意图理解
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董宏凯 committed
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@@ -40,6 +40,8 @@ import { AIWritingTimeline } from './AIWritingTimeline';
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import { InterventionCard } from './InterventionCard';
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import { WritingSkeletonCard } from './WritingSkeletonCard';
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import { DraftStreamingView } from './DraftStreamingView';
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import { IntentResultCard } from './IntentResultCard';
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import { IntentStreamingView } from './IntentStreamingView';
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import { OutlineStreamingView } from './OutlineStreamingView';
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import { ReviewStreamingView } from './ReviewStreamingView';
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import { RevisionSummaryCard } from './RevisionSummaryCard';
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@@ -343,6 +345,17 @@ export function AIWritingPanel() {
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<AIWritingForm onSubmit={startWriting} />
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)}
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{/* 意图解析：流式 → 结果卡片承接。
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放在 timeline 之上 —— 解析后 timeline 会从 step_done 起一直长，
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如果把意图块塞在 timeline 下面就会随 timeline 增长被推走；
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放上面则恒定锚定在内容最顶部，刷到底也能滚回顶部看清楚意图理解结果。 */}
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{(state.intentStreamingText || state.intentThinkingText) && (
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<IntentStreamingView text={state.intentStreamingText} thinkingText={state.intentThinkingText} />
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)}
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{state.parsedIntent && !state.intentStreamingText && (
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<IntentResultCard intent={state.parsedIntent} articleTypes={articleTypes} />
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)}
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{/* 运行中或完成后显示时间线 */}
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{!isIdle && (
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<AIWritingTimeline
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@@ -394,7 +407,9 @@ export function AIWritingPanel() {
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{showSkeleton &&
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state.status !== 'writing_draft' &&
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!(state.status === 'writing_outline' && (state.outlineStreamingText || state.outlineThinkingText)) &&
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!(state.status === 'reviewing' && state.streamingReviewItems.length > 0) && (
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!(state.status === 'reviewing' && state.streamingReviewItems.length > 0) &&
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/* 意图解析流式过程中（LLM 还在吐 JSON）不要显示 skeleton */
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!state.intentStreamingText && !state.intentThinkingText && (
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<div className="px-4 pb-4">
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<WritingSkeletonCard status={state.status} researchProgress={state.researchProgress} />
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</div>
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@@ -0,0 +1,51 @@
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import { Sparkles } from 'lucide-react';
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import type { ParsedIntent } from '../../types/ai-writing';
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import type { ArticleType } from '../../api/ai-writing-article-types';
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interface IntentResultCardProps {
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intent: ParsedIntent;
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articleTypes: ArticleType[];
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}
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/**
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* 意图解析完成后的结构化卡片。展示 LLM 把用户原始意图理解成的四项：
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* 文章类型 / 目标读者 / 字数 / 精炼意图。在素材收集开始前作为"承上启下"的视觉锚点。
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*/
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export function IntentResultCard({ intent, articleTypes }: IntentResultCardProps) {
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// article_type 后端给的是 key（如 "analysis"），UI 展示用 label（如 "分析报道"）
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const typeLabel =
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articleTypes.find((t) => t.key === intent.articleType)?.label ?? intent.articleType;
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return (
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<div className="px-4 pb-2">
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<div className="rounded-xl border border-blue-200 dark:border-blue-900 bg-blue-50/40 dark:bg-blue-950/20 overflow-hidden shadow-sm">
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<div className="flex items-center gap-2 px-3 py-2 border-b border-blue-200/60 dark:border-blue-900/60 bg-blue-100/40 dark:bg-blue-900/30">
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<Sparkles className="size-3.5 text-blue-500 shrink-0" />
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<span className="text-xs font-medium text-blue-600 dark:text-blue-400">
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意图解析完成
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</span>
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</div>
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<div className="px-3 py-2.5 space-y-1.5 text-xs">
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<Row label="文章类型" value={typeLabel} />
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<Row label="目标读者" value={intent.targetAudience} />
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<Row label="字数目标" value={`${intent.wordCountTarget} 字`} />
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{intent.refinedIntent && (
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<Row label="精炼意图" value={intent.refinedIntent} multiline />
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)}
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</div>
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</div>
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</div>
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);
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}
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function Row({ label, value, multiline }: { label: string; value: string; multiline?: boolean }) {
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return (
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<div className={multiline ? 'flex flex-col gap-0.5' : 'flex items-baseline gap-2'}>
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<span className="shrink-0 text-muted-foreground">{label}</span>
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<span className={multiline ? 'text-foreground leading-relaxed' : 'text-foreground truncate'}>
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{value}
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</span>
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</div>
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);
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}
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@@ -0,0 +1,101 @@
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import { useEffect, useRef, useState } from 'react';
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import { Sparkles, ChevronDown, ChevronRight, Brain } from 'lucide-react';
