第三步结构化输出优化
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@@ -0,0 +1,106 @@
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/**
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* Step3 流程图「后端抽取 + 校验」SSE 客户端。
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*
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* 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
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* 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
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* → 返回校验后的结构化图数据 `{title, nodes, edges}`。
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*
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* 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。
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*/
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import { apiFetch } from "@/core/api/fetch-client";
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import { getBackendBaseURL } from "@/core/config";
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import type { FlowGraphData } from "@/roundtable-planning/lib/flow-graph";
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export interface FlowExtractRequest {
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/** 报告 Markdown（优先数据来源）。 */
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reportMd?: string | null;
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/** 各席位交付（无报告时的数据来源）。 */
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seatDeliveries?: Array<{ sender: string; content: string }>;
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/** 业务链层级（stages 的 goal）。 */
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chainLevels?: string[];
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/** 业务链总控编排提示（写明层级时优先用它）。 */
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coordinatorPrompt?: string | null;
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/** 推理模型名（沿用第三步所选）。 */
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model?: string;
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/** Phase 2 问答修改：当前流程图 + 指令 → 按指令改图（其余不变）。 */
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currentGraph?: FlowGraphData | null;
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instruction?: string;
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signal?: AbortSignal;
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/** 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）。 */
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onExtractDelta?: (text: string) => void;
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/** 抽取结束、进入校验阶段。 */
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onValidating?: () => void;
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/** 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）。 */
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onValidateDelta?: (text: string) => void;
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}
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/**
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* 消费 SSE，返回校验后的最终图。抽取阶段通过 `onExtractDelta` 实时回吐；
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* 出错（后端 error 帧 / 网络）抛异常。
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*/
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export async function streamFlowExtract(req: FlowExtractRequest): Promise<FlowGraphData> {
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const res = await apiFetch(`${getBackendBaseURL()}/api/flow-extract/stream`, {
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method: "POST",
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify({
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report_md: req.reportMd ?? null,
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seat_deliveries: (req.seatDeliveries ?? []).map((s) => ({ sender: s.sender, content: s.content })),
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chain_levels: req.chainLevels ?? [],
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coordinator_prompt: req.coordinatorPrompt ?? null,
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model: req.model,
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current_graph: req.currentGraph ?? null,
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instruction: req.instruction ?? null,
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}),
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signal: req.signal,
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});
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if (!res.ok || !res.body) {
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const text = await res.text().catch(() => "");
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throw new Error(`flow-extract failed (${res.status}): ${text}`);
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}
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const reader = res.body.getReader();
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const decoder = new TextDecoder();
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let buffer = "";
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let graph: FlowGraphData | null = null;
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let errored: string | null = null;
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while (true) {
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const { done, value } = await reader.read();
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if (done) break;
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buffer += decoder.decode(value, { stream: true });
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const lines = buffer.split("\n");
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buffer = lines.pop() ?? "";
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for (const rawLine of lines) {
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const line = rawLine.trimEnd();
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if (!line.startsWith("data:")) continue;
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const data = line.slice(5).trim();
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if (!data) continue;
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let frame: Record<string, unknown>;
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try {
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frame = JSON.parse(data) as Record<string, unknown>;
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} catch {
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continue;
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}
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const event = typeof frame.event === "string" ? frame.event : "";
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if (event === "extract_delta") {
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const c = typeof frame.content === "string" ? frame.content : "";
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if (c) req.onExtractDelta?.(c);
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} else if (event === "validating") {
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req.onValidating?.();
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} else if (event === "validate_delta") {
