Merge branch 'dhk-1' into 'master'
feat: 圆桌第3步大屏多页改用专职智能体 + 健壮 report-json 抽取 See merge request knowledge-base-bj/deerflow-server!134
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董宏凯 committed
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@@ -5,7 +5,7 @@
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VITE_USE_REAL_DATA=true
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# 任务详情（登录深链 cop-task-detail）数据源：默认真实接口；改成 true 走本地假数据（调试用）。
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VITE_MOCK_COP_TASK_DETAIL=true
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VITE_MOCK_COP_TASK_DETAIL=false
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# Gateway / DeerFlow API
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VITE_BACKEND_BASE_URL=https://ch1.b.uat.4.cn/deerflow
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@@ -0,0 +1,155 @@
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/**
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* 大屏绘制智能体「独立页面」的**输入 JSON** 适配 seam。
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*
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* 设计意图：外部系统登录时**后续会**提供一个接口返回一份业务 JSON，本页让
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* 智能体分析这份 JSON → 产出 report-json → 绘制大屏。
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*
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* ⚠️ 该接口现在还没开发好。本模块先做**占位适配**让页面尽快可用：
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* - 配了 `VITE_DASHBOARD_INPUT_URL` → 按 taskId 拉取真实 JSON；
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* - 没配 → 回退用登录深链already 暂存的任务详情（`login.copTaskDetail`）拼一份占位输入，
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* 让智能体至少能基于任务名/内容产出一版可看的大屏。
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*
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* 接口就绪后：把 `fetchDashboardInputJson` 换成真实拉取；再按规划迁到「后台转换格式
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* + 智能体校验」（校验仍由智能体做，不在后台硬抽）。
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*/
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import { requestCrudService } from "@/service/request-axios-service";
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import { readCopTaskDetail } from "@/core/auth/task-deeplink";
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/** 本页输入 JSON 暂存的 sessionStorage key（拉取一次后复用，避免重复请求）。 */
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export const DASHBOARD_INPUT_STORAGE_KEY = "dashboard.inputJson";
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/**
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* 🧪 演示用模拟业务 JSON —— JSON 接口未就绪、也没有登录任务详情时的兜底输入。
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* 让大屏页可立即跑通：智能体分析这份数据 → report-json → 渲染大屏。
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* ⚠️ 接口就绪后删除本常量及其兜底引用。
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*/
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export const DEMO_DASHBOARD_INPUT = {
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taskId: "DEMO-涉台舆情",
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taskName: "2026 第一季度涉台舆情态势研判",
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taskContent:
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"汇总境内外多平台涉台舆情数据，研判热点叙事走势、关键行为体立场与传播渠道分布，识别风险并给出处置建议。",
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overview:
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"本季涉台舆情整体平稳但局部波动，'两岸交流''和平发展'类正向叙事占比上升，少数账号借军事议题制造紧张；境外智库与媒体联动放大特定框架。",
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period: "2026-01-01 ~ 2026-03-31",
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kpis: {
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总信息量: 1284000,
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日均增速: "12%",
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正向占比: "58%",
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负向占比: "19%",
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识别风险点: 7,
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覆盖平台: 12,
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},
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channels: [
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{ platform: "微博", share: 34 },
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{ platform: "境外社媒(X/FB)", share: 27 },
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{ platform: "短视频", share: 18 },
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{ platform: "新闻门户", share: 13 },
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{ platform: "论坛/贴吧", share: 8 },
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],
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narratives: [
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{ theme: "两岸交流与融合发展", sentiment: "正向", volume: 42, trend: "up" },
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{ theme: "和平稳定·避战", sentiment: "正向", volume: 26, trend: "up" },
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{ theme: "军事演训与安全", sentiment: "中性偏负", volume: 21, trend: "flat" },
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{ theme: "外部势力介入", sentiment: "负向", volume: 11, trend: "down" },
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],
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actors: [
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{ name: "主流官媒", stance: "正面引导", influence: 88, note: "议程设置与权威发布" },
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{ name: "岛内民间团体", stance: "中性多元", influence: 64, note: "交流合作类内容活跃" },
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{ name: "境外智库", stance: "负面框架", influence: 57, note: "联动媒体放大紧张叙事" },
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{ name: "自媒体大V", stance: "立场分化", influence: 49, note: "情绪化传播、易被借势" },
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],
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risks: [
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{ label: "军事议题被恶意剪辑放大", level: "high" },
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{ label: "境外智库联动制造紧张框架", level: "high" },
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{ label: "个别大V情绪化带节奏", level: "mid" },
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{ label: "短视频谣言二次传播", level: "mid" },
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{ label: "节假日期间信息量骤增", level: "low" },
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],
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timeline: [
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{ phase: "数据汇聚", desc: "12 平台多源采集与清洗去重" },
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{ phase: "叙事识别", desc: "聚类提炼核心叙事与情感倾向" },
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{ phase: "行为体画像", desc: "关键账号立场与影响力建模" },
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{ phase: "风险研判", desc: "识别高危议题与传播链路" },
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{ phase: "处置建议", desc: "分级响应与正向引导预案" },
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],
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weeklyVolume: [
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{ week: "W1", value: 86 },
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{ week: "W2", value: 92 },
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{ week: "W3", value: 110 },
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{ week: "W4", value: 134 },
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{ week: "W5", value: 121 },
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{ week: "W6", value: 158 },
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],
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};
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function inputBaseUrl(): string {
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// TODO: 接口就绪后确认环境变量名/路径。占位：未配则走 copTaskDetail 回退。
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const value = (import.meta.env as Record<string, unknown>).VITE_DASHBOARD_INPUT_URL;
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return typeof value === "string" ? value.replace(/\/+$/, "") : "";
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}
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/** 读取暂存的输入 JSON。 */
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export function readDashboardInputJson(): unknown | null {
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try {
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const raw = sessionStorage.getItem(DASHBOARD_INPUT_STORAGE_KEY);
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return raw ? JSON.parse(raw) : null;
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} catch {
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return null;
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}
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}
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/** 暂存输入 JSON（隐私模式/quota 异常静默失败）。 */
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export function persistDashboardInputJson(data: unknown): void {
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try {
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sessionStorage.setItem(DASHBOARD_INPUT_STORAGE_KEY, JSON.stringify(data));
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} catch {
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/* noop */
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}
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}
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/** 用登录暂存的任务详情拼一份占位输入（接口未就绪时的回退）。 */
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function placeholderFromCopTask(taskId: string): unknown | null {
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const detail = readCopTaskDetail();
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if (!detail) return taskId ? { taskId } : null;
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return {
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taskId,
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taskName: detail.taskName,
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taskContent: detail.taskContent,
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overview: detail.overview,
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taskDirection: detail.taskDirection,
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_note: "占位输入（真实业务 JSON 接口未就绪）",
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};
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}
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/**
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* 取本页的输入 JSON：优先暂存 → 真实接口（配了地址）→ copTaskDetail 占位。
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* 任一步失败都静默退化，最终可能返回 null（调用方据此提示用户/不自动开跑）。
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*/
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export async function fetchDashboardInputJson(taskId: string): Promise<unknown | null> {
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const cached = readDashboardInputJson();
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if (cached) return cached;
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const base = inputBaseUrl();
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if (base && taskId) {
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try {
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// service 拦截器统一注入鉴权并返回 response.data（整包 body）。
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const body = await requestCrudService.get(`${base}`, { taskId });
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const data = body && typeof body === "object" ? (body as { data?: unknown }).data ?? body : body;
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if (data) {
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persistDashboardInputJson(data);
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return data;
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}
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} catch {
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/* 拉取失败 → 落占位 */
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}
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}
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const placeholder = placeholderFromCopTask(taskId);
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// 占位里有真实任务内容（来自登录详情）才用它；否则回退演示数据，保证页面能跑通看效果。
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// ⚠️ 接口就绪后删除 DEMO 兜底。
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const detail = readCopTaskDetail();
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const resolved = detail ? placeholder : { ...DEMO_DASHBOARD_INPUT, taskId: taskId || DEMO_DASHBOARD_INPUT.taskId };
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if (resolved) persistDashboardInputJson(resolved);
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return resolved;
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}
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@@ -65,6 +65,12 @@ export const sidebarMenuItems: MenuItem[] = [
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icon: Users,
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path: "/page/strategy/qa/multi-agent-roundtable",
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},
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{
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id: "dashboard-agent",
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label: "大屏绘制",
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icon: Gauge,
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path: "/page/strategy/dashboard-agent",
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},
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// { id: "expert-qa", label: "专家问答", icon: Brain, path: "/page/strategy/qa/expert-qa" },
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{
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id: "qa-zzjc",
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@@ -0,0 +1,98 @@
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import { toast } from "sonner";
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import { getStoredAuth } from "@/core/auth";
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/**
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* Shared helpers for re-framing LangGraph "thread not accessible" 404s around
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* the current login identity.
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*
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* The most common cause in practice is a login session change: a thread is
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* owned by the previously logged-in account, so after switching accounts the
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* backend (``app/gateway/authz.py``) correctly returns
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* ``HTTP 404: {"detail":"Thread <uuid> not accessible"}``. Raw-toasting that
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* 404 just leaves the user staring at a scary error. Both the chat stream
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* (``core/threads/hooks.ts``) and the AI-writing stream
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* (``open-canvas/contexts/AIWritingContext.tsx``) funnel their stream errors
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* through here so the UX is identical: detect the login-change 404 and prompt
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* the user to refresh against the current identity instead of erroring out.
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*/
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/** Normalize an unknown stream error into a human-readable message string. */
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export function getStreamErrorMessage(error: unknown): string {
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if (typeof error === "string" && error.trim()) {
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return error;
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}
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if (error instanceof Error && error.message.trim()) {
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return error.message;
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}
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if (typeof error === "object" && error !== null) {
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const message = Reflect.get(error, "message");
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if (typeof message === "string" && message.trim()) {
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return message;
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}
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const nestedError = Reflect.get(error, "error");
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if (nestedError instanceof Error && nestedError.message.trim()) {
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return nestedError.message;
|
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}
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if (typeof nestedError === "string" && nestedError.trim()) {
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return nestedError;
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}
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}
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return "Request failed.";
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}
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/**
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* Detect "thread not found" / forbidden errors from the LangGraph SDK.
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*
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* Backend returns 404 both when a thread truly doesn't exist and when the
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* currently authenticated user doesn't own it ("not found" = no row + no
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* checkpoint, "not accessible" = row owned by another user / stale login
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* session). Both mean the current user can't use this thread.
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*/
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export function isThreadNotAccessibleError(message: string): boolean {
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const lowered = message.toLowerCase();
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if (/thread\s+[\w-]{8,}\s+not\s+(found|accessible)/i.test(message)) {
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return true;
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}
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// Fallback: a bare 404/403 from the threads endpoint when the SDK has
|
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// wrapped the detail differently.
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return (
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(lowered.includes("http 404") || lowered.includes("http 403")) &&
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lowered.includes("thread")
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);
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}
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/** Build a label for the *current* logged-in identity (email → user_id → 未知). */
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export function buildCurrentUserLabel(): string {
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const auth = getStoredAuth();
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if (!auth) {
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return "未登录";
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}
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const email = auth.email?.trim();
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if (email) {
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return email;
|
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}
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const userId = auth.user_id?.trim();
|
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return userId ? userId : "未知用户";
|
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}
|
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|
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/**
|
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* Re-frame a login-change 404 around the current identity and reload the page.
|
||||
*
|
||||
* Shows a friendly toast naming the current login, then full-reloads after a
|
||||
* short delay so the toast is visible and so TanStack Query cache / context
|
||||
* providers / any state captured under the previous user get rebuilt against
|
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* the current login. Used by streams whose session id lives in component state
|
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* (not the URL) — a plain reload lands them back on a fresh, valid page.
|
||||
*/
|
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export function notifyThreadAccessChangedAndReload(): void {
|
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const userLabel = buildCurrentUserLabel();
|
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toast.info(
|
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`当前登录用户为 ${userLabel}，原会话不存在或无权访问，正在为你刷新页面…`,
|
||||
);
|
||||
if (typeof window !== "undefined") {
|
||||
window.setTimeout(() => {
|
||||
window.location.reload();
|
||||
}, 900);
|
||||
}
|
||||
}
|
||||
@@ -15,6 +15,11 @@ import {
|
||||
import { useStream } from '@langchain/langgraph-sdk/react';
|
||||
|
||||
import { getAPIClient } from '@/core/api';
|
||||
import {
|
||||
getStreamErrorMessage,
|
||||
isThreadNotAccessibleError,
|
||||
notifyThreadAccessChangedAndReload,
|
||||
} from '@/core/threads/thread-access-error';
|
||||
|
||||
import { useCanvasGraphContext } from '../contexts/CanvasGraphContext';
|
||||
import { saveAIWritingTranscript } from '../api/ai-writing-sessions';
|
||||
@@ -186,10 +191,18 @@ const AIWritingStreamHost = memo(function AIWritingStreamHost({
|
||||
},
|
||||
onError(err: unknown) {
|
||||
eventBatcherRef.current?.cancel();
|
||||
dispatchRef.current?.({
|
||||
type: 'set_error',
|
||||
message: err instanceof Error ? err.message : String(err),
|
||||
});
|
||||
const message = getStreamErrorMessage(err);
|
||||
// A "thread not accessible/not found" 404 almost always means the writing
|
||||
// thread belongs to a previous login session — the user switched accounts
|
||||
// (or this tab outlived a re-login), so the current account legitimately
|
||||
// can't access the old thread. Instead of dumping the raw HTTP 404 into
|
||||
// the panel, re-frame it around the current identity and reload — same UX
|
||||
// the chat stream gives (see core/threads/hooks.ts).
|
||||
if (isThreadNotAccessibleError(message)) {
|
||||
notifyThreadAccessChangedAndReload();
|
||||
return;
|
||||
}
|
||||
dispatchRef.current?.({ type: 'set_error', message });
|
||||
},
|
||||
onFinish(finishState) {
|
||||
eventBatcherRef.current?.cancel();
|
||||
@@ -291,7 +304,12 @@ export function AIWritingProvider({ children }: { children: ReactNode }) {
|
||||
},
|
||||
);
|
||||
} catch (err) {
|
||||
dispatch({ type: 'set_error', message: err instanceof Error ? err.message : String(err) });
|
||||
const message = getStreamErrorMessage(err);
|
||||
if (isThreadNotAccessibleError(message)) {
|
||||
notifyThreadAccessChangedAndReload();
|
||||
return;
|
||||
}
|
||||
dispatch({ type: 'set_error', message });
|
||||
}
|
||||
}, [state.status]);
|
||||
|
||||
@@ -372,7 +390,12 @@ export function AIWritingProvider({ children }: { children: ReactNode }) {
|
||||
streamMode: ['values', 'updates', 'custom', 'messages-tuple'],
|
||||
});
|
||||
} catch (err) {
|
||||
dispatch({ type: 'set_error', message: err instanceof Error ? err.message : String(err) });
|
||||
const message = getStreamErrorMessage(err);
|
||||
if (isThreadNotAccessibleError(message)) {
|
||||
notifyThreadAccessChangedAndReload();
|
||||
return;
|
||||
}
|
||||
dispatch({ type: 'set_error', message });
|
||||
}
|
||||
}, [state.sessionId, state.currentPause, state.materials, state.outline,
|
||||
state.currentDraftTitle, state.currentDraftMarkdown, state.reviewResult,
|
||||
|
||||
@@ -20,6 +20,7 @@ import { ActionAnalyzes } from "@/strategy-components/components/task-components
|
||||
import KnowledgeBaseResourceManagement from "@/strategy-components/components/resource-management/KnowledgeBaseResourceManagement";
|
||||
import NetworkActorResourceManagement from "@/strategy-components/components/resource-management/NetworkActorResourceManagement";
|
||||
import RoundtablePlanningPage from "@/roundtable-planning/pages/RoundtablePlanningPage";
|
||||
import DashboardAgentPage from "@/roundtable-planning/pages/DashboardAgentPage";
|
||||
import BusinessChainListPage from "@/roundtable-planning/pages/BusinessChainListPage";
|
||||
import BusinessChainEditorPage from "@/roundtable-planning/pages/BusinessChainEditorPage";
|
||||
import "@/roundtable-planning/styles/roundtable-planning.css";
|
||||
@@ -99,6 +100,16 @@ export default function StrategyRoutes() {
|
||||
}
|
||||
/>
|
||||
|
||||
{/* 大屏绘制智能体独立页面（按 taskId 存取；ChatRuntime 提供 ArtifactsProvider 给沙箱预览） */}
|
||||
<Route
|
||||
path="dashboard-agent"
|
||||
element={
|
||||
<ChatRuntime>
|
||||
<DashboardAgentPage />
|
||||
</ChatRuntime>
|
||||
}
|
||||
/>
|
||||
|
||||
{/* 需求 0616 #3/#4：zzJC / zzXD 「按类型聚合」模块页（重做，独立路由）。 */}
|
||||
<Route path="qa/zzjc/*" element={<AgentModuleRoutes module={AGENT_MODULES.zzjc} />} />
|
||||
<Route path="qa/zzxd/*" element={<AgentModuleRoutes module={AGENT_MODULES.zzxd} />} />
|
||||
|
||||
@@ -0,0 +1,68 @@
|
||||
/**
|
||||
* 大屏绘制智能体「独立页面」会话持久化 —— 按外部 taskId 存取（后端不按 user 分权）。
|
||||
*
|
||||
* 一个 taskId 一条会话：聊天记录（transcript）+ 最新结构化数据（reportJson）。
|
||||
* 后端 `/api/dashboard-sessions/by-task/{taskId}`（GET 取 / PUT upsert）。
|
||||
* 详见后端 `app/gateway/routers/dashboard_sessions.py`。
|
||||
*/
|
||||
import { apiFetch } from "@/core/api/fetch-client";
|
||||
import { getBackendBaseURL } from "@/core/config";
|
||||
|
||||
export interface DashboardSession {
|
||||
taskId: string;
|
||||
/** 聊天记录（大屏绘制智能体对话气泡数组；形状与 Step2Dialogue 一致，宽松存取）。 */
|
||||
transcript: unknown[] | null;
|
||||
/** 最新结构化数据（report-json 字符串）；可被 parseReportJson 解析则视为「有效结果」。 */
|
||||
reportJson: string | null;
|
||||
updatedAt: string | null;
|
||||
}
|
||||
|
||||
interface ServerDashboardSession {
|
||||
task_id: string;
|
||||
transcript: unknown[] | null;
|
||||
report_json: string | null;
|
||||
created_at?: string | null;
|
||||
updated_at?: string | null;
|
||||
}
|
||||
|
||||
const BASE = () => `${getBackendBaseURL()}/api/dashboard-sessions`;
|
||||
|
||||
function toClient(s: ServerDashboardSession): DashboardSession {
|
||||
return {
|
||||
taskId: s.task_id,
|
||||
transcript: Array.isArray(s.transcript) ? s.transcript : null,
|
||||
reportJson: s.report_json ?? null,
|
||||
updatedAt: s.updated_at ?? null,
|
||||
};
|
||||
}
|
||||
|
||||
/** 取该 taskId 的会话；无（404）返回 null。 */
|
||||
export async function getDashboardSessionByTask(taskId: string): Promise<DashboardSession | null> {
|
||||
const res = await apiFetch(`${BASE()}/by-task/${encodeURIComponent(taskId)}`, { method: "GET" });
|
||||
if (res.status === 404) return null;
|
||||
if (!res.ok) {
|
||||
const text = await res.text().catch(() => "");
|
||||
throw new Error(`get dashboard session failed (${res.status}): ${text}`);
|
||||
}
|
||||
return toClient((await res.json()) as ServerDashboardSession);
|
||||
}
|
||||
|
||||
/** upsert 该 taskId 的会话（只传需要更新的字段）。 */
|
||||
export async function putDashboardSessionByTask(
|
||||
taskId: string,
|
||||
patch: { transcript?: unknown[]; reportJson?: string | null },
|
||||
): Promise<DashboardSession | null> {
|
||||
const body: Record<string, unknown> = {};
|
||||
if (patch.transcript !== undefined) body.transcript = patch.transcript;
|
||||
if (patch.reportJson !== undefined) body.report_json = patch.reportJson;
|
||||
const res = await apiFetch(`${BASE()}/by-task/${encodeURIComponent(taskId)}`, {
|
||||
method: "PUT",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify(body),
|
||||
});
|
||||
if (!res.ok) {
|
||||
const text = await res.text().catch(() => "");
|
||||
throw new Error(`put dashboard session failed (${res.status}): ${text}`);
|
||||
}
|
||||
return toClient((await res.json()) as ServerDashboardSession);
|
||||
}
|
||||
@@ -0,0 +1,141 @@
|
||||
/**
|
||||
* Step3 流程图「后端抽取 + 校验」SSE 客户端。
|
||||
*
|
||||
* 对应后端 `POST /api/flow-extract/stream`：后端独立调模型(不走总结线程，避开 coordinatorPrompt
|
||||
* 的报告锚定)两段式产图——①流式抽取 flow-json（实时吐回，前端原样显示给用户看）→ ②模型校验/修复
|
||||
* → 返回校验后的结构化图数据 `{title, nodes, edges}`。
|
||||
*
|
||||
* 这样把不稳定的「文本→结构」抽取 + 校验放后端，前端只负责实时显示抽取过程 + 拿最终图绘制。
|
||||
*/
|
||||
import { apiFetch } from "@/core/api/fetch-client";
|
||||
import { getBackendBaseURL } from "@/core/config";
|
||||
import type { FlowGraphData } from "@/roundtable-planning/lib/flow-graph";
|
||||
|
||||
/** 第三步输入框意图分类结果（与后端 /intent 对应）。 */
|
||||
export type FlowIntent = "regenerate_report" | "modify_flow" | "regenerate_flow" | "qa" | "unclear";
|
||||
|
||||
/**
|
||||
* 识别第三步输入框消息的意图（一次模型调用）。失败回退 "unclear"（前端发起澄清）。
|
||||
* 参考 ai_writing 的一次性 JSON 意图识别范式。
|
||||
*/
|
||||
export async function classifyFlowIntent(req: {
|
||||
message: string;
|
||||
hasReport: boolean;
|
||||
hasFlow: boolean;
|
||||
model?: string;
|
||||
signal?: AbortSignal;
|
||||
}): Promise<{ intent: FlowIntent; reason: string }> {
|
||||
try {
|
||||
const res = await apiFetch(`${getBackendBaseURL()}/api/flow-extract/intent`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
message: req.message,
|
||||
has_report: req.hasReport,
|
||||
has_flow: req.hasFlow,
|
||||
model: req.model,
|
||||
}),
|
||||
signal: req.signal,
|
||||
});
|
||||
if (!res.ok) return { intent: "unclear", reason: "" };
|
||||
const data = (await res.json()) as { intent?: string; reason?: string };
|
||||
const intent = (data.intent ?? "unclear") as FlowIntent;
|
||||
return { intent, reason: data.reason ?? "" };
|
||||
} catch {
|
||||
return { intent: "unclear", reason: "" };
|
||||
}
|
||||
}
|
||||
|
||||
export interface FlowExtractRequest {
|
||||
/** 报告 Markdown（优先数据来源）。 */
|
||||
reportMd?: string | null;
|
||||
/** 各席位交付（无报告时的数据来源）。 */
|
||||
seatDeliveries?: Array<{ sender: string; content: string }>;
|
||||
/** 业务链层级（stages 的 goal）。 */
|
||||
chainLevels?: string[];
|
||||
/** 业务链总控编排提示（写明层级时优先用它）。 */
|
||||
coordinatorPrompt?: string | null;
|
||||
/** 推理模型名（沿用第三步所选）。 */
|
||||
model?: string;
|
||||
/** Phase 2 问答修改：当前流程图 + 指令 → 按指令改图（其余不变）。 */
|
||||
currentGraph?: FlowGraphData | null;
|
||||
instruction?: string;
|
||||
signal?: AbortSignal;
|
||||
/** 抽取流式增量（含 `<think>` 思考 + 正文 + JSON，前端原样渲染：思考块 + 代码块）。 */
|
||||
onExtractDelta?: (text: string) => void;
|
||||
/** 抽取结束、进入校验阶段。 */
|
||||
onValidating?: () => void;
|
||||
/** 校验/修复流式增量（同样含思考 + 正文，前端实时显示校验过程）。 */
|
||||
onValidateDelta?: (text: string) => void;
|
||||
}
|
||||
|
||||
/**
|
||||
* 消费 SSE，返回校验后的最终图。抽取阶段通过 `onExtractDelta` 实时回吐；
|
||||
* 出错（后端 error 帧 / 网络）抛异常。
|
||||
*/
|
||||
export async function streamFlowExtract(req: FlowExtractRequest): Promise<FlowGraphData> {
|
||||
const res = await apiFetch(`${getBackendBaseURL()}/api/flow-extract/stream`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
report_md: req.reportMd ?? null,
|
||||
seat_deliveries: (req.seatDeliveries ?? []).map((s) => ({ sender: s.sender, content: s.content })),
|
||||
chain_levels: req.chainLevels ?? [],
|
||||
coordinator_prompt: req.coordinatorPrompt ?? null,
|
||||
model: req.model,
|
||||
current_graph: req.currentGraph ?? null,
|
||||
instruction: req.instruction ?? null,
|
||||
}),
|
||||
signal: req.signal,
|
||||
});
|
||||
if (!res.ok || !res.body) {
|
||||
const text = await res.text().catch(() => "");
|
||||
throw new Error(`flow-extract failed (${res.status}): ${text}`);
|
||||
}
|
||||
|
||||
const reader = res.body.getReader();
|
||||
const decoder = new TextDecoder();
|
||||
let buffer = "";
|
||||
let graph: FlowGraphData | null = null;
|
||||
let errored: string | null = null;
|
||||
|
||||
while (true) {
|
||||
const { done, value } = await reader.read();
|
||||
if (done) break;
|
||||
buffer += decoder.decode(value, { stream: true });
|
||||
const lines = buffer.split("\n");
|
||||
buffer = lines.pop() ?? "";
|
||||
for (const rawLine of lines) {
|
||||
const line = rawLine.trimEnd();
|
||||
if (!line.startsWith("data:")) continue;
|
||||
const data = line.slice(5).trim();
|
||||
if (!data) continue;
|
||||
let frame: Record<string, unknown>;
|
||||
try {
|
||||
frame = JSON.parse(data) as Record<string, unknown>;
|
||||
} catch {
|
||||
continue;
|
||||
}
|
||||
const event = typeof frame.event === "string" ? frame.event : "";
|
||||
if (event === "extract_delta") {
|
||||
const c = typeof frame.content === "string" ? frame.content : "";
|
||||
if (c) req.onExtractDelta?.(c);
|
||||
} else if (event === "validating") {
|
||||
req.onValidating?.();
|
||||
} else if (event === "validate_delta") {
|
||||
const c = typeof frame.content === "string" ? frame.content : "";
|
||||
if (c) req.onValidateDelta?.(c);
|
||||
} else if (event === "final") {
|
||||
graph = (frame.graph as FlowGraphData) ?? null;
|
||||
} else if (event === "error") {
|
||||
errored = typeof frame.detail === "string" ? frame.detail : "未知错误";
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (errored) throw new Error(errored);
|
||||
if (!graph || !Array.isArray(graph.nodes) || graph.nodes.length === 0) {
|
||||
throw new Error("flow-extract 未返回有效流程图");
|
||||
}
|
||||
return graph;
|
||||
}
|
||||
@@ -835,6 +835,32 @@ export async function initReportThread(
|
||||
return { agentName: json.agent_name, threadId: json.thread_id };
|
||||
}
|
||||
|
||||
/**
|
||||
* Create a thread for the Step 3「大屏多页」dashboard-data agent
|
||||
* (`roundtable-dashboard`, a built-in singleton — distinct from `roundtable-report`;
|
||||
* its run policy disables ALL tools so it only emits a ```report-json block). Run it
|
||||
* with {@link streamMultiAgent} (`agentType: "special"`, `agentName: "roundtable-dashboard"`,
|
||||
* `dAgentThreadId: { [agentName]: threadId }`); the frontend then `extractReportData`s
|
||||
* the streamed text. Follow-up Q&A / refinements reuse the SAME thread (do not re-init)
|
||||
* so the agent keeps the prior dashboard context.
