fengxiangdebaoziandClaude Opus 4.8
3907034a1d
- 席位执行模式：选择参会智能体弹窗加模式选择器，映射 thinking/reasoning/subagent 透传 recommend/chain/dag 三条编排；后端 _special_run 覆盖 seat policy（仅席位， 总控不变）；ultra 解锁席位 task 子代理（custom 流回传 task_running 实时进度）。 - 子智能体任务卡片：复用主聊天 SubtaskCard + SubtaskContext，从席位流解析 task 调用/结果，在 Step2 气泡渲染多张任务卡片（覆盖业务链条路径）。 - 后台挂起也按所选模式跑（StartParams→inprocess gateway→run_seat 覆盖 policy）。 - 后台作业完成全工作区跨页通知：RoundtableJobNotifier 挂在 PageLayout 常驻层， 作业终态弹「达成共识」同款浮窗。 - 业务链条可配「默认执行模式」并持久化（roundtable_chains.seat_mode；选链时弹窗 默认用它，仍可临时改）。 - 修复后台 Step3 HTML 看板概率截断：deepseek max_tokens=4096 单轮写不完长 HTML， 产物自愈重试上限 2→5，让 append 续写写完整（同惠及 summary/dashboard）。 Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
562 lines
26 KiB
Python
562 lines
26 KiB
Python
"""圆桌真网关（Phase 3b）—— 进程内驱动真实 leader / seat / report agent run。
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实现 ``RoundtableGateway`` 协议（定义在 harness 的 roundtable_orchestrator.types），
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但**进程内**跑真模型：复用 ``services.start_run``（scheduled task 同款）驱动一轮 agent，
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跑完从 checkpointer 读最终 messages，用 ``multi_agent`` 的纯解析函数解析派活 / 澄清。
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不走 HTTP、不需要 Request（用 ``SimpleNamespace(app=app, state=...)`` 当虚拟 request，
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``set_current_user`` 由执行器设好）。
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本网关位于 app 层（依赖 app.state + multi_agent helpers），实现 harness 定义的协议——
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不违反 harness→app 边界（harness 永不 import app；app import harness 合法）。
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⚠️ 需在能起完整后端栈 + 真模型的环境验证（见 scripts/probe_roundtable_job.py）。
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"""
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from __future__ import annotations
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import json
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import logging
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import uuid
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from datetime import UTC, datetime
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from types import SimpleNamespace
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from typing import Any
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from langgraph.checkpoint.base import empty_checkpoint
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from app.gateway.roundtable_run_policy import roundtable_run_policy
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from app.gateway.routers._roundtable_seed import (
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COORDINATOR_AGENT_ID,
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REPORT_AGENT_ID,
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SUMMARY_AGENT_ID,
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ensure_roundtable_functional_agents,
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)
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from deerflow.agents.roundtable_orchestrator.types import (
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LeaderTurn,
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ReportResult,
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SeatRef,
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SeatTurn,
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SummaryResult,
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)
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from deerflow.config.paths import get_paths
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from deerflow.runtime.serialization import serialize_channel_values
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logger = logging.getLogger(__name__)
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_ROUNDTABLE_THREAD_METADATA: dict[str, Any] = {"thread_type": "roundtable", "system": True}
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_REPORT_FILE = "方案总览.html"
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_SUMMARY_FILE = "方案总结报告.md"
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# 各角色的工具裁剪 / thinking / reasoning 全部取自单一数据源 roundtable_run_policy
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# （与 multi_agent 的 _leader_run / _special_run 同源），不再在本文件硬编码——历史上
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# 这里漏禁了 web_search，导致后台席位 / 报告能联网搜索而前端不能，现由 policy 统一。
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# ── 进程内底层操作 ───────────────────────────────────────────────────────────
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async def _create_thread(app) -> str:
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"""同进程建一个带圆桌 metadata 的 thread + 空 checkpoint（镜像 _create_thread_direct）。"""
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checkpointer = app.state.checkpointer
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thread_store = app.state.thread_store