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const c = typeof frame.content === "string" ? frame.content : "";
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if (c) req.onValidateDelta?.(c);
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} else if (event === "final") {
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graph = (frame.graph as FlowGraphData) ?? null;
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} else if (event === "error") {
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errored = typeof frame.detail === "string" ? frame.detail : "未知错误";
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}
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}
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}
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if (errored) throw new Error(errored);
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if (!graph || !Array.isArray(graph.nodes) || graph.nodes.length === 0) {
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throw new Error("flow-extract 未返回有效流程图");
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}
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return graph;
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}
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@@ -15,6 +15,7 @@ import {
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useRef,
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useState,
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} from "react";
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import { createPortal } from "react-dom";
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import {
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applyNodeChanges,
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Background,
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@@ -32,11 +33,14 @@ import {
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} from "@xyflow/react";
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import "@xyflow/react/dist/style.css";
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import {
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Check,
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FileText,
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Loader2,
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LocateFixed,
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Maximize2,
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Minimize2,
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Pencil,
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Trash2,
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Workflow,
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X,
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ZoomIn,
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@@ -45,6 +49,8 @@ import {
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import { cn } from "@/lib/utils";
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import {
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applyNodeEdit,
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deleteNodeFromGraph,
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FLOW_NODE_TYPE_META,
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FLOW_NODE_W,
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layoutFlowGraph,
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@@ -59,6 +65,8 @@ interface FlowGraphPanelProps {
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onClose: () => void;
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/** 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。 */
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onSwitchToReport?: () => void;
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/** 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。 */
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onChange?: (next: FlowGraphData) => void;
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}
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// ── 自定义节点（按 type 着色的卡片）────────────────────────────────────────
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@@ -106,6 +114,7 @@ function FlowCanvasInner({
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onToggleFullscreen,
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onClose,
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onSwitchToReport,
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onChange,
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}: FlowGraphPanelProps & { fullscreen: boolean; onToggleFullscreen: () => void }) {
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const rf = useReactFlow();
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const [selectedId, setSelectedId] = useState<string | null>(null);
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@@ -276,13 +285,14 @@ function FlowCanvasInner({
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</div>
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</div>
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{/* 节点详情弹窗 */}
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{/* 节点详情弹窗（传入 onChange 时可编辑 / 删除节点） */}
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{selectedNode && (
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<NodeDetailModal
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node={selectedNode}
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data={data}
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onClose={() => setSelectedId(null)}
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onJump={(id) => setSelectedId(id)}
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onChange={onChange}
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/>
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)}
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</div>
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@@ -317,17 +327,38 @@ function NodeDetailModal({
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data,
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onClose,
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onJump,
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onChange,
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}: {
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node: FlowGraphNode;
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data: FlowGraphData;
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onClose: () => void;
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onJump: (id: string) => void;
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/** Phase 3：传入则可编辑 / 删除该节点（回写整张新图）。 */
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onChange?: (next: FlowGraphData) => void;
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}) {
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const meta = FLOW_NODE_TYPE_META[node.type];
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const labelOf = (id: string) => data.nodes.find((n) => n.id === id)?.label ?? id;
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const incoming = data.edges.filter((e) => e.to === node.id);
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const outgoing = data.edges.filter((e) => e.from === node.id);