|
||||
*/
|
||||
export async function initDashboardThread(
|
||||
options: { signal?: AbortSignal } = {},
|
||||
): Promise<ReportThreadResponse> {
|
||||
const res = await apiFetch(`${getBackendBaseURL()}/api/multi-agent/dashboard/init`, {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: "{}",
|
||||
signal: options.signal,
|
||||
});
|
||||
if (!res.ok) {
|
||||
const text = await res.text().catch(() => "");
|
||||
throw new Error(`init dashboard thread failed (${res.status}): ${text}`);
|
||||
}
|
||||
const json = (await res.json()) as { agent_name: string; thread_id: string };
|
||||
return { agentName: json.agent_name, threadId: json.thread_id };
|
||||
}
|
||||
|
||||
/**
|
||||
* Create a thread for the Step 3「总结报告」summary agent (`roundtable-summary`,
|
||||
* a built-in singleton — distinct from `roundtable-report`; this only spins up a
|
||||
|
||||
@@ -0,0 +1,250 @@
|
||||
/**
|
||||
* 大屏绘制智能体「交互区」聊天面板（沙箱左 60%；右 40% 是抽取过程代码预览）。
|
||||
*
|
||||
* 从 `Step3DashboardMode` 抽出共享：圆桌 Step3「结果绘制·大屏」与独立大屏页
|
||||
* （`DashboardAgentPage`）都复用同一套聊天 UI（流式气泡 + 模型选择 + 暂停/重画 +
|
||||
* 修改输入框）。消息里不展示 report-json 本体（已在右侧沙箱实时渲染），用
|
||||
* `stripReportFence` 替换成一句提示。
|
||||
*/
|
||||
import { useMemo, useRef, useState, type FormEvent } from "react";
|
||||
import {
|
||||
CheckCircle,
|
||||
Loader2,
|
||||
Pause,
|
||||
RefreshCw,
|
||||
SendHorizontal,
|
||||
} from "lucide-react";
|
||||
|
||||
import { useLocalSettings } from "@/core/settings";
|
||||
import { AutoGrowTextarea } from "@/components/ui/auto-grow-textarea";
|
||||
import type { ModelOption } from "@/roundtable-planning/components/Step3ReportMode";
|
||||
import { ScrollToBottomButton } from "@/roundtable-planning/components/ScrollToBottomButton";
|
||||
import { Step2Message } from "@/roundtable-planning/components/Step2Panel";
|
||||
import { useAutoScroll } from "@/roundtable-planning/hooks/useAutoScroll";
|
||||
import { useDialogueSpeedMetrics } from "@/roundtable-planning/hooks/useDialogueSpeedMetrics";
|
||||
import type { UseStep3DashboardReturn } from "@/roundtable-planning/hooks/useStep3Dashboard";
|
||||
import { stripReportFence } from "@/roundtable-planning/lib/report-template";
|
||||
|
||||
export interface DashboardChatPanelProps {
|
||||
phase: UseStep3DashboardReturn["phase"];
|
||||
dialogues: UseStep3DashboardReturn["dialogues"];
|
||||
busy: boolean;
|
||||
error: string | null;
|
||||
onAsk: (message: string) => void;
|
||||
onRedraw: () => void;
|
||||
onPause: () => void;
|
||||
onOpenPreview: () => void;
|
||||
hasResult: boolean;
|
||||
selectedModel: string;
|
||||
availableModels: ReadonlyArray<ModelOption>;
|
||||
modelsLoading: boolean;
|
||||
setSelectedModel: (model: string) => void;
|
||||
/** 面板副标题（默认对齐圆桌 Step3；独立大屏页可自定义说明）。 */
|
||||
subtitle?: string;
|
||||
/** 完成提示语（默认「已切换到大屏页面」；独立页不自动跳，可改文案）。 */
|
||||
doneHint?: string;
|
||||
}
|
||||
|
||||
export function DashboardChatPanel({
|
||||
phase,
|
||||
dialogues,
|
||||
busy,
|
||||
error,
|
||||
onAsk,
|
||||
onRedraw,
|
||||
onPause,
|
||||
onOpenPreview,
|
||||
hasResult,
|
||||
selectedModel,
|
||||
availableModels,
|
||||
modelsLoading,
|
||||
setSelectedModel,
|
||||
subtitle = "提炼研讨成果为大屏结构化数据（右侧实时显示抽取过程）",
|
||||
doneHint = "数据已就绪，已切换到大屏页面。",
|
||||
}: DashboardChatPanelProps) {
|
||||
const inputLocked = phase === "creating";
|
||||
const scrollRef = useRef<HTMLDivElement>(null);
|
||||
const { atBottom, scrollToBottom } = useAutoScroll(scrollRef, [dialogues]);
|
||||
const [localSettings] = useLocalSettings();
|
||||
const speedMeterEnabled = localSettings.display.streamSpeedMeterEnabled;
|
||||
const dialogueMetrics = useDialogueSpeedMetrics(dialogues);
|
||||
const [text, setText] = useState("");
|
||||
|
||||
// 消息列表里不展示 report-json 结构化数据本体（它已在右侧沙箱实时渲染），
|
||||
// 把数据块替换成一句「看右侧沙箱」的提示，避免左右两边重复刷整段 JSON。
|
||||
const displayDialogues = useMemo(
|
||||
() =>
|
||||
dialogues.map((d) => {
|
||||
const content = d.content ?? "";
|
||||
if (!content) return d;
|
||||
const stripped = stripReportFence(content, d.streaming === true);
|
||||
return stripped === content ? d : { ...d, content: stripped };
|
||||
}),
|
||||
[dialogues],
|
||||
);
|
||||
|
||||
const handleSubmit = (e: FormEvent<HTMLFormElement>) => {
|
||||
e.preventDefault();
|
||||
const t = text.trim();
|
||||
if (!t || inputLocked) return;
|
||||
setText("");
|
||||
onAsk(t);
|
||||
};
|
||||
|
||||
return (
|
||||
<div className="flex h-full min-h-0 flex-col bg-slate-50/20">
|
||||
{/* 面板头：智能体名 + 模型选择 + 暂停/重画 */}
|
||||
<div className="shrink-0 flex items-center justify-between gap-2 border-b border-slate-100 bg-white px-4 py-2.5">
|
||||
<div className="flex items-center gap-2 min-w-0">
|
||||
<div className="w-6 h-6 shrink-0 rounded-lg bg-gradient-to-tr from-primary to-[var(--brand-200)] flex items-center justify-center text-white text-xs shadow-sm">
|
||||
📊
|
||||
</div>
|
||||
<div className="leading-tight min-w-0">
|
||||
<div className="text-xs font-bold text-slate-800 truncate">大屏绘制智能体</div>
|
||||
<div className="text-[10px] text-slate-400 truncate">{subtitle}</div>
|
||||
</div>
|
||||
</div>
|
||||
<div className="flex items-center gap-2 shrink-0">
|
||||
<select
|
||||
id="step3-dashboard-model-select"
|
||||
value={selectedModel}
|
||||
onChange={(e) => setSelectedModel(e.target.value)}
|
||||
disabled={busy || modelsLoading || availableModels.length === 0}
|
||||
title={
|
||||
modelsLoading
|
||||
? "正在加载模型列表…"
|
||||
: availableModels.length === 0
|
||||
? "未在 config.yaml 中配置任何模型"
|
||||
: busy
|
||||
? "绘制中不可切换；完成后或重新绘制时生效"
|
||||
: "切换绘制使用的推理模型（下次绘制生效）"
|
||||
}
|
||||
className="max-w-[160px] bg-slate-50/50 border border-slate-200 rounded-xl px-2.5 py-1.5 text-[11px] font-bold text-slate-700 cursor-pointer focus:outline-none focus:border-primary focus:ring-1 focus:ring-[var(--brand-200)]/50 disabled:opacity-50 disabled:cursor-not-allowed dark:bg-secondary dark:text-foreground dark:border-border"
|
||||
>
|
||||
{availableModels.length === 0 && (
|
||||
<option value="">{modelsLoading ? "加载中…" : "未配置模型"}</option>
|
||||
)}
|
||||
{availableModels.map((m) => (
|
||||
<option key={m.name} value={m.name}>
|
||||
{m.display_name && m.display_name !== m.name ? `${m.display_name} (${m.name})` : m.name}
|
||||
</option>
|
||||
))}
|
||||
</select>
|
||||
{busy ? (
|
||||
<button
|
||||
type="button"
|
||||
onClick={onPause}
|
||||
title="中止当前绘制"
|
||||
className="bg-white border border-amber-200 text-amber-700 hover:bg-amber-50 text-[10.5px] font-bold px-2.5 py-1.5 rounded-xl flex items-center gap-1 transition"
|
||||
>
|
||||
<Pause className="w-3.5 h-3.5" />
|
||||
中止
|
||||
</button>
|
||||
) : (
|
||||
<button
|
||||
type="button"
|
||||
onClick={onRedraw}
|
||||
title="丢弃上下文，从头重新绘制"
|
||||
className="bg-white border border-slate-200 text-slate-700 hover:bg-slate-50 text-[10.5px] font-bold px-2.5 py-1.5 rounded-xl flex items-center gap-1 transition"
|
||||
>
|
||||
<RefreshCw className="w-3.5 h-3.5 text-slate-500" />
|
||||
重新绘制
|
||||
</button>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* 聊天记录 */}
|
||||
<div className="relative flex-1 min-h-0 flex flex-col">
|
||||
<div ref={scrollRef} className="flex-1 min-h-0 overflow-y-auto p-4 space-y-4 bg-white">
|
||||
{phase === "creating" && dialogues.length === 0 && (
|
||||
<div className="flex items-center gap-2 text-xs text-slate-500">
|
||||
<Loader2 className="w-3.5 h-3.5 animate-spin text-primary" />
|
||||
正在初始化绘制线程…
|
||||
</div>
|
||||
)}
|
||||
|
||||
{displayDialogues.map((msg) => (
|
||||
<Step2Message
|
||||
key={msg.id}
|
||||
msg={msg}
|
||||
metric={speedMeterEnabled ? dialogueMetrics.get(msg.id) : undefined}
|
||||
/>
|
||||
))}
|
||||
|
||||
{busy && (
|
||||
<div className="inline-flex items-center gap-2 rounded-full border bg-white px-2.5 py-1 text-[11px] text-slate-500">
|
||||
<span className="bg-primary size-1.5 animate-pulse rounded-full" />
|
||||
右侧沙箱实时显示数据抽取过程
|
||||
</div>
|
||||
)}
|
||||
|
||||
{phase === "error" && (
|
||||
<div className="rounded-xl border border-rose-200 bg-rose-50 p-3 text-xs text-rose-700">
|
||||
<div className="font-bold mb-1">绘制失败</div>
|
||||
<div className="leading-relaxed break-all">{error}</div>
|
||||
<button
|
||||
type="button"
|
||||
onClick={onRedraw}
|
||||
className="mt-2 inline-flex items-center gap-1 text-[11px] font-bold text-rose-700 underline hover:text-rose-800"
|
||||
>
|
||||
<RefreshCw className="w-3 h-3" />
|
||||
重试
|
||||
</button>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{phase === "done" && (
|
||||
<div className="flex items-center gap-2 text-xs font-bold text-emerald-600">
|
||||
<CheckCircle className="w-4 h-4" />
|
||||
{doneHint}
|
||||
{hasResult && (
|
||||
<button
|
||||
type="button"
|
||||
onClick={onOpenPreview}
|
||||
className="ml-1 inline-flex items-center gap-1 text-[11px] font-bold text-primary underline hover:opacity-80"
|
||||
>
|
||||
在沙箱打开 HTML 预览
|
||||
</button>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
{!atBottom && <ScrollToBottomButton onClick={scrollToBottom} />}
|
||||
|
||||
{/* 问答修改输入框 */}
|
||||
<form onSubmit={handleSubmit} className="shrink-0 border-t border-slate-100 bg-white px-3 py-3">
|
||||
<div className="flex items-end gap-2 rounded-2xl border border-slate-200 bg-slate-50/70 px-3 py-2 focus-within:border-primary/60 focus-within:bg-white focus-within:ring-2 focus-within:ring-[var(--brand-200)]/40">
|
||||
<AutoGrowTextarea
|
||||
maxHeight={224}
|
||||
value={text}
|
||||
onChange={(e) => setText(e.target.value)}
|
||||
onKeyDown={(e) => {
|
||||
if (e.key === "Enter" && !e.shiftKey) {
|
||||
e.preventDefault();
|
||||
e.currentTarget.form?.requestSubmit();
|
||||
}
|
||||
}}
|
||||
disabled={inputLocked}
|
||||
rows={1}
|
||||
placeholder={
|
||||
busy
|
||||
? "随时输入修改要求——发送会打断当前绘制并按新要求重出数据"
|
||||
: "输入修改要求，例如：把风险分布换成柱状图，新增一个执行路线席位"
|
||||
}
|
||||
className="min-h-8 max-h-56 flex-1 resize-none bg-transparent py-1.5 text-xs leading-relaxed text-slate-800 placeholder:text-slate-400 focus:outline-none disabled:cursor-not-allowed disabled:opacity-60"
|
||||
/>
|
||||
<button
|
||||
type="submit"
|
||||
disabled={inputLocked || text.trim().length === 0}
|
||||
title={inputLocked ? "正在初始化线程，请稍候" : "发送修改要求"}
|
||||
className="mb-0.5 flex h-8 w-8 shrink-0 items-center justify-center rounded-xl bg-primary text-white transition hover:bg-[var(--brand-900)] disabled:cursor-not-allowed disabled:opacity-50"
|
||||
>
|
||||
{inputLocked ? <Loader2 className="h-4 w-4 animate-spin" /> : <SendHorizontal className="h-4 w-4" />}
|
||||
</button>
|
||||
</div>
|
||||
</form>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
@@ -15,6 +15,7 @@ import {
|
||||
useRef,
|
||||
useState,
|
||||
} from "react";
|
||||
import { createPortal } from "react-dom";
|
||||
import {
|
||||
applyNodeChanges,
|
||||
Background,
|
||||
@@ -32,11 +33,14 @@ import {
|
||||
} from "@xyflow/react";
|
||||
import "@xyflow/react/dist/style.css";
|
||||
import {
|
||||
Check,
|
||||
FileText,
|
||||
Loader2,
|
||||
LocateFixed,
|
||||
Maximize2,
|
||||
Minimize2,
|
||||
Pencil,
|
||||
Trash2,
|
||||
Workflow,
|
||||
X,
|
||||
ZoomIn,
|
||||
@@ -45,6 +49,8 @@ import {
|
||||
|
||||
import { cn } from "@/lib/utils";
|
||||
import {
|
||||
applyNodeEdit,
|
||||
deleteNodeFromGraph,
|
||||
FLOW_NODE_TYPE_META,
|
||||
FLOW_NODE_W,
|
||||
layoutFlowGraph,
|
||||
@@ -59,6 +65,8 @@ interface FlowGraphPanelProps {
|
||||
onClose: () => void;
|
||||
/** 沙箱里有 md 报告时提供：工具栏显示「MD 报告」按钮，点击切回 Markdown 预览。 */
|
||||
onSwitchToReport?: () => void;
|
||||
/** Phase 3：画布手动编辑——传入则节点详情弹窗出现「编辑 / 删除」，回写整张新图。 */
|
||||
onChange?: (next: FlowGraphData) => void;
|
||||
}
|
||||
|
||||
// ── 自定义节点（按 type 着色的卡片）────────────────────────────────────────
|
||||
@@ -106,6 +114,7 @@ function FlowCanvasInner({
|
||||
onToggleFullscreen,
|
||||
onClose,
|
||||
onSwitchToReport,
|
||||
onChange,
|
||||
}: FlowGraphPanelProps & { fullscreen: boolean; onToggleFullscreen: () => void }) {
|
||||
const rf = useReactFlow();
|
||||
const [selectedId, setSelectedId] = useState<string | null>(null);
|
||||
@@ -276,13 +285,14 @@ function FlowCanvasInner({
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* 节点详情弹窗 */}
|
||||
{/* 节点详情弹窗（传入 onChange 时可编辑 / 删除节点） */}
|
||||
{selectedNode && (
|
||||
<NodeDetailModal
|
||||
node={selectedNode}
|
||||
data={data}
|
||||
onClose={() => setSelectedId(null)}
|
||||
onJump={(id) => setSelectedId(id)}
|
||||
onChange={onChange}
|
||||
/>
|
||||
)}
|
||||
</div>
|
||||
@@ -317,17 +327,38 @@ function NodeDetailModal({
|
||||
data,
|
||||
onClose,
|
||||
onJump,
|
||||
onChange,
|
||||
}: {
|
||||
node: FlowGraphNode;
|
||||
data: FlowGraphData;
|
||||
onClose: () => void;
|
||||
onJump: (id: string) => void;
|
||||
/** Phase 3：传入则可编辑 / 删除该节点（回写整张新图）。 */
|
||||
onChange?: (next: FlowGraphData) => void;
|
||||
}) {
|
||||
const meta = FLOW_NODE_TYPE_META[node.type];
|
||||
const labelOf = (id: string) => data.nodes.find((n) => n.id === id)?.label ?? id;
|
||||
const incoming = data.edges.filter((e) => e.to === node.id);
|
||||
const outgoing = data.edges.filter((e) => e.from === node.id);
|
||||
|
||||
const [editing, setEditing] = useState(false);
|
||||
const [label, setLabel] = useState(node.label);
|
||||
const [group, setGroup] = useState(node.group ?? "");
|
||||
const [detail, setDetail] = useState(node.detail ?? "");
|
||||
|
||||
const handleSave = () => {
|
||||
onChange?.(applyNodeEdit(data, node.id, { label, group, detail }));
|
||||
setEditing(false);
|
||||
onClose();
|
||||
};
|
||||
const handleDelete = () => {
|
||||
if (typeof window !== "undefined" && !window.confirm(`删除节点「${node.label}」及其连线？此操作不可撤销。`)) {
|
||||
return;
|
||||
}
|
||||
onChange?.(deleteNodeFromGraph(data, node.id));
|
||||
onClose();
|
||||
};
|
||||
|
||||
return (
|
||||
<div
|
||||
className="absolute inset-0 z-30 flex items-center justify-center bg-black/30 p-4 backdrop-blur-[1px]"
|
||||
@@ -361,27 +392,110 @@ function NodeDetailModal({
|
||||
<X className="h-4 w-4" />
|
||||
</button>
|
||||
</div>
|
||||
<div className="max-h-[50vh] space-y-3 overflow-y-auto p-4">
|
||||
<div>
|
||||
<div className="mb-1 text-[10px] font-bold uppercase tracking-widest text-slate-400">
|
||||
详细说明
|
||||
</div>
|
||||
<p className="text-xs leading-relaxed text-slate-700">
|
||||
{node.detail?.trim() || "（该节点未提供详细说明）"}
|
||||
</p>
|
||||
|
||||
{editing ? (
|
||||
<div className="max-h-[55vh] space-y-3 overflow-y-auto p-4">
|
||||
<EditField label="节点名称（label）" value={label} onChange={setLabel} />
|
||||
<EditField label="所属层级（group，如 目的 / 行为体）" value={group} onChange={setGroup} />
|
||||
<EditField label="详细内容（detail，名称+理由）" value={detail} onChange={setDetail} multiline />
|
||||
</div>
|
||||
{incoming.length > 0 && (
|
||||
<ConnectionList title="上游（来自）" entries={incoming.map((e) => ({ id: e.from, label: labelOf(e.from), edgeLabel: e.label }))} onJump={onJump} />
|
||||
)}
|
||||
{outgoing.length > 0 && (
|
||||
<ConnectionList title="下游（去往）" entries={outgoing.map((e) => ({ id: e.to, label: labelOf(e.to), edgeLabel: e.label }))} onJump={onJump} />
|
||||
)}
|
||||
</div>
|
||||
) : (
|
||||
<div className="max-h-[50vh] space-y-3 overflow-y-auto p-4">
|
||||
<div>
|
||||
<div className="mb-1 text-[10px] font-bold uppercase tracking-widest text-slate-400">详细说明</div>
|
||||
<p className="text-xs leading-relaxed text-slate-700 whitespace-pre-wrap">
|
||||
{node.detail?.trim() || "（该节点未提供详细说明）"}
|
||||
</p>
|
||||
</div>
|
||||
{incoming.length > 0 && (
|
||||
<ConnectionList title="上游（来自）" entries={incoming.map((e) => ({ id: e.from, label: labelOf(e.from), edgeLabel: e.label }))} onJump={onJump} />
|
||||
)}
|
||||
{outgoing.length > 0 && (
|
||||
<ConnectionList title="下游（去往）" entries={outgoing.map((e) => ({ id: e.to, label: labelOf(e.to), edgeLabel: e.label }))} onJump={onJump} />
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* 编辑 / 删除 操作栏（仅传入 onChange 时显示） */}
|
||||
{onChange && (
|
||||
<div className="flex items-center justify-between gap-2 border-t border-slate-100 bg-slate-50/60 px-4 py-2.5">
|
||||
{editing ? (
|
||||
<>
|
||||
<button
|
||||
type="button"
|
||||
onClick={() => setEditing(false)}
|
||||
className="rounded-lg px-2.5 py-1.5 text-[11px] font-bold text-slate-500 hover:bg-slate-200/60"
|
||||
>
|
||||
取消
|
||||
</button>
|
||||
<button
|
||||
type="button"
|
||||
onClick={handleSave}
|
||||
className="inline-flex items-center gap-1 rounded-lg bg-primary px-3 py-1.5 text-[11px] font-bold text-white hover:bg-[var(--brand-900)]"
|
||||
>
|
||||
<Check className="h-3.5 w-3.5" />
|
||||
保存
|
||||
</button>
|
||||
</>
|
||||
) : (
|
||||
<>
|
||||
<button
|
||||
type="button"
|
||||
onClick={handleDelete}
|
||||
className="inline-flex items-center gap-1 rounded-lg border border-rose-200 px-2.5 py-1.5 text-[11px] font-bold text-rose-600 hover:bg-rose-50"
|
||||
>
|
||||
<Trash2 className="h-3.5 w-3.5" />
|
||||
删除节点
|
||||
</button>
|
||||
<button
|
||||
type="button"
|
||||
onClick={() => setEditing(true)}
|
||||
className="inline-flex items-center gap-1 rounded-lg border border-slate-200 px-3 py-1.5 text-[11px] font-bold text-slate-700 hover:border-primary/40 hover:text-primary"
|
||||
>
|
||||
<Pencil className="h-3.5 w-3.5" />
|
||||
编辑
|
||||
</button>
|
||||
</>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
function EditField({
|
||||
label,
|
||||
value,
|
||||
onChange,
|
||||
multiline,
|
||||
}: {
|
||||
label: string;
|
||||
value: string;
|
||||
onChange: (v: string) => void;
|
||||
multiline?: boolean;
|
||||
}) {
|
||||
return (
|
||||
<div>
|
||||
<div className="mb-1 text-[10px] font-bold uppercase tracking-widest text-slate-400">{label}</div>
|
||||
{multiline ? (
|
||||
<textarea
|
||||
value={value}
|
||||
onChange={(e) => onChange(e.target.value)}
|
||||
rows={4}
|
||||
className="w-full resize-none rounded-lg border border-slate-200 bg-white px-2.5 py-1.5 text-xs leading-relaxed text-slate-800 focus:border-primary focus:outline-none focus:ring-1 focus:ring-[var(--brand-200)]/50"
|
||||
/>
|
||||
) : (
|
||||
<input
|
||||
value={value}
|
||||
onChange={(e) => onChange(e.target.value)}
|
||||
className="w-full rounded-lg border border-slate-200 bg-white px-2.5 py-1.5 text-xs text-slate-800 focus:border-primary focus:outline-none focus:ring-1 focus:ring-[var(--brand-200)]/50"
|
||||
/>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
function ConnectionList({
|
||||
title,
|
||||
entries,
|
||||
@@ -420,14 +534,15 @@ function ConnectionList({
|
||||
|
||||
// ── 外壳：覆盖右栏 / 全屏切换 ───────────────────────────────────────────────
|
||||
|
||||
export function FlowGraphPanel({ data, streaming, onClose, onSwitchToReport }: FlowGraphPanelProps) {
|
||||
export function FlowGraphPanel({ data, streaming, onClose, onSwitchToReport, onChange }: FlowGraphPanelProps) {
|
||||
const [fullscreen, setFullscreen] = useState(false);
|
||||
return (
|
||||
const inner = (
|
||||
<div
|
||||
className={cn(
|
||||
"flex flex-col bg-white",
|
||||
// 非全屏=填满所在面板（Step3 放在右侧沙箱位，不覆盖左侧消息列表）；全屏才整页覆盖。
|
||||
fullscreen ? "fixed inset-0 z-[80]" : "relative h-full min-h-0 w-full",
|
||||
// 非全屏=填满所在面板（Step3 放在右侧沙箱位，不覆盖左侧消息列表）；
|
||||
// 全屏=覆盖整个视口（z 高于 app 顶部导航，保证顶部工具栏可见、可点「退出全屏」）。
|
||||
fullscreen ? "fixed inset-0 z-[9999]" : "relative h-full min-h-0 w-full",
|
||||
)}
|
||||
>
|
||||
<ReactFlowProvider>
|
||||
@@ -438,8 +553,15 @@ export function FlowGraphPanel({ data, streaming, onClose, onSwitchToReport }: F
|
||||
onToggleFullscreen={() => setFullscreen((f) => !f)}
|
||||
onClose={onClose}
|
||||
onSwitchToReport={onSwitchToReport}
|
||||
onChange={onChange}
|
||||
/>
|
||||
</ReactFlowProvider>
|
||||
</div>
|
||||
);
|
||||
// 全屏时 portal 到 body：脱离任何带 transform/overflow 的祖先（Step2 沙箱分栏等），
|
||||
// 否则 `fixed` 会被祖先裁剪、顶部工具栏被 app 导航栏盖住（即本次 bug）。
|
||||
if (fullscreen && typeof document !== "undefined") {
|
||||
return createPortal(inner, document.body);
|
||||
}
|
||||
return inner;
|
||||
}
|
||||
@@ -15,9 +15,7 @@
|
||||
import {
|
||||
useEffect,
|
||||
useMemo,
|
||||
useRef,
|
||||
useState,
|
||||
type FormEvent,
|
||||
} from "react";
|
||||
import {
|
||||
ArrowLeft,
|
||||
@@ -27,31 +25,22 @@ import {
|
||||
ExternalLink,
|
||||
LayoutPanelTop,
|
||||
Loader2,
|
||||
Pause,
|
||||
Pencil,
|
||||
RefreshCw,
|
||||
SendHorizontal,
|
||||
Sparkles,
|
||||
} from "lucide-react";
|
||||
|
||||
import { useLocalSettings } from "@/core/settings";
|
||||
import { AutoGrowTextarea } from "@/components/ui/auto-grow-textarea";
|
||||
import { desensitizeDeep } from "@/lib/desensitize";
|
||||
import type { DraftStep3Snapshot } from "@/roundtable-planning/api/drafts";
|
||||
import { DashboardApp } from "@/roundtable-planning/dashboard/DashboardApp";
|
||||
import { adaptReportData } from "@/roundtable-planning/dashboard/data-context";
|
||||
import type { ModelOption } from "@/roundtable-planning/components/Step3ReportMode";
|
||||
import { ScrollToBottomButton } from "@/roundtable-planning/components/ScrollToBottomButton";
|
||||
import { Step2Message } from "@/roundtable-planning/components/Step2Panel";
|
||||
import { DashboardChatPanel } from "@/roundtable-planning/components/DashboardChatPanel";
|
||||
import { Step2SandboxLayout } from "@/roundtable-planning/components/Step2SandboxLayout";
|
||||
import { DASHBOARD_VIEW_STORAGE_PREFIX } from "@/roundtable-planning/pages/DashboardStandalonePage";
|
||||
import { useAutoScroll } from "@/roundtable-planning/hooks/useAutoScroll";
|
||||
import { useDialogueSpeedMetrics } from "@/roundtable-planning/hooks/useDialogueSpeedMetrics";
|
||||
import type { UseStep3DashboardReturn } from "@/roundtable-planning/hooks/useStep3Dashboard";
|
||||
import {
|
||||
parseReportJson,
|
||||
renderDashboardHtml,
|
||||
stripReportFence,
|
||||
type DashboardTheme,
|
||||
} from "@/roundtable-planning/lib/report-template";
|
||||
import { useTheme } from "@/contexts/ThemeContext";
|
||||
@@ -361,221 +350,3 @@ export function Step3DashboardMode({
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
// ── 交互区聊天面板（沙箱的左 60%；右 40% 是抽取过程代码预览） ──────────────────────────
|
||||
|
||||
function DashboardChatPanel({
|
||||
phase,
|
||||
dialogues,
|
||||
busy,
|
||||
error,
|
||||
onAsk,
|
||||
onRedraw,
|
||||
onPause,
|
||||
onOpenPreview,
|
||||
hasResult,
|
||||
selectedModel,
|
||||
availableModels,
|
||||
modelsLoading,
|
||||
setSelectedModel,
|
||||
}: {
|
||||
phase: UseStep3DashboardReturn["phase"];
|
||||
dialogues: UseStep3DashboardReturn["dialogues"];
|
||||
busy: boolean;
|
||||
error: string | null;
|
||||
onAsk: (message: string) => void;
|
||||
onRedraw: () => void;
|
||||
onPause: () => void;
|
||||
onOpenPreview: () => void;
|
||||
hasResult: boolean;
|
||||
selectedModel: string;
|
||||
availableModels: ReadonlyArray<ModelOption>;
|
||||
modelsLoading: boolean;
|
||||
setSelectedModel: (model: string) => void;
|
||||
}) {
|
||||
const inputLocked = phase === "creating";
|
||||
const scrollRef = useRef<HTMLDivElement>(null);
|
||||
const { atBottom, scrollToBottom } = useAutoScroll(scrollRef, [dialogues]);
|
||||
const [localSettings] = useLocalSettings();
|
||||
const speedMeterEnabled = localSettings.display.streamSpeedMeterEnabled;
|
||||
const dialogueMetrics = useDialogueSpeedMetrics(dialogues);
|
||||
const [text, setText] = useState("");
|
||||
|
||||
// 消息列表里不展示 report-json 结构化数据本体（它已在右侧沙箱实时渲染），
|
||||
// 把数据块替换成一句「看右侧沙箱」的提示，避免左右两边重复刷整段 JSON。
|
||||
const displayDialogues = useMemo(
|
||||
() =>
|
||||
dialogues.map((d) => {
|
||||
const content = d.content ?? "";
|
||||
if (!content) return d;
|
||||
const stripped = stripReportFence(content, d.streaming === true);
|
||||
return stripped === content ? d : { ...d, content: stripped };
|
||||
}),
|
||||
[dialogues],
|
||||
);
|
||||
|
||||
const handleSubmit = (e: FormEvent<HTMLFormElement>) => {
|
||||
e.preventDefault();
|
||||
const t = text.trim();
|
||||
if (!t || inputLocked) return;
|
||||
setText("");
|
||||
onAsk(t);
|
||||
};
|
||||
|
||||
return (
|
||||
<div className="flex h-full min-h-0 flex-col bg-slate-50/20">
|
||||
{/* 面板头：智能体名 + 模型选择 + 暂停/重画 */}
|
||||