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thread_id = str(uuid.uuid4())
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await thread_store.create(thread_id, assistant_id=None, metadata=dict(_ROUNDTABLE_THREAD_METADATA))
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config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
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await checkpointer.aput(
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config,
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empty_checkpoint(),
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{"step": -1, "source": "input", "writes": None, "parents": {}, "created_at": datetime.now(UTC).isoformat()},
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{},
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)
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return thread_id
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async def _read_thread_messages(app, thread_id: str) -> list[dict[str, Any]]:
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"""读 thread 最新 checkpoint 的 messages（已 serialize 成 dict 列表）。"""
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checkpointer = app.state.checkpointer
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config = {"configurable": {"thread_id": thread_id, "checkpoint_ns": ""}}
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ct = await checkpointer.aget_tuple(config)
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if ct is None:
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return []
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checkpoint = getattr(ct, "checkpoint", {}) or {}
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values = serialize_channel_values(checkpoint.get("channel_values", {}) or {})
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return values.get("messages") or []
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def _latest_ai_message(messages: list[dict[str, Any]]) -> dict[str, Any] | None:
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for m in reversed(messages):
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if not isinstance(m, dict):
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continue
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if (m.get("type") or m.get("role")) in {"ai", "assistant"}:
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return m
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return None
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def _message_text(message: dict[str, Any] | None) -> str:
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if not message:
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return ""
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content = message.get("content")
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if isinstance(content, str):
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return content
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if isinstance(content, list):
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parts = [p.get("text", "") for p in content if isinstance(p, dict) and p.get("type") == "text"]
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return "\n".join(parts)
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return ""
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def _latest_ai_text(messages: list[dict[str, Any]]) -> str:
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"""取**最后一条有正文**的 AI 消息文本。
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席位常以「调一堆工具」的 AIMessage 收尾（正文为空、只有 tool_calls，甚至 dangling），
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直接取最后一条 AI 消息会拿到空串 → 报告就拿不到席位产物。这里向前找到真正有文字的那条。
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"""
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for m in reversed(messages):
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if not isinstance(m, dict):
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continue
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if (m.get("type") or m.get("role")) not in {"ai", "assistant"}:
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continue
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text = _message_text(m)
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if text.strip():
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return text
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return ""
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def _normalize_tool_calls(tool_calls: Any) -> list[dict[str, Any]]:
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"""把 checkpoint 里的 tool_calls 归一化成 {name, args, id}（丢掉无名项）。"""
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out: list[dict[str, Any]] = []
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for tc in tool_calls or []:
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if not isinstance(tc, dict):
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continue