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interface IntentStreamingViewProps {
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text: string;
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thinkingText?: string;
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}
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/**
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* 意图解析流式视图：与 OutlineStreamingView / DraftStreamingView 同样的打字机机制，
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* 颜色用蓝色主题区分意图理解阶段。展示 LLM 边吐 JSON 边滚动的过程，结果由
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* IntentResultCard 接力展示结构化字段。
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*/
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export function IntentStreamingView({ text, thinkingText }: IntentStreamingViewProps) {
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const preRef = useRef<HTMLPreElement>(null);
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const thinkRef = useRef<HTMLPreElement>(null);
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const [thinkOpen, setThinkOpen] = useState(true);
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useEffect(() => {
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if (preRef.current) {
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preRef.current.scrollTop = preRef.current.scrollHeight;
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}
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}, [text]);
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useEffect(() => {
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if (thinkOpen && thinkRef.current) {
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thinkRef.current.scrollTop = thinkRef.current.scrollHeight;
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}
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}, [thinkingText, thinkOpen]);
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if (!text && !thinkingText) return null;
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return (
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<div className="px-4 pb-2 space-y-2">
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{thinkingText && (
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<div className="rounded-xl border border-amber-200 dark:border-amber-900 bg-amber-50/50 dark:bg-amber-950/20 overflow-hidden shadow-sm">
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<button
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type="button"
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onClick={() => setThinkOpen((v) => !v)}
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className="w-full flex items-center gap-2 px-3 py-2 border-b border-amber-200/60 dark:border-amber-900/60 bg-amber-100/40 dark:bg-amber-900/30 hover:bg-amber-100/70 dark:hover:bg-amber-900/50 transition-colors"
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>
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<Brain className="size-3.5 text-amber-500 shrink-0" />
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<span className="text-xs font-medium text-amber-600 dark:text-amber-400">
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思考过程
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</span>
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<span className="ml-auto text-amber-400">
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{thinkOpen
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? <ChevronDown className="size-3.5" />
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: <ChevronRight className="size-3.5" />}
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</span>
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</button>
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{thinkOpen && (
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<pre
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ref={thinkRef}
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className="text-[11px] font-mono leading-relaxed p-3 text-amber-700/70 dark:text-amber-300/60 whitespace-pre-wrap max-h-40 overflow-y-auto italic"
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>
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{thinkingText}
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{!text && (
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<span className="inline-block w-0.5 h-3.5 bg-amber-400 ml-0.5 align-text-bottom animate-[blink_1s_step-end_infinite]" />
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)}
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</pre>
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)}
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</div>
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)}
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{text && (
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<div className="rounded-xl border border-blue-200 dark:border-blue-900 bg-blue-50/50 dark:bg-blue-950/20 overflow-hidden shadow-sm">
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<div className="flex items-center gap-2 px-3 py-2 border-b border-blue-200/60 dark:border-blue-900/60 bg-blue-100/40 dark:bg-blue-900/30">
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<Sparkles className="size-3.5 text-blue-500 shrink-0" />
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<span className="text-xs font-medium text-blue-600 dark:text-blue-400">
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意图解析中…
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</span>
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<span className="ml-auto inline-flex gap-1">
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{[0, 1, 2].map((i) => (
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<span
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key={i}
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className="size-1 rounded-full bg-blue-400 animate-bounce"
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style={{ animationDelay: `${i * 150}ms` }}
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/>
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))}
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</span>
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</div>
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<pre
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ref={preRef}
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className="text-[11px] font-mono leading-relaxed p-3 text-muted-foreground whitespace-pre-wrap max-h-52 overflow-y-auto"
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>
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{text}
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<span className="inline-block w-0.5 h-3.5 bg-blue-400 ml-0.5 align-text-bottom animate-[blink_1s_step-end_infinite]" />
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</pre>
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</div>
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)}
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<style>{`
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@keyframes blink {
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0%, 100% { opacity: 1; }
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50% { opacity: 0; }
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}
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`}</style>
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</div>
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);
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}
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@@ -36,6 +36,7 @@ import type {
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AIOutline,
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BlockedSection,
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CompletedIntervention,
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ParsedIntent,
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ProgressEvent,
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ProgressEventType,