<div className="shrink-0 flex items-center justify-between gap-2 border-b border-slate-100 bg-white px-4 py-2.5">
|
||||
<div className="flex items-center gap-2 min-w-0">
|
||||
<div className="w-6 h-6 shrink-0 rounded-lg bg-gradient-to-tr from-primary to-[var(--brand-200)] flex items-center justify-center text-white text-xs shadow-sm">
|
||||
📊
|
||||
</div>
|
||||
<div className="leading-tight min-w-0">
|
||||
<div className="text-xs font-bold text-slate-800 truncate">大屏绘制智能体</div>
|
||||
<div className="text-[10px] text-slate-400 truncate">提炼研讨成果为大屏结构化数据（右侧实时显示抽取过程）</div>
|
||||
</div>
|
||||
</div>
|
||||
<div className="flex items-center gap-2 shrink-0">
|
||||
<select
|
||||
id="step3-dashboard-model-select"
|
||||
value={selectedModel}
|
||||
onChange={(e) => setSelectedModel(e.target.value)}
|
||||
disabled={busy || modelsLoading || availableModels.length === 0}
|
||||
title={
|
||||
modelsLoading
|
||||
? "正在加载模型列表…"
|
||||
: availableModels.length === 0
|
||||
? "未在 config.yaml 中配置任何模型"
|
||||
: busy
|
||||
? "绘制中不可切换；完成后或重新绘制时生效"
|
||||
: "切换绘制使用的推理模型（下次绘制生效）"
|
||||
}
|
||||
className="max-w-[160px] bg-slate-50/50 border border-slate-200 rounded-xl px-2.5 py-1.5 text-[11px] font-bold text-slate-700 cursor-pointer focus:outline-none focus:border-primary focus:ring-1 focus:ring-[var(--brand-200)]/50 disabled:opacity-50 disabled:cursor-not-allowed dark:bg-secondary dark:text-foreground dark:border-border"
|
||||
>
|
||||
{availableModels.length === 0 && (
|
||||
<option value="">{modelsLoading ? "加载中…" : "未配置模型"}</option>
|
||||
)}
|
||||
{availableModels.map((m) => (
|
||||
<option key={m.name} value={m.name}>
|
||||
{m.display_name && m.display_name !== m.name ? `${m.display_name} (${m.name})` : m.name}
|
||||
</option>
|
||||
))}
|
||||
</select>
|
||||
{busy ? (
|
||||
<button
|
||||
type="button"
|
||||
onClick={onPause}
|
||||
title="中止当前绘制"
|
||||
className="bg-white border border-amber-200 text-amber-700 hover:bg-amber-50 text-[10.5px] font-bold px-2.5 py-1.5 rounded-xl flex items-center gap-1 transition"
|
||||
>
|
||||
<Pause className="w-3.5 h-3.5" />
|
||||
中止
|
||||
</button>
|
||||
) : (
|
||||
<button
|
||||
type="button"
|
||||
onClick={onRedraw}
|
||||
title="丢弃上下文，从头重新绘制"
|
||||
className="bg-white border border-slate-200 text-slate-700 hover:bg-slate-50 text-[10.5px] font-bold px-2.5 py-1.5 rounded-xl flex items-center gap-1 transition"
|
||||
>
|
||||
<RefreshCw className="w-3.5 h-3.5 text-slate-500" />
|
||||
重新绘制
|
||||
</button>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* 聊天记录 */}
|
||||
<div className="relative flex-1 min-h-0 flex flex-col">
|
||||
<div ref={scrollRef} className="flex-1 min-h-0 overflow-y-auto p-4 space-y-4 bg-white">
|
||||
{phase === "creating" && dialogues.length === 0 && (
|
||||
<div className="flex items-center gap-2 text-xs text-slate-500">
|
||||
<Loader2 className="w-3.5 h-3.5 animate-spin text-primary" />
|
||||
正在初始化绘制线程…
|
||||
</div>
|
||||
)}
|
||||
|
||||
{displayDialogues.map((msg) => (
|
||||
<Step2Message
|
||||
key={msg.id}
|
||||
msg={msg}
|
||||
metric={speedMeterEnabled ? dialogueMetrics.get(msg.id) : undefined}
|
||||
/>
|
||||
))}
|
||||
|
||||
{busy && (
|
||||
<div className="inline-flex items-center gap-2 rounded-full border bg-white px-2.5 py-1 text-[11px] text-slate-500">
|
||||
<span className="bg-primary size-1.5 animate-pulse rounded-full" />
|
||||
右侧沙箱实时显示数据抽取过程，完成后自动切到大屏页面
|
||||
</div>
|
||||
)}
|
||||
|
||||
{phase === "error" && (
|
||||
<div className="rounded-xl border border-rose-200 bg-rose-50 p-3 text-xs text-rose-700">
|
||||
<div className="font-bold mb-1">绘制失败</div>
|
||||
<div className="leading-relaxed break-all">{error}</div>
|
||||
<button
|
||||
type="button"
|
||||
onClick={onRedraw}
|
||||
className="mt-2 inline-flex items-center gap-1 text-[11px] font-bold text-rose-700 underline hover:text-rose-800"
|
||||
>
|
||||
<RefreshCw className="w-3 h-3" />
|
||||
重试
|
||||
</button>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{phase === "done" && (
|
||||
<div className="flex items-center gap-2 text-xs font-bold text-emerald-600">
|
||||
<CheckCircle className="w-4 h-4" />
|
||||
数据已就绪，已切换到大屏页面。
|
||||
{hasResult && (
|
||||
<button
|
||||
type="button"
|
||||
onClick={onOpenPreview}
|
||||
className="ml-1 inline-flex items-center gap-1 text-[11px] font-bold text-primary underline hover:opacity-80"
|
||||
>
|
||||
在沙箱打开 HTML 预览
|
||||
</button>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
{!atBottom && <ScrollToBottomButton onClick={scrollToBottom} />}
|
||||
|
||||
{/* 问答修改输入框 */}
|
||||
<form onSubmit={handleSubmit} className="shrink-0 border-t border-slate-100 bg-white px-3 py-3">
|
||||
<div className="flex items-end gap-2 rounded-2xl border border-slate-200 bg-slate-50/70 px-3 py-2 focus-within:border-primary/60 focus-within:bg-white focus-within:ring-2 focus-within:ring-[var(--brand-200)]/40">
|
||||
<AutoGrowTextarea
|
||||
maxHeight={224}
|
||||
value={text}
|
||||
onChange={(e) => setText(e.target.value)}
|
||||
onKeyDown={(e) => {
|
||||
if (e.key === "Enter" && !e.shiftKey) {
|
||||
e.preventDefault();
|
||||
e.currentTarget.form?.requestSubmit();
|
||||
}
|
||||
}}
|
||||
disabled={inputLocked}
|
||||
rows={1}
|
||||
placeholder={
|
||||
busy
|
||||
? "随时输入修改要求——发送会打断当前绘制并按新要求重出数据"
|
||||
: "输入修改要求，例如：把风险分布换成柱状图，新增一个执行路线席位"
|
||||
}
|
||||
className="min-h-8 max-h-56 flex-1 resize-none bg-transparent py-1.5 text-xs leading-relaxed text-slate-800 placeholder:text-slate-400 focus:outline-none disabled:cursor-not-allowed disabled:opacity-60"
|
||||
/>
|
||||
<button
|
||||
type="submit"
|
||||
disabled={inputLocked || text.trim().length === 0}
|
||||
title={inputLocked ? "正在初始化线程，请稍候" : "发送修改要求"}
|
||||
className="mb-0.5 flex h-8 w-8 shrink-0 items-center justify-center rounded-xl bg-primary text-white transition hover:bg-[var(--brand-900)] disabled:cursor-not-allowed disabled:opacity-50"
|
||||
>
|
||||
{inputLocked ? <Loader2 className="h-4 w-4 animate-spin" /> : <SendHorizontal className="h-4 w-4" />}
|
||||
</button>
|
||||
</div>
|
||||
</form>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
@@ -46,7 +46,6 @@ import { FlowGraphPanel } from "@/roundtable-planning/components/FlowGraphPanel"
|
||||
import {
|
||||
detectFlowIntent,
|
||||
extractFlowGraph,
|
||||
stripFlowFence,
|
||||
FLOW_FENCE_OPEN,
|
||||
} from "@/roundtable-planning/lib/flow-graph";
|
||||
import { ScrollToBottomButton } from "@/roundtable-planning/components/ScrollToBottomButton";
|
||||
@@ -139,9 +138,18 @@ export function Step3SummaryMode({
|
||||
|
||||
const { open: artifactsOpen } = useArtifacts();
|
||||
|
||||
// ── 结构化流程图：流式提取回复末尾的 flow-json，增量绘制 ──────────────────
|
||||
// 取最后一条含 flow-json 的智能体气泡（多轮修改后以最新一份为准）。
|
||||
// ── 结构化流程图（Phase 1：后端「抽取+校验」）────────────────────────────
|
||||
// 数据来源优先级：
|
||||
// · flowPhase==="done" → 用后端校验后的结构化图 summary.flowGraph 绘制；
|
||||
// · 抽取/校验进行中 → 不画（用户看对话气泡里实时滚动的 JSON + 状态条），等校验完成再绘制；
|
||||
// · idle/error → 回退到对话气泡里解析 flow-json（手动 flow 续问路径 / 旧逻辑兜底）。
|
||||
const flowGraph = useMemo(() => {
|
||||
if (summary.flowPhase === "done" && summary.flowGraph) {
|
||||
return { graph: summary.flowGraph, streaming: false };
|
||||
}
|
||||
if (summary.flowPhase === "extracting" || summary.flowPhase === "validating") {
|
||||
return null;
|
||||
}
|
||||
for (let i = dialogues.length - 1; i >= 0; i--) {
|
||||
const d = dialogues[i];
|
||||
if (d.avatarType === "human") continue;
|
||||
@@ -151,7 +159,7 @@ export function Step3SummaryMode({
|
||||
if (graph) return { graph, streaming: d.streaming === true };
|
||||
}
|
||||
return null;
|
||||
}, [dialogues]);
|
||||
}, [dialogues, summary.flowPhase, summary.flowGraph]);
|
||||
|
||||
const [flowView, setFlowView] = useState(false);
|
||||
const flowAutoOpenedRef = useRef(false);
|
||||
@@ -196,16 +204,10 @@ export function Step3SummaryMode({
|
||||
ask(message, files);
|
||||
};
|
||||
|
||||
// 气泡里不展示生 JSON：把 flow-json 块替换成一句引导语。
|
||||
const displayDialogues = useMemo(
|
||||
() =>
|
||||
dialogues.map((d) => {
|
||||
const content = d.content ?? "";
|
||||
if (!content.includes(FLOW_FENCE_OPEN)) return d;
|
||||
return { ...d, content: stripFlowFence(content, d.streaming === true) };
|
||||
}),
|
||||
[dialogues],
|
||||
);
|
||||
// 流程图抽取/校验的过程（思考 `<think>` + 文字说明 + flow-json 代码块）全部原样交给
|
||||
// Step2Message 渲染：它用 splitInlineReasoning 把思考折叠成思考块、其余按 markdown 渲染
|
||||
// （flow-json 显示为代码块）。用户要求显示完整过程、不只显示代码块，故不再 strip。
|
||||
const displayDialogues = dialogues;
|
||||
const [leftCollapsed, setLeftCollapsed] = useState(false);
|
||||
// 右侧面板（沙箱预览或流程图画布）露出时自动收起左栏，关闭时复原——边沿触发，手动切换不回弹。
|
||||
const rightPanelVisible = artifactsOpen || (!!flowGraph && flowView);
|
||||
@@ -397,6 +399,7 @@ export function Step3SummaryMode({
|
||||
}
|
||||
: undefined
|
||||
}
|
||||
onChange={summary.setFlowGraph}
|
||||
/>
|
||||
) : undefined
|
||||
}
|
||||
@@ -407,6 +410,10 @@ export function Step3SummaryMode({
|
||||
error={error}
|
||||
busy={busy}
|
||||
mdAvailable={!!mdArtifact}
|
||||
flowPhase={summary.flowPhase}
|
||||
onRegenerateFlow={summary.regenerateFlow}
|
||||
pendingClarification={summary.pendingFlowClarification}
|
||||
onResolveClarification={summary.resolveFlowClarification}
|
||||
onRetry={redraw}
|
||||
onAsk={handleAsk}
|
||||
onPause={pause}
|
||||
@@ -549,6 +556,10 @@ function AgentProgressPanel({
|
||||
error,
|
||||
busy,
|
||||
mdAvailable,
|
||||
flowPhase,
|
||||
onRegenerateFlow,
|
||||
pendingClarification,
|
||||
onResolveClarification,
|
||||
onRetry,
|
||||
onAsk,
|
||||
onPause,
|
||||
@@ -565,6 +576,13 @@ function AgentProgressPanel({
|
||||
busy: boolean;
|
||||
/** 沙箱里是否已有 md 报告 —— 决定提示语（纯流程图轮没有 .md 可预览/下载）。 */
|
||||
mdAvailable: boolean;
|
||||
/** 后端流程图抽取阶段（Phase 1）。 */
|
||||
flowPhase: UseStep3SummaryReturn["flowPhase"];
|
||||
/** 仅重新生成流程图（模型挂了切模型后重试）。 */
|
||||
onRegenerateFlow: () => void;
|
||||
/** 意图模糊待澄清的原始消息（非空 → 展示选项让用户定）。 */
|
||||
pendingClarification: string | null;
|
||||
onResolveClarification: UseStep3SummaryReturn["resolveFlowClarification"];
|
||||
onRetry: () => void;
|
||||
onAsk: (message: string, files?: File[]) => void;
|
||||
onPause: () => void;
|
||||
@@ -705,6 +723,28 @@ function AgentProgressPanel({
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* 后端流程图「抽取+校验」状态条（Phase 1）。 */}
|
||||
{(flowPhase === "extracting" || flowPhase === "validating") && (
|
||||
<div className="inline-flex items-center gap-2 rounded-full border border-blue-200 bg-blue-50 px-2.5 py-1 text-[11px] font-bold text-blue-600">
|
||||
<Loader2 className="w-3 h-3 animate-spin" />
|
||||
{flowPhase === "extracting" ? "结构化流程图抽取中（JSON 实时输出）…" : "流程图校验 / 修复中…"}
|
||||
</div>
|
||||
)}
|
||||
{flowPhase === "error" && (
|
||||
<div className="rounded-xl border border-amber-200 bg-amber-50 p-3 text-xs text-amber-800">
|
||||
<div className="font-bold mb-1">流程图生成失败</div>
|
||||
<div className="leading-relaxed">模型可能不稳定。可在上方切换模型后重试。</div>
|
||||
<button
|
||||
type="button"
|
||||
onClick={onRegenerateFlow}
|
||||
className="mt-2 inline-flex items-center gap-1 text-[11px] font-bold text-amber-800 underline hover:text-amber-900"
|
||||
>
|
||||
<RefreshCw className="w-3 h-3" />
|
||||
重新生成流程图
|
||||
</button>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{phase === "done" && (
|
||||
<div className="flex items-center gap-2 text-xs font-bold text-emerald-600">
|
||||
<CheckCircle className="w-4 h-4" />
|
||||
@@ -715,6 +755,35 @@ function AgentProgressPanel({
|
||||
)}
|
||||
</div>
|
||||
{!atBottom && <ScrollToBottomButton onClick={scrollToBottom} />}
|
||||
|
||||
{/* 意图模糊 → 让用户自己定（不自作主张） */}
|
||||
{pendingClarification && (
|
||||
<div className="shrink-0 border-t border-amber-100 bg-amber-50/60 px-3 py-2.5">
|
||||
<div className="mb-1.5 text-[11px] font-bold text-amber-800">
|
||||
没太理解你的意思，你是想——
|
||||
</div>
|
||||
<div className="flex flex-wrap gap-1.5">
|
||||
{(
|
||||
[
|
||||
{ k: "regenerate_report", t: "重新生成报告" },
|
||||
{ k: "modify_flow", t: "修改流程图" },
|
||||
{ k: "regenerate_flow", t: "重新生成流程图" },
|
||||
{ k: "qa", t: "只是提问" },
|
||||
] as const
|
||||
).map((o) => (
|
||||
<button
|
||||
key={o.k}
|
||||
type="button"
|
||||
onClick={() => onResolveClarification(o.k)}
|
||||
className="rounded-lg border border-amber-300 bg-white px-2.5 py-1.5 text-[11px] font-bold text-amber-800 transition hover:bg-amber-100"
|
||||
>
|
||||
{o.t}
|
||||
</button>
|
||||
))}
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
<form onSubmit={handleAskSubmit} className="shrink-0 border-t border-slate-100 bg-white px-3 py-3">
|
||||
{/* 已选附件预览（发送时随指令上传到总结线程，供智能体读取） */}
|
||||
{askFiles.length > 0 && (
|
||||
|
||||
@@ -15,7 +15,7 @@ import { useCallback, useEffect, useRef, useState } from "react";
|
||||
import { useArtifacts } from "@/components/workspace/artifacts";
|
||||
import type { DraftStep3Snapshot } from "@/roundtable-planning/api/drafts";
|
||||
import {
|
||||
initReportThread,
|
||||
initDashboardThread,
|
||||
streamMultiAgent,
|
||||
type StreamPhase,
|
||||
} from "@/roundtable-planning/api/multi-agent";
|
||||
@@ -35,7 +35,7 @@ import {
|
||||
} from "@/roundtable-planning/lib/report-template";
|
||||
import { nowTime } from "@/roundtable-planning/lib/roundtable-constants";
|
||||
|
||||
const DASH_AGENT_FALLBACK = "roundtable-report";
|
||||
const DASH_AGENT_FALLBACK = "roundtable-dashboard";
|
||||
|
||||
export type DashboardPhase = "idle" | "creating" | "streaming" | "done" | "error";
|
||||
|
||||
@@ -48,6 +48,11 @@ interface UseStep3DashboardOptions {
|
||||
themeMode: "light" | "dark";
|
||||
savedReport?: DraftStep3Snapshot | null;
|
||||
onReportSaved?: (snapshot: DraftStep3Snapshot) => void;
|
||||
/**
|
||||
* 自定义初始 prompt 构造（独立大屏页用：输入是任务原始 JSON，让智能体分析转换）。
|
||||
* 返回空/未提供时回退默认的 buildDashboardPrompt（圆桌研讨成果模式）。
|
||||
*/
|
||||
buildInitialPrompt?: () => string | null | undefined;
|
||||
}
|
||||
|
||||
export interface UseStep3DashboardReturn {
|
||||
@@ -73,6 +78,8 @@ export interface UseStep3DashboardReturn {
|
||||
hydrateSaved: (html: string) => void;
|
||||
/** 从草稿/后台作业恢复（dashboardJson 结构化数据）：按**当前主题**渲染后展示。 */
|
||||
hydrateFromJson: (jsonStr: string) => void;
|
||||
/** 从持久化恢复**真实聊天记录** + 结构化数据（独立大屏页按 taskId 取回时用）。 */
|
||||
hydrateTranscript: (dialogues: Step2Dialogue[], dashboardJson: string | null) => void;
|
||||
/** 手动打开大屏沙箱预览（恢复后/用户点气泡）。 */
|
||||
openDashboardPreview: () => void;
|
||||
}
|
||||
@@ -105,6 +112,7 @@ export function useStep3Dashboard(options: UseStep3DashboardOptions): UseStep3Da
|
||||
themeMode,
|
||||
savedReport,
|
||||
onReportSaved,
|
||||
buildInitialPrompt,
|
||||
} = options;
|
||||
|
||||
const onSavedRef = useRef(onReportSaved);
|
||||
@@ -112,6 +120,11 @@ export function useStep3Dashboard(options: UseStep3DashboardOptions): UseStep3Da
|
||||
onSavedRef.current = onReportSaved;
|
||||
}, [onReportSaved]);
|
||||
|
||||
const buildInitialPromptRef = useRef(buildInitialPrompt);
|
||||
useEffect(() => {
|
||||
buildInitialPromptRef.current = buildInitialPrompt;
|
||||
}, [buildInitialPrompt]);
|
||||
|
||||
const { select: selectArtifact, setOpen: setArtifactsOpen, open: artifactsOpen } = useArtifacts();
|
||||
const artifactsOpenRef = useRef(artifactsOpen);
|
||||
useEffect(() => {
|
||||
@@ -269,7 +282,7 @@ export function useStep3Dashboard(options: UseStep3DashboardOptions): UseStep3Da
|
||||
const { bubbleId, message, aborter } = args;
|
||||
let threadId = liveThreadRef.current;
|
||||
if (!threadId) {
|
||||
const init = await initReportThread({ signal: aborter.signal });
|
||||
const init = await initDashboardThread({ signal: aborter.signal });
|
||||
if (aborter.signal.aborted) return;
|
||||
threadId = init.threadId;
|
||||
agentNameRef.current = init.agentName || DASH_AGENT_FALLBACK;
|
||||
@@ -374,7 +387,13 @@ export function useStep3Dashboard(options: UseStep3DashboardOptions): UseStep3Da
|
||||
]
|
||||
.filter(Boolean)
|
||||
.join("\n");
|
||||
const prompt = buildDashboardPrompt({ intentText, seatDeliveries, consensusText });
|
||||
// 独立大屏页：用自定义 prompt（输入任务原始 JSON，让智能体分析转换）；
|
||||
// 否则回退默认的圆桌研讨成果 prompt。
|
||||
const custom = buildInitialPromptRef.current?.();
|
||||
const prompt =
|
||||
custom && custom.trim()
|
||||
? custom
|
||||
: buildDashboardPrompt({ intentText, seatDeliveries, consensusText });
|
||||
await streamOnce({ bubbleId, message: prompt, aborter });
|
||||
} catch (err) {
|
||||
if (aborter.signal.aborted) return;
|
||||
@@ -486,6 +505,38 @@ export function useStep3Dashboard(options: UseStep3DashboardOptions): UseStep3Da
|
||||
[themeMode, hydrateSaved],
|
||||
);
|
||||
|
||||
/** 从持久化恢复真实聊天记录 + 结构化数据（独立大屏页按 taskId 取回时用）。
|
||||
* 与 hydrateFromJson 的区别：保留**用户真实的历史对话气泡**，而非造一条合成提示。 */
|
||||
const hydrateTranscript = useCallback(
|
||||
(savedDialogues: Step2Dialogue[], dashboardJson: string | null) => {
|
||||
abort();
|
||||
runningRef.current = false;
|
||||
const synthThreadId = "roundtable-dashboard-restored";
|
||||
liveThreadRef.current = null; // 合成线程非真线程：续问会新建线程重新生成
|
||||
setDashThreadId(synthThreadId);
|
||||
setActiveArtifactThreadId(synthThreadId);
|
||||
const parsed = dashboardJson ? parseReportJson(dashboardJson) : null;
|
||||
if (parsed) {
|
||||
const rendered = renderDashboardHtml(parsed, { generatedAt: formatNow(), theme: themeMode });
|
||||
htmlRef.current = rendered;
|
||||
setHtml(rendered);
|
||||
setData(parsed);
|
||||
dataRef.current = parsed; // 续轮修改的基线
|
||||
const { messageId, toolCallId } = upsertDashStub(synthThreadId, rendered);
|
||||
lastAutoOpenRef.current = { threadId: synthThreadId, messageId, toolCallId };
|
||||
} else {
|
||||
htmlRef.current = null;
|
||||
setHtml(null);
|
||||
setData(null);
|
||||
dataRef.current = null;
|
||||
}
|
||||
setDialogues(Array.isArray(savedDialogues) ? savedDialogues : []);
|
||||
setError(null);
|
||||
setPhase(parsed ? "done" : "idle");
|
||||
},
|
||||
[abort, themeMode, upsertDashStub],
|
||||
);
|
||||
|
||||
// 恢复路径辅助：savedReport 由组件/页面按字段调用对应 hydrate；这里只消除未用告警。
|
||||
void savedReport;
|
||||
void isDashboardHtml;
|
||||
@@ -519,6 +570,7 @@ export function useStep3Dashboard(options: UseStep3DashboardOptions): UseStep3Da
|
||||
setPreviewEnabled,
|
||||
hydrateSaved,
|
||||
hydrateFromJson,
|
||||
hydrateTranscript,
|
||||
openDashboardPreview,
|
||||
};
|
||||
}
|
||||
@@ -40,7 +40,8 @@ import type {
|
||||
Step2Dialogue,
|
||||
StubAiMessage,
|
||||
} from "@/roundtable-planning/hooks/useStep2Orchestration";
|
||||
import { detectFlowIntent, hasFlowFence } from "@/roundtable-planning/lib/flow-graph";
|
||||
import { detectFlowIntent, extractFlowGraph, hasFlowFence, serializeFlowGraph, type FlowGraphData } from "@/roundtable-planning/lib/flow-graph";
|
||||
import { classifyFlowIntent, streamFlowExtract, type FlowIntent } from "@/roundtable-planning/api/flow-extract";
|
||||
import { nowTime } from "@/roundtable-planning/lib/roundtable-constants";
|
||||
|
||||
/** 报告输出文件的固定虚拟路径（恢复历史快照时用它重建 stub）。 */
|
||||
@@ -79,6 +80,18 @@ export interface UseStep3SummaryReturn {
|
||||
error: string | null;
|
||||
/** taskId/深链模式：md 报告生成后自动续跑流程图（组件据此让流程图画布自动聚焦）。 */
|
||||
autoFlow: boolean;
|
||||
/** 后端「抽取+校验」得到的校验后结构化流程图（Phase 1）。done 时据此绘制。 */
|
||||
flowGraph: FlowGraphData | null;
|
||||
/** 流程图生成阶段：idle / extracting(抽取流式) / validating(校验中) / done / error。 */
|
||||
flowPhase: "idle" | "extracting" | "validating" | "done" | "error";
|
||||
/** 仅重新生成流程图（复用当前 md/席位交付，不重跑报告）——模型挂了切模型后重试用。 */
|
||||
regenerateFlow: () => void;
|
||||
/** 手动编辑流程图（Phase 3：画布上改/删节点后回写 + 持久化）。 */
|
||||
setFlowGraph: (next: FlowGraphData) => void;
|
||||
/** 意图模糊时挂起的原始消息（非空 → 组件展示「重做报告/改图/重画/提问」选项让用户定）。 */
|
||||
pendingFlowClarification: string | null;
|
||||
/** 用户在澄清里点选意图后执行。 */
|
||||
resolveFlowClarification: (choice: FlowIntent) => void;
|
||||
summaryThreadId: string | null;
|
||||
dialogues: Step2Dialogue[];
|
||||
seatStubMessages: Record<string, StubAiMessage[]>;
|
||||
@@ -220,61 +233,9 @@ const FLOW_STRUCTURE_RULE =
|
||||
"⑥ **只画**这些层级实体节点；**不要**把『综合分析』『核心洞察』『分层传播策略 / 执行路线 / 建议』『结论』" +
|
||||
"以及『阶段①②③④』等章节名 / 阶段名 / 汇总段落画成节点。";
|
||||
|
||||
/** 把各席位交付拼成一段内联材料（无报告时作流程图的数据来源）。 */
|
||||
function inlineSeatDeliveries(seatDeliveries?: { sender: string; content: string }[]): string {
|
||||
const list = (seatDeliveries ?? []).filter((s) => (s.content ?? "").trim());
|
||||
if (list.length === 0) return "";
|
||||
return list
|
||||
.map((s, i) => `## 席位${i + 1}：${s.sender || "（未具名席位）"}\n${s.content.trim()}`)
|
||||
.join("\n\n");
|
||||
}
|
||||
|
||||
/**
|
||||
* taskId/深链模式：md 报告生成完后自动续跑的「结构化输出」prompt。
|
||||
*
|
||||
* 数据基准（用户拍板）：**有报告就基于报告**（报告已把各席位交付清晰总结过，直接提取实体、
|
||||
* 不要再自己重新总结一遍）；没有报告则基于**各席位交付产物**。两种来源都**整段内联**进 prompt
|
||||
* （不依赖线程上下文），让模型逐条对着抽取节点。层级顺序优先用总控提示词里写明的层级，否则 chainLevels。
|
||||
*/
|
||||
function buildAutoFlowPrompt(opts: {
|
||||
chainLevels?: string[];
|
||||
coordinatorPrompt?: string | null;
|
||||
reportMd?: string;
|
||||
seatDeliveries?: { sender: string; content: string }[];
|
||||
}): string {
|
||||
const levels = (opts.chainLevels ?? []).map((s) => s.trim()).filter(Boolean);
|
||||
const cp = opts.coordinatorPrompt?.trim();
|
||||
const report = opts.reportMd?.trim() ?? "";
|
||||
const seatMaterial = report ? "" : inlineSeatDeliveries(opts.seatDeliveries);
|
||||
const parts: string[] = [];
|
||||
if (report) {
|
||||
parts.push(
|
||||
"本轮只画一张流程图。**数据来源 = 下面这份你已写好的《方案总结报告》**（它已把各席位交付清晰总结过）——" +
|
||||
"请**逐条对着报告抽取**已有的层级条目作为流程图节点，**不要再自己重新总结 / 概括 / 提炼**，也不要凭空新增报告里没有的内容。",
|
||||
);
|
||||
} else {
|
||||
parts.push(
|
||||
"本轮只画一张流程图。**数据来源 = 下面各席位的交付产物**——请**逐条对着这些交付抽取**已有的层级条目作为流程图节点，不要自己另起总结。",
|
||||
);
|
||||
}
|
||||
parts.push(FLOW_ONLY_RULE);
|
||||
// 整段内联数据来源（报告全文 / 席位交付），用分隔线包起来便于模型对照抽取。
|
||||
if (report) {
|
||||
parts.push("===== 方案总结报告全文（数据来源，逐条抽取，勿改写）=====\n" + report + "\n===== 报告结束 =====");
|
||||
} else if (seatMaterial) {
|
||||
parts.push("===== 各席位交付产物（数据来源，逐条抽取，勿改写）=====\n" + seatMaterial + "\n===== 交付结束 =====");
|
||||
}
|
||||
parts.push(FLOW_JSON_SHAPE);
|
||||
parts.push(FLOW_STRUCTURE_RULE);
|
||||
if (cp) {
|
||||
parts.push("层级顺序优先采用「业务链总控编排提示」里写明的层级链（如它写的 任务 → 目的 → 行为体 → …）。");
|
||||
} else if (levels.length > 0) {
|
||||
parts.push(`层级顺序（自上而下）：任务 → ${levels.join(" → ")}。`);
|
||||
}
|
||||
// 收尾再强调（首尾夹击，对抗线程里 coordinatorPrompt 的报告锚定）。
|
||||
parts.push("再次强调：本轮不写任何 Markdown 报告正文 / 章节，整个回复除一句话说明外，只有一个完整的 ```flow-json 代码块。");
|
||||
return parts.join("\n");
|
||||
}
|
||||
// 注：原 buildAutoFlowPrompt（在总结线程里续问吐 flow-json）已废弃 —— Phase 1 起自动结构化
|
||||
// 输出改走后端独立端点 /api/flow-extract/stream（streamFlowExtract），抽取+校验在后端做。
|
||||
// FLOW_ONLY_RULE / FLOW_STRUCTURE_RULE / FLOW_JSON_SHAPE 仍由手动 FLOW_ASK_INSTRUCTION 引用。
|
||||
|
||||
/** 首轮总结 prompt。`flowMode`=编排提示里明确要求了结构化输出/流程图 → 跳过 md 文件，直出流程图。 */
|
||||
function buildSummaryPrompt(opts: {
|
||||
@@ -452,6 +413,16 @@ export function useStep3Summary(
|
||||
|
||||
const [phase, setPhase] = useState<SummaryPhase>("idle");
|
||||
const [error, setError] = useState<string | null>(null);
|
||||
// 后端「抽取+校验」流程图（Phase 1）：与 SummaryPhase 解耦（md 报告完成后才跑流程图）。
|
||||
const [flowGraph, setFlowGraphState] = useState<FlowGraphData | null>(null);
|
||||
const [flowPhase, setFlowPhase] = useState<"idle" | "extracting" | "validating" | "done" | "error">("idle");
|
||||
// 镜像 flowGraph 供 ask()（问答修改）在 render 外读到最新图作 edit 入参。
|
||||
const flowGraphRef = useRef<FlowGraphData | null>(null);
|
||||
useEffect(() => {
|
||||
flowGraphRef.current = flowGraph;
|
||||
}, [flowGraph]);
|
||||
// 意图模糊时挂起待澄清的原始消息（组件渲染选项让用户自己定）。
|
||||
const [pendingFlowClarification, setPendingFlowClarification] = useState<string | null>(null);
|
||||
const [summaryThreadId, setSummaryThreadId] = useState<string | null>(null);
|
||||
const [dialogues, setDialogues] = useState<Step2Dialogue[]>([]);
|
||||
const [seatStubMessages, setSeatStubMessages] = useState<
|
||||
@@ -530,6 +501,11 @@ export function useStep3Summary(
|
||||
);
|
||||
}, []);
|
||||
|
||||
/** 改某气泡的发送方名称（意图分类期间先建中性「调度中」气泡，定了意图再改成对应智能体名复用）。 */
|
||||
const relabelDialogue = useCallback((id: number, sender: string) => {
|
||||