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name = tc.get("name") or ""
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if not name:
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continue
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out.append({"name": name, "args": tc.get("args") or {}, "id": tc.get("id") or ""})
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return out
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def _tool_calls_since_last_human(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
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"""收集「最后一条 human 消息之后」各 AI 消息的 tool_calls（即**本轮**调用），保序。
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席位 thread 跨轮累积历史，直接扫全量会把往轮的工具调用也算进来；以最后一条 human
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（本轮 run 的输入）为界，只取其后的 AI 工具调用，对应前端「这一条气泡的步骤卡」。
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"""
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last_human = -1
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for i, m in enumerate(messages):
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if isinstance(m, dict) and (m.get("type") or m.get("role")) in {"human", "user"}:
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last_human = i
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out: list[dict[str, Any]] = []
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for m in messages[last_human + 1 :]:
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if not isinstance(m, dict):
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continue
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if (m.get("type") or m.get("role")) not in {"ai", "assistant"}:
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continue
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out.extend(_normalize_tool_calls(m.get("tool_calls")))
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return out
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async def _run_agent_turn(
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app,
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*,
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thread_id: str,
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agent_name: str,
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message: str,
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model: str | None,
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excluded_tools: list[str],
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skill_stop_names: list[str],
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thinking_enabled: bool,
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reasoning_effort: str,
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subagent_enabled: bool = False,
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) -> list[dict[str, Any]]:
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"""进程内驱动 agent_name 在 thread_id 上跑一轮（发 message），阻塞到完成，返回最终 messages。
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镜像 multi_agent ``_run_payload`` 的 body 形状：agent 通过 ``context.agent_name`` 选，
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assistant_id 固定 lead_agent；关闭 memory recall / subagent / plan mode（临时研讨任务）。
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"""
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from app.gateway.routers.thread_runs import RunCreateRequest
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from app.gateway.services import start_run
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body = RunCreateRequest(
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assistant_id="lead_agent",
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input={"messages": [{"type": "human", "content": [{"type": "text", "text": message}], "additional_kwargs": {}}]},
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config={"recursion_limit": 1000},
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context={
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"agent_name": agent_name,
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"model_name": model or "",
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"mode": "pro",
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"reasoning_effort": reasoning_effort,
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"thinking_enabled": thinking_enabled,
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"is_plan_mode": False,
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# ultra 席位才置真 → 解锁 task 子代理工具（席位 policy 未禁 task）。leader 恒 False。
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"subagent_enabled": subagent_enabled,
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"thread_id": thread_id,
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"memory_recall_disabled": True,
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# 与 multi_agent._run_payload 对齐:关 builtin 记忆注入,避免单例总控/席位