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ReviewIssue,
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@@ -66,6 +67,9 @@ const INITIAL_STATE: AIWritingState = {
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streamingDraftSections: [],
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outlineStreamingText: '',
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outlineThinkingText: '',
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intentStreamingText: '',
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intentThinkingText: '',
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parsedIntent: null,
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researchProgress: '',
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streamingReviewItems: [],
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revisionSummary: null,
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@@ -146,6 +150,41 @@ function handleEvent(prev: AIWritingState, event: ProgressEvent): AIWritingState
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case 'step_done':
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return base;
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case 'intent_chunk': {
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const text = (event as unknown as { text: string }).text ?? '';
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const isThinking = (event as unknown as { is_thinking: boolean }).is_thinking ?? false;
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return {
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...prev,
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intentStreamingText: isThinking ? prev.intentStreamingText : prev.intentStreamingText + text,
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intentThinkingText: isThinking ? prev.intentThinkingText + text : prev.intentThinkingText,
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};
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}
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case 'intent_ready': {
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const ev = event as unknown as {
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articleType?: string;
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article_type?: string;
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targetAudience?: string;
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target_audience?: string;
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wordCountTarget?: number;
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word_count_target?: number;
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refinedIntent?: string;
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refined_intent?: string;
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};
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const parsed: ParsedIntent = {
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articleType: ev.articleType ?? ev.article_type ?? '',
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targetAudience: ev.targetAudience ?? ev.target_audience ?? '',
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wordCountTarget: Number(ev.wordCountTarget ?? ev.word_count_target ?? 800),
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refinedIntent: ev.refinedIntent ?? ev.refined_intent ?? '',
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};
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return {
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...base,
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parsedIntent: parsed,
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intentStreamingText: '',
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intentThinkingText: '',
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};
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}
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case 'research_progress':
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return { ...base, researchProgress: event.message ?? '' };
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@@ -403,6 +442,7 @@ function customToProgressEvent(raw: unknown, sessionId: string): ProgressEvent |
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// 后端 _save_draft_to_repo 等 helper 不走 custom 通道，type 必为节点 emit 的 chunk 类型
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if (!type) return null;
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const allowed: ProgressEventType[] = [
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'intent_chunk', 'intent_ready',
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'research_progress', 'outline_chunk', 'draft_chunk',
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'review_chunk', 'review_item_done',
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];
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@@ -84,6 +84,8 @@ export type ProgressEventType =
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| 'connected'
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| 'step_start'
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| 'step_done'
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| 'intent_chunk'
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| 'intent_ready'
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| 'research_progress'
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| 'materials_ready'
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| 'outline_chunk'
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@@ -213,6 +215,14 @@ export interface CompletedIntervention {
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// ── Hook 状态 ─────────────────────────────────────────────────────────────────
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/** 意图解析最终结构化字段，来自 intent_ready 事件。 */
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export interface ParsedIntent {
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articleType: string;
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targetAudience: string;
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wordCountTarget: number;
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refinedIntent: string;
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}
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export interface AIWritingState {
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sessionId: string | null;
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status: WritingStatus;
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@@ -234,6 +244,10 @@ export interface AIWritingState {
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// 流式大纲（正文 / 思考分开）
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outlineStreamingText: string;
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outlineThinkingText: string;
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// 意图解析（流式 + 结果）
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intentStreamingText: string;
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intentThinkingText: string;
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parsedIntent: ParsedIntent | null;
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// 素材收集实时进度（最新一条提示文案）
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researchProgress: string;
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// 流式审核（逐条到达）
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+70
-25
@@ -6,8 +6,16 @@ import re
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from langchain_core.runnables import RunnableConfig
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from langchain_core.messages import HumanMessage, SystemMessage
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from ..state import AIWritingState
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from ._utils import llm_json
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from ._utils import (
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ThinkTagParser,
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_content_to_text,
