'), agentId, name, content, cycle }。
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# 让用户跑完 / 进行中都能看到真实研讨内容（leader 说明 + 各席位交付），而非只有流程图。
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dialogues: Mapped[str | None] = mapped_column(PortableLongText, nullable=True)
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# Step3 报告快照：JSON object { html, generatedAt, model, summary }。done 时写入，
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# 使前端从单个 job 即可拿到完整结果（对话 + 报告）。
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step3: Mapped[str | None] = mapped_column(PortableLongText, nullable=True)
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# Terminal error detail when status=error.
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error: Mapped[str | None] = mapped_column(Text, nullable=True)
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# 时间列必须用 BeijingDateTime（项目规范，与 ai_writing_sessions / roundtable_drafts 对齐）。
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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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