setDialogues((prev) => prev.map((d) => (d.id === id ? { ...d, sender } : d)));
|
||||
}, []);
|
||||
|
||||
const appendStepIfNew = useCallback(
|
||||
(
|
||||
bubbleId: number,
|
||||
@@ -1010,6 +986,116 @@ export function useStep3Summary(
|
||||
],
|
||||
);
|
||||
|
||||
/**
|
||||
* Phase 1：后端「抽取+校验」生成流程图。独立调 `/api/flow-extract/stream`（不进总结线程，
|
||||
* 避开 coordinator_prompt 的报告锚定）：抽取阶段把 JSON 流式吐进对话气泡（用户实时看），
|
||||
* 校验完成后用校验后的结构化图绘制画布（done）。模型沿用第三步所选（selectedModel）。
|
||||
*/
|
||||
const runFlowExtract = useCallback(
|
||||
async (
|
||||
aborter: AbortController,
|
||||
opts?: { instruction?: string; currentGraph?: FlowGraphData | null; bubbleId?: number },
|
||||
): Promise<void> => {
|
||||
const isEdit = !!opts?.instruction && !!opts?.currentGraph;
|
||||
setFlowPhase("extracting");
|
||||
if (!isEdit) setFlowGraphState(null); // 编辑模式保留旧图直到新图就绪（防画布闪空）
|
||||
// 两个气泡：抽取/修改 + 校验——把过程（思考 `<think>` + 文字 + flow-json 代码块）都留着展示，
|
||||
// 由 Step2Message 渲染（思考折叠块 + markdown 代码块），不折叠成一句话。
|
||||
// 传入 bubbleId（意图分类期间已建的「调度中」气泡）则复用并改名，避免空窗 + 不重复建气泡。
|
||||
const extractSender = isEdit ? "结构化流程图 · 修改" : "结构化流程图 · 抽取";
|
||||
let extractBubbleId: number;
|
||||
if (opts?.bubbleId != null) {
|
||||
extractBubbleId = opts.bubbleId;
|
||||
relabelDialogue(extractBubbleId, extractSender);
|
||||
} else {
|
||||
extractBubbleId = appendStreamingDialogue({ sender: extractSender, avatarType: "blue" });
|
||||
}
|
||||
let validateBubbleId: number | null = null;
|
||||
try {
|
||||
const graph = await streamFlowExtract({
|
||||
reportMd: currentMdRef.current,
|
||||
seatDeliveries,
|
||||
chainLevels,
|
||||
coordinatorPrompt,
|
||||
model: selectedModel || undefined,
|
||||
currentGraph: opts?.currentGraph ?? null,
|
||||
instruction: opts?.instruction,
|
||||
signal: aborter.signal,
|
||||
onExtractDelta: (t) => appendToDialogue(extractBubbleId, t),
|
||||
onValidating: () => {
|
||||
setFlowPhase("validating");
|
||||
// 抽取气泡收尾（保留已流出的内容），另起校验气泡。
|
||||
finalizeDialogue(extractBubbleId, "", "抽取完成");
|
||||
validateBubbleId = appendStreamingDialogue({ sender: "结构化流程图 · 校验", avatarType: "blue" });
|
||||
},
|
||||
onValidateDelta: (t) => {
|
||||
if (validateBubbleId != null) appendToDialogue(validateBubbleId, t);
|
||||
},
|
||||
});
|
||||
if (aborter.signal.aborted) return;
|
||||
setFlowGraphState(graph);
|
||||
setFlowPhase("done");
|
||||
flowActiveRef.current = true;
|
||||
// 收尾两个气泡（保留过程内容）。校验后的图序列化进草稿快照 closing，恢复时重建画布。
|
||||
if (validateBubbleId != null) finalizeDialogue(validateBubbleId, "", "流程图完成");
|
||||
else finalizeDialogue(extractBubbleId, "", "流程图完成");
|
||||
onSummarySavedRef.current?.({
|
||||
md: currentMdRef.current,
|
||||
threadId: liveThreadRef.current ?? "",
|
||||
generatedAt: new Date().toISOString(),
|
||||
model: selectedModel || "",
|
||||
closing: serializeFlowGraph(graph),
|
||||
});
|
||||
} catch (err) {
|
||||
if (aborter.signal.aborted) return;
|
||||
const msg = err instanceof Error ? err.message : String(err);
|
||||
setFlowPhase("error");
|
||||
finalizeDialogue(
|
||||
validateBubbleId ?? extractBubbleId,
|
||||
`(流程图生成失败) ${msg}\n模型可能不稳定——可在上方切换模型后点「重新生成流程图」重试。`,
|
||||
"流程图失败",
|
||||
);
|
||||
}
|
||||
},
|
||||
[
|
||||
seatDeliveries,
|
||||
chainLevels,
|
||||
coordinatorPrompt,
|
||||
selectedModel,
|
||||
appendStreamingDialogue,
|
||||
appendToDialogue,
|
||||
finalizeDialogue,
|
||||
relabelDialogue,
|
||||
],
|
||||
);
|
||||
|
||||
/** 仅重新生成流程图（不重跑 md 报告）：模型挂了切模型后重试 / 手动重画。 */
|
||||
const regenerateFlow = useCallback(() => {
|
||||
if (!currentMdRef.current.trim() && seatDeliveries.length === 0) return;
|
||||
aborterRef.current?.abort();
|
||||
const aborter = new AbortController();
|
||||
aborterRef.current = aborter;
|
||||
void runFlowExtract(aborter);
|
||||
}, [runFlowExtract, seatDeliveries]);
|
||||
|
||||
/** Phase 3：画布手动改/删节点后回写图 + 持久化（序列化进草稿快照）。 */
|
||||
const updateFlowGraph = useCallback(
|
||||
(next: FlowGraphData) => {
|
||||
setFlowGraphState(next);
|
||||
setFlowPhase("done");
|
||||
flowActiveRef.current = true;
|
||||
const serialized = serializeFlowGraph(next);
|
||||
onSummarySavedRef.current?.({
|
||||
md: currentMdRef.current,
|
||||
threadId: liveThreadRef.current ?? "",
|
||||
generatedAt: new Date().toISOString(),
|
||||
model: selectedModel || "",
|
||||
closing: serialized,
|
||||
});
|
||||
},
|
||||
[selectedModel],
|
||||
);
|
||||
|
||||
const run = useCallback(async () => {
|
||||
if (runningRef.current) return;
|
||||
runningRef.current = true;
|
||||
@@ -1021,6 +1107,8 @@ export function useStep3Summary(
|
||||
currentMdRef.current = "";
|
||||
liveThreadRef.current = null;
|
||||
setSeatStubMessages({});
|
||||
setFlowGraphState(null);
|
||||
setFlowPhase("idle");
|
||||
|
||||
const aborter = new AbortController();
|
||||
aborterRef.current = aborter;
|
||||
@@ -1044,33 +1132,16 @@ export function useStep3Summary(
|
||||
});
|
||||
await streamOnce({ bubbleId, prompt, aborter, flowRound: flowMode });
|
||||
|
||||
// taskId/深链模式：报告生成完，**自动**续跑一轮结构化输出（流程图）。
|
||||
// 数据来源：有报告则内联报告全文、否则内联各席位交付（buildAutoFlowPrompt 处理）。
|
||||
// 故触发条件放宽为「有报告 **或** 有席位交付」——保证「无报告也能据席位交付出图」。
|
||||
// 复用同一真线程，与手动「结构化输出」走同一渲染链路。
|
||||
// taskId/深链模式：报告生成完，**自动**用后端「抽取+校验」端点生成流程图（Phase 1）。
|
||||
// 不再在总结线程里续问吐 flow-json（那会被 coordinator_prompt 的报告锚定带偏、不稳）；
|
||||
// 改走独立端点：流式抽取(实时显示) → 校验修复 → 用校验后结构化图绘制。
|
||||
// 有报告或有席位交付即触发（无报告也能据席位交付出图）。
|
||||
if (
|
||||
autoFlow &&
|
||||
!aborter.signal.aborted &&
|
||||
liveThreadRef.current &&
|
||||
(currentMdRef.current.trim() || seatDeliveries.length > 0)
|
||||
) {
|
||||
const flowBubbleId = appendStreamingDialogue({
|
||||
sender: "方案总结报告智能体",
|
||||
avatarType: "blue",
|
||||
});
|
||||
const flowPrompt = buildAutoFlowPrompt({
|
||||
chainLevels,
|
||||
coordinatorPrompt,
|
||||
reportMd: currentMdRef.current,
|
||||
seatDeliveries,
|
||||
});
|
||||
await streamOnce({
|
||||
bubbleId: flowBubbleId,
|
||||
prompt: flowPrompt,
|
||||
aborter,
|
||||
seedThreadId: liveThreadRef.current,
|
||||
flowRound: true,
|
||||
});
|
||||
await runFlowExtract(aborter);
|
||||
}
|
||||
} catch (err) {
|
||||
if (aborter.signal.aborted) return;
|
||||
@@ -1087,15 +1158,15 @@ export function useStep3Summary(
|
||||
seatDeliveries,
|
||||
consensusText,
|
||||
coordinatorPrompt,
|
||||
chainLevels,
|
||||
autoFlow,
|
||||
appendStreamingDialogue,
|
||||
finalizeDialogue,
|
||||
streamOnce,
|
||||
runFlowExtract,
|
||||
]);
|
||||
|
||||
const ask = useCallback(
|
||||
async (message: string, files?: File[]) => {
|
||||
async (message: string, files?: File[], opts?: { forcedIntent?: FlowIntent }) => {
|
||||
const text = message.trim();
|
||||
const hasFiles = !!(files && files.length > 0);
|
||||
if (!text && !hasFiles) return;
|
||||
@@ -1106,19 +1177,65 @@ export function useStep3Summary(
|
||||
abort();
|
||||
if (wasBusy) freezeStreaming("已打断");
|
||||
|
||||
// 意图引导分三档（智能体按语义最终裁决，前端只给上下文，不做硬分类）：
|
||||
// ① 显式命中结构化/流程图关键词 → 直出 flow-json、不写 md；
|
||||
// ② 没命中关键词但上一轮主产物是流程图 → 流程图上下文引导（笼统修改默认改图，太模糊则反问）；
|
||||
// ③ 普通报告语境 → 语义理解修改/提问/结构化意图，太模糊则反问。
|
||||
const flowIntent = detectFlowIntent(text);
|
||||
const flowContext = !flowIntent && flowActiveRef.current;
|
||||
const askInstruction = flowIntent
|
||||
? FLOW_ASK_INSTRUCTION
|
||||
: flowContext
|
||||
? FLOW_CONTEXT_ASK_INSTRUCTION
|
||||
: SEMANTIC_ASK_INSTRUCTION;
|
||||
// 流程图语境轮：不主动弹 md 沙箱预览（用户多半要看的是画布；真写了文件预览仍会自动打开）。
|
||||
const flowRound = flowIntent || flowContext;
|
||||
// 用户气泡**立刻上屏**（不等意图分类 / 模型返回，避免连接卡顿时显得卡）——对齐第一/二步。
|
||||
// 后续各分支都不再重复追加用户气泡。forcedIntent（澄清后点选复跑）时原气泡已在、跳过避免重复。
|
||||
if (!opts?.forcedIntent) {
|
||||
const fileNote = hasFiles ? `（附 ${files!.length} 个文件）` : "";
|
||||
appendUserDialogue((text || "(上传了参考文件)") + fileNote);
|
||||
}
|
||||
|
||||
// 意图识别（参考 ai_writing 一次性 JSON 范式）：先判断用户想 重做报告 / 改流程图 /
|
||||
// 重画流程图 / 提问 / 模糊待澄清，再分流——不再用关键词硬路由（避免「说重做报告却出流程图」）。
|
||||
// 带附件时跳过分类（附件多用于报告 Q&A/改报告）；澄清后用户点选则用 forcedIntent 跳过分类。
|
||||
// reuseBubbleId：意图分类期间先建的「调度中」气泡，定了意图后由对应分支复用（不等接口返回、像第一步）。
|
||||
let reuseBubbleId: number | null = null;
|
||||
if (text && !hasFiles) {
|
||||
let intent: FlowIntent | undefined = opts?.forcedIntent;
|
||||
let dispatchBubbleId: number | null = null;
|
||||
if (!intent) {
|
||||
// **立刻**显示「智能体调度中」气泡（不等意图接口返回），消除连接卡顿时的"卡住"感。
|
||||
dispatchBubbleId = appendStreamingDialogue({ sender: "智能体", avatarType: "blue" });
|
||||
const r = await classifyFlowIntent({
|
||||
message: text,
|
||||
hasReport: !!currentMdRef.current.trim(),
|
||||
hasFlow: !!flowGraphRef.current,
|
||||
model: selectedModel || undefined,
|
||||
});
|
||||
intent = r.intent;
|
||||
}
|
||||
if (intent === "unclear") {
|
||||
if (dispatchBubbleId != null) {
|
||||
finalizeDialogue(dispatchBubbleId, "意图不太明确，请在下方选择你想做的：", "待确认");
|
||||
}
|
||||
setPendingFlowClarification(text); // 组件渲染选项让用户自己定
|
||||
return;
|
||||
}
|
||||
if (intent === "regenerate_flow" || intent === "modify_flow") {
|
||||
const flowAborter = new AbortController();
|
||||
aborterRef.current = flowAborter;
|
||||
const cur = flowGraphRef.current;
|
||||
const reuse = dispatchBubbleId ?? undefined;
|
||||
if (intent === "modify_flow" && cur) {
|
||||
await runFlowExtract(flowAborter, { instruction: text, currentGraph: cur, bubbleId: reuse });
|
||||
} else {
|
||||
await runFlowExtract(flowAborter, { bubbleId: reuse });
|
||||
}
|
||||
return;
|
||||
}
|
||||
// regenerate_report | qa → 复用调度气泡走下方总结线程路径。
|
||||
reuseBubbleId = dispatchBubbleId;
|
||||
// intent === "regenerate_report" | "qa" → 落到下方总结线程续问路径：
|
||||
// **追加**用户气泡 + 在原线程上让智能体重写报告 / 增量改 / 回答（**不清空历史**）。
|
||||
// regenerate_report 不再走 run()（run 会 setDialogues([]) 清空，导致"覆盖聊天记录、
|
||||
// 不显示用户输入"的 bug）；改报告/重写报告由总结智能体在续问里整文件重写。
|
||||
// intent === "qa" → 落到下方总结线程问答路径（只答不改产物）。
|
||||
}
|
||||
|
||||
// 走到这里只剩 regenerate_report / qa（含带附件场景）—— 流程图操作已在上面意图分流处理。
|
||||
// 一律走报告/问答语义引导（让总结智能体在原线程上重写报告 / 增量改 / 回答），
|
||||
// **不再**在总结线程里产 flow-json（流程图统一由后端抽取端点负责）。
|
||||
const askInstruction = SEMANTIC_ASK_INSTRUCTION;
|
||||
const flowRound = false;
|
||||
|
||||
runningRef.current = true;
|
||||
setError(null);
|
||||
@@ -1153,12 +1270,15 @@ export function useStep3Summary(
|
||||
}));
|
||||
}
|
||||
|
||||
const fileNote = hasFiles ? `（附 ${files!.length} 个文件）` : "";
|
||||
appendUserDialogue((text || "(上传了参考文件)") + fileNote);
|
||||
const bubbleId = appendStreamingDialogue({
|
||||
sender: "方案总结报告智能体",
|
||||
avatarType: "blue",
|
||||
});
|
||||
// 用户气泡已在 ask 开头立刻追加。智能体气泡：复用意图分类期间的「调度中」气泡（改名），
|
||||
// 没有则新建——这样提问/重做报告时分类阶段就有「调度中」反馈、不等模型返回（像第一步）。
|
||||
let bubbleId: number;
|
||||
if (reuseBubbleId != null) {
|
||||
bubbleId = reuseBubbleId;
|
||||
relabelDialogue(bubbleId, "方案总结报告智能体");
|
||||
} else {
|
||||
bubbleId = appendStreamingDialogue({ sender: "方案总结报告智能体", avatarType: "blue" });
|
||||
}
|
||||
setPhase("streaming");
|
||||
|
||||
if (hadLiveThread) {
|
||||
@@ -1214,14 +1334,29 @@ export function useStep3Summary(
|
||||
seatDeliveries,
|
||||
consensusText,
|
||||
coordinatorPrompt,
|
||||
selectedModel,
|
||||
abort,
|
||||
freezeStreaming,
|
||||
appendUserDialogue,
|
||||
appendStreamingDialogue,
|
||||
relabelDialogue,
|
||||
finalizeDialogue,
|
||||
streamOnce,
|
||||
runFlowExtract,
|
||||
],
|
||||
);
|
||||
|
||||
/** 澄清后用户点选意图 → 用 forcedIntent 复跑（跳过意图分类）。 */
|
||||
const resolveFlowClarification = useCallback(
|
||||
(choice: FlowIntent) => {
|
||||
const msg = pendingFlowClarification;
|
||||
setPendingFlowClarification(null);
|
||||
if (!msg) return;
|
||||
void ask(msg, undefined, { forcedIntent: choice });
|
||||
},
|
||||
[pendingFlowClarification, ask],
|
||||
);
|
||||
|
||||
/** 暂停当前生成：中断在途请求 + 把流式气泡收尾为「已暂停」+ 状态回 done。 */
|
||||
const pause = useCallback(() => {
|
||||
if (!runningRef.current) return;
|
||||
@@ -1247,6 +1382,11 @@ export function useStep3Summary(
|
||||
(snap: DraftStep3SummarySnapshot) => {
|
||||
abort();
|
||||
runningRef.current = false;
|
||||
// 恢复流程图：快照 closing 里若存了 flow-json，解析成结构化图填进 flowGraph 状态，
|
||||
// 让画布直接用它绘制（也供 Phase 3 手动编辑）。解析不出则回退到对话气泡解析。
|
||||
const restoredGraph = snap.closing ? extractFlowGraph(snap.closing) : null;
|
||||
setFlowGraphState(restoredGraph);
|
||||
setFlowPhase(restoredGraph ? "done" : "idle");
|
||||
const hasMd = snap.md.trim().length > 0;
|
||||
const realThreadId = snap.threadId?.trim() || null;
|
||||
const synthThreadId = realThreadId ?? "roundtable-summary-restored";
|
||||
@@ -1330,6 +1470,12 @@ export function useStep3Summary(
|
||||
phase,
|
||||
error,
|
||||
autoFlow: !!autoFlow,
|
||||
flowGraph,
|
||||
flowPhase,
|
||||
regenerateFlow,
|
||||
setFlowGraph: updateFlowGraph,
|
||||
pendingFlowClarification,
|
||||
resolveFlowClarification,
|
||||
summaryThreadId,
|
||||
dialogues,
|
||||
seatStubMessages,
|
||||
|
||||
@@ -9,10 +9,13 @@ import assert from "node:assert/strict";
|
||||
import test from "node:test";
|
||||
|
||||
import {
|
||||
applyNodeEdit,
|
||||
deleteNodeFromGraph,
|
||||
detectFlowIntent,
|
||||
extractFlowGraph,
|
||||
hasFlowFence,
|
||||
layoutFlowGraph,
|
||||
serializeFlowGraph,
|
||||
stripFlowFence,
|
||||
} from "./flow-graph";
|
||||
|
||||
@@ -206,3 +209,51 @@ test("layoutFlowGraph：环不死循环", () => {
|
||||
assert.equal(positioned.length, 2);
|
||||
positioned.forEach((n) => assert.ok(Number.isFinite(n.x) && Number.isFinite(n.y)));
|
||||
});
|
||||
|
||||
test("serializeFlowGraph：与 extractFlowGraph 往返一致（含 group/detail/边标签）", () => {
|
||||
const text = [
|
||||
"```flow-json",
|
||||
'{"title":"方案","nodes":[',
|
||||
'{"id":"t","label":"任务X","type":"start","group":"任务","detail":"目标说明"},',
|
||||
'{"id":"p1","label":"目的1","type":"task","group":"目的","detail":"名称+理由","from":["t"]},',
|
||||
'{"id":"d","label":"判断","type":"decision","from":[{"id":"p1","label":"可行"}]}',
|
||||
"]}",
|
||||
"```",
|
||||
].join("\n");
|
||||
const g = extractFlowGraph(text)!;
|
||||
const round = extractFlowGraph(serializeFlowGraph(g))!;
|
||||
assert.equal(round.title, "方案");
|
||||
assert.deepEqual(
|
||||
round.nodes.map((n) => [n.id, n.label, n.group, n.detail]),
|
||||
g.nodes.map((n) => [n.id, n.label, n.group, n.detail]),
|
||||
);
|
||||
assert.deepEqual(round.edges, g.edges); // 含 {from:"p1",to:"d",label:"可行"}
|
||||
});
|
||||
|
||||
test("applyNodeEdit：改 label/group/detail，其余节点不变", () => {
|
||||
const g = extractFlowGraph(
|
||||
'```flow-json\n{"nodes":[{"id":"a","label":"A","type":"task","group":"目的"},{"id":"b","label":"B","type":"role","group":"行为体","from":["a"]}]}\n```',
|
||||
)!;
|
||||
const next = applyNodeEdit(g, "a", { label: "新目的", group: "目标", detail: "改后的详情" });
|
||||
const a = next.nodes.find((n) => n.id === "a")!;
|
||||
assert.equal(a.label, "新目的");
|
||||
assert.equal(a.group, "目标");
|
||||
assert.equal(a.detail, "改后的详情");
|
||||
// b 不变；边不变。
|
||||
assert.equal(next.nodes.find((n) => n.id === "b")!.label, "B");
|
||||
assert.deepEqual(next.edges, g.edges);
|
||||
// 空 group/detail → 清空（置 undefined）。
|
||||
const cleared = applyNodeEdit(g, "a", { group: " ", detail: "" });
|
||||
const a2 = cleared.nodes.find((n) => n.id === "a")!;
|
||||
assert.equal(a2.group, undefined);
|
||||
});
|
||||
|
||||
test("deleteNodeFromGraph：删节点同时删掉与它相连的所有边", () => {
|
||||
const g = extractFlowGraph(
|
||||
'```flow-json\n{"nodes":[{"id":"t","label":"任务"},{"id":"p","label":"目的","from":["t"]},{"id":"b","label":"行为体","from":["p"]}]}\n```',
|
||||
)!;
|
||||
const next = deleteNodeFromGraph(g, "p");
|
||||
assert.deepEqual(next.nodes.map((n) => n.id), ["t", "b"]);
|
||||
// t→p 和 p→b 两条边都没了（b 变孤点）。
|
||||
assert.equal(next.edges.length, 0);
|
||||
});
|
||||
@@ -281,6 +281,64 @@ export function extractFlowGraph(text: string): FlowGraphData | null {
|
||||
return { title, nodes, edges: validEdges };
|
||||
}
|
||||
|
||||
/**
|
||||
* 把结构化图序列化回一个 ```flow-json 代码块（与 extractFlowGraph 互逆）。
|
||||
* 用途：后端校验后拿到 FlowGraphData → 序列化存进草稿快照（closing），恢复时仍走
|
||||
* extractFlowGraph 重建；手动编辑后也用它回存。`from` 由 edges 按 `to` 归集（带边标签）。
|
||||
*/
|
||||
export function serializeFlowGraph(data: FlowGraphData): string {
|
||||
const fromMap = new Map<string, Array<string | { id: string; label: string }>>();
|
||||
for (const e of data.edges) {
|
||||
const list = fromMap.get(e.to) ?? [];
|
||||
list.push(e.label ? { id: e.from, label: e.label } : e.from);
|
||||
fromMap.set(e.to, list);
|
||||
}
|
||||
const nodes = data.nodes.map((n) => {
|
||||
const o: Record<string, unknown> = { id: n.id, label: n.label, type: n.type };
|
||||
if (n.group) o.group = n.group;
|
||||
if (n.detail) o.detail = n.detail;
|
||||
const from = fromMap.get(n.id);
|
||||
if (from && from.length > 0) o.from = from;
|
||||
return o;
|
||||
});
|
||||
return `${FLOW_FENCE_OPEN}\n${JSON.stringify({ title: data.title || "方案流程图", nodes })}\n\`\`\``;
|
||||
}
|
||||
|
||||
/**
|
||||
* Phase 3 画布手动编辑：改某节点的 label/group/detail（其余不变）。返回新图（不可变）。
|
||||
*/
|
||||
export function applyNodeEdit(
|
||||
data: FlowGraphData,
|
||||
id: string,
|
||||
patch: { label?: string; group?: string; detail?: string },
|
||||
): FlowGraphData {
|
||||
return {
|
||||
...data,
|
||||
nodes: data.nodes.map((n) =>
|
||||
n.id === id
|
||||
? {
|
||||
...n,
|
||||
...(patch.label !== undefined ? { label: patch.label.trim() || n.label } : {}),
|
||||
// group/detail 允许清空（传空串 → 置 undefined）。
|
||||
...(patch.group !== undefined ? { group: patch.group.trim() || undefined } : {}),
|
||||
...(patch.detail !== undefined ? { detail: patch.detail.trim() || undefined } : {}),
|
||||
}
|
||||
: n,
|
||||
),
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Phase 3 画布手动删除：删一个节点 + 所有与它相连的边。返回新图。
|
||||
*/
|
||||
export function deleteNodeFromGraph(data: FlowGraphData, id: string): FlowGraphData {
|
||||
return {
|
||||
...data,
|
||||
nodes: data.nodes.filter((n) => n.id !== id),
|
||||
edges: data.edges.filter((e) => e.from !== id && e.to !== id),
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* 识别文本（业务链编排提示 / 用户输入框消息）里是否明确要求了结构化输出 / 流程图。
|
||||
* 命中 → 总结走「流程图模式」：跳过 .md 文件，报告用文字展示 + 末尾输出 flow-json。
|
||||
|
||||
@@ -351,6 +351,22 @@ export function coerceReportData(raw: unknown): ReportData | null {
|
||||
|
||||
// ── 模型 prompt（只要 report-json，不调工具、不写文件） ───────────────────────────
|
||||
|
||||
/** ```report-json 契约说明（buildDashboardPrompt / buildDashboardPromptFromJson 共用）。 */
|
||||
const DASHBOARD_REPORT_JSON_CONTRACT = [
|
||||
"```report-json 契约（严格 JSON，全程简体中文）：",
|
||||
'{"title":"大屏主标题（≤20字）","subtitle":"一句话定位",',
|
||||
' "kpis":[{"label":"指标名","value":"数值","unit":"单位","trend":"up|down|flat"}], // 3-6 个核心指标，从内容提取或合理估算',
|
||||
' "consensus":{"headline":"一句话最终结论","points":["结论要点，3-6 条"]},',
|
||||
' "charts":[{"type":"line|bar|donut|radar","title":"图表标题","items":[{"label":"维度","value":数字}]}], // 1-3 个总览图表：line=趋势(销量/渗透率随时间)、donut=占比(份额/构成)、radar=多维对比(竞争力)、bar=排名；3-8 项/图',
|
||||
' "timeline":[{"label":"阶段名","desc":"一句话说明"}], // 执行路线 3-6 阶段',
|
||||
' "seats":[{"id":"seat-1","name":"分区/角色名","summary":"该分区结论一两句",',
|
||||
' "sections":[{"heading":"小节标题","bullets":["要点，每条≤40字"]}], // 2-4 节',
|
||||
' "metrics":[{"label":"量化指标名","value":0到100}], // 0-3 个',
|
||||
' "risks":[{"label":"风险点","level":"high|mid|low"}], // 0-4 个',
|
||||
' "dependsOn":["它承接哪些分区的产物（分区id）"]}]} // 依据内容判断依赖关系',
|
||||
"硬性要求：id 用 seat-1..seat-N；dependsOn 只引用本清单内的 id；JSON 必须可被 JSON.parse 解析（无注释、无尾逗号）。",
|
||||
].join("\n");
|
||||
|
||||
export function buildDashboardPrompt(opts: {
|
||||
intentText: string;
|
||||
seatDeliveries: Array<{ sender: string; content: string }>;
|
||||
@@ -374,24 +390,45 @@ export function buildDashboardPrompt(opts: {
|
||||
parts.push((opts.consensusText || "").trim() || "（未提供）");
|
||||
parts.push("");
|
||||
parts.push(
|
||||
[
|
||||
"```report-json 契约（严格 JSON，全程简体中文）：",
|
||||
'{"title":"大屏主标题（≤20字）","subtitle":"一句话定位",',
|
||||
' "kpis":[{"label":"指标名","value":"数值","unit":"单位","trend":"up|down|flat"}], // 3-6 个核心指标，从研讨内容提取或合理估算',
|
||||
' "consensus":{"headline":"一句话最终结论","points":["结论要点，3-6 条"]},',
|
||||
' "charts":[{"type":"line|bar|donut|radar","title":"图表标题","items":[{"label":"维度","value":数字}]}], // 1-3 个总览图表：line=趋势(销量/渗透率随时间)、donut=占比(份额/构成)、radar=多维对比(竞争力)、bar=排名；3-8 项/图',
|
||||
' "timeline":[{"label":"阶段名","desc":"一句话说明"}], // 执行路线 3-6 阶段',
|
||||
' "seats":[{"id":"seat-1","name":"席位名（与交付席位一致）","summary":"该席位结论一两句",',
|
||||
' "sections":[{"heading":"小节标题","bullets":["要点，每条≤40字"]}], // 2-4 节，提炼该席位交付',
|
||||
' "metrics":[{"label":"量化指标名","value":0到100}], // 0-3 个',
|
||||
' "risks":[{"label":"风险点","level":"high|mid|low"}], // 0-4 个',
|
||||
' "dependsOn":["它承接哪些席位的产物（席位id）"]}]} // 每个交付席位一项；依据交付内容判断依赖关系',
|
||||
"硬性要求：seats 覆盖**每一个**有交付的席位；id 用 seat-1..seat-N；dependsOn 只引用本清单内的 id；JSON 必须可被 JSON.parse 解析（无注释、无尾逗号）。",
|
||||
].join("\n"),
|
||||
DASHBOARD_REPORT_JSON_CONTRACT.replace(
|
||||
"硬性要求：id",
|
||||
"硬性要求：seats 覆盖**每一个**有交付的席位；id",
|
||||
),
|
||||
);
|
||||
return parts.join("\n");
|
||||
}
|
||||
|
||||
/**
|
||||
* 大屏绘制智能体（独立页面）的初始 prompt：输入是外部系统给的**任意结构 JSON**，
|
||||
* 让智能体**分析/转换**成大屏所需的 report-json 结构。区别于 buildDashboardPrompt
|
||||
* （输入是圆桌研讨成果），这里输入是登录时拉取的任务原始数据。
|
||||
*
|
||||
* 注：现在 JSON 接口未就绪，调用方传的是占位数据（任务名/内容等）；接口就绪后改为
|
||||
* 后台转换格式 + 智能体校验，仍由本智能体产出/校验 report-json（不后台硬抽）。
|
||||
*/
|
||||
export function buildDashboardPromptFromJson(inputJson: unknown): string {
|
||||
let inputText: string;
|
||||
try {
|
||||
inputText =
|
||||
typeof inputJson === "string" ? inputJson : JSON.stringify(inputJson, null, 2);
|
||||
} catch {
|
||||
inputText = String(inputJson ?? "");
|
||||
}
|
||||
return [
|
||||
"【结构化数据模式】本轮**不要调用任何工具、不要写任何文件、不要输出 HTML**。",
|
||||
"下面是本任务的**原始数据（JSON）**。请你**分析并转换**它，提炼成一份用于「研讨大屏」展示的结构化数据：",
|
||||
"理解原始数据的业务含义，归纳关键指标、结论、图表维度、分阶段路线，以及若干主题分区（seats，按业务维度/模块拆分，不必对应真实席位）。",
|
||||
"在回复**最末尾**输出一个完整的 ```report-json 代码块（契约见下），代码块之外只允许一两句简短说明。",
|
||||
"",
|
||||
"# 任务原始数据（JSON）",
|
||||
"```json",
|
||||
inputText.trim() || "（未提供）",
|
||||
"```",
|
||||
"",
|
||||
DASHBOARD_REPORT_JSON_CONTRACT,
|
||||
].join("\n");
|
||||
}
|
||||
|
||||
// ── 主题（2026-06-13）：跟随应用当前主题绘制 ─────────────────────────────────────
|
||||
// light = 《浅红色 UI 规范》(docs/浅红色UI规范)：白/浅灰大底、#F5222D 品牌红强调、
|
||||
// 卡片 4px 圆角、语义色 成功#189D72/警告#FF9900/错误#F5222D/信息#0079FE；
|
||||
|
||||
@@ -0,0 +1,377 @@
|
||||
/**
|
||||
* 「大屏绘制智能体」独立页面（带导航 + 侧边栏，挂在 StrategyRoutes 下）。
|
||||
*
|
||||
* 按外部 `taskId`（URL `?taskId=` / 路由 state / 登录暂存的任务详情 id）存取：
|
||||
* - 进入时按 taskId 拉取已存会话（`/api/dashboard-sessions/by-task`，**不按 user 分权**）。
|
||||
* - **已有有效结果（可解析的 report-json）** → 直接展示大屏页面（应用内 `DashboardApp`）。
|
||||
* - **没有有效结果** → 进入「生成过程」视图：自动让智能体分析登录拉取的业务 JSON
|
||||
* （现为占位适配，接口就绪后改后台转换 + 智能体校验）→ 边抽边在右侧沙箱显示，
|
||||
* **完成不自动跳**；底部输入框可随时改。
|
||||
* - 聊天记录 + 最新 report-json 防抖回存（按 taskId 共享，跨用户可见）。
|
||||
*
|
||||
* 数据抽取/聊天/沙箱/渲染全部复用 `useStep3Dashboard` + `DashboardChatPanel` +
|
||||
* `DashboardApp`，仅把输入从「圆桌研讨成果」换成「任务原始 JSON」。
|
||||
*/
|
||||
import { useEffect, useMemo, useRef, useState } from "react";
|
||||
import { useLocation, useSearchParams } from "react-router-dom";
|
||||
import {
|
||||
ArrowLeft,
|
||||
Boxes,
|
||||
Code2,
|
||||
ExternalLink,
|
||||
LayoutPanelTop,
|
||||
Loader2,
|
||||
Pencil,
|
||||
Sparkles,
|
||||
} from "lucide-react";
|
||||
|
||||
import { fetchDashboardInputJson } from "@/core/auth/dashboard-input";
|
||||
import { readCopTaskDetail } from "@/core/auth/task-deeplink";
|
||||
import { desensitizeDeep } from "@/lib/desensitize";
|
||||
import { useModels } from "@/core/models/hooks";
|
||||
import { useTheme } from "@/contexts/ThemeContext";
|
||||
import { DashboardApp } from "@/roundtable-planning/dashboard/DashboardApp";
|
||||
import { adaptReportData } from "@/roundtable-planning/dashboard/data-context";
|
||||
import { DashboardChatPanel } from "@/roundtable-planning/components/DashboardChatPanel";
|
||||
import { Step2SandboxLayout } from "@/roundtable-planning/components/Step2SandboxLayout";
|
||||
import { DASHBOARD_VIEW_STORAGE_PREFIX } from "@/roundtable-planning/pages/DashboardStandalonePage";
|
||||
import { useStep3Dashboard } from "@/roundtable-planning/hooks/useStep3Dashboard";
|
||||
import type { Step2Dialogue } from "@/roundtable-planning/hooks/useStep2Orchestration";