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# 被上轮会话 memory.json 带偏(臆造席位 / 直接干活)。详见该处注释。
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"memory_injection_enabled": False,
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},
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excluded_tools=excluded_tools,
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skill_stop_names=skill_stop_names,
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on_disconnect="continue",
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multitask_strategy="reject",
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)
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# 虚拟 request：带上空 ``state``（auth 中间件在真请求上会写 state.user / state.auth；
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# 进程内没有这层，下游凡是读 request.state.X 的代码必须能拿到 None 而不是 AttributeError）。
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request = SimpleNamespace(app=app, state=SimpleNamespace(user=None, auth=None))
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record = await start_run(body, thread_id, request) # type: ignore[arg-type]
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if getattr(record, "task", None) is not None:
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await record.task # 阻塞到这轮 agent 跑完，checkpoint 已落盘
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return await _read_thread_messages(app, thread_id)
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def _read_report_html(app, thread_id: str, user_id: str) -> str | None:
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"""从 report thread 的 outputs 目录读出报告 HTML。
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优先 ``方案总览.html``；找不到就扫目录里**任意 .html**（取体积最大的那个），
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容忍报告 agent 用了别的文件名。
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"""
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try:
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outputs_dir = get_paths().sandbox_outputs_dir(thread_id, user_id=user_id)
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preferred = outputs_dir / _REPORT_FILE
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if preferred.exists():
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return preferred.read_text(encoding="utf-8")
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if outputs_dir.exists():
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htmls = sorted(
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outputs_dir.glob("*.html"),
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key=lambda p: p.stat().st_size,
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reverse=True,
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)
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if htmls:
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logger.info("report html: 方案总览.html not found, using %s", htmls[0].name)
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return htmls[0].read_text(encoding="utf-8")
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logger.warning("report html: no .html found in %s", outputs_dir)
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except Exception: # noqa: BLE001
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logger.warning("read report html failed for thread %s", thread_id, exc_info=True)
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return None
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def _read_summary_md(app, thread_id: str, user_id: str) -> str | None:
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"""从 summary thread 的 outputs 目录读出总结报告 Markdown。
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优先 ``方案总结报告.md``；找不到就扫目录里**任意 .md**（取体积最大的那个），
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容忍总结 agent 用了别的文件名。镜像 ``_read_report_html``。
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"""
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try:
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outputs_dir = get_paths().sandbox_outputs_dir(thread_id, user_id=user_id)
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preferred = outputs_dir / _SUMMARY_FILE
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if preferred.exists():
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return preferred.read_text(encoding="utf-8")
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if outputs_dir.exists():
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mds = sorted(
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outputs_dir.glob("*.md"),
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key=lambda p: p.stat().st_size,
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reverse=True,
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)
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if mds:
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logger.info("summary md: 方案总结报告.md not found, using %s", mds[0].name)
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return mds[0].read_text(encoding="utf-8")