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get_model,
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parse_json,
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safe_get_stream_writer,
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)
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logger = logging.getLogger(__name__)
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@@ -138,42 +146,79 @@ async def intent_parser_node(state: AIWritingState, config: RunnableConfig) -> d
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if fallback_type not in valid_keys:
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fallback_type = default_key
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result = await llm_json(
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system,
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user,
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fallback={
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"article_type": fallback_type,
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"target_audience": state.get("target_audience", "普通读者"),
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"word_count_target": state.get("word_count_target", 800),
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"refined_intent": state["user_intent"],
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},
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config=config,
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)
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fallback_result = {
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"article_type": fallback_type,
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"target_audience": state.get("target_audience", "普通读者"),
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"word_count_target": state.get("word_count_target", 800),
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"refined_intent": state["user_intent"],
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}
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# 意图解析模型调用失败时以非致命 notice 提示用户（llm_json 已用 fallback 兜底，
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# 流程仍可继续，但用户需知道结构化配置可能不准）。
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# 流式调用：与 writer_outline / writer_draft 同套机制 —— LLM 逐字吐出 JSON，
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# 通过 ``intent_chunk`` 推给前端打字机；完成后 ``parse_json`` 解析结构化结果，
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# 再通过 ``intent_ready`` 推一份结构化字段给前端结果卡片。
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writer = safe_get_stream_writer()
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llm_error: str | None = None
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full_text = ""
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parser = ThinkTagParser()
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try:
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model = get_model(config)
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async for chunk in model.astream(
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[SystemMessage(content=system), HumanMessage(content=user)]
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):
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delta = _content_to_text(chunk.content)
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if not delta:
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continue
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for is_thinking, text in parser.feed(delta):
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if not is_thinking:
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full_text += text
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writer({
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"type": "intent_chunk",
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"text": text,
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"is_thinking": is_thinking,
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})
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except Exception as exc:
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# 模型 / 网络挂掉 —— 不阻断流程，用 fallback 继续。
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logger.error("intent_parser streaming failed: %s", exc, exc_info=True)
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llm_error = f"{type(exc).__name__}: {exc}"
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result = parse_json(full_text, fallback_result) if full_text else fallback_result
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# 意图解析模型调用失败时以非致命 notice 提示用户（fallback 已兜底，流程仍可继续）
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progress_events: list[dict] = []
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if result.get("_llm_error"):
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if llm_error:
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progress_events.append({
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"type": "notice",
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"agent_name": "系统",
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"title": "模型错误",
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"detail": f"意图解析失败，已按默认配置兜底。原因：{result['_llm_error']}",
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"detail": f"意图解析失败，已按默认配置兜底。原因：{llm_error}",
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})
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progress_events.append({"type": "step_done", "agent_name": "系统", "message": "意图解析完成"})
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updates: dict = {"status": "researching", "progress_events": progress_events}
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# 校验 LLM 返回的 article_type 是否在有效范围内，不合法时回退到默认值
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raw_type = result.get("article_type", "")
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updates["article_type"] = raw_type if raw_type in valid_keys else fallback_type
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if result.get("target_audience"):
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updates["target_audience"] = result["target_audience"]
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final_article_type = raw_type if raw_type in valid_keys else fallback_type
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final_target_audience = result.get("target_audience") or fallback_result["target_audience"]
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word_count = _coerce_word_count(result.get("word_count_target"))
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final_word_count = word_count if word_count is not None else fallback_result["word_count_target"]
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final_refined_intent = result.get("refined_intent") or state["user_intent"]
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# 推送结构化结果给前端 —— 结果卡片显示文章类型 / 目标读者 / 字数 / 精炼意图
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writer({
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"type": "intent_ready",
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"article_type": final_article_type,
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"target_audience": final_target_audience,
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"word_count_target": final_word_count,
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||||
"refined_intent": final_refined_intent,
|
||||
})
|
||||
|
||||
progress_events.append({"type": "step_done", "agent_name": "系统", "message": "意图解析完成"})
|
||||
|
||||
updates: dict = {"status": "researching", "progress_events": progress_events}
|
||||
updates["article_type"] = final_article_type
|
||||
if result.get("target_audience"):
|
||||
updates["target_audience"] = final_target_audience
|
||||
if word_count is not None:
|
||||
updates["word_count_target"] = word_count
|
||||
updates["word_count_target"] = final_word_count
|
||||
if result.get("refined_intent"):
|
||||
updates["user_intent"] = result["refined_intent"]
|
||||
updates["user_intent"] = final_refined_intent
|
||||
|
||||
return updates
|
||||
Reference in new issue
Block a user