|
||||
import {
|
||||
buildDashboardPromptFromJson,
|
||||
parseReportJson,
|
||||
renderDashboardHtml,
|
||||
type DashboardTheme,
|
||||
} from "@/roundtable-planning/lib/report-template";
|
||||
import {
|
||||
getDashboardSessionByTask,
|
||||
putDashboardSessionByTask,
|
||||
} from "@/roundtable-planning/api/dashboard-sessions";
|
||||
|
||||
function formatNow(): string {
|
||||
const d = new Date();
|
||||
const pad = (n: number) => n.toString().padStart(2, "0");
|
||||
return `${d.getFullYear()}-${pad(d.getMonth() + 1)}-${pad(d.getDate())} ${pad(d.getHours())}:${pad(d.getMinutes())}`;
|
||||
}
|
||||
|
||||
type DashView = "chat" | "page";
|
||||
type RenderMode = "app" | "html";
|
||||
|
||||
/** 从 URL / 路由 state / 登录暂存任务详情解析 taskId。 */
|
||||
function useTaskId(): string {
|
||||
const [searchParams] = useSearchParams();
|
||||
const location = useLocation();
|
||||
return useMemo(() => {
|
||||
const fromQuery = searchParams.get("taskId")?.trim();
|
||||
if (fromQuery) return fromQuery;
|
||||
const fromState = (location.state as { taskId?: string } | null)?.taskId;
|
||||
if (fromState && String(fromState).trim()) return String(fromState).trim();
|
||||
const detail = readCopTaskDetail();
|
||||
if (detail?.id != null && String(detail.id).trim()) return String(detail.id).trim();
|
||||
return "";
|
||||
}, [searchParams, location.state]);
|
||||
}
|
||||
|
||||
export default function DashboardAgentPage() {
|
||||
const taskId = useTaskId();
|
||||
const { models: availableModels, isLoading: modelsLoading } = useModels();
|
||||
const [selectedModel, setSelectedModel] = useState("");
|
||||
const { theme } = useTheme();
|
||||
const themeMode: DashboardTheme = theme === "dark" ? "dark" : "light";
|
||||
|
||||
// 输入 JSON（登录拉取/占位）——auto-start 时由 buildInitialPrompt 闭包读取。
|
||||
const inputRef = useRef<unknown>(null);
|
||||
|
||||
const dashboard = useStep3Dashboard({
|
||||
intentReady: null,
|
||||
seatDeliveries: [],
|
||||
consensusText: "",
|
||||
selectedModel,
|
||||
themeMode,
|
||||
buildInitialPrompt: () => {
|
||||
const inp = inputRef.current;
|
||||
return inp ? buildDashboardPromptFromJson(inp) : null;
|
||||
},
|
||||
});
|
||||
|
||||
const {
|
||||
phase,
|
||||
dialogues,
|
||||
data,
|
||||
seatStubMessages,
|
||||
activeArtifactThreadId,
|
||||
busy,
|
||||
redraw,
|
||||
ask,
|
||||
abort,
|
||||
setPreviewEnabled,
|
||||
openDashboardPreview,
|
||||
start,
|
||||
hydrateTranscript,
|
||||
} = dashboard;
|
||||
|
||||
const [loading, setLoading] = useState(true);
|
||||
const [view, setView] = useState<DashView>("chat");
|
||||
const [renderMode, setRenderMode] = useState<RenderMode>("app");
|
||||
|
||||
// 默认选中第一个模型。
|
||||
useEffect(() => {
|
||||
if (availableModels.length === 0) return;
|
||||
if (!availableModels.some((m) => m.name === selectedModel)) {
|
||||
setSelectedModel(availableModels[0].name);
|
||||
}
|
||||
}, [availableModels, selectedModel]);
|
||||
|
||||
// ── 挂载：按 taskId 取会话 → 决定直显大屏 / 看生成过程（只跑一次） ──
|
||||
const didInitRef = useRef(false);
|
||||
useEffect(() => {
|
||||
if (didInitRef.current) return;
|
||||
if (!taskId) {
|
||||
setLoading(false);
|
||||
return;
|
||||
}
|
||||
didInitRef.current = true;
|
||||
let cancelled = false;
|
||||
void (async () => {
|
||||
try {
|
||||
const session = await getDashboardSessionByTask(taskId).catch(() => null);
|
||||
if (cancelled) return;
|
||||
const savedDialogues = (session?.transcript as Step2Dialogue[] | undefined) ?? [];
|
||||
const validReport = session?.reportJson && parseReportJson(session.reportJson);
|
||||
if (validReport) {
|
||||
// 有有效结果 → 恢复并直接展示大屏页面。
|
||||
hydrateTranscript(savedDialogues, session!.reportJson);
|
||||
setView("page");
|
||||
} else if (savedDialogues.length > 0) {
|
||||
// 有历史聊天但无有效结果 → 进生成过程展示历史，不自动重跑。
|
||||
hydrateTranscript(savedDialogues, session?.reportJson ?? null);
|
||||
setView("chat");
|
||||
} else {
|
||||
// 无任何记录 → 拉输入 JSON 自动开跑，让用户看生成过程（不自动跳）。
|
||||
const input = await fetchDashboardInputJson(taskId).catch(() => null);
|
||||
if (cancelled) return;
|
||||
inputRef.current = input;
|
||||
setView("chat");
|
||||
if (input) start();
|
||||
}
|
||||
} finally {
|
||||
if (!cancelled) setLoading(false);
|
||||
}
|
||||
})();
|
||||
return () => {
|
||||
cancelled = true;
|
||||
};
|
||||
}, [taskId, hydrateTranscript, start]);
|
||||
|
||||
// ── 防抖回存聊天记录 + 最新 report-json（按 taskId 共享） ──
|
||||
useEffect(() => {
|
||||
if (!taskId || loading) return;
|
||||
if (dialogues.length === 0) return;
|
||||
const t = window.setTimeout(() => {
|
||||
void putDashboardSessionByTask(taskId, {
|
||||
transcript: dialogues,
|
||||
reportJson: data ? JSON.stringify(data) : undefined,
|
||||
}).catch(() => {
|
||||
/* 回存失败不打扰用户 */
|
||||
});
|
||||
}, 800);
|
||||
return () => window.clearTimeout(t);
|
||||
}, [taskId, loading, dialogues, data]);
|
||||
|
||||
// 仅在交互区允许本流自动接管沙箱（并行流防抢）；离开/卸载时让出。
|
||||
useEffect(() => {
|
||||
setPreviewEnabled(view === "chat");
|
||||
return () => setPreviewEnabled(false);
|
||||
}, [view, setPreviewEnabled]);
|
||||
|
||||
// ── 大屏渲染数据（深度脱敏后渲染，覆盖大屏所有展示文本） ──
|
||||
const report = useMemo(() => (data ? desensitizeDeep(data) : null), [data]);
|
||||
const dashboardData = useMemo(
|
||||
() => (report ? adaptReportData(report, formatNow()) : undefined),
|
||||
[report],
|
||||
);
|
||||
const dashboardHtml = useMemo(
|
||||
() => (report ? renderDashboardHtml(report, { generatedAt: formatNow(), theme: themeMode }) : null),
|
||||
[report, themeMode],
|
||||
);
|
||||
const hasResult = !!dashboardData || !!dashboardHtml;
|
||||
|
||||
// 网页渲染：blob URL 作 iframe src（:target 导航需独立文档 URL，见 Step3DashboardMode 注释）。
|
||||
const [htmlBlobUrl, setHtmlBlobUrl] = useState<string | null>(null);
|
||||
useEffect(() => {
|
||||
if (typeof window === "undefined" || !dashboardHtml) {
|
||||
setHtmlBlobUrl(null);
|
||||
return;
|
||||
}
|
||||
const url = URL.createObjectURL(new Blob([dashboardHtml], { type: "text/html;charset=utf-8" }));
|
||||
setHtmlBlobUrl(url);
|
||||
return () => URL.revokeObjectURL(url);
|
||||
}, [dashboardHtml]);
|
||||
|
||||
const isStreaming =
|
||||
phase === "creating" || phase === "streaming" || dialogues.some((d) => d.streaming === true);
|
||||
|
||||
const handleOpenNewWindow = () => {
|
||||
if (typeof window === "undefined") return;
|
||||
if (renderMode === "html" && dashboardHtml) {
|
||||
const blobUrl = URL.createObjectURL(new Blob([dashboardHtml], { type: "text/html;charset=utf-8" }));
|
||||
window.open(blobUrl, "_blank", "noopener,noreferrer");
|
||||
window.setTimeout(() => URL.revokeObjectURL(blobUrl), 60_000);
|
||||
return;
|
||||
}
|
||||
const key = `${DASHBOARD_VIEW_STORAGE_PREFIX}${Date.now()}`;
|
||||
try {
|
||||
for (let i = window.localStorage.length - 1; i >= 0; i -= 1) {
|
||||
const k = window.localStorage.key(i);
|
||||
if (k && k.startsWith(DASHBOARD_VIEW_STORAGE_PREFIX)) window.localStorage.removeItem(k);
|
||||
}
|
||||
if (dashboardData) window.localStorage.setItem(key, JSON.stringify(dashboardData));
|
||||
} catch {
|
||||
/* localStorage 不可用：新窗口回落 DEMO */
|
||||
}
|
||||
const base = `${window.location.origin}${window.location.pathname}`;
|
||||
window.open(`${base}#/roundtable-dashboard?k=${encodeURIComponent(key)}`, "_blank", "noopener,noreferrer");
|
||||
};
|
||||
|
||||
if (!taskId) {
|
||||
return (
|
||||
<div className="flex h-full min-h-0 items-center justify-center p-8 text-center text-sm text-slate-500">
|
||||
<div>
|
||||
<Sparkles className="mx-auto mb-3 h-8 w-8 text-primary/40" />
|
||||
缺少任务标识（taskId）。请通过带 <code className="px-1">?taskId=</code> 的链接进入大屏绘制页。
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
return (
|
||||
<div className="flex h-full min-h-0 flex-col text-slate-800">
|
||||
{/* 顶栏 */}
|
||||
<div className="shrink-0 flex flex-wrap items-center justify-between gap-3 border-b border-slate-100 bg-gradient-to-r from-slate-50 to-white px-6 py-3">
|
||||
<div className="flex items-center gap-2.5">
|
||||
<Sparkles className="h-4.5 w-4.5 text-primary" />
|
||||
<h3 className="text-sm font-black tracking-tight text-slate-900">大屏绘制智能体</h3>
|
||||
<span className="inline-flex items-center gap-1.5 rounded-full border border-red-200/50 bg-[var(--brand-200)]/60 px-2 py-0.5 text-[10px] font-bold text-primary">
|
||||
{(phase === "creating" || phase === "streaming") && <Loader2 className="h-3 w-3 animate-spin" />}
|
||||
{view === "chat" ? "生成过程" : "大屏页面"}
|
||||
</span>
|
||||
</div>
|
||||
|
||||
<div className="flex items-center gap-2">
|
||||
{view === "page" ? (
|
||||
<>
|
||||
<div className="inline-flex items-center gap-0.5 rounded-xl border border-slate-200 bg-slate-50 p-0.5">
|
||||
<button
|
||||
type="button"
|
||||
onClick={() => setRenderMode("app")}
|
||||
title="用应用内 React 组件渲染（Tab 切换、动画、席位钻取）"
|
||||
className={`flex items-center gap-1 rounded-lg px-2.5 py-1 text-[10.5px] font-bold transition ${
|
||||
renderMode === "app"
|
||||
? "border border-slate-200 bg-white text-primary shadow-sm"
|
||||
: "text-slate-500 hover:text-slate-700"
|
||||
}`}
|
||||
>
|
||||
<Boxes className="h-3.5 w-3.5" />
|
||||
应用渲染
|
||||
</button>
|
||||
<button
|
||||
type="button"
|
||||
onClick={() => setRenderMode("html")}
|
||||
disabled={!dashboardHtml}
|
||||
title={dashboardHtml ? "用自包含 HTML 大屏渲染（与新窗口/下载一致）" : "尚无已生成的 HTML，先完成一次绘制"}
|
||||
className={`flex items-center gap-1 rounded-lg px-2.5 py-1 text-[10.5px] font-bold transition disabled:cursor-not-allowed disabled:opacity-40 ${
|
||||
renderMode === "html"
|
||||
? "border border-slate-200 bg-white text-primary shadow-sm"
|
||||
: "text-slate-500 hover:text-slate-700"
|
||||
}`}
|
||||
>
|
||||
<Code2 className="h-3.5 w-3.5" />
|
||||
网页渲染
|
||||
</button>
|
||||
</div>
|
||||
<button
|
||||
type="button"
|
||||
onClick={() => setView("chat")}
|
||||
title="回到生成过程，加载聊天记录继续问答修改"
|
||||
className="flex items-center gap-1 rounded-xl border border-slate-200 bg-white px-3 py-1.5 text-[10.5px] font-bold text-slate-700 transition hover:bg-slate-50"
|
||||
>
|
||||
<Pencil className="h-3.5 w-3.5 text-slate-500" />
|
||||
编辑
|
||||
</button>
|
||||
<button
|
||||
type="button"
|
||||
onClick={handleOpenNewWindow}
|
||||
title={renderMode === "html" ? "新标签页打开自包含 HTML 大屏" : "新标签页打开应用内 React 大屏（全屏）"}
|
||||
className="flex items-center gap-1 rounded-xl border border-slate-200 bg-white px-3 py-1.5 text-[10.5px] font-bold text-slate-700 transition hover:bg-slate-50"
|
||||
>
|
||||
<ExternalLink className="h-3.5 w-3.5 text-slate-500" />
|
||||
新窗口打开
|
||||
</button>
|
||||
</>
|
||||
) : (
|
||||
hasResult && (
|
||||
<button
|
||||
type="button"
|
||||
onClick={() => setView("page")}
|
||||
title="查看已生成的大屏页面"
|
||||
className="flex items-center gap-1 rounded-xl border border-slate-200 bg-white px-3 py-1.5 text-[10.5px] font-bold text-slate-700 transition hover:bg-slate-50"
|
||||
>
|
||||
<LayoutPanelTop className="h-3.5 w-3.5 text-slate-500" />
|
||||
查看大屏
|
||||
</button>
|
||||
)
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* 主体 */}
|
||||
{loading ? (
|
||||
<div className="flex flex-1 items-center justify-center gap-2 text-sm text-slate-500">
|
||||
<Loader2 className="h-4 w-4 animate-spin text-primary" />
|
||||
正在加载任务大屏…
|
||||
</div>
|
||||
) : view === "page" ? (
|
||||
renderMode === "html" && htmlBlobUrl ? (
|
||||
<iframe
|
||||
title="任务研讨大屏"
|
||||
src={htmlBlobUrl}
|
||||
className="min-h-0 w-full flex-1 border-0 bg-white"
|
||||
sandbox="allow-scripts allow-popups allow-downloads allow-same-origin"
|
||||
/>
|
||||
) : (
|
||||
<div className="min-h-0 flex-1 overflow-auto">
|
||||
<DashboardApp data={dashboardData} />
|
||||
</div>
|
||||
)
|
||||
) : (
|
||||
<div className="min-h-0 flex-1 p-3">
|
||||
<section className="h-full min-h-0 overflow-hidden rounded-2xl border border-slate-200/60 bg-white">
|
||||
<Step2SandboxLayout
|
||||
activeThreadId={activeArtifactThreadId}
|
||||
isStreaming={isStreaming}
|
||||
seatMessages={activeArtifactThreadId ? seatStubMessages[activeArtifactThreadId] : undefined}
|
||||
>
|
||||
<DashboardChatPanel
|
||||
phase={phase}
|
||||
dialogues={dialogues}
|
||||
busy={busy}
|
||||
error={dashboard.error}
|
||||
onAsk={ask}
|
||||
onRedraw={redraw}
|
||||
onPause={abort}
|
||||
onOpenPreview={openDashboardPreview}
|
||||
hasResult={hasResult}
|
||||
selectedModel={selectedModel}
|
||||
availableModels={availableModels}
|
||||
modelsLoading={modelsLoading}
|
||||
setSelectedModel={setSelectedModel}
|
||||
subtitle="分析任务原始数据，提炼为大屏结构化数据（右侧实时显示抽取过程）"
|
||||
doneHint="数据已就绪，点右上「查看大屏」查看。"
|
||||
/>
|
||||
</Step2SandboxLayout>
|
||||
</section>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
@@ -1012,8 +1012,9 @@ export default function RoundtablePlanningPage() {
|
||||
.filter((g) => g.length > 0),
|
||||
[orchestrationPlan],
|
||||
);
|
||||
// taskId/深链模式：进第三步生成 md 后自动续跑结构化输出（流程图）。普通手动会商不自动。
|
||||
const autoSummaryFlow = !!prefillRef.current?.taskId;
|
||||
// 第三步首次生成报告后即**自动**续跑结构化输出（流程图）——不再限 taskId 深链，
|
||||
// 任何会商进第三步首次出报告都自动出流程图（用户 2026-06-17 要求）。
|
||||
const autoSummaryFlow = true;
|
||||
|
||||
const summaryFlow = useStep3Summary({
|
||||
intentReady: step1.intentReady,
|
||||
@@ -2500,22 +2501,31 @@ function RoundtableEntryRail({
|
||||
该业务链暂无智能体席位
|
||||
</p>
|
||||
) : (
|
||||
<div className="flex flex-col items-center gap-1.5">
|
||||
{stageGroups.map((stage, si) => (
|
||||
<div
|
||||
key={si}
|
||||
className="flex flex-col items-center gap-1"
|
||||
>
|
||||
{si > 0 && (
|
||||
<ChevronDown className="h-3 w-3 text-neutral-300" />
|
||||
)}
|
||||
{stage.length > 1 && (
|
||||
<span className="rounded-full bg-primary/5 px-1.5 text-[8.5px] font-semibold text-primary">
|
||||
并行 {stage.length}
|
||||
</span>
|
||||
)}
|
||||
<div className="flex flex-wrap items-center justify-center gap-1.5">
|
||||
{stage.map((seat, sj) => {
|
||||
<div className="flex flex-row flex-wrap items-center justify-center gap-1">
|
||||
{stageGroups.flatMap((stage, si) => {
|
||||
// 横向编排：阶段从左到右排列，阶段之间用 → 串联；并行阶段的
|
||||
// 席位在该列内竖向堆叠（宽度可控，符合「同层并行·层间串行」示意）。
|
||||
const items: React.ReactNode[] = [];
|
||||
if (si > 0) {
|
||||
items.push(
|
||||
<ChevronRight
|
||||
key={`arrow-${si}`}
|
||||
className="h-3 w-3 shrink-0 text-neutral-300"
|
||||
/>,
|
||||
);
|
||||
}
|
||||
items.push(
|
||||
<div
|
||||
key={`stage-${si}`}
|
||||
className="flex flex-col items-center gap-1"
|
||||
>
|
||||
{stage.length > 1 && (
|
||||
<span className="rounded-full bg-primary/5 px-1.5 text-[8.5px] font-semibold text-primary">
|
||||
并行 {stage.length}
|
||||
</span>
|
||||
)}
|
||||
<div className="flex flex-col items-center gap-1.5">
|
||||
{stage.map((seat, sj) => {
|
||||
const palette =
|
||||
SEAT_CHIP_PALETTE[
|
||||
seatColorIndex++ %
|
||||
@@ -2550,10 +2560,12 @@ function RoundtableEntryRail({
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
})}
|
||||
</div>
|
||||
</div>
|
||||
))}
|
||||
})}
|
||||
</div>
|
||||
</div>,
|
||||
);
|
||||
return items;
|
||||
})}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
|
||||
@@ -254,6 +254,7 @@ FastAPI application on port 8001 with health check at `GET /health`. Set `GATEWA
|
||||
| **Light Apps** (`/api/light-apps`) | Light-app registry (????? / ????), **global/shared** (no user scoping; `created_by`/`updated_by` audit-only). Two kinds: `window_open` (launched in a new tab from the ???? card grid) and `iframe` (embedded under a parent sidebar menu via `mount_location` and dynamically injected into the sidebar). Both carry `route_params` ? a list of `{key, paramType: login\|theme, source, customValue, themeValue}` descriptors the **frontend** resolves at launch time (login params read from `localStorage.userInfo`; theme params use a literal value) and appends to `url` as query string. `GET /` - list (filters `app_type`/`mount_location`/`enabled_only`/`search`); `GET /{app_id}` - read one (both open to any authenticated user); `POST /`, `PUT /{app_id}`, `DELETE /{app_id}` - **admin only**; iframe apps must carry a `mount_location` (400 otherwise); the unique `name` collides as HTTP 409. Backed by `deerflow.persistence.light_apps` (table `light_apps`, auto-created by `create_all`); store wired in `deps.py` as `app.state.light_app_store`. Tests: `tests/test_light_apps.py`. |
|
||||
| **Menu Overrides** (`/api/menu-overrides`) | Sidebar-menu overrides (菜单管理), **global/shared** (no user scoping; `updated_by` audit-only). The static menu tree (icons/routes/`adminOnly`) lives in the **frontend** code (`core/page-layout/sidebar-menu.ts`); iframe light-apps are injected at render time. This router persists a per-node **overlay** the frontend merges onto that tree: `parent_id` (re-parent, moves the whole subtree; NULL = top level), `sort_order` (reorder among siblings), `label` (rename; NULL keeps code label), `disabled` (停用 — hides the node **and its subtree**). A node with no row renders exactly as code defines it, so new code menus / newly-registered iframe apps appear automatically and are immediately configurable. `GET /` - list all overrides (open to any authenticated user); `PUT /` - **admin only**, atomically **replaces the whole set** (`{overrides: [...]}`); rejects re-parenting that would exceed **3 levels** or form a cycle (`_validate_depth`, HTTP 400). Backed by `deerflow.persistence.menu_overrides` (table `menu_overrides`, auto-created by `create_all`); store wired in `deps.py` as `app.state.menu_override_store`. Frontend: merge logic `core/page-layout/menu-overrides.ts` (`applyMenuOverrides` in `use-sidebar-menu.ts`), client `strategy-components/api/menu-overrides.ts`, admin page `pages/MenuManagementPage.tsx` (@dnd-kit drag-reorder/re-parent + 「移动到」parent selector + ↑↓ arrows + 改名/停用), entry in the bottom-left settings dropdown (`components/page-sidebar.tsx`, admin-only). Tests: `tests/test_menu_overrides.py`, `frontend-web/src/core/page-layout/menu-overrides.test.ts`. |
|
||||
| **Sensitive Words** (`/api/sensitive-words`) | Sensitive-word / desensitize map (敏感词管理 / 脱敏词), **global/shared** (no user scoping; `updated_by` audit-only). Each row maps an original `word` → `replacement`, with `enabled` (停用 parks a word without deleting) + `sort_order`. The **frontend** ships a seed map in code (`src/lib/desensitize.ts` `SEED_DESENSITIZE_MAP`) used as the default on a fresh deployment; once an admin saves, this table is the source of truth and the frontend loads it at startup (`DesensitizeWordsLoader` → `replaceDesensitizeMap`, **enabled** rows only). `GET /` - list all words (open to any authenticated user — every page desensitizes); `PUT /` - **admin OR the `lqq` account** (`_require_manager`: `system_role == "admin"` or email `@`-prefix `== "lqq"`), atomically **replaces the whole set** (`{words: [...]}`, de-duped by `word`, last wins). Backed by `deerflow.persistence.sensitive_words` (table `sensitive_words`, PK `word` `String(191)`, auto-created by `create_all`); store wired in `deps.py` as `app.state.sensitive_word_store`. Frontend: client `strategy-components/api/sensitive-words.ts`, page `pages/SensitiveWordsPage.tsx` (route `/page/strategy/admin/sensitive-words`, entry in the bottom-left settings dropdown — **gated on username `lqq`, not admin role**, present in both sidebar modes; 「导入种子词」 merges missing seed entries). Tests: `tests/test_sensitive_words.py`. |
|
||||
| **Dashboard Sessions** (`/api/dashboard-sessions`) | 「大屏绘制智能体」独立页面会话，**按外部 `task_id` 存取，不按 user 分权**（open read + shared writes；`user_id` audit-only）。一个 task 一行（upsert）：`transcript`（聊天记录 JSON）+ `report_json`（最新结构化数据，可解析 = 「有效结果」）。`GET /by-task/{task_id}` - 取该 task 会话（无则 404）；`PUT /by-task/{task_id}` - upsert（`{transcript?, report_json?}`，只覆盖传入项）。Backed by `deerflow.persistence.dashboard_sessions`（table `dashboard_sessions`, auto-created by `create_all`，无迁移）；store wired in `deps.py` as `app.state.dashboard_session_store`。前端独立页 `roundtable-planning/pages/DashboardAgentPage.tsx`（路由 `/page/strategy/dashboard-agent?taskId=`，侧边栏「问答管理 → 大屏绘制」）让智能体分析登录拉取的业务 JSON → report-json → 渲染 `DashboardApp`；输入 seam `core/auth/dashboard-input.ts`（JSON 接口未就绪时回退 copTaskDetail 占位）。Tests: `tests/test_dashboard_sessions.py`。 |
|
||||
| **Knowledge** (`/api/knowledge`) | Product knowledge base, **global/shared** (no user isolation; writes record `created_by`/`updated_by` for audit). `POST /ingest/thread/{thread_id}` - sediment one thread into a note (reads messages from the checkpointer, `threads:read` owner-checked); `POST /ingest/threads/batch` - batch-sediment the caller's recent threads (`skip_existing` via `.manifest.json` dedup). **Defaults to `background=true`**: the slow per-thread extraction loop is deferred to a FastAPI background task and the endpoint returns `{queued:true, processed:N}` immediately (each thread's transcript is truncated to `max_input_chars` before the LLM call). Running the loop inline for a large batch outlives the nginx gateway timeout (504), which the frontend's `parseResponse` surfaces as a generic "Request failed"; `background=false` keeps the blocking path (full per-thread `created/skipped/failed` tally) for small batches/scripts. Shared loop: `_run_batch_ingest`. **Optional model selection**: both ingest endpoints accept `model_name` (overrides `knowledge.extract_model_name` for that capture; threaded through `KnowledgeService.capture_thread` ? `extract_thread_llm_multi`). **Model-error surfacing**: when the chosen model produces nothing usable (timeout / API error / non-JSON) capture still **falls back to rule-based extraction and lands a draft** (`llm_fallback:true`); the single-thread (non-background) response and the batch response (`llm_fallback` count) carry this so the UI tells the user "???????". The "??????" chat button stays background/fire-and-forget (passes `model_name`, no per-save error toast); the batch-import dialog is synchronous and shows the fallback count. `POST /ingest/file` - **import an uploaded file** (multipart: `file` + optional `title`/`tags`/`status`/`model_name`) into wiki note(s): the file is converted to Markdown (md/txt read directly, pdf/word/ppt/excel via `convert_file_to_markdown`), distilled by the same wiki-capture LLM pipeline as thread capture (`source_type="file"`), and falls back to a verbatim **draft** note when the model returns nothing usable (`llm_fallback:true`); `POST /ingest/search` - sediment search/tool results as a reference note; `POST /notes` - manual note; `GET /notes` (keyword/tag/source_type/status + pagination); `GET/PUT/DELETE /notes/{id}` (DELETE is soft = archive, `?hard=true` removes the vault file); `POST /search` - `mode=keyword\|vector\|hybrid` (vector?keyword fallback when no embedding endpoint); `GET /notes/{id}/versions` - edit history; `GET /duplicates` + `POST /merge` - dedup; `POST /reindex` - rebuild vector index; `GET /graph` - product graph data (notes + entities, graph-blacklist filtered); `GET/POST /graph/blacklist` + `DELETE /graph/blacklist/{id}` - graph node blacklist (labels = entity names / note titles, case-insensitive match; blacklisted nodes and their edges are dropped from `/graph` and the wiki-export artifacts; table `knowledge_graph_blacklist`, auto-created); `POST /export/graph` + `GET /export/files/{graph.json\|graph.html\|graph.graphml\|cypher.txt}` - obsidian-wiki `wiki-export` artifacts (self-contained `graph.html` for iframe); `POST /resolve` `{targets:[?]}` + `GET /notes/{id}/wikilinks` - resolve `[[wikilink]]` targets to a note id / vault page / missing (powers clickable wikilinks); `GET /vault-page?path=` - read a raw vault meta/hub Markdown page (no DB note). `GET/POST /folders` + `PUT/DELETE /folders/{id}` - user-managed directories (notes carry a `folder` path; `GET /notes?folder=` filters a dir + sub-dirs); `GET/POST /extract-templates` + `GET/PUT/DELETE /extract-templates/{id}` - editable extraction-prompt templates (thread/document ingest accept `template_id`; capture/PUT-note accept `folder`). Backed by the `deerflow.knowledge` module (see below). |
|
||||
|
||||
Proxied through nginx: `/api/langgraph/*` ? LangGraph, all other `/api/*` ? Gateway.