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const [editing, setEditing] = useState(false);
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const [label, setLabel] = useState(node.label);
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const [group, setGroup] = useState(node.group ?? "");
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const [detail, setDetail] = useState(node.detail ?? "");
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const handleSave = () => {
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onChange?.(applyNodeEdit(data, node.id, { label, group, detail }));
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setEditing(false);
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onClose();
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};
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const handleDelete = () => {
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if (typeof window !== "undefined" && !window.confirm(`删除节点「${node.label}」及其连线？此操作不可撤销。`)) {
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return;
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}
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onChange?.(deleteNodeFromGraph(data, node.id));
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onClose();
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};
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return (
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<div
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className="absolute inset-0 z-30 flex items-center justify-center bg-black/30 p-4 backdrop-blur-[1px]"
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@@ -361,27 +392,110 @@ function NodeDetailModal({
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<X className="h-4 w-4" />
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</button>
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</div>
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<div className="max-h-[50vh] space-y-3 overflow-y-auto p-4">
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<div>
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<div className="mb-1 text-[10px] font-bold uppercase tracking-widest text-slate-400">
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详细说明
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</div>
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<p className="text-xs leading-relaxed text-slate-700">
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{node.detail?.trim() || "（该节点未提供详细说明）"}
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</p>
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{editing ? (
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<div className="max-h-[55vh] space-y-3 overflow-y-auto p-4">
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<EditField label="节点名称（label）" value={label} onChange={setLabel} />
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<EditField label="所属层级（group，如 目的 / 行为体）" value={group} onChange={setGroup} />
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<EditField label="详细内容（detail，名称+理由）" value={detail} onChange={setDetail} multiline />
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</div>
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{incoming.length > 0 && (
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<ConnectionList title="上游（来自）" entries={incoming.map((e) => ({ id: e.from, label: labelOf(e.from), edgeLabel: e.label }))} onJump={onJump} />
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)}
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{outgoing.length > 0 && (
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<ConnectionList title="下游（去往）" entries={outgoing.map((e) => ({ id: e.to, label: labelOf(e.to), edgeLabel: e.label }))} onJump={onJump} />
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)}
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</div>
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) : (
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<div className="max-h-[50vh] space-y-3 overflow-y-auto p-4">
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<div>
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<div className="mb-1 text-[10px] font-bold uppercase tracking-widest text-slate-400">详细说明</div>
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<p className="text-xs leading-relaxed text-slate-700 whitespace-pre-wrap">
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{node.detail?.trim() || "（该节点未提供详细说明）"}
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</p>
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</div>
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{incoming.length > 0 && (
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<ConnectionList title="上游（来自）" entries={incoming.map((e) => ({ id: e.from, label: labelOf(e.from), edgeLabel: e.label }))} onJump={onJump} />
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)}
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{outgoing.length > 0 && (
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<ConnectionList title="下游（去往）" entries={outgoing.map((e) => ({ id: e.to, label: labelOf(e.to), edgeLabel: e.label }))} onJump={onJump} />
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)}
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</div>
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)}
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{/* 编辑 / 删除 操作栏（仅传入 onChange 时显示） */}
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{onChange && (
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<div className="flex items-center justify-between gap-2 border-t border-slate-100 bg-slate-50/60 px-4 py-2.5">
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{editing ? (
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<>
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<button
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type="button"
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onClick={() => setEditing(false)}
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className="rounded-lg px-2.5 py-1.5 text-[11px] font-bold text-slate-500 hover:bg-slate-200/60"
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>
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取消
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</button>
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<button
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type="button"
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onClick={handleSave}
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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)]"
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>
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<Check className="h-3.5 w-3.5" />
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保存
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</button>
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</>
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) : (
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<>
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<button
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type="button"