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logger.warning("summary md: no .md found in %s", outputs_dir)
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except Exception: # noqa: BLE001
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logger.warning("read summary md failed for thread %s", thread_id, exc_info=True)
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return None
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# Step3 产物自愈重试次数（首轮之外的纠偏轮数）。
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# 实测痛点（2026-06-12，h:\xiugai\后台日志.txt）：deepseek 报告 agent 说「let me write
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# it all at once」后发出超长 write_file，单次输出被 max_tokens 截断 → 工具调用从未执行、
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# run 以 success 收尾但 outputs 下没有 .html → step3.html 回退成开场白文字。
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# 自愈：检测产物缺失/截断后，同线程追加纠偏消息（要求 write_file 分段 + append 续写）重试。
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#
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# 取值依据（2026-06-15 修复「后台 HTML 看板只生成一半、概率渲染失败」）：模型单轮输出受
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# ``config.yaml`` 的 ``max_tokens``（deepseek-chat = 4096）硬截。一份完整看板 HTML 几十 KB
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# ≈ 上万 token，单轮写不完；旧值 2（最多 3 轮 ≈ 12k token）对长报告不够 → 落库半截 HTML →
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# 渲染失败。调到 5（最多 6 轮 append ≈ 24k token，足够绝大多数看板）显著降低截断概率。
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# 循环命中完整产物（``</html>`` / 非空 md / 合法 json）即 break，短报告不会白跑额外轮次。
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_ARTIFACT_RETRY_LIMIT = 5
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_REPORT_RETRY_MISSING = (
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f"检测到 /mnt/user-data/outputs/{_REPORT_FILE} 还没有生成——你上一轮的 write_file 没有执行成功"
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"（最常见原因：试图一次写入整份长 HTML，输出超长被截断，工具调用没有真正落盘）。\n"
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"请立即重新写入，并**严格分段**：\n"
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f"1. 先用 write_file 写入 /mnt/user-data/outputs/{_REPORT_FILE} 的开头部分（<!doctype html> 起，约 200-300 行）；\n"
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"2. 再用 write_file 的 append=true 参数分多次续写后续片段，每段同样控制在 300 行以内；\n"
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"3. 直到文档以 </html> 收尾。\n"
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"不要在对话里粘贴 HTML 源码，写完用一两句话总结即可。"
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)
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_REPORT_RETRY_TRUNCATED = (
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f"检测到 /mnt/user-data/outputs/{_REPORT_FILE} 已写入但**不完整**（没有 </html> 收尾，疑似上一轮输出被截断）。\n"
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"请用 write_file 的 append=true 参数从中断处**继续补全剩余内容**（不要重写已有部分），"
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"每段控制在 300 行以内，分多次续写直到文档以 </html> 收尾。不要在对话里粘贴源码。"
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)
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_SUMMARY_RETRY_MISSING = (
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f"检测到 /mnt/user-data/outputs/{_SUMMARY_FILE} 还没有生成——你上一轮的 write_file 没有执行成功"
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"（可能是单次输出过长被截断）。请立即用 write_file 重新写入完整报告；若内容很长，"
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"先写前半部分、再用 write_file 的 append=true 参数分段续写。写完后把报告正文也输出到对话回复里。"
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)
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_DASHBOARD_FENCE = "```report-json"
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_DASHBOARD_RETRY = (
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"你刚才没有输出有效的 ```report-json 代码块（或其中的 JSON 无法解析）。"
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"请重新在回复**最末尾**输出一个完整且**严格合法**的 ```report-json 代码块"
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"（标准 JSON：无注释、无尾逗号、字符串用双引号），代码块之外只允许一两句说明；"
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"不要调用任何工具、不要写文件、不要输出 HTML。"
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)
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def _extract_report_json(text: str) -> str | None:
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"""从模型回复里提取最后一个 ```report-json 代码块的 JSON 字符串（须可解析）。"""
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start = text.rfind(_DASHBOARD_FENCE)
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if start < 0:
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return None
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after = text[start + len(_DASHBOARD_FENCE) :]
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close = after.find("```")
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body = (after[:close] if close >= 0 else after).strip()
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if not body:
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return None
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try:
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json.loads(body)
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except Exception: # noqa: BLE001
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return None
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return body
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def _html_complete(html: str | None) -> bool:
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"""报告 HTML 是否完整可用（非空 + 有 </html> 收尾，截断检测）。"""
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return bool(html and html.strip()) and "</html>" in html.lower()
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def _build_seat_intro(agents: list[SeatRef]) -> str:
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"""镜像 init 注入 coordinator 的「可调度席位清单」。"""
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lines = [f"- agent_name: {a.agent_id}（{a.name}）" for a in agents]
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return (
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"【圆桌系统初始化】本次研讨可调度的席位清单如下，请严格按 agent_name 通过 "
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"agent_orchestration 技能派活，不要派给不在清单内的 agent：\n" + "\n".join(lines)
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)
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# ── 网关实现 ─────────────────────────────────────────────────────────────────
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|
||
|
||
class InProcessRoundtableGateway:
|
||
"""进程内真网关。executor 先调 ``init_threads`` 建好线程，再跑编排引擎。"""
|
||
|
||
def __init__(
|
||
self,
|
||
app,
|
||
*,
|
||
user_id: str,
|
||
agents: list[SeatRef],
|
||
model: str | None = None,
|
||
seat_thinking_enabled: bool | None = None,
|
||
seat_reasoning_effort: str | None = None,
|
||
seat_subagent_enabled: bool = False,
|
||
) -> None:
|
||
self._app = app
|
||
self._user_id = user_id
|
||
self._agents = list(agents)
|
||
self._model = model
|
||
# 席位执行模式（沿用 Step2 选择）：覆盖 seat policy 的 thinking/reasoning（None → policy 默认），
|
||
# subagent 仅 ultra 档为真。只作用 special 席位；leader 派活不受影响。
|
||
self._seat_thinking_enabled = seat_thinking_enabled
|
||
self._seat_reasoning_effort = seat_reasoning_effort
|
||
self._seat_subagent_enabled = seat_subagent_enabled
|
||
self._seat_intro = ""
|
||
self._first_leader = True
|
||
|
||
async def init_threads(self) -> tuple[dict[str, str], str]:
|
||
"""建 coordinator + 各席位 thread，返回 (thread_ids, coordinator_name)。
|
||
|
||
席位清单通过「首轮 leader 消息前缀」注入（coordinator thread 跨轮累积历史，
|
||
首轮带上即长期可见），无需单独写 checkpoint。
|
||
"""
|
||
ensure_roundtable_functional_agents()
|
||
thread_ids: dict[str, str] = {}
|
||
for a in self._agents:
|
||
thread_ids[a.agent_id] = await _create_thread(self._app)
|
||
thread_ids[COORDINATOR_AGENT_ID] = await _create_thread(self._app)
|
||
self._seat_intro = _build_seat_intro(self._agents)
|
||
return thread_ids, COORDINATOR_AGENT_ID
|
||
|
||
async def run_leader(self, *, thread_ids, coordinator_name, message, model) -> LeaderTurn:
|
||
msg = message
|
||
if self._first_leader and self._seat_intro:
|
||
msg = self._seat_intro + "\n\n" + message
|
||
self._first_leader = False
|
||
policy = roundtable_run_policy("leader")
|
||
messages = await _run_agent_turn(
|
||
self._app,
|
||
thread_id=thread_ids[coordinator_name],
|
||
agent_name=coordinator_name,
|
||
message=msg,
|
||
model=model or self._model,
|
||
excluded_tools=policy.excluded_tools,
|
||
skill_stop_names=policy.skill_stop_names,
|
||
thinking_enabled=policy.thinking_enabled,
|
||
reasoning_effort=policy.reasoning_effort,
|
||
)
|
||
ai = _latest_ai_message(messages)
|
||
# tool_calls 取最后一条 AI 消息（派活就在这条）；正文取最后一条有文字的 AI 消息。
|
||
final_text = _latest_ai_text(messages)
|
||
tool_calls = (ai.get("tool_calls") if ai else None) or []
|
||
# 本轮工具调用（派活 / 澄清），带进 LeaderTurn 供草稿 run 重建步骤卡。
|
||
turn_tool_calls = _normalize_tool_calls(tool_calls)
|
||
|
||
# 澄清优先
|
||
clar = next((tc for tc in tool_calls if (tc.get("name") or "").lower() == "ask_clarification"), None)
|
||
if clar is not None:
|
||
cargs = clar.get("args") or {}
|
||
question = str(cargs.get("question") or "").strip() or "总控需要补充信息，请补充后继续。"
|
||
return LeaderTurn(final_text=final_text, clarification=question, tool_calls=turn_tool_calls)
|
||
|
||
# 派活解析（复用 multi_agent 纯函数）
|
||
from app.gateway.routers.multi_agent import _extract_dispatch
|
||
|
||
dispatched: list[tuple[str, str]] = []
|
||
for tc in tool_calls:
|
||
try:
|
||
name, task = _extract_dispatch(tc)
|
||
if name and task:
|
||
dispatched.append((name, task))
|
||
except Exception: # noqa: BLE001
|
||
logger.warning("dispatch parse failed for tool_call", exc_info=True)
|
||
return LeaderTurn(final_text=final_text, dispatched=dispatched, tool_calls=turn_tool_calls)
|
||
|
||
async def run_seat(self, *, thread_ids, agent_id, task, model) -> SeatTurn:
|
||
policy = roundtable_run_policy("seat")
|
||
# 席位模式覆盖 policy 的 thinking/reasoning（None → policy 默认），与前端 _special_run 一致。
|
||
messages = await _run_agent_turn(
|
||
self._app,