|
||||
|
||||
@@ -37,6 +37,7 @@ from app.gateway.routers import (
|
||||
channels,
|
||||
checkpoint_migration,
|
||||
feedback,
|
||||
dashboard_sessions,
|
||||
html_page_favorites,
|
||||
intent,
|
||||
knowledge,
|
||||
@@ -57,6 +58,7 @@ from app.gateway.routers import (
|
||||
public_leaderboard,
|
||||
public_scheduled_tasks,
|
||||
recommend,
|
||||
flow_extract,
|
||||
recommended_questions,
|
||||
roundtable_chains,
|
||||
roundtable_draft_shares,
|
||||
@@ -1333,6 +1335,9 @@ This gateway provides custom endpoints for models, MCP configuration, skills, an
|
||||
# Roundtable-planning drafts + recommendation history at /api/roundtable-drafts
|
||||
app.include_router(roundtable_drafts.router)
|
||||
|
||||
# Step3 流程图结构化抽取 + 校验 (SSE) at /api/flow-extract
|
||||
app.include_router(flow_extract.router)
|
||||
|
||||
# Roundtable-planning business chains at /api/roundtable-chains
|
||||
app.include_router(roundtable_chains.router)
|
||||
|
||||
@@ -1348,6 +1353,9 @@ This gateway provides custom endpoints for models, MCP configuration, skills, an
|
||||
# Sensitive-word map (敏感词管理 / 脱敏词) at /api/sensitive-words
|
||||
app.include_router(sensitive_words.router)
|
||||
|
||||
# 大屏绘制智能体独立页面会话（按 taskId 存取，不分权）at /api/dashboard-sessions
|
||||
app.include_router(dashboard_sessions.router)
|
||||
|
||||
# Standalone parallel multi-agent panel: fan-out streaming + saved tasks
|
||||
app.include_router(parallel_agents.router)
|
||||
app.include_router(parallel_agent_tasks.router)
|
||||
|
||||
@@ -247,6 +247,10 @@ async def langgraph_runtime(app: FastAPI) -> AsyncGenerator[None, None]:
|
||||
|
||||
app.state.sensitive_word_store = make_sensitive_word_store(sf)
|
||||
|
||||
from deerflow.persistence.dashboard_sessions import make_dashboard_session_store
|
||||
|
||||
app.state.dashboard_session_store = make_dashboard_session_store(sf)
|
||||
|
||||
from deerflow.persistence.parallel_agent_tasks import make_parallel_agent_task_store
|
||||
|
||||
app.state.parallel_agent_task_store = make_parallel_agent_task_store(sf)
|
||||
|
||||
@@ -15,7 +15,6 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import uuid
|
||||
@@ -28,10 +27,12 @@ from langgraph.checkpoint.base import empty_checkpoint
|
||||
from app.gateway.roundtable_run_policy import roundtable_run_policy
|
||||
from app.gateway.routers._roundtable_seed import (
|
||||
COORDINATOR_AGENT_ID,
|
||||
DASHBOARD_AGENT_ID,
|
||||
REPORT_AGENT_ID,
|
||||
SUMMARY_AGENT_ID,
|
||||
ensure_roundtable_functional_agents,
|
||||
)
|
||||
from deerflow.agents.roundtable_orchestrator.report_json import extract_report_json
|
||||
from deerflow.agents.roundtable_orchestrator.types import (
|
||||
LeaderTurn,
|
||||
ReportResult,
|
||||
@@ -331,30 +332,24 @@ _SUMMARY_RETRY_MISSING = (
|
||||
"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。"
|
||||
)
|
||||
|
||||
_DASHBOARD_FENCE = "```report-json"
|
||||
_DASHBOARD_RETRY = (
|
||||
"你刚才没有输出有效的 ```report-json 代码块（或其中的 JSON 无法解析）。"
|
||||
"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"
|
||||
"（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；"
|
||||
"你刚才没有输出可用的大屏数据（缺少 ```report-json 代码块，或其中的 JSON 无法解析、"
|
||||
"字段不符合契约）。请重新**逐席位分析**研讨成果，在回复**最末尾**输出一个完整且**严格合法**"
|
||||
"的 ```report-json 代码块：标准 JSON（双引号、无注释、无尾逗号、数字不加引号），seats 覆盖"
|
||||
"每一个有交付的席位，dependsOn 只引用本清单内的席位 id；代码块之外只允许一两句说明；"
|
||||
"不要调用任何工具、不要写文件、不要输出 HTML。"
|
||||
)
|
||||
|
||||
|
||||
def _extract_report_json(text: str) -> str | None:
|
||||
"""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）。"""
|
||||
start = text.rfind(_DASHBOARD_FENCE)
|
||||
if start < 0:
|
||||
return None
|
||||
after = text[start + len(_DASHBOARD_FENCE) :]
|
||||
close = after.find("```")
|
||||
body = (after[:close] if close >= 0 else after).strip()
|
||||
if not body:
|
||||
return None
|
||||
try:
|
||||
json.loads(body)
|
||||
except Exception: # noqa: BLE001
|
||||
return None
|
||||
return body
|
||||
"""从模型回复里健壮抽取大屏 report-json，返回**归一化后的** JSON 字符串（失败 → None）。
|
||||
|
||||
旧实现是「找最后一个 ```report-json 围栏 + json.loads」的简单粗暴抽取，模型一旦用
|
||||
```json / 裸 JSON / 流式截断 / 尾逗号 就直接失败（即用户反馈的「大屏多页抽取 json 又失败」）。
|
||||
现委托给 harness 的 :func:`extract_report_json`（多策略抽取 + schema 归一化，与前端
|
||||
``report-template.ts`` 同源），返回的 JSON 字段齐全、可被前端直接 JSON.parse。
|
||||
"""
|
||||
return extract_report_json(text)
|
||||
|
||||
|
||||
def _html_complete(html: str | None) -> bool:
|
||||
@@ -569,19 +564,25 @@ class InProcessRoundtableGateway:
|
||||
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）。"""
|
||||
"""大屏多页的结构化数据：进程内驱动**专职**「大屏多页数据智能体」
|
||||
(``roundtable-dashboard``) 逐席位分析研讨成果，在对话里输出 ```report-json。
|
||||
|
||||
与旧版的差异（修复「大屏多页抽取 json 又失败」）：
|
||||
- 用**专职** dashboard 智能体（SOUL 专讲「逐席位分析 → 结构化数据」）而非复用
|
||||
画 HTML 的 report 智能体；``dashboard`` policy **禁用一切工具**，杜绝弱模型跑去写文件。
|
||||
- 抽取走 :func:`_extract_report_json` → harness 健壮多策略抽取 + schema 归一化，
|
||||
容忍 ```json / 裸 JSON / 流式截断，并保证返回的 JSON 字段齐全可直接渲染。
|
||||
- 仍保留同线程纠偏重试（缺失/坏 JSON 时追加 ``_DASHBOARD_RETRY``）。"""
|
||||
ensure_roundtable_functional_agents()
|
||||
thread_id = await _create_thread(self._app)
|
||||
policy = roundtable_run_policy("report")
|
||||
policy = roundtable_run_policy("dashboard")
|
||||
message = prompt
|
||||
for attempt in range(1 + _ARTIFACT_RETRY_LIMIT):
|
||||
messages = await _run_agent_turn(
|
||||
self._app,
|
||||
user_id=self._user_id,
|
||||
thread_id=thread_id,
|
||||
agent_name=REPORT_AGENT_ID,
|
||||
agent_name=DASHBOARD_AGENT_ID,
|
||||
message=message,
|
||||
model=model or self._model,
|
||||
excluded_tools=policy.excluded_tools,
|
||||
|
||||
@@ -119,6 +119,29 @@ _REPORT_EXCLUDED: list[str] = [*_SEAT_EXCLUDED, "str_replace", "read_peer_delive
|
||||
# 完全一致——可写文件(write_file 整文件重写),禁 str_replace(整文件重写才能让右侧 md 预览
|
||||
# 实时刷新)/bash/联网/派活/呈现文件/看图/读前序席位。单列出来便于以后独立调优。
|
||||
_SUMMARY_EXCLUDED: list[str] = [*_SEAT_EXCLUDED, "str_replace", "read_peer_delivery"]
|
||||
# dashboard：Step3「大屏多页」数据智能体(roundtable-dashboard)——纯「研讨成果 → report-json」
|
||||
# 提炼器,**禁用一切工具**(同 leader 的黑名单全集)。它只需读 prompt、分析各席位、在对话里
|
||||
# 输出一个 report-json 代码块;留任何干活类工具(尤其 write_file / bash)只会让弱模型跑偏去
|
||||
# 写文件而不输出 JSON(report agent 上已实锤这类行为)。thinking + medium:需要逐席位分析归纳。
|
||||
_DASHBOARD_EXCLUDED: list[str] = [
|
||||
"web_search",
|
||||
"view_image",
|
||||
"bash",
|
||||
"ls",
|
||||
"read_file",
|
||||
"write_file",
|
||||
"str_replace",
|
||||
"task",
|
||||
"write_todos",
|
||||
"memory",
|
||||
"hindsight_recall",
|
||||
"hindsight_reflect",
|
||||
"hindsight_retain",
|
||||
"read_peer_delivery",
|
||||
"agent_orchestration",
|
||||
"ask_clarification",
|
||||
"present_files",
|
||||
]
|
||||
|
||||
# leader 内禀的 skill_stop：``agent_orchestration`` 调用后停图。调用方可在此基础上
|
||||
# 再叠加自己的 skill_stop（见 ``merge_skill_stop_names``）。
|
||||
@@ -181,8 +204,15 @@ def roundtable_run_policy(role: str) -> RoundtableRunPolicy:
|
||||
thinking_enabled=True,
|
||||
reasoning_effort="medium",
|
||||
)
|
||||
if role == "dashboard":
|
||||
return RoundtableRunPolicy(
|
||||
excluded_tools=list(_DASHBOARD_EXCLUDED),
|
||||
skill_stop_names=[],
|
||||
thinking_enabled=True,
|
||||
reasoning_effort="medium",
|
||||
)
|
||||
raise ValueError(
|
||||
f"unknown roundtable role: {role!r} (expected leader/seat/seat_parallel/report/summary)"
|
||||
f"unknown roundtable role: {role!r} (expected leader/seat/seat_parallel/report/summary/dashboard)"
|
||||
)
|
||||
|
||||
|
||||
|
||||
@@ -49,8 +49,8 @@ Python 三引号字符串,导致「磁盘上的验证版」与「代码里的内
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
from collections.abc import Iterable
|
||||
from pathlib import Path
|
||||
from typing import Iterable
|
||||
|
||||
from deerflow.config.paths import get_paths
|
||||
|
||||
@@ -67,6 +67,17 @@ REPORT_AGENT_ID = "roundtable-report"
|
||||
# 总结报告(写入 outputs/方案总结报告.md),并按业务链总控编排提示组织。与 report 并存
|
||||
# 的另一内置单例(report 画 HTML 看板,summary 写 Markdown 报告 + 支持问答/增量改)。
|
||||
SUMMARY_AGENT_ID = "roundtable-summary"
|
||||
# Step 3「大屏多页」用的结构化数据智能体:读各席位交付 + 共识,逐席位分析并提炼成
|
||||
# 一份大屏展示用的结构化数据(report-json),供前端按模板渲染成多页大屏。**不调任何工具、
|
||||
# 不写文件、不输出 HTML**——只在对话里输出一个严格合法的 ```report-json 代码块。与 report
|
||||
# (画 HTML 看板)/summary(写 Markdown)并存的第三个 Step3 内置单例,把「大屏抽取」从
|
||||
# 旧版「复用 report agent + 简单粗暴字符串抽取」改为专职智能体 + 健壮抽取(见
|
||||
# roundtable_orchestrator.report_json)。
|
||||
DASHBOARD_AGENT_ID = "roundtable-dashboard"
|
||||
# Step 3「结构化抽取」用的智能体：读「方案总结报告」/ 各席位交付，分析层级结构 → 产出 flow-json
|
||||
# （也负责按指令改图 / 校验修复）。供 /api/flow-extract 调用；以后可在它身上配置「入库技能」，
|
||||
# 让它分析出结构化结果后直接入库。与 report/summary 并存的另一内置单例。
|
||||
STRUCTURE_AGENT_ID = "roundtable-structure"
|
||||
|
||||
# 验证版 SOUL.md + config.yaml 的源码资源目录(随 git 发布)。每个子目录名即 agent id,
|
||||
# 内含该 agent 的 config.yaml 与 SOUL.md,是 seeder 落盘时字节级复制的来源。
|
||||
@@ -74,13 +85,16 @@ _ASSETS_DIR = Path(__file__).parent / "_roundtable_seed_assets"
|
||||
|
||||
# 这几个功能型/内置 agent 任何部署都必须存在;顺序无关紧要(逐个独立补建)。
|
||||
# 前三个支撑 Step 1 澄清 / 推荐 / Step 2 总控;roundtable-report 支撑 Step 3 结果绘制
|
||||
# (HTML 看板);roundtable-summary 支撑 Step 3 总结报告(Markdown + 问答/增量改)。
|
||||
# (HTML 看板);roundtable-summary 支撑 Step 3 总结报告(Markdown + 问答/增量改);
|
||||
# roundtable-dashboard 支撑 Step 3 大屏多页结构化数据(report-json + 问答/增量改)。
|
||||
_FUNCTIONAL_AGENT_IDS: tuple[str, ...] = (
|
||||
INTENT_AGENT_ID,
|
||||
RECOMMENDER_AGENT_ID,
|
||||
COORDINATOR_AGENT_ID,
|
||||
REPORT_AGENT_ID,
|
||||
SUMMARY_AGENT_ID,
|
||||
DASHBOARD_AGENT_ID,
|
||||
STRUCTURE_AGENT_ID,
|
||||
)
|
||||
|
||||
# seeder 会复制资源目录里的这些文件(存在即复制,缺失则跳过)。
|
||||
|
||||
+57
@@ -0,0 +1,57 @@
|
||||
# 圆桌大屏多页数据智能体
|
||||
|
||||
## 核心定位
|
||||
你是多智能体圆桌研讨 **Step 3** 的「大屏多页数据智能体」。各席位（情报收集 / 环境评估 / 方案设计 / 风险审查 / 执行规划 / 总结陈述等）已经完成研讨并产出交付，总控也给出了最终共识。
|
||||
|
||||
你的**唯一职责**：通读这些文字成果，**逐个席位地分析、提炼**，把整场研讨归纳成一份**大屏展示用的结构化数据**，在回复**最末尾**输出一个**严格合法**的 ```report-json 代码块。
|
||||
|
||||
这份数据会被前端用既定模板渲染成「多页大屏」（总览页 + 每个席位一页）。所以数据的**结构正确性与可解析性是第一位的**——宁可内容朴素，也必须是一份能被 `JSON.parse` 解析、字段齐全的 JSON。
|
||||
|
||||
你**不**调研、**不**联网搜索、**不**派活、**不**追问用户、**不**写文件、**不**输出 HTML——你是一个「研讨成果 → 结构化数据」的提炼器，只在对话里输出一个 report-json 代码块。
|
||||
|
||||
## 输入说明
|
||||
用户消息里会给你三部分原料（可能有缺省）：
|
||||
1. **任务意图摘要**：目标、约束、关键假设（来自 Step 1）；
|
||||
2. **各席位最终交付**：每个席位的名称 + 它产出的完整正文；
|
||||
3. **总控最终共识结论**。
|
||||
|
||||
## 工作流程（先分析，再产出）
|
||||
1. **逐席位分析**：依次读每个有交付的席位，在思考里为它提炼出——该席位的一句话结论（summary）、2–4 个小节要点（sections）、0–3 个可量化指标（metrics，0–100 评分）、0–4 个风险点（risks，high/mid/low）、它承接了哪些其它席位的产物（dependsOn，只能引用本次清单里的席位 id）。
|
||||
2. **抽取全局总览**：从跨席位信息里提炼总标题、一句话定位、3–6 个核心 KPI、1–3 个总览图表（风险分布 / 资源占比 / 方案评分 / 竞争力雷达等）、3–6 段执行路线（timeline），以及总控的最终结论 headline + 要点 points。
|
||||
3. **缺数字时合理估算**：没有明确数字时，按研讨内容把定性的「高/中/低」映射成评分、把阶段描述排成时间线等，**合理估算而非凭空编造与原料矛盾的数据**；估算的指标在 label 里可带「(估)」字样。
|
||||
4. **输出**：在回复最末尾输出一个 ```report-json 代码块。代码块之外只允许一两句简短说明，**不要**把数据重复贴进正文。
|
||||
|
||||
## report-json 契约（严格 JSON，全程简体中文）
|
||||
```report-json
|
||||
{"title":"大屏主标题（≤20字）","subtitle":"一句话定位",
|
||||
"kpis":[{"label":"指标名","value":"数值","unit":"单位","trend":"up|down|flat"}],
|
||||
"consensus":{"headline":"一句话最终结论","points":["结论要点，3-6 条"]},
|
||||
"charts":[{"type":"line|bar|donut|radar","title":"图表标题","items":[{"label":"维度","value":数字}]}],
|
||||
"timeline":[{"label":"阶段名","desc":"一句话说明"}],
|
||||
"seats":[{"id":"seat-1","name":"席位名（与交付席位一致）","summary":"该席位结论一两句",
|
||||
"sections":[{"heading":"小节标题","bullets":["要点，每条≤40字"]}],
|
||||
"metrics":[{"label":"量化指标名","value":0到100}],
|
||||
"risks":[{"label":"风险点","level":"high|mid|low"}],
|
||||
"dependsOn":["它承接哪些席位的产物（席位id）"]}]}
|
||||
```
|
||||
|
||||
### 字段硬性要求
|
||||
- `seats` 覆盖**每一个有交付的席位**，一个席位一项；`id` 用 `seat-1`..`seat-N`，`name` 与交付席位名一致；
|
||||
- `dependsOn` 只能引用本清单内出现过的席位 `id`，不能自指，不能编造不存在的 id；
|
||||
- `charts` 的 `type` 只能是 `line` / `bar` / `donut` / `radar`；`kpis` 的 `trend` 只能是 `up` / `down` / `flat`；`risks` 的 `level` 只能是 `high` / `mid` / `low`；
|
||||
- 数值字段写数字（不带引号、不带单位）；`kpis.value` 可带文字（如「约 30」），单位单独放 `unit`；
|
||||
- **JSON 必须严格合法**：双引号、无注释、无尾逗号、无单引号、布尔/数字不加引号。
|
||||
|
||||
## 多轮修改（用户会在输入框里继续提要求）
|
||||
数据生成后，用户可能继续发来修改要求（如「把风险分布换成柱状图」「新增一个执行路线席位」「第 2 个席位的结论改细些」）。处理规则：
|
||||
1. 把新消息当作对**上一份 report-json 的增量修改**：只改用户提到的部分，其余字段尽量保持不变；
|
||||
2. 仍在回复最末尾输出**完整的、严格合法的** ```report-json 代码块（不是只给 diff）；
|
||||
3. 若用户只是提问而非要求改数据，就只用文字回答、不重出代码块。
|
||||
|
||||
## 禁止事项
|
||||
- ❌ 调用任何工具（write_file / bash / web_search / 派活 / 看图 / 澄清 等一律不用）
|
||||
- ❌ 写任何文件、输出任何 HTML / Markdown 表格作为「产物」（产物只有那个 report-json 代码块）
|
||||
- ❌ 输出非法 JSON（注释、尾逗号、单引号、数字带引号、缺闭合括号）
|
||||
- ❌ 代码块之外大段复述数据，或把 JSON 也贴一份到正文
|
||||
- ❌ 编造与原料明显矛盾的数据，或编造不存在的席位 id
|
||||
- ❌ 在数据里添加「AI 自动生成」之类的水印字样
|
||||
+3
@@ -0,0 +1,3 @@
|
||||
description: 圆桌大屏多页数据智能体，读取各席位的最终交付与总控共识，逐席位分析并提炼成一份大屏展示用的结构化数据（report-json）。本身不调研、不联网、不派活、不写文件、不输出 HTML，只做分析归纳与结构化抽取。
|
||||
id: roundtable-dashboard
|
||||
name: 大屏多页数据
|
||||
+75
@@ -0,0 +1,75 @@
|
||||
# 结构化抽取智能体（roundtable-structure）
|
||||
|
||||
你是圆桌「方案总结」第三步的**结构化抽取智能体**。你的职责：**读懂**已生成的《方案总结报告》或各席位交付内容，**分析**其内在层级结构，把它**抽取**成一张可交互流程图的结构化数据（`flow-json`）。你也负责**按用户指令修改**这张流程图、以及**校验修复**流程图。
|
||||
|
||||
你专职「内容 → 结构」：不写报告、不联网、不派活、不画 HTML。
|
||||
|
||||
**输出方式（重要）**：你要**先用简短文字说明你的分析过程**——让用户看到进展（尤其模型较慢时也有实时反馈）：例如「我识别出本次层级链为 任务→目的→行为体→…；目的层有 3 条、各对应 1 个行为体；据此组织如下」，约 2–4 句即可，不要长篇大论；**然后**在回复**末尾**输出一个完整的 ```flow-json 代码块。前端会把这段分析说明展示给用户、并据末尾的 flow-json 绘制流程图。
|
||||
|
||||
> 说明：本轮要做哪件事（首次抽取 / 校验修复 / 按指令修改）由发给你的用户消息指明；数据来源（报告全文 / 各席位交付 / 当前流程图）也在用户消息里给全。你只需专注分析内容、产出结构化结果。
|
||||
|
||||
## 一、输出契约（flow-json 形状）
|
||||
|
||||
在前述简短分析说明之后，于回复**末尾**输出一个 ```flow-json 代码块，形状如下（示例仅示意结构，勿照抄内容）：
|
||||
|
||||
```flow-json
|
||||
{"title":"流程图标题","nodes":[
|
||||
{"id":"n1","label":"任务简短名","type":"start","group":"任务","detail":"任务目标"},
|
||||
{"id":"n2","label":"目的核心短语","type":"task","group":"目的","detail":"名称+理由要点(2-4句,单行)","from":["n1"]},
|
||||
{"id":"n3","label":"行为体核心短语","type":"role","group":"行为体","detail":"名称+理由要点","from":["n2"]}
|
||||
]}
|
||||
```
|
||||
|
||||
字段：
|
||||
- `id`：n1/n2… 唯一短串；
|
||||
- `label`：≤12 字，节点标题，取该条目核心短语；
|
||||
- `type`：取 `start/end/stage/task/decision/risk/deliverable/role` 之一，**仅决定配色**；
|
||||
- `group`：**该节点所在的层级名称**（如 任务 / 目的 / 行为体 / 预期行为 / 驱动因素 / 叙事），**必填**——画布按它给节点打分类标签；
|
||||
- `detail`：该条目的**名称 + 完整理由 / 说明**——**原样取自报告、不要简化或精简理由**（理由要完整保留，不要删减内容）；只需**写成一行**——内部不要出现真实换行，需要分隔用「；」或「/」（避免 JSON 里出现裸换行）；
|
||||
- `from`：前驱节点 id 列表，只引用**前面已出现**的节点（也可用顶层 `edges:[{from,to,label}]`，两种都支持）。
|
||||
|
||||
节点按先后排列、输出**全量**数据、全程简体中文。
|
||||
|
||||
### ✅ 正确节点示例 vs ❌ 错误（必看）
|
||||
|
||||
**✅ 正确**（`group` 标对层级，且**带上 `from` 连到前驱**——每个非根节点都必须连上前一层级对应的节点）：
|
||||
|
||||
```
|
||||
{"id":"n2","label":"精准追踪管理公众情绪曲线","type":"task","group":"目的","detail":"目的一：精准追踪并管理公众情绪曲线，识别关键情绪拐点；理由：暑期文旅涉及高频消费场景，情绪易受价格、服务、灾害影响呈多峰多谷震荡，情绪拐点是危机化解窗口，需精细跟踪作为前哨感知维度","from":["n1"]}
|
||||
```
|
||||
|
||||
**❌ 错误**（缺了 `from`，导致节点孤立、流程图平铺——这是最常见的错误，务必避免）：
|
||||
|
||||
```
|
||||
{"id":"n2","label":"精准追踪管理公众情绪曲线","type":"task","group":"目的","detail":"…"} ← 少了 "from"
|
||||
```
|
||||
|
||||
**硬性要求**：除根节点（任务，n1）外，**每个节点都必须有 `from`**（连到它在上一层级里对应的那个节点）；目的i 连 任务，行为体i 连 目的i，预期行为i 连 行为体i，叙事i 连 预期行为i……一组一条自上而下的链，不要让任何节点悬空。
|
||||
|
||||
## 二、画法（严格遵守）
|
||||
|
||||
① 根节点 n1 = 本次任务本身（报告 / 任务总览里的任务名称），`label` 用任务简短名，`group` 填「任务」，**不要**用「任务」「目标」这种泛称当 `label`；
|
||||
|
||||
② 从第二层起按层级逐层展开：每个层级在报告 / 交付里通常列了**多条具体条目**（如 目的层有 目的1 / 目的2 / 目的3、行为体层有各自对应的行为体，后面还有 预期行为·驱动因素、叙事…），把**每一条具体条目各画成一个节点**，**不要**把一整层合并成一个笼统节点；
|
||||
|
||||
③ 每个节点的 `group` 填它所在层级的名称（取自报告里该层级的真实名称）；
|
||||
|
||||
④ **连线**：你要做对三件事 ——
|
||||
(a) 每个节点的 `group` 标对层级；
|
||||
(b) **各层内的条目按"相同的对应顺序"排列**——报告里相互对应的一组（目的1 ↔ 它对应的行为体 ↔ 对应的预期行为 ↔ 对应的叙事）在各自层里**都排同一位次**（第 1 组都排第 1 位、第 2 组都排第 2 位…）；
|
||||
(c) **每个非根节点都填上 `from`**，连到它在上一层级里对应的那个节点（目的i 连 任务、行为体i 连 目的i、预期行为i 连 行为体i、叙事i 连 预期行为i……一组一条自上而下的链）——**最常见的错误就是漏填 from 导致节点平铺，务必每个都填**。
|
||||
系统还会按 group 层级关系**校正 / 补全连线**作为兜底，但你必须先把 from 填全、填对；
|
||||
|
||||
⑤ **务必把每个层级的每一条都输出成节点、不要漏层、不要只输出开头一两个节点就停**（典型规模约 13–16 个节点）；
|
||||
|
||||
⑥ **只画**这些层级实体节点；**不要**把「综合分析 / 核心洞察 / 执行路线 / 建议 / 结论」以及「阶段①②③④」等章节名 / 阶段名 / 汇总段落画成节点；节点 `label` 也**绝不能**是「阶段1 / 阶段2 / 第N阶段」这类阶段编号。
|
||||
|
||||
## 三、层级顺序
|
||||
|
||||
- 若数据来源里给了「业务链总控编排提示」并写明了层级链（如 任务 → 目的 → 行为体 → 预期行为 → 驱动因素 → 叙事），**优先按它的层级**组织；
|
||||
- 否则按给出的「默认层级」；都没有时，以「任务」为根、按报告 / 交付的逻辑结构合理分层。
|
||||
|
||||
## 四、修改 / 校验时
|
||||
|
||||
- **修改**：在「当前流程图」基础上**只按用户指令改动**（增 / 删 / 改节点或连线、改 label/group/detail），未被指令涉及的部分**保持原样**；删节点要同时去掉与它相关的连线；节点 id 尽量沿用原有的；输出**修改后的完整** flow-json。
|
||||
- **校验修复**：对照数据来源检查——根节点是任务本身、每层每条都在没漏、连边按对应关系成分支无悬空边、每个节点都有 `group`、没有汇总 / 阶段编号节点；发现问题就改对，没问题就原样规范化输出完整 flow-json。
|
||||
+3
@@ -0,0 +1,3 @@
|
||||
description: 圆桌结构化抽取智能体。读取「方案总结报告」或各席位交付内容，分析其层级结构，抽取成可交互流程图的结构化数据（flow-json）；也负责按指令修改流程图、校验修复流程图。专职「内容→结构」，不写报告、不联网、不画 HTML。后续可在此智能体上配置「入库技能」，让它分析出结构化结果后直接入库。
|
||||
id: roundtable-structure
|
||||
name: 结构化抽取
|
||||
@@ -0,0 +1,84 @@
|
||||
"""按 taskId 存取的「大屏绘制智能体」独立页面会话存储 API。
|
||||
|
||||
独立大屏页（前端 ``DashboardAgentPage``）按外部 ``task_id`` 存取：聊天记录
|
||||
（``transcript``）+ 最新结构化数据（``report_json``）。**按 task 共享、不按 user
|
||||
分权**（open read + 写放开），镜像 ``roundtable_drafts`` 的 by-task 语义。
|
||||
|
||||
Routes (prefix ``/api/dashboard-sessions``):
|
||||
GET /by-task/{task_id} 取该 task 的会话（无则 404）
|
||||
PUT /by-task/{task_id} upsert（body: {transcript?, report_json?}）
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from fastapi import APIRouter, HTTPException, Request
|
||||
from pydantic import BaseModel
|
||||
|
||||
from deerflow.runtime.user_context import get_effective_user_id
|
||||
|
||||
router = APIRouter(prefix="/api/dashboard-sessions", tags=["dashboard-sessions"])
|
||||
|
||||
|
||||
class DashboardSessionResponse(BaseModel):
|
||||
task_id: str
|
||||
transcript: Any | None = None
|
||||
report_json: str | None = None
|
||||
created_at: str | None = None
|
||||
updated_at: str | None = None
|
||||
|
||||
|
||||
class DashboardSessionUpsertRequest(BaseModel):
|
||||
# 只传需要更新的字段：聊天记录 / 结构化数据，二者皆可单独更新。
|
||||
transcript: Any | None = None
|
||||
report_json: str | None = None
|
||||
|
||||
|
||||
def _current_user_id(request: Request) -> str:
|
||||
user = getattr(request.state, "user", None)
|
||||
if user is not None:
|
||||
return str(user.id)
|
||||
return get_effective_user_id()
|
||||
|
||||
|
||||
def _get_store(request: Request):
|
||||
store = getattr(request.app.state, "dashboard_session_store", None)
|
||||
if store is None:
|
||||
raise HTTPException(status_code=503, detail="Dashboard session store not available")
|
||||
return store
|
||||
|
||||
|
||||
def _project(row: dict[str, Any]) -> DashboardSessionResponse:
|
||||
return DashboardSessionResponse(
|
||||
task_id=row.get("task_id", ""),
|
||||
transcript=row.get("transcript"),
|
||||
report_json=row.get("report_json"),
|
||||
created_at=row.get("created_at"),
|
||||
updated_at=row.get("updated_at"),
|
||||
)
|
||||
|
||||
|
||||
@router.get("/by-task/{task_id}", response_model=DashboardSessionResponse)
|
||||
async def get_dashboard_session(request: Request, task_id: str) -> DashboardSessionResponse:
|
||||
"""取该 task 的大屏会话（**不按 user 分权**）；无则 404。"""
|
||||
store = _get_store(request)
|
||||
row = await store.get_by_task(task_id)
|
||||
if row is None:
|
||||
raise HTTPException(status_code=404, detail="No dashboard session for this task")
|
||||
return _project(row)
|
||||
|
||||
|
||||
@router.put("/by-task/{task_id}", response_model=DashboardSessionResponse)
|
||||
async def upsert_dashboard_session(
|
||||
request: Request, task_id: str, body: DashboardSessionUpsertRequest
|
||||
) -> DashboardSessionResponse:
|
||||
"""按 task upsert 大屏会话（**不按 user 分权**），返回最新快照。"""
|
||||
store = _get_store(request)
|
||||
row = await store.upsert_by_task(
|
||||
task_id,
|
||||
transcript=body.transcript,
|
||||
report_json=body.report_json,
|
||||
user_id=_current_user_id(request),
|
||||
)
|
||||
return _project(row)
|
||||
@@ -0,0 +1,713 @@
|
||||
"""流程图结构化抽取 + 校验 的 SSE 端点。
|
||||
|
||||
第三步「方案总结」之后，要把报告 / 各席位交付的层级结论抽成一张**流程图**（节点 + 连线）。
|
||||
之前让总结智能体在同一对话线程里顺手吐 ``flow-json`` 很不稳：业务链 ``coordinator_prompt``
|
||||
常写着「输出结构化结论 / 按阶段产出报告」，模型被线程上下文锚定，要么不出 flow-json、要么
|
||||
结构残缺（只剩两三个节点）。
|
||||
|
||||
这里改成**独立的一次性模型调用**（不走 lead_agent / 不带线程历史），两段式：
|
||||
① 抽取（``model.astream``）：把报告/席位交付按层级抽成 flow-json，**流式**把过程吐给前端实时显示；
|
||||
② 校验/修复（``model.ainvoke``）：把抽取结果连同源材料再过一遍模型，补全漏层、修正连边、
|
||||
去掉阶段/汇总节点，产出干净的 flow-json；后端再做容错解析 + 结构校验，返回最终图数据。
|
||||
|
||||
SSE 帧（``data: {json}\n\n``）：
|
||||
- ``{"event":"extract_delta","content":"…"}`` 抽取流式增量（前端把 JSON 实时显示出来）
|
||||
- ``{"event":"extract_done"}`` 抽取结束，进入校验
|
||||
- ``{"event":"validating"}`` 校验中
|
||||
- ``{"event":"final","graph":{title,nodes,edges}}`` 校验后的最终图（前端据此绘制）
|
||||
- ``{"event":"error","detail":"…"}``
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
from collections.abc import AsyncIterator
|
||||
from typing import Any
|
||||
|
||||
from fastapi import APIRouter, Body, Request
|
||||
from fastapi.responses import StreamingResponse
|
||||
from langchain_core.messages import HumanMessage, SystemMessage
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from app.gateway.routers._roundtable_seed import (
|
||||
STRUCTURE_AGENT_ID,
|
||||
ensure_roundtable_functional_agents,
|
||||
)
|
||||
from deerflow.config.agents_config import load_agent_config, load_agent_soul
|
||||
from deerflow.models.factory import create_chat_model
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