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onClick={handleDelete}
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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"
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>
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<Trash2 className="h-3.5 w-3.5" />
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删除节点
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</button>
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<button
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type="button"
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onClick={() => setEditing(true)}
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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"
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>
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<Pencil className="h-3.5 w-3.5" />
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编辑
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</button>
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</>
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)}
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</div>
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)}
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</div>
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</div>
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);
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}
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function EditField({
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label,
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value,
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onChange,
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multiline,
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}: {
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label: string;
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value: string;
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onChange: (v: string) => void;
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multiline?: boolean;
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}) {
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return (
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<div>
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<div className="mb-1 text-[10px] font-bold uppercase tracking-widest text-slate-400">{label}</div>
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{multiline ? (
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<textarea
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value={value}
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onChange={(e) => onChange(e.target.value)}
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rows={4}
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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"
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/>
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) : (
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<input
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value={value}
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onChange={(e) => onChange(e.target.value)}
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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"
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/>
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)}
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</div>
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);
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}
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function ConnectionList({
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title,
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entries,
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@@ -420,14 +534,15 @@ function ConnectionList({
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// ── 外壳：覆盖右栏 / 全屏切换 ───────────────────────────────────────────────
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export function FlowGraphPanel({ data, streaming, onClose, onSwitchToReport }: FlowGraphPanelProps) {
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export function FlowGraphPanel({ data, streaming, onClose, onSwitchToReport, onChange }: FlowGraphPanelProps) {
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const [fullscreen, setFullscreen] = useState(false);
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return (
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const inner = (
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<div
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className={cn(
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"flex flex-col bg-white",
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// 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。
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fullscreen ? "fixed inset-0 z-[80]" : "relative h-full min-h-0 w-full",
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// 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）；
|
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// 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」）。
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fullscreen ? "fixed inset-0 z-[9999]" : "relative h-full min-h-0 w-full",
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)}
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>
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<ReactFlowProvider>
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@@ -438,8 +553,15 @@ export function FlowGraphPanel({ data, streaming, onClose, onSwitchToReport }: F
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onToggleFullscreen={() => setFullscreen((f) => !f)}
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onClose={onClose}
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onSwitchToReport={onSwitchToReport}
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onChange={onChange}
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/>
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</ReactFlowProvider>
|
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</div>
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);
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// 全屏时 portal 到 body：脱离任何带 transform/overflow 的祖先（Step2 沙箱分栏等），
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// 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）。
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if (fullscreen && typeof document !== "undefined") {
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return createPortal(inner, document.body);
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}
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return inner;
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}
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@@ -46,7 +46,6 @@ import { FlowGraphPanel } from "@/roundtable-planning/components/FlowGraphPanel"
|
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import {
|
||||
detectFlowIntent,
|
||||
extractFlowGraph,
|
||||
stripFlowFence,
|
||||
FLOW_FENCE_OPEN,
|
||||
} from "@/roundtable-planning/lib/flow-graph";
|
||||