|
||
thread_id=thread_ids[agent_id],
|
||
agent_name=agent_id,
|
||
message=task,
|
||
model=model or self._model,
|
||
excluded_tools=policy.excluded_tools,
|
||
skill_stop_names=policy.skill_stop_names,
|
||
thinking_enabled=(
|
||
policy.thinking_enabled
|
||
if self._seat_thinking_enabled is None
|
||
else self._seat_thinking_enabled
|
||
),
|
||
reasoning_effort=(
|
||
policy.reasoning_effort
|
||
if self._seat_reasoning_effort is None
|
||
else self._seat_reasoning_effort
|
||
),
|
||
subagent_enabled=self._seat_subagent_enabled,
|
||
)
|
||
return SeatTurn(final_text=_latest_ai_text(messages), tool_calls=_tool_calls_since_last_human(messages))
|
||
|
||
async def create_report_thread(self) -> str:
|
||
ensure_roundtable_functional_agents()
|
||
return await _create_thread(self._app)
|
||
|
||
async def run_report(self, *, report_thread_id, prompt, model) -> ReportResult:
|
||
"""跑报告 agent，**带产物自愈**：首轮后 HTML 缺失/截断（无 </html>）→ 同线程追加
|
||
纠偏消息（分段 + append 续写）重试，至多 ``_ARTIFACT_RETRY_LIMIT`` 轮。"""
|
||
policy = roundtable_run_policy("report")
|
||
summary = ""
|
||
html: str | None = None
|
||
message = prompt
|
||
for attempt in range(1 + _ARTIFACT_RETRY_LIMIT):
|
||
messages = await _run_agent_turn(
|
||
self._app,
|
||
thread_id=report_thread_id,
|
||
agent_name=REPORT_AGENT_ID,
|
||
message=message,
|
||
model=model or self._model,
|
||
excluded_tools=policy.excluded_tools,
|
||
skill_stop_names=policy.skill_stop_names,
|
||
thinking_enabled=policy.thinking_enabled,
|
||
reasoning_effort=policy.reasoning_effort,
|
||
)
|
||
summary = _latest_ai_text(messages) or summary
|
||
html = _read_report_html(self._app, report_thread_id, self._user_id)
|
||
if _html_complete(html):
|
||
break
|
||
if attempt >= _ARTIFACT_RETRY_LIMIT:
|
||
break
|
||
message = _REPORT_RETRY_TRUNCATED if (html and html.strip()) else _REPORT_RETRY_MISSING
|
||
logger.warning(
|
||
"report html %s after attempt %d for thread %s; retrying with corrective prompt",
|
||
"truncated" if (html and html.strip()) else "missing",
|
||
attempt + 1,
|
||
report_thread_id,
|
||
)
|
||
# 截断的 HTML 也比纯文字强（浏览器容忍未闭合标签），仅在完全没有文件时才回退正文。
|
||
final_html = html if (html and html.strip()) else summary
|
||
return ReportResult(html=final_html, summary=summary, model=model or self._model)
|
||
|
||
async def create_summary_thread(self) -> str:
|
||
ensure_roundtable_functional_agents()
|
||
return await _create_thread(self._app)
|
||
|
||
async def run_summary(self, *, summary_thread_id, prompt, model) -> SummaryResult:
|
||
"""进程内驱动 roundtable-summary 写「方案总结报告.md」+ 对话正文（与前端
|
||
useStep3Summary 同一智能体 / 同一 run policy / 同款产物），返回与
|
||
DraftStep3SummarySnapshot 对齐的结果。线程是真实持久线程——加载草稿后可续问。
|
||
|
||
带产物自愈：md 文件缺失时同线程追加纠偏消息重试（同 ``run_report``）。"""
|
||
policy = roundtable_run_policy("summary")
|
||
closing = ""
|
||
md: str | None = None
|
||
message = prompt
|
||
for attempt in range(1 + _ARTIFACT_RETRY_LIMIT):
|
||
messages = await _run_agent_turn(
|
||
self._app,
|
||
thread_id=summary_thread_id,
|
||
agent_name=SUMMARY_AGENT_ID,
|
||
message=message,
|
||
model=model or self._model,
|
||
excluded_tools=policy.excluded_tools,
|
||
skill_stop_names=policy.skill_stop_names,
|
||
thinking_enabled=policy.thinking_enabled,
|
||
reasoning_effort=policy.reasoning_effort,
|
||
)
|
||
closing = _latest_ai_text(messages) or closing
|
||
md = _read_summary_md(self._app, summary_thread_id, self._user_id)
|
||
if md and md.strip():
|
||
break
|
||
if attempt >= _ARTIFACT_RETRY_LIMIT:
|
||
break
|
||
message = _SUMMARY_RETRY_MISSING
|
||
logger.warning(
|
||
"summary md missing after attempt %d for thread %s; retrying with corrective prompt",
|
||
attempt + 1,
|
||
summary_thread_id,
|
||
)
|
||
final_md = md if (md and md.strip()) else closing
|
||
return SummaryResult(md=final_md, closing=closing, model=model or self._model, thread_id=summary_thread_id)
|
||
|
||
async def run_dashboard_data(self, *, prompt, model) -> str | None:
|
||
"""大屏多页（模式二）的结构化数据：进程内驱动 roundtable-report 输出
|
||
```report-json（prompt 已声明禁工具/禁文件），返回可解析的 JSON 字符串。
|
||
缺失 / 坏 JSON → 同线程纠偏重试（同 run_report/run_summary 的自愈语义）。"""
|
||
ensure_roundtable_functional_agents()
|
||
thread_id = await _create_thread(self._app)
|
||
policy = roundtable_run_policy("report")
|
||
message = prompt
|
||
for attempt in range(1 + _ARTIFACT_RETRY_LIMIT):
|
||
messages = await _run_agent_turn(
|
||
self._app,
|
||
thread_id=thread_id,
|
||
agent_name=REPORT_AGENT_ID,
|
||
message=message,
|
||
model=model or self._model,
|
||
excluded_tools=policy.excluded_tools,
|
||
skill_stop_names=policy.skill_stop_names,
|
||
thinking_enabled=policy.thinking_enabled,
|
||
reasoning_effort=policy.reasoning_effort,
|
||
)
|
||
body = _extract_report_json(_latest_ai_text(messages))
|
||
if body:
|
||
return body
|
||
if attempt >= _ARTIFACT_RETRY_LIMIT:
|
||
break
|
||
message = _DASHBOARD_RETRY
|
||
logger.warning(
|
||
"dashboard report-json missing/invalid after attempt %d for thread %s; retrying",
|
||
attempt + 1,
|
||
thread_id,
|
||
)
|
||
return None
|