router = APIRouter(prefix="/api/flow-extract", tags=["flow-extract"])
|
||||
|
||||
|
||||
# ── schemas ──────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
class SeatDelivery(BaseModel):
|
||||
sender: str = ""
|
||||
content: str = ""
|
||||
|
||||
|
||||
class FlowExtractRequest(BaseModel):
|
||||
# 数据来源：有报告优先用报告，否则用各席位交付。
|
||||
report_md: str | None = Field(default=None)
|
||||
seat_deliveries: list[SeatDelivery] = Field(default_factory=list)
|
||||
# 层级骨架：优先 coordinator_prompt 里写明的层级，否则用 chain_levels。
|
||||
chain_levels: list[str] = Field(default_factory=list)
|
||||
coordinator_prompt: str | None = Field(default=None)
|
||||
model: str | None = Field(default=None)
|
||||
# Phase 2 问答修改：带上当前图 + 用户指令 → 进 edit 模式（按指令改图，其余不变）。
|
||||
current_graph: dict[str, Any] | None = Field(default=None)
|
||||
instruction: str | None = Field(default=None)
|
||||
|
||||
|
||||
class FlowIntentRequest(BaseModel):
|
||||
"""第三步输入框意图识别：判断用户想 重做报告 / 改流程图 / 重画流程图 / 提问 / 模糊待澄清。"""
|
||||
|
||||
message: str = ""
|
||||
has_report: bool = False
|
||||
has_flow: bool = False
|
||||
model: str | None = Field(default=None)
|
||||
|
||||
|
||||
# 意图枚举（与前端路由一一对应）。
|
||||
_INTENTS = {"regenerate_report", "modify_flow", "regenerate_flow", "qa", "unclear"}
|
||||
|
||||
|
||||
# ── flow-json 容错解析（Python 版，对齐前端 lib/flow-graph.ts）──────────────
|
||||
|
||||
_FENCE = "```flow-json"
|
||||
_VALID_TYPES = {"start", "end", "stage", "task", "decision", "risk", "deliverable", "role"}
|
||||
|
||||
|
||||
def _as_str(v: Any) -> str:
|
||||
return v.strip() if isinstance(v, str) else ""
|
||||
|
||||
|
||||
def _loose_nodes(body: str) -> list[dict[str, Any]]:
|
||||
"""正则逐字段抽取节点（strict json.loads 失败时回退）。容忍裸换行 / 尾逗号。"""
|
||||
# 取 "nodes" 后的 [ ... ]（到与之配对的 ] 或文本结尾）。
|
||||
key = body.find('"nodes"')
|
||||
if key < 0:
|
||||
return []
|
||||
arr = body.find("[", key)
|
||||
if arr < 0:
|
||||
return []
|
||||
# 平衡括号切出每个顶层 {…}
|
||||
slices: list[str] = []
|
||||
depth = 0
|
||||
start = -1
|
||||
in_str = False
|
||||
esc = False
|
||||
for i in range(arr + 1, len(body)):
|
||||
ch = body[i]
|
||||
if in_str:
|
||||
if esc:
|
||||
esc = False
|
||||
elif ch == "\\":
|
||||
esc = True
|
||||
elif ch == '"':
|
||||
in_str = False
|
||||
continue
|
||||
if ch == '"':
|
||||
in_str = True
|
||||
elif ch == "{":
|
||||
if depth == 0:
|
||||
start = i
|
||||
depth += 1
|
||||
elif ch == "}":
|
||||
depth -= 1
|
||||
if depth == 0 and start >= 0:
|
||||
slices.append(body[start : i + 1])
|
||||
start = -1
|
||||
elif ch == "]" and depth == 0:
|
||||
break
|
||||
out: list[dict[str, Any]] = []
|
||||
for sl in slices:
|
||||
out.append(_loose_one(sl))
|
||||
return out
|
||||
|
||||
|
||||
def _loose_edges(body: str) -> list[dict[str, Any]]:
|
||||
"""非法 JSON 回退路径下，从顶层 `"edges":[ {from,to,label} … ]` 容错抽边。"""
|
||||
key = body.find('"edges"')
|
||||
if key < 0:
|
||||
return []
|
||||
arr = body.find("[", key)
|
||||
if arr < 0:
|
||||
return []
|
||||
depth = 0
|
||||
start = -1
|
||||
in_str = False
|
||||
esc = False
|
||||
slices: list[str] = []
|
||||
for i in range(arr + 1, len(body)):
|
||||
ch = body[i]
|
||||
if in_str:
|
||||
if esc:
|
||||
esc = False
|
||||
elif ch == "\\":
|
||||
esc = True
|
||||
elif ch == '"':
|
||||
in_str = False
|
||||
continue
|
||||
if ch == '"':
|
||||
in_str = True
|
||||
elif ch == "{":
|
||||
if depth == 0:
|
||||
start = i
|
||||
depth += 1
|
||||
elif ch == "}":
|
||||
depth -= 1
|
||||
if depth == 0 and start >= 0:
|
||||
slices.append(body[start : i + 1])
|
||||
start = -1
|
||||
elif ch == "]" and depth == 0:
|
||||
break
|
||||
|
||||
def pick(slice_: str, *keys: str) -> str:
|
||||
for k in keys:
|
||||
m = re.search(r'"' + k + r'"\s*:\s*"([^"]*)"', slice_)
|
||||
if m:
|
||||
return m.group(1)
|
||||
return ""
|
||||
|
||||
out: list[dict[str, Any]] = []
|
||||
for sl in slices:
|
||||
frm = pick(sl, "from", "source")
|
||||
to = pick(sl, "to", "target")
|
||||
if frm and to:
|
||||
e: dict[str, Any] = {"from": frm, "to": to}
|
||||
lbl = pick(sl, "label")
|
||||
if lbl:
|
||||
e["label"] = lbl
|
||||
out.append(e)
|
||||
return out
|
||||
|
||||
|
||||
def _loose_one(slice_: str) -> dict[str, Any]:
|
||||
def pick(*keys: str) -> str | None:
|
||||
for k in keys:
|
||||
m = re.search(r'"' + k + r'"\s*:\s*"((?:\\.|[^"\\])*)"', slice_)
|
||||
if m:
|
||||
raw = m.group(1)
|
||||
try:
|
||||
return json.loads('"' + raw.replace("\n", "\\n").replace("\r", "") + '"')
|
||||
except Exception: # noqa: BLE001
|
||||
return raw.replace("\\n", "\n").replace('\\"', '"')
|
||||
return None
|
||||
|
||||
obj: dict[str, Any] = {}
|
||||
for k, dst in (("id", "id"), ("type", "type"), ("detail", "detail")):
|
||||
v = pick(k)
|
||||
if v:
|
||||
obj[dst] = v
|
||||
label = pick("label", "name")
|
||||
if label:
|
||||
obj["label"] = label
|
||||
group = pick("group", "level", "tier", "layer")
|
||||
if group:
|
||||
obj["group"] = group
|
||||
fm = re.search(r'"(?:from|prev)"\s*:\s*\[([\s\S]*?)\]', slice_)
|
||||
if fm:
|
||||
inner = fm.group(1)
|
||||
ids: list[Any] = [{"id": x} for x in re.findall(r'\{[^{}]*?"id"\s*:\s*"([^"]+)"[^{}]*?\}', inner)]
|
||||
if not ids:
|
||||
ids = re.findall(r'"([^"]+)"', inner)
|
||||
if ids:
|
||||
obj["from"] = ids
|
||||
return obj
|
||||
|
||||
|
||||
def _norm_node(raw: dict[str, Any]) -> dict[str, Any] | None:
|
||||
nid = _as_str(raw.get("id"))
|
||||
label = _as_str(raw.get("label")) or _as_str(raw.get("name"))
|
||||
if not nid or not label:
|
||||
return None
|
||||
rtype = _as_str(raw.get("type")).lower()
|
||||
ntype = rtype if rtype in _VALID_TYPES else "task"
|
||||
node: dict[str, Any] = {"id": nid, "label": label, "type": ntype}
|
||||
detail = _as_str(raw.get("detail")) or _as_str(raw.get("description"))
|
||||
if detail:
|
||||
node["detail"] = detail
|
||||
group = _as_str(raw.get("group")) or _as_str(raw.get("level")) or _as_str(raw.get("tier")) or _as_str(raw.get("layer"))
|
||||
if group:
|
||||
node["group"] = group
|
||||
return node
|
||||
|
||||
|
||||
def _edges_from(raw: dict[str, Any], nid: str) -> list[dict[str, Any]]:
|
||||
frm = raw.get("from") or raw.get("prev")
|
||||
if not isinstance(frm, list):
|
||||
return []
|
||||
out: list[dict[str, Any]] = []
|
||||
for e in frm:
|
||||
if isinstance(e, str) and e.strip():
|
||||
out.append({"from": e.strip(), "to": nid})
|
||||
elif isinstance(e, dict):
|
||||
fid = _as_str(e.get("id")) or _as_str(e.get("from"))
|
||||
if fid:
|
||||
edge = {"from": fid, "to": nid}
|
||||
lbl = _as_str(e.get("label"))
|
||||
if lbl:
|
||||
edge["label"] = lbl
|
||||
out.append(edge)
|
||||
return out
|
||||
|
||||
|
||||
def parse_flow_graph(text: str) -> dict[str, Any] | None:
|
||||
"""从（可能带 fence / 非法 JSON 的）文本里解析出 ``{title, nodes, edges}``。无节点→None。"""
|
||||
start = text.rfind(_FENCE)
|
||||
body = text[start + len(_FENCE) :] if start >= 0 else text
|
||||
close = body.find("```")
|
||||
if close >= 0:
|
||||
body = body[:close]
|
||||
body = body.strip()
|
||||
|
||||
title = ""
|
||||
nodes_raw: list[dict[str, Any]] = []
|
||||
obj: Any = None
|
||||
for candidate in (body, re.sub(r",(\s*[}\]])", r"\1", body)): # 原文 + 去尾逗号
|
||||
try:
|
||||
obj = json.loads(candidate)
|
||||
break
|
||||
except Exception: # noqa: BLE001
|
||||
obj = None
|
||||
if isinstance(obj, dict):
|
||||
title = _as_str(obj.get("title"))
|
||||
raw_list = obj.get("nodes")
|
||||
nodes_raw = raw_list if isinstance(raw_list, list) else []
|
||||
else:
|
||||
m = re.search(r'"title"\s*:\s*"([^"]*)"', body)
|
||||
if m:
|
||||
title = m.group(1)
|
||||
nodes_raw = _loose_nodes(body)
|
||||
|
||||
nodes: list[dict[str, Any]] = []
|
||||
edges: list[dict[str, Any]] = []
|
||||
seen: set[str] = set()
|
||||
for raw in nodes_raw:
|
||||
if not isinstance(raw, dict):
|
||||
continue
|
||||
node = _norm_node(raw)
|
||||
if not node or node["id"] in seen:
|
||||
continue
|
||||
seen.add(node["id"])
|
||||
nodes.append(node)
|
||||
edges.extend(_edges_from(raw, node["id"]))
|
||||
if not nodes:
|
||||
return None
|
||||
|
||||
# 兼容**独立 `edges` 数组**：模型可能把连线写成节点上的 `from`，也可能写成顶层
|
||||
# `edges:[{from,to,label}]`（或 source/target 别名）。后者若不处理，节点全无连线（bug）。
|
||||
raw_edges: Any = obj.get("edges") if isinstance(obj, dict) else None
|
||||
if not isinstance(raw_edges, list):
|
||||
raw_edges = _loose_edges(body)
|
||||
for e in raw_edges or []:
|
||||
if not isinstance(e, dict):
|
||||
continue
|
||||
frm = _as_str(e.get("from")) or _as_str(e.get("source"))
|
||||
to = _as_str(e.get("to")) or _as_str(e.get("target"))
|
||||
if not frm or not to:
|
||||
continue
|
||||
edge: dict[str, Any] = {"from": frm, "to": to}
|
||||
lbl = _as_str(e.get("label"))
|
||||
if lbl:
|
||||
edge["label"] = lbl
|
||||
edges.append(edge)
|
||||
# 只保留两端都存在的边 + 去重。
|
||||
valid_edges: list[dict[str, Any]] = []
|
||||
keys: set[str] = set()
|
||||
for e in edges:
|
||||
if e["from"] not in seen or e["to"] not in seen or e["from"] == e["to"]:
|
||||
continue
|
||||
k = f'{e["from"]}->{e["to"]}'
|
||||
if k in keys:
|
||||
continue
|
||||
keys.add(k)
|
||||
valid_edges.append(e)
|
||||
|
||||
# 连线选取（两全：尊重模型 from + 弱模型兜底）：
|
||||
# · 模型把连线**连全了**（除根节点外每个节点都有入边）→ 直接用模型的 from/edges；
|
||||
# · 模型**漏连**（≥2 个节点没入边，最常见就是漏 from 导致平铺）→ 按 group 层级关系
|
||||
# **确定性推导补全**，保证不平铺、连线质量与模型能力无关。
|
||||
incoming = {e["to"] for e in valid_edges}
|
||||
orphans = [n for n in nodes if n["id"] not in incoming]
|
||||
derived = _derive_layered_edges(nodes)
|
||||
if valid_edges and len(orphans) <= 1:
|
||||
final_edges = valid_edges # 模型连全（仅根无入边）→ 用模型连边
|
||||
elif derived is not None:
|
||||
final_edges = derived # 模型漏连 → 按层级推导补全（永不平铺）
|
||||
else:
|
||||
final_edges = valid_edges
|
||||
return {"title": title, "nodes": nodes, "edges": final_edges}
|
||||
|
||||
|
||||
def _derive_layered_edges(nodes: list[dict[str, Any]]) -> list[dict[str, Any]] | None:
|
||||
"""按节点的 `group`（层级）确定性推导连线，**不依赖模型给的 from/edges**。
|
||||
|
||||
规则（对应「任务 → 各目的 → 各自对应的行为体 → … → 叙事」这种分层链）：
|
||||
· 按 group 首次出现顺序排出层 L0,L1,…（L0 通常是「任务」单节点）；
|
||||
· 相邻层 a→b 连边：|a|==1 → a 扇出到 b 全部；|b|==1 → a 全部汇入 b；
|
||||
|a|==|b| → 按下标一一对应（目的i→行为体i）；否则该层对不规则 → 整体放弃推导(返回 None)。
|
||||
返回 None 表示「无法可靠推导」（无 group / 只有一层 / 存在不规则层对），调用方回退模型连边。
|
||||
"""
|
||||
# 按 group 分层，保持每层内节点的原始顺序 + 层的首次出现顺序。
|
||||
# 加固：个别节点漏 group（弱模型可能漏标）→ 按发射顺序**就近归入前一个层级**（carry-forward），
|
||||
# 不再因一个漏标就整体放弃；只有连开头节点都没 group / 全程无任何 group 才放弃。
|
||||
order: list[str] = []
|
||||
layers: dict[str, list[str]] = {}
|
||||
last_group = ""
|
||||
any_group = False
|
||||
for n in nodes:
|
||||
g = _as_str(n.get("group"))
|
||||
if g:
|
||||
any_group = True
|
||||
last_group = g
|
||||
else:
|
||||
g = last_group # 漏 group → 沿用前一节点的层级
|
||||
if not g:
|
||||
return None # 开头节点就没 group，无法锚定层级 → 回退模型连边
|
||||
if g not in layers:
|
||||
layers[g] = []
|
||||
order.append(g)
|
||||
layers[g].append(n["id"])
|
||||
if not any_group or len(order) < 2:
|
||||
return None # 全程无 group / 只有一层 → 回退模型连边
|
||||
out: list[dict[str, Any]] = []
|
||||
for k in range(len(order) - 1):
|
||||
a = layers[order[k]]
|
||||
b = layers[order[k + 1]]
|
||||
if len(a) == 1:
|
||||
out.extend({"from": a[0], "to": nid} for nid in b)
|
||||
elif len(b) == 1:
|
||||
out.extend({"from": nid, "to": b[0]} for nid in a)
|
||||
elif len(a) == len(b):
|
||||
out.extend({"from": a[i], "to": b[i]} for i in range(len(a)))
|
||||
else:
|
||||
# 不规则层对（两边都 >1 且不等，多因模型漏/多了某层条目）：**不放弃、不平铺**，
|
||||
# 按比例把下层每个节点连到上层对应位置，保证每个节点都有入边（连上 > 连得完美）。
|
||||
for j, nid in enumerate(b):
|
||||
out.append({"from": a[j * len(a) // len(b)], "to": nid})
|
||||
return out
|
||||
|
||||
|
||||
# ── prompts ────────────────────────────────────────────────────────────────
|
||||
|
||||
_SHAPE = (
|
||||
"flow-json 严格用下面这个形状（一个 JSON 对象，nodes 是节点数组；示例仅示意结构、勿照抄内容）：\n"
|
||||
"```flow-json\n"
|
||||
'{"title":"流程图标题","nodes":[\n'
|
||||
' {"id":"n1","label":"任务简短名","type":"start","group":"任务","detail":"任务目标"},\n'
|
||||
' {"id":"n2","label":"目的核心短语","type":"task","group":"目的","detail":"名称+理由要点(2-4句,单行)","from":["n1"]},\n'
|
||||
' {"id":"n3","label":"行为体核心短语","type":"role","group":"行为体","detail":"名称+理由要点","from":["n2"]}\n'
|
||||
"]}\n"
|
||||
"```\n"
|
||||
"字段：id(n1/n2…唯一)、label(≤12字,节点标题)、type(start/end/stage/task/decision/risk/deliverable/role 之一,仅决定配色)、"
|
||||
"group(该节点所在层级名称,如 任务/目的/行为体/预期行为/驱动因素/叙事,必填)、"
|
||||
"detail(该条目的名称+**完整理由/说明,原样取自报告、不要简化或精简理由**;只需写成一行——内部换行用「；」或「/」替代,不要内含真实换行)、"
|
||||
"from(前驱节点 id 列表,只引用前面已出现的节点)。"
|
||||
)
|
||||
|
||||
_STRUCTURE = (
|
||||
"【画法·严格遵守】"
|
||||
"① 根节点 n1=本次任务本身(报告/任务总览里的任务名称),label 用任务简短名,group 填「任务」,不要用「任务」泛称当 label;"
|
||||
"② 从第二层起按层级逐层展开:每层在报告/交付里通常列了多条具体条目(如 目的层有 目的1/2/3、行为体层有各自对应行为体,后面还有 预期行为·驱动因素、叙事…),"
|
||||
"把每一条具体条目各画成一个节点,不要把整层合并成一个笼统节点;"
|
||||
"③ 每个节点 group 填它所在层级名称(取自报告里该层级真实名称);"
|
||||
"④ **连线**:(a)group 标对层级;(b)各层内条目按**相同对应顺序**排列(相互对应的一组在各层都排同一位次);"
|
||||
"(c)**每个非根节点都必须填 from**,连到上一层级里对应的那个节点(目的i连任务、行为体i连目的i、预期行为i连行为体i、叙事i连预期行为i…)——**最常见错误就是漏 from 导致平铺,务必每个都填**;系统还会按层级校正/补全连线兜底;"
|
||||
"⑤ 务必把每层每一条都输出成节点、不要漏层、不要只输出开头一两个节点就停(典型约13-16个节点);"
|
||||
"⑥ 只画这些层级实体节点,不要把『综合分析/核心洞察/执行路线/建议/结论』及『阶段①②③④』等汇总段落/阶段名画成节点。"
|
||||
)
|
||||
|
||||
|
||||
def _material(req: FlowExtractRequest) -> str:
|
||||
report = (req.report_md or "").strip()
|
||||
if report:
|
||||
return "===== 方案总结报告全文（数据来源，逐条抽取，勿改写）=====\n" + report + "\n===== 报告结束 ====="
|
||||
seats = [s for s in req.seat_deliveries if (s.content or "").strip()]
|
||||
if seats:
|
||||
body = "\n\n".join(f"## 席位{i + 1}：{s.sender or '（未具名席位）'}\n{s.content.strip()}" for i, s in enumerate(seats))
|
||||
return "===== 各席位交付产物（数据来源，逐条抽取，勿改写）=====\n" + body + "\n===== 交付结束 ====="
|
||||
return ""
|
||||
|
||||
|
||||
def _hierarchy_hint(req: FlowExtractRequest) -> str:
|
||||
if (req.coordinator_prompt or "").strip():
|
||||
return "层级顺序优先采用下面「业务链总控编排提示」里写明的层级链(如它写的 任务→目的→行为体→…):\n" + req.coordinator_prompt.strip()
|
||||
levels = [s.strip() for s in req.chain_levels if s.strip()]
|
||||
if levels:
|
||||
return "层级顺序(自上而下)：任务 → " + " → ".join(levels) + "。"
|
||||
return "层级顺序：以「任务」为根，按报告/交付的逻辑结构合理分层展开。"
|
||||
|
||||
|
||||
# 兜底系统提示：当 roundtable-structure 的 SOUL 缺失时用它（与 SOUL 同款契约）。
|
||||
_DEFAULT_SYSTEM = (
|
||||
"你是「结构化抽取智能体」。职责：读懂《方案总结报告》/ 各席位交付内容，分析其层级结构，"
|
||||
"抽取成可交互流程图的结构化数据(flow-json)；也按指令改图、校验修复。"
|
||||
"请**先用简短文字(2-4句)说明分析过程**(识别的层级链、每层条目数、分支对应)让用户看到进展，"
|
||||
"**再**在回复末尾输出一个完整的 ```flow-json 代码块（除这段简短说明外不写长篇正文）。全程简体中文。\n"
|
||||
+ _SHAPE
|
||||
+ "\n"
|
||||
+ _STRUCTURE
|
||||
)
|
||||
|
||||
|
||||
def _system_prompt() -> str:
|
||||
"""结构化抽取的系统提示 = roundtable-structure 智能体的 SOUL（可在智能体管理页编辑、
|
||||
未来挂入库技能）；SOUL 缺失则回退内置 _DEFAULT_SYSTEM。"""
|
||||
soul = load_agent_soul(STRUCTURE_AGENT_ID)
|
||||
return soul.strip() if soul and soul.strip() else _DEFAULT_SYSTEM
|
||||
|
||||
|
||||
def _resolve_model(payload_model: str | None) -> str | None:
|
||||
"""模型优先级：前端所选(payload) > 智能体配置的 model > None(用 config 默认模型)。"""
|
||||
if payload_model:
|
||||
return payload_model
|
||||
try:
|
||||
cfg = load_agent_config(STRUCTURE_AGENT_ID)
|
||||
if cfg and cfg.model:
|
||||
return cfg.model
|
||||
except Exception: # noqa: BLE001
|
||||
pass
|
||||
return None
|
||||
|
||||
|
||||
def _extract_messages(req: FlowExtractRequest) -> list[Any]:
|
||||
user = (
|
||||
_material(req)
|
||||
+ "\n\n"
|
||||
+ _hierarchy_hint(req)
|
||||
+ "\n\n请分析以上数据来源，按你的职责设定输出一个完整的 ```flow-json 代码块。"
|
||||
)
|
||||
return [SystemMessage(content=_system_prompt()), HumanMessage(content=user)]
|
||||
|
||||
|
||||
def _edit_messages(req: FlowExtractRequest) -> list[Any]:
|
||||
"""Phase 2 问答修改：按用户指令改当前流程图（增/删/改节点或连线），其余保持不变。"""
|
||||
cur = json.dumps(req.current_graph or {}, ensure_ascii=False)
|
||||
user = (
|
||||
_material(req)
|
||||
+ "\n\n===== 当前流程图（flow-json，待按指令修改）=====\n"
|
||||
+ cur
|
||||
+ "\n===== 当前流程图结束 =====\n\n用户修改指令：\n"
|
||||
+ (req.instruction or "").strip()
|
||||
+ "\n\n请按指令修改这张流程图：未被指令涉及的部分保持原样、节点 id 尽量沿用、删节点连带删边，"
|
||||
+ "输出**修改后的完整** ```flow-json 代码块。"
|
||||
)
|
||||
return [SystemMessage(content=_system_prompt()), HumanMessage(content=user)]
|
||||
|
||||
|
||||
def _validate_messages(req: FlowExtractRequest, extracted: str) -> list[Any]:
|
||||
user = (
|
||||
_material(req)
|
||||
+ "\n\n===== 已抽取的 flow-json（待校验修复）=====\n"
|
||||
+ extracted
|
||||
+ "\n===== 待校验结束 =====\n\n请对照数据来源，**逐个节点检查上面这份 flow-json 是不是缺字段**、并修复它："
|
||||
+ "\n**校验要点（务必遵守）**："
|
||||
+ "① **连接关系 `from`（最重要，必查必补）**：**逐个**检查上面每个节点——除根任务外，"
|
||||
+ "**只要某个节点缺了 `from`（或 `from` 为空），就必须给它补上正确的 `from`**：连到它在上一层级里对应的那个节点"
|
||||
+ "（目的i 连 任务、行为体i 连 目的i、预期行为i 连 行为体i、叙事i 连 预期行为i…）。"
|
||||
+ "缺 `from` 是这份 JSON 最常见、最致命的错漏（会导致流程图平铺），**绝不能让任何非根节点悬空**；"
|
||||
+ "② **每层每条都在、没漏层没漏条目**，节点数只能 ≥ 原图、不能变少；"
|
||||
+ "③ 每个节点的 `group`（层级名）标对；各层内条目按相同对应顺序排列（相互对应的一组在各层排同一位次）；"
|
||||
+ "④ 每个节点的 `detail` **原样保留、不要简化或精简理由**——理由要完整，不要删减内容；"
|
||||
+ "⑤ 没有汇总/阶段编号节点；只在确有错漏时才动，没问题就原样规范化输出。"
|
||||
+ "\n输出修复后的完整 ```flow-json 代码块（含全部节点 + 每个非根节点的 from）。"
|
||||
)
|
||||
return [SystemMessage(content=_system_prompt()), HumanMessage(content=user)]
|
||||
|
||||
|
||||
# ── endpoint ─────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def _frame(data: dict[str, Any]) -> str:
|
||||
return f"data: {json.dumps(data, ensure_ascii=False, default=str)}\n\n"
|
||||
|
||||
|
||||
def _chunk_parts(chunk: Any) -> tuple[str, str]:
|
||||
"""从流式 chunk 取 (正文增量, 思考增量)。思考走 additional_kwargs.reasoning_content
|
||||
（DeepSeek/兼容 OpenAI 推理模型的单列字段），正文走 content。"""
|
||||
content = chunk.content if isinstance(getattr(chunk, "content", None), str) else ""
|
||||
ak = getattr(chunk, "additional_kwargs", None) or {}
|
||||
reasoning = ""
|
||||
if isinstance(ak, dict):
|
||||
r = ak.get("reasoning_content") or ak.get("reasoning")
|
||||
if isinstance(r, str):
|
||||
reasoning = r
|
||||
return content, reasoning
|
||||
|
||||
|
||||
@router.post("/stream")
|
||||
async def extract_flow_stream(request: Request, payload: FlowExtractRequest = Body(...)) -> StreamingResponse:
|
||||
"""流式抽取流程图 JSON + 流式校验修复（抽取/校验过程含思考都实时吐前端）→ 返回最终结构化图。"""
|
||||
|
||||
ensure_roundtable_functional_agents() # 自愈：确保 roundtable-structure 的 SOUL/config 存在
|
||||
resolved_model = _resolve_model(payload.model)
|
||||
|
||||
async def gen() -> AsyncIterator[str]:
|
||||
try:
|
||||
# 带 instruction + current_graph → Phase 2 问答修改（按指令改当前图）；否则首次抽取。
|
||||
is_edit = bool((payload.instruction or "").strip()) and payload.current_graph is not None
|
||||
if not _material(payload).strip() and not is_edit:
|
||||
yield _frame({"event": "error", "detail": "缺少数据来源（报告或各席位交付均为空）"})
|
||||
return
|
||||
|
||||
# ① 抽取 / 编辑（正文 + 思考(reasoning) 拼成 `<think>…</think>正文` 增量逐帧吐，前端实时显示）
|
||||
round1_messages = _edit_messages(payload) if is_edit else _extract_messages(payload)
|
||||
model = create_chat_model(name=resolved_model, thinking_enabled=True)
|
||||
extracted = ""
|
||||
think_open = False
|
||||
async for chunk in model.astream(round1_messages):
|
||||
c, r = _chunk_parts(chunk)
|
||||
out = ""
|
||||
if r:
|
||||
if not think_open:
|
||||
out += "<think>"
|
||||
think_open = True
|
||||
out += r
|
||||
if c:
|
||||
if think_open:
|
||||
out += "</think>"
|
||||
think_open = False
|
||||
out += c
|
||||
extracted += c
|
||||
if out:
|
||||
yield _frame({"event": "extract_delta", "content": out})
|
||||
if think_open:
|
||||
yield _frame({"event": "extract_delta", "content": "</think>"})
|
||||
yield _frame({"event": "extract_done"})
|
||||
|
||||
# ② 校验 / 修复（同样流式 + 思考实时显示）
|
||||
yield _frame({"event": "validating"})
|
||||
validated = ""
|
||||
graph: dict[str, Any] | None = None
|
||||
try:
|
||||
vmodel = create_chat_model(name=resolved_model, thinking_enabled=True)
|
||||
think_open = False
|
||||
async for chunk in vmodel.astream(_validate_messages(payload, extracted)):
|
||||
c, r = _chunk_parts(chunk)
|
||||
out = ""
|
||||
if r:
|
||||
if not think_open:
|
||||
out += "<think>"
|
||||
think_open = True
|
||||
out += r
|
||||
if c:
|
||||
if think_open:
|
||||
out += "</think>"
|
||||
think_open = False
|
||||
out += c
|
||||
validated += c
|
||||
if out:
|
||||
yield _frame({"event": "validate_delta", "content": out})
|
||||
if think_open:
|
||||
yield _frame({"event": "validate_delta", "content": "</think>"})
|
||||
graph = parse_flow_graph(validated)
|
||||
except Exception: # noqa: BLE001 — 校验失败不致命，回退抽取结果
|
||||
logger.warning("flow-extract validation failed, fall back to extraction", exc_info=True)
|
||||
graph = None
|
||||
extracted_graph = parse_flow_graph(extracted)
|
||||
# 防回归兜底：校验**绝不能让结构变差**。若校验后的图节点或连线比抽取结果更少
|
||||
# （模型校验时把连线删没了、或漏了节点——实测会发生），直接回退用抽取结果，
|
||||
# 保住所有节点与连线。校验只在「不丢东西」时才采用。
|
||||
if graph is not None and extracted_graph is not None:
|
||||
if len(graph.get("nodes", [])) < len(extracted_graph.get("nodes", [])) or len(
|
||||
graph.get("edges", [])
|
||||
) < len(extracted_graph.get("edges", [])):
|
||||
logger.info(
|
||||
"flow-extract: validation reduced nodes/edges (%d/%d → %d/%d), keep extraction",
|
||||
len(extracted_graph.get("nodes", [])),
|
||||
len(extracted_graph.get("edges", [])),
|
||||
len(graph.get("nodes", [])),
|
||||
len(graph.get("edges", [])),
|
||||
)
|
||||
graph = extracted_graph
|
||||
if graph is None:
|
||||
graph = extracted_graph
|
||||
if graph is None:
|
||||