import { ScrollToBottomButton } from "@/roundtable-planning/components/ScrollToBottomButton";
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||||
@@ -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 +410,8 @@ export function Step3SummaryMode({
|
||||
error={error}
|
||||
busy={busy}
|
||||
mdAvailable={!!mdArtifact}
|
||||
flowPhase={summary.flowPhase}
|
||||
onRegenerateFlow={summary.regenerateFlow}
|
||||
onRetry={redraw}
|
||||
onAsk={handleAsk}
|
||||
onPause={pause}
|
||||
@@ -549,6 +554,8 @@ function AgentProgressPanel({
|
||||
error,
|
||||
busy,
|
||||
mdAvailable,
|
||||
flowPhase,
|
||||
onRegenerateFlow,
|
||||
onRetry,
|
||||
onAsk,
|
||||
onPause,
|
||||
@@ -565,6 +572,10 @@ 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;
|
||||
onRetry: () => void;
|
||||
onAsk: (message: string, files?: File[]) => void;
|
||||
onPause: () => void;
|
||||
@@ -705,6 +716,28 @@ 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" />
|
||||
|
||||
@@ -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 { streamFlowExtract } 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 +80,14 @@ 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;
|
||||
summaryThreadId: string | null;
|
||||
dialogues: Step2Dialogue[];
|
||||
seatStubMessages: Record<string, StubAiMessage[]>;
|
||||
@@ -220,61 +229,9 @@ 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 +409,14 @@ 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]);
|
||||
const [summaryThreadId, setSummaryThreadId] = useState<string | null>(null);
|
||||
const [dialogues, setDialogues] = useState<Step2Dialogue[]>([]);
|
||||
const [seatStubMessages, setSeatStubMessages] = useState<
|
||||
@@ -1010,6 +975,110 @@ 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 },
|
||||
): 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。
|
||||
const extractBubbleId = appendStreamingDialogue({
|
||||
sender: isEdit ? "结构化流程图 · 修改" : "结构化流程图 · 抽取",
|
||||
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,
|
||||
],
|
||||
);
|
||||
|
||||
/** 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 +1090,8 @@ export function useStep3Summary(
|
||||
currentMdRef.current = "";
|
||||
liveThreadRef.current = null;
|
||||
setSeatStubMessages({});
|
||||
setFlowGraphState(null);
|
||||
setFlowPhase("idle");
|
||||
|
||||
const aborter = new AbortController();
|
||||
aborterRef.current = aborter;
|
||||
@@ -1044,33 +1115,16 @@ 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;
|
||||
@@ -1087,11 +1141,11 @@ export function useStep3Summary(
|
||||
seatDeliveries,
|
||||
consensusText,
|
||||
coordinatorPrompt,
|
||||
chainLevels,
|
||||
autoFlow,
|
||||
appendStreamingDialogue,
|
||||
finalizeDialogue,
|
||||
streamOnce,
|
||||
runFlowExtract,
|
||||
]);
|
||||
|
||||
const ask = useCallback(
|
||||
@@ -1106,6 +1160,25 @@ export function useStep3Summary(
|
||||
abort();
|
||||
if (wasBusy) freezeStreaming("已打断");
|
||||
|
||||
// 流程图相关消息 → 走后端「抽取/编辑 + 校验」端点（流式显示过程 → 校验后绘制），
|
||||
// 不再在总结线程吐 flow-json 做实时绘制。判定：
|
||||
// · 已有流程图 且（显式 flow 关键词 或 上一轮主产物是流程图）→ **问答修改**（带当前图+指令，Phase 2）；
|
||||
// · 还没有流程图 但显式要流程图 → **首次生成**。
|
||||
// 普通报告修改 / 问答（无 flow 上下文）仍走下方总结线程路径。
|
||||
const flowIntentNow = detectFlowIntent(text);
|
||||
const curGraph = flowGraphRef.current;
|
||||
if (flowIntentNow || (curGraph && flowActiveRef.current)) {
|
||||
appendUserDialogue(text);
|
||||
const flowAborter = new AbortController();
|
||||
aborterRef.current = flowAborter;
|
||||
if (curGraph) {
|
||||
await runFlowExtract(flowAborter, { instruction: text, currentGraph: curGraph });
|
||||
} else {
|
||||
await runFlowExtract(flowAborter);
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
// 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）；
|
||||
@@ -1219,6 +1292,7 @@ export function useStep3Summary(
|
||||
appendUserDialogue,
|
||||
appendStreamingDialogue,
|
||||
streamOnce,
|
||||
runFlowExtract,
|
||||
],
|
||||
);
|
||||
|
||||
@@ -1247,6 +1321,11 @@ 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 +1409,10 @@ export function useStep3Summary(
|
||||
phase,
|
||||
error,
|
||||
autoFlow: !!autoFlow,
|
||||
flowGraph,
|
||||
flowPhase,
|
||||
regenerateFlow,
|
||||
setFlowGraph: updateFlowGraph,
|
||||
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。
|
||||
|
||||
@@ -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,
|
||||
|
||||
@@ -57,6 +57,7 @@ from app.gateway.routers import (
|
||||
public_leaderboard,
|
||||
public_scheduled_tasks,
|
||||
recommend,
|
||||
flow_extract,
|
||||
recommended_questions,
|
||||
roundtable_chains,
|
||||
roundtable_draft_shares,
|
||||
@@ -1333,6 +1334,9 @@ 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)
|
||||
|
||||
|
||||
@@ -0,0 +1,510 @@
|
||||
"""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 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)
|
||||
|
||||
|
||||
# ── 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)
|
||||
return {"title": title, "nodes": nodes, "edges": valid_edges}
|
||||
|
||||
|
||||
# ── 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(该条目名称+理由要点,约2-4句,**写成一行**不要内含换行)、"
|
||||
"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 连边严格按报告里的对应关系串成分支:根任务→各目的;每个目的→它对应的行为体;每个行为体→对应的预期行为·驱动因素→对应的叙事……每组各成一条自上而下的分支(有几组就几条,组间不混连);"
|
||||
"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。"
|
||||
|
||||
|
||||
def _extract_messages(req: FlowExtractRequest) -> list[Any]:
|
||||
sys = (
|
||||
"你是「流程图结构化抽取器」。你的唯一产物是一张流程图的 flow-json 数据(节点+连线),"
|
||||
"不是文字报告、不是章节、不是结论清单。只输出一个 ```flow-json 代码块,不要写任何其它正文。全程简体中文。\n"
|
||||
+ _SHAPE
|
||||
+ "\n"
|
||||
+ _STRUCTURE
|
||||
)
|
||||
user = _material(req) + "\n\n" + _hierarchy_hint(req) + "\n\n请据此输出一个完整的 ```flow-json 代码块。"
|
||||
return [SystemMessage(content=sys), 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)
|
||||
sys = (
|
||||
"你是「流程图编辑器」。下面给你 当前流程图(flow-json) + 数据来源 + 用户的修改指令。"
|
||||
"请**按用户指令修改流程图**（可增/删/改节点、改连线、改 label/group/detail），"
|
||||
"**未被指令涉及的部分保持原样**，输出**修改后的完整 flow-json**(只输出一个 ```flow-json 代码块,不要解释)。"
|
||||
"节点 id 尽量沿用原有的；删节点要同时去掉与它相关的连线。全程简体中文。\n"
|
||||
+ _SHAPE
|
||||
)
|
||||
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()
|
||||
)
|
||||
return [SystemMessage(content=sys), HumanMessage(content=user)]
|
||||
|
||||
|
||||
def _validate_messages(req: FlowExtractRequest, extracted: str) -> list[Any]:
|
||||
sys = (
|
||||
"你是「流程图校验修复器」。下面给你 数据来源 和 一份已抽取的 flow-json。"
|
||||
"请对照数据来源校验并修复这份 flow-json,输出**修复后**的完整 flow-json(只输出一个 ```flow-json 代码块,不要解释)。\n"
|
||||
"校验要点:① 根节点是任务本身(group=任务);② 每个层级的每一条具体条目都在(没漏层、没漏条目);"
|
||||
"③ 连边按对应关系成分支、无悬空边、根可达;④ 每个节点都有 group(层级名),label≤12字,detail 为单行的名称+理由要点;"
|
||||
"⑤ 没有『综合分析/执行路线/结论』及『阶段编号』这类无关节点。发现问题就改对,没问题就原样规范化输出。\n"
|
||||
+ _SHAPE
|
||||
)
|
||||
user = _material(req) + "\n\n===== 已抽取的 flow-json（待校验修复）=====\n" + extracted + "\n===== 待校验结束 ====="
|
||||
return [SystemMessage(content=sys), 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。"""
|
||||
|
||||
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=payload.model or None, 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=payload.model or None, 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
|
||||
if graph is None:
|
||||
graph = parse_flow_graph(extracted)
|
||||
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"},
|
||||
)
|
||||
@@ -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
|
||||
|
||||
Reference in new issue
Block a user