yield _frame({"event": "error", "detail": "未能从模型输出解析出流程图节点"})
|
||||
return
|
||||
yield _frame({"event": "final", "graph": graph})
|
||||
except Exception as exc: # noqa: BLE001
|
||||
logger.exception("flow-extract stream failed")
|
||||
yield _frame({"event": "error", "detail": str(exc)})
|
||||
|
||||
return StreamingResponse(
|
||||
gen(),
|
||||
media_type="text/event-stream",
|
||||
headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no", "Connection": "keep-alive"},
|
||||
)
|
||||
|
||||
|
||||
# ── 意图识别（一次性 JSON，参考 ai_writing/intent_parser 范式）─────────────────
|
||||
|
||||
|
||||
def _parse_intent_json(text: str) -> dict[str, Any] | None:
|
||||
"""从模型输出里容错解析 {intent, reason}。strict → 去围栏/去尾逗号 → 平衡括号。"""
|
||||
candidates: list[str] = []
|
||||
t = text.strip()
|
||||
candidates.append(t)
|
||||
m = re.search(r"```(?:json)?\s*([\s\S]+?)\s*```", t)
|
||||
if m:
|
||||
candidates.append(m.group(1).strip())
|
||||
s = t.find("{")
|
||||
e = t.rfind("}")
|
||||
if s >= 0 and e > s:
|
||||
candidates.append(t[s : e + 1])
|
||||
for c in candidates:
|
||||
for cand in (c, re.sub(r",(\s*[}\]])", r"\1", c)):
|
||||
try:
|
||||
obj = json.loads(cand)
|
||||
if isinstance(obj, dict):
|
||||
return obj
|
||||
except Exception: # noqa: BLE001
|
||||
continue
|
||||
return None
|
||||
|
||||
|
||||
_INTENT_SYS = (
|
||||
"你是圆桌「方案总结」第三步输入框的**意图识别器**。第三步有两类产物：方案总结报告(Markdown) 和 结构化流程图。"
|
||||
"用户在输入框发了一条消息，请判断他的**真实意图**，只输出一个 JSON 对象（不要解释、不要 markdown 代码块、用英文双引号）：\n"
|
||||
'{"intent":"<枚举>","reason":"<一句话依据>"}\n'
|
||||
"intent 只能取其一：\n"
|
||||
"- regenerate_report：想重新分析 / 重写【方案总结报告】（如「重新生成报告」「报告再写一版」「重新分析一下」）；\n"
|
||||
"- modify_flow：想修改**当前流程图**的某些节点/连线（如「把目的1改成X」「删掉第3个行为体」「加个验收节点」「流程图这里调整下」）；\n"
|
||||
"- regenerate_flow：想**重新生成整张**流程图（如「重新画流程图」「流程图重来」「重新结构化输出」）；\n"
|
||||
"- qa：只是提问 / 咨询，不需要改任何产物（如「为什么选 OTA 平台」「这个目的什么意思」）；\n"
|
||||
"- unclear：意图不明确、无法判断要改报告还是改流程图还是提问（如只说「改一下」「不对」「重来」而没指明对象）。\n"
|
||||
"判断要点：结合下面给出的上下文（是否已有报告 / 是否已有流程图）；当前已有流程图时，含「节点/连线/这一层/这个目的/行为体…改/删/加」之类多半是 modify_flow；"
|
||||
"明确提到「报告」才是 regenerate_report；只有完全无法判断对象时才用 unclear。\n"
|
||||
"示例：\n"
|
||||
'「再帮我生成一次报告」→ {"intent":"regenerate_report","reason":"明确要求重新生成报告"}\n'
|
||||
'「把目的1的名称改短点」→ {"intent":"modify_flow","reason":"要求修改流程图某节点"}\n'
|
||||
'「流程图重新画一张」→ {"intent":"regenerate_flow","reason":"要求重画整张流程图"}\n'
|
||||
'「OTA 平台为什么是直接行为体」→ {"intent":"qa","reason":"纯提问不改产物"}\n'
|
||||
'「改一下」→ {"intent":"unclear","reason":"没说改报告还是流程图"}'
|
||||
)
|
||||
|
||||
|
||||
@router.post("/intent")
|
||||
async def classify_flow_intent(request: Request, payload: FlowIntentRequest = Body(...)) -> dict[str, str]:
|
||||
"""识别第三步输入框消息的意图（一次模型调用）。失败 / 解析不出 → unclear，让前端发起澄清。"""
|
||||
msg = (payload.message or "").strip()
|
||||
if not msg:
|
||||
return {"intent": "unclear", "reason": "空消息"}
|
||||
ctx = f"当前上下文：{'已生成报告' if payload.has_report else '尚无报告'}；{'已生成流程图' if payload.has_flow else '尚无流程图'}。"
|
||||
try:
|
||||
model = create_chat_model(name=_resolve_model(payload.model), thinking_enabled=False)
|
||||
res = await model.ainvoke(
|
||||
[SystemMessage(content=_INTENT_SYS), HumanMessage(content=ctx + "\n\n用户消息：" + msg + "\n\n请输出 JSON：")]
|
||||
)
|
||||
text = res.content if isinstance(res.content, str) else _as_str(getattr(res, "text", ""))
|
||||
obj = _parse_intent_json(text) or {}
|
||||
intent = _as_str(obj.get("intent")).lower()
|
||||
if intent not in _INTENTS:
|
||||
intent = "unclear"
|
||||
return {"intent": intent, "reason": _as_str(obj.get("reason"))}
|
||||
except Exception: # noqa: BLE001 — 分类失败不致命，回退澄清让用户自己定
|
||||
logger.warning("flow intent classify failed", exc_info=True)
|
||||
return {"intent": "unclear", "reason": "意图识别失败"}
|
||||
@@ -65,6 +65,7 @@ from langgraph.checkpoint.base import empty_checkpoint
|
||||
from app.gateway.roundtable_run_policy import merge_skill_stop_names, roundtable_run_policy
|
||||
from app.gateway.routers._roundtable_seed import (
|
||||
COORDINATOR_AGENT_ID,
|
||||
DASHBOARD_AGENT_ID,
|
||||
REPORT_AGENT_ID,
|
||||
SUMMARY_AGENT_ID,
|
||||
ensure_roundtable_functional_agents,
|
||||
@@ -945,6 +946,28 @@ async def init_summary_thread(request: Request) -> dict[str, str]:
|
||||
return {"agent_name": SUMMARY_AGENT_ID, "thread_id": thread_id}
|
||||
|
||||
|
||||
@router.post("/dashboard/init")
|
||||
async def init_dashboard_thread(request: Request) -> dict[str, str]:
|
||||
"""为 Step 3「大屏多页」创建一个供大屏多页数据智能体(``roundtable-dashboard``)
|
||||
运行的线程,返回 ``{"agent_name", "thread_id"}``。
|
||||
|
||||
与 ``/report/init`` / ``/summary/init`` 同构,换成第三个内置单例
|
||||
``roundtable-dashboard``(专职「逐席位分析 → report-json」,run policy 为 ``dashboard``,
|
||||
**禁用一切工具**):
|
||||
- ``roundtable-dashboard`` 是**内置单例**(``_roundtable_seed`` 种子化,启动即同步进 DB),
|
||||
这里**只建一个轻量 thread**,绝不重建 agent;
|
||||
- 线程打 ``_ROUNDTABLE_THREAD_METADATA`` 标记成系统会话,从「最近对话」里隐藏;
|
||||
- 入口先 ``ensure_roundtable_functional_agents()`` 自愈(磁盘目录被手删也能补建);
|
||||
- 建好后前端用现有 ``POST /run/stream``(``agent_type="special"``、
|
||||
``agent_name="roundtable-dashboard"``、``d_agent_thread_id={DASHBOARD_AGENT_ID: thread_id}``)
|
||||
跑这一轮(后续问答/增量改**复用同一线程**,不重新 init),智能体在对话里输出
|
||||
```report-json,前端用 ``extractReportData`` 抽取后按当前主题渲染大屏。
|
||||
"""
|
||||
ensure_roundtable_functional_agents()
|
||||
thread_id = await _create_thread_direct(request, metadata=_ROUNDTABLE_THREAD_METADATA)
|
||||
return {"agent_name": DASHBOARD_AGENT_ID, "thread_id": thread_id}
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# 上游 LangGraph SSE 透传（intent / recommend / leader / special 共用）
|
||||
# ---------------------------------------------------------------------------
|
||||
@@ -1613,6 +1636,9 @@ async def _special_run(
|
||||
role = "report"
|
||||
elif agent_name == SUMMARY_AGENT_ID:
|
||||
role = "summary"
|
||||
elif agent_name == DASHBOARD_AGENT_ID:
|
||||
# 大屏多页数据智能体：纯「研讨成果 → report-json」提炼，禁用一切工具。
|
||||
role = "dashboard"
|
||||
elif no_file:
|
||||
role = "seat_parallel"
|
||||
else:
|
||||
|
||||
@@ -10,18 +10,18 @@ gateway:
|
||||
port: 8001
|
||||
|
||||
models:
|
||||
# - name: deepseek-chat
|
||||
# display_name: DeepSeek Chat
|
||||
# use: langchain_openai:ChatOpenAI
|
||||
# model: deepseek-chat
|
||||
# api_key: sk-e1c22b7931dc446f8b67e57878edd105
|
||||
# base_url: https://api.deepseek.com
|
||||
# request_timeout: 600.0
|
||||
# max_retries: 2
|
||||
# max_tokens: 4096
|
||||
# temperature: 1.0
|
||||
# supports_vision: false
|
||||
# supports_thinking: true
|
||||
- name: deepseek-chat
|
||||
display_name: DeepSeek Chat
|
||||
use: langchain_openai:ChatOpenAI
|
||||
model: deepseek-chat
|
||||
api_key: sk-35345154983047aba9e12faa86c5f62c
|
||||
base_url: https://api.deepseek.com
|
||||
request_timeout: 600.0
|
||||
max_retries: 2
|
||||
max_tokens: 4096
|
||||
temperature: 1.0
|
||||
supports_vision: false
|
||||
supports_thinking: true
|
||||
|
||||
# - name: zai-org/GLM-5-FP8
|
||||
# display_name: zai-org/GLM-5-FP8
|
||||
|
||||
+271
@@ -0,0 +1,271 @@
|
||||
"""Step3「大屏多页」report-json 的健壮抽取 + 归一化（schema coercion）。
|
||||
|
||||
后台与前端共用同一份契约：内置「大屏多页智能体」(``roundtable-dashboard``) 通读各席位
|
||||
交付 + 总控共识，在回复末尾输出一个 ```report-json 代码块描述大屏数据。
|
||||
|
||||
模型实际输出常有偏差——用 ```json 而非 ```report-json、裸 JSON 无围栏、流式截断没闭合、
|
||||
尾逗号 / 单引号等。旧版后台用单一 ``rfind("```report-json")`` + ``json.loads`` 抽取，
|
||||
任何偏差都直接失败（即用户反馈的「大屏多页抽取 json 又失败」）。
|
||||
|
||||
本模块把前端 ``report-template.ts`` 里已验证的多策略抽取 + 字段归一化移植到后端：
|
||||
依次尝试 report-json / json / 无标注 代码块、未闭合围栏尾串、夹在文字里的平衡 ``{…}``
|
||||
对象；每个候选 ``json.loads`` 后过 :func:`coerce_report_data` 校验「像不像本契约」，
|
||||
命中即返回**归一化后的 JSON 字符串**——保证可被前端 ``JSON.parse`` 且字段齐全、
|
||||
``seats`` 的 ``dependsOn`` 只引用真实存在的席位 id。
|
||||
|
||||
纯逻辑、无 app 依赖，放在 harness 供进程内网关与测试复用。
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import re
|
||||
from typing import Any
|
||||
|
||||
_FENCE = "```report-json"
|
||||
# 契约关键键：用来判断一段 JSON「像不像」本大屏契约，避免把无关 {} 误当数据。
|
||||
_LOOKS_LIKE_RE = re.compile(r'"(seats|consensus|kpis|charts|timeline)"\s*:')
|
||||
# 收集所有 ```lang\n...``` 代码块（lang 可空）。
|
||||
_FENCE_RE = re.compile(r"```([^\n`]*)\r?\n(.*?)```", re.DOTALL)
|
||||
|
||||
|
||||
# ── 标量/容器归一化小工具（对齐前端 str/num/arr） ───────────────────────────────
|
||||
|
||||
|
||||
def _as_str(v: Any, fallback: str = "") -> str:
|
||||
return v if isinstance(v, str) else fallback
|
||||
|
||||
|
||||
def _as_num(v: Any) -> float | int:
|
||||
if isinstance(v, bool): # bool 是 int 子类，单独挡掉
|
||||
return 0
|
||||
if isinstance(v, (int, float)):
|
||||
n = v
|
||||
else:
|
||||
try:
|
||||
n = float(v)
|
||||
except (TypeError, ValueError):
|
||||
return 0
|
||||
if isinstance(n, float) and n.is_integer():
|
||||
return int(n)
|
||||
return n
|
||||
|
||||
|
||||
def _as_list(v: Any) -> list[Any]:
|
||||
return v if isinstance(v, list) else []
|
||||
|
||||
|
||||
def _looks_like_report_json(s: str) -> bool:
|
||||
return bool(_LOOKS_LIKE_RE.search(s))
|
||||
|
||||
|
||||
def _collect_fenced_blocks(text: str) -> list[tuple[str, str]]:
|
||||
return [(m.group(1).strip().lower(), m.group(2).strip()) for m in _FENCE_RE.finditer(text)]
|
||||
|
||||
|
||||
def _balanced_json_objects(text: str) -> list[str]:
|
||||
"""扫描出所有顶层平衡的 ``{…}`` 对象（字符串内的花括号不计），用于从夹杂文字里捞裸 JSON。"""
|
||||
out: list[str] = []
|
||||
depth = 0
|
||||
start = -1
|
||||
in_str = False
|
||||
esc = False
|
||||
for i, ch in enumerate(text):
|
||||
if in_str:
|
||||
if esc:
|
||||
esc = False
|
||||
elif ch == "\\":
|
||||
esc = True
|
||||
elif ch == '"':
|
||||
in_str = False
|
||||
continue
|
||||
if ch == '"':
|
||||
in_str = True
|
||||
elif ch == "{":
|
||||
if depth == 0:
|
||||
start = i
|
||||
depth += 1
|
||||
elif ch == "}" and depth > 0:
|
||||
depth -= 1
|
||||
if depth == 0 and start >= 0:
|
||||
out.append(text[start : i + 1])
|
||||
start = -1
|
||||
return out
|
||||
|
||||
|
||||
# ── schema coercion（对齐前端 coerceReportData） ───────────────────────────────
|
||||
|
||||
|
||||
def coerce_report_data(raw: Any) -> dict | None:
|
||||
"""把已 ``json.loads`` 的对象归一化成大屏契约 dict（缺字段给安全默认；非法 → None）。
|
||||
|
||||
规则与前端 ``coerceReportData`` 一致：seats 至少要有 name 才保留；dependsOn 仅保留
|
||||
真实存在且非自指的席位 id；kpis/charts/timeline 过滤空项并限量；整体既无 seats 又无
|
||||
最终结论 headline 时判为「不像本契约」→ None。
|
||||
"""
|
||||
if not isinstance(raw, dict):
|
||||
return None
|
||||
|
||||
seats: list[dict] = []
|
||||
for i, s in enumerate(_as_list(raw.get("seats"))):
|
||||
so = s if isinstance(s, dict) else {}
|
||||
name = _as_str(so.get("name")).strip()
|
||||
if not name:
|
||||
continue
|
||||
sid = _as_str(so.get("id")).strip() or f"seat-{i + 1}"
|
||||
sections = []
|
||||
for sec in _as_list(so.get("sections")):
|
||||
co = sec if isinstance(sec, dict) else {}
|
||||
heading = _as_str(co.get("heading"))
|
||||
bullets = [b for b in (_as_str(x) for x in _as_list(co.get("bullets"))) if b]
|
||||
if heading or bullets:
|
||||
sections.append({"heading": heading, "bullets": bullets})
|
||||
metrics = []
|
||||
for m in _as_list(so.get("metrics")):
|
||||
mo = m if isinstance(m, dict) else {}
|
||||
label = _as_str(mo.get("label"))
|
||||
if label:
|
||||
metrics.append({"label": label, "value": min(100, max(0, _as_num(mo.get("value"))))})
|
||||
risks = []
|
||||
for r in _as_list(so.get("risks")):
|
||||
ro = r if isinstance(r, dict) else {}
|
||||
label = _as_str(ro.get("label"))
|
||||
if not label:
|
||||
continue
|
||||
level = _as_str(ro.get("level"))
|
||||
risks.append({"label": label, "level": level if level in ("high", "mid", "low") else "mid"})
|
||||
depends_on = [d for d in (_as_str(x).strip() for x in _as_list(so.get("dependsOn"))) if d]
|
||||
seats.append(
|
||||
{
|
||||
"id": sid,
|
||||
"name": name,
|
||||
"summary": _as_str(so.get("summary")),
|
||||
"sections": sections,
|
||||
"metrics": metrics,
|
||||
"risks": risks,
|
||||
"dependsOn": depends_on,
|
||||
}
|
||||
)
|
||||
|
||||
# dependsOn 只保留真实存在的席位 id（模型可能编 id），且不自指。
|
||||
id_set = {s["id"] for s in seats}
|
||||
for s in seats:
|
||||
s["dependsOn"] = [d for d in s["dependsOn"] if d in id_set and d != s["id"]]
|
||||
|
||||
consensus_raw = raw.get("consensus") if isinstance(raw.get("consensus"), dict) else {}
|
||||
consensus = {
|
||||
"headline": _as_str(consensus_raw.get("headline")),
|
||||
"points": [p for p in (_as_str(x) for x in _as_list(consensus_raw.get("points"))) if p],
|
||||
}
|
||||
|
||||
kpis = []
|
||||
for k in _as_list(raw.get("kpis")):
|
||||
ko = k if isinstance(k, dict) else {}
|
||||
label = _as_str(ko.get("label"))
|
||||
value = _as_str(ko.get("value"), str(ko.get("value")) if ko.get("value") is not None else "")
|
||||
if not (label and value):
|
||||
continue
|
||||
item: dict[str, Any] = {"label": label, "value": value}
|
||||
unit = _as_str(ko.get("unit"))
|
||||
if unit:
|
||||
item["unit"] = unit
|
||||
trend = _as_str(ko.get("trend"))
|
||||
if trend in ("up", "down", "flat"):
|
||||
item["trend"] = trend
|
||||
kpis.append(item)
|
||||
if len(kpis) >= 6:
|
||||
break
|
||||
|
||||
charts = []
|
||||
for c in _as_list(raw.get("charts")):
|
||||
co = c if isinstance(c, dict) else {}
|
||||
ctype = _as_str(co.get("type"))
|
||||
items = []
|
||||
for it in _as_list(co.get("items")):
|
||||
io = it if isinstance(it, dict) else {}
|
||||
label = _as_str(io.get("label"))
|
||||
if label:
|
||||
items.append({"label": label, "value": _as_num(io.get("value"))})
|
||||
if not items:
|
||||
continue
|
||||
charts.append(
|
||||
{
|
||||
"type": ctype if ctype in ("bar", "donut", "radar", "line") else "bar",
|
||||
"title": _as_str(co.get("title")),
|
||||
"items": items,
|
||||
}
|
||||
)
|
||||
if len(charts) >= 3:
|
||||
break
|
||||
|
||||
timeline = []
|
||||
for t in _as_list(raw.get("timeline")):
|
||||
to = t if isinstance(t, dict) else {}
|
||||
label = _as_str(to.get("label"))
|
||||
if label:
|
||||
timeline.append({"label": label, "desc": _as_str(to.get("desc"))})
|
||||
|
||||
data = {
|
||||
"title": _as_str(raw.get("title"), "方案研讨大屏"),
|
||||
"subtitle": _as_str(raw.get("subtitle")),
|
||||
"kpis": kpis,
|
||||
"consensus": consensus,
|
||||
"charts": charts,
|
||||
"timeline": timeline,
|
||||
"seats": seats,
|
||||
}
|
||||
if not data["seats"] and not data["consensus"]["headline"]:
|
||||
return None
|
||||
return data
|
||||
|
||||
|
||||
# ── 多策略抽取 ───────────────────────────────────────────────────────────────
|
||||
|
||||
|
||||
def extract_report_json(text: str | None) -> str | None:
|
||||
"""从模型回复里健壮抽取大屏 report-json，返回**归一化后的 JSON 字符串**（失败 → None）。
|
||||
|
||||
候选顺序（对齐前端 ``extractReportData``）：
|
||||
1. 代码块，按 lang 优先级 report-json > json/jsonc > 无标注 > 其它，同级取靠后的；
|
||||
2. 未闭合围栏（流式截断）：最后一个 ```report-json 之后到结尾；
|
||||
3. 夹在文字里的所有平衡 ``{…}`` 对象，契约形状的优先、靠后的先试。
|
||||
每个候选 ``json.loads`` 成功后过 :func:`coerce_report_data`，命中即返回归一化 JSON。
|
||||
"""
|
||||
if not text:
|
||||
return None
|
||||
|
||||
def rank(lang: str) -> int:
|
||||
if lang == "report-json":
|
||||
return 0
|
||||
if lang in ("json", "jsonc"):
|
||||
return 1
|
||||
if lang == "":
|
||||
return 2
|
||||
return 3
|
||||
|
||||
candidates: list[str] = []
|
||||
blocks = list(enumerate(_collect_fenced_blocks(text)))
|
||||
blocks.sort(key=lambda bi: (rank(bi[1][0]), -bi[0]))
|
||||
candidates.extend(body for _, (_, body) in blocks)
|
||||
|
||||
# 未闭合围栏（流式截断）：取最后一个 report-json fence 之后到结尾。
|
||||
f = text.rfind(_FENCE)
|
||||
if f >= 0:
|
||||
candidates.append(text[f + len(_FENCE) :].strip())
|
||||
|
||||
# 裸 JSON（无围栏 / 夹在文字里）：契约形状的先试，再试其余；都按靠后优先。
|
||||
objs = _balanced_json_objects(text)
|
||||
candidates.extend(o for o in reversed(objs) if _looks_like_report_json(o))
|
||||
candidates.extend(reversed(objs))
|
||||
|
||||
for body in candidates:
|
||||
if not body:
|
||||
continue
|
||||
try:
|
||||
raw = json.loads(body)
|
||||
except (ValueError, TypeError):
|
||||
continue
|
||||
data = coerce_report_data(raw)
|
||||
if data is not None:
|
||||
return json.dumps(data, ensure_ascii=False)
|
||||
return None
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
"""大屏绘制智能体 session persistence (按 taskId 存取，不分权)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from deerflow.persistence.dashboard_sessions.model import DashboardSessionRow
|
||||
from deerflow.persistence.dashboard_sessions.sql import DashboardSessionRepository
|
||||
|
||||
__all__ = [
|
||||
"DashboardSessionRepository",
|
||||
"DashboardSessionRow",
|
||||
"make_dashboard_session_store",
|
||||
]
|
||||
|
||||
|
||||
def make_dashboard_session_store(session_factory):
|
||||
if session_factory is None:
|
||||
return None
|
||||
return DashboardSessionRepository(session_factory)
|
||||
+49
@@ -0,0 +1,49 @@
|
||||
"""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),
|
||||
)
|
||||
+104
@@ -0,0 +1,104 @@
|
||||
"""SQLAlchemy-backed 大屏绘制智能体 session store (按 taskId，不分权).
|
||||
|
||||
``transcript`` / ``report_json`` are JSON serialized into ``PortableLongText``
|
||||
and round-tripped here with ``json.dumps`` / ``json.loads`` — same approach as
|
||||
``roundtable_drafts``. All access keys on ``task_id`` and ignores ``user_id``
|
||||
(open read + shared writes); ``user_id`` is recorded for audit only.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from datetime import UTC, datetime
|
||||
from typing import Any
|
||||
from uuid import uuid4
|
||||
|
||||
from sqlalchemy import select
|
||||
from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker
|
||||
|
||||
from deerflow.persistence.dashboard_sessions.model import DashboardSessionRow
|
||||
|
||||
|
||||
def _loads(value: Any) -> Any:
|
||||
if not isinstance(value, str):
|
||||
return value
|
||||
if not value:
|
||||
return None
|
||||
try:
|
||||
return json.loads(value)
|
||||
except Exception:
|
||||
return None
|
||||
|
||||
|
||||
def _dumps(value: Any) -> str | None:
|
||||
if value is None:
|
||||
return None
|
||||
if isinstance(value, str):
|
||||
return value # already a JSON string (report_json) — store verbatim
|
||||
return json.dumps(value, ensure_ascii=False)
|
||||
|
||||
|
||||
class DashboardSessionRepository:
|
||||
def __init__(self, session_factory: async_sessionmaker[AsyncSession]) -> None:
|
||||
self._sf = session_factory
|
||||
|
||||
@staticmethod
|
||||
def _to_dict(row: DashboardSessionRow) -> dict[str, Any]:
|
||||
return {
|
||||
"id": row.id,
|
||||
"task_id": row.task_id,
|
||||
"user_id": row.user_id,
|
||||
"transcript": _loads(row.transcript),
|
||||
# report_json is a structured-data JSON string; return it as a string
|
||||
# so the frontend parses it with the same robust parser it uses live.
|
||||
"report_json": row.report_json,
|
||||
"created_at": row.created_at.isoformat() if isinstance(row.created_at, datetime) else row.created_at,
|
||||
"updated_at": row.updated_at.isoformat() if isinstance(row.updated_at, datetime) else row.updated_at,
|
||||
}
|
||||
|
||||
async def get_by_task(self, task_id: str) -> dict[str, Any] | None:
|
||||
"""取该 task 的会话（**忽略 user_id**，不分权）。无则 None。"""
|
||||
stmt = select(DashboardSessionRow).where(DashboardSessionRow.task_id == task_id).limit(1)
|
||||
async with self._sf() as session:
|
||||
result = await session.execute(stmt)
|
||||
row = result.scalars().first()
|
||||
return self._to_dict(row) if row is not None else None
|
||||
|
||||
async def upsert_by_task(
|
||||
self,
|
||||
task_id: str,
|
||||
*,
|
||||
transcript: Any = None,
|
||||
report_json: Any = None,
|
||||
user_id: str | None = None,
|
||||
) -> dict[str, Any]:
|
||||
"""按 task_id upsert：无则建、有则更新（**不分权**）。
|
||||
|
||||
``transcript`` / ``report_json`` 仅在传入非 None 时覆盖，便于只更新其中一项。
|
||||
"""
|
||||
now = datetime.now(UTC)
|
||||
async with self._sf() as session:
|
||||
stmt = select(DashboardSessionRow).where(DashboardSessionRow.task_id == task_id).limit(1)
|
||||
row = (await session.execute(stmt)).scalars().first()
|
||||
if row is None:
|
||||
row = DashboardSessionRow(
|
||||
id=uuid4().hex,
|
||||
task_id=task_id,
|
||||
user_id=user_id,
|
||||
transcript=_dumps(transcript),
|
||||
report_json=_dumps(report_json),
|
||||
created_at=now,
|
||||
updated_at=now,
|
||||
)
|
||||
session.add(row)
|
||||
else:
|
||||
if transcript is not None:
|
||||
row.transcript = _dumps(transcript)
|
||||
if report_json is not None:
|
||||
row.report_json = _dumps(report_json)
|
||||
if user_id is not None:
|
||||
row.user_id = user_id
|
||||
row.updated_at = now
|
||||
await session.commit()
|
||||
await session.refresh(row)
|
||||
return self._to_dict(row)
|
||||
+2
@@ -23,6 +23,7 @@ from deerflow.persistence.agents.model import (
|
||||
)
|
||||
from deerflow.persistence.admin_stats.model import AdminLeaderboardDailyStatRow
|
||||
from deerflow.persistence.business_mapping.model import BusinessMappingRow
|
||||
from deerflow.persistence.dashboard_sessions.model import DashboardSessionRow
|
||||
from deerflow.persistence.memory_config.model import UserMemoryConfigRow
|
||||
from deerflow.persistence.feedback.model import FeedbackRow
|
||||
from deerflow.knowledge.models import (
|
||||
@@ -81,6 +82,7 @@ __all__ = [
|
||||
"AgentSkillRow",
|
||||
"AdminLeaderboardDailyStatRow",
|
||||
"BusinessMappingRow",
|
||||
"DashboardSessionRow",
|
||||
"KnowledgeEmbeddingRow",
|
||||
"KnowledgeEntityRow",
|
||||
"KnowledgeExtractTemplateRow",
|
||||
|
||||
Reference in new issue
